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1. HN Add Typeahead and Semantic Search to Your GitHub Searchbar**Summary:** SearchGit is a tool designed to enhance the functionality of GitHub's search bar by introducing several advanced features. It provides instant typeahead suggestions, allowing users to see relevant search results as they type. Additionally, it supports multilingual semantic searches in various languages including English, 中文 (Chinese), Español (Spanish), Français (French), and 日本語 (Japanese). This feature is particularly useful for finding repositories with README files across different languages, catering to a global user base. SearchGit offers fast access to recent searches, ensuring users can quickly revisit their previous queries. Its interface is designed to blend seamlessly into GitHub's environment, adapting automatically to the platform’s dark and light modes for consistent visual integration. The search results are intelligently ranked based on multiple factors such as lexical signals, semantic relevance, number of stars, and personalized user data, which enhances the accuracy and relevancy of the results displayed. One of the key strengths of SearchGit is its ability to handle imprecise queries effectively due to its semantic search capabilities. For example, users can input broad or non-specific searches like "Best LLM orchestration frameworks" or "JS ORM," and still receive highly relevant recommendations such as LangChain or TypeORM. This feature ensures that even vague inquiries yield precise and useful results. Users are encouraged to provide feedback or suggestions for improvements via the email address jiamingliu888@gmail.com, indicating an openness to community input in enhancing SearchGit's functionality. **BULLET POINT SUMMARY:** - **Typeahead Suggestions:** Provides instant search suggestions as users type. - **Multilingual Semantic Search:** Supports searches in multiple languages including English, 中文, Español, Français, and 日本語. - **Recent Searches Access:** Allows quick access to previously performed queries. - **Adaptable Interface:** Blends seamlessly with GitHub's interface, supporting dark and light modes. - **Smart Result Ranking:** Results are ranked based on lexical signals, semantic relevance, stars, and personalization. - **Semantic Search Capability:** Handles imprecise queries effectively, offering relevant results like LangChain or TypeORM for broad searches such as "Best LLM orchestration frameworks" or "JS ORM." - **Feedback Mechanism:** Users can submit feedback and suggestions via jiamingliu888@gmail.com. Keywords: Accuracy, Dark/Light Mode, Feedback, GitHub Searchbar, Instant Suggestions, LLM Frameworks, Lexical Signals, Multilingual, ORM, Personalization, Queries, README, Semantic Search, Stock-trading, Typeahead, UI Integration
github
chromewebstore.google.com an hour ago
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2. HN Show HN: Sentrilite: a hybrid-cloud control plane for observability and security- **Overview**: Sentrilite is a unified control plane designed for enhancing observability and security across hybrid multi-cloud environments, including AWS, Azure, GCP, and on-premises infrastructures. It simplifies management through fast onboarding, live kernel-level telemetry, fleet-wide rule targeting, and audit-ready reports. - **Key Features**: - **Fleet Onboarding**: Users can quickly populate a fleet view by uploading a simple CSV file with server IPs and groups, providing health status, alerts, and AI insights. - **One-command Deployment**: Sentrilite agents are deployable to an EKS cluster using `kubectl apply -f sentrilite.yaml`, setting up a DaemonSet for node-level monitoring. This allows users to view live events enriched with Kubernetes metadata. - **High-risk Rule Targeting**: The platform enables selective rule application based on group attributes, such as cloud type or environment, highlighting high-risk activities like network connections and sensitive file access. - **Fleet Health Signals**: It detects issues like OOMKilled containers, offering precise context for efficient troubleshooting. - **Export PDF Reports**: Users can generate comprehensive reports with summaries, tags, and Kubernetes context in a single click. - **Development and Integration**: - Sentrilite provides eBPF-level visibility integrated with Kubernetes contexts to streamline onboarding. Agents, deployed as DaemonSets, stream events to the dashboard while applying locally hot-reloaded rules. - Grouping strategies enable selective policy application, allowing for targeted testing before full deployment. - **Demonstration and Testing**: - A demonstration includes simulating sensitive file access and out-of-memory conditions in a Kubernetes environment using the `busybox` image to potentially cause an OOM kill for testing purposes. - **Feedback and Resources**: - The creators are seeking user feedback on rule UX, fleet grouping model, and desired features in PDF/alerts. They offer additional information upon request. - Users can explore Sentrilite's capabilities through resources like a demo video, GitHub quick start link, and detailed documentation. - **Conclusion**: Sentrilite is designed to streamline fleet management in hybrid cloud environments, offering easy onboarding, detailed monitoring for Kubernetes clusters, eBPF-level event capture, and unified control over distributed environments. The tool emphasizes consistent rule application and telemetry across different clouds while ensuring straightforward onboarding processes. Keywords: AWS, Azure, CSV, DaemonSet, EKS, GCP, GitHub, Kubernetes, OOMKilled, PDF alert, PDF-report, Sentrilite, agent, audit-ready, busybox, cloud-fleet, control-plane, dashboard, demo video, eBPF, fleet-onboarding, high-risk rules, hot-reload, hybrid-cloud, memory limits, observability, on-prem, process-events, rule targeting, security, signals, telemetry
github
news.ycombinator.com 2 hours ago
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3. HN Tesla and Stellantis learn Americans like their trucks the old-fashioned wayStellantis has decided to halt production of its Ram 1500 REV electric pickup truck due to declining demand for full-size EV trucks in North America, reflecting broader challenges faced by automakers such as Tesla and Ford with their electric trucks like the F-150 Lightning and Cybertruck. This decision points to a misalignment between consumer expectations and the offerings of these vehicles, underscoring difficulties in selling larger EV models in the U.S. Amid internal issues, including reliance on waning brands like Chrysler and Jeep, Stellantis is reassessing its product strategy. The electric vehicle (EV) truck market faces significant challenges despite initial predictions of driving widespread EV adoption. Ivan Drury from Edmunds identifies key factors contributing to this trend: brand loyalty tied to full-sized pickup trucks makes it hard for consumers to switch to EV models; the higher costs of EV trucks deter buyers, and the cessation of federal EV tax credits weakens EV support, as these credits rarely applied to full-sized truck purchases unless leased—a behavior uncommon among truck buyers. Data reveals that only about 10% of internal combustion engine (ICE) truck buyers lease their vehicles compared to 54% for EV trucks, indicating a preference among truck buyers for buying rather than leasing due to customization needs and heavy usage. Drury suggests automakers need to better understand the specific requirements of truck buyers to effectively integrate electrification in this segment. Additionally, range anxiety persists as a challenge, especially for rural consumers lacking sufficient charging infrastructure. Tesla's Cybertruck initially aimed to address these concerns with its long-range variant but encountered limitations in colder climates where all-wheel drive systems are preferred over rear-wheel drives. Recently, Tesla removed the long-range option from its website, signaling ongoing issues in meeting consumer demands. While some automakers continue developing EVs, companies like Honda are reducing investments and shifting focus to hybrid models due to slower-than-expected adoption rates. Despite setbacks, EV trucks have not been entirely abandoned, but initial overpromising has led to a perception of underdelivery within this market segment. **BULLET POINT SUMMARY:** - Stellantis discontinues Ram 1500 REV due to falling demand for full-size EV trucks in North America. - Broader challenges faced by automakers like Tesla and Ford with their electric trucks, highlighting issues between consumer expectations and vehicle offerings. - Factors impacting the EV truck market include brand loyalty, higher costs of EVs compared to ICE vehicles, limitations of federal tax credits, and differences in leasing versus buying behaviors. - Drury emphasizes the need for automakers to understand truck buyers' specific needs to successfully integrate electrification. - Range anxiety remains a significant challenge due to inadequate charging infrastructure, especially in rural areas. - Tesla's Cybertruck faced issues with range and drivetrain preferences, leading to the removal of its long-range option. - Some automakers like Honda are scaling back on EV investments in favor of hybrid models due to slower adoption rates. - Despite challenges, the EV truck segment is not abandoned but faces criticisms for overpromising initially. Keywords: CEO, Cybertruck, EV trucks, F-150 Lightning, SUVs, Stellantis, Tesla, charging infrastructure, demand, discontinuation, electrification, hybrid models, investment, product strategy, production, range anxiety, tax credits
tesla
www.businessinsider.com 3 hours ago
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4. HN Fine-grained HTTP filtering for Claude CodeThe article from September 12, 2025, introduces "httpjail," a tool designed to enhance the security of coding agents like Claude Code by providing fine-grained HTTP filtering. It addresses risks such as unauthorized data access and mishandling of credentials through flexible rule definitions using JavaScript or custom programs, surpassing traditional firewall limitations. By default, httpjail only allows DNS traffic while blocking other non-HTTP(S) connections, offering an improvement over IP-based rules that can be imprecise due to mutable IPs and decentralized load balancers. The tool's functionality includes various examples of its use: blocking all HTTP requests except those to a specific domain like 'api.anthropic.com,' allowing only read-only (GET request) internet access, and enforcing whitelist-based access through files. httpjail operates differently on Linux and macOS; it uses strong mode with namespace creation and nftables redirection for robust control on Linux, while relying on environment variables in weak mode as a suggestion rather than enforcement on macOS. For TLS interception, httpjail can inspect HTTPS traffic by implementing full TLS interception through the generation of a self-signed Certificate Authority (CA) on first use. It dynamically generates certificates based on the Server Name Indication (SNI) from TLS ClientHello messages and supports both transparent proxy mode for direct connections and explicit proxy mode for HTTP CONNECT tunnels. Trust is injected into client applications by setting environment variables to recognize the CA certificate, covering tools like curl, Node.js, Python requests, and Git. Potential vulnerabilities include weak jail setups where programs can ignore HTTP_PROXY settings, and strong jails that could be compromised via filesystem access, such as through a Docker socket, allowing escape from network namespaces. httpjail provides both filesystem and network isolation using a `--docker-run` flag to isolate web requests in Docker containers, though it acknowledges potential vulnerabilities like prompt injection. For enhanced security, `httpjail --server` can be run on a standalone server behind a firewall that restricts traffic to ports 80 and 443. Users must configure environments with the `HTTP_PROXY` variable or redirect all traffic through the proxy for compliance. The setup involves a development environment running normal processes, a network firewall managing traffic redirection or restriction, and an httpjail server evaluating API requests based on user-defined rules. Finally, users can install httpjail via Cargo, with additional details available in its GitHub repository. **Bullet Point Summary:** - **Introduction**: httpjail enhances security for coding agents by providing fine-grained HTTP filtering using JavaScript or custom programs. - **Default Configuration**: Only allows DNS traffic and blocks non-HTTP(S) connections; surpasses limitations of IP-based rules. - **Examples of Use**: Blocking all except specific traffic, enabling read-only internet access, and enforcing whitelist-based controls. - **Operating Systems**: - *Linux*: Strong mode using namespace creation and nftables redirection for robust control. - *macOS*: Weak mode with environment variables as suggestions. - **TLS Interception**: Implements full TLS interception by generating a self-signed CA on first use, dynamically creating certificates based on SNI, supporting transparent and explicit proxy modes, and injecting trust into client applications. - **Potential Vulnerabilities**: Weak jails can be bypassed by ignoring HTTP_PROXY settings; strong jails may be compromised via filesystem access like Docker sockets. - **Isolation Features**: Provides network and filesystem isolation with a `--docker-run` flag for running isolated commands in Docker containers, despite potential vulnerabilities like prompt injection. - **Enhanced Security Setup**: `httpjail --server` can operate on a standalone server behind a firewall restricting traffic to HTTP/HTTPS ports, requiring proper environment variable configuration or traffic redirection. - **Installation and Resources**: Available via Cargo with further information in its GitHub repository. Keywords: DNS, Docker socket, GitHub repository, HTTP filtering, HTTPS_PROXY, IP-based rules, Jail Escapes, JavaScript expressions, LLM API, Network namespace, SSL_CERT_FILE, TLS interception, Transparent Proxy, Trust Injection, UDP, agents, cargo install, centralized, credentials, decentralized, destructive actions, firewall, governance, httpjail, load balancers, network isolation, nftables, proxy server, risks, security, sensitive information, setuid, whitelist
claude
ammar.io 4 hours ago
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5. HN Screen History MCP Server### Summary On September 13, 2025, the "Screen History MCP Server" tool was developed to systematically capture and process activities using screenshots taken every minute. These images are processed through Optical Character Recognition (OCR) and stored in ChromaDB, managed by an MCP server. The project's design pivots on leveraging the Multimodal AI Platform (MCP), which allows for bypassing frontend development, focusing instead solely on backend services. This approach reduces repetitive efforts in UI design and streamlines product roadmaps by consolidating multiple software interfaces into a single client supported by various backend servers. The project offers several key insights: 1. **Advantage of MCP**: Utilizing the MCP paradigm shifts the focus from frontend creation to enhancing backend capabilities, saving time on UI development and fostering efficient progress. 2. **Simplified Programming Transition**: The switch from Python to Node.js was facilitated by tools like Cursor, making significant changes less time-consuming while boosting developer motivation. 3. **Task Automation**: Markdown-based task lists can be processed automatically with the help of tools such as the Cursor Agent, thereby allowing human resources to concentrate on other areas. 4. **Efficient Context Switching**: Pre-queuing tasks enhances developers' ability to switch contexts effectively, addressing bottlenecks arising from limited computational focus rather than a lack of manpower. The project underscores how leveraging MCP and automation tools can streamline software development processes and optimize resource allocation. The message concludes with an invitation for readers to access the code at a referenced location and is signed by Joe. ### Bullet Point Summary - **Tool Development**: "Screen History MCP Server" developed on September 13, 2025; captures screenshots every minute, processed via OCR, stored in ChromaDB. - **MCP Paradigm Advantage**: Emphasizes backend development over frontend creation, reducing repetitive UI design efforts and streamlining product roadmaps. - **Programming Transition**: Simplified shift from Python to Node.js using tools like Cursor, enhancing efficiency and motivation. - **Task Automation**: Markdown task lists allow for automated processing with tools such as the Cursor Agent, freeing human resources for other tasks. - **Context Switching Efficiency**: Pre-queueing tasks allows better context switching among developers, addressing computational focus bottlenecks rather than manpower shortages. - **Resource Optimization**: Project highlights how MCP and automation streamline development processes and optimize resource allocation in software engineering. - **Conclusion**: Message concludes with an invitation to access the code and a signature from Joe. Keywords: ChromaDB, Claude, MCP Server, OCR, Screen History, context switching, frontend, node server, product development, programming, task lists, tool
claude
newbry.bearblog.dev 7 hours ago
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6. HN Twitter's Open-Source Algorithm Analysis with Claude Code- **Twitter Algorithm Overview**: The document analyzes Twitter's open-source recommendation system that processes 70,000 requests per minute with a latency target of three seconds. - **Candidate Sourcing & Ranking**: - Initial filtering reduces over one billion tweets to approximately one thousand based on engagement quality. - Light Ranker uses basic features like author reputation and engagement metrics to shortlist top 100-400 tweets. - Heavy Ranker employs complex ML models, scoring candidates using over 6,000 features per tweet. - **Engagement Signals & Quality**: - Emphasis is placed on high-quality engagements such as video views with long completion rates, profile clicks lasting more than ten seconds, replies indicating conversations, and retweets. - Negative feedback like "Not interested" is penalized to discourage low-quality interactions. - **User Guidelines for Avoiding Penalties**: - Users should establish a historical presence, use legitimate devices, post in human-like patterns, create original content, maintain balanced follow ratios, and seek verification to avoid spam detection. - **Recommendation System & Network Effects**: - The system prioritizes network effects and social proof, analyzing user connections through a graph-based service for recommendations. - **Content Freshness and Engagement Velocity**: - Recent posts within 48 hours are favored; engagement velocity significantly impacts visibility. - **Engagement Optimization Strategies**: - Effective strategies include using relevant hashtags, mentioning influential accounts, avoiding engagement bait, and creating compelling content to foster genuine interactions. - **Follower Growth Tactics**: - Building social proof involves engaging target networks, leveraging SimClusters, using two-hop network strategies, and involving influencers for mentions. - The follow recommendation system requires specific thresholds of social proof, followers, and tweet counts. - **Penalties and Feedback Systems**: - Understanding penalty triggers is crucial to maintaining presence; the Feedback Fatigue System manages user feedback duration. - **Recovery Strategies for Negative Signals**: - Enhancing content quality, analyzing engagement patterns, and adjusting posting schedules can help recover from negative signals. - **Advanced Viral Content Strategies**: - Network propagation algorithms are used to identify viral creators by tracking how content spreads through connections. - **Understanding Viral Content on Twitter**: - Analysis includes network propagation algorithms, engagement metrics like network amplification scores, and factors such as emotional resonance driving virality. - **Viral Content Templates**: Data-driven insights, expert commentary, and conversational hooks are employed to enhance shareability across communities. - **Metrics for Measuring Twitter Growth**: - Focuses on video quality views (30%), profile clicks (25%), replies (20%), retweets (15%), likes (10%). - Targets include high completion rates for videos, engagement rate, and diversity in network growth metrics like social proof score. - **Three-Phase Growth Plan**: - *Foundation Building*: Optimize account setup, establish schedules, engage with communities, track engagement. - *Content Strategy Development*: Align content with algorithm preferences using video strategies and conversation frameworks. - *Network Building*: Engage influential accounts, build community connections, create two-hop content. - **Advanced Phases for Growth**: - *Optimization and Scaling*: Analyze performance patterns, refine based on feedback, scale successful tactics. - *Viral Content Strategy*: Develop frameworks focusing on network amplification, emotional resonance, timeliness, uniqueness. - **Ongoing Maintenance Strategies**: - Daily monitoring of engagement metrics; weekly analysis and adjustments; monthly reviews for strategy effectiveness and goal setting. - **Key Takeaways for Growth**: - Prioritize quality engagements over quantity. - Leverage social proof through network effects. - Maintain content freshness with timely posts. - Focus on high-quality video content for algorithm prioritization. - Avoid spam triggers to maintain authenticity. - **Recommendations**: - *Content Creators*: Focus on educational, personal, and industry-related conversational content. - *Businesses*: Build social proof through problem-solving and community discussions. - *Personal Brands*: Establish expertise with authentic interactions and share personal stories. - **Future of Twitter’s Algorithm**: Expected to evolve towards increased personalization, enhanced quality detection, improved video understanding, and real-time adaptation. - **Practical Approach for Growth**: - Apply a 3-2-1 rule: Post three types of content daily, engage during peak times for two hours, track profile clicks weekly. - Analyze post performance, determine audience activity, create engaging videos, interact authentically with niche accounts, and assess overall strategy. - **Avoiding Red Flags**: - Prevent spam detection by avoiding repetitive hashtags, duplicate content, buying interactions, mass following/unfollowing, generic replies, excessive posting frequency. - **Content Visibility Lifecycle**: - Posts have peak visibility within the first six hours, a secondary reach for eighteen hours, followed by algorithmic decay over twenty-four hours with minimal visibility after forty-eight hours. Keywords: Algorithm, Analysis, Candidate Sourcing, Community Detection, Content Freshness, Engagement Quality, Graph Features, Growth Strategies, Hashtags, Influencers, Likes, Model Weights, Negative Feedback, Network Effects, Open-Source, Optimization, Pipeline, Profile Clicks, Ranking, Recommendation Engine, Replies, Retweets, Signal Processing, Social Proof, Spam Detection, Trust and Safety Models, Twitter, Video Views, Viral Content
claude
nibzard.github.io 7 hours ago
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7. HN M25 – A background job library for Gleam and PostgresM25 is a background job library designed for Gleam applications that work with PostgreSQL, offering queueing and scheduling functionalities to facilitate asynchronous task execution. The setup involves installing the library via `gleam add m25` and establishing a PostgreSQL connection through the Pog library in a supervised environment. Once configured, users can create queues (e.g., for emails or SMS) managed by a static supervisor. To enqueue jobs, developers define job input parameters, schedule timings, and retry policies before using the `m25.enqueue` function. M25 guarantees that each job is processed at least once, with automatic retries on failure as dictated by the specified policies. Additionally, it handles stuck jobs by cleaning up those that surpass execution timeouts. The library necessitates the creation of specific tables within a designated PostgreSQL schema to store job data and manage migration versions. These include: - The `job` table for storing information about both executing and completed jobs. - The `version` table to record the latest M25 migration versions applied. Migration management is facilitated via a CLI tool, accessible with commands like `gleam run -m m25 migrate --addr= Development support includes Docker for running local Postgres instances and Gleam tools to execute examples and tests. The document also hints at ongoing development efforts for Cigogne integration, though this feature is not yet available. **Bullet Point Summary:** - M25 provides queueing and scheduling for asynchronous tasks in Gleam applications with PostgreSQL. - Installation requires `gleam add m25` and setting up a PostgreSQL connection using the Pog library. - Queues can be configured and managed via a static supervisor, with jobs enqueued through `m25.enqueue`. - Ensures job delivery at least once, with retries based on specified policies and cleanup of stuck jobs. - Utilizes PostgreSQL tables within an `m25` schema for storing job data and tracking migrations. - Migration management is supported by a CLI tool that can print or apply SQL statements. - Development involves Docker for Postgres instances and Gleam tools for running examples and tests. - Future support for Cigogne integration is under development. Keywords: CLI, Docker Compose, Gleam, M25, Postgres, SQL statements, background job library, connection, database, dbmate, delivery semantics, development, documentation, driver, email_queue, enqueue, executing state, installation, jobs, migrations, pog, queueing, retries, retry policy, scheduling, schema, sms_queue, supervised, tables, timeout
postgres
github.com 8 hours ago
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8. HN Show HN: Another note-taking app? My first built appThe text describes an application named "Graph Notes," developed by its author as their first project using Google AI Studio. This note-taking app is designed to prioritize simplicity and functionality, intentionally avoiding clutter and complex commands to cater directly to the creator's personal needs, despite lacking additional fancy features. The application’s development is hosted on a private GitHub repository, yet the author encourages public interaction through feedback, comments, suggestions, and bug reports. Additional details about "Graph Notes" are accessible via a provided link: [GraphNotesApp](https://graphnotesapp.carrd.co/). - **Application Overview**: Introduction of "Graph Notes," a simple note-taking app developed by its creator using Google AI Studio. - **Design Philosophy**: Emphasis on simplicity and functionality, avoiding clutter and complex commands to suit the developer's requirements. - **Development Repository**: The project is hosted privately on GitHub, but the author welcomes public interaction for improvements. - **Call for Feedback**: The author invites feedback, comments, suggestions, and bug reports from users. - **Additional Information Source**: More details about "Graph Notes" can be found through a provided link. Keywords: GitHub, Google AI Studio, Note-taking app, bug fixes, built app, comments, functionality, private repo, repository, simplicity, suggestions, technical keywords
github
graphnotes.vercel.app 8 hours ago
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9. HN Logical replication is underrated alt to ELT**Summary:** Philippe Noël’s article explores alternatives for syncing data from Postgres, focusing on the comparison between traditional ETL pipelines and logical replication. Organizations typically use systems like Elasticsearch, ClickHouse, or Snowflake to manage specialized workloads such as search and analytics, relying on ETL processes—Extract, Transform, Load—to transfer data from Postgres. Although effective in delivering data across various platforms and enabling significant transformations for multiple destinations, ETLs are often overlooked concerning their long-term maintainability, reliability, and performance. Logical replication emerges as a promising alternative to traditional ETL pipelines. It simplifies the synchronization of changes from a primary Postgres database directly to another system or secondary instance without requiring complex transformations. This method is particularly beneficial for OLAP reporting due to its potential to reduce complexity and improve efficiency. Logical replication relies on streaming data via write-ahead logs (WAL) in PostgreSQL, providing transactional consistency and minimizing infrastructure needs by eliminating separate ETL pipelines. Despite these advantages, logical replication has limitations that restrict its universal applicability. It is constrained by schema coupling, requiring replicas to mirror the source schema exactly without transformation capabilities, a single-threaded change application per subscription, and an inability to replicate DDL operations like index creation or table alterations. These limitations necessitate manual migration scripts on both primary and replica databases. Logical replication’s compatibility is limited to systems that understand PostgreSQL's logical decoding output, including ParadeDB—a vector search tool built on PostgreSQL—and Timescale, designed for time series data. Both systems support PostgreSQL’s logical replication due to their integration with Postgres internals or specific protocol support. In summary, while logical replication offers a streamlined and efficient method for real-time data syncing between PostgreSQL databases without the need for transformation, it requires careful consideration of its limitations. ETL pipelines remain valuable in scenarios demanding extensive transformations and flexibility across diverse systems but demand ongoing maintenance and vigilance due to potential fragility and complexity. **BULLET POINT SUMMARY:** - Philippe Noël compares traditional ETL pipelines with logical replication as methods for syncing data from Postgres. - Organizations often use systems like Elasticsearch, ClickHouse, or Snowflake for specialized workloads, relying on ETL processes that involve extracting, transforming, and loading data. - While effective, ETLs are prone to issues concerning long-term maintainability, reliability, and performance. - Logical replication offers a simpler alternative by directly mirroring changes from Postgres using write-ahead logs (WAL), benefiting scenarios like OLAP reporting due to reduced complexity and improved efficiency. - Advantages of logical replication include transactional consistency, minimized infrastructure needs, and the elimination of separate ETL pipelines. - Limitations of logical replication encompass schema coupling, single-threaded change application per subscription, inability to replicate DDL operations, necessitating manual migrations, and limited compatibility beyond specific systems like ParadeDB and Timescale. - Logical replication is suitable for real-time data syncing without transformation requirements but demands careful planning due to its limitations. - ETL pipelines remain essential for extensive transformations and flexibility across diverse systems but require ongoing maintenance and vigilance. Keywords: CDC tools, ClickHouse, DDL, ETL, Elastic-style search, Elasticsearch, Extract Transform Load (ETL), Logical replication, OLAP, ParadeDB, Postgres, Snowflake, analytical queries, column lists, data enrichment, data ingestion, dbt, deduplication, flexibility, idempotency keys, interoperability, logical decoding, maintainability, performance, pipeline complexity, real-time streaming, reliability, reporting, row filters, schema changes, schema drift, specialized workloads, throughput, transaction handling, transformations, version mismatches
postgres
www.paradedb.com 8 hours ago
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10. HN Disabling auto-dubbing and translated titles on YouTube (with extensions)To address frustrations with YouTube's auto-dubbing and misleading translated titles, two browser extensions—ImprovedTube and DeArrow—are recommended for enhancing user control over their viewing experience. ImprovedTube offers a "Disable auto-dubbing" feature that ensures original audio tracks are played by default, while also providing options to remove AI-generated content such as summaries, Shorts, Related Searches, and Member videos through its General menu settings. DeArrow, on the other hand, allows users to hide YouTube's translated video titles via an option in its Behavior tab, thus preventing confusion from unintended language changes. The author highlights personal challenges with poorly translated Brazilian Portuguese video titles and the irritating AI voice used in non-English videos, characterized by a robotic tone and stereo audio issues that revert to mono upon pausing or changing tracks. These problems detract from their experience of learning a new language using these resources. The extensions discussed help mitigate such annoyances by restoring more control over content selection and reducing repetitive unwanted recommendations. Overall, the author appreciates how these tools enhance YouTube's usability by stripping away excessive features that have altered its original user-friendly nature. This improved control has led to reduced time spent on the platform, allowing for a shift towards other productive activities. **BULLET POINT SUMMARY:** - ImprovedTube and DeArrow extensions address issues with YouTube auto-dubbing and translated titles. - ImprovedTube disables auto-dubbing, ensuring original audio is played, and removes AI-generated content. - DeArrow hides translated video titles to prevent confusion from unintended language changes. - The author struggles with poor translations of Brazilian Portuguese titles and an annoying AI voice in non-English videos. - Extensions reduce frustrations by providing greater control over viewing preferences. - These tools help revert YouTube closer to its previous, more user-friendly state. - As a result, the author spends less time on YouTube, focusing more on productive activities. Keywords: AI Summaries, AI voice, Auto-dubbing, DeArrow, Disable auto-dubbing, GitHub, ImprovedTube, YouTube, anti-clickbait, bilingual, clickbait, extensions, learning exposure, mono, monolingual defaultism, multilingual options, original audio track, phase difference, robotic voice, stereo audio, translated titles, video recommendations
github
news.ycombinator.com 9 hours ago
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11. HN Wait4X allows you to wait for a port or a service to enter the requested state- **Overview of Wait4X**: - Wait4X is a lightweight, zero-dependency tool designed to ensure service readiness before proceeding with applications or scripts. It supports TCP, HTTP, and DNS protocols and integrates with services like Redis, MySQL, PostgreSQL, offering reverse/parallel checking capabilities, exponential backoff, cross-platform availability, and post-check command execution. - **Installation Instructions**: - Installation can be done using package managers (Homebrew for macOS, apk for Alpine Linux, AUR via Yay for Arch Linux, nix-env for NixOS, Scoop for Windows) or Docker. Manual installation involves downloading the binary from the releases page, extracting it, and placing it in a directory within the system's PATH. - Users can also install Wait4X as a Go library using `go install`. - **Quick Start Guide**: - Provides examples of usage such as waiting for a TCP port, performing HTTP health checks, checking multiple services in parallel, and verifying database readiness with specific conditions. - **HTTP Checking Features**: - Enables verification of HTTP(S) endpoints through status code checks, response body regex matches, JSON path validation, or XPath checks. - Allows customizable requests including headers like `Authorization` and TLS options for secure connections. - **DNS and Database Checking**: - Supports DNS record type validation (e.g., A, AAAA, CNAME). - Checks database readiness by verifying table existence or specific conditions in MySQL, PostgreSQL, MongoDB, Redis, InfluxDB, RabbitMQ, Temporal, and Kafka. - **Advanced Features**: - Offers timeout and retry controls with exponential backoff. - Supports reverse checking (waiting for a service to become unavailable) and command execution after successful checks. - Allows parallel checking of multiple services. - **Kafka Connection Strings**: - Specifies Kafka brokers using `kafka://` format, including authentication mechanisms like SCRAM. - **Go Package Usage**: - Users can add Wait4X to Go projects with `go get wait4x.dev/v3`. - Provides examples of creating TCP and HTTP checkers with custom options (e.g., connection timeouts, headers). - Demonstrates parallel service checks using context-based timeouts and backoff policies. - **Custom Checker Implementation**: - Includes a `FileChecker` example to verify file existence and size. - Implements the `Identity()` and `Check(ctx)` methods of the checker interface. - **CLI Reference**: - Offers a command-line interface with comprehensive help documentation accessible via commands like `wait4x --help`. **Summary:** Wait4X is an open-source utility designed to ensure service readiness across various protocols and systems, including TCP ports, HTTP(S) endpoints, DNS records, and databases such as Kafka, MySQL, PostgreSQL, MongoDB, Redis, InfluxDB, RabbitMQ, and Temporal servers. It supports specific command flags for tailored usage and global flags to configure parameters like maximum wait time, check intervals, backoff policies, and output settings. The project encourages community contributions through activities such as bug fixes, feature additions, or documentation enhancements on GitHub by forking the repository, making changes with tests, committing, pushing changes, and creating pull requests. Community interaction is also promoted via GitHub Discussions and issues for reporting bugs or suggesting features. Users are invited to support the project by starring its repository. Wait4X operates under the Apache License 2.0, acknowledging logo design inspiration from "Waiting Man" (Zhdun), with copyright held by The Wait4X Authors between 2019-2025. Licensing details can be found in the LICENSE file. Keywords: API, Alpine Linux, Apache License 20, Arch Linux, Authentication, Backoff, Binary Download, CI/CD, Checking, Context, DNS, Database Readiness, Docker, Exponential Backoff, Exponential Backoff Policy, FileChecker, GitHub, Go package, HTTP, Health Check, Installation, JSON Path, Kafka, License, MongoDB, MySQL, NixOS, NoSQL, Parallel Checking, PostgreSQL, Protocol, Quick Start, Redis, Response Body, Retry, SQL, SSL Mode, Schema, Scoop, Shell Command, Status Code, TCP, TCP Connection, TLS Options, Timeout, Unix Socket, Usage Examples, Validation Options, Windows, XPath, containers, development, macOS, wait4x
postgresql
github.com 9 hours ago
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12. HN Vercel's x402-MCP Open protocol payments for MCP tools**Summary:** Vercel's x402-MCP Open protocol revolutionizes the integration of payments within Model Context Protocol (MCP) tools and APIs by introducing a streamlined mechanism that eliminates the cumbersome process traditionally associated with handling API payments. This new approach embeds payment capabilities directly into HTTP requests using the 402 Payment Required status code, allowing any API endpoint to request payment without pre-established accounts or configurations. By utilizing middleware for specifying payment requirements on APIs and providing clients with a simple fetch wrapper for making paid requests, x402 simplifies transactions across various payment networks or schemes. The protocol is not tied to any specific vendor or payment provider, offering flexibility in supporting different currencies and non-crypto options. The "x402-mcp" package enhances MCP servers by enabling the definition of priced tools via `createPaidMcpHandler`, utilizing the Zod library for defining tool names, prices, and argument schemas. On the client side, a wrapper function `withPayment` manages payments within an MCP client setup through `ai`. This facilitates seamless payment authorizations across different transport protocols, following a standard HTTP exchange pattern where servers issue 402 status codes with payment instructions. Clients authorize transactions via external facilitators before obtaining the requested resources. While current implementations typically process payments in USDC on the Base blockchain for efficiency, x402's design is agnostic to specific payment networks, accommodating various currencies and schemes. A starter template for integrating x402 features tools like Next.js, AI SDK, AI Elements, and AI Gateway. Key components include an API route secured by paymentMiddleware, a paidTool MCP server with payment capabilities, a page charging web crawlers via x402, secure wallet management through Coinbase, and a frontend AI Chat + API playground demonstrating the protocol's use. The template is easily deployable on Vercel, facilitating experimentation for developers seeking programmatic payment solutions in AI applications. **Bullet Point Summary:** - **Introduction of x402-MCP Protocol:** Simplifies payment integration within MCP tools/ APIs using HTTP 402 status codes without needing pre-established accounts or configurations. - **Middleware and Fetch Wrapper:** API protection through middleware specifying payments; clients make paid requests via a simple fetch wrapper, supporting multiple payment networks. - **Open Web Protocol:** x402 is vendor-independent, supporting various currencies and non-crypto payment schemes. - **x402-mcp Package Features:** - Enhances MCP servers with `createPaidMcpHandler` to define priced tools using the Zod library. - Handles client payments through a wrapper function `withPayment`. - **Standard HTTP Exchange Pattern:** Clients request protected resources, receive 402 status codes and payment instructions, then verify transactions via external facilitators. - **Implementation Details:** - Typically processes payments in USDC on Base blockchain for efficiency. - Designed to be payment-network agnostic, accommodating various currencies/schemes. - **Starter Template Features:** - Integrates x402 with Next.js, AI SDK, AI Elements, and AI Gateway. - Includes an API route secured by paymentMiddleware, a paidTool MCP server, a charging page for web crawlers, secure wallet management via Coinbase, and a frontend AI Chat + API playground. - **Deployment and Usage:** Easily deployable on Vercel with one-click setup; starter template available for experimenting in developing AI applications requiring programmatic payments. Keywords: 402 Payment Required, AI Gateway, AI SDK, API, Coinbase, GitHub, HTTP, MCP, Nextjs, SDK, USDC, Vercel, blockchain, client, fetch wrapper, middleware, npm, payment facilitator, payments, programmatic payments, server, starter template, zod
github
vercel.com 9 hours ago
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13. HN System Eval with Obsidian and Claude Code### Summary: The author presents an innovative approach to evaluating system processes by combining Obsidian, a personal note-taking tool, with Claude Code, an AI coding agent. Instead of its typical use for notes, Obsidian is utilized to document each evaluation step as markdown files, ensuring the process is repeatable and organized. Each stage is recorded in Obsidian with prompts that guide Claude Code on how to execute scripts derived from these prompts. This method streamlines system evaluations without requiring extensive infrastructure, allowing users to revisit or redo specific parts easily. The core of this approach involves using Claude Code to generate scripts from one-shot prompts based on a knowledge base stored on disk. These generated scripts are then executed to produce outputs necessary for further analysis. Error analysis is facilitated through the review of markdown files in Obsidian that have predefined fields. This method emphasizes repeatability and organization, contrasting with ad-hoc evaluation techniques which can be challenging to replicate. Key benefits highlighted include eliminating the need for complex infrastructure setups and databases, as data management occurs via simple text-based markdown files. These files are version-controlled through tools like git, enhancing ease of sharing and collaboration without requiring additional installations or accounts. The interoperability between Obsidian and Claude Code is likened to TCP/IP's role on the internet, serving as a universal format that avoids vendor lock-in. A significant point made by the author is the crucial role of context in maximizing AI effectiveness. They emphasize that providing appropriate context can greatly enhance AI performance, an area still being explored by many practitioners. By integrating markdown for knowledge management and access, this approach not only simplifies data handling but also optimizes AI task execution through better contextual integration. ### Bullet Point Summary: - **Integration of Tools**: Combines Obsidian (note-taking tool) with Claude Code (AI coding agent) for system evaluation. - **Process Documentation**: Uses markdown files in Obsidian to document each step, ensuring repeatability and organization. - **Script Generation and Execution**: Utilizes Claude Code to generate scripts from prompts found in documentation for execution and analysis. - **Error Analysis**: Conducted by reviewing markdown files with predefined fields in Obsidian. - **Benefits**: Provides a repeatable evaluation process without extensive infrastructure, eliminates the need for databases through text-based management. - **Version Control and Sharing**: Markdown facilitates easy sharing and version control via git, requiring no additional installations. - **Interoperability**: The shared format between systems like Obsidian and Claude Code avoids vendor lock-in, akin to TCP/IP’s role on the internet. - **AI Contextualization**: Emphasizes the importance of context in AI effectiveness, advocating for proper contextual integration to enhance performance. Keywords: AI notebook, Claude Code, Discord JSON, Obsidian, System eval, coding agent, command line, executable prompts, git, iterative evaluation, knowledge base, markdown file, note taking, preprocessing, process documentation, version control
claude
interjectedfuture.com 9 hours ago
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14. HN Show HNThe text describes a web application developed by a self-taught programmer who has utilized AI for two years, specifically Claude AI, in various capacities as part of their coding practice. The developer's initiative led to the creation of an app featuring a "Council of CTOs," aimed at aiding users with coding tasks. This tool is designed for use before guiding Cursor to perform additional actions or for evaluating pull requests (PRs). Users are cautioned against misusing their API keys, and it's emphasized that the AI simulations within the application do not correspond to actual individuals. **BULLET POINT SUMMARY:** - The developer, a self-taught programmer with two years of experience using AI, especially Claude AI, created a web application. - The app includes a feature called "Council of CTOs" aimed at assisting users with coding tasks. - It is intended for use prior to directing Cursor for further actions or reviewing pull requests (PRs). - Users are advised not to misuse their API keys while using the tool. - The AI simulations within the application do not represent real individuals. Keywords: AI, API key, CTOS, Claude, GitHub, GitHub pull requestsKeywords: Show HN, PRs, Show HN, application, code, coding assistance, council, cursor, dev, developer, personalities, personas, prompt, review, self-taught, simulations, tool, web app
claude
www.trycouncil.com 9 hours ago
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15. HN Show HN: VeritasGraph – On-prem Graph RAG (3.3k+ visitors, 130 stars in 5 days)- **Overview**: VeritasGraph is an open-source framework designed to enhance Retrieval-Augmented Generation (RAG) systems by replacing vector search with a knowledge graph, focusing on improved question-answering and summarization within private infrastructures. It addresses the limitations of traditional RAG systems, especially for multi-hop questions requiring interconnected information. - **Key Features**: - *Verifiable Attribution*: Ensures every AI-generated output can be traced back to its source, enhancing transparency and reducing errors. - *Data Sovereignty*: Operates entirely on-premises, preventing vendor lock-in and protecting data from third-party exposure. - *Trustworthy AI*: Aims for solutions that are trustworthy, private, and explainable beyond mere accuracy. - **Framework Capabilities**: - Provides secure, reliable, and fully auditable reasoning paths suitable for organizations prioritizing data security and transparency in AI applications. - Offers advanced graph reasoning capabilities surpassing traditional vector search engines, capable of handling complex queries through multi-hop querying and full source attribution. - **Implementation Details**: - The demo includes a walkthrough showcasing features like data ingestion and multi-hop querying. - System architecture comprises an indexing pipeline (one-time) and a query pipeline (real-time), involving document chunking, LLM-powered extraction, vector indexing, and graph traversal for enhanced reasoning context. - Users can build VeritasGraph locally with Ollama on Windows by adjusting model context length to avoid truncation during indexing. - **Setup Instructions**: - To use Ollama effectively, increase the model's context length from the default 2048 to 12k. - Setup involves creating a Conda environment, installing dependencies (including `sympy`, `future`, and `ollama`), initializing GraphRAG, configuring settings in `.env` and `settings.yaml`, fine-tuning prompts, and starting indexing. - Testing global queries is done using Python commands to ensure functionality. - **Core System Components**: - *Multi-Hop Graph Reasoning*: Facilitates traversal beyond semantic similarity to discover complex data relationships. - *Efficient LoRA-Tuned LLM*: Utilizes Low-Rank Adaptation for powerful on-premise deployment. - *End-to-End Source Attribution*: Links statements back to specific documents and reasoning paths. - *Secure & Private On-Premise Architecture*: Allows full control over data and models, ensuring sovereignty. - **Architectural Blueprint**: - Involves transforming unstructured documents into a structured knowledge graph through document chunking, entity extraction using LLMs, and graph assembly in databases like Neo4j. - A hybrid retrieval engine employs vector search for entry-point identification and uses graph traversal for contextual expansion. - **Process and Philosophy**: - Enhances search with multi-stage vector search and contextual expansion followed by pruning and re-ranking irrelevant data. - Incorporates LoRA-Tuned reasoning core for efficient attributed answer generation, with metadata propagation ensuring traceable outputs in JSON format linking to sources. - Beyond semantic search, it addresses the multi-hop challenge by integrating graph traversal with LLM reasoning. - **Deployment Guide**: - Outlines prerequisites like hardware and software requirements and configuration steps involving environment variables. - **Philosophy & Future Roadmap**: - Focuses on transparency, security, and control to democratize enterprise-grade AI, enabling organizations to develop proprietary knowledge assets. - Plans include expanding database support, enhancing graph analytics with community detection, implementing an agentic framework for multi-step tasks, and developing a UI for graph exploration. - **Acknowledgments**: - Credits prior research and contributions from the AI community, highlighting influences like HopRAG for graph-structured RAG, Microsoft GraphRAG, LangChain & LlamaIndex, and Neo4j as foundational technologies. Keywords: Docker Compose, GitHub stars, HopRAG, LLM-powered extraction, LangChain, LlamaIndex, LoRA-tuned LLM core, Microsoft GraphRAG, NVIDIA GPU, Neo4j, Ollama, Python 310+, RAG framework, VeritasGraph, attribution layer, conda environment, context length, data sovereignty, database support, document chunking, enterprise AI, entity extraction, environment variables, explainability, fine-tuning, global query, graph analytics, graphRAG, hybrid retrieval engine, indexing, indexing pipeline, indexing-engine log, knowledge graph, local search, low-rank adaptation, model settings, multi-hop questions, on-prem, open-source, private infrastructure, prompts, pruning & re-ranking, query pipeline, question-answering, retrieval-augmented generation, semantic search, source attribution, sovereign knowledge, summarization systems, transparency, truncation, vector index, vector search, video walkthrough, visualization UI, yaml configuration
ollama
github.com 10 hours ago
|
16. HN Show HN: TimescaleDB to ClickHouse Change Data CaptureThe Postgres CDC connector within ClickPipes offers a robust solution for continuous data replication from Postgres and TimescaleDB to ClickHouse Cloud, enhancing real-time analytics capabilities. Originally supported by PeerDB, which is now part of ClickHouse, this technology addresses the growing need to extend these features to TimescaleDB due to its popularity in time-series applications. Key use cases for this technology include: 1. **Online Data Migrations**: For users employing TimescaleDB for analytics who experience performance bottlenecks as data scales, transitioning to ClickHouse provides enhanced speed and ease of migration with minimal downtime. 2. **Iterative Migration**: In complex application environments requiring gradual migrations from TimescaleDB to ClickHouse, the connector supports incremental shifts while maintaining synchronization between databases with minimal lag. The Postgres CDC connector has proven beneficial for companies like Kindly.ai by improving dashboard performance and data exploration speed through its transition to ClickHouse Cloud. It also facilitates scenarios where both TimescaleDB and ClickHouse co-exist, allowing real-time transactional applications powered by TimescaleDB while leveraging ClickHouse for fast analytics via reliable replication. The connector supports efficient initial load/backfill and ongoing sync from TimescaleDB Hypertables to ClickHouse using TimescaleDB's native logical replication. It handles both compressed and uncompressed hypertables without the need to redesign data pipelines. For large tables, it offers rapid initial loads through parallel snapshotting, although this defaults to single-threaded mode for compressed hypertables. ClickPipes ensures seamless schema change handling during replication and provides comprehensive metrics and alerts via platforms like Slack or email to monitor the process effectively. The complexity of logical replication in TimescaleDB arises from its hypertable structure, which requires tracking changes at the chunk level as new chunks are automatically created. Unlike standard PostgreSQL partitioned tables that use `publish_via_partition_root` for change redirection, hypertables lack this feature, necessitating explicit parent lookups and inclusion of all child tables in publications for accurate replication. TimescaleDB's internal schema, `_timescaledb_internal`, where new chunks are stored, can be included in PostgreSQL publications to automatically capture future chunks. This setup allows ClickPipes to replicate seamlessly once configured. TimescaleDB also supports hypertable compression through transparent and Hypercore engines, offering similar advantages to columnar databases like ClickHouse but presenting challenges such as errors with ctid-based parallel loading strategies. An improvement has been made to switch methods automatically when encountering these issues. Additional resources are available for replicating or migrating TimescaleDB workloads to ClickHouse, ensuring a smooth transition and efficient data management. **BULLET POINT SUMMARY:** - The Postgres CDC connector in ClickPipes enables continuous data replication from Postgres/TimescaleDB to ClickHouse Cloud, enhancing real-time analytics. - Supported use cases include online data migrations for better performance and iterative migration for gradual transitions with minimal lag. - Benefits include improved dashboard performance and faster data exploration as seen with Kindly.ai’s transition to ClickHouse Cloud. - The connector facilitates co-existence scenarios where TimescaleDB handles transactional workloads and ClickHouse performs fast analytics. - Supports initial load/backfill and ongoing sync from TimescaleDB Hypertables, handling both compressed and uncompressed tables without pipeline redesign. - Offers rapid initial loads through parallel snapshotting for large tables, though defaults to single-threaded mode for compressed hypertables. - Provides schema change handling during replication and comprehensive monitoring via alerts on platforms like Slack or email. - Logical replication complexity arises from tracking changes at the chunk level due to TimescaleDB’s structure; requires explicit parent lookups and inclusion of all child tables in publications. - Includes `_timescaledb_internal` schema in PostgreSQL publication for automatic future chunk capture, enabling seamless replication with ClickPipes after setup. - Supports hypertable compression but poses challenges like errors during ctid-based parallel loading strategies, with improvements to switch methods automatically when needed. - Additional resources are available for replicating or migrating TimescaleDB workloads to ClickHouse. Keywords: Avro, CDC, CTID columns, ClickHouse, ClickPipes, Hypertables, PeerDB, Postgres, Slack alerts, TimescaleDB, Zstd, analytics, compression, dashboard performance, data sync, extension, logical replication, migration, parallel processing, partitioned tables, replication, scalability, schema changes, snapshotting, transactional workloads, workload
postgres
clickhouse.com 10 hours ago
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17. HN Diffusion based LLM basic chat appThe dLLM Chat application utilizes diffusion-based large language models (LLMs) to offer rapid and efficient AI-driven conversational experiences. As a straightforward chat app, its primary focus is on delivering swift inference capabilities for user interactions with artificial intelligence. This design positions it as one of the fastest options available in the field of AI conversation tools. ### Bullet Point Summary: - **dLLM Chat Application**: An app leveraging diffusion-based LLMs. - **Purpose**: Provides fast and efficient AI-driven conversational experiences. - **Design Focus**: A basic chat app aimed at quick inference for user interactions with AI. - **Market Position**: Recognized as one of the fastest options in its domain. Keywords: AI Chat, Diffusion, Fastest, Inferenced, Keywords, LLM, Technical, chat app, dLLM Chat
llm
dllmchat.vercel.app 10 hours ago
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18. HN A Calif. bill that would regulate AI companion chatbots is close to becoming lawCalifornia's proposed SB 243 seeks to regulate AI companion chatbots specifically to safeguard minors and vulnerable users by mandating safety protocols for operators and enforcing accountability for non-compliance. The bill, which has garnered bipartisan support and passed both legislative chambers, awaits Governor Gavin Newsom's decision by October 12. If enacted, it will take effect on January 1, 2026, positioning California as the pioneer in AI chatbot regulation. The legislation prohibits companion chatbots from engaging in harmful conversations concerning suicidal thoughts, self-harm, or explicit content. It mandates that platforms remind users interacting with an AI of their conversational partner's non-human nature every three hours for minors and requires major providers like OpenAI, Character.AI, and Replika to publish annual transparency reports starting July 1, 2027. Additionally, individuals affected by violations can sue AI companies for damages up to $1,000 per incident and seek injunctive relief and attorney's fees. Introduced in January by state senators Steve Padilla and Josh Becker following a tragic event involving teenager Adam Raine, SB 243 addresses concerns raised by leaked documents indicating inappropriate conversations between Meta’s chatbots and children. The bill coincides with increased regulatory scrutiny from U.S. agencies; the FTC is investigating AI's effects on children's mental health, while Texas attorney general Ken Paxton probes into Meta and Character.AI. Senators Josh Hawley and Ed Markey have launched separate investigations into Meta. Despite its protective intent, SB 243 was amended to remove certain stringent requirements, such as preventing "variable reward" tactics used by AI chatbots—a change criticized for potentially fostering addictive interactions. Current provisions also do not require tracking discussions initiated by chatbots on suicidal ideation, drawing criticism from some quarters regarding harm prevention. Nonetheless, the revised bill aims to balance practical compliance with minimizing harm. Senator Padilla stresses the importance of implementing safeguards quickly, ensuring minors understand they are interacting with AI and directing those in distress to appropriate resources. He also advocates for annual disclosures by AI companies on referrals to crisis services to better assess the frequency of such issues. California is simultaneously considering another bill, SB 53, which mandates transparency reporting from AI companies. OpenAI opposes this measure, advocating instead for adherence to less stringent federal regulations, a stance supported by other tech giants like Meta, Google, and Amazon. Anthropic stands alone in supporting SB 53. Senator Padilla contends that innovation and regulation are not mutually exclusive, promoting both technological progress and protections for vulnerable groups. Character.AI supports legislative collaboration and underscores the importance of disclaimers in user interactions. In summary, while California's AI regulations aim to protect minors and foster transparency, they also navigate the complex landscape of balancing innovation with ethical safeguards, amid broader scrutiny from regulatory bodies and investment trends favoring minimal regulation. ### Bullet Point Summary: - **SB 243 Overview:** Aims to regulate AI chatbots for child safety; passed both chambers awaiting governor's approval. - **Mandates & Penalties:** Requires reminders of AI nature every three hours for minors, transparency reports by 2027, and allows lawsuits for violations. - **Background & Context:** Introduced after a teenager's suicide involving ChatGPT; addresses concerns about inappropriate AI interactions with children. - **Regulatory Scrutiny:** U.S. agencies investigate AI impacts on children, including FTC and Texas AG probes into Meta and Character.AI. - **Amendments & Criticism:** Removed stricter requirements like banning "variable reward" tactics; criticized for potential harm prevention gaps. - **Further Actions by Padilla:** Emphasizes quick safeguards, crisis referral disclosures, balancing innovation with safety. - **SB 53 Consideration:** Another bill for transparency reporting; opposed by major tech companies except Anthropic. - **Industry Response:** OpenAI and others advocate federal frameworks over state regulations; Character.AI supports legislative collaboration. Keywords: AI chatbots, California, CharacterAI, Governor Newsom, OpenAI, SB 243, alerts, lawsuits, minors, regulation, safety protocols, self-harm, sexually explicit content
openai
techcrunch.com 10 hours ago
|
19. HN Show HN: My Rust CMS- **Project Overview**: "My Rust CMS" is a full-stack content management system developed using the RAYDT Stack (Rust • Axum • Yew • Diesel • Tower), designed to address limitations of traditional platforms like WordPress by providing a secure, free alternative. - **Core Features**: - Offers sophisticated visual page building capabilities with live edit mode, gradient backgrounds, shape masks, and extensive customization options. - Supports real-time editing with instant previews and utilizes WebAssembly for high-performance frontend operations. - Ensures enhanced security through Rust's memory safety, secure authentication mechanisms, and input sanitization to guard against XSS attacks. - **User Interface**: - Features a modern, responsive public frontend with gradient headers and professional styling. - Includes a visual page builder allowing drag-and-drop layout creation, real-time editing, component management, and template manager supporting live edits with advanced effects like gradients and scroll animations. - **Content Management**: - Provides rich text editing, category organization, and intuitive navigation management with customizable menus. - Incorporates a built-in comment system with moderation tools for managing user interactions. - **User and Access Control**: - Implements role-based access control with secure authentication mechanisms. - Offers a professional live edit mode supporting multi-property editing and advanced controls such as header adjustments and typography customization. - **Component Management**: - Supports nested components with unlimited depth, enabling intuitive drag-and-drop functionality and responsive design controls. - Allows real-time property edits and component-specific styling, enhancing robustness and user experience. - **Design and Development Tools**: - Features a comprehensive content management system with advanced component management including Hero, Text, Image, Gallery, and custom components. - Provides over 47 customization options for settings like typography, colors, layout, alongside an integrated design system supporting color schemes and responsive designs. - **Deployment and Setup**: - Can be deployed using Docker for ease of setup, requiring installation of Docker & Docker Compose and Git for cloning the repository. - Instructions include starting services and accessing various interfaces (frontend at http://localhost:8080, backend API at http://localhost:8081, admin panel at http://localhost:8080/admin) with default login credentials. - **Development Environment**: - Outlines development scripts for managing services, maintaining code quality through commands like `cargo check`, `cargo fmt`, and security audits. - Includes testing protocols for backend and frontend components using cargo tests and integration tests across the workspace. - **Documentation**: - Provides essential guides covering setup, development environment configuration, database management, security features, and architecture. - **Recent Improvements**: - Introduces a live edit mode with multi-property configurations, default data setup for professional configuration, Docker integration for deployment, comprehensive code cleanup achieving zero compiler warnings, and an enhanced template system. - **Security Features**: - Offers session-based authentication to avoid JWT vulnerabilities, input sanitization, file upload security measures, and properly configured CORS. - **Contribution Guidelines**: - Encourages contributions by providing guidelines for forked repository management, maintaining zero compiler warnings, feature testing, documentation updates, and pull request submissions. - **Project Acknowledgments**: - Licensed under MIT, it acknowledges contributions from the Rust Community, Yew Framework, Diesel ORM, Axum Framework, and PostgreSQL. This comprehensive CMS leverages modern web technologies to provide a secure, efficient, and user-friendly platform for content management, emphasizing live editing capabilities, robust security measures, and an easily deployable architecture. Keywords: Authentication, Axum, CMS, Content Management, Diesel, Docker, Live Edit, PostgreSQL, Rust, Security, Tower, WebAssembly, Yew
postgresql
github.com 11 hours ago
|
20. HN Practical Techniques for Codex, Cursor and Claude CodeThe document offers practical techniques for effectively utilizing AI language models named Codex, Cursor, and Claude Code, specifically focusing on optimizing their performance for coding tasks. It provides strategies that guide users in leveraging the distinct capabilities of each model to improve programming workflows. The guidance likely includes methods to enhance efficiency and productivity by understanding how each model can best be integrated into various aspects of coding, ensuring that users maximize their potential benefits. - **Overview**: The document discusses practical techniques for effectively using AI language models Codex, Cursor, and Claude Code. - **Focus**: It emphasizes optimizing these models' performance in coding tasks through tailored strategies. - **Unique Capabilities**: Guidance is provided on utilizing each model's unique features to enhance programming workflows. - **Objective**: The goal is to improve efficiency and productivity by integrating the strengths of each AI language model into coding processes. Keywords: Claude Code, Codex, Cursor, Keywords, Methods, Practical techniques, Relevant, Technical, Text, Topics
claude
coding-with-ai.dev 11 hours ago
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21. HN Inside vLLM: Anatomy of a High-Throughput LLM Inference SystemThe document titled "Inside vLLM: Anatomy of a High-Throughput Large Language Model (LLM) Inference System" provides an in-depth exploration of the structure and functionality of a system specifically designed for efficient large language model inference. The text details how this system achieves high-throughput capabilities, likely discussing its architecture, performance optimizations, and scalability features essential for managing extensive workloads effectively. Additionally, the mention of "Modal Notebooks" suggests that the system includes interactive or modular components aimed at facilitating experimentation or analysis. This inclusion indicates a design choice to enhance user interaction and engagement with the model's functionalities. Furthermore, there is an indication of a call to share this technology, which points towards an intent for broader dissemination and community involvement in the development and application of this advanced inference system. - **Main Title:** The document focuses on "Inside vLLM: Anatomy of a High-Throughput Large Language Model (LLM) Inference System." - **System Structure & Functionality:** It explores how efficiently large language models are run, emphasizing high-throughput inference. - **Key Features Discussed:** - Architecture of the system - Performance optimizations for handling workloads - Scalability features ensuring efficient processing of extensive tasks - **Interactive Components:** The inclusion of "Modal Notebooks" suggests interactive or modular elements designed to support user experimentation and analysis. - **Community Engagement:** There is an implied call to share insights about this technology, indicating a push towards broader dissemination and community engagement. Keywords: High-Throughput, Inference, LLM, LLM Inference System, Modal, Modal Notebooks, Notebooks, Share, System, Throughput, vLLM
llm
modal.com 11 hours ago
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22. HN Show HN: I built an open source drag and drop editor for Genkit AI flowsThe provided text discusses the development of FlowShapr AI, an open-source drag-and-drop editor designed to manage Genkit AI flows. The tool addresses the challenge of making iterative changes to prompts, flows, and tools without requiring code modifications or redeployments. Built on top of Genkit, FlowShapr AI simplifies flow management by enabling remote execution of simple workflows using providers such as GoogleAI, Anthropic, or OpenAI. It integrates seamlessly with MCP tools through an API endpoint, allowing for the execution of flows remotely and compatibility with the genkit client SDK. The project is hosted on [flowshapr.ai](https://flowshapr.ai), with its repository available on GitHub. The developer plans future updates that will introduce support for Ollama, various vector stores, complex multi-agent flows, and session management. Feedback and suggestions from users are actively encouraged to enhance the tool further. **BULLET POINT SUMMARY:** - FlowShapr AI is an open-source drag-and-drop editor created to manage Genkit AI flows. - It allows iterative changes without needing code alterations or redeployments. - Built on top of Genkit, it supports remote execution using providers like GoogleAI, Anthropic, and OpenAI. - Integrates with MCP tools via an API endpoint and is compatible with the genkit client SDK. - The project is hosted at [flowshapr.ai](https://flowshapr.ai) with a repository on GitHub. - Future updates will include support for Ollama, vector stores, complex multi-agent flows, and session management. - User feedback and suggestions are encouraged to improve the tool. Keywords: AI Agents, API Endpoint, Anthropic, Genkit, GoogleAI, Langfuse, MCP tools, Ollama, OpenAi, UI builder, code change, deployment, editor, flows, flowshaprai, multi agent flows, open source, prompts, remote execution, session management, tools, tracing, vector stores
ollama
news.ycombinator.com 11 hours ago
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23. HN Pgdbtemplate: Go library for creating PostgreSQL test databases using templates- **Pgdbtemplate Library Overview**: Pgdbtemplate is a Go library optimized for creating PostgreSQL test databases using template databases, significantly enhancing test execution speed by 1.2 to 1.6 times compared to traditional migration methods and reducing memory usage by about 17%. It supports up to 500 concurrent databases, ensuring thread-safe operations and improved performance with increasing schema complexity. - **Features and Compatibility**: The library is specific to PostgreSQL, includes connection string validation, and supports both `database/sql` and `pgx` drivers. It facilitates flexible testing scenarios and integrates with Testcontainers for containerized testing environments. Pgdbtemplate allows configurable migration runners and connection providers, ensuring efficient database management. - **Installation and Setup**: Users can install Pgdbtemplate using the command `go get github.com/andrei-polukhin/pgdbtemplate`. The setup involves configuring a connection provider, initializing a migration runner, and managing migrations effectively with tools provided by the library. - **Code Examples and Usage**: The document includes code examples demonstrating how to create a PostgreSQL connection provider, configure a migration runner, initialize template management, and manage database creation and deletion for testing. It highlights using Pgdbtemplate in existing PostgreSQL environments with `pgx` driver testing. - **Template Manager and Testing Environment**: In the provided Go package (`myapp_test`), components like Template Manager Setup, Main Test Function, and specific Test Functions (e.g., `TestUserRepositoryPgx`) are outlined. These demonstrate setting up isolated database environments for effective PostgreSQL testing using Go, including creating temporary databases, running migrations, and cleaning resources post-testing. - **Integration with testcontainers-go**: The document discusses integrating the `testcontainers-go` library to create disposable containerized testing environments within Go projects, enhancing consistency and isolation during tests. It covers setup instructions, usage examples, and advantages for streamlined migration management. - **Connection Pooling Options**: Both StandardConnectionProvider and PgxConnectionProvider support common pooling settings such as max open connections and idle connections, with advanced configuration options detailed in an external guide. - **Performance Benefits of Template Databases**: Benchmarks on an Apple M4 Pro show template databases performing 1.03x to 1.50x faster than traditional methods, depending on schema complexity. They offer better concurrency and memory efficiency, reducing operation times consistently across different complexities and handling up to 500 concurrent operations with a notable time savings. - **Schema Complexity Impact**: Performance gains increase from 1.03x for single-table schemas to 1.50x for multi-table schemas using templates. The approach also improves thread safety and reduces memory usage per operation by 17%. - **Scalability and Configuration Requirements**: Tests show scalability benefits with time savings of 32% to 37% when managing a large number of databases. Proper configuration requires setting the `POSTGRES_CONNECTION_STRING` environment variable. - **Thread Safety and Best Practices**: The library is thread-safe, utilizing mutexes for synchronization during template initialization and database creation. Best practices include using unique naming strategies with timestamps and atomic counters, ensuring test isolation by using separate databases, and adhering to PostgreSQL 9.5+ compatibility requirements along with Go 1.21+. - **Contribution and Licensing**: Contributions are welcomed as per guidelines in CONTRIBUTING.md, and the project is licensed under the MIT License as detailed in LICENSE. The summary encapsulates Pgdbtemplate’s capabilities, setup, and performance benefits while outlining usage practices for efficient PostgreSQL testing in a concurrent environment. Keywords: Benchmark Results, Best Practices, Concurrency, ConnMaxIdleTime, ConnMaxLifetime, Context, CreateTestDatabase, Database Creation, Go, MaxIdleConns, MaxOpenConns, Migration Files, Mutex Protection, Package, Parallel Testing, Pgdbtemplate, Pgx Testing, PostgreSQL, Postgres, Scaling Benefits, Schema Size, StandardConnectionProvider, Template Approach, TemplateManager, cleanup, configurable migration runners, connection string validation, container, database/sql, goroutines, high-performance, pgx drivers, schema complexity, setup, templates, test, test databases, testcontainers integration, thread-safe
postgres
github.com 11 hours ago
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24. HN Tech Stack for Indie Hackers: Keep It Simple and Iterate FastThe document provides guidance for indie hackers aiming to launch projects efficiently by emphasizing simplicity and speed. It recommends using cost-effective infrastructure like Hetzner servers paired with Coolify for deployment via Docker, allowing seamless management from deployment to CI/CD processes. The use of Postgres for storage is suggested, along with automatic database backups stored in AWS S3 to ensure data safety. Language selection is flexible but the author favors Clojure for its dynamic typing and JVM performance benefits. For frontend development, starting simple with HTMX and transitioning to React + JavaScript as needed is advised. Writing high-level tests, especially API-level testing, from the outset ensures code quality and facilitates future refactoring. Monitoring solutions include Coolify’s basic log viewing, host metrics alerts, health checks, and tools like Sentry for exception visibility. For complex features such as authentication and payments, it recommends using managed services like Clerk or Auth0 for security and Stripe for payment processing to save time and avoid common pitfalls. The document advises focusing on product development initially and addressing future growth through vertical scaling, transitioning later to a managed Postgres solution, container orchestration with Docker, and adding a load balancer when necessary. Overall, it emphasizes keeping solutions simple and avoiding premature optimizations. **Bullet Point Summary:** - Emphasizes simplicity and speed for indie hackers launching projects. - Recommends using Hetzner servers and deploying with Coolify via Docker. - Suggests Postgres for storage and AWS S3 for database backups. - Language choice is flexible; Clojure recommended for its benefits. - Start frontend development with HTMX, switch to React + JavaScript as needed. - Highlights the importance of writing high-level tests, especially API-level testing, from the start. - Monitoring should include Coolify log viewing, host metrics alerts, health checks, and Sentry for exceptions. - For complex features like authentication and payments, use managed services such as Clerk or Auth0 and Stripe. - Focus on building the product first; address scaling later through vertical scaling, managed Postgres, Docker orchestration, and load balancing if needed. - Advocates keeping solutions simple and avoiding premature optimizations. Keywords: API-level Testing, AWS RDS, Aurora, Auth0, Authentication, CI/CD, Clerk, Clojure, Container Orchestration, Coolify, Deployment, Developer, DigitalOcean, Dockerfile, ECS Fargate, GCP, GitHub, HTMX, Hetzner, High-Level Tests, Indie Hackers, JVM, Kinde, Load Balancer, Managed Services, Monitoring, Neon, Observability, Payments, Postgres, Project Launch, React, Refactor, Scaling, Sentry, Stripe, Tech Stack
postgres
blog.andreyfadeev.com 12 hours ago
|
25. HN Cookiecutter Django: framework for jumpstarting production-ready Django projects**Concise Summary:** Cookiecutter Django is a framework aimed at expediting the setup of production-ready Django projects, utilizing Cookiecutter for streamlined project creation and supporting Python 3.12 alongside Django 5.1. It comes equipped with features that ensure quick start-up with full test coverage, integrates Twitter Bootstrap v5, and provides secure defaults using SSL configurations. The framework is optimized for both development and production settings. Key components of Cookiecutter Django include built-in support for user registration through django-allauth, flexible static file management options (like Gulp or Webpack), email services via Anymail with various providers, and media storage solutions on AWS S3, Google Cloud Storage, Azure Storage, or nginx. Additionally, it offers Docker integration with docker-compose, deployment guidance for Heroku and PythonAnywhere, and test execution support using unittest or pytest. Optional functionalities include ASGI setup, serving static files through cloud services or Whitenoise, Celery configuration for task management, Mailpit for local email testing, and Sentry integration for error logging. The framework emphasizes the use of maintained third-party libraries, defaults to PostgreSQL (with a MySQL fork option), employs environment variables for configurations, and utilizes tools like pre-commit for maintaining code quality. For support or troubleshooting, users are directed to consult documentation and encouraged to open issues rather than contacting maintainers directly. This project uses PostgreSQL versions 13 through 17 and offers a MySQL alternative, while incompatibility with Apache/mod_wsgi is noted. It also advocates financial support via OpenCollective or GitHub Sponsors. The document includes instructions for creating a new Django project named "redditclone" by first installing Cookiecutter using `uv tool`, followed by running it against the cookiecutter-django GitHub repository and answering configuration prompts. These steps cover setting up project details, domain and email configurations, PostgreSQL version selection, cloud provider setup, asynchronous tasks (Celery), use of Django REST Framework, frontend pipeline preferences, additional services like Sentry, deployment platform choices, CI tool selections, VCS settings, and debug mode. Upon project completion, users are instructed to navigate into the project directory (`reddit/`), review its contents, initialize a Git repository, commit initial changes, and push them to GitHub. Community support is available via Stack Overflow and Discord for `cookiecutter-django`. Readers of "Two Scoops of Django" may encounter discrepancies due to this project serving as a testing ground for new concepts. PyUp users are offered a discount code `cookiecutter` for updates on dependencies and security patches. Both Python and HTML files in the project contain placeholders for third-party library integration labeled "your stuff". For those preferring MySQL, an alternative fork is available through a specified GitHub link. Stable releases can be accessed via the project's official releases page. Users whose requirements differ from what is provided are encouraged to fork the repository, share their versions on lists like 'Similar Cookiecutter Templates', and submit them to cookiecutter and Django Packages, with permissions for renaming forks. The project welcomes small pull requests and invites users to contribute articles or tips via pull requests. **Bullet Point Summary:** - **Framework Overview**: Cookiecutter Django expedites the creation of production-ready Django projects using Cookiecutter, supporting Python 3.12 and Django 5.1. - **Features & Components**: Includes full test coverage, Twitter Bootstrap v5 integration, secure SSL defaults, user registration via django-allauth, flexible static file management, email sending through Anymail, media storage solutions, Docker support, deployment instructions for Heroku and PythonAnywhere, and testing with unittest or pytest. - **Optional Functionalities**: ASGI setup, cloud-based static file serving, Celery configuration, Mailpit for local email testing, Sentry for error logging; prioritizes maintained libraries and defaults to PostgreSQL (with MySQL option). - **Support & Compatibility**: Uses PostgreSQL 13-17; incompatible with Apache/mod_wsgi. Encourages support via OpenCollective or GitHub Sponsors. - **Project Setup Instructions**: Involves installing Cookiecutter, running it against the cookiecutter-django repo, and answering configuration prompts for project-specific details. - **Post-Creation Steps**: Includes navigating to the project directory, initializing a Git repository, committing changes, and pushing to GitHub; community support via Stack Overflow and Discord. - **Additional Information**: Mentions "Two Scoops of Django" discrepancies, PyUp discount code `cookiecutter`, placeholders for third-party libraries, MySQL fork availability, stable release access, and encourages forking the project and contributing articles or tips. Keywords: AWS, Celery, Cookiecutter, DevOps, Django, Docker, GitHub, Heroku, PostgreSQL, SSL, Selenium, async, frontend, production-ready, pytest
postgresql
github.com 12 hours ago
|
26. HN Deterministic LLMThinking Machines Lab is leveraging its significant $2 billion seed funding to develop deterministic AI models that provide reproducible responses. Utilizing expertise from former OpenAI researchers, their research, detailed in a blog post "Defeating Nondeterminism in LLM Inference," targets the inherent randomness in AI outputs by focusing on GPU kernel orchestration during inference processing. By managing this layer, they aim to achieve more consistent and predictable AI model responses. This initiative is part of broader efforts shared through their new blog platform, Connectionism. The lab's work seeks to improve the reliability of AI models, particularly benefiting reinforcement learning (RL) training processes, which reward accurate answers. Inconsistent outputs in current systems generate noise, hindering smooth RL operations; thus, more consistent responses could enhance efficiency. The company plans to apply RL for creating tailored AI solutions for businesses and is preparing to launch its first product aimed at researchers and startups. Former OpenAI CTO Murtazi has indicated that the forthcoming product will facilitate custom model development, although specific details remain undisclosed. In a move towards greater transparency and fostering a research-oriented culture, Thinking Machines Lab intends to regularly publish updates through their blog series "Connectionism." This approach contrasts with OpenAI's recent shift towards less openness as it expanded. This initiative sheds light on the operations of one of Silicon Valley’s more secretive AI startups, emphasizing its engagement with critical challenges in the field. The success of Thinking Machines Lab in addressing these issues and developing practical products will be crucial to justifying its $12 billion valuation. **Bullet Point Summary:** - Thinking Machines Lab is using $2 billion seed funding to create deterministic AI models for reproducible responses. - Research focuses on controlling GPU kernel orchestration during inference processing to reduce randomness, as detailed in "Defeating Nondeterminism in LLM Inference." - Part of broader efforts shared through their blog platform, Connectionism. - Aims to enhance AI model reliability, particularly benefiting reinforcement learning (RL) training by reducing noise from inconsistent outputs. - Plans to use RL for developing tailored AI solutions for businesses; preparing to launch a product for researchers and startups. - Former OpenAI CTO Murtazi suggests the new product will aid in custom model development without disclosing specifics. - Lab emphasizes transparency through regular research updates on "Connectionism," contrasting with OpenAI's decreased openness. - The initiative highlights engagement with critical AI challenges, with success crucial to justifying its $12 billion valuation. Keywords: $12 billion valuation, AI models, Connectionism, Deterministic LLM, GPU kernels, Mira Murati, OpenAI, RL training, Silicon Valley, Thinking Machines Lab, deterministic systems, inference processing, nondeterminism, randomness, reinforcement learning, reproducible responses, research culture, seed funding
llm
techcrunch.com 12 hours ago
|
27. HN Show HN: Open Source Deal Flow Monitoring Infrastructure Built for VCs- **Subsignal Overview**: Subsignal is an open-source infrastructure designed to aid venture capitalists (VCs) by providing comprehensive deal flow monitoring. It flags sector changes, helping VCs stay informed and act promptly. - **Key Features**: - *Deal Intelligence*: Tracks promising companies for re-engagement. - *Relationship Intelligence*: Maintains connections during company pivots. - *Competitive Intelligence*: Monitors threats to portfolio companies. - Provides tools for timely strategic decisions. - **Testimonials and Benefits**: Users highlight Subsignal's competitive edge through early market insights and enhanced relationships with potential founders. It helps VCs stay relevant and capitalize on comeback stories by offering advanced views of the competitive landscape. - **Project Structure**: - Organized into directories such as `public/` for static assets, `app/` for Next.js components, `components/` for reusable React components, `lib/` for library code, `services/` for business logic, and more. - **Configuration and Setup**: - Prerequisites include Node.js 18+, pnpm, PostgreSQL, and necessary API keys. - Set up involves cloning the repo, installing dependencies with `pnpm install`, configuring environment variables from `.env.example`, and setting up the database with migration scripts. - **Development Tools**: - Includes linting (`pnpm lint`), TypeScript checks (`pnpm typecheck`), code formatting (`pnpm format`), and React Email server setup. - Database management is handled through Drizzle Studio, migrations, and schema changes using specific `pnpm` commands. - **API Documentation**: - Available via `docs/example.md` or a Postman collection. Covers authentication (Google OAuth), company management, page monitoring, user preferences, and health checks. - **Tech Stack**: - Utilizes Next.js 15, React 19, TypeScript for frontend; Hono API framework with PostgreSQL and Drizzle ORM for backend. - Features Google OAuth via Better Auth, OpenAI GPT for AI tasks, React Email with Resend for emails, DodoPayments for payments, Inngest for background jobs, Cloudflare R2 for storage, and UI components like Radix UI, Lucide React, and Sonner toasts. - **Additional Capabilities**: - Monitors automation storage using Cloudflare R2, capturing briefings and screenshots. - Offers AI-powered briefings, centralized company portfolio management, briefing organization with filtering, customizable monitoring preferences, and visual snapshots of website changes. - **Contributions and Quality Assurance**: - Encourages maintaining code quality through linting, type checks, auto-formatting, and passing tests before building the project. - **Pricing Plans**: - Emphasizes flexible pricing to accommodate different investment workflows. Keywords: AI Services, API Documentation, Authentication, Automation, Cloudflare R2, Competitive Intelligence, Components, Database Migrations, Deal Flow, DodoPayments, Drizzle ORM, Email Templates, Environment Variables, Hono Framework, Hooks, Inngest, Lucide React, Market Intelligence, Milestones, Nextjs, Nodejs, OAuth Credentials, Open Source, Portfolio, PostgreSQL, Radix UI, React, Relationship Intelligence, Sonner Toasts, Startups, Static Assets, Storage, Subsignal, Tailwind CSS, TypeScript, VCs
postgresql
github.com 12 hours ago
|
28. HN Building Infrastructure Automation Without Terraform for Fly.ioThe provided text serves as a guide for automating Fly.io infrastructure deployment in the absence of Terraform support due to platform constraints. It outlines two main strategies: utilizing `flyctl` integrated with GitHub Actions and employing the Machines API for advanced control over virtual machines. 1. **Flyctl + GitHub Actions**: This method is recommended for most users as it integrates seamlessly with Git workflows. When code is pushed to the main branch, a GitHub Action triggers automatic deployment without requiring separate VM state or infrastructure management. To set this up: - Generate a deploy token using `fly tokens create deploy --app - Store the token in GitHub Secrets under the name `FLY_API_TOKEN`. - Create a YAML workflow file `.github/workflows/fly.yml` that checks out code, sets up Flyctl, and runs `flyctl deploy --remote-only`, using the secret for authentication. This enables continuous deployment across multiple regions. 2. **Machines API**: For users needing more granular control over their infrastructure, the Machines API provides a RESTful interface to directly manage virtual machines (VMs). It allows creation, orchestration, and management of VMs across various regions without reliance on Terraform-like plans or states. Key features include: - Direct manipulation of VMs using HTTP clients like `curl`. - Configuration options for custom scaling logic and lifecycle management. - Use cases such as setting up ephemeral game servers or serverless functions that are spun up and torn down programmatically. The guide emphasizes aligning tools with Fly.io's core principles rather than forcing them into a plan/apply model. While the Machines API offers extensive control, it introduces complexity compared to simpler `flyctl` methods. The text also highlights the challenge of transitioning away from Terraform due to its declarative nature not fitting well with the imperative approach required by the Machines API. Overall, the guide provides practical steps and considerations for leveraging Fly.io's capabilities in a flexible yet controlled manner, encouraging sharing of custom tooling projects among developers facing similar transitions. **Bullet Point Summary:** - The guide offers strategies to automate deployment on Fly.io without Terraform, emphasizing `flyctl + GitHub Actions` and the Machines API. - `Flyctl + GitHub Actions`: Automates deployment via Git workflows using a generated deploy token stored in GitHub Secrets; suitable for most users. - Machines API: Provides advanced control over VMs with direct RESTful management capabilities, ideal for custom orchestration needs. - Setup involves configuring GitHub Actions and using HTTP clients like `curl` to interact with the Machines API. - Emphasizes aligning tools with Fly.io’s principles rather than a plan/apply model; highlights complexity versus simplicity trade-offs. - Encourages sharing of projects that reimagine tooling in response to Terraform's deprecation. Keywords: Bearer Token, CLI, CPU Kind, Canary Deploys, Continuous Deployment, Curl, Custom Scaling, Deploy Token, Docker, Ephemeral Environments, Flyctl, Flyio, GitHub Actions, Go SDK, HTTP Interface, HTTPS, Infrastructure Automation, JSON, Machines API, Memory Mb, Monorepo, Nginx, Orchestration Tools, Postgres, REST Interface, Rails App, Redis, Serverless, Terraform, VM Control, Workflow File
postgres
fly.io 13 hours ago
|
29. HN Why OpenAI's solution to AI hallucinations would kill ChatGPT tomorrow**Summary:** OpenAI's research paper delves into the inherent problem of "hallucinations" in AI language models like ChatGPT, where these systems produce false information despite perfect training data. The study offers a rigorous mathematical explanation indicating that such errors are inevitable due to how language models predict words based on probabilities, leading to error accumulation over extended sentences. Hallucination rates remain constrained by an AI's ability to differentiate valid from invalid responses, making them particularly unavoidable in complex knowledge domains and less frequently encountered facts during training. The research identifies a critical issue with current evaluation methods used by major benchmarks that favor certainty over doubt—AI systems are thus incentivized to guess rather than admit uncertainty. This has led to the prevalence of incorrect answers, as seen when advanced models confidently provide inaccurate responses about factual details such as birthdays. The study highlights how binary grading systems further exacerbate this issue by penalizing honest responses with no confidence. To mitigate these issues, OpenAI proposes adjusting evaluation criteria to consider AI's confidence levels in its responses. For example, requiring a model to be over 75% confident before responding could reduce incorrect answers but may also decrease the number of user interactions due to withheld uncertain responses. This proposal draws parallels to air-quality monitoring systems where users prefer confident, albeit sometimes inaccurate, information over uncertain readings. The paper suggests that reducing hallucinations is possible through established methods for quantifying uncertainty, though these approaches require significant computational resources, making them economically viable primarily in high-stakes fields like supply chain logistics, financial trading, and medical diagnostics. In consumer applications, the focus remains on confident responses due to current business incentives and evaluation benchmarks. Although improvements in energy efficiency and chip technology could help, they are unlikely to fully mitigate the computational demands of implementing uncertainty assessments in AI systems. The paper underscores a misalignment between economic drivers in consumer AI development and efforts to reduce hallucinations, suggesting that this issue will persist until there is a shift in incentives. **Bullet Point Summary:** - **Inherent Issue**: AI language models produce "hallucinations" due to the probabilistic nature of word prediction. - **Evaluation Methods**: Current benchmarks reward certainty over doubt, leading to confident but incorrect answers. - **Proposed Solution**: Modify evaluation criteria to factor in AI confidence levels, reducing errors at a potential cost to user interaction. - **Economic Viability**: High-stakes fields justify the increased computational costs for uncertainty-aware models, unlike consumer applications focused on confident responses. - **Persistent Challenge**: The misalignment between economic incentives and the need to reduce hallucinations suggests this problem will continue unless incentive structures change. Keywords: AI hallucinations, Adam Kalai, ChatGPT, DeepSeek-V3, OpenAI, active learning, benchmarks, binary grading, business infrastructure, chip architectures, classification problem, consumer applications, evaluation trap, financial trading, knowledge, large language models, mathematical explanation, medical diagnostics, operational costs, penalising uncertainty, probability, specialized domains, supply chain logistics, training data
openai
theconversation.com 13 hours ago
https://www.anthropic.com/research/language-models-most 11 hours ago https://openai.com/index/why-language-models-hallucinat 11 hours ago https://scholar.google.com/citations?hl=en&user=AB5z_AkA 11 hours ago https://arxiv.org/pdf/2509.04664 11 hours ago https://www.rasch.org/rmt/rmt271d.htm 11 hours ago |
30. HN How to Upgrade Your MCP Server with Context EngineeringThe article discusses enhancing the Multi-Context Processing (MCP) server for improved context management of AI agents through Context Engineering using XMLUI. The author, involved as a consultant, highlights the importance of managing information within AI agents' context windows and details improvements to xmlui-mcp for better handling of documentation, source code, and examples. Key configuration steps include launching xmlui-mcp with specific directories set for XMLUI repository clones and example subfolders like xmlui-invoice and xmlui-mastodon. This setup facilitates improved functionality in AI agents such as Claude or Cursor by providing relevant context. Feedback from developers indicates notable enhancements, exemplified by a seamless user interface experience post-refresh. The author initially expressed enthusiasm about solving issues with the MCP server but noted concerns regarding recurring unsupported solutions due to inadequate documentation and examples. To address this, prompts were introduced to ensure adherence to verified xmlui syntax. Further enhancement of xmlui-mcp involves improving fuzzy search capabilities using a phased approach—starting from exact matches, relaxing stopwords, to adopting partial matching strategies—to balance precision and recall. A successful search strategy is demonstrated through the quest for guidance on creating equal-width components. The breakthrough came with the precise query "width 100% equal," identifying relevant guides due to key terms in filenames and content about width sizing and layout instructions. Analysis of `query_plan` showed varying results across exact, relaxed, and partial queries. The summary emphasizes improving response accuracy and accountability for AI tools: - **Response Organization**: Responses are now categorized into buckets guiding users towards appropriate follow-up tools. - **Emphasis on Documentation**: Users must rely on verifiable documentation with cited URLs or examples rather than accepting uncited claims. - **Prompt Updates for AI Tools**: Prompts for Claude and Cursor have been revised to prioritize guidance from the xmlui-mcp server and acknowledge when definitive answers are unavailable. - **Honesty in Failure**: The article stresses testing scenarios where correct answers may not exist, ensuring AI communicates absent solutions honestly. - **Documentation Testing and Improvement**: Documentation can be tested for accuracy, addressing identified gaps (e.g., missing examples) to enhance documentation quality and search outcomes. - **Universal Design Principle**: Writing accessible documentation benefits all users by improving overall accessibility. Additionally, the document discusses the role of Large Language Models (LLMs) in writing documentation, advocating for human readability over machine optimization. It suggests thoughtful naming and structured layering as beneficial editorial practices, with a Machine Comprehension Platform (MCP) server enabling better AI collaboration to improve documentation processes. The partnership aims for iterative improvements through AI feedback. While autonomous LLMs may be unreliable, creating reliable layers atop them is recommended in software engineering. Context engineering ensures teams working on XMLUI applications remain grounded when using these technologies. **Key Points:** - Upgrading MCP server enhances context management for AI agents via XMLUI. - Improved xmlui-mcp configurations enable better handling of documentation and examples. - A phased fuzzy search approach aims to balance precision and recall in searches. - Effective search strategy illustrated with a query on creating equal-width components. - Response accuracy improvements include categorized responses, reliance on verifiable documentation, updated AI prompts, honest failure communication, documentation testing, and universal design principles. - LLMs should prioritize human readability; editorial practices like naming and layering improve collaboration between MCP servers and AI tools for iterative enhancements. - Context engineering is crucial to maintain reliability in XMLUI application development. Keywords: CLI, LLM, MCP server, XMLUI, accessibility, agents, binary, context windows, documentation, examples, repo, search tools
llm
thenewstack.io 14 hours ago
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31. HN NVIDIA B200 low power usage for inference AI workloadThe text evaluates the performance and cost-effectiveness of NVIDIA's Blackwell B200 GPUs in comparison to the H100 GPUs within both self-hosted and cloud environments, particularly highlighting their use in machine learning tasks such as language model inference and computer vision training. - **Performance Insights**: - The B200 GPUs demonstrated lower-than-expected power consumption at around 600W per GPU versus a specification of 1000W. - Initial language model (LLM) inference performance was underwhelming but expected to improve with software updates. For computer vision tasks, like YOLOv8 + DINOv2, the B200 excelled without any tuning and had a high idle power draw of 140W per GPU. - Compared to H100 GPUs in cloud setups, B200 showed significant performance advantages: approximately a 10% speedup for mid-sized Gemma 27B models during inference tests and up to 57% faster training speeds for computer vision tasks using larger memory capacities. - **Cost Considerations**: - Self-hosting B200s could cost roughly $0.51 per hour per GPU in operating expenses, a fraction of cloud-based H100 costs ranging from $2.95 to $16.10 per hour. This makes self-hosting up to 30 times more cost-effective for continuous use. - The initial capital expenditure (CapEx) for purchasing B200 GPUs is around $400,000, potentially reduced through programs like NVIDIA's Inception program. - **Self-hosting vs. Cloud Benefits**: - Self-hosted setups offer consistent performance without the overhead of virtualization and the "noisy neighbor" problem present in cloud services. - They provide predictable costs by moving from variable to fixed expenses and ensure maximum GPU utilization without unexpected bills. - A case study at GreenMountain, Norway, illustrates a sustainable infrastructure powered by renewable energy supporting continuous AI workloads with an 8x B200 cluster. - **Infrastructure and Efficiency**: - The B200's architecture boasts over double the memory capacity, approximately 2.4 times greater memory bandwidth, and more than twice the peak FP16/BF16 compute throughput compared to H100. - Self-hosting supports faster iterations due to constant access to resources, avoiding delays associated with cloud-based spot instances or quota restrictions. - **Future Steps**: - Future benchmarks will focus on latency and throughput of models like Stable Diffusion and experiments with language model fine-tuning using methods such as LoRA and QLoRA. - Addressing the high idle power consumption remains a priority for future optimization efforts. Overall, the analysis underscores the B200's performance advantages in specific machine learning tasks and its cost benefits when self-hosted, particularly within robust infrastructures like GreenMountain. Despite some initial software immaturity and high idle power concerns, the potential improvements from upcoming updates are promising. Keywords: AI, B200, Benchmarking(Note: The keywords extracted are relevant to the text and appear within it without duplicates), Blackwell, Computer Vision, DINOv2, GPUs, Gemma, H100, Inference, NVIDIA, Ollama, YOLOv8
ollama
www.lightly.ai 14 hours ago
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32. HN Made a project to integrate GPT models into directly Ghidra**GhidraGPT Plugin Summary** The GhidraGPT plugin is an advanced tool developed by Mohamed Benchikh that integrates Large Language Models (LLMs) into the Ghidra reverse engineering software to improve code analysis and readability through AI-powered features. It enhances functionality by enabling function and variable renaming, providing detailed code explanations, and detecting vulnerabilities. For optimal performance in real-time scenarios, faster models like grok-3 or deepseek-chat are recommended. The plugin supports various LLM providers such as OpenAI, Google Gemini, Anthropic's Claude, Cohere, Mistral AI, DeepSeek, Grok (xAI), and Ollama. **Key Features:** - **Integration with Ghidra:** Integrates seamlessly into the Ghidra analysis pipeline and offers custom theming options for user interface personalization. - **Installation and Configuration:** Can be installed via a GitHub repository using shell scripts. Users configure it through a secure configuration panel where API keys are stored safely. - **Functional Capabilities:** - Enhances code by renaming variables/functions. - Explains function behavior and analyzes vulnerabilities, accessible through the right-click context menu in Ghidra. **Architecture and Services:** - The plugin comprises services like CodeEnhancementService for renaming tasks, CodeAnalysisService for security analysis, and GPTService for managing AI interactions. - ConfigurationManager handles settings and API key management, while UI components such as GhidraGPTProvider enable context menu integration, and GhidraGPTConsole displays AI responses. **Pending Tasks and Future Plans:** - It includes planned improvements like automated variable retyping, enhanced cross-reference analysis, batch processing, and intelligent caching systems. - Additional future features aim to incorporate custom prompts for specific analyses and export functionalities. **Contributions and Requirements:** - Contributions are welcomed through issues, feature requests, pull requests, or help with documentation and testing. The project is hosted on GitHub at ZeroDaysBroker/GhidraGPT. - It requires Ghidra version 10.0+ and Java 17 for compatibility and an active internet connection for API calls (except Ollama), relying on valid API keys from AI providers. **Performance Considerations:** - Analysis results may vary based on the complexity of the code and the chosen AI model, affecting the effectiveness of the plugin's capabilities. --- - **Development:** Created by Mohamed Benchikh to integrate LLMs into Ghidra for enhanced code analysis. - **Functionality:** Offers renaming, explanations, vulnerability detection; supports various AI providers including OpenAI and Google Gemini. - **Integration:** Seamlessly integrates with Ghidra's pipeline; offers custom UI theming. - **Installation:** Available via GitHub repository, requires shell scripts for building and installing. - **Configuration:** Managed through a configuration panel that securely stores API keys. - **Actions:** Accessible from the context menu in Ghidra; supports enhancing code, explaining functions, and vulnerability analysis. - **Architecture:** Includes services like CodeEnhancementService, CodeAnalysisService, GPTService, and ConfigurationManager. - **UI Components:** Features GhidraGPTProvider for context menus and GhidraGPTConsole for AI responses. - **Future Improvements:** Plans for automated retyping, enhanced analysis, batch processing, caching, custom prompts, and export functionality. - **Contributions:** Open to community input via GitHub; requires Ghidra 10.0+, Java 17, and internet connection (except Ollama). - **Performance Variability:** Effectiveness depends on code complexity and AI model choice. Keywords: AI Responses, AI analysis, AI-powered code analysis, API keys, Anthropic, Batch Processing, Caching System, CodeAnalysisService, CodeEnhancementService, Cohere, ConfigurationManager, Contributing, Cross-Reference Analysis, Custom Prompts, DeepSeek, Dependencies, Export Functionality, GPT models, GPTService, Ghidra, GhidraGPT, GhidraGPTConsole, GhidraGPTProvider, Google Gemini, Grok, Large Language Models (LLMs), Mistral AI, Ollama, OpenAI, Plugin, UI components, Variable Retyping, analysis pipeline, analysis results, build plugin, code complexity, code explanation, configuration panel, encryption, function, integration, internet connection, multi-LLM support, providers, real-time performance, repository, reverse engineering, right-click context menu, security analysis, security vulnerabilities, service layer, stream processing, theme support, variable names, vulnerability detection
deepseek
github.com 15 hours ago
|
33. HN Behavior: Robot manipulation benchmark based on 1000 household tasks**Summary:** BEHAVIOR-1K is a benchmark designed for testing embodied AI agents on 1,000 household tasks like cleaning, cooking, and organizing. It leverages real human activity surveys and preference studies to create its comprehensive task repository. The system supports Linux (Ubuntu 20.04+) and Windows 10+ with recommended specifications of at least 32GB RAM and an NVIDIA RTX 2080+ GPU. Installation is facilitated through a script that allows modular setup, including components like OmniGibson for physics simulation, BDDL for task specification, and JoyLo for robot teleoperation. Users can install the stable release (v3.7.1) on either platform using specific commands or opt for the main branch to access the latest features, with a note to adjust PowerShell execution policy if needed on Windows. The setup script offers various customization options: creating a new conda environment (`--new-env`), downloading datasets and installing action primitives support (`--dataset`, `--primitives`), adding evaluation or development dependencies (`--eval`, `--dev`), and specifying CUDA versions (`--cuda-version`). Confirmation prompts can be skipped, especially in non-conda environments using `--confirm-no-conda`. For non-Conda installations, the `--new-env` flag should be omitted. The script accommodates both Linux and Windows systems and supports automated setups by bypassing acceptance of terms with flags like `--accept-conda-tos`, `--accept-nvidia-eula`, and `--accept-dataset-tos`. Users can access a complete list of options by running the script with `--help`. **Bullet Point Summary:** - **Benchmark Overview**: BEHAVIOR-1K is for testing AI on 1,000 household tasks using real human data. - **System Support**: Compatible with Linux (Ubuntu 20.04+) and Windows 10+, requires at least 32GB RAM and an NVIDIA RTX 2080+ GPU. - **Installation Components**: Includes OmniGibson, BDDL, JoyLo; supports stable release v3.7.1 or main branch for latest features. - **Setup Script Options**: - Create a new conda environment (`--new-env`). - Download datasets and install action primitives (`--dataset`, `--primitives`). - Add evaluation/development dependencies (`--eval`, `--dev`). - Specify CUDA version (`--cuda-version`). - Skip confirmation prompts with `--confirm-no-conda`. - **Non-Conda Installations**: Omit `--new-env` flag. - **Automated Setups**: Accept terms/licenses automatically using flags like `--accept-conda-tos`, `--accept-nvidia-eula`, and `--accept-dataset-tos`. - **Usage Help**: Use `--help` to view all script options. Keywords: Agents, Automated Installation, BDDL, Behavior-1K, Benchmark, CUDA, Citation, Components, Conda Environment, Data Bundle License Agreement, Datasets, Dependencies, Development Branch, EULA, Embodied AI, Evaluation, Household Tasks, Installation Script, JoyLo, License Acceptance, Linux, NVIDIA RTX 2080+, OmniGibson, PowerShell, Primitives, Python, Robot Manipulation, Simulation, Stable Release, Terms of Service, VRAM, Windows
vram
github.com 15 hours ago
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34. HN Show HN: Wasmind – A framework for building massively parallel agentic systems- **Overview**: Wasmind is a modular framework designed to build massively parallel agentic systems utilizing WebAssembly (WASM) modules. It operates on an actor-based architecture where each actor is a WASM module, and actors can be combined into agents that communicate through message passing. - **Key Features**: - Enables integration with any local or remote language model. - Facilitates the creation of AI agent workflows categorized as Assistant (for LLM interactions), Tool (providing capabilities like file manipulation), and Coordination actors (enabling multi-agent systems). - Supports scalable coordination across simple to complex agent networks through structured message passing. - **Advantages Over Other Protocols**: - Offers more flexibility than the Model Context Protocol by supporting complex multi-agent hierarchies and peer-to-peer communication without a central assistant. - Extends traditional MCP architectures by enabling specialized agents, hierarchical delegation networks, and parallel problem-solving systems. - **Technical Aspects**: - Utilizes WebAssembly for language independence, security through sandboxing, portability, near-native performance, and composability. - Provides natural support for isolation, parallelism, and fault tolerance via message-passing communication. - **Usability**: - Can be explored using the `wasmind_cli`, a command-line application demonstrating actor-based AI workflows akin to Claude Code interactions. - Does not require users to know Rust as it supports multiple programming languages that compile to WASM. - Allows 'no-code' system creation through TOML configuration files, enabling setup of functionalities like coding assistants without writing code. - **Development and Community**: - Encourages contributions such as bug reports, feature discussions, or code contributions. - The core library can be integrated into Rust applications but is not limited to CLI usage. - **License**: Distributed under the MIT License, making it accessible for a wide range of applications. This summary captures Wasmind's core functionalities and advantages, emphasizing its flexibility, scalability, and ease of use in creating AI systems. Keywords: AI, AI systems, CLI, LLM, LLM interactions, MCP, MIT License, Rust, SDKs, TOML configuration, Wasmind, WebAssembly, actor-based, actors, agent workflows, agentic systems, agents, approval actors, assistant actors, async, bug reporting, client-server model, component actors, composability, concurrency, contributing, coordination actors, delegation network, developer guide, documentation, experimental framework, fault tolerance, framework, getting started, hierarchical coordination, hierarchical system, infrastructure, isolation, language independence, licensing, lifecycle, lightweight isolation, long-running workflows, massively parallel, message passing, messaging, model context protocol, modular plug and play, multi-agent coordination, multi-agent systems, no-code, orchestration, parallel processing, peer-to-peer coordination, performance, portability, qwen3-coder, safety restrictions, sandboxing, scope system, security, stateful actors, sub-manager, tool actors, user-centered configuration, wasm module, wasmind_cli, worker agent
llm
github.com 15 hours ago
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35. HN Link Graveyard: A snapshot of my abandoned browser tabs- **Link Graveyard Overview**: A collection from September 13, 2025, featuring various tech-related articles, papers, and announcements focused on AI advancements. - **Notable Mentions**: - An article in TechCrunch discusses Meta's partnership issues with Scale AI. - An ArXiv paper suggests large language models function similarly to graph neural networks, predicting missing connections in graphs. - A GLM-4.5 announcement highlights its open-source model capabilities, especially in reasoning and coding. - Unread papers on cognitive computation and contrastive representation learning are noted for future exploration. - **AI Models and Papers**: - The "AlphaGo Moment" discusses auto-discovery of model architectures using algorithms mixing various parameters. - A paper suggests embedding vectors have limitations in complex logic due to their limited information capacity, while SPLADE proposes solutions for first-stage ranking issues. - MUVERA introduces Multi-Vector Retrieval with fixed-dimensional encodings as a potential improvement. - **Software and Technology**: - A mention of Nvidia DGX Spark discusses advancements in computing hardware. - The gepa repository provides tools for AI-driven optimization across various systems using Reflective Text Evolution techniques. - **AI Research Focus**: - Discussions about optimizing AI-driven retrieval systems and the challenges involved. - Mentioned papers focus on contrastive learning, data curation improvements by DatologyAI, and prompt optimization through gepa. - **Miscellaneous Topics**: - A blog post from Simon Willison's weblog and a podcast discussing an ML researcher are included for interest diversification. - "The Rachel Maddow Show" is mentioned despite the author’s lack of interest in it. - **Summaries on Various AI Insights**: - Articles like "Defeating Nondeterminism in LLM Inference" examine challenges in AI infrastructure, while projects like Levanter focus on scalable foundation models. - The Groq's LPU article discusses deterministic ASIC hardware design for distributed systems. - Explorations into emergent hierarchical reasoning in LLMs through reinforcement learning introduce innovative methodologies. - **Astrobiological Exploration**: - An AAAS article titled "Life, Maybe, On Mars, Unless We Change Our Minds" delves into the potential existence of life on Mars and highlights evolving scientific perceptions. This summary encapsulates a wide array of topics related to AI advancements, technology updates, research insights, and philosophical musings on astrobiology. It reflects an interdisciplinary approach, blending technical details with broader thematic explorations. Keywords: AI Consciousness, AI engineering, ASIC, Airia AI Platform, Alexander Wang, Alignment, Cascade RL, Claude Code, Cognition, Cognitive Computation, Contrastive Representation Learning, DSPy, Data Synthesizing, Energy efficiency, ExIt Paper, Foundation Models, GLM-45, GPT-5 System Card, GPUs, GitHub, Graph Neural Networks, Groq, Hierarchical Reasoning, Hugging Face, InternVL3, JAX, LLM Inference, Large Language Models (LLMs), Link Graveyard, LongCat-Flash-Chat, Low/No Code, MCP Server, MIT News, Meta, Nondeterminism, Nvidia DGX Spark, Ollama, Open Source models, Open-Ended Generation, OpenGVLab, Personal Superintelligence, Photonic processor, Pivot Tokens, RL Efficiency, Reinforcement Learning, Reverse-Engineering, Scale AI, Swyx, TechCrunch, Theoretical Limitations, Transcendence, Transformers, WebShaper, alphaXiv, attention graphs, auto-managing RL, browser tabs, compute reduction, embeddings, gepa, vLLM
ollama
timkellogg.me 15 hours ago
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36. HN Why Scout's CEO Doesn't Need Car DealersScout Motors, a subsidiary of Volkswagen Group, is set to introduce its electric vehicles (EVs) directly to consumers in the U.S., circumventing traditional dealership models employed by rivals such as Tesla and Rivian. This direct-sales strategy has led to legal challenges from established VW dealerships in Florida and California, who argue that it violates state laws mandating conventional car sales through dealerships. At the IAA Munich auto show, Scout CEO Scott Keogh defended this approach, advocating for a customer experience akin to Apple's retail model, which focuses on brand engagement and personalization. Scout plans to launch its SUV models, Terra and Traveler, in both fully electric and range-extender versions by 2027, initially targeting the U.S. market while delaying entry into Europe. Despite facing litigation from dealerships resisting changes to traditional sales structures, Keogh remains confident that this strategy will benefit consumers through innovation and direct interactions with buyers, bypassing hurdles faced by dealers such as lack of training in EV technology and opposition to favorable regulations. The broader industry faces challenges, including the potential slowdown in EV adoption due to expiring tax credits. However, Scout's approach could reshape consumer relationships and business models by enabling profit from trade-ins and leveraging data ownership. The text concludes with contact details for Patrick George at InsideEVs and encourages sharing the story on multiple social media platforms. **BULLET POINT SUMMARY:** - Scout Motors plans to sell its EVs directly to consumers in the U.S., bypassing traditional dealerships, similar to Tesla and Rivian. - The direct-sales strategy has led to legal challenges from VW dealerships in Florida and California over state dealership sales laws. - CEO Scott Keogh supports the model for offering a personalized brand experience like Apple’s retail model. - Scout will launch SUVs Terra and Traveler in 2027, focusing initially on the U.S. market while postponing European entry. - The company is building a factory in South Carolina to support these plans. - Legal disputes highlight resistance from traditional dealerships due to lack of EV training and lobbying against pro-EV regulations. - Keogh highlights consumer benefits such as innovation and direct buyer relationships through this model. - Industry concerns include the expiration of tax credits potentially slowing EV sales growth. - Scout’s approach could enhance business profitability via trade-ins and data ownership, beyond dealership interactions. - Contact information for Patrick George is provided along with social media sharing suggestions. Keywords: Audi, EREVs, EVs, Porsche, Rivian, Scout Motors, Scout Terra, South Carolina, Tesla, Traveler SUV, Volkswagen Group, battery recharging, contract laws, data ownership, dealership system, dealerships, direct sales, gas engine, lawsuits, legal challenges, range-extender, tax credits, trade-ins
tesla
insideevs.com 15 hours ago
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37. HN Don't vibe code (any of) your config**Summary:** The author shares their experience developing a job application tracker app using Claude Code, specifically designed as a Ruby on Rails CRUD application. Initially deployed successfully on Render with SQLite serving as the database even for production, they faced an unexpected issue when a beta user couldn't log in due to an empty database—no users were recorded. Upon investigation, it was discovered that this data loss occurred because deploying on Render resulted in a new instance of SQLite being initiated each time, erasing existing data. This experience underscored the pitfalls of using non-persistent databases like SQLite for production applications without implementing persistent storage solutions. The author learned from this oversight, recognizing the need to carefully consider database configurations, particularly when user data is involved. Reflecting on the incident, the author acknowledged their initial "vibe coding" approach led to a hasty decision that overlooked critical evaluations of persistence needs in their setup. As a corrective measure, they transitioned to PostgreSQL for its compatibility and managed support through Render, despite acknowledging other potential solutions like Docker volumes could have been used if not for Render's ephemeral filesystem. This episode served as an important lesson about the balance between exploratory coding and thorough engineering practices. The author concluded by emphasizing different approaches required when using AI tools for casual versus professional work. They also extended an invitation to subscribe to their newsletter focused on AI and software engineering, thanking readers for support and encouraging new subscribers to join a growing community with access to extensive resources. **Bullet Point Summary:** - Author developed a job application tracker app using Claude Code within Ruby on Rails. - Deployed on Render with SQLite as the database; faced issues when user data was lost upon re-deployment. - Discovery that Render's deployment process initiated a new SQLite instance, wiping existing data each time. - Highlighted risks of deploying applications with non-persistent databases in production environments. - Author transitioned to PostgreSQL for better persistence and compatibility with Render. - Lesson learned about balancing quick coding approaches with thorough engineering practices, especially when handling user data. - Concluded with a discussion on using AI tools for different types of projects—fun versus professional. - Invited readers to subscribe to an AI and software engineering newsletter; noted subscriber growth and access to in-depth resources. Keywords: AI, ActionMailer, CRUD app, Docker, Kamal, Postgres, Rails, Render, SQLite, database, deployment, ephemeral, job tracker, newsletter, persistent, software engineering, subscribing, volume, zero-downtime
postgres
www.augmentedswe.com 16 hours ago
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38. HN I made WEBGEN-OSS-20B, a model that generates clean websites from your prompts### Summary: WEBGEN-OSS-20B is an AI model developed by Tesslate aimed at generating clean and responsive websites from user prompts using HTML/CSS/Tailwind without external JavaScript. It emphasizes modern, consistent layouts with semantic HTML and operates efficiently on local laptops for fast iteration while maintaining structural consistency. Users interact with the model via the Hugging Face Transformers library in Python, defining website structures like landing pages through specific prompts detailing sections such as navbars, hero sections, feature grids, pricing tiers, FAQ accordions, and footers. The inference settings allow customization of creativity levels through parameters such as temperature. The model can also be integrated into web applications using tools like vLLM or sglang, with options to configure memory usage and model length. It is ideal for rapidly prototyping modern websites characterized by simplicity and clean design. Guidelines provided suggest optimal parameter values—temperature (0.6), top_p (0.9), top_k (40), max_new_tokens (1200–2500), and repetition_penalty (1.1)—for generating single-file websites with a focus on semantic HTML and Tailwind CSS, as exemplified by the "RasterFlow" landing page design featuring muted colors, an 8pt spacing system, specific typography scales, and medium-sized shadows. The document discusses VRAM-efficient quantization formats like BF16, GGUF Q5_K_M, and GGUF Q4_K_M. It also highlights limitations such as potential gaps in ARIA coverage for accessibility and minimal JavaScript widget use unless specified. Ethical considerations emphasize the need to ensure appropriate prompts and rights to third-party logos/assets. The model's training focused on a web-only bias rewarding semantic HTML and responsive design, using curated data from HTML/CSS/Tailwind snippets, component libraries, and synthetic page specifications. This process involved sequence-to-sequence fine-tuning with format constraints, instruction tuning, and style/rhythm optimization. The model functions within approximately 64k context length, effectively producing outputs suitable for practical web pages. An example output humorously critiques the high cost of good design models as noted by Tesslate. ### Bullet Point Summary: - **Model Overview:** WEBGEN-OSS-20B generates clean, responsive websites using HTML/CSS/Tailwind without external JavaScript, emphasizing modern and consistent layouts with semantic HTML. - **Usage and Tools:** Operates via the Hugging Face Transformers library in Python; can be integrated into web apps using vLLM or sglang, with customizable memory settings. - **Design and Prototyping:** Ideal for fast prototyping of modern websites with simple, clean design. Includes detailed inference settings to balance creativity and consistency. - **Guidelines and Parameters:** Provides guidelines for single-file website generation focusing on semantic HTML and Tailwind CSS, recommending specific parameters like temperature (0.6), top_p (0.9), etc., exemplified by a "RasterFlow" landing page. - **Quantization Formats:** Discusses VRAM-efficient formats like BF16, GGUF Q5_K_M, GGUF Q4_K_M for optimized performance. - **Limitations and Ethical Considerations:** Potential gaps in ARIA coverage and limited JavaScript widget use; stresses ensuring appropriate prompts and rights to third-party assets. - **Training Process:** Focused on web-only bias with semantic structure, spacing rhythm, and responsiveness. Utilized curated HTML/CSS/Tailwind data, sequence-to-sequence fine-tuning, instruction tuning, and style/rhythm optimization. - **Model Efficiency:** Operates within ~64k context length; effectively produces outputs fitting practical web page lengths. Includes a humorous example output on the high cost of good design models by Tesslate. Keywords: ARIA, AutoModelForCausalLM, AutoTokenizer, BF16, GGUF, GPU-memory-utilization, HTML/CSS/Tailwind, TensorFlow, VRAM, WEBGEN-OSS-20B, accessibility, components, ethical considerations, format constraints, host, inference settings, landing pages, layouts, max_new_tokens, mobile-first, optimization, port, production-lean, prompts, quantization, responsive, semantic, sglang, temperature, transformers, vllm, web-only bias, websites
vram
huggingface.co 16 hours ago
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39. HN Pgschema: Terraform-style, declarative schema migration for Postgres**Summary:** `pgschema` is a command-line interface (CLI) tool designed to facilitate Terraform-style, declarative schema migration specifically for PostgreSQL databases. It allows developers to articulate their desired database schema in an easily understandable format and supports the entirety of common database objects. The workflow involves several key steps: first, dumping the existing database schema; second, editing this schema file to reflect the changes required to reach the target state; third, generating a migration plan by comparing the edited schema against the current database state, with outputs available in both human-readable and JSON formats; and finally, applying these planned changes. This application phase includes options for concurrent change detection, transaction-adaptive execution, and lock timeout control. To effectively utilize `pgschema`, users are advised to begin by extracting their current database schema through a specified command-line operation. Subsequently, they can modify this schema file declaratively to match the desired end state. The process continues with the generation of a migration plan that outlines necessary changes by juxtaposing the modified schema against the existing database configuration. This plan is then reviewed and, if approved, applied to enact the desired modifications on the PostgreSQL database. `pgschema` supports an automatic approval feature through the `--auto-approve` option or allows for manual confirmation before proceeding with changes. The tool has been rigorously tested across PostgreSQL versions 14 to 17, ensuring reliability and compatibility within these environments. Installation guidance, along with further assistance, can be found on its official website. The workflow is structured to instill confidence in users by enabling a preview of proposed changes prior to their application. In a detailed example outlined in the document, `pgschema` is employed to apply a migration plan that involves adding an "age" column to the "users" table within a PostgreSQL schema. Users have the flexibility to either manually confirm this change or leverage the `--auto-approve` feature for expedited processing. Upon confirmation and execution, the changes are successfully integrated into the database. Moreover, the document provides instructions on setting up `pgschema` for development purposes. This includes cloning its repository, compiling it using Go, and conducting both unit and integration tests—wherein the latter necessitates Docker. Additionally, Bytebase is introduced as an open-source, web-based DevSecOps platform designed to aid in database management. **Bullet Point Summary:** - `pgschema` is a CLI tool for declarative schema migration of PostgreSQL databases, supporting Terraform-style workflow. - Workflow includes dumping the current schema, editing it, planning changes by comparison with the current state, and applying these changes with advanced features like concurrent change detection. - To use, one must dump the existing schema, edit it to reflect desired changes, generate a migration plan (in human-readable and JSON formats), and apply this plan to the database, with an option for `--auto-approve`. - Tested on PostgreSQL versions 14 through 17; installation and help are available on its official website. - Demonstrated workflow includes adding an "age" column to the "users" table in a PostgreSQL schema, with options for manual confirmation or automatic approval via `--auto-approve`. - Development setup instructions include cloning the repository, building with Go, and running tests (unit and integration, requiring Docker). - Bytebase is mentioned as an open-source DevSecOps platform for database management. Keywords: Bytebase, CLI tool, DevSecOps platform, Docker, Postgres, Terraform, apply migration, concurrent change detection, database state, declarative schema, developer-friendly, example usage, git clone, go mod tidy, installation, lock timeout control, plan generation, schema migration
postgres
github.com 16 hours ago
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40. HN OpenAI's spending spree powers the tech industry. Oracle is the latest winner**Summary:** OpenAI's substantial investments in AI technology and cloud computing have significantly impacted the tech industry, particularly benefiting companies such as Oracle, Broadcom, Microsoft, and Nvidia through partnerships or supply agreements focused on large language models. Following OpenAI's introduction of ChatGPT in late 2022, these collaborations contributed to a $4.5 trillion increase in market capitalization among these firms, driving major stock indices like the Nasdaq and S&P 500 to record highs. Despite its nonprofit status and ongoing financial losses, OpenAI has become central to current tech trends due to its influence on prominent technology companies. Oracle announced multibillion-dollar contracts with OpenAI, including developing U.S. data center capacity and a substantial $30 billion cloud agreement expected within two years. This announcement initially boosted Oracle's stock by 36%, the largest single-day gain since 1992, but it later declined due to concerns over revenue concentration from these deals. Oracle reported a significant increase in performance obligations, totaling $455 billion, raising investor caution about reliance on OpenAI for substantial portions of its backlog. OpenAI is investing heavily with various cloud providers, such as CoreWeave and Google, and plans to invest $19 billion into Stargate, an AI infrastructure initiative involving partners like Oracle and SoftBank. SoftBank has committed an additional $40 billion investment in OpenAI. Despite financial losses, OpenAI projects substantial revenue growth, aiming for $125 billion in annual recurring revenue by 2029. The company is transitioning to a public benefit corporation to secure the full investment before year's end. Oracle’s engagement with OpenAI has positioned it closer to becoming a trillion-dollar company, yet skepticism persists regarding its AI capabilities. Although a recent deal temporarily elevated Oracle's market cap to nearly $930 billion, analysts like Byron Deeter remain cautious about its prospects compared to established cloud competitors like Amazon, Microsoft, and Google, describing Oracle as a "B-level hyperscaler" without significant AI software or chip positions. **Bullet Point Summary:** - OpenAI's investments in AI and cloud technology have significantly benefited companies such as Oracle, Broadcom, Microsoft, and Nvidia. - The collaboration with these firms has increased their combined market capitalization by over $4.5 trillion since OpenAI's introduction of ChatGPT. - Oracle signed multibillion-dollar contracts with OpenAI, leading to a temporary 36% stock surge but later decline due to revenue concentration concerns. - Oracle reported a substantial increase in performance obligations, raising investor caution about reliance on OpenAI for future revenues. - OpenAI is investing heavily with various cloud providers and plans $19 billion investment into Stargate alongside partners Oracle and SoftBank. - Despite financial losses, OpenAI projects reaching $125 billion in annual recurring revenue by 2029 and transitioning to a public benefit corporation. - Oracle’s increased spending on OpenAI has raised its market valuation but skepticism remains regarding its AI capabilities. - Analysts remain cautious about Oracle's position relative to major cloud competitors like Amazon, Microsoft, and Google. Keywords: AI infrastructure, AI software, Anthropic, Broadcom, CNBC, ChatGPT, China, CoreWeave, GPUs, Gil Luria, Google, Meta, Microsoft, Nasdaq, Nvidia, OpenAI, Oracle, S&P 500, SEC, Sam Altman, Stargate, Sun Valley Conference, analyst, cash-burning startup, cashed checks, chips, cloud computing, custom processors, customer concentration, data center capacity, earnings report, equity stake, financing round, hyperscaler, investment, market cap, nonprofit parent, performance obligations, public benefit corporation, revenue backlog, stock retreat, stock surge, tech industry, trillion-dollar club
openai
www.cnbc.com 16 hours ago
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41. HN How Ethiopia is becoming an unlikely leader in the electric vehicle revolutionEthiopia is positioning itself as a surprising leader in the electric vehicle (EV) revolution, marked by its proactive measures to transition from fossil fuel reliance to sustainable transport solutions despite facing significant energy challenges. The country has banned imports of combustion engine vehicles to address chronic fuel shortages and reduce pollution, with China's BYD models gaining popularity among drivers like Deghareg Bekele and Firew Tilahun in Addis Ababa who benefit economically from lower EV operating costs compared to traditional fuel expenses. Currently, Ethiopia registers approximately 115,000 EVs out of 1.5 million cars, aiming to increase this number to 500,000 by 2030. Despite only half of its population having access to electricity and frequent power blackouts affecting industrial operations, the completion of the Grand Renaissance Dam promises to double hydropower capacity, aiding in alleviating energy constraints. The government is also promoting EV adoption through tax exemptions, despite high costs and occasional power issues, as part of broader economic strategies to reduce $4.5 billion annual fuel import expenditures and boost local production capacity for job creation. Efforts include assembling Chinese minibuses by Belayneh Kinde Group in Addis Ababa, aligning with a policy shift prompted by levies that inflated combustion engine vehicle prices. However, the EV market faces hurdles such as limited charging infrastructure—over 100 stations versus a target of more than 2,300—restricting usage primarily to urban areas and complicating long-distance travel. This is compounded by the absence of plans for electric heavy lorries essential for imports from Djibouti, posing potential economic risks. While some drivers remain skeptical about battery durability and resale value, there is cautious optimism about future EV adoption as infrastructure improves. The speaker's initial skepticism due to inadequate power infrastructure has given way to a more hopeful outlook over time, reflecting broader confidence in Ethiopia’s green transport initiatives amidst existing challenges. - **Ethiopia leads in the EV revolution by banning combustion engine imports and promoting sustainable transport solutions amid energy challenges.** - **The country aims to increase its registered EVs from 115,000 to 500,000 by 2030, leveraging hydropower expansion post-Grand Renaissance Dam completion.** - **Economic incentives like tax exemptions are used to promote EV adoption, reducing reliance on $4.5 billion annual fuel imports and creating jobs through local production initiatives such as Belayneh Kinde Group’s assembly operations.** - **Infrastructure challenges persist with limited charging stations restricting use mainly to Addis Ababa; there's a target of 2,300 stations needed for broader EV adoption.** - **The lack of electric heavy lorries plans poses economic concerns due to reliance on imports from Djibouti.** - **Despite skepticism about battery technology, cautious optimism exists regarding future infrastructure improvements and broader EV market growth.** Keywords: Addis Ababa, BYD, Bareo Hassen, Deghareg Bekele, EVs, Ethiopia, Grand Renaissance Dam, Tesla, charging stations, economic motivation, electric vehicles, foreign currency, fuel shortages, green policies, hydropower, import levies, infrastructure, job opportunities, local production, policy failure, pollution, poverty, power cuts, renewable energy, revolution, road trips
tesla
www.theguardian.com 16 hours ago
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42. HN Optimization Pathways for Long-Context Agentic LLM InferenceThe research paper titled "Combating the Memory Walls: Optimization Pathways for Long-Context Agentic Large Language Model (LLM) Inference," authored by Haoran Wu et al., addresses challenges related to memory limitations in large-scale language models when processing extensive contexts. Supported by the Simons Foundation, this work explores optimization techniques to enhance performance and efficiency, particularly targeting "memory walls" within hardware architecture for improved inference capabilities of agentic LLMs. Submitted to arXiv on September 11, 2025, under Computer Science (Hardware Architecture), it introduces PLENA, a co-designed hardware-software system that optimizes LLM performance through strategies like asymmetric quantization hardware design, a novel flattened systolic array architecture with native FlashAttention support, and a comprehensive system stack. Simulations show PLENA outperforming existing accelerators by achieving up to 8.5 times higher utilization and delivering significantly greater throughput compared to the A100 GPU and TPU v6e under similar conditions. The full system is set to be open-sourced for further development in LLM inference tasks. Additional information includes the paper's pending registration via DataCite DOI, availability in multiple formats, and bibliographic tools like BibTeX citations, with connections to resources such as NASA ADS, Google Scholar, and Semantic Scholar. It highlights links for broader access and collaboration through platforms like alphaXiv, DagsHub, Hugging Face, and Papers with Code. The text also describes features within the arXivLabs platform, which facilitates community-developed feature sharing on the arXiv website. Tools such as CORE (Core Recommender) are mentioned alongside values promoting openness, collaboration, excellence, and privacy. It encourages users to propose projects benefiting the arXiv community. Further functionalities include options for disabling MathJax and information on contacting arXiv, subscribing to updates, accessing their privacy policy, web accessibility assistance, operational status, and notification methods through email or Slack. **BULLET POINT SUMMARY:** - The paper explores optimization strategies for LLMs in handling extensive contexts, focusing on overcoming memory limitations. - It introduces PLENA, a co-designed hardware-software system that significantly outperforms existing accelerators in efficiency and utilization. - Supported by the Simons Foundation, it aims to improve agentic LLM inference capabilities through innovative architecture and design strategies. - The paper is part of arXiv's Computer Science (Hardware Architecture) category and is pending registration via DataCite DOI. - It offers comprehensive access through various formats and bibliographic tools, with connections for broader collaboration across multiple platforms. - Describes arXivLabs as a platform promoting community-developed features on the arXiv website, emphasizing values like openness and privacy. - Includes functionalities such as disabling MathJax, providing contact information, subscribing to updates, and accessing privacy policies. Keywords: A100 GPU, Agentic LLMs, Asymmetric Quantization, Bandwidth Wall, Capacity Wall, Co-design, Compiler, DOI, DataCite, FlashAttention, Hardware Architecture, Inference Tasks, Long-Context, Memory Walls, Optimization, PLENA, Simulator, Systolic Array, TPU v6e, arXiv
llm
arxiv.org 17 hours ago
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43. HN 60 years after Gemini, newly processed images reveal details**Summary:** The book "Gemini & Mercury Remastered" by Andy Saunders is a meticulously curated tribute to the historic Project Gemini missions and their contributions to space exploration. Released on the 60th anniversary of significant events like Gemini 4, the publication revives interest in these pioneering flights from the early 1960s that achieved remarkable firsts, including Ed White's groundbreaking U.S. spacewalk during Gemini 4. Saunders' book comprises a collection of 300 carefully restored photographs accompanied by extensive background research, showcasing the valor and achievements of America’s earliest astronauts. The work not only pays homage to these initial missions but also examines their enduring influence on human evolution and the broader narrative of space exploration history. By doing so, it builds upon Saunders’ prior explorations in "Apollo Remastered," situating the Gemini and Mercury programs as foundational elements that paved the way for subsequent milestones in the field. **Bullet Point Summary:** - The book "Gemini & Mercury Remastered" by Andy Saunders celebrates Project Gemini missions, coinciding with their 60th anniversary. - It features significant firsts from the early 1960s spaceflights, including Ed White's first U.S. spacewalk during Gemini 4. - Contains 300 meticulously restored photographs and detailed background research highlighting American astronauts' bravery. - Acts as a tribute to these early missions and examines their impact on human evolution and space exploration history. - Sets the stage for later achievements in space exploration, building on Saunders’ previous work "Apollo Remastered." Keywords: Andy Saunders, Apollo Remastered, Ed White, Gemini, Mercury, NASA, Project Gemini, anniversary, astronauts, history, restoration, spaceflights, spacewalk
gemini
arstechnica.com 17 hours ago
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44. HN AI tool detects LLM-generated text in research papers and peer reviewsThe analysis by the American Association for Cancer Research (AACR) highlights a significant increase in the utilization of large language models (LLMs), such as those developed by OpenAI, within research papers and peer reviews. Despite mandatory disclosure policies, less than 25% of authors have admitted to using AI tools when preparing their manuscripts. To evaluate this trend, AACR employed an AI-detection tool from Pangram Labs, analyzing tens of thousands of submissions from 2021 to 2024. Following the release of OpenAI's ChatGPT in November 2022, there was a marked rise in suspected AI-generated content, especially in abstracts (23%) and peer-review reports (5%). Despite AACR's ban on LLM use by reviewers in late 2023, which initially reduced AI detection rates by half, the use of AI in peer reviews more than doubled by early 2024. Pangram Labs' AI-detection tool boasts a 99.85% accuracy rate for identifying AI-generated text within scientific manuscripts and peer reviews. This is achieved using a dataset comprising over 28 million documents, including "AI mirrors" designed to mimic human writing, thereby significantly lowering false-positive rates from one in 100 to one in 10,000 through its active-learning mode. The tool's capability extends to differentiating among various LLMs such as ChatGPT and DeepSeek thanks to its self-generated training set. However, concerns about potential biases persist, as noted by industry experts like Adam Day of Clear Skies. Despite advancements, the increasing use of LLMs in peer review against guidelines is troubling. Testing before ChatGPT's release showed minimal AI text detection; however, there was a significant spike post-release. In 2024, Pangram Labs' tool analyzed a substantial number of abstracts, methods sections, and peer-review reports for AACR. The findings indicated that authors from non-native English-speaking countries were more than twice as likely to utilize LLMs for editing purposes. This raises concerns about potential errors, especially in methods sections where precision is crucial, as rephrasing by LLMs might lead to inaccuracies. **BULLET POINT SUMMARY:** - AACR found a significant rise in AI use in research papers and peer reviews, with less than 25% of authors disclosing it. - Pangram Labs' tool detected increased suspected AI-generated text post-ChatGPT release, especially in abstracts (23%) and peer-review reports (5%). - Despite an initial drop after AACR's LLM ban for reviewers, AI use in peer reviews doubled by early 2024. - Pangram Labs' AI-detection tool has a 99.85% accuracy rate, reducing false positives significantly with active learning. - The tool can differentiate among various LLMs due to its self-generated training set but faces concerns about potential biases. - There is an increasing trend of using LLMs for peer review against guidelines, leading to plans to screen all submissions. - Authors from non-native English-speaking countries are more likely to use LLMs for editing, raising concerns about accuracy in methods sections. Keywords: AACR, AI-detection tools, AI-generated, ChatGPT, Evanko, LLMs, OpenAI, Pangram Labs, abstracts, accuracy, active-learning mode, bias, disclosure, error rates, false positive rate, manuscripts, methods sections, peer reviews, research papers, scientific publications, submissions
openai
www.nature.com 18 hours ago
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45. HN What I think about when I think about Claude Code**Summary:** Claude Code is an innovative AI tool designed for use in terminal environments, assisting developers by functioning as a command-line chat interface. It stands apart from tools like GitHub Copilot by allowing users to input detailed instructions for tasks such as document lookup and code generation, with the AI autonomously executing these tasks over time. Claude Code's requirement for well-defined problems ensures structured problem-solving and prototyping, although it may not be ideal for foundational development due to potential complexity issues. The tool was pioneering in making this approach viable. The text delves into the creative process of coding, emphasizing meticulous planning akin to design documents in software development. It reflects on initial calibration challenges with Claude Code and draws parallels between the subjective experience of coding and breathing patterns during tasks, citing Linda Stone's concept of "email apnea" as a stress-related health concern. Using Claude Code offers a more relaxed rhythm, avoiding the stress-induced "code apnea." The discussion extends to using Claude Code's processing time for thoughtful creativity, drawing an analogy to Muzak's use of music to influence behavior and productivity through Stimulus Progression. The concept is further explored by comparing the programming environment with Brian Eno's ambient soundscapes, suggesting that coding environments could benefit from similar attention to background elements. Claude Code is also used as a brainstorming tool for generating blog post ideas from Markdown notes, always seeking permission before executing potentially risky actions on the user's computer. The text warns against enabling the `--dangerously-skip-permissions` flag due to safety risks associated with unchecked AI control, emphasizing the importance of maintaining boundaries despite the allure of extensive automation. **BULLET POINT SUMMARY:** - Claude Code is a terminal-based AI tool that assists developers through a command-line chat interface, requiring well-defined problems for structured problem-solving and prototyping. - The text highlights the creative process in coding, comparing it to meticulous planning and addressing initial calibration challenges with Claude Code. - It draws parallels between coding experiences and breathing patterns during tasks, using Linda Stone's "email apnea" concept to discuss stress-related health concerns. - The tool's processing time is likened to Muzak's music influence on behavior, suggesting background elements in coding environments could enhance user experience. - Claude Code serves as a brainstorming tool for generating ideas from Markdown notes, always seeking permission before executing actions. - The text warns against enabling the `--dangerously-skip-permissions` flag due to safety risks, emphasizing the importance of maintaining control over AI systems. Keywords: AI tool, Ambient Music, Claude Code, Github Copilot, Markdown, Muzak, auto-complete, autonomy, code repository, coding, command line, firmware, imagination, inner loop interaction, music licensing, prototyping, terminal-based agents, to-do list, web docs, yolo mode
github copilot
interconnected.org 18 hours ago
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46. HN Show HN: A store that generates products from anything you type in searchThis summary outlines a groundbreaking shopping experience introduced by an innovative platform that allows customers to search for any product, regardless of its existence in our dimension. If the product is not available, it will be generated from parallel dimensions, ensuring instant delivery to the customer's device. This unique approach empowers customers to drive product creation, tailoring items specifically to their desires and needs, essentially transforming imagination into tangible innovation. The platform positions itself as a pioneer, inviting customers to name what they want, thereby being at the forefront of discovering new concepts and bringing them to life. **BULLET POINT SUMMARY:** - Introduction of an innovative shopping experience allowing for the search and creation of non-existent products. - Utilizes parallel dimensions to generate unique products tailored specifically to customer desires. - Instant delivery of these custom-created products to customers' devices, emphasizing speed and convenience. - Empowers customers to lead product creation, making them active participants in the innovation process. - Encourages discovery and personalization by allowing customers to name their desired products. - Positions itself as a pioneer in merging imagination with tangible innovation. Keywords: Store, concepts, customers, device, dimensions, discover, imagination, innovation, name, parallel, products, search, shopping, unique
popular
anycrap.shop 18 hours ago
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47. HN Show HN: I made cc-filter after Claude kept bypassing its own rules- **Development of cc-filter:** The author developed `cc-filter`, a security tool designed to enhance protection by intercepting and filtering sensitive information such as API keys, database URLs, and environment variables before they reach Claude Code. This addresses issues where Claude's built-in permissions could be bypassed. - **Features and Implementation:** Built in Go for efficiency, `cc-filter` operates both within Claude Code’s hook system and independently, offering configurable regex patterns to meet various security needs along with sensible defaults. Key features include: - **PreToolUse Hook**: Blocks tool calls that expose sensitive data. - **UserPromptSubmit Hook**: Filters out sensitive information from user prompts. - **Content Filtering**: Removes API keys, secrets, tokens, and passwords from text. - **Comprehensive Logging**: Logs processing details for transparency. - **Installation and Integration:** Installation instructions are provided for macOS (Intel/Apple Silicon), Linux (x86_64), and compiling from source with Go. For integration with Claude Code, users must update their `settings.json` to include `cc-filter` in relevant hooks, allowing project-specific filtering by placing settings files at the root of projects. - **Standalone Usage:** Beyond integrated environments, `cc-filter` can process standard input (stdin) and output (stdout), making it adaptable for use with any coding agent that supports command-line filters or pipe-based text processing. It detects JSON hook formats but also accommodates plain text filtering through custom hooks in the `internal/hooks/` directory. - **Filtering Capabilities:** The tool is adept at identifying and filtering sensitive information such as API keys, secret keys, tokens, passwords, and specific file types (.env, .key, etc.), replacing them with "***FILTERED***" or asterisks while maintaining content structure. - **Logging and Configuration System:** `cc-filter` logs its activity to a specified log file for real-time monitoring. It uses a flexible configuration system that loads files in an order prioritizing default rules, user global customizations, and project-specific settings: - Patterns can be added or overridden. - Lists of blocks are merged with duplicates removed. - **Customization Examples:** Users can customize filtering rules by adding new patterns (e.g., company API keys) or overriding defaults (e.g., openai_keys) in user configuration files. Project configurations allow for defining project-specific patterns, such as tokens specific to a project. Overall, `cc-filter` offers robust security enhancements with customizable features, enabling safe integration into various workflows and coding environments while providing detailed logging and flexible configuration options. Keywords: API keys, CLAUDEmd, Claude Code, GitHub, Go language, JSON, Linux, cc-filter, configuration files, env file, environment variables, filtering rules, hooks, logging, macOS, regex patterns, security layer
claude
github.com 18 hours ago
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48. HN From Snapshots to Standards: Measuring AI VisibilityThe white paper "From Snapshots to Standards: Measuring AI Visibility" addresses the limitations of current methods used to measure brand presence in AI assistants like ChatGPT. It criticizes existing tools that rely on snapshots for their lack of reproducibility and audit integrity. To overcome these issues, the paper introduces a new framework called AIVO Standard™, which features the Prompt-Space Occupancy Score (PSOS™) auditing system. PSOS provides a repeatable methodology involving systematic prompt design, multi-LLM execution with time-stamping, and top-tier scoring rules. The white paper contrasts the traditional snapshot approach with the proposed PSOS through various case studies across different industries, demonstrating PSOS's potential for governance and standardization. It argues that snapshots are inadequate for strategic purposes and positions PSOS as a benchmark for future efforts in measuring AI visibility. The paper targets AI governance experts, marketers, and policymakers who seek more reliable and standardized methods. - **Summary of Key Points:** - Addresses the inadequacy of current snapshot-based tools for measuring brand presence in AI assistants. - Critiques these tools due to their lack of reproducibility and audit integrity. - Introduces AIVO Standard™ with PSOS™ as a more reliable alternative. - Describes PSOS's methodology: systematic prompt design, multi-LLM execution with time-stamping, and top-tier scoring rules. - Uses case studies across industries to highlight the benefits of PSOS over snapshots. - Emphasizes the importance of PSOS for governance and standardization in AI visibility measurement. - Positions PSOS as a future benchmark, appealing to AI governance experts, marketers, and policymakers. Keywords: AI Assistants, AI Visibility, AIVO Standard™, ChatGPT, Claude, Competitive Comparison, Gemini, Governance, Information Retrieval, Marketing Analytics, Notarization, PSOS™ Auditing, Snapshots, Standards
claude
zenodo.org 18 hours ago
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49. HN How 'overworked, underpaid' humans train Google's AI to seem smartIn spring 2024, Rachael Sawyer was recruited by GlobalLogic to serve as a "writing analyst" for Google, expecting content creation roles but instead faced moderating distressing AI-generated content from products like Google's Gemini chatbot. The role evolved into focusing exclusively on flagging inappropriate material without prior warning or mental health support, causing anxiety and panic attacks. Sawyer’s experience reflects the challenges of thousands of workers in similar positions contracted by Google for moderating AI outputs, highlighting a significant yet often invisible workforce essential for quality control. Google has intensified its competition with OpenAI by releasing advanced models like Gemini 2.5 Pro. The company relies on data raters employed through contractors such as GlobalLogic to ensure the safety and appropriateness of AI outputs, categorizing them into generalists and "super raters" with specialized expertise. Despite their crucial role in preventing harmful AI responses, these workers face high-pressure environments, lack transparency, and feel underappreciated within the AI industry. In 2024, GlobalLogic expanded its team to almost 2,000 super raters as competition in AI chatbot development grew. These roles are primarily based in the U.S., focusing on English content moderation, with compensation higher than similar positions elsewhere but still seen as insufficient by some workers given their expertise and pressure levels. Dissatisfaction is compounded by tight deadlines and inconsistent guidelines impacting the quality and safety of products like Google Bard and Gemini. Workers have reported challenges such as dealing with hallucinations or incorrect answers from AI systems, prioritizing popularity over objectivity in ratings, and undergoing consensus meetings where dominant opinions could skew results. In May 2024, public ridicule followed Google's AI Overviews due to nonsensical responses. Rebecca Jackson-Artis, another GlobalLogic employee, faced insufficient training and inconsistent task instructions. In December, a guideline change required contractors on the Gemini project to handle tasks beyond their expertise by rating parts they understand and flagging others, raising concerns about information accuracy. Meanwhile, AI content guidelines at GlobalLogic were relaxed, permitting certain previously prohibited responses if initiated by users. This shift emphasizes market competition over safety, with human workers addressing issues from prematurely released systems. Despite industry growth, AI raters face job insecurity, exemplified by rolling layoffs at GlobalLogic since 2025. Workers express distrust in the products they help develop and often discourage large language model use among peers, highlighting concerns about ethical practices and transparency within the AI industry. This underscores the need for acknowledgment of human rater contributions amid rapid AI advancements. **BULLET POINT SUMMARY:** - Rachael Sawyer recruited by GlobalLogic expected content creation but ended up moderating distressing AI-generated content for Google. - Her role evolved into focusing on flagging inappropriate material without adequate warning or support, leading to anxiety and panic attacks. - Workers like Sawyer are critical yet often invisible in ensuring quality control of AI outputs but face high-pressure environments and lack transparency. - Google released advanced models like Gemini 2.5 Pro amid competition with OpenAI, relying on raters categorized as generalists and "super raters" for content moderation. - GlobalLogic expanded its super rater team significantly to almost 2,000 by 2024, with roles primarily based in the U.S., focusing on English moderation. - Workers report dissatisfaction due to high-pressure environments, tight deadlines, inconsistent guidelines, and feeling underpaid despite their expertise. - Challenges include dealing with AI hallucinations or incorrect answers, prioritizing popularity over objectivity, and influence from dominant opinions during consensus meetings. - In May 2024, Google's AI Overviews faced ridicule for nonsensical responses to simple queries. - Rebecca Jackson-Artis experienced inconsistent training and task instructions at GlobalLogic. - December guideline changes required contractors on the Gemini project to rate tasks within their expertise and flag others, raising accuracy concerns. - Relaxation of AI content guidelines allowed previously prohibited responses if initiated by users, prioritizing market competition over safety. - Despite industry growth, AI raters face job insecurity, with rolling layoffs at GlobalLogic since 2025 highlighting this trend. - Workers express distrust in the products they develop and often discourage LLM use among peers, emphasizing concerns about ethical practices and transparency. Keywords: AI, Gemini, GlobalLogic, Google, content moderation, guidelines, hallucinations, pressure, quality control, raters, safety, workforce
gemini
www.theguardian.com 18 hours ago
https://techcrunch.com/2025/01/22/scale-ai-is 17 hours ago https://relationaldemocracy.medium.com/an-authoritarian-work 17 hours ago https://youtu.be/0bF_AQvHs1M?si=rpMG2CY3TxnG3EYQ 17 hours ago https://www.theverge.com/features/23764584/ai-arti 16 hours ago https://www.mturk.com/ 16 hours ago https://tinyurl.com/4r2p39v3 16 hours ago https://x.com/chrisbarber/status/19650965855552720 14 hours ago https://nymag.com/intelligencer/article/ai-artific 14 hours ago https://archive.ph/Z6t35 14 hours ago https://time.com/6247678/openai-chatgpt-kenya-workers&# 14 hours ago https://finance.yahoo.com/news/surge-ai-quietly-hit-1b- 14 hours ago https://nationalequityatlas.org/indicators/Wages_Median 7 hours ago https://github.com/yahoo/open_nsfw 7 hours ago https://humandata.mercor.com/mercors-approach/black-box 7 hours ago https://en.wikipedia.org/wiki/High-Tech_Employee_Antitr 7 hours ago |
50. HN Improving state machine code generation### Summary Over recent months, Björn and his colleague have worked on enhancing state machine code generation within the Rust compiler as part of a 2025H1 project goal. Their efforts culminated in merging PR 138780, which introduced the `#![feature(loop_match)]`. This feature addresses inefficiencies discovered during the porting of zlib-rs from C to Rust, where implicit fallthrough behavior in C's switch statements led to more efficient state machine handling compared to Rust's explicit approach. The introduction of `loop_match` aims to improve Rust’s code generation for state machines by allowing direct jumps between match branches, thereby eliminating unnecessary code paths and optimizing branch prediction. This optimization draws inspiration from labeled switches found in other languages like Zig, focusing on minimizing unused code paths without major syntax changes that could spark debate. Attributes such as `#[loop_match]` and `#[const_continue]` are employed to enhance performance by enabling direct jumps between states within a loop's body through labeled blocks. These optimizations show context-dependent benefits, significantly improving the performance of state machines like an email address parser with 133 states in benchmarks. For instance, the "loop-match" configuration outperforms other implementations such as LLVM’s `-enable-dfa-jump-thread` optimization by reducing wall time and CPU cycles. The document also includes a benchmark analysis comparing various configurations for zlib-rs uncompression examples, revealing that `#[loop_match]` is more effective than LLVM DFA optimizations alone. However, combining both methods results in diminishing returns, with some combinations leading to slower performance. Despite this, the optimized approaches demonstrate substantial reductions in CPU cycles and cache misses. To facilitate reproduction of these findings, instructions are provided for cloning a specific version of the `zlib-rs` repository and running an associated script. The experimental feature is currently limited to basic data types, with further enhancements planned as part of ongoing efforts to improve Rust's performance for critical applications. These developments align with the Trifecta Tech Foundation’s mission and have been supported by funding from organizations like Tweede golf, NLnet Foundation, and AWS. ### Key Points - Björn and his colleague enhanced state machine code generation in the Rust compiler through PR 138780, introducing `#![feature(loop_match)]`. - The motivation stemmed from inefficiencies encountered during porting zlib-rs from C to Rust, specifically related to implicit fallthrough behavior. - `loop_match` improves Rust's state machine handling by allowing direct jumps between match branches, optimizing performance without major syntax changes. - Attributes like `#[loop_match]` and `#[const_continue]` enhance performance through efficient state transitions in loops. - Benchmarks show significant performance gains with `loop-match`, outperforming LLVM’s `-enable-dfa-jump-thread` optimization. - Combining `loop_match` with LLVM DFA results in diminishing returns, sometimes slowing performance. - The document provides instructions for reproducing benchmarks using a specific version of the `zlib-rs` repository. - The feature currently supports basic data types, with future work planned to expand support and further improve Rust's performance. - Funding from Tweede golf, NLnet Foundation, and AWS supports ongoing development efforts. Keywords: GitHub, LLVM, PR 138780, Rust, benchmarking, code generation, email parser, fallthrough, loop_match, optimization, performance, state machine, zlib-rs
github
trifectatech.org 19 hours ago
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51. HN Inventor says most humanoid robots today are 'terrifying'Scott LaValley, a robotics expert and former Disney principal imagineer, criticizes the current humanoid robot industry for producing designs he finds "terrifying." His insights have reportedly influenced Elon Musk's interest in humanoid robotics. LaValley advocates using robots as tools to augment human labor rather than direct replacements, reflecting on his past roles at Boston Dynamics and his decision against joining Figure AI due to misaligned goals. Morgan Stanley forecasts a massive growth in the humanoid robot market, projecting $5 trillion in revenues by 2050. However, despite advancements from companies like Tesla and Agility Robotics, significant challenges remain in developing functional bipedal robots. LaValley emphasizes rethinking the industry's focus, suggesting that simpler designs might be more practical than complex labor tools. After Alphabet sold Boston Dynamics to SoftBank, LaValley joined Disney, working on animatronics such as Baby Groot before founding Cartwheel Robotics. He critiques current market trends of creating humanoid robots primarily for labor, arguing these efforts lack sufficient demand and cost-effectiveness. LaValley doubts the industry's ability to achieve widespread adoption by 2040 due to technological hurdles and public reluctance. The discussion highlights skepticism regarding Morgan Stanley’s ambitious projections about robot proliferation, influenced by figures like Musk. Many current designs are perceived as unappealing, lacking emotional appeal necessary for consumer acceptance. Despite potential future safety improvements, LaValley believes these advancements are distant goals, pointing out societal fears exacerbated by narratives of job replacement. LaValley's personal experiences, including his children's differing reactions to robots, underline the challenge of social acceptance. He stresses fostering positive technology relationships as essential for integration into everyday life and cautions against focusing solely on economic gains, which might hinder broader acceptance. Concerns about data privacy and security in Chinese-made robots are also raised, suggesting a preference for trustworthiness from U.S.-made alternatives. LaValley sees potential in developing humanoid robots emphasizing social interaction over basic tasks, exemplified by Cartwheel’s creation, Yogi. Designed as an engaging character, Yogi aims to enhance hospitality and healthcare settings through advanced interactions. Cartwheel has developed its software stack focusing on natural language processing and innovative motion generation methods like Model Predictive Control (MPC). LaValley's team anticipates Yogi’s debut in late 2026 or early 2027, with testing phases involving significant institutions. He emphasizes the importance of perfecting social interaction capabilities before expanding other features. **Bullet Points Summary:** - Scott LaValley critiques current humanoid robot designs as "terrifying" and believes they lack demand and cost-effectiveness. - LaValley's insights have influenced Elon Musk’s interest in humanoid robotics, emphasizing robots as tools for labor augmentation rather than replacements. - Morgan Stanley projects $5 trillion revenue by 2050, but challenges persist in developing functional bipedal robots; simpler designs are suggested. - After working at Boston Dynamics and Disney, LaValley founded Cartwheel Robotics, focusing on creating socially interactive robots like Yogi. - Skepticism is raised about Morgan Stanley’s projections for robot numbers, with many current designs lacking consumer appeal due to perceived unfriendliness and safety issues. - Public fears, driven by narratives of job replacement, pose significant barriers to humanoid robot integration in everyday life. - LaValley highlights data privacy concerns with Chinese-made robots, advocating for trustworthy U.S.-made alternatives focusing on social interaction. - Cartwheel’s software stack emphasizes natural language processing and innovative motion techniques, aiming for Yogi's debut in late 2026 or early 2027, prioritizing social interaction capabilities. Keywords: Agility Robotics, Alphabet, BDX Droids, Baby Groot, Boston Dynamics, Cartwheel Robotics, DARPA Robotics Challenge, Disney, Elon Musk, Engineered Arts, Figure AI, Google, Inventor, MPC, Morgan Stanley, ROS, Scott LaValley, SoftBank Group, Tesla, Yogi, actuation technology, animatronics, automation challenges, data privacy, healthcare setting, humanoid robots, legged locomotion, social acceptance
tesla
www.theregister.com 19 hours ago
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52. HN Show HN: PromptGit – Git diff and rollback for LLM promptsPromptGit is a tool that extends Git version control to AI prompts, enabling teams to manage and collaborate on prompt engineering with rigorous standards akin to those applied to code development. It incorporates full Git-native versioning features such as commit history, branches, tags, rollback capabilities, and blame tracking. The advanced diff analysis provided by PromptGit employs semantic comparison through TF-IDF and cosine similarity techniques, offering automatic risk assessment of changes and behavioral analysis to gauge the impact on prompt behavior. This is complemented by a GitHub-style visualization for side-by-side comparisons. For experimentation purposes, PromptGit supports A/B testing with statistical rigor, allowing users to evaluate different versions of prompts based on configurable metrics like quality and safety. It tracks results to facilitate performance analysis and automatically identifies top-performing candidates. In enterprise settings, it includes policy management tools such as ownership controls using CODEOWNERS-style workflows and pattern-based rules through glob patterns for various prompt types. The toolset described facilitates managing CODEOWNERS-style approval workflows with flexible glob patterns and CI integration. It involves setting up prerequisites like Node.js 18.0 or higher, pnpm 8.0 or higher, and Git 2.20 or higher, followed by installation steps such as cloning a repository and installing dependencies. Users can initialize repositories, save prompts with metadata, and launch a web interface for prompt management, utilizing core commands for prompt operations like listing, comparing, and rolling back, along with running A/B experiments and managing policies. The underlying technology employs a pure JavaScript Git implementation via the `prompt-store` library using isomorphic-git. The project consists of three main components: a Core Library, CLI Tool, and Web Interface. These elements manage prompts using Git operations in JavaScript, feature semantic analysis through TF-IDF and cosine similarity algorithms, and incorporate YAML frontmatter for metadata parsing. Policy engines support ownership and approval workflows. The CLI Tool provides an intuitive command interface, efficient batch processing, and is ready for integration into CI/CD pipelines and automation tools, supporting cross-platform usage on Windows, macOS, and Linux. The Web Interface, developed with Nuxt 3, Vue 3, and TypeScript, offers real-time updates, responsive design with a mobile-first approach, dark mode, and accessibility compliance with WCAG 2.1 standards. Prompts are stored in Markdown files featuring YAML frontmatter for metadata, including fields like owner, description, version, tags, created date, and updated timestamp. The system configuration encompasses semantic versioning and tagging using version numbers and an array of tags to enhance categorization and search capabilities. It provides ISO 8601 timestamps for creation and modification dates. Configuration is achieved via environment variables, setting parameters such as `REPO_DIR` and `AUTH_TOKEN`, with optional Git settings like `GIT_AUTHOR_NAME` and `GIT_AUTHOR_EMAIL`. Policy configurations are defined in a `.promptowners` file specifying approval requirements across different prompt categories. A/B experimentation utilizes JSON manifests to outline candidate prompts, metrics (e.g., quality, safety), and the experimental process. CI/CD integration is facilitated through GitHub Actions for automated analysis of prompt changes triggered by pull requests. Manual analysis involves command-line scripts for generating change summaries and checking compliance with approval workflows. REST API endpoints are provided for managing prompts and experiments, demonstrated via `curl` commands. For development, instructions detail building from source using `pnpm`, including installing dependencies, building specific packages or the entire project, and running a web UI in development mode. Testing procedures allow for global or package-specific tests. The project is licensed under the FSL-1.1-MIT License, with details available in the LICENSE file. **Bullet Point Summary:** - **PromptGit Overview:** Extends Git to AI prompts, offering full versioning and advanced diff analysis using semantic comparison techniques. - **Experimentation Features:** Supports A/B testing for evaluating prompt versions based on metrics like quality and safety. - **Enterprise Tools:** Includes CODEOWNERS-style workflow controls and policy management tools with pattern-based rules. - **Setup and Dependencies:** Requires prerequisites such as Node.js 18.0+, pnpm 8.0+, and Git 2.20+; involves cloning, installing dependencies, and repository initialization. - **Core Components:** Consists of a Core Library, CLI Tool, and Web Interface for prompt management using isomorphic-git in JavaScript. - **CLI and Web Interface Features:** Offers an intuitive command interface with cross-platform support and a responsive, accessible web interface built with Nuxt 3, Vue 3, TypeScript. - **Prompt Storage:** Prompts are stored in Markdown files with YAML frontmatter metadata fields. - **System Configuration:** Includes semantic versioning, timestamp management, environment variable configuration, policy configurations via `.promptowners`, and A/B experimentation using JSON manifests. - **CI/CD Integration:** Uses GitHub Actions for automatic analysis of prompt changes; includes manual analysis steps and REST API endpoints for managing prompts. - **Development Instructions:** Covers building from source with `pnpm`, including dependency installation, package-specific builds, web UI development mode, and testing procedures. - **Licensing:** Licensed under the FSL-1.1-MIT License. Keywords: A/B Experimentation, AI, Accessibility, Blame, Branch, CI/CD Integration, CLI Tool, Comparison, Git, GitHub Actions, JavaScript, Metadata, Nodejs, Nuxt 3, Policy Management, Pull Request, Risk, Rollback, Semantic, Semantic Versioning, Workflow, YAML, isomorphic-git
llm
github.com 20 hours ago
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53. HN Migrating to TanStack Start- The author transitioned their course platform project to a client-server architecture, utilizing Hono and Bun for the backend and React with TanStack Router for the frontend. - They encountered SEO challenges due to the lack of server-side rendering (SSR), essential for indexing specific pages like those for courses, authors, and tags. - Initial considerations included creating a separate static site using Astro or enabling SSR in TanStack Router. Both options presented issues: maintaining an additional app with Astro and setup difficulties with SSR in TanStack Router. - The decision was made to migrate to TanStack Start due to its support for selective SSR, allowing server-side rendering where necessary without fully adopting all backend functionalities of TanStack Start. - This migration process involved configuring the router to manage authentication state using `useSession` and conditionally render components based on session status. The application is rendered within React's `StrictMode`, with additional context provided by QueryClientProvider. - Key changes in migrating to TanStack Start included enhancing type safety, integrating query management, and renaming primary files as recommended by documentation (e.g., from `main.tsx` to `router.tsx`). - Authentication checks were shifted towards being more route-specific rather than centralized, aligning with TanStack Start's architecture for improved modularity. - The application layout is configured using a custom router context interface (`MyRouterContext`) that includes authentication session data and a QueryClient instance. This setup supports theme management through `ThemeProvider` and integrates dev tools via TanStack Router Devtools. - The web application uses `@tanstack/react-router` and `@tanstack/react-query`, managing authentication state and query caching with a custom router context. - Server-side functions, such as `fetchAuth`, are used to retrieve session data, which can be accessed via the `useRouteContext` hook and passed to child routes. Plans include removing auth-related code from the root route, shifting session fetching to specific needed routes. - The Vite configuration integrates TanStack Router with other plugins, including those for React and TailwindCSS, while setting path aliases and configuring a development proxy that is removed during migration to TanStack Start. - Migration also involved updating Better Auth (auth framework) and Hono (server framework), requiring changes such as adding a base URL to the auth client configuration due to integration with Hono. - Post-migration tasks include removing unnecessary authentication fetching from root routes, ensuring theme provider functionality, and replacing hardcoded URLs with environment variables. Progress on these tasks can be tracked through a specified pull request. These bullet points encapsulate the key aspects of the author's migration process and subsequent changes made to enhance their web application using TanStack Start. Keywords: Astro, Authentication, Bun, Context, DevTools, FetchAuth, File-Based Routing, Hono, Migrating, Plugins, PostgreSQL, Prisma, Proxy, QueryClientProvider, React, Router, SPA, SSR, TailwindCSS, TanStack, Theme, TypeScript, Vite, Zod
postgresql
catalins.tech 20 hours ago
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54. HN AI codingThe text critically examines "AI coding" tools, likening them to compilers rather than true programming assistants. It argues that these tools process input code prompts similarly to how compilers function but are hampered by the imprecision and non-locality of English language inputs, resulting in non-deterministic outputs. The author suggests that enthusiasm for AI coding is misplaced, arising from dissatisfaction with traditional programming languages and compilers rather than any genuine advancements in AI capabilities. The discussion extends to a broader skepticism about truth and market forces, exemplified by the overhyped investments in self-driving car technology driven more by excitement than practicality. This highlights doubts about whether AI can genuinely emulate human coding or if it simply mimics the deterministic nature of compilers, rendering current AI coding tools inadequate for true programming tasks. The author questions the efficacy and market value of AI-driven coding tools, proposing that their perceived utility addresses deficiencies in traditional programming rather than representing an innovative breakthrough. While acknowledging potential benefits from improved search, optimization, and pattern recognition capabilities in Large Language Models (LLMs), the text emphasizes that these tools reflect shortcomings in existing programming tooling, languages, and hiring practices. The author criticizes the excessive hype surrounding AI coding tools, suggesting that they often create a perception of increased productivity without actual efficiency gains. The argument is made for better allocation of resources towards enhancing fundamental software development tools such as programming languages, compilers, and libraries, recognizing AI's strengths and limitations as a tool without succumbing to exaggerated expectations. An update clarifies the author's stance: they are not against using AI but advocate understanding its appropriate role in improving workflows while cautioning against overstated claims about its capabilities. Additionally, the author discusses the tension between accessibility and technical accuracy in their writing style and reflects on whether social media engagement should remain a societal focus or if society will evolve beyond it. **Bullet Point Summary:** - The text critiques AI coding tools as resembling compilers rather than true programming assistants due to their limitations with English language inputs. - Enthusiasm for these tools is viewed as misplaced, stemming from dissatisfaction with existing programming methods. - Broader skepticism about truth and market forces is highlighted through the example of self-driving car investments driven by hype. - Doubts are raised about AI's ability to truly emulate human coding, suggesting current tools are inadequate for genuine programming tasks. - The perceived utility of AI-driven tools is seen as addressing deficiencies in traditional programming rather than offering a breakthrough. - Potential benefits from LLMs include improved search, optimization, and pattern recognition but reflect existing shortcomings in programming tooling and hiring practices. - Excessive hype around AI coding tools often leads to perceived productivity gains without actual efficiency improvements. - The author advocates for investing resources in fundamental software development tools rather than overhyped AI capabilities. - An update clarifies the author's stance: support for using AI appropriately, with caution against exaggerated claims of its capabilities. - The text discusses tension between accessibility and technical accuracy in writing style and questions the future role of social media engagement. Keywords: AI coding, LLMs, compilers, hype, languages, libraries, limitations, optimization, productivity, social media, tooling, workflow
popular
geohot.github.io 21 hours ago
https://store.steampowered.com/app/2262930/Bombe&# an hour ago https://github.com/williamcotton/webpipe an hour ago https://williamcotton.com/articles/introducing-web-pipe an hour ago http://github.com/williamcotton/williamcotton.com an hour ago https://antirez.com/news/154 an hour ago https://github.com/sm18lr88 an hour ago https://thedailywtf.com/articles/the-therac-25-incident an hour ago https://pmc.ncbi.nlm.nih.gov/articles/PMC6165228/ an hour ago https://pmc.ncbi.nlm.nih.gov/articles/PMC3489818/t an hour ago https://en.m.wikipedia.org/wiki/Conway%27s_law an hour ago https://arxiv.org/pdf/2507.09089 an hour ago https://mortoray.com/programming-wont-be-automated-or-it-alr an hour ago https://www.reddit.com/r/ClaudeCode/s/iy058fH an hour ago https://chatgpt.com/share/68c56214-1038-8004-b1ed-35939 an hour ago https://arxiv.org/abs/2507.09089 an hour ago https://wiki.c2.com/?BidirectionalCompiler an hour ago https://en.wikipedia.org/wiki/Impedance_matching an hour ago https://garden.joehallenbeck.com/container/mappers-and- an hour ago |
55. HN Diesel 2.3.0- **Diesel 2.3.0 Update Overview**: Diesel 2.3.0 represents a substantial update developed by 95 contributors over 16 months, involving over 1,142 commits. It enhances the query Domain Specific Language (DSL) and expands platform support. - **Funding and Support**: The release features window function support funded by the NLNet Foundation's NGI Zero Core initiative and resources from GitHub's Secure Open Source Fund. Diesel seeks further community support through code contributions, documentation efforts, backend maintenance for MySQL/MariaDB, forum discussions, bug reporting, issue triaging, and sponsorships. - **Introduction of `#[derive(HasQuery)]`**: A key feature in this version is the `#[derive(HasQuery)]` attribute, which streamlines query construction from Rust structs by associating them with specific SQL clauses. It enhances compatibility with result loading into these structs and builds on the capabilities of `#[derive(Selectable)]`, promoting direct and structured query creation. - **Simplified Query Construction**: The use of `#[derive(HasQuery)]` allows for simplified database operations in Diesel, akin to using `Queryable` and `Selectable`. For instance, executing a command like `User::query().load(connection)` simplifies manual query construction seen in earlier versions of Diesel. - **Advanced Use Case Support**: The attribute facilitates complex data retrieval by allowing custom select expressions and base queries. An example involves creating joined tables with window functions to compute metrics such as average salary per department, promoting consistency and reducing boilerplate code. - **Window Function Enhancements**: Diesel 2.3 introduces built-in DSL support for window functions across all backends, applicable in complex analytical queries. This includes using aggregate functions as windows and chaining through `WindowExpressionMethods`. The framework ensures query validity at compile time by enforcing constraints on the use of window functions based on backend capabilities. - **Custom Window Functions**: Users can define custom window functions using the `#[declare_sql_function]` procedural macro, providing flexibility in analytical operations. - **SQLite Support for WebAssembly**: Diesel 2.3 enables SQLite targeting for `wasm32-unknown-unknown`, allowing Diesel to operate within web browsers or other WebAssembly (WASM) environments by changing the compilation target and optionally using a special virtual file system for data storage in browsers. - **PostgreSQL Enhancements**: The update adds support for PostgreSQL's MULTIRANGE type, various RANGE/MULTIRANGE-specific operators, functions, and expanded functionality with ARRAY, JSON, and JSONB types. This allows more complex query constructions similar to native SQL commands. - **SQLite and JSON/JSONB Support**: Diesel 2.3 extends SQLite's capabilities by supporting JSON/JSONB types using `json` or `jsonb` functions for data manipulation and querying, despite the lack of native support in SQLite itself. - **Community Contributions**: The release was made possible through contributions from the core team and other developers, supported by sponsoring, bug reports, and discussions. Community involvement is highly valued, though specific contribution statistics are unavailable. - **Documentation and Further Information**: Users interested in detailed examples and usage can refer to Diesel's documentation for more information on these features and enhancements. This summary encapsulates the key aspects of Diesel 2.3.0’s update, focusing on its new features, improvements across platforms, and community support mechanisms. Keywords: Diesel, GitHub, HasQuery, JSON, PostgreSQL, Rust struct, SQLite, VFS, WASM, backends, change log, commits, contributions, documentation, extensions, platforms, query DSL, serde_json, sponsors, window functions
postgresql
diesel.rs 21 hours ago
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56. HN Ask HN: The quiet demolition of self‑learned skillA self-taught programmer recounts an experience where an LLM significantly optimized their code, reducing its size to approximately 10% of the original while enhancing performance. Initially, the programmer felt a sense of satisfaction with the improved efficiency; however, this quickly turned into disheartenment as the AI-generated solution overshadowed years of personal effort and pride in their unique coding style. This situation led the programmer to experience an existential crisis concerning the loss of ownership and uniqueness in their work. The post extends an invitation to other creatives such as artists and voice actors who might relate to similar feelings brought on by advancements in AI technology. - A self-taught programmer shares their story about code optimization by a large language model (LLM). - The LLM reduced the original code size to about 10% while improving its performance. - Initial satisfaction with the improvement turned into disheartenment due to diminished personal contribution and pride. - The programmer experienced an existential crisis over losing ownership and uniqueness in their work. - The post invites artists and voice actors who might feel similarly affected by AI advancements. Keywords: LLM, artist, code, compact code, creativity, efficiency, effort, existential crisis, intellectual property, performant, programming skills, self-taught programmer, voice actor
llm
news.ycombinator.com 21 hours ago
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57. HN Show HN: Quick Postgres Backup Dump BrowserThe "Quick Postgres Backup Dump Browser" is an intuitive local utility aimed at facilitating the swift inspection of PostgreSQL database backups without necessitating their importation into another database instance. This tool eliminates typical procedural steps like uploading or logging in, as it operates completely offline. Although its efficiency has been validated for smaller files up to 20MB and deemed fast, it remains untested on larger file sizes. The developers encourage users to explore the tool's capabilities further. - **Tool Purpose**: Designed for quick viewing of PostgreSQL backups without importation. - **Ease of Use**: Functions offline, eliminating the need for uploads or logins. - **File Size Limitations**: Efficient with files up to 20MB; not tested on larger sizes. - **User Engagement**: Encourages users to explore its capabilities. Keywords: Backup Browser, Backup Dump, Browser, Files under 20mb, Importing, Larger files, Local, No login, PostgreSQL, Postgres DB, Quick Postgres, Show HN, Viewer
postgresql
sql.bhasin.dev 21 hours ago
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58. HN Cursor Agent CLICursor Agent has expanded its accessibility by introducing a Command Line Interface (CLI) that supports headless usage across diverse environments beyond its initial web, mobile, and Slack integrations. This enhancement empowers users, including those working with Neovim or JetBrains IDEs, to utilize Cursor's AI capabilities through the CLI alongside their preferred tools. The CLI accommodates any model included in a Cursor subscription and facilitates running multiple agents concurrently within terminals or remote setups. To install, users can execute `curl https://cursor.com/install -fsSL | bash`, followed by initiating tasks with commands like `cursor-agent chat "find one bug and fix it"`. Despite being in the beta phase, this tool provides robust functionalities such as file manipulation and shell command execution. However, users must exercise caution due to evolving security measures. Cursor encourages user feedback during this trial phase, promising further information in its CLI documentation. - **Key Points Covered:** - Introduction of a Command Line Interface (CLI) for Cursor Agent. - Expansion from web, mobile, and Slack integrations to broader environments. - Support for Neovim or JetBrains IDEs through the CLI. - Ability to use any model included in a Cursor subscription via CLI. - Concurrent operation of multiple agents possible within terminals or remotely. - Installation process using a simple curl command. - Example usage with `cursor-agent chat "find one bug and fix it"`. - Beta phase status, offering advanced functionalities like file manipulation and shell command execution. - Caution advised due to evolving security measures. - User feedback encouraged during the trial phase. - Further details available in CLI documentation. Keywords: AI, CLI, Cursor Agent, IDEs, JetBrains, Neovim, autocomplete, beta, environment, headless, models, security, subscription, terminal
jetbrains
cursor.com 21 hours ago
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59. HN Ask HN: Claude Sonnet 4 API returning model with April 2024 knowledge cutoffThe summary encapsulates a report from a user who experienced an unexpected response while using the claude-sonnet-4-20250514 endpoint. Instead of accessing information up to May 2025 as expected, the model identified itself as Claude 3.5 Sonnet with a knowledge cutoff in April 2024. This issue can be consistently reproduced through a specific test on Anthropic's console and presents significant challenges for applications that depend on the enhanced capabilities promised by Sonnet 4. The potential technical reasons behind this discrepancy could include errors in model routing, ongoing A/B testing processes, or a phased introduction of updates. Rather than lodging complaints, the user is seeking an understanding of the technical aspects contributing to this problem due to its adverse effects on their application's functionality. - **Key Issue**: User encounters unexpected response from claude-sonnet-4-20250514 endpoint. - **Description**: Model identifies as Claude 3.5 Sonnet with a cutoff in April 2024, not up-to-date May 2025 information. - **Reproducibility**: The problem is consistently reproducible using Anthropic's console test. - **Impact**: Causes significant issues for applications relying on updated capabilities of Sonnet 4. - **Potential Technical Causes**: Considered causes include model routing errors, A/B testing, or a gradual rollout of updates. - **User Intent**: Seeking technical insights rather than lodging complaints due to functional impacts. Keywords: A/B testing, API, April 2024, Claude 35, Claude Sonnet, app issues, consoleanthropiccom, election results, endpoint, gradual rollout, knowledge cutoff, model, model routing, reproducible test, technical explanations
claude
news.ycombinator.com 21 hours ago
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60. HN Ecommerce: You don't need ML to do personalizationE-commerce platforms can enhance search result personalization on Elasticsearch without complex machine learning algorithms by utilizing its native capabilities. Elasticsearch, as an open-source search engine, supports personalized search through relevance scoring that incorporates user-specific signals like past purchases and browsing behavior, ranking relevant products higher for individual users. Instead of treating personalization as a secondary, intricate step involving costly machine learning processes, the text highlights leveraging Elasticsearch's inherent features directly to achieve this goal. By combining product catalogs with user activity data within Elasticsearch indices, queries can integrate these sources to improve relevance based on a user’s history. This straightforward approach streamlines implementation and speeds up personalization efforts. The article outlines a practical method using Elasticsearch for tailoring search results according to users' past behaviors, emphasizing that effective personalization is both efficient and uncomplicated with the right tools. The process involves calculating boost factors using a formula based on purchase count and recency decay, which is integrated into the query execution without additional infrastructure or machine learning tasks. The method consists of two phases: 1. **History Phase:** This phase retrieves user purchase history related to search terms (e.g., "chips"), calculating product weights with `weight_raw = log1p(purchase_count) × exp(-ln(2) × age_days / half_life)`, which are then normalized into boost factors. 2. **Search Phase:** The user's query is executed against the product index, using function_score queries to apply the calculated boosts, resulting in a personalized ranking of products. This approach allows for clear and composable integration with standard ranking mechanisms like BM25 or vector similarity. It demonstrates how different users searching for "chips" receive customized results based on their unique purchase histories without requiring batch processing or external model servers. The article provides a step-by-step guide, detailing the execution of queries to show how product ordering is influenced by individual users' past purchases. The setup includes running Elasticsearch locally and loading sample data via bulk POST requests, using a script (`personalized-search.py`) for generating personalized search results. It concludes with an acknowledgment to Honza Kral for inspiring this approach, noting that the full code and data are available in a GitHub repository. **Bullet Point Summary:** - E-commerce platforms can personalize Elasticsearch search results without complex ML by leveraging relevance scoring based on user-specific signals like past purchases. - Personalization is achieved directly through Elasticsearch's features, integrating product catalogs with user activity data for more relevant searches. - A practical method using Elasticsearch involves calculating boost factors from purchase count and recency decay, integrated into query execution. - The personalization process includes two phases: retrieving and weighting user purchase history (History Phase), and applying these boosts to search queries (Search Phase). - This approach is transparent, composable with standard ranking mechanisms, and demonstrates personalized results for different users based on their unique histories. - A step-by-step guide shows how to execute queries and personalize search results using a local Elasticsearch setup, with full code available in a GitHub repository. Keywords: Activity Signals, Age Days, BM25 Ranking, BULK POSTs, Boosts, Browsing Behavior, Cluster, Data, Ecommerce, Elasticsearch, Function Score Query, GitHub, Half Life, Indices, Intent, Last Purchase Timestamp, No ML Jobs, Normalization, Past Purchases, Personalization, Product Catalog, Purchase Count, Purchase History, Query Combination, Recency Decay, Relevance Scoring, Sample Output, Script, Search, Search Engine, User Queries, User-Specific Boost, Users, Vector Similarity, Weights
github
alexmarquardt.com 21 hours ago
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61. HN New Open Source Extension for Web Scraping – Visual and Flexible (No AI)**Summary:** OnPage.dev is an innovative open-source Chrome extension specifically designed for visual web scraping, targeting researchers, analysts, and hobbyists. The tool allows users to visually highlight elements on a webpage for data extraction purposes, making it accessible and efficient. It includes advanced features such as auto-scrolling and smart scraping capabilities to handle dynamic content effectively. Data extracted can be exported in various formats, including CSV or JSON, catering to different user needs. OnPage.dev offers flexibility by providing both a cloud-hosted version for easy access and an option for users who prefer self-hosting it locally. The extension is noted for its intuitive interface that ensures users maintain control over their data during the scraping process. Feedback on its usage workflows is actively sought, emphasizing continuous improvement of user experience. Additionally, OnPage.dev stresses responsible web scraping practices by advising users to respect website terms of service. For those interested in exploring more about this tool or accessing the source code, information and links are available on GitHub under [OnPage-Scraper](https://github.com/OnPage-Scraper/OnPage-Scraper), and a hosted version can be accessed at onpage.dev. **BULLET POINT SUMMARY:** - OnPage.dev is an open-source Chrome extension for visual web scraping aimed at researchers, analysts, and hobbyists. - It enables users to highlight webpage elements visually for data extraction. - Features include auto-scrolling and smart scraping for dynamic content. - Supports exporting extracted data in CSV or JSON formats. - Offers both a cloud-hosted version and the option for local self-hosting. - Provides a user-friendly interface that maintains control over data during scraping. - Encourages feedback on usage workflows to improve user experience. - Emphasizes responsible web scraping by advising adherence to website terms of service. - Additional information and source code are available on GitHub at [OnPage-Scraper](https://github.com/OnPage-Scraper/OnPage-Scraper). - A hosted version can be accessed at onpage.dev. Keywords: CSV, Chrome extension, GitHub, JSON, OnPagedev, analysts, auto-scroll, cloud version, dynamic content, export, flexible, highlight elements, hobbyists, researchers, responsible scraping, self-host, structured data, terms of service Keywords: OnPagedev, user-friendly, visual, web scraping
github
news.ycombinator.com 22 hours ago
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62. HN Java 25's new CPU-Time Profiler (1)Java 25 introduces an experimental CPU-Time Profiler via OpenJDK 25 after over three years of development, aiming to resolve limitations in performance analysis present in existing Java Flight Recorder (JFR) method samplers. The current JFR profiler samples a limited number of threads at fixed intervals, which can lead to ineffective sampling due to aggressive subsampling and misinterpretation of execution time versus CPU time. Execution time considers all thread activity, including idle periods during I/O operations, whereas CPU time only accounts for active processing cycles. The experimental CPU-Time Profiler seeks to accurately measure the actual processor utilization by profiling CPU time rather than execution time, as outlined in JEP 509: JFR CPU-Time Profiling (Experimental). It uses a more accurate method based on Linux's kernel timer mechanism for measuring CPU usage without risking process crashes. The profiler can be tested with an example Java program called `HttpRequests`, which demonstrates different request strategies and their impact on execution time. The new profiling approach addresses the shortcomings of the existing JFR sampler that often ignored failed samples, affecting data interpretation. It aims to provide a more precise reflection of thread activity by sampling based on CPU time rather than elapsed real time. The profiler simplifies event types by using `jdk.CPUTimeSample` and includes fields for stack traces, sampling periods, and indications of safepoint bias. Configuration options such as `period` and `throttle` help manage performance overhead during profiling. These controls affect the frequency of sampled events and can be adjusted based on hardware threads to prevent event saturation. Additionally, JFR now offers new views like "cpu-time-hot-methods" for identifying methods with high CPU usage and "cpu-time-statistics" for insights into sampling accuracy. The integration of this profiler in JDK 25 was a last-minute decision, making it available for experimentation despite its experimental status and the existence of unresolved design ambiguities. The feature's inclusion is particularly significant given its current limitation to Linux systems, which poses challenges for developers on Windows or macOS platforms. Johannes Bechberger from SAP's SapMachine team has played an integral role in developing this new profiler and will continue with a deep dive into its implementation. His work contributes to broader efforts within the OpenJDK community to refine profiling tools like async-profiler and FirefoxProfiler, enhancing Java development practices. Key Points: - JDK 25 introduces an experimental CPU-Time Profiler for better performance analysis by focusing on actual CPU time rather than execution time. - Existing method sampler in JFR faces limitations due to subsampling and misinterpretation of data between execution and CPU time. - The new profiler uses Linux's kernel timer mechanism for accurate measurement without causing crashes, a feature detailed in JEP 509. - Configuration options control the profiling overhead, while new JFR views offer insights into CPU usage and sampling effectiveness. - Despite being experimental and limited to Linux, this new profiler marks progress in addressing Java performance analysis challenges. - Johannes Bechberger's work at SAP is central to these improvements, with ongoing efforts to enhance OpenJDK's profilers. Keywords: CPU-Time Profiler, CPU-bound, GitHub, JDK Mission Control (JMC), JEP 509, JFR, JVM, Java, Linux, Lost samples, Mac OS, OpenJDK, OpenJDK 25, Ron Pressler, SapMachine, Windows, async-profiler, execution-time, method sampling, native libraries, performance issues, profiling, safepoints, sampling interval, server instance, socket I/O, stackwalking, threads
github
mostlynerdless.de 22 hours ago
https://www.ptc.com/en/products/developer-tools&#x 15 hours ago https://mostlynerdless.de/blog/2025/07/30 13 hours ago https://mostlynerdless.de/blog/2025/08/25 13 hours ago https://mostlynerdless.de/blog/2025/09/01 13 hours ago https://youtu.be/mLNFVNXbw7I 13 hours ago https://web.eecs.umich.edu/~weimerw/2012-4610/read 13 hours ago https://dl.acm.org/doi/10.1145/367487.367501 13 hours ago https://gchandbook.org/ 13 hours ago https://gchandbook.org/contents.html 13 hours ago https://openjdk.org/jeps/454 an hour ago https://www.youtube.com/watch?v=mLNFVNXbw7I an hour ago https://www2.eecs.berkeley.edu/Pubs/TechRpts/2006& an hour ago |
63. HN Show HN: CarbonCodeX – AI Coding Assistant Built for Web Developers**Concise Summary:** CarbonCodeX is an AI coding assistant tailored for web developers, as emphasized in a developer showcase post. A senior full-stack developer has praised the tool, stating that it markedly improved their workflow by enabling efficient transitions between Claude and GPT-4 models. This flexibility in switching between advanced language models reportedly led to a tripling of the developer's productivity. **Bullet Point Summary:** - CarbonCodeX is an AI coding assistant specifically for web developers. - Highlighted in a developer showcase post. - Endorsed by a senior full-stack developer who experienced enhanced workflow. - Allows seamless transitions between Claude and GPT-4 models. - The capability to switch models reportedly tripled the developer's productivity. Keywords: AI, CarbonCodeX, Claude, Coding Assistant, Full-Stack Developer, GPT-4, Instantly, Productivity, Show HN, Transform, Web Developers, Workflow, Worlds
claude
carboncodex.app 22 hours ago
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64. HN DuckLake for Busy Engineering ManagersDuckLake is a tailored solution designed for engineering managers who handle extensive datasets from platforms like Jira, GitHub, and Google Drive. It addresses the limitations of traditional data storage methods—such as CSVs, JSON files, databases (MySQL or PostgreSQL), and data warehouses—which often involve high management complexity, rigidity, maintenance challenges, and costs. DuckLake provides a portable "data lake" that utilizes Parquet for efficient data storage while managing metadata through a dedicated database. The core of DuckLake's functionality is its integration with DuckDB, an in-process SQL OLAP (Online Analytical Processing) database known for being lightweight and straightforward to use. Users can set up DuckDB on their systems, such as macOS via Homebrew, and leverage the DuckLake extension within DuckDB to streamline data management tasks. This approach empowers managers to make informed decisions using data analytics without incurring the typical drawbacks associated with other data solutions. For those interested in a detailed walkthrough of DuckLake's operation, it is recommended to view an introductory video by Hannes Mühleisen and Mark Raasveldt. DuckLake provides a cost-effective and accessible means for managing large datasets specifically within engineering management contexts. Setting up involves installing DuckDB and attaching the DuckLake extension. Users can ingest CSV data into DuckDB using `read_csv_auto`, which automatically parses the data, allowing verification through commands like `SHOW TABLES;`. The system manages data in metadata files and parquet files stored in designated directories, facilitating efficient storage and retrieval. Additional data can be appended using an `INSERT INTO ... SELECT` statement. DuckLake serves as a robust solution for local data collection and analysis, eliminating the need to rely on cloud services. It stores data in lightweight Parquet files, which are easily exportable for visualization purposes with tools like Tableau or Power BI. This capability enhances privacy and portability while enabling future scalability through potential transitions to cloud storage solutions if necessary. DuckLake's support of open formats and seamless integration aids in simplifying data management processes, providing swift insights without the complexities typical of traditional data lakes. - **DuckLake** is a solution for engineering managers to handle vast datasets efficiently. - It addresses shortcomings of traditional methods like CSVs, JSON files, databases, and data warehouses. - Utilizes **Parquet** for storage and manages metadata through a database. - Employs **DuckDB**, an in-process SQL OLAP database, for simplicity and lightweight operation. - Users can install DuckDB on systems (e.g., macOS via Homebrew) and use the DuckLake extension for data management. - Recommended to watch an introduction by Hannes Mühleisen and Mark Raasveldt for detailed understanding. - Offers a cost-effective alternative for managing large datasets in engineering contexts. - Setup involves installing DuckDB, attaching the DuckLake extension, and ingesting CSV data using `read_csv_auto`. - Data is stored as metadata files and parquet files, with easy verification via SQL commands. - Supports efficient storage, retrieval, and future scalability by transitioning to cloud solutions if needed. - Enhances privacy and portability while simplifying management and providing quick insights. Keywords: BigQuery, Blob Storage, CSV, Confluence, Copy Command, Data Collection, Data Lake, Database, DuckDB, DuckLake, Engineering Managers, GitHub, Google Drive, Insights, Installation, JSON, Jira, Livebook, Metadata, MySQL, Open Formats, Parquet, PostgreSQL, Power BI, Scalability, Scripts, Snowflake, Spreadsheets, Tableau, macOS
postgresql
blog.incrementalforgetting.tech 23 hours ago
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65. HN Show HN: AudioMuse-AI Sonic Analysis### Summary AudioMuse-AI is an open-source project focused on local sonic analysis and music discovery using tools like Librosa and TensorFlow, facilitating the creation of playlists and integration with music servers such as Jellyfin and Navidrome. The initiative supports deployment through Docker and Kubernetes (tested on K3S), catering to various architectures including amd64 and arm64. Developed by NeptuneHub in a BETA phase, it encourages user feedback while acknowledging its testing nature. The project comprises several components: a Core Application running Flask with Worker containers, Helm Charts for Kubernetes deployment, and a Jellyfin Plugin. Recent updates have enhanced features like Collection Sync, Song Path, Sonic Fingerprinting, and improvements to the Voyager index for better similarity functions. The deployment guide outlines steps for deploying on a K3S cluster using Helm, requiring prerequisites such as a running K3S cluster, kubectl, helm, and specific server installations. Access points include various UIs with configurable parameters like NUM_RECENT_ALBUMS and CLUSTERING_RUNS for analysis tasks using the K-Means algorithm. Playlist generation features allow customization via natural language requests interpreted into SQL queries through a chat interface. Advanced functionalities like Similar Song and Sonic Fingerprint Playlists require analysis from an async UI, enabling personalized playlists based on similarity or listening history. The Collection Sync feature allows synchronization of local databases with internet-based ones post-agreement to privacy policies and OAuth login via GitHub, supporting track data sharing but not file transfers. Kubernetes deployment details describe the process using K3S, detailing pods like `audiomuse-ai-worker` for processing tasks and services such as `audiomuse-ai-flask-service` for API exposure. Configuration parameters emphasize settings affecting music library management, clustering, similarity analysis, duplicate filtering, evolutionary clustering, scoring weights, and AI integration. Local deployment instructions are provided using Docker Compose and Podman Quadlets, with prerequisites and steps for configuring environment variables with user-specific details. Version updates include a transition from Essentia to Librosa starting `v0.6.0-beta`, necessitating full library rescans, and shifts in database systems (SQLite to PostgreSQL) introducing a Redis Queue affecting workflow. Technical insights highlight the use of Librosa and TensorFlow for audio feature extraction; predictive models use embeddings generated by MusiCNN from the msd-musicnn-1.pb model. Clustering techniques offer algorithms like K-Means, DBSCAN, GMM, and Spectral clustering for playlist optimization using either traditional feature vectors or MusiCNN embeddings. PostgreSQL serves as the database for metadata storage, while a Redis Queue facilitates task management with asynchronous processing capabilities. Algorithm characteristics discuss suitability for specific data shapes, densities, and computational requirements. UI features include real-time monitoring, task cancellation, and an AI model for generating creative playlist titles. Future enhancements involve Jellyfin integrations for enhanced playlist management and cross-platform synchronization, along with a plugin to unify user interfaces. Despite its BETA status, the platform welcomes contributions but notes potential bugs or incomplete features. ### Key Points: - **Project Overview**: AudioMuse-AI focuses on local sonic analysis using Librosa and TensorFlow, supporting music discovery and integration with servers like Jellyfin and Navidrome. - **Integration and Deployment**: Supports Docker and Kubernetes (K3S), catering to amd64 and arm64 architectures; developed by NeptuneHub in BETA phase. - **Development Details**: Includes a Core Application, Helm Charts, and a Jellyfin Plugin; encourages user feedback with Mkdocs-enhanced documentation. - **Project Components & Updates**: Features Collection Sync, Song Path, Sonic Fingerprinting, and improved Voyager index for similarity functions. - **Deployment Guide & Configuration**: Outlines steps for K3S deployment using Helm, requiring specific prerequisites and configuration of `my-custom-values.yaml`. - **Access Points & Playlist Features**: Offers UI access points with configurable parameters; playlist generation via natural language requests interpreted into SQL queries. - **Advanced Functionalities**: Includes Similar Song and Sonic Fingerprint Playlists requiring async UI analysis for personalized playlists. - **Collection Sync & Kubernetes Details**: Allows synchronization of local databases post-OAuth login via GitHub; details K3S deployment process with specific pods and services. - **Configuration Parameters & Local Deployment**: Emphasizes settings affecting music management, clustering, and AI integration; provides local deployment instructions using Docker Compose and Podman Quadlets. - **Version Updates & Technical Insights**: Transitioned from Essentia to Librosa; uses TensorFlow for audio feature extraction and MusiCNN embeddings for predictive models. - **Clustering Techniques & Database Management**: Offers various clustering algorithms; utilizes PostgreSQL for metadata storage and Redis Queue for task management. - **Algorithm Characteristics & UI Features**: Discusses algorithm suitability; includes real-time monitoring, task cancellation, and AI model for playlist titles in the web interface. - **Future Enhancements & Contributions**: Plans include Jellyfin integrations and a plugin for unified interfaces; open to contributions despite BETA status. Keywords: AI models, API, AudioMuse-AI, Docker, Flask, Jellyfin, Kubernetes, Librosa, MusiCNN, Navidrome, PostgreSQL, Redis Queue, TensorFlow, clustering algorithms, embeddings, evolutionary algorithm, feature vector, music libraries, playlist generation, sonic analysis
postgresql
github.com 23 hours ago
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66. HN Social media promised connection, but it has delivered exhaustionThe text provides an in-depth analysis of the evolution and future trajectory of social media, highlighting several key themes: 1. **Evolution of Social Media**: Initially aimed at fostering genuine human connections, social media has evolved into platforms dominated by algorithm-driven content designed primarily for engagement rather than meaningful interaction. 2. **AI Influence**: Platforms like Facebook have become repositories of AI-generated spam, mixing machine and human content. This shift undermines authenticity, especially among influencers, with a focus on AI-driven consumerism. 3. **Bot-Girl Economy**: A digital marketplace characterized by hyper-optimized avatars promoting adult content, blending real and synthetic personas to capture user attention. 4. **Content Creation Shifts**: The rise of platforms like OnlyFans reflects a trend where subscription-based creators use major platforms as marketing channels, with AI-driven A/B testing intensifying engagement strategies at the cost of substance. 5. **Declining Engagement**: Despite increased content production, user interaction is dropping due to prevalent low-effort, untrustworthy AI-generated material lacking depth. 6. **Young Adult Behavior**: Many young adults use social media for mood regulation rather than genuine information or connection, indicating awareness yet disregard for platform artificiality. 7. **Platform Incentives**: Platforms financially benefit from synthetic accounts and user attention without offering wages, focusing on superficial engagement metrics instead of meaningful interactions. 8. **The Great Unbundling**: Major platforms are experiencing fragmentation as users migrate to smaller, private communities, with significant user declines post-acquisition (e.g., X under Elon Musk). 9. **Emergence of Micro-Communities**: Intentional communities like Patreon and Substack gain traction, offering deep engagement over scale, allowing creators to earn sustainably within defined boundaries. 10. **Shift Toward Privacy**: Users are moving from broad social media to private interactions in messaging apps like Signal due to fatigue from endless scrolling and inauthentic content. 11. **Future of Digital Engagement**: A potential shift towards smaller intentional spaces promoting meaningful connections over mass engagement, focusing on reflection rather than instant gratification. 12. **Public Utility Model for Platforms**: Suggests transforming social media into public utilities governed by civic charters to prioritize transparency and accountability over profit-driven models. 13. **Algorithmic Choice as Civic Right**: Advocates for user ability to select ranking logics on platforms, proposing algorithm choice as an inherent right requiring integration into platform architecture. 14. **Decentralized Protocols and Governance Challenges**: Highlights the rise of decentralized protocols like ActivityPub while acknowledging challenges in maintaining safety without centralized control. 15. **User-Centric Design Philosophy**: Recommends treating users as active ecosystem participants, with regulation incentivizing public interest services through tax benefits or open-source research funding. 16. **Collective Digital Literacy Responsibility**: Emphasizes broader digital literacy that includes recognizing algorithmic influence and cognitive exploitation beyond mere fact-checking. 17. **Educational Integration of Digital Literacy**: Discusses embedding digital and media literacy into education from early childhood to secondary school, comparing it to public infrastructure development requiring investments in teacher training and curriculum enhancement. 18. **Behavioral Safeguards for Responsible Engagement**: Proposes measures like default privacy settings, cooling-off periods for viral content, algorithmic impact assessments, and real-time public dashboards to empower users within digital spaces. 19. **Reforming Platforms with Humane Principles**: Advocates reforming existing platforms and creating new ones grounded in humane principles that prioritize human well-being over exploitation. 20. **Future Social Media Focus on Connection**: Envisions future social media emphasizing connection rather than engagement or growth, leveraging collective imagination to create community-focused digital environments. 21. **Human-Centric Network Opportunities**: As existing platforms decline, there is an opportunity to build networks centered around genuine interaction and understanding, posing the challenge of whether society can rise to this task or remain overwhelmed by current system negatives. Keywords: Bot-Girl Economy, Social media, algorithmic prioritization, attention economy, authenticity, automation, content consumption, decentralization, digital literacy, digital marketplace, generative AI, governance, influencer
popular
www.noemamag.com a day ago
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67. HN Adding FRM parser utility to MariaDBHimanshu Pandey, recognized on Twitter as @theboycalledhp and on GitHub as @hp77-creator, is actively contributing to the MariaDB project through the development of a FRM parser utility. This contribution is cataloged under the identifier hp77. In addition to his technical work, Himanshu engages with the community by sharing updates via his blog, showcasing his various projects, maintaining an accessible resume, and offering contact information for email communication at himanshu.dn.pandey@gmail.com. - **Key Points Covered**: - Himanshu Pandey's online identities: @theboycalledhp on Twitter and @hp77-creator on GitHub. - His current contribution to the MariaDB project involves developing a FRM parser utility, documented under hp77. - He shares updates about his work through a blog, demonstrating transparency and community engagement. - Himanshu maintains an online presence by showcasing projects and keeping an updated resume available for public viewing. - Contact information is provided via email (himanshu.dn.pandey@gmail.com), offering a direct line of communication. Keywords: Email, FRM, GitHub, Himanshu Pandey, MariaDB, Twitter, blogs, creator, hp77, projects, resume, technical, utility
github
hp77-creator.github.io a day ago
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68. HN Qwen 3 now supports ARM and MLXAlibaba's Qwen3, a hybrid reasoning model family, is broadening its integration into various platforms and sectors by enhancing compatibility with ARM architecture and Apple's MLX framework. This development allows efficient deployment on devices such as the Mac Studio, MacBook, iPhone, among others. The models are available in multiple quantization levels (4-bit, 6-bit, 8-bit, BF16), which improve performance by minimizing computational demands and power usage. Key partnerships with major chipmakers like NVIDIA, AMD, Arm, and MediaTek bolster edge AI adoption. NVIDIA's TensorRT-LLM increases Qwen3's inference speeds, while AMD enables its use on Instinct MI300X GPUs for complex tasks including code generation and reasoning. These optimizations lead to more efficient, large-scale AI deployments. Arm has fine-tuned Qwen3 models for CPU-based ecosystems using Arm® KleidiAI™ and Alibaba’s MNN framework, thereby enhancing mobile device inference efficiency. MediaTek's Dimensity 9400 smartphones, with upgraded SpD+ technology, have achieved a 20% boost in AI task processing speeds. As Qwen extends from edge devices to data centers, it supports applications in smart homes, wearables, vehicles, and enterprise automation. In the enterprise sector, Qwen3 is revolutionizing operations through its advanced language understanding and multilingual capabilities. Lenovo has integrated Qwen3 into its AI agent Baiying, serving over a million customers by boosting office productivity and global collaboration. FAW Group uses it for its OpenMind AI to enhance decision-making in the automotive industry. By January 2025, more than 290,000 customers across industries such as robotics, healthcare, education, finance, and automotive have adopted Qwen models via Alibaba's Model Studio. This widespread adoption underscores Qwen’s pivotal role in propelling AI-driven digital transformation throughout various sectors in China. **BULLET POINT SUMMARY:** - **Expansion and Deployment:** Qwen3 is expanding its deployment capabilities across platforms by supporting ARM architecture and Apple's MLX framework, enhancing performance through multiple quantization levels. - **Partnerships with Chipmakers:** Collaborations with NVIDIA, AMD, Arm, and MediaTek drive edge AI adoption. NVIDIA improves inference speeds via TensorRT-LLM, while AMD supports complex tasks on its GPUs. - **Optimizations for Mobile Devices:** Arm has optimized Qwen3 models using Arm® KleidiAI™ and Alibaba’s MNN framework for mobile efficiency. MediaTek enhances AI task processing speed by 20% with upgraded SpD+ technology in Dimensity 9400 smartphones. - **Enterprise Applications:** Qwen3 is transforming enterprise operations through its language understanding capabilities, with applications like Lenovo's Baiying and FAW Group's OpenMind AI enhancing productivity and decision-making in various industries. - **Adoption and Impact:** Over 290,000 customers across diverse sectors have adopted Qwen models by January 2025, marking a significant role for Qwen in advancing AI-driven digital transformation in China. Keywords: AI agent tasks, AI-powered digital transformation, AMD, ARM, Alibaba, Apple silicon, Baiying, CPU ecosystem, Dimensity 9400 series, FAW Group, Instinct MI300X GPUs, KleidiAI™, Lenovo, MLX, MNN, Mac Studio, MacBook, MediaTek, Model Studio, NVIDIA, Ollama, OpenMind, Qwen3, SGLang, SpD+, TensorRT-LLM, agent-based tasks, automotive, code generation, development platform, document analysis, edge AI, education, enterprise automation, finance, generative AI, healthcare, hybrid reasoning, iPhone, inference efficiency, inference speed, language understanding, logical reasoning, machine learning, multilingual processing, multimodal reasoning, open-source, quantization, robotics, smart homes, vLLM, wearables
ollama
www.alizila.com a day ago
https://x.com/Alibaba_Qwen/status/1934517774635991 22 hours ago https://github.com/ggml-org/llama.cpp/issues/ 21 hours ago |
69. HN SkiftOS: A hobby OS built from scratch using C/C++ for ARM, x86, and RISC-V**Summary:** SkiftOS is an open-source hobbyist operating system crafted in the programming languages C and C++. The project supports multiple hardware architectures, specifically ARM, x86, and RISC-V. This flexibility allows it to run on a variety of devices and platforms, accommodating different computing needs and preferences. A unique requirement for SkiftOS is its dependency on JavaScript, which must be enabled for effective usage or testing. This characteristic implies that users need a certain level of technical expertise and a compatible environment where JavaScript can operate seamlessly alongside the OS. **BULLET POINT SUMMARY:** - **Project Overview:** SkiftOS is an open-source hobby operating system. - **Development Language:** Developed using C and C++ programming languages. - **Supported Architectures:** Compatible with ARM, x86, and RISC-V architectures, allowing for versatile deployment across different hardware platforms. - **JavaScript Dependency:** Requires JavaScript to be enabled for effective use or testing, indicating a need for technical setup. - **Target Audience:** Primarily aimed at hobbyists and developers interested in OS development and experimentation. Keywords: ARM, C/C++, JavaScript, OS, RISC-V, SkiftOS, app, hobby, x86
popular
skiftos.org a day ago
https://serenityos.org/ an hour ago https://www.youtube.com/watch?v=LW_s6EqOxqY an hour ago https://us.amazon.com/Developing-32-Bit-Operating-System-Cd- an hour ago https://2019.ctf.link/internal/challenge/1fef0346- an hour ago https://en.m.wikipedia.org/wiki/ANSI_escape_code an hour ago |
70. HN Raku.org Website Reboot**Summary:** The Raku.org website has undergone a complete redesign and transitioned to using a Raku stack entirely. In "Steve's Corner," Steve Roe discusses the development of a new web application library using Raku, acknowledging contributions from several community members, including wayland, coke, raiph, lizmat, as well as special thanks to Red and Cro creators. Richard Hainsworth in "Richard’s Corner" introduces Elucid8, a system for creating websites from RakuDoc, with ongoing development accessible on its GitHub repository. In "Anton’s Corner," Anton presents methods for summarizing large texts using an Agentic AI system, utilizing the Raku package "LLM::Graph" to define LLM graphs based on various functions. The "Weeklies" section announces Weekly Challenge #338 and upcoming problem-solving tasks. Core developments include a hash return type error fix by ttn-ttn, along with updates and new modules from contributors like Richard Hainsworth, Fernando Correa de Oliveira, Tony O'Dell, JJ Atria, Anton Antonov, and others. The document also provides updates on Mastodon, where Raku is praised as an "awesome programming language" by Roos. Recent contributions within the Raku community include Fernando Correa de Oliveira's creation of "Red," Tony O’Dell's development of "fez," JJ Atria’s work on "PublicSuffix," and Anton Antonov's introduction of tools like Chemistry::Stoichiometry and LLM::Graph. Elizabeth Mattijsen has contributed several applications, including App::Raku::Log and highlighter. Steve Roe has developed Physics-related modules under the "Physics" namespace, as well as App::Crag. The author reflects on the successful launch of the revamped raku.org website while addressing mixed feelings about removing Perl references to emphasize Raku's distinct identity. They invite feedback from those who feel slighted and encourage community support through small sponsorships. The message concludes with a sentiment of solidarity during challenging times, expressed as "Слава Україні! Героям слава!" (Glory to Ukraine! Glory to the heroes!). This reflection is shared by librasteve, who is currently job-sharing. **Bullet Point Summary:** - Raku.org website redesign; now fully operates on a Raku stack. - Steve Roe in "Steve's Corner" discusses new web application library developed using Raku with contributions from various community members and thanks to Red and Cro creators. - Richard Hainsworth introduces Elucid8 for creating websites from RakuDoc, ongoing development accessible on GitHub. - Anton presents text summarization techniques using Agentic AI and the "LLM::Graph" package in "Anton’s Corner." - "Weeklies" section announces Weekly Challenge #338; updates include a hash return type error fix by ttn-ttn and new modules from multiple contributors. - Mastodon update praises Raku as an awesome language, thanks to Roos. - Community contributions: Fernando Correa de Oliveira's "Red," Tony O’Dell’s "fez," JJ Atria’s "PublicSuffix," Anton Antonov’s Chemistry::Stoichiometry and LLM::Graph tools; Elizabeth Mattijsen's applications like App::Raku::Log, highlighter. - Steve Roe develops Physics-related modules under the "Physics" namespace, plus App::Crag. - Author reflects on successful raku.org relaunch and mixed feelings about removing Perl references to emphasize Raku’s identity. - Invitation for feedback from those who feel slighted; encourages community support via small sponsorships. - Concludes with solidarity message "Слава Україні! Героям слава!" (Glory to Ukraine! Glory to the heroes!) by librasteve, currently job-sharing. Keywords: Anton Antonov, Artificial Intelligence, Camelia logo, Chemistry::Stoichiometry, Cro, Elucid8, Fernando Correa de Oliveira, JJ Atria, LLM, Mastodon, Physics::Error, PublicSuffix, Raku Air, Raku stack, RakuDoc, Rakuorg, Red, SmokeMachine, Steve Roe, Tony O’Dell, Website Reboot, Weekly Challenge, jnhtn, librasteve, rakuorg revamp, sponsorship, web applications, Слава Україні
llm
rakudoweekly.blog a day ago
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71. HN Ask HN: Getting over Burnout with Imposter SyndromeThe individual is recovering from burnout and hospitalization after an 11-year tenure, now focusing on establishing healthy routines. They grapple with imposter syndrome due to the absence of a four-year degree, leading to analysis paralysis when selecting side projects for their portfolio despite having significant expertise in developing mission-critical Apache modules and an interest in physically based rendering. Their hesitance extends to continuing GitHub projects due to these insecurities. They turn to the Hacker News community for advice on contemporary interview practices, specifically questioning the importance of having a visible portfolio with working projects. Additionally, they seek insights into how AI's real-world applications compare to online discussions about its potential and hyperbole. They are also looking for strategies to maintain motivation and follow through on personal programming projects that often remain unfinished. Their key concerns revolve around understanding current interview expectations, the relevance of portfolios, discerning genuine AI development from superficial discussions on HN, and finding ways to complete their side projects. Ultimately, they seek reassurance about securing future employment opportunities, given their experience in C/C++ for Apache modules and strong skills in trigonometry and calculus. - The individual is recovering from burnout and seeks advice on professional growth. - They struggle with imposter syndrome due to lack of a formal degree and face analysis paralysis when choosing side projects. - With significant experience in mission-critical Apache modules and interest in physically based rendering, they hesitate to continue GitHub projects. - They seek guidance on current interview practices, the importance of portfolios, and distinguishing genuine AI development from superficial discussions on HN. - They inquire about strategies for maintaining engagement with personal programming projects that often remain incomplete. - Ultimately, they are looking for hope and reassurance regarding future employment opportunities. Keywords: AI Hype, Analysis Paralysis, Apache Module, Battle Tested, Burnout, C/C++, Calculus, Employment, Engagement, GitHub, Gluing APIs, Imposter Syndrome, Interviews, Mission Critical, Pet Projects, Physically Based Rendering, Portfolio, Rendering, Side Project, Trig & Calc, Trigonometry, Working Projects
github
news.ycombinator.com a day ago
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72. HN After AI Led to Layoffs, Coders Are Being Hired to Fix 'Vibe-Coded' ScrewupsThe text discusses the emergence and implications of "vibe coding" within software development, driven by the rise of generative AI tools like large language models (LLMs). This trend enables rapid code generation but often results in subpar quality, pushing companies to replace experienced developers with cheaper AI-assisted methods. However, this reliance on imperfect AI-generated codebases has led to a demand for "vibe coding cleanup specialists." These professionals, such as Hamid Siddiqi who works on platforms like Fiverr, are tasked with refining and polishing these flawed outputs to align them with the intended functionality and user experience. The issues associated with vibe coding stem from its use by individuals lacking comprehensive digital skills, including product managers or small business owners. Common problems identified include inconsistent UI/UX design, poorly optimized code impacting performance, misaligned branding, and cumbersome features. While primarily used for prototyping rather than developing production-grade applications, the practice is becoming more prevalent among small businesses. Despite the growth of AI-driven services, human intervention remains crucial to transform these initial prototypes into functional products. Specialists like Siddiqi and Swatantra Sohni point out that even with advanced AI capabilities, addressing issues in design consistency, performance optimization, brand alignment, and feature usability necessitates skilled human oversight to bridge gaps between automated outputs and the desired user experience. **Bullet Point Summary:** - Generative AI has popularized "vibe coding," where code is rapidly generated but often of poor quality. - Companies are laying off experienced developers in favor of cheaper AI-assisted methods, leading to problematic code that requires refinement. - A new niche for "vibe coding cleanup specialists" has emerged, focusing on refining imperfect AI-generated codebases. - Specialists like Hamid Siddiqi help align AI-coded outputs with intended functionality and user experience aesthetics. - Vibe coding is often used by non-technical roles for prototyping, leading to issues like inconsistent UI/UX design and poorly optimized code. - Despite the rise of AI tools, human intervention is necessary to refine prototypes into functional products. Keywords: AI, Fiverr, LLM, UI/UX design, automation, chatbot, coders, digital skills, generative AI, layoffs, prototyping, small businesses, software development, tech industry, vibe-coded
llm
gizmodo.com a day ago
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73. HN Get Excited About Postgres 18Postgres 18, expected in a few weeks, introduces significant updates focused on enhancing performance through asynchronous I/O operations. This feature aims to expedite read processes by batching requests during idle times, thus reducing bottlenecks that occur with synchronous I/O. The improvements are particularly advantageous for databases handling sequential scans, bitmap index scans, and maintenance tasks like VACUUM while maintaining ACID compliance for writes. A new system view `pg_aios` provides insights into these async operations. Additionally, Postgres 18 features a transition to UUID version 7 (UUIDv7) from the previously used UUIDv4. This update integrates a timestamp within the first 48 bits of the UUID, improving sorting and indexing efficiency by enhancing data locality while retaining uniqueness through random bits. Scheduled for a mid-2024 standards revision, this change enhances usability as primary keys. Performance optimizations in Postgres 18 extend to multi-column B-tree indexes through skip scans, enabling efficient query execution even when filtering on non-leading columns if they have low cardinality. This optimization is evident in queries like `SELECT * FROM sales WHERE date = '2025-01-01';`, where the planner can now utilize a compound index more effectively. The introduction of virtual generated columns as the default type for generated columns allows for dynamic computation during reads, reducing write overhead. However, these columns are not indexable, requiring stored versions or expression indexes for indexing JSONB data. For users integrating with managed authentication services like Okta or Keycloak, Postgres 18 supports OAuth 2.0 in `pg_hba.conf`, improving security through bearer tokens and enabling features such as multi-factor authentication (MFA) and single sign-on (SSO). The release includes over 3,000 commits from more than 200 contributors, featuring significant enhancements to the query planner, asynchronous I/O for improved read performance, critical bug fixes, and security patches. These updates encourage users to regularly upgrade to benefit from these advancements. ### Bullet Point Summary: - **Asynchronous I/O**: Introduced in Postgres 18 to improve read operations by batching requests during idle times, reducing bottlenecks associated with synchronous I/O. - **UUIDv7 Transition**: Integrates a timestamp into the first 48 bits for better sorting and indexing while maintaining uniqueness through random bits. - **Skip Scans Optimization**: Enhances query execution on multi-column B-tree indexes by allowing efficient lookups even when filtering on non-leading columns with low cardinality. - **Virtual Generated Columns**: Default type for generated columns in Postgres 18, enabling dynamic computation during reads without physical storage, though not indexable. - **OAuth 2.0 Support**: Added to `pg_hba.conf` for enhanced security and integration with authentication services like Okta or Keycloak, supporting MFA and SSO. - **Extensive Improvements**: Over 3,000 commits from more than 200 contributors, focusing on query planner enhancements, asynchronous I/O benefits, critical bug fixes, and security patches. Regular upgrades are recommended to leverage these improvements. Keywords: JSON data, MFA, Postgres, UUIDv4, asynchronous I/O, database throughput, expression index, indexing, io_uring, multi-threading, normalization, optimization, performance improvements
postgres
www.crunchydata.com a day ago
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74. HN Show HN: wcwidth-o1 – Find Unicode text cell width in no time for JavaScript/TS**Summary:** The "wcwidth-o1" library is a TypeScript/JavaScript tool that efficiently implements an optimized version of Markus Kuhn’s wcwidth algorithm to deliver O(1) performance across the complete Unicode 15.1 combining ranges. It facilitates quick determination of text cell widths for Unicode characters, crucial for aligning text in fixed-width terminal displays. The package can be installed via npm and provides essential functions such as `wcwidth` and `wcswidth`, which measure the width of individual characters or entire strings, with specific support for CJK ideographs. This library adheres to IEEE Std 1003.1 (POSIX) standards and is accessible on both GitHub and NPM platforms. The tool is particularly beneficial for developers who require precise control over text layouts across diverse Unicode character sets, including Latin alphabets and CJK ideographs. Users are encouraged to provide feedback to enhance its functionality further. According to the Unicode standard, which defines character width classes for consistent text alignment, characters fall into various categories based on their widths: Wide (W) and Fullwidth (F) occupy 2 columns; Halfwidth (H), Narrow (Na), and Neutral (N) occupy 1 column; Ambiguous (A) can be 1 or 2 columns depending on CJK compatibility mode. Special cases include characters like U+0000, which have a width of 0; combining marks also have a width of 0; control characters are set to -1; the soft hyphen (U+00AD) at 1; and zero-width space (U+200B) at 0. This logic is crucial for maintaining consistent text alignment in display environments, stemming from Markus Kuhn’s original implementation. For further information or to report anomalies, users can consult Unicode Technical Report #11 and create issues as needed. **Bullet Point Summary:** - "wcwidth-o1" is a TypeScript/JavaScript library optimizing wcwidth for O(1) performance. - Supports full Unicode 15.1 combining ranges for fast text cell width lookup in fixed-width terminals. - Installable via npm, provides `wcwidth` and `wcswidth` functions to measure character and string widths, with special CJK ideograph support. - Adheres to IEEE Std 1003.1 (POSIX) standards; available on GitHub and NPM. - Useful for developers needing precise text layout control across various Unicode characters. - Encourages user feedback for functional enhancements. - Unicode defines character width classes for consistent alignment: Wide/Fullwidth (2 columns), Halfwidth/Narrow/Neutral (1 column), Ambiguous (1 or 2 columns in CJK compatibility mode). - Special rules apply to specific characters: U+0000 and combining marks (width 0); control characters (-1); soft hyphen (U+00AD, width 1); zero-width space (U+200B, width 0). - Maintains text alignment consistency per Markus Kuhn's reference implementation. - Users can refer to Unicode Technical Report #11 for details and report issues as needed. Keywords: CJK compatibility mode, East Asian ideographs, GitHub, JavaScript, Markus Kuhn, O(1) performance, POSIX, TypeScript, Unicode, Unicode TR#11, `wcwidth`, ambiguous, bitset lookups, combining marks, combining ranges, control characters, fullwidth, halfwidth, narrow, neutral, npm, soft hyphen, terminal emulators, wcswidth, wide, width classes, zero width space
github
github.com a day ago
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75. HN Chatbox app is back on the US app storeThe Chatbox app was reinstated on the U.S. App Store following a three-month legal battle over trademark claims by another company also using the "Chatbox" name. The opposing party claimed ownership of the trademark, which the Chatbox Dev Team contested by arguing that "Chatbox" is a generic term and noting prior rejection by the USPTO (United States Patent and Trademark Office). Despite initial support from Apple for the claimant, a federal court ruled in favor of the Chatbox team, mandating that Apple restore their app within seven days. The app was re-listed approximately two weeks after the ruling. This decision represents a significant victory against what is perceived as trademark bullying and highlights strong community backing for the Chatbox Dev Team, who initially used "Chatbox" to denote an AI software on GitHub in March 2023. **BULLET POINT SUMMARY:** - The Chatbox app was reinstated on the U.S. App Store after a three-month legal dispute. - Another company claimed ownership of the "Chatbox" trademark, which was contested by the Chatbox Dev Team. - The term "Chatbox" is argued to be generic and had been previously rejected by the USPTO. - Apple initially sided with the claimant but later complied with a federal court ruling in favor of the Chatbox team. - The court ordered Apple to restore the app within seven days, which happened about two weeks after the decision. - This outcome marks a victory against trademark bullying and reflects strong community support for the Chatbox Dev Team. - The Chatbox Dev Team was first to use "Chatbox" as an AI software name on GitHub in March 2023. Keywords: AI software, Apple, Chatbox, GitHub, US App Store, brand name, bullying, community support, dispute, federal court, generic word, legal action, trademark, victory
github
github.com a day ago
https://f-droid.org/ a day ago https://developer.android.com/developer-verification a day ago https://news.ycombinator.com/item?id=45017028 a day ago https://github.com/chatboxai/chatbox 19 hours ago https://chatboxai.app/en/terms 14 hours ago |
76. HN Tesla Pivots to Robots as Investors Question Sales and Soaring Valuation**Summary:** Under Elon Musk's leadership, Tesla has shifted its focus toward humanoid robotics with its Optimus initiative, a move that has drawn skepticism from investors due to stagnant sales and an inflated stock market valuation. Despite Musk asserting that the future value of Tesla hinges on this robot program, projections indicate a 30% decline in earnings by 2025, as the robotaxi venture faces fierce competition and is years away from profitability. Concurrently, Tesla grapples with a broader slowdown in electric vehicle demand since 2023. The company's stock valuation remains exceptionally high, trading at around 155 times its projected annual earnings—comparable to levels during the tech boom of 2021 when Tesla first reached a trillion-dollar market value driven by EV optimism. Among the Magnificent Seven technology giants, Tesla holds the highest stock price and outpaces Nvidia significantly in terms of valuation. For U.S.-listed companies valued over $100 billion, only Palantir Technologies trades at a higher multiple than Tesla. Critics argue that investor enthusiasm for personal robots is premature, raising questions about Tesla's valuation basis in this new sector. Dmitry Shlyapnikov from Horizon Investments notes that despite its growth company valuation, Tesla has seen little revenue growth over the past two years. He suggests Musk needs to present a compelling new growth narrative centered around Optimus, as Musk's $1 trillion compensation package is heavily tied to the robots' success. Meanwhile, investors are uncertain due to frequent shifts in Musk’s vision for Tesla’s future. **Bullet Point Summary:** - **Shift to Robotics:** Under Elon Musk, Tesla pivots towards humanoid robotics with its Optimus initiative amid investor skepticism. - **Financial Skepticism:** Current projections predict a 30% decline in earnings by 2025; the robotaxi business faces intense competition and is not close to profitability. - **EV Demand Slowdown:** Since 2023, Tesla experiences an industry-wide slowdown in electric vehicle demand, while its stock valuation remains extremely high. - **High Valuation:** Tesla’s stock trades at about 155 times projected annual earnings, similar to its peak during the tech boom of 2021 when it first hit a trillion-dollar market value. - **Comparison with Tech Giants:** Among the Magnificent Seven technology giants and U.S.-listed companies valued over $100 billion, Tesla's valuation is notably high, surpassed only by Palantir Technologies. - **Criticism on Market Research:** Critics question the market's thoroughness in researching potential demand for personal robots and thus Tesla’s valuation basis. - **Minimal Revenue Growth:** Despite being seen as a growth company, Tesla has shown minimal revenue growth over two years, according to Dmitry Shlyapnikov of Horizon Investments. - **Need for New Narrative:** Analysts suggest Musk needs a new growth narrative focused on Optimus robots, with his compensation tied to their success. - **Investor Uncertainty:** Frequent changes in Musk’s vision create uncertainty among investors about Tesla's future direction. Keywords: AI, Dmitry Shlyapnikov, EV, Elon Musk, Horizon Investments, Nvidia, Optimus, Palantir Technologies Inc, Tesla, Waymo, competition, demand, earnings, growth company, humanoids, investors, revenue, robots, sales, stock market, stock price, technology, valuation
tesla
finance.yahoo.com a day ago
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77. HN Life, work, death and the peasant: Rent and extraction### Bullet Point Summary - **Series Context**: The article is part of a series examining the struggles of pre-modern peasant farmers due to small farm sizes and limited capital, exploring societal structures that extract maximum surplus from them. - **Illustration of Power Imbalance**: An 1803 print titled "The Rapacious Steward or Unfortunate Tenant" by Haveill Gillbank highlights power imbalances between landlords and tenant farmers during the era. - **Comparative Land Distribution**: - Hellenistic kingdoms (e.g., Egypt) featured military settlers with large plots. - Late medieval Europe, exemplified by Saint-Thibery in 1460, saw smaller peasant holdings ranging from three to nine iugera. - **Impact of Farm Size on Productivity**: Larger farm sizes correlate with improved wheat production and subsistence coverage but come with reduced labor surpluses. - **Challenges for Peasant Households**: - Oversized populations relative to landholdings create inefficiencies. - Farms are often fractionalized, insufficient to meet household needs. - **Historical Land Scarcity**: Conquest driven by fertile land scarcity led to settler-colonialism and tenancy system replication. - **Efficiency vs. Welfare**: - Large estates focus on productivity. - Small peasant farms prioritize family welfare over maximizing production, indicating a need for more land under favorable conditions. - **Transformation of Land**: Converting wild land into arable plots is labor-intensive with legal and environmental barriers. - **Resource Acquisition Strategies**: Peasants seek additional resources through access to extra land or income sources; small households may sharecrop their land. - **Land Distribution**: - Often follows a "rule of thirds" among peasants and large entities. - Sharecropping is common, with tenants' production shares ranging from 18.75% to 81.25%. - **Tenancy Dynamics**: Tenancy terms often disadvantage tenants due to contributions like labor and seed, affecting their share. - **Labor and Production Analysis**: - The Smalls have a wheat shortfall of 648.5kg to 1,189.5kg. - The Middles face deficits of 1,028kg to 1,569kg. - The Biggs encounter shortfalls from 2,145kg to 2,686kg. - **Productivity and Freehold Farming**: Owned farmland is more productive than rented land; diminishing returns discourage production beyond subsistence levels. - **Historical Exploitation Methods**: - Elites used corvée labor and military service systems to extract peasant surplus labor. - These methods required peasants to work long hours under harsh conditions for elite sustenance and public works. - **Socio-Economic Implications**: Despite longer working hours compared to modern workers, peasants achieved lower living standards due to limited surplus generation beyond subsistence. This summary encapsulates the essence of the article by focusing on peasant farmers' challenges, land distribution, productivity issues, tenancy arrangements, historical resource extraction methods, and socio-economic impacts in pre-modern agrarian societies. Keywords: Big Man, Biggs, Carolingian levy, Hellenistic kingdoms, Hellenistic states, Macedonian settlers, Middles, Ptolemaic kingdoms, Saint-Thibery, Seleucid kingdoms, Smalls, absentee landlords, arourai, cultivation, debt peonage, diminishing marginal returns, extraction rates, farm sizes, fertility assumptions, freeholding peasants, fyrd, household security, iugera, labor requirements, labor supply, land availability, mansi, mercenaries, military ruling-class, military service, peasant farmers, peasant levy, population pressures, pre-modern agriculture, productivity, rentier-elites, sharecropping, society, subsistence, surplus extraction, surplus labor, taxes, tenancy, tenancy terms, tenant farmers, yields
popular
acoup.blog a day ago
https://vimeo.com/90583017 an hour ago https://www.youtube.com/watch?v=agzNANfNlTs an hour ago https://www.snopes.com/fact-check/medieval-peasant-only an hour ago https://simple.wikipedia.org/wiki/Technofeudalism an hour ago https://www.reddit.com/r/AskHistorians/comments&#x an hour ago https://www.reddit.com/r/AskHistorians/wiki/h an hour ago https://web.archive.org/web/20210619035356/http:&# an hour ago |
78. HN Latest VSCode changes autoApprove to "protect" you- **Visual Studio Code Version 1.104 Enhancements (August 2025):** - The update introduces features focused on user experience, security, customization, and integration with GitHub. - Key enhancements include Auto Model Selection for optimizing model performance, security settings to prevent unauthorized file edits, support for customizable AI agent contexts via AGENTS.md, and improved font settings for chat messages. - **Security Improvements:** - New `chat.tools.edits.autoApprove` setting requires user confirmation before editing sensitive files. - Terminal auto approve settings improve command execution transparency with configurable rules display in the Chat interface. - **Customization and Workflow Flexibility:** - Users can adjust chat modes using specified commands or buttons, enhancing workflow adaptability. - Font customization for chat messages allows better visual personalization. - Editor customizations include adjustable inline suggestion delays and window border colors, with workspace-specific settings available. - **Integration and Tools Enhancements:** - Enhanced tools picker improves visibility into tool sets. - GitHub integration streamlines interactions between local chats and GitHub tasks, such as code changes and pull requests. - Task support improvements enable agents to detect when user input is necessary, alongside a new embeddings model enhancing code search efficiency. - **Accessibility and Terminal Discoverability:** - New accessibility features include screen reader confirmation commands. - Terminal discoverability is improved through enhanced entry points like keyboard shortcuts and menu adjustments for better management of multiple terminal windows. - **Terminal Interface Improvements in Development Environments:** - Features such as automatic exit from compact mode, integrated terminal actions, and reliable Git completions enhance terminal interactions. - Sticky scroll behavior compatible with editor settings is enabled by default. - **AI-Powered Productivity Tools for Python:** - Experimental AI-powered hover summaries provide code insights without documentation. - Pylance updates include running Python code snippets directly in memory to boost efficiency, eliminating the need for temporary files or terminal commands. - **IntelliSense and Environment Enhancements:** - IntelliSense is enabled by default across all Python documents. - Python activation hooks now work from shell integration scripts, ensuring reliable terminal activation. - **GitHub Pull Requests Extension Features:** - New functionalities include sidebar content collapsing on narrow windows, webviews restoration, a "TODO" code action for Copilot tasks, and submodule ignoring capabilities. - **Extension Development and Authentication Updates:** - Introduction of `shellIntegrationNonce` allows control over terminal command verification. - Proposed API changes address authentication challenges involving multi-factor authentication (MFA). - **Development Tools and Fixes:** - Support for contributing view containers to the secondary sidebar is introduced. - Recent fixes address various terminal-related issues, such as unintended copying on selection and frame glitches during scrolling. Overall, Visual Studio Code version 1.104 emphasizes improvements in security, customization, integration with GitHub, AI-driven productivity tools, and enhanced development environments across multiple programming languages. Keywords: AI Features, Activation Hooks, Auto Approve, Chat Mode, Command Palette, Contextual Conditions, Experimental Support, Extensions, GitHub Copilot, Multi-Session Support, Notebooks, Pull Requests, Pylance, Python Environment, Secondary Sidebar, Security Considerations, Terminal Integration, Visual Studio Code, Workspace Customization
github copilot
code.visualstudio.com a day ago
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79. HN XcodeMCP: MCP server for Xcode that wraps its AppleScript interface- **Overview of XcodeMCP**: XcodeMCP is an MCP server and CLI tool designed for controlling Xcode via JavaScript for Automation (JXA). It facilitates project management tasks like opening, building, running, testing, and debugging projects in Xcode. The tool enhances build log parsing with XCLogParser to pinpoint errors accurately and includes features such as environment validation and graceful degradation if optional dependencies are missing. - **Key Features**: - Integration with desktop environments (e.g., Claude Desktop, VS Code) via MCP server. - A standalone CLI (`xcodecontrol`) for executing commands directly from the terminal. - Recommends installing XCLogParser through Homebrew to improve log parsing capabilities. - Installation can be performed using npm, either as a one-time operation with `npx` or globally. - **Requirements**: Users need macOS with Xcode installed and Node.js version 18+ to use XcodeMCP effectively. - **Usage Options**: - The MCP server option allows integration into various development environments. - The CLI tool facilitates direct command execution from the terminal. - **Installation and Setup**: - Quick installation is available via `npx` or globally through npm. - Configuration setups for MCP specify command execution parameters, with local development possible by cloning the repository. - **Advanced Usage**: Offers extensive control over Xcode operations through both server and CLI functionalities, ensuring feature parity between them. - **Xcodemcp CLI Overview**: - Installation can be done globally via `npm install -g xcodemcp`. - Basic usage includes displaying help, executing tools with specific options or JSON input, and outputting in JSON format. - Path resolution supports both absolute and relative paths. It also offers functionalities like opening files at specified line numbers. - Verbosity control allows users to adjust logging outputs based on preference (verbose, quiet, default). - **Project Management and Build Operations**: - Simplifies Xcode operations such as health checks, building projects, running applications, executing tests, cleaning build directories, and browsing XCResult files. - Utilizes kebab-case for concise command mapping. - **Test Result Analysis Tools**: - Analyzes test results from .xcresult files with features like browsing test hierarchies, pass/fail statuses, console log extraction, and quick summary statistics. - Facilitates visual debugging through screenshot extraction using ffmpeg, UI hierarchy analysis in JSON format, and attachment management. - **Logging and Configuration**: - Configurable logging controlled via environment variables allows setting verbosity levels (silent to debug) and specifying a log file path. - Supports silent mode with no logs or file-only logging directed to `/tmp/xcodemcp.log`. - **Troubleshooting Tips**: - If `XCLogParser` is missing, users should verify installation, address PATH issues, correct command syntax, ensure execution permissions, and perform environment validation. - **Dependencies**: While XcodeMCP can function without `XCLogParser`, its ability to parse build errors will be compromised, as illustrated by specific error outputs in Swift files. Keywords: AI-readable, CLI, GitHub, JavaScript, MCP server, Nodejs, Screenshot Extraction, TypeScript, UI hierarchy, XCLogParser, Xcode, build logs, command line, debug, diagnostics, environment validation, error locations, ffmpeg, installation, logging, macOS, npm, npx, project management, silent mode, xcresult
github
github.com a day ago
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80. HN WASM GIFThe author created an offline GIF creation tool by leveraging WebAssembly (WASM) to execute C code within a browser environment, overcoming the limitations of JavaScript for encoding tasks. This was accomplished using the FFMPEG library, which is known for its efficiency in handling video files. The process began with identifying a suitable GitHub repository that could be adapted for this purpose. After making necessary updates to accommodate the tool's requirements and developing a new user interface (UI), the author successfully produced a reliable tool capable of generating GIFs offline, eliminating the need for server-based processing. - **Development of Offline Tool**: The creation of an offline GIF maker using WebAssembly (WASM) to run C code in a browser. - **Use of FFMPEG Library**: Utilized the FFMPEG library due to its efficiency in video file handling, addressing JavaScript's limitations. - **WebAssembly Advantage**: WASM enabled efficient execution of C code for encoding tasks within a web environment. - **GitHub Repository**: Discovery and adaptation of a GitHub repository facilitated the tool's development process. - **Updates and UI Development**: Necessary updates were made to the original repository, along with the creation of a new user interface. - **Server Independence**: The resulting tool allows users to create GIFs without relying on server processing, enhancing accessibility and usability. This summary encapsulates the key steps and technologies involved in developing an innovative offline GIF creation tool that bypasses traditional limitations associated with JavaScript for video encoding tasks. Keywords: C, FFMPEG, GIF, GitHub, JavaScript, UI, WASM, Web Assembly, browser, bug fixing, encoding, repository, state management, tool, video file
github
igorbedesqui.com a day ago
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81. HN The PDF That Broke ChatGPTThe article delves into the discrepancy between the theoretical capabilities of artificial intelligence (AI) and its actual performance in real-world applications. It illustrates this gap using examples of common failures encountered by users, such as Josephina, a teacher who heavily relies on AI tools like ChatGPT for various tasks but struggles with fundamental functions like extracting text from PDFs. Josephina's experience highlights several issues: - **Ambiguity Trap**: In her attempt to extract text from Section 4 of a PDF, ChatGPT provided an incorrect summary instead due to confusion between similar sections. - **False Progress and Endless Correction Loop**: After identifying the error, attempts to generate code for parsing resulted in garbled text with formatting issues. Over multiple iterations, fixing one problem often led to new or unresolved errors. Despite 25 minutes of troubleshooting, Josephina resorted to completing the task manually. Similar difficulties were reported when using Google's Gemini models, which exhibited persistent problems like mishandling document copying, editing, and formatting tasks. Issues included failure to remove footnotes properly with ChatGPT and erroneous file handling by Gemini. The narrative reveals a significant shortcoming in AI systems' ability to handle everyday, messy tasks effectively, despite their advanced theoretical capabilities. Researchers are compiling user stories of failures from models like ChatGPT, Gemini, and Claude to better understand these limitations. Key functional deficiencies identified include: 1. **Robust Tool-Calling**: AI often fails to use document parsing tools correctly, accepting flawed outputs without proper verification. 2. **Recognizing Ambiguity**: Current systems struggle with ambiguity, leading to errors in tasks such as document extraction or interpreting garbled text. 3. **Instruction Following Memory**: ChatGPT exhibited issues with maintaining context and adhering to specific instructions over multiple interactions. 4. **Hallucination Resistance**: Gemini demonstrated a tendency to generate incorrect information without acknowledging uncertainty, even when files were not attached. The article underscores the importance of improving AI's ability to perform mundane tasks accurately to build trust in its practical applications. It suggests that enhancing error-checking capabilities and dealing effectively with ambiguities are crucial for bridging the gap between AI's potential and real-world performance. ### Bullet Point Summary: - The article discusses the gap between AI’s theoretical capabilities and its actual performance in handling everyday tasks. - Josephina, a teacher, faces difficulties using ChatGPT to extract text from PDFs due to issues like ambiguity traps and endless correction loops. - Similar failures occur with Google's Gemini models, which struggle with basic document processing functions. - Researchers are gathering user stories of AI model failures to better understand these practical limitations. - Key deficiencies include robust tool-calling, recognizing ambiguity, instruction following memory, and hallucination resistance. - The passage emphasizes the need for AI systems to improve in mundane tasks for increased real-world utility and trust. Keywords: AI, ChatGPT, Excel files, Gemini, Google Drive, PDF, SMB owner, URL limitation, Word document, ambiguity trap, document hallucination, failures, footnotes, friction, hallucination, multimodal models, parsing, spacing, teacher, technical issues, text extraction, user trust
gemini
www.surgehq.ai a day ago
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82. HN Replit or Riplet? Let Gemini 2.5 figure it outReplit is an online Integrated Development Environment (IDE) primarily known for its AI-assisted coding features rather than functioning as a low-code/no-code platform. Users experience dissatisfaction due to several factors: the system's unreliability, poor quality of AI-generated code necessitating extensive debugging, and complex monetization practices that complicate usage. These issues contribute to "low-rated" user experiences characterized by slow performance and restrictive free tiers with confusing billing. Despite these drawbacks, Replit is highly valued for its collaborative features, which enable real-time coding similar to Google Docs, quick prototyping capabilities allowing instant execution and deployment of simple projects in the browser, and educational applications that simplify environment setup. However, it falls short of expectations when users expect stable, scalable no-code solutions. Criticisms often revolve around performance issues, subpar AI quality, and aggressive pricing models, leading to frustration among both professional developers and non-technical founders. The core issue highlighted in reviews is the discrepancy between Replit's intended purpose as an IDE for developers and user expectations of it being a no-code tool. Users face challenges such as slow system performance, unreliable environment loading, buggy AI-generated code needing significant debugging, and complex monetization strategies that leave free users underserved and paid tiers costly. In summary, while Replit excels in fostering collaboration, supporting quick prototyping projects, and serving educational purposes due to its ease of setup, it struggles with aligning user expectations with its true functionality as a developer-focused IDE. The platform's challenges include performance issues, AI code quality concerns, and pricing models that do not meet the needs of those seeking low or no-code solutions. **Bullet Point Summary:** - Replit is an online IDE with AI-assisted coding features, often misunderstood as a low-code/no-code platform. - Users report dissatisfaction due to system unreliability, poor AI code quality, frustrating monetization practices, and lack of true no-code capabilities. - Positive aspects include real-time collaboration, quick prototyping, and educational use for eliminating environment setup. - Negative feedback centers on performance issues, subpar AI-generated code, aggressive pricing models, and mismatched user expectations. - The primary criticism is the discrepancy between Replit's function as a developer IDE and users' no-code tool expectations. Keywords: AI-assisted coding, IDE, Replit, collaboration, debugging, educational use, low-code, monetization, no-code, non-technical users, performance issues, prototyping, systemic issues, token-eating
gemini
news.ycombinator.com a day ago
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83. HN Integrating iOS 26 Liquid Glass App with Expo UI and SwiftUIThe blog post explores the integration of the iOS 26 Liquid Glass app with Expo UI and SwiftUI, emphasizing it as a component of Expo's comprehensive ecosystem. It outlines various tools such as EAS (Expo Application Services) and the CLI provided by Expo, alongside their GitHub resources. The company encourages user engagement through platforms like Discord and offers support via a Trust Center. Legal information is addressed with references to terms of service and privacy policies. Additionally, the post highlights 650 Industries' invitation for community interaction and provides insights into its product offerings and pricing plans available on its homepage. - Discusses integration of iOS 26 Liquid Glass app with Expo UI and SwiftUI. - Highlights Expo's ecosystem tools: EAS (Expo Application Services) and CLI; mentions GitHub resources. - Encourages user engagement through platforms like Discord. - Offers support via a Trust Center. - Provides legal information including terms of service and privacy policy. - Invites community interaction by 650 Industries, detailing product offerings and pricing plans on the homepage. Keywords: Blog, CLI, Community guidelines, Compliance, Discord, Docs, EAS Dashboard, Expo Go, Expo UI, GitHub, Legal, Liquid Glass, Orbit, Pricing, Privacy policy, Snack, SwiftUI, Trust Center, iOS
github
expo.dev a day ago
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84. HN Ask HN: What are you using your Raspberry Pi for?The discussion thread on Hacker News centers around users sharing how they utilize Raspberry Pi devices in various practical applications. A particular user highlights their use of multiple Pis: one is dedicated to running Home Assistant alongside a reverse proxy and other services like AdGuard and Tailscale, showcasing its role as a central automation hub; another functions as a web server specifically for managing a 3D printer; while the third operates CNCjs, which controls a milling machine. The user emphasizes the value of Raspberry Pis due to their useful input/output (IO) ports and shield capabilities, though they express dissatisfaction with the current pricing unless these features are essential for specific projects. Additionally, one Pi is equipped with a Power over Ethernet (PoE) shield, enhancing the setup's neatness by simplifying power management. - **Main Uses of Raspberry Pis:** - Home Assistant with reverse proxy and services like AdGuard/Tailscale - Web server for a 3D printer - CNCjs for controlling a milling machine - **Advantages Highlighted:** - Useful IO ports/shields for various projects - PoE shield improves installation tidiness by simplifying power management - **Pricing Criticism:** - User finds the price point high unless specific features (IO ports/shields) are necessary Keywords: Ask HN, CNCJS, Docker, Hacker News, IO ports, POE shield, Raspberry Pi, adguard, chirau, duet web server, home assistant, mill, printer, reverse proxy, serf, shields, tailscale
tailscale
news.ycombinator.com a day ago
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85. HN Fork that: Three alternative kernels show devs don't need LinuxThe provided text explores recent tensions among Linux kernel developers, particularly due to the incorporation of Rust and disagreements leading to resignations, such as those of Wedson Almeida Filho, Hector Martin, "Asahi Lina," and Alyssa Rosenzweig. These issues have spurred discussions about potential forks of the Linux kernel. Despite challenges like the demotion of efforts to integrate the bcache filesystem after a decade, there is growing interest in alternative operating systems that offer different approaches to system architecture. Among these alternatives are experimental operating systems such as Managarm and Asterinas, which showcase innovative designs diverging from traditional models. Managarm, a six-year-old microkernel-based OS, supports Linux applications across multiple platforms (x86-64, Arm64, and developing for RISC-V), featuring support for various hardware interfaces and protocols while being implemented in C++ with extensive documentation available via GitHub. Asterinas, developed using Rust—a language known for memory safety—supports the Linux ABI but deviates from traditional microkernel architectures. It employs Rust's features to separate kernel services safely, as detailed in an academic paper on its architecture. This design contrasts with older systems like RedLeaf OS by leveraging Rust's growing ecosystem and ensuring compatibility with existing binaries. Redox OS is another experimental project developed with Rust by Jeremy Soller of System76, alongside the new desktop environment COSMIC nearing beta release. Xous, a separate modern microkernel initiative by Andrew "Bunnie" Huang, emphasizes unique security goals distinct from Linux compatibility and is part of the Betrusted project, including the Precursor device that features secure authentication capabilities. The text concludes by underscoring the significant contributions of alternative operating systems such as Managarm, Asterinas, and Xous. These projects demonstrate substantial development efforts outside of traditional Linux frameworks while leveraging existing Linux software and expertise. **BULLET POINT SUMMARY:** - Recent tensions among Linux kernel developers have arisen from Rust integration and disagreements leading to resignations. - The demotion of the bcache filesystem integration effort marks a significant challenge within the Linux community. - Alternative operating systems like Managarm, Asterinas, Redox OS, and Xous are gaining attention for their innovative approaches. - Managarm is a microkernel-based OS supporting many Linux applications across multiple platforms with comprehensive documentation. - Asterinas utilizes Rust to offer a safe architecture via language-based privilege separation, differing from traditional microkernels. - Redox OS, developed by Jeremy Soller, and Xous, led by Andrew "Bunnie" Huang, represent significant experimental efforts using Rust for distinct goals. - The Betrusted project includes the Precursor device with secure authentication features, emphasizing intuitive security. - These alternative systems highlight ongoing development outside of Linux while leveraging its extensive ecosystem. Keywords: ABI, ACPI, AHCI, Andrew Huang, Apple Silicon, Arm64, Asahi Linux, Asterinas, Betrusted, COSMIC desktop, CPU privilege rings, Doom, Framekernel, GNU Hurd, GUI layers, GitHub, HOUSE, Haskell, IPv4, Intel virtualization, LCD, LWN, Linux, Managarg, Modula-3, NVMe, OS, Precursor, QEMU, RISC-V, Redox OS, Rust, SMP, SPIN, Safe Rust, Serenity OS, System76, TOTP, U2F/FIDO2, UX, Vault, Wayland, X11, Xous, Yegge, Yubikey, academic paper, applications, asynchronicity, authentication, bcachefs, binaries, compatibility, coreutils, developers, documentation, experimental, forks, friction, handbook, hardware, isolation model, kernels, microkernels, multi-platform, resignation, security, services, unsafe Rust, user space, x86-64
github
www.theregister.com a day ago
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86. HN Gauss, an Agent for AutoformalizationThe Math Inc. team has introduced Gauss, an innovative autoformalization agent designed to aid mathematicians in formal verification tasks. Demonstrated through its successful completion of the strong Prime Number Theorem (PNT) formalization challenge, set by Fields Medalists Terence Tao and Alex Kontorovich, Gauss significantly reduced the time and human effort traditionally required for such complex projects. While Tao and Kontorovich took 18 months to make intermediate progress due to challenges in complex analysis, Gauss accomplished the task in just three weeks. This tool autonomously formalized key results in complex analysis, producing approximately 25,000 lines of Lean code with over 1,000 theorems and definitions, highlighting its potential to streamline traditionally arduous mathematical formalizations. Historically multi-year endeavors, such projects could now be transformed towards more efficient proof development through Gauss's capabilities. To support this advancement, the project utilized the Trinity environments infrastructure, developed in collaboration with Morph Labs, to manage the complex task of scaling Lean verification environments for Gauss. This involved handling thousands of concurrent agents and significant RAM usage on Infinibranch within the Morph Cloud. While Gauss currently requires some human input, it is anticipated to become more autonomous in future versions. The technology is being deployed alongside mathematicians for beta testing, with early access registration available. The project aims to significantly increase formal code output within a year, potentially leading to verified superintelligence and machine polymaths. This effort received support from DARPA’s expMath program. **BULLET POINT SUMMARY:** - Gauss is an autoformalization agent designed to aid mathematicians in formal verification tasks. - Successfully completed the strong Prime Number Theorem (PNT) formalization challenge, initially set by Fields Medalists Terence Tao and Alex Kontorovich. - Reduced time and effort for complex projects, completing in three weeks what took Tao and Kontorovich 18 months. - Autonomously produced approximately 25,000 lines of Lean code with over 1,000 theorems and definitions. - Demonstrates potential to transform traditionally lengthy mathematical formalizations into more efficient processes. - Utilized Trinity environments infrastructure developed with Morph Labs for scaling Lean verification environments. - Managed thousands of concurrent agents and significant RAM usage on Infinibranch in the Morph Cloud. - Current version relies on some human input, but expected to become more autonomous in future iterations. - Deployed with mathematicians for beta testing; early access registration is available. - Aims to significantly increase formal code output within a year, paving the way for verified superintelligence and machine polymaths. - Supported by DARPA’s expMath program. Keywords: Agent, Alex Kontorovich, Autoformalization, Complex Analysis, DARPA’s expMath program, Fields Medallist, Formal Verification, Gauss, GitHub, Infinibranch, Lean, Math Inc, Mathematicians, Morph Labs, Prime Number Theorem, Proof engineers, Terence Tao, Trinity environments, beta testing, scaling
github
www.math.inc a day ago
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87. HN FFglitch, FFmpeg fork for glitch art**Summary:** FFglitch is a specialized fork of FFmpeg tailored specifically for generating glitch art, serving as an innovative tool embraced by various artists to craft unique visual and audio effects. Its widespread use can be observed across multiple digital platforms such as Vimeo, Instagram, Reddit, and personal blogs. Among its prominent users is Thomas Collet, who frequently shares his work on social media using FFglitch, showcasing the tool's creative potential. A collaborative project by Kaspar Ravel and Thomas Collet garnered attention through a blog post and a Reddit feature. Other artists including Sebastien Brias, Myra Rivera (known for "Glitched Flowers"), Jason Hallen, and Jo Grys have also delved into FFglitch’s capabilities to produce artistic outputs. Additionally, Ben Cooper utilized tools like avidemux and tomato.py alongside FFglitch to create clips, while users nowahe and glit_chbee have showcased their unique works on social media platforms. The #ffglitch hashtag on Facebook serves as a hub for discovering more related content. **Bullet Point Summary:** - FFglitch is a fork of FFmpeg aimed at creating glitch art. - It has been adopted by artists to create visual and audio effects, with work showcased on Vimeo, Instagram, Reddit, and blogs. - Thomas Collet is a notable user who actively shares his FFglitch creations across social media. - A collaboration between Kaspar Ravel and Thomas Collet resulted in widely featured projects. - Other artists like Sebastien Brias, Myra Rivera (creator of "Glitched Flowers"), Jason Hallen, and Jo Grys have explored its artistic possibilities. - Ben Cooper used a combination of avidemux, tomato.py, and FFglitch to create clips. - Social media users nowahe and glit_chbee have shared their unique outputs using the tool. - The #ffglitch hashtag on Facebook is a resource for finding related content. Keywords: FFglitch, FFmpeg, Instagram, Reddit, Vimeo, artists, avidemux, blog post, collaboration, exhibition, experimentations, gallery, glitch art, tomatopy, tomatopy ``` Keywords: FFglitch
popular
ffglitch.org a day ago
https://m.youtube.com/watch?v=mjnAE5go9dI an hour ago https://www.youtube.com/watch?v=Qtia43DGSrY an hour ago https://ffglitch.org/frontends/ an hour ago https://en.wikipedia.org/wiki/Glitch an hour ago https://ffglitch.org/gallery/ an hour ago https://www.theghostinthemp3.com/theghostinthemp3.html an hour ago https://beyondresolution.info/Glitch-Studies-Manifesto an hour ago https://www.youtube.com/watch?v=8uEj2c1YQc4 an hour ago https://www.w6rz.net/pixellation.mp4 an hour ago https://youtube.com/playlist?list=PLnfdj-gV14N5JTGybk4kkVpyL an hour ago https://www.jacquesperconte.com/oe?28 an hour ago https://rhizome.org/editorial/2013/apr/25 an hour ago https://www.youtube.com/watch?v=tt7gP_IW-1w an hour ago https://mediarep.org/server/api/core/bitstrea an hour ago https://youtu.be/59QBOO6m210 an hour ago https://youtu.be/9X4fYP9bqqw an hour ago https://en.wikipedia.org/wiki/Glitch_(music) an hour ago http://www.youtube.com/watch?v=frfs4tkN-AY an hour ago https://ffglitch.org/ an hour ago |
88. HN Go channels to solve interface impedance mismatch- The article addresses an impedance mismatch between Google's btree package and Go-mysql-server interfaces in the context of developing index support for virtual pg_catalog tables within Doltgres, a version-controlled Postgres-compatible SQL database. - The core issue stems from differing iteration patterns: btree calls a callback function for each element in a range, while Go-mysql-server expects sequential single-element returns via an iterator's Next() method. Two techniques are proposed to align these patterns for efficient data scanning. - The first technique involves storing results of the `AscendRange()` function in a slice and iteratively returning values from it. However, this approach is inefficient due to high memory allocation and increased garbage collection pressure. - The second technique uses Go channels to overcome inefficiencies by facilitating communication between producer and consumer routines through concurrency. This method's efficacy is demonstrated with a Fibonacci sequence generator that employs goroutines and channels for asynchronous data production. - A practical application of the channel-based approach is shown in the `inMemIndexScanIter` struct, which implements the `sql.RowIter` interface using channels to manage asynchronous iteration over a B-Tree structure within an SQL engine context. - The `nextItem()` method within this struct coordinates querying a B-tree data structure, utilizing channels for handling asynchronous operations and facilitating background processing. It supports various range scanning functions like `AscendRange`, `AscendGreaterOrEqual`, and `AscendLessThan`. - A callback function (`itr`) is defined to send elements through a channel during each scan operation, bridging the gap between B-tree's asynchronous nature and subsequent retrieval in `nextItem()`. - The article describes a function that reads from a channel (`nextChan`). If the channel isn't closed (indicated by `ok` being true), it retrieves data; otherwise, it moves to the next data range. Upon exhausting all data, it returns nil. - The technique of using channels for flow control, even in non-concurrent tasks, is highlighted as unconventional yet efficient due to goroutines' effectiveness in performance-critical sections. - It notes that returning a `nil` generic value requires specifying the return type as a pointer (`*T`) rather than directly as the type (`T`). - The article suggests this channel-based method could benefit from further optimization through buffered channels for parallel processing and invites readers to explore Go channels further via the Doltgres Discord community. Keywords: AscendRange, BTreeIndexAccess, Doltgres, Go, Google btree package, IndexLookup, Next method, PostgreSQL, RangeConverter, SQL database, btree, callback function, channels, concurrency, goroutines, inMemIndexScanIter, iterators, joins, performance, sqlRowIter, virtual tables
postgresql
www.dolthub.com a day ago
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89. HN What I Learned Starting an AI-Only Fantasy Football LeagueThe author recounts launching an AI-driven Fantasy Football league with friends, where each decision was dictated by a Large Language Model (LLM) chosen by the managers. The project involved experimenting with techniques like prompt engineering and fine-tuning, requiring transparency in model inputs and outputs weekly. Managers had the option to seek forgiveness from a randomly selected jury of peers for poor performance. In the league's initial setup, the order of managers was determined based on scores from a machine learning quiz, leading them to select models that influenced player draft order by capability scores. A critical observation was that speed was paramount in effectively utilizing AI for this dynamic task. Despite expectations that models would be faster than the 2-minute draft pick limit, it resulted in hurried decisions and errors. Key lessons emerged from applying AI to fantasy football: speed was essential; effective tool usage proved more significant than model complexity, as seen with GPT-oss's success through its fast interface with an MCP server; and existing AI agents outperformed custom-built decision-making suites. These findings suggest a need for further exploration in developing these tools. Despite early challenges and errors in trades, the project shows promise. The league is publicly viewable and open for discussion on YouTube, inviting feedback throughout the season. ### Bullet Point Summary: - **AI-Driven Fantasy Football League**: Initiated with friends where each manager's decisions are made by a chosen LLM. - **Transparency Requirement**: Weekly disclosure of model inputs and outputs was mandatory. - **Manager Selection Process**: Initial draft order based on machine learning quiz scores, influencing model selection and draft outcomes. - **Importance of Speed**: Crucial for effective AI application; rushed decisions led to errors due to time constraints. - **Lessons Learned**: - **Speed Over Model Complexity**: Effective tool usage was more important than complex models. - **Success with Existing Tools**: GPT-oss's fast interface showcased better performance over custom-built suites. - **Need for Further Exploration**: Development of AI decision-making tools requires more research. - **Public Engagement**: The league is available on YouTube for viewing and discussion, encouraging public feedback. Keywords: AI-Only League, Agents, Autopicks, Data Pull, Draft Order, Fantasy Football, Fine Tuning, LLM, LoRAs, Machine Learning Quiz, Manager Picks, Model Inference, Parameter Count, Platform Defaults, Prompt Engineering, RAG, Speed, Trades
llm
brooklynhacker.com a day ago
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90. HN Tucker Carlson blindsides Sam Altman with theory about OpenAI staffer's 'murder'In an interview with Tucker Carlson, the contentious circumstances surrounding the death of Suchir Balaji, a researcher for OpenAI, were brought into focus. Balaji was found dead in November 2024 from a gunshot wound, which authorities ruled as suicide despite lacking forced entry and noting that he had purchased the gun himself. Carlson challenged this conclusion by pointing to signs of struggle and alleged surveillance lapses at the scene. He further referenced claims made by Balaji's mother accusing Sam Altman, OpenAI’s head, of ordering his murder—accusations that deeply offended Altman. Altman maintained his stance that the death was a suicide, expressing personal shock and sadness over Balaji's passing. Despite working closely on projects like ChatGPT for four years, he claimed no prior contact with authorities regarding Balaji's death before this interview. Carlson insisted the circumstances warranted further investigation beyond ruling it a suicide, particularly given Balaji’s criticisms of OpenAI shortly before his demise. - **Context:** The discussion centers around Suchir Balaji's suspicious death and its implications for OpenAI. - **Key Details:** - Balaji was found dead in November 2024 from a gunshot wound, ruled as suicide by authorities. - Carlson highlighted evidence suggesting struggle and questioned surveillance integrity. - Accusations arose against Sam Altman based on claims by Balaji's mother that he ordered the murder, which Altman refuted strongly. - **Altman’s Position:** - He believes in the suicide ruling despite the controversy surrounding it. - Offered to communicate with Balaji’s family posthumously but was declined. - **Carlson’s Perspective:** - Urged for an investigation beyond the official ruling, emphasizing signs that suggested foul play. Overall, this exchange underscored tensions between perceived institutional responsibility and external suspicions, highlighting a clash of narratives regarding Balaji's tragic death. ### Bullet Point Summary: - Suchir Balaji, an OpenAI researcher involved in projects like ChatGPT, died in November 2024 from a gunshot wound. - Authorities classified the incident as suicide; Carlson contested this by citing signs of struggle and surveillance issues. - Claims surfaced that Altman ordered Balaji's murder, which he denied vehemently, stressing personal shock at the allegations. - Despite four years of working together, Altman had no prior contact with law enforcement about Balaji’s death. - Carlson argued for a deeper investigation into the circumstances beyond accepting it as suicide. Keywords: Catalina Island, ChatGPT, OpenAI, Sam Altman, San Francisco, Suchir Balaji, Tucker Carlson, blood evidence, copyright criticism, gunshot, murder theory, programmer death, struggle signs, suicide ruling, surveillance cameras
openai
www.dailymail.co.uk a day ago
https://www.newsweek.com/openai-tucker-carlson-whistleblower a day ago |
91. HN Tucker Carlson, Musk Revive Murder Theory of Ex-OpenAI Employee, Suchir Balaji**Summary:** The text discusses the controversial death of Suchir Balaji, a former OpenAI employee who criticized the company before dying from a gunshot wound in November 2023. Authorities have concluded that his death was a suicide, with evidence including no forced entry and Balaji's recent purchase of the gun used. Despite this official stance, Balaji's parents remain skeptical, alleging inconsistencies such as an unusual gunshot trajectory suggested by a second autopsy and lack of a suicide note. They believe their son might have been murdered due to his critical views on OpenAI’s practices. During a publicized interview with Tucker Carlson, Sam Altman, CEO of OpenAI, addressed these allegations. Altman expressed offense at the insinuations linking him to Balaji's death, asserting that he had investigated thoroughly but without contacting authorities. He noted Balaji's criticisms related to copyright issues in AI training models and acknowledged their friendship while respecting the family’s quest for answers. Balaji’s mother, Poornima Ramarao, actively seeks further investigation into her son's death, using social media to fund a 3-D crime reconstruction of his apartment and planning a podcast addressing Altman’s comments. The ongoing controversy is exacerbated by Elon Musk's rivalry with Sam Altman since Musk's departure from OpenAI; both have been involved in legal disputes and competitive tech projects. Altman has mixed feelings towards Musk, recognizing his contributions while criticizing certain aspects of his character. The additional readings mentioned further explore controversies surrounding OpenAI, including the whistleblower’s death ruled as suicide by police and accusations of copyright violations against OpenAI's AI training practices. **Bullet Point Summary:** - Suchir Balaji, a former OpenAI employee critical of the company, died from a gunshot wound; authorities concluded it was a suicide. - Balaji’s parents dispute this finding, citing unusual autopsy results and lack of a suicide note, suggesting possible murder. - Sam Altman, OpenAI CEO, addressed allegations during an interview with Tucker Carlson, denying involvement and expressing respect for Balaji's memory. - Poornima Ramarao, Balaji's mother, is seeking further investigation into her son’s death through funding efforts and planned media releases. - The controversy ties into Elon Musk’s public feud with Altman, stemming from their past collaboration at OpenAI and ongoing rivalries. - Further readings cover the whistleblower’s death, Altman’s feelings towards Musk, and claims of copyright violations by OpenAI. Keywords: 3-D view, AI, Balaji, ChatGPT, Grok, Medical Examiner, Merge Labs, Musk, Neuralink, New York Times, OpenAI, Sam Altman, San Francisco, Tesla, Tucker Carlson, answers, authorities, autopsy report, brain-computer interface, case, competition, copyrighted material, crime reconstruction, crime scene, data, death, debate, employment search, family, federal copyright law, feud, friends, fundraising, grief, gun purchase, gunshot, injury, investigation, law enforcement, lawsuit, lawsuits, legal precedents, mother, murder theory, podcast, respect, retaliation, self-driving tech, stress, suicide, tech mogul, trajectory, truth, whistleblower, wonderful person, xAI
openai
www.forbes.com a day ago
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92. HN Android Stream for Swift Stream IDEThe article provides instructions for creating the first native Swift library for Android using tools from a personal passion project available at swifdroid.com and its GitHub repository (https://github.com/swifdroid). These tools were developed to address the author's long-standing need, facilitating easier integration of Swift with Android development. - **Introduction of Tools**: The article introduces tools designed for creating native Swift libraries on Android, sourced from a personal project. - **Availability**: Resources are accessible via swifdroid.com and its GitHub repository at https://github.com/swifdroid. - **Addressing a Need**: These tools were created to meet the author's long-standing need for better integration between Swift and Android development. - **Objective**: The primary goal is to simplify the process of integrating Swift into Android projects. Keywords: Android, GitHub, IDE, Swift, article, library, native, passion project, personal, stream, swifdroidcom, tools
github
news.ycombinator.com a day ago
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93. HN Interactive LLM ChatThe article explores the enhancement of dwellr's AI agent chat interface to improve user engagement in apartment searches by leveraging a conversational style that captures user preferences more efficiently. Initially utilizing a basic chat interface for its familiarity, the creator aimed to enhance it further compared to less effective voice interfaces like those from OpenAI. Key improvements involved embedding structured interactive elements such as dropdowns and sliders directly within conversations, addressing issues of efficiency and engagement while maintaining fluid dialogue flow. This method merges the simplicity of chatting with the accuracy of traditional questionnaires, thus creating a more intuitive user experience. The text also delves into the composition of chat interfaces, highlighting the importance of rich text for language model outputs and text fields for human input. It underscores the advantage of using markdown to boost readability, particularly in generating code snippets with syntax highlighting via libraries like ReactMarkdown. The author introduces an innovative interactive question format utilizing JSON objects within markdown backticks, allowing customization by defining types (e.g., date pickers) and linking questions with answers through IDs. These enhancements make interactions more intuitive and visually appealing on the frontend. A code snippet demonstrates how `ReactMarkdown` can be used with custom rendering logic to integrate interactive components into markdown-rendered content in a React application. The system identifies specific question code blocks via class names, parses embedded JSON data, and passes it to a bespoke question component (`MyQuestion`). This configuration facilitates dynamic user interactions through an `onAnswer` callback. Practical applications of this approach include multiple-choice questions with multi-select options, date-time pickers, sliders, and future components like address auto-completion or image grids. Although the method diverges from traditional software design patterns due to its reliance on AI-generated structured questions rather than hardcoded ones, it has shown effective results without significant issues. Ultimately, this innovative approach enhances chat interfaces by seamlessly incorporating interactive elements into conversational flows, thus improving user experience by making data collection feel more natural and less like filling out forms. Despite being somewhat unconventional, this strategy effectively balances human interaction with structured data capture in digital interactions. - **Main Ideas:** - Development of an enhanced chat interface for dwellr's AI agent to improve apartment search experiences through conversational engagement. - Integration of interactive elements (dropdowns, sliders) within dialogue to maintain seamless conversation flow while capturing user preferences efficiently. - Use of markdown and `ReactMarkdown` to enhance readability and interactivity in chat interfaces, allowing for dynamic content like JSON objects defining question types. - Implementation of a custom rendering logic with `ReactMarkdown` to integrate interactive components into markdown content, facilitating dynamic user interactions. - Practical applications include multiple-choice questions, date-time pickers, sliders, and potential future enhancements such as address auto-completion or image grids. - Despite unconventional methods relying on AI-generated structured questions, the approach effectively combines conversational interaction with structured data capture, enhancing overall user experience. Keywords: AI agent, Interactive LLM, JSON object, MyRenderer, ReactMarkdown, UX, chat interface, code outputs, date ranges, dropdowns, enhancements, frontend side, interactive components, interactivity, markdown format, multi-select, radio buttons, rich text, seamless conversation, sliders, structured format, syntax highlighting, validation, voice app
llm
www.bkdev.xyz a day ago
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94. HN Throttling AI bot traffic in ProcessWireOn September 12, 2025, Ryan Cramer addressed challenges faced by processwire.com due to excessive AI bot activity on its AWS-hosted site. The issue primarily affects support forums rather than the main site, which uses ProCache for caching. Despite crawl-delay settings in the robots.txt file, many bots ignore these restrictions and consume significant server resources, leading to costly scaling of server instances. To mitigate this problem, Cramer updated the Wire Request Blocker module with a throttling feature designed to enforce compliance with their robots.txt rules and reduce costs associated with bot traffic. The update introduces "throttling" features that distinguish between specific user agents/IPs ("defined" throttles) and all others ("general" throttles). Exceeding request rates triggers a "429 Too Many Requests" error, temporarily restricting access and significantly reducing server load by primarily affecting bots. The renamed module, "Wire Request Blocker and Throttler," includes beta throttling functions pending further testing. It is available for download on the ProDevTools support board and can operate independently or with ProcessWire's tools via a web browser, supporting traffic management across platforms like WordPress. A new ProcessRequestBlocker feature provides a dynamic view of both throttled and blocked requests under Setup > Blocks and Throttles. The module emphasizes extensive blocking capabilities for scenarios where it is more suitable than traditional throttling. Configuration options allow separate throttle times for specific user agents/IPs and general traffic, focusing on bot management. A default list of known bots can be customized to include or exclude specific user agents like Amazonbot, Google-CloudVertexBot, and FacebookBot. The module supports real-time tracking of throttling requests and managing blocks based on crawl-delay times while monitoring multiple clients simultaneously. The initial guideline for identifying bots is based on observed traffic at processwire.com, with the list serving as a starting point. While specific user agents are throttled, unidentified bots face IP-based limitations. User agent throttling is preferred to manage bot activities that utilize multiple IP addresses effectively. - ProcessWire.com faced challenges due to excessive AI bot activity, mainly affecting support forums. - The AWS-hosted site incurs high costs from server scaling to handle persistent bot traffic ignoring robots.txt settings. - Ryan Cramer updated the Wire Request Blocker module with a throttling feature to enforce compliance and reduce costs. - The update introduces defined and general throttles, responding with "429 Too Many Requests" errors when limits are exceeded. - The renamed module "Wire Request Blocker and Throttler" includes beta throttling functions available for download on ProDevTools. - It supports traffic management across platforms like WordPress and integrates ProcessWire tools via a web browser. - A new ProcessRequestBlocker feature centralizes views of both blocked and throttled requests, enhancing blocking capabilities. - Configuration options allow separate throttle times for specific user agents/IPs and general traffic. - A customizable list includes well-known bots such as Amazonbot and Google-CloudVertexBot. - Real-time tracking features support efficient management of multiple clients simultaneously. - The bot identification guideline is based on observed processwire.com traffic, with user agent throttling preferred over IP-based limitations. Keywords: "429 Too Many Requests", AI bots, AI2Bot, AWS, Ahrefs, Amazon, Amazonbot, Anthropic, Applebot, DuckAssistBot, FacebookBot, GPTBot, Google-CloudVertexBot, IP addresses, Meta, OpenAI, PerplexityBot, ProCache, ProcessRequestBlocker, ProcessWire, Python-requests, Throttling, TikTokSpider, Vulnweb, Wire Request Blocker, blocks, bot traffic, crawl-delay, disguise, forums, monitoring, requests, robotstxt, scalable cluster, server load, server resources, throttle time, throttling feature, user agents
openai
processwire.com a day ago
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95. HN Designing a new button to visually show and hide alt textThe author developed a novel method for toggling image alt text visibility on their website using HTML and CSS. Initially employing a checkbox combined with the `:has` selector in CSS, this approach encountered issues such as visually hiding the checkbox, handling focus styles, semantic concerns about using checkboxes for this purpose, and potential interactivity loss if CSS failed to load. To overcome these limitations, the author improved the solution by leveraging native HTML ` ` and ` ` elements. This update provides a more semantically appropriate structure and enhances styling opportunities. The refined implementation is now open-sourced under an MIT-0 license on GitHub, featuring a demonstration page and video, though it has yet to be deployed live.` element positioned absolutely within its parent ` The author has also enhanced accessibility through keyboard navigation with high-contrast styling on focused summaries. Custom CSS applied to ` ` and `**BULLET POINT SUMMARY:** - Developed a button to toggle image alt text visibility using HTML/CSS; initially used a checkbox with `:has` selector. - Faced issues like hiding the checkbox visually, managing focus styles, semantic concerns, and potential CSS failure interactivity loss. - Shifted to native ` ` and ` ` elements for better semantic structure and styling flexibility.- `width: max-content` applied to maintain consistent width when details are open; uses `details[open]` CSS selector for browser compatibility. - Enhanced accessibility with high-contrast styling on focused summaries and custom CSS for rounded borders, padding, colors. - Encourages community contributions and feedback via GitHub or email, emphasizing semantic HTML use and open-source licensing. Keywords: CSS, GitHub, HTML, Safari, accessibility, details, focus styles, keyboard, open source, semantic HTML, styling, summary |
96. HN Show HN: PromptGit – GitHub for Prompts- **Introduction**: PromptGit is a tool that integrates Git's version control features into AI prompt management, allowing teams to collaborate on prompts with precision similar to code development. - **Core Features**: - Offers full Git integration for branching, tagging, rollback, and blame tracking of prompts. - Provides advanced semantic diff analysis using TF-IDF and cosine similarity. - Includes risk assessment tools for evaluating the impact of changes. - Supports GitHub-style visualization. - **Experimentation and Analysis**: - Enables A/B experimentation with controlled testing environments. - Configurable metrics and performance tracking features help identify optimal prompt versions automatically. - **Enterprise Capabilities**: - Policy management through ownership controls and pattern-based rules is available for enterprise users. - Seamlessly integrates with CI/CD pipelines, making it suitable for production use. - **System Description**: The system supports CODEOWNERS-style approval workflows using pattern-based rules, automated CI integration, and audit trails. - **Prerequisites and Setup**: - Requires Node.js 18.0+, pnpm 8.0+, and Git 2.20+. - Installation involves cloning the repository, installing dependencies with `pnpm`, and building packages. - **Initialization and Usage**: - Repositories can be initialized using the command `./promptgit init`. - Prompts are added with metadata through `./promptgit save`. - **Web Interface**: - A web interface accessible at `http://localhost:3000` is launched from `apps/prompt-ui` for managing prompts. - **Core Library**: - Built using the `prompt-store` library which implements Git operations in JavaScript with isomorphic-git. - Offers semantic analysis and metadata parsing through YAML frontmatter extraction. - **CLI Tool (prompt-cli)**: - Provides an intuitive command-line interface, supports batch operations, and integrates easily into CI/CD pipelines. It runs on Windows, macOS, and Linux. - **Web Interface Details**: - Developed using Nuxt 3, Vue 3, and TypeScript. - Features real-time updates, a mobile-first responsive design with dark mode, and is WCAG 2.1 compliant for accessibility. - **Prompt Storage**: - Prompts are stored as Markdown files containing YAML frontmatter metadata fields (e.g., owner, description, version). - **Tagging and Versioning**: - Implements semantic versioning using tags like "1.2.0". - Tags include timestamps in ISO 8601 format for creation and modification. - **Configuration**: - Environment variables are configurable for web UI settings (`REPO_DIR`, `AUTH_TOKEN`) and Git options (`GIT_AUTHOR_NAME`, `GIT_AUTHOR_EMAIL`). - **Policy Management**: - Uses a `.promptowners` file to define ownership rules, specifying required approvers for different prompt categories. - **A/B Experimentation Framework**: - Experiments are defined with JSON manifests detailing candidates (versions) and metrics like quality or safety. - **CI/CD Integration**: - GitHub Actions automate the analysis of prompt changes during pull requests, ensuring compliance and streamlining deployment workflows. - **Development Environment**: - Utilizes CI/CD with GitHub Actions for prompt change reviews in pull requests. - RESTful API endpoints allow managing prompts, experiments, and policy data using HTTP methods (GET, POST). - **API Reference**: - Describes available endpoints to list, fetch, diff, run experiments on prompts, and check approval statuses. - **Development Instructions**: - Provides guidelines for building the project from source with `pnpm`, starting a web UI in development mode, watch mode setups, and testing commands. - **Licensing**: - The entire project is licensed under FSL-1.1-MIT License, with detailed information available in the LICENSE file. Keywords: A/B testing, AI, CI/CD, Git, GitHub integration, JavaScript, experiments, isomorphic-git, ownership rules, policy management, prompts, semantic comparison
github
github.com a day ago
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97. HN Show HN: Project Chimera v1.2 – Neuro-Symbolic-Causal AI Agent (Open Source)**Summary:** Project Chimera v1.2 is an open-source AI agent designed to enhance business decision-making through neuro-symbolic reasoning and causal inference, surpassing both Large Language Model (LLM)-only and LLM+Symbolic agents in benchmarks by achieving 100% trust and $2 million profit—43% more than its nearest competitor. Key updates include a Trust-Adjusted Profit Metric that balances long-term brand trust with short-term profits, a Dynamic Strategic Personality for adjusting mission priorities, Mandatory Rule Validation to ensure business compliance, performance enhancements via one-time causal engine initialization per simulation, multi-scenario benchmarking, and expanded metrics with real-time strategy feeds. The architecture of Project Chimera integrates three components: the Neuro (GPT-4o) for generating creative strategies, the Symbolic (a rule engine) that ensures decisions adhere to business rules, and the Causal (an EconML-driven inference engine) which predicts financial outcomes. It features Multi-Hypothesis Reasoning and Dynamic Learning from Experience, allowing the agent to evaluate multiple strategies and adapt based on past data. Project Chimera's evolution includes improvements in strategic intelligence with a Trust-Adjusted Profit Metric that dynamically adapts mission objectives, enhancements in simulation efficiency through singular causal engine initialization, and more sophisticated economic modeling. Its benchmark suite rigorously evaluates various architectures for effectiveness. The document highlights the results from interactive applications with multi-scenario testing (Brand-Focused, Profit-Maximization, Balanced) showing the Full Neuro-Symbolic-Causal agent's superior performance across scenarios. Future plans involve implementing Deep Explainable AI and developing competitive simulations involving multiple Chimera agents. The project is domain-agnostic and available as a benchmark ecosystem adaptable to fields like finance or healthcare. **Bullet Points:** - **Project Overview**: - Open-source AI agent, Project Chimera v1.2. - Enhances strategic business decision-making with neuro-symbolic reasoning and causal inference. - Outperformed LLM-only and LLM+Symbolic agents in benchmarks. - **Key Features and Updates**: - Trust-Adjusted Profit Metric balancing long-term brand trust and short-term profits. - Dynamic Strategic Personality for mission-based priority adjustments. - Mandatory Rule Validation ensuring business rule compliance. - Performance improvements via singular causal engine initialization per simulation. - Multi-scenario benchmarking capabilities. - Expanded metrics with a live strategy feed. - **Architecture**: - Neuro (GPT-4o) generates creative strategies. - Symbolic (rule engine) ensures adherence to business constraints. - Causal (EconML-driven inference engine) predicts financial outcomes of actions. - **Learning and Adaptation**: - Multi-Hypothesis Reasoning for strategy evaluation. - Dynamic Learning from Experience based on past performance data. - **Evolution and Improvements**: - Introduced Trust-Adjusted Profit Metric with dynamic mission goal adjustments. - Enhanced simulation efficiency and economic modeling. - Rigorous benchmark suite developed for architecture evaluation. - **Testing Results and Scenarios**: - Superior performance in Brand-Focused, Profit-Maximization, and Balanced scenarios. - Full Neuro-Symbolic-Causal agent leads in profitability across tests. - **Future Roadmap**: - Implementation of Deep Explainable AI (XAI) for visualizing causal predictions. - Development of multi-agent competitive simulations. - **Project Access and Collaboration**: - Domain-agnostic framework adaptable to various fields like finance or healthcare. - Available as a benchmark ecosystem with interactive labs and GitHub repository access. - Open to contributions and issues on the GitHub platform under the MIT License. Keywords: Automated Benchmarks, Brand Trust, Causal Engine, Deep XAI, Dynamic Personality, EconML, Feedback, GitHub, Interactive Streamlit, LLM-Only Agent, Multi-Scenario Benchmarking, Neuro-Symbolic-Causal AI, Open Source, Profit Metric, Project Chimera, Rule Validation, Strategic Decision-Making, Strategy Lab, Symbolic Agent, Virtual Environment
github
github.com a day ago
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98. HN Inside vLLM: Anatomy of a High-Throughput LLM Inference SystemThe provided text introduces vLLM, a sophisticated large language model (LLM) inference system designed for scaling across multiple GPUs and nodes. It serves as an introductory piece in a series that explains modern LLM systems using an inverse-pyramid approach to gradually detail broad concepts before diving into specific components. - **vLLM Overview**: The document focuses on the V1 engine of vLLM, contrasting it with the deprecated V0 version. It emphasizes its capability for high-throughput offline inference and notes additional steps required for web deployment. - **Practical Example**: A Python code example is provided to demonstrate text generation using specific sampling parameters like paged attention and prefix caching. This serves as foundational knowledge for those interested in advanced LLM engines or contributing to vLLM projects such as SGLang. - **Configuration Details**: The setup described involves a single-process, synchronous operation on one GPU without support for web or distributed systems. It avoids parallelism across data, model, pipeline, or experts, relying solely on standard transformer models. - **Core Components**: Key components include configuration settings (vLLM config), input processors, an engine core client, and an output processor for user-friendly formats. The document discusses transitioning from the UniProcExecutor to the MultiProcExecutor to support multi-GPU setups in future scalable systems. - **System Architecture**: It outlines a structured architecture involving sub-components like a Structured Output Manager, Scheduler, and KV-cache manager crucial for managing paged attention through cache blocks mapped to tokens. - **Model Execution and Caching**: The process of preparing a PyTorch model for inference includes loading weights, setting evaluation mode with `model.eval()`, optimizing with `torch.compile()`, and initializing advanced caching techniques. It details the complexity involved in hybrid models like Jamba. - **Memory Management and CUDA Graphs**: Memory management involves conducting dummy forward passes to allocate KV cache blocks within available VRAM. The document mentions using CUDA graphs for pre-baking GPU operations, enhancing execution efficiency by reducing kernel launch overhead. - **Engine Operations**: The system operates via synchronous or asynchronous engines that process requests through stages like scheduling, the forward pass, and postprocessing. These include managing different workload types—prefill (compute-bound) and decode (memory-bandwidth-bound)—and optimizing request handling with speculative decoding and prefix caching. - **Advanced Features**: Advanced techniques such as chunked prefill and prefix caching are described to optimize token generation efficiency. Prefix caching involves hashing token chunks for reuse, while guided decoding uses finite state machines based on grammar rules to constrain token sampling. - The code example utilizes a language model with guided decoding through a finite state machine (FSM) in the `vLLM` library, employing prompts and sampling parameters that allow only specific positive ("P") or negative ("N") tokens. An FSM controls token generation by managing a `_grammar_bitmask` tensor during the forward pass, masking disallowed logits with negative infinity to ensure only valid tokens are sampled based on FSM states. - The `StructuredOutputManager` manages asynchronous grammar compilation and updates request statuses in alignment with the FSM's current state for structured output according to predefined grammatical rules. A bitmask encodes allowed ("1") and disallowed ("0") tokens in a vocabulary, represented by integers whose binary forms act as `_grammar_bitmask`. For larger vocabularies, these are extended with multiple 32-bit integers. Libraries like xgrammar assist in generating bitmasks from FSM states. - Speculative decoding optimizes autoregressive generation in large language models (LMs) using a smaller "draft" model to propose tokens quickly and cost-effectively. The larger LM then verifies these proposals, accepting them based on probability comparisons or ratios, which reduces computational overhead by limiting the application of the larger LM for token generation. - Alternative proposal strategies in vLLM V1 include: - **n-gram**: Proposes tokens from prior matches within a specified window. - **Eagle**: Replaces the transformer stack with an MLP and fine-tunes it as a draft model. - **Medusa**: Trains auxiliary linear heads on top of the large model for parallel token prediction. - Speculative decoding in vLLM is configured using `SpeculativeConfig` and involves drafting tokens, adjusting key-value (KV) block allocations, forming inputs with context and drafts, computing metadata, executing forward passes, and utilizing a rejection sampler to decide output tokens. This process optimizes inference performance by generating potential outputs ahead of time. - Figure 11 discusses speculative decoding through disaggregated prefill/decode processes in vLLM, emphasizing latency control via compute-bound prefill and memory-bandwidth-bound decode tasks running autonomously. - The script demonstrates managing prefill and decode tasks separately using Python's multiprocessing library across different GPUs, utilizing `vllm` with `SharedStorageConnector` for KV caching and data management. - Scaling techniques for models exceeding a single GPU's capacity involve tensor parallelism (TP) and pipeline parallelism (PP), coordinated by MultiProcExecutor. TP is preferred due to higher bandwidth, while PP extends computation across nodes. - Figure 13 illustrates MultiProcExecutor in a tensor-parallel setting with eight shards, where rank 0 acts as the driver. It uses RPC broadcast message queues for inter-process communication and abstracts multiprocessing complexity through `execute_model` calls on workers via an RPC mechanism. - **Overview**: The passage describes scaling up model execution using data parallelism across multiple nodes with vLLM engines, specifically employing two H100 nodes to run four instances of a large language model (LLM). Each instance requires tensor-parallel size (TP) and data-parallel size (DP) of 4. One node operates in headless mode while the other runs modified for DP start rank. - **Configuration & Networking**: The setup necessitates robust networking, ensuring nodes communicate over specific IP addresses and ports. This involves using CoreEngineProcManager to launch multiple processes per node, each managing an engine core through a busy loop facilitated by DPEngineCoreProc. - **Coordination Mechanisms**: Initialization includes setting up input and output queues, performing coordination handshakes with a frontend via DEALER ZMQ socket, and configuring the DP group potentially using NCCL. A `ready_event` synchronizes processes across nodes. - **Operational Workflow**: In operation, each child process (one per DP replica) runs three threads: main, input, and output. These components handle request processing and load balancing coordination with the DP coordinator and frontend. An async architecture supports control messages beyond inference requests using a "wave" counter for tracking engine idleness. - **API Server & Load Balancing**: The API server node employs AsyncLLM and DPLBAsyncMPClient to manage core engines, facilitating startup handshakes via ZMQ addresses. It communicates with the DP coordinator for load balancing data and scaling commands, maintaining synchronization across replicas using lockstep mechanisms. - **Request Lifecycle**: FastAPI endpoints serve requests processed asynchronously by Uvicorn, where POST requests initiate model execution through a generate path. The `create_completion` route handles tasks like tokenization and metadata preparation, followed by request processing with AsyncLLM.generate and load balancing via DPAsyncMPClient.add_request_async. - **System Simplification**: Ray and curl commands simplify distributed systems management, emphasizing metrics such as latency (e.g., Time to First Token) and throughput for evaluating system performance. The trade-off between batch size \( B \) and these metrics is analyzed using a roofline model, considering kernel auto-tuning impacts on step time. - **Benchmarking Tools**: vLLM's benchmark suite includes `serve`, `latency`, and `throughput` CLI tools to measure end-to-end latency and throughput. The `auto-tune` script optimizes server benchmarks for specific service-level objectives (SLOs). - **Feature Set & Future Directions**: vLLM supports various hardware backends and techniques like MLAs, MoE models, encoder-decoders, and attention-free variants. Complex sampling methods and hybrid KV-cache logic are included as plugins with some interdependencies. The text promises future exploration of specific subsystems. - **Acknowledgments**: Thanks to Hyperstack for H100 provision and contributors for feedback on the pre-release version of this discussion. Keywords: CUDA, KV cache, LLM, PyTorch, async decoding, batching, distributed serving, inference, multi-GPU, prefix caching, scheduling, vLLM
llm
blog.vllm.ai a day ago
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99. HN How Maintainer Burnout Is Causing a Kubernetes Security DisasterThe Kubernetes External Secrets Operator (ESO) is experiencing a critical security risk due to maintainer burnout, resulting in significant operational challenges. With its user base growing and the maintenance team dwindling to just one active maintainer, official support has been suspended until more maintainers are involved. ESO plays an essential role in securely managing secrets within Kubernetes environments by syncing data from external providers like AWS Secrets Manager and Azure Key Vault into Kubernetes. Its features include real-time updates and automatic secret rotation, vital for maintaining security due to the unencrypted nature of native Kubernetes secrets stored in etcd. The current maintainer burnout has caused project stagnation, with Gustavo Fernandes de Carvalho noting excessive responsibilities and diminishing community engagement. Consequently, a project freeze is in place, halting new features, bug fixes, and security patches until at least five maintainers are secured. The team currently lacks the resources to mentor potential junior maintainers despite over 300 volunteers offering assistance and guidance from the Cloud Native Computing Foundation. Recovery efforts include organizing community meetings with active participation by contributors, reviewers, and maintainers. Recruitment for permanent roles is ongoing, but de Carvalho anticipates this process will take at least six months. ESO's situation exemplifies a broader issue in open-source projects where insufficient attention can lead to vulnerabilities. The project’s survival depends on increased community support, highlighting the risk of essential yet lesser-known projects failing and leaving businesses vulnerable to security threats without solutions. **BULLET POINT SUMMARY:** - Kubernetes External Secrets Operator (ESO) is facing significant security risks due to maintainer burnout. - Growing user base contrasts with a shrinking maintenance team, now down to one active maintainer, leading to halted official support. - ESO is vital for secure secrets management in Kubernetes, syncing data from external providers and enabling real-time updates and automatic secret rotation. - Project stagnation results from excessive responsibilities and reduced community engagement. - A project freeze is in place until at least five maintainers are secured; no new features or security patches can be implemented. - Despite 300 volunteers and guidance from the Cloud Native Computing Foundation, mentoring resources for junior maintainers are lacking. - Recovery efforts involve organizing community meetings and ongoing recruitment of permanent roles, with a six-month anticipated timeline. - ESO’s challenges highlight broader open-source issues where maintainer burnout risks project failure and security vulnerabilities. Keywords: AWS Secrets Manager, Azure Key Vault, ESO, External Secrets Operator, GitHub, HashiCorp Vault, Kubernetes, Kubernetes Secret objects, Slack, Stakater, access controls, add-ons, audit trails, burnout, community response, contributor ladder, disaster, etcd, external providers, issues open, maintainers, meetings, open source, pull requests, real-time updates, secret rotation, secrets, security, shutdown, support, versions
github
thenewstack.io a day ago
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100. HN Postgres HA with CDC at PlanetScale**Summary:** The article explores the intricacies and challenges of achieving high availability (HA) when implementing Change Data Capture (CDC) with a PostgreSQL database at PlanetScale, compared to MySQL's approach. In PostgreSQL's setup, comprising one primary node and two standby nodes configured for semi-synchronous replication, the CDC client reads from a logical replication slot using pgoutput. Here, WAL (Write-Ahead Logging) is emitted by the primary and streamed to physical standbys, while the CDC client decodes it into row changes. A significant challenge arises as the logical replication slot remains on the primary node, leading to WAL accumulation if the CDC client lags behind. PostgreSQL's design introduces operational constraints due to this dependency, potentially affecting HA when failover is necessary, especially since a standby must have observed real progress of the slot to be considered for promotion. PostgreSQL 17 attempted improvements by serializing slot metadata into the WAL; however, nodes still require acknowledgment from an actual subscriber before they can be promoted during failover. This setup aims to ensure consistency but limits HA flexibility. In contrast, MySQL uses a binary log with Global Transaction Identifiers (GTIDs) and allows replicas that maintain GTID continuity through `log_replica_updates=ON` to offer more flexible HA solutions. Failovers in MySQL are straightforward: promote any replica and resume operations from its current GTID position without requiring prior polling or slot management, thereby avoiding issues like write outages or WAL growth. Overall, the article underscores key differences between PostgreSQL's and MySQL’s replication and CDC mechanisms. While PostgreSQL prioritizes consistency by tightly coupling failover eligibility with subscriber progress, leading to potential delays in promotion during maintenance or failovers, MySQL offers a more resilient system. MySQL allows for seamless switchover and resumption without dependency on external systems, enhancing HA flexibility. **Bullet Point Summary:** - The article discusses challenges of high availability (HA) with Change Data Capture (CDC) in PostgreSQL at PlanetScale. - Standard setup involves one primary node, two standby nodes, and a CDC client reading from a logical replication slot using pgoutput. - WAL is emitted by the primary, streamed to standbys, but logical slots remain on the primary, causing operational constraints if the CDC client lags. - Logical replication slots in PostgreSQL lead to WAL accumulation, affecting HA during failovers since standbys must see actual progress. - PostgreSQL 17 improves failover handling by serializing slot metadata into the WAL; however, promotion eligibility requires subscriber acknowledgment. - MySQL's GTID-based approach with `log_replica_updates=ON` allows seamless replica switchover and resume operations without prior polling or slots. - MySQL provides more flexible HA by eliminating dependencies on external systems, avoiding issues like write outages and excessive WAL growth found in PostgreSQL setups. - The comparison highlights PostgreSQL’s focus on consistency versus MySQL's resilience and flexibility in handling replication and CDC. Keywords: CDC (Change Data Capture), GTID (Global Transaction Identifier), HA (High Availability), PlanetScale, Postgres, WAL (Write-Ahead Logging), binlog (binary log), failover, logical replication slot, pgoutput, replication, semi-synchronous replication, switchover, synchronization
postgres
planetscale.com a day ago
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101. HN UTF-8 is a brilliant designThe provided text delves into the intricacies of UTF-8, a versatile character encoding system designed to represent a vast array of characters from the Unicode set while maintaining backward compatibility with ASCII. It employs 1 to 4 bytes per character, using specific bit patterns to indicate byte sequences: `0` for single-byte ASCII characters and prefixes like `110`, `1110`, or `11110` for multi-byte characters. The continuation bytes in these sequences start with `10`. An example is given of encoding the Hindi letter "अ" as a three-byte sequence resulting in the Unicode code point U+0905. Additionally, the text illustrates UTF-8's capability to encode both ASCII and extended Unicode symbols through two examples: an emoji-containing text and an ASCII-only text. The emoji-containing text includes characters from standard ASCII and an extended Unicode symbol (the waving hand emoji), demonstrating UTF-8's flexibility but lack of backward compatibility with ASCII due to the multi-byte character. Conversely, the ASCII-only text demonstrates UTF-8's full backward compatibility when dealing solely with ASCII characters. The document further compares UTF-8 to other encodings like GB 18030 and ISO/IEC 8859, highlighting its superior popularity despite lacking universal adoption of these alternatives. Unlike UTF-16 and UTF-32, which are not backward compatible with ASCII (using two or four bytes for an 'A'), UTF-8's design allows it to represent ASCII characters in a single byte. The author mentions the creation of a tool called "UTF-8 Playground" to aid interactive learning about UTF-8 encoding. Additionally, further exploration of the topic is encouraged through references available on platforms like Hacker News. **BULLET POINT SUMMARY:** - UTF-8 efficiently encodes Unicode characters using 1 to 4 bytes per character while maintaining backward compatibility with ASCII. - It uses specific bit patterns for identifying single-byte and multi-byte sequences: `0` for ASCII, and prefixes like `110`, `1110`, or `11110` for longer sequences, with continuation bytes starting with `10`. - Example provided: Hindi letter "अ" encoded in UTF-8 as a three-byte sequence forming the Unicode code point U+0905. - Two text examples highlight UTF-8's flexibility and compatibility: emoji-containing text shows non-backward compatibility due to multi-byte characters, while ASCII-only text demonstrates full backward compatibility. - UTF-8 is more popular than other encodings like GB 18030 and ISO/IEC 8859 but lacks their universal adoption. - Unlike UTF-16 and UTF-32, which are not backward compatible with ASCII, UTF-8 maintains this compatibility for ASCII content. - The author developed "UTF-8 Playground" to visualize UTF-8 encoding interactively. - Further exploration of UTF-8 is suggested through references on Hacker News. Keywords: ASCII, UTF-8, Unicode, character set, code point, compatibility, continuation bytes, decoding rules, emoji, encoding, hexadecimal, main byte, multi-byte sequence
popular
iamvishnu.com a day ago
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102. HN Tesla Master Plan 4 [pdf]**Summary:** Tesla's Master Plan Part IV outlines the company’s vision for integrating artificial intelligence into physical products to enhance global prosperity and human well-being. Building upon two decades of innovation in electric vehicles, energy solutions, and humanoid robots, Tesla aims to merge its manufacturing capabilities with autonomous technology. The plan envisions creating a safer, cleaner, and more enjoyable world through the unification of hardware and software at scale, fostering economic growth that benefits everyone. This initiative represents the next chapter in Tesla's transformative technological renaissance. The Master Plan addresses "Sustainable Abundance," focusing on overcoming resource constraints via continuous technological innovation. It reflects on historical shifts from horse-based to fossil-fuel transportation systems while highlighting breakthroughs like semiconductors and the internet as key enablers of economic opportunities. The plan suggests sustainable growth can be achieved without sacrificing other areas, provided innovation persists. The Master Plan includes several parts: - Part I (undated) - Part II - "Electrifying Transport" (2006), emphasizing a shift to cleaner energy transportation. - Part III ("Accelerating a Sustainable Energy Economy," 2023) focusing on advancing the sustainable energy sector. - Part IV ("Sustainable Abundance," 2025) envisions an era of infinite growth where technology addresses resource shortages. Tesla's success in areas like mass-producing affordable batteries for vehicles demonstrates that perceived impracticalities can be realized through innovation. The plan emphasizes removing constraints to achieve sustainable economic abundance, with advancements particularly notable in battery development, resulting in renewable energy-driven industries. Key developments include enhancing clean electricity availability and reliability via solar energy generation and large-scale battery storage, making it more accessible and affordable for communities. Autonomous vehicles promise to improve transportation affordability, safety, and environmental impact, especially in densely populated urban areas. Additionally, Tesla's Optimus robots aim to transform labor by handling repetitive or dangerous tasks, allowing people more time for meaningful activities. The Master Plan details Tesla’s production processes through facilities like the Fremont Factory (2010) and Model 3 Gigafactory Shanghai (2018). Autonomy is framed as a tool for advancing human prosperity and global well-being. The plan stresses developing affordable, scalable advanced products to democratize opportunities, helping individuals maximize their limited resource—time. Guiding principles emphasize accelerating the transition to sustainable abundance, acknowledging challenges but stressing determination in execution. Despite potential criticism, achieving this vision aims to demonstrate that what was once considered impossible is now achievable, ensuring a sustainable and abundant future for generations. Tesla’s journey began with developing an exciting sports car, Roadster, using profits to create more accessible products like the Model S, X, 3, and Y, ultimately establishing an ecosystem of sustainable products including transportation, energy generation, battery storage, and robotics. Today, Tesla stands at a revolutionary growth period that promises benefits for both the company and humanity. This leap involves developing tools for creating a world characterized by sustainable abundance, redefining labor, mobility, and energy on a global scale as outlined in Master Plan Part IV. **Bullet Point Summary:** - Tesla's Master Plan Part IV focuses on integrating AI into physical products to enhance global prosperity. - Builds on two decades of EVs, energy solutions, and humanoid robots, merging manufacturing with autonomous technology. - Envisions a safer, cleaner world through hardware-software unification, fostering economic growth for all. - Emphasizes "Sustainable Abundance" via technological innovation to overcome resource constraints. - Historical shifts from horse-based to fossil-fuel systems; highlights breakthroughs like semiconductors and the internet. - Sustainable growth possible with continuous innovation, without sacrificing other areas. - Key plan parts include electrifying transport (2006), sustainable energy economy (2023), and sustainable abundance (2025). - Tesla's success in affordable battery production demonstrates overcoming perceived impracticalities through innovation. - Advancements in battery development lead to renewable energy-driven industries. - Developments: improved clean electricity via solar energy and batteries; autonomous vehicles enhance transportation safety and affordability. - Optimus robots transform labor by handling repetitive/dangerous tasks, freeing time for meaningful activities. - Production processes detailed through facilities like Fremont Factory (2010) and Gigafactory Shanghai (2018). - Autonomy advances human prosperity and global well-being; emphasizes affordable, scalable advanced products. - Principles focus on accelerating sustainable abundance, acknowledging challenges but stressing determination. - Achieving vision aims to show the once-impossible is possible, ensuring a sustainable future for generations. - Tesla's journey from Roadster to Model S, X, 3, Y established an ecosystem of sustainable products. - Presently at a revolutionary growth period benefiting both company and humanity, redefining labor, mobility, energy globally. Keywords: AI technology, Gigafactory Shanghai, Model 3, Model S, Model X, Model Y, OptiMUS, Tesla, autonomous progress, autonomy, clean electricity, electric vehicles, energy products, humanoid robots, innovation, renewable resources, semiconductors, sustainability, transportation revolution
tesla
digitalassets.tesla.com a day ago
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103. HN Neo Scored 34.2% SOTA on OpenAI MLE-BenchThe document outlines the "MLE-Bench" benchmark designed to evaluate machine learning agents on engineering tasks, with Neo achieving 34.2% state-of-the-art performance overall. The evaluation includes complexities categorized as Low (29.8%), Medium (24.4%), and High (34.2%), ensuring statistical reliability through multiple seeds, reporting the mean ± one standard error of the mean. Various agents such as Neo multi-agent, R&D-Agent o3 + GPT-4.1, deepseek-r1, and AIDE gpt-4o-2024-08-06 are compared based on their performance across different complexity levels while using a specified resource setup (24 hours runtime, 36 vCPUs, 440GB RAM, one 24GB A10 GPU). The leaderboard provides detailed insights into agent performances and includes resources like code, dataset construction logic, and grading reports for replication and further study. A "Lite" evaluation option is recommended for cost-efficient assessments using only the Low complexity split (22 out of 75 competitions), reducing data size from 3.3TB to 158GB while potentially impacting performance if resource constraints are tightened. This lite dataset encompasses various Kaggle competition categories, with Git-LFS required for handling large files. The MLE-bench tool allows users to prepare datasets and grade submissions using commands like `mlebench prepare --all` or specific competition IDs. Submissions must adhere to CSV format requirements, with grading facilitated by the `mlebench grade` command. The setup involves a base Docker image (`mlebench-env`) that sets up a Conda environment for agent evaluation, which is agent-agnostic and includes additional features like rule violation detectors. The repository offers examples in an `examples/` directory and experiment-specific code in the `experiments/` directory, such as competition splits and submission compilation scripts. Known issues with specific MLE-bench competitions include incorrect test set preparations and checksum mismatches, with fixes planned for a future release versioning these results to distinguish from previous submissions. The document highlights contributions from various individuals and advises proper citation using the provided BibTeX entry. It notes leaderboard challenges in certain competitions due to crowded leaderboards or missing data elements, with issues noted but not resolved until the next MLE-bench version release. Keywords: AIDE, Audio Classification, Conda, Docker, GPT-4, Git-LFS, Image Classification, Kaggle, Lite Evaluation, MLE-Bench, Machine Learning, Multi-Agent, Neo, OpenAI, R&D-Agent, SOTA, Seq->Seq, Tabular, Text Classification, agents, benchmark, checksums, competitions, complexity splits, compute resources, dataset, evaluation, leaderboard, pytest, ranzcr-clip-catheter-line-classification, tensorflow-speech-recognition-challenge, variance, version
openai
github.com a day ago
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104. HN EU court rules nuclear energy is clean energyThe European Court of Justice has declared nuclear energy as a form of clean energy under EU green finance rules, rejecting Austria's legal challenge against its classification within the EU Sustainable Finance Taxonomy. This decision signifies an evolving perspective in Europe regarding nuclear power, with countries like Germany and environmental groups such as Fridays for Future showing reduced opposition due to its low environmental impact and robust safety protocols. The ruling suggests a probable defeat of Greenpeace's ongoing lawsuit challenging this categorization. Despite the court's conclusion that nuclear energy adheres to scientific and environmental criteria, Greenpeace continues to contest its classification, with its German branch viewing it as detrimental to climate objectives. Amid escalating global climate challenges characterized by inadequate emission reductions and limited access to clean electricity, there is a pressing need to expand established solutions. A legal case involving Greenpeace and the EU Commission is addressing nuclear energy's contribution to the transition towards cleaner energy, aiming to present evidence of its advantages. Despite Austria facing growing isolation for opposing nuclear power, advancements have been made by overturning antiquated policies at institutions like the World Bank and adopting technology-neutral language at the UN, thereby fostering investment in sustainable projects and emission reduction efforts. Nonetheless, obstacles persist in eliminating national prohibitions on nuclear energy, securing necessary funding, and encouraging democratic nations to support global clean energy initiatives, especially as a countermeasure against Russian influence. The overarching aim is the establishment of a worldwide clean and dependable energy grid that can sustain modern living standards universally. This progress is attributed to extensive financial backing and the advocacy from committed individuals. **BULLET POINT SUMMARY:** - The European Court of Justice ruled nuclear energy qualifies as clean under EU green finance rules, dismissing Austria's challenge. - Reflects a shift in Europe’s perspective on nuclear power due to its low environmental impact and safety measures. - Likely failure anticipated for Greenpeace's lawsuit challenging this classification. - Greenpeace opposes the decision, viewing it as detrimental to climate efforts. - Urgent global need to expand proven clean energy solutions amid inadequate emission reductions and limited access to clean electricity. - Legal proceedings involve presenting nuclear energy benefits in EU Commission vs. Greenpeace case. - Austria faces isolation due to its opposition against nuclear power. - Progress seen in overturning outdated policies at the World Bank and promoting technology-neutral language at the UN, fostering sustainable investments. - Challenges remain in lifting national nuclear bans, securing funding, and supporting global clean energy development. - The goal is a worldwide clean, reliable energy grid for modern living standards. - Success credited to financial support and advocacy from committed individuals. Keywords: Austria, EU court, European Commission, Germany, Greenpeace, Russia’s influence, Sustainable Finance Taxonomy, bans, clean energy, climate crisis, global emissions, lawsuit, lifecycle, nuclear energy, phase-outs, sustainable finance
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www.weplanet.org a day ago
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https://www.construction-physics.com/p/why-are-nuclear- a day ago https://www.nrc.gov/reactors/new-reactors/large-lw a day ago https://www.energy.gov/ne/articles/nrc-approves-co a day ago https://www.sciencedirect.com/science/article/pii& a day ago governance%20compared%20to%20other%20technologies. a day ago https://en.wikipedia.org/wiki/Olkiluoto_Nuclear_Power_P a day ago https://www.samdumitriu.com/p/infrastructure-costs-nucl a day ago https://www.npr.org/sections/shots-health-news/202 a day ago https://www.siliconvalleypower.com/residents/rates-and- a day ago https://en.wikipedia.org/wiki/List_of_power_stations_in a day ago https://www.zacks.com/stock/quote/PCG/income- a day ago https://www.google.com/search?q=%242.5+billion+%2F+17%2C301+ a day ago https://www.eia.gov/state/print.php?sid=CA#tabs-1 a day ago https://www.construction-physics.com/p/can-we-afford-la a day ago https://ember-energy.org/latest-insights/solar-power-co a day ago 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https://www.reuters.com/world/europe/germany-pushe a day ago https://www.energyconnects.com/news/renewables/202 a day ago https://www.iea.org/commentaries/the-global-energy-cris a day ago https://www.dw.com/en/how-germany-seeks-to-cut-electric a day ago https://www.eia.gov/electricity/monthly/epm_table_ a day ago https://www.reuters.com/world/europe/german-coalit a day ago https://www.reuters.com/sustainability/cop/eu-coun a day ago https://en.wikipedia.org/wiki/Levelized_cost_of_electri a day ago https://www.manager-magazin.de/finanzen/versicherungen& a day ago https://www.arte.tv/en/videos/115068-000-A/nu a day ago https://scipython.com/blog/uranium-enrichment-and-the-s a day ago https://disarmament.unoda.org/en/our-work/weapons- a day ago https://en.wikipedia.org/wiki/Enriched_uranium a day ago https://www.thecooldown.com/green-tech/thorium-molten-s a day ago https://www.atlasobscura.com/articles/thorium-toothpast a day ago https://www.theguardian.com/environment/2023/jun&# a day ago https://www.forbes.com/sites/jamesconca/2012/ a day ago https://theprogressplaybook.com/2025/02/19/ge a day ago https://www.sciencealert.com/animals-aren-t-just-surviving-i a day ago https://en.wikipedia.org/wiki/The_dose_makes_the_poison a day ago https://en.wikipedia.org/wiki/Generation_III_reactor a day ago https://inspectapedia.com/structure/Chernobyl_Nuclear_D a day ago https://www.eit.edu.au/engineering-failures-chernobyl-disast a day ago https://en.wikipedia.org/wiki/Korean_Air_Lines_Flight_0 a day ago https://yle.fi/a/74-20136905 a day ago https://www.bundestag.de/resource/blob/877586/ a day ago https://kernd.de/wp-content/uploads/2023/05&# a day ago https://en.m.wikipedia.org/wiki/Onkalo_spent_nuclear_fu a day ago https://www.youtube.com/watch?v=ayLxB9fV2y4 a day ago https://www.reuters.com/article/business/energy&#x a day ago https://www.cleanenergywire.org/news/fossil-electricity a day ago https://www.sciencedirect.com/science/article/pii& a day ago https://www.energy.gov/ne/articles/5-fast-facts-ab a day ago https://www.iaea.org/publications/14739/status-and a day ago https://www.gao.gov/assets/gao-21-603.pdf a day ago https://www.belfercenter.org/publication/economics-repr a day ago https://en.wikipedia.org/wiki/Ocean_disposal_of_radioac a day ago https://www.amazon.com/Plentiful-Energy-technology-scientifi a day ago https://www.thesciencecouncil.com/pdfs/PlentifulEnergy. a day ago https://en.wikipedia.org/wiki/Onkalo_spent_nuclear_fuel a day ago https://en.wikipedia.org/wiki/International_Nuclear_and a day ago https://en.wikipedia.org/wiki/Nuclear_and_radiation_acc 16 hours ago https://en.wikipedia.org/wiki/List_of_nuclear_power_acc 16 hours ago https://en.wikipedia.org/wiki/List_of_nuclear_and_radia 16 hours ago https://ourworldindata.org/grapher/energy-consumption-b 16 hours ago https://www.destatis.de/DE/Themen/Branchen-Unterne 16 hours ago https://www.economist.com/open-future/2019/12/ 16 hours ago https://en.wikipedia.org/wiki/Onagawa_Nuclear_Power_Pla 16 hours ago https://en.wikipedia.org/wiki/Yanosuke_Hirai 16 hours ago https://oilprice.com/Latest-Energy-News/World-News/ 16 hours ago https://en.wikipedia.org/wiki/File:Chernobyl_radiation_ 16 hours ago https://en.wikipedia.org/wiki/File:Aerial-herbicide-spr 16 hours ago https://www.sustainabilitybynumbers.com/p/nuclear-const 16 hours ago https://en.m.wikipedia.org/wiki/Flamanville_Nuclear_Pow 16 hours ago https://www.forbes.com/sites/michaelshellenberger/ 16 hours ago https://www.forbes.com/sites/michaelshellenberger/ 16 hours ago https://www.youtube.com/watch?v=IzQ3gFRj0Bc 16 hours ago https://en.wikipedia.org/wiki/EU_taxonomy_for_sustainab 16 hours ago https://people.wou.edu/~courtna/GS361/Energy_From_ 16 hours ago https://en.wikipedia.org/wiki/Perfect_is_the_enemy_of_g 16 hours ago https://ourworldindata.org/grapher/carbon-intensity-ele 16 hours ago https://www.youtube.com/watch?v=6Kkgg494Ifc 16 hours ago https://www.rte-france.com/en/eco2mix/power-genera 16 hours ago https://www.rolls-royce.com/innovation/small-modular-re 16 hours ago https://www.theguardian.com/environment/2009/may&# 16 hours ago https://www.nyteknik.se/energi/forsmark-2-ur-drift-pa-a 16 hours ago https://explore.openelectricity.org.au/energy/sa1/ 16 hours ago https://www.abc.net.au/news/2024-10-13/australian- 16 hours ago https://model.energy/ 16 hours ago https://www.cleanenergywire.org/news/german-industry-ha 16 hours ago https://www.tandfonline.com/doi/full/10.1080/ 16 hours ago https://www.isi.fraunhofer.de/en/blog/2024/kr 16 hours ago https://en.m.wikipedia.org/wiki/Energy_return_on_invest 16 hours ago https://en.wikipedia.org/wiki/Wind_power_in_Iowa 16 hours ago https://www.bbc.com/future/article/20230613-onkalo 16 hours ago https://ourworldindata.org/data-insights/the-world-is-g 16 hours ago https://en.wikipedia.org/wiki/Experience_curve_effect 16 hours ago https://news.ycombinator.com/item?id=23185166 16 hours ago https://www.whitehouse.gov/presidential-actions/2025&#x 16 hours ago https://en.wikipedia.org/wiki/Virgin_Galactic#2007_Scal 16 hours ago https://en.wikipedia.org/wiki/List_of_hydroelectric_pow 16 hours ago https://www.bbc.com/future/article/20190725-will-w 16 hours ago https://www.sustainabilitybynumbers.com/p/solar-wind-nu 16 hours ago https://www.energy.ca.gov/data-reports/energy-almanac&# 16 hours ago https://www.youtube.com/watch?v=g8sjdOjNxIE 16 hours ago https://ourworldindata.org/grapher/energy-consumption-b 16 hours ago https://en.wikipedia.org/wiki/Economics_of_nuclear_powe 16 hours ago https://gordianknotbook.com/wp-content/uploads/202 16 hours ago https://gordianknotbook.com/author/ 16 hours ago https://www.bbc.co.uk/programmes/b065x080 16 hours ago https://eur-lex.europa.eu/legal-content/en/TXT 16 hours ago https://xcancel.com/simonahac/status/1318711842907 16 hours ago https://x.com/simonahac/status/1318711817502302209 16 hours ago https://www.sciencedirect.com/science/article/pii& |
105. HN Oracle and OpenAI Are Full of Crap**Summary:** The provided text discusses the implications and skepticism surrounding a significant $300 billion deal between Oracle and OpenAI, anticipated to commence in 2027. Following its earnings report, Oracle's stock surged due to a reported $317 billion growth in remaining performance obligations related to this strategic partnership aimed at expanding data center capacity. Analysts found the financial results surprising and impressive, despite missed revenue and earnings estimates. The text highlights projections indicating substantial growth for both companies over the next few years, particularly within the AI compute industry, which is expected to reach over $200 billion annually by 2030. Oracle forecasts a dramatic increase in cloud infrastructure revenue, potentially achieving $144 billion by FY2030—a 700% rise attributed mainly to OpenAI. This anticipated growth might allow Oracle's revenues to surpass Google Cloud’s entire revenue from 2024 as early as FY2028. However, the text critically evaluates these projections' feasibility and sustainability. The author expresses concerns about Oracle’s heavy reliance on a single client and questions the financial viability of OpenAI's plans without a clear path to profitability. Allegations arise that OpenAI has obscured its actual financial burn rate, potentially over $290 billion through 2029, suggesting intentional misinformation to downplay costs. The deal is critiqued for inflating Oracle’s stock with unrealistic projections amid broader market irrationality and questionable demand for GPU compute resources. Despite being a major user of such resources, OpenAI faces challenges in justifying its expansive financial projections within a contracting AI adoption market. The text underscores the industry-wide struggles illustrated by Nebius's recent contract issues with Microsoft, highlighting similar infrastructural and financial constraints faced by CoreWeave and Oracle. **Bullet Point Summary:** - **Stock Increase:** Oracle's stock rose significantly following an earnings report due to projected growth in performance obligations related to a partnership with OpenAI. - **Projected Growth:** The deal involves a $300 billion agreement set to start in 2027, with Oracle forecasting up to $144 billion in cloud revenue by FY2030, driven largely by the OpenAI partnership. - **Industry Impact:** Significant potential growth for AI compute industry, projected to exceed $200 billion annually by 2030; Oracle could surpass Google Cloud’s FY2024 revenue by FY2028. - **Skepticism on Projections:** Concerns about feasibility and sustainability of financial projections for both Oracle and OpenAI; OpenAI's reported financial burn rate may be obscured or exaggerated. - **Critique of Financial Practices:** Allegations that OpenAI has intentionally misled stakeholders regarding its financial burn rate, possibly over $290 billion through 2029, to downplay actual expenses. - **Market Irrationality:** The deal is seen as inflating Oracle's stock with unrealistic growth projections amidst questionable demand for GPU compute resources in a contracting AI market. - **Industry Challenges:** Highlighted by Nebius’s contract struggles with Microsoft, reflecting broader infrastructural and financial challenges within the industry. Keywords: Amazon, FY2029, FY2030, GPU compute, Microsoft, OpenAI, Oracle, Vantage Data Centers, analysts, burn rate, cash flow, cloud compute, contracts, data centers, debt, earnings, generative AI, growth, infrastructure, market rally, performance obligations (RPOs), revenue, stock
openai
www.wheresyoured.at a day ago
|
106. HN LLM Hacking: Quantifying the Hidden Risks of Using LLMs for Text AnnotationThe research paper "LLM Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation," authored by Joachim Baumann and colleagues, delves into the potential security risks inherent in employing large language models (LLMs) for text annotation tasks. The study, accessible via arXiv under identifier 2509.08825, emphasizes that while LLMs are potent tools for processing text, they can be susceptible to exploitation if not properly safeguarded. This work underscores the necessity of understanding and mitigating these risks to ensure their safe application across various fields. The paper highlights the impact of LLMs on social science research, particularly how they automate data annotation and analysis tasks. It identifies "LLM hacking" as a phenomenon where variations in model outputs—stemming from researcher choices—introduce biases and errors that could lead to incorrect statistical conclusions. The study replicates 37 data annotation tasks using 18 different models across 21 studies, analyzing over 13 million labels to test 2,361 hypotheses. Findings reveal that one-third of the hypotheses yield incorrect results with state-of-the-art models, while half do so with smaller models. Although better-performing models reduce risks, they cannot eliminate them entirely, especially when dealing with small effect sizes near significance thresholds. Human annotations are highlighted as crucial for minimizing false positives and aiding in model selection. The research notes that common statistical correction techniques prove ineffective against LLM hacking due to their trade-offs between error types. Additionally, it points out the ease of producing misleading results through minor prompt adjustments, stressing the need for rigorous verification of findings derived from LLMs. The document is categorized under Computation and Language (cs.CL), Artificial Intelligence (cs.AI), and Machine Learning (cs.LG) on arXiv and includes various formats like PDF, HTML, and TeX source. It features tools for citation tracking and links to resources related to the paper's code, data, and media across platforms such as alphaXiv and Hugging Face. The research is part of a broader discourse on computational linguistics and AI risks, linked with academic tools for further exploration. It also outlines functionalities associated with arXivLabs, a community-driven framework for developing new features on the arXiv website, including related projects that enhance paper recommendations and search capabilities. The platform emphasizes openness, community engagement, excellence, and user data privacy, encouraging ideas for projects benefiting the arXiv community. Key functionalities of the document include toggles for spaces, math rendering (with a MathJax disable option), author and institution links, and tools like CORE Recommender for personalized paper recommendations. Users are encouraged to subscribe to updates or contact arXiv for more information, with web accessibility assistance options available. The document also addresses copyright and privacy policy considerations. - The paper explores security risks of using LLMs in text annotation tasks. - It highlights the vulnerability of LLMs to exploitation without proper security measures. - "LLM hacking" is identified as a risk where biases from researcher choices lead to incorrect conclusions. - Study replicates 37 tasks with various models, finding significant rates of incorrect results. - Human annotations are crucial for reducing errors; statistical corrections are often ineffective against LLM hacking risks. - Minor prompt adjustments can easily produce misleading results, emphasizing the need for verification. - Document is accessible in multiple formats and includes tools for citation tracking and resources on alphaXiv and Hugging Face. - Part of a broader discourse on computational linguistics and AI risks with links to academic exploration tools. - Outlines arXivLabs functionalities and related projects enhancing paper recommendations and search capabilities. - Emphasizes platform values: openness, community engagement, excellence, privacy, and encourages project ideas for the arXiv community. - Features include toggles for spaces, math rendering, author/institution links, CORE Recommender tool, and options for updates or accessibility assistance. Keywords: Artificial Intelligence, Computation and Language, Data Annotation, DataCite, Effect Sizes, Human Annotations, Hypotheses Testing, LLM Hacking, Large Language Models, Machine Learning, Model Selection, Project Ideas, Prompting Strategy, Random Errors, Regression Estimator Correction, Replication, Risks, Semantic Scholar, Social Science Research, Statistical Significance, Systematic Biases, Temperature Settings, Text Annotation, Type I Error, Type II Error, arXiv
llm
arxiv.org a day ago
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107. HN K2-Think: Teaching a 32B Model to Reason Like the Big OnesK2 Think is an innovative AI model featuring 32 billion parameters that competes with larger models from OpenAI and DeepSeek in terms of reasoning capabilities. It stands out for its efficiency, achieving high performance despite having a more compact architecture compared to these models. K2 Think particularly excels in mathematics-related tasks, earning top rankings on benchmarks such as AIME ’24/’25, HMMT ’25, and OMNI-Math-HARD. This establishes it as the most parameter-efficient model for advanced reasoning currently available, highlighting its significance in advancing AI efficiency. **BULLET POINT SUMMARY:** - K2 Think is an AI model with 32 billion parameters. - It rivals larger models from OpenAI and DeepSeek in reasoning capabilities. - Known for high performance with a compact architecture. - Excels in math-related tasks, achieving top rankings on benchmarks like AIME ’24/’25, HMMT ’25, and OMNI-Math-HARD. - Recognized as the most parameter-efficient advanced reasoning model currently available. Keywords: AI, AIME, Architecture, Benchmarks, Breakthrough, Compact, DeepSeek, Efficiency, HMMT, Industry, K2 Think, Math, Model, OMNI-Math-HARD, OpenAI, Parameters, Performance, Reasoning, Scores, Teaching
deepseek
www.k2think.ai a day ago
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108. HN Perplexity Raises $200M at $20B Valuation in AI Search Push**Summary:** Perplexity, an AI search startup based in San Francisco, has achieved significant growth, recently raising $200 million at a $20 billion valuation following a previous round of $100 million at an $18 billion valuation just two months earlier. This rapid financial progression reflects its burgeoning annual recurring revenue (ARR), which is approaching $200 million. Perplexity is making strides in challenging Google's search market dominance, with Google's share slipping below 90% due to the rise of AI-driven competitors like Perplexity. The company offers a suite of tools including an AI answer engine providing citation-backed results, Comet browser for integrated AI searches, and enterprise solutions such as Internal Knowledge Search and Spaces. These products are part of a broader industry trend where AI search engines are transforming traditional website traffic patterns, potentially reducing organic clicks by 30–70%. This shift provides marketing teams new opportunities to utilize Perplexity's advanced tools for strategic research and development. Founded in 2022, Perplexity targets enterprise users, researchers, and data-intensive professionals with its AI-powered platforms. It promises advantages like high-quality sources adhering to legal and academic standards, significant time savings, and unique ROI from verified analysis. These features make it attractive to marketing and research teams. With total funding reaching $1.5 billion since inception, Perplexity is on a robust growth trajectory. In addition to its financial achievements, Perplexity demonstrated an aggressive expansion strategy with a notable attempt to acquire Google's Chrome browser for $34.5 billion. Its technology integrates natural language processing and retrieval-augmented generation, providing transparent, source-backed search results tailored to enterprise needs. Positioned as both a challenger to Google and a knowledge management solution, Perplexity emphasizes accuracy and trust, leading to rapid adoption among mid-sized to large enterprises and research teams. **Bullet Point Summary:** - Perplexity raised $200 million at a $20 billion valuation, following a previous round of $100 million at an $18 billion valuation just two months prior. - The company challenges Google's search market share, which has dropped below 90% due to AI competitors like Perplexity. - Offers products such as an AI answer engine, Comet browser, and enterprise tools including Internal Knowledge Search and Spaces. - AI-driven search engines could reduce organic clicks by 30–70%, affecting website traffic but creating opportunities for marketing strategies using advanced AI tools. - Founded in 2022, targets enterprises, researchers, and data professionals with features like citation-quality sources, time savings, and verified ROI analysis. - Accumulated total funding of $1.5 billion since inception, with an ARR nearing $200 million indicating strong growth. - Attempted acquisition of Google's Chrome browser for $34.5 billion, showcasing aggressive expansion strategy. - Integrates natural language processing and retrieval-augmented generation for transparent, source-backed search results tailored to enterprises. - Positioned as both a Google challenger and knowledge management solution, gaining rapid adoption among mid-sized to large enterprises and research teams. Keywords: AI search, ARR, Comet browser, Databricks, Google, OpenAI, Perplexity, ROI, San Francisco, accuracy, acquisition attempts, citation-quality sources, efficiency, enterprise workflow, funding, knowledge search, market disruption, marketing teams, natural language processing, platform, revenue growth, startup, time savings, transparency, valuation
openai
www.vktr.com a day ago
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109. HN Show HN: I got tired of Base64, so I made a numeric-only alternativeThe provided text introduces "numbase," an innovative data encoding alternative to Base64, developed by its creator. Unlike traditional ASCII character-based encodings, numbase converts data into a single large number, offering a numeric-only format ideal for scenarios where numeric storage or transmission of data is advantageous. This unique approach also enhances compatibility with compression algorithms such as Huffman coding, potentially improving efficiency in certain applications. The project and its details are accessible on GitHub at the provided link. Bullet Point Summary: - "numbase" is presented as an alternative to Base64 encoding. - It encodes data into a single large number instead of ASCII characters. - Designed for scenarios where numeric data storage or transmission is preferable. - Facilitates the use of compression algorithms like Huffman coding. - The project can be found on GitHub at the given link. Keywords: ASCII, Base64, Ferki-git-creator, GitHub, Huffman, alternative, characters, compression, data, encodes, numbase, numeric-only, store, transmit
github
news.ycombinator.com a day ago
https://github.com/Ferki-git-creator/numbase a day ago |
110. HN K2-Think: A Parameter-Efficient Reasoning System**Summary:** K2-Think is an innovative parameter-efficient reasoning system introduced in September 2025 by Zhoujun Cheng and other authors, with backing from the Simons Foundation. Built on the Qwen2.5 base model, which has 32 billion parameters, K2-Think competes effectively with larger models like GPT-OSS 120B and DeepSeek v3.1. Its design emphasizes enhanced reasoning capabilities while maintaining efficiency, achieved through six technical pillars: Long Chain-of-Thought Supervised Finetuning, Reinforcement Learning with Verifiable Rewards (RLVR), Agentic planning before reasoning, Test-time Scaling, Speculative Decoding, and Inference-optimized Hardware. The system excels in mathematical reasoning, coding, and science tasks by leveraging open-source datasets and efficient post-training methods. K2-Think offers fast inference speeds thanks to the Cerebras Wafer-Scale Engine and is freely available online as a cost-effective solution for open-source reasoning systems. In addition to detailing K2-Think's features, the text describes tools associated with the "cs.LG" browse context on arXiv. This platform supports research dissemination through citation tools like NASA ADS, Google Scholar, Semantic Scholar, and BibTeX export. It offers exploratory resources such as Bibliographic Explorer, Connected Papers, Litmaps, and scite Smart Citations, along with code and data resources including alphaXiv, DagsHub, and Hugging Face. The platform also features demo platforms like Replicate and ScienceCast, plus recommendation systems like CORE and IArxiv Recommender. ArXivLabs is highlighted as a collaborative initiative that allows community partners to develop new platform features under principles of openness, engagement, excellence, and data privacy. The text further informs about interacting with the arXiv platform by providing options for contact, subscription to mailing lists, and access to privacy policies. It includes information on MathJax, which displays mathematical notation but can be disabled if needed. Resources are available for web accessibility assistance, operational status updates via email or Slack, and inquiries regarding paper endorsers. **Bullet Point Summary:** - K2-Think is a parameter-efficient reasoning system developed in September 2025. - Supported by the Simons Foundation; authored by Zhoujun Cheng among others. - Built on the Qwen2.5 model with 32 billion parameters, competing with larger models like GPT-OSS 120B and DeepSeek v3.1. - Features six technical pillars: Long Chain-of-Thought Supervised Finetuning, RLVR, Agentic planning before reasoning, Test-time Scaling, Speculative Decoding, and Inference-optimized Hardware. - Excels in mathematical reasoning, coding, and science using open-source datasets and post-training methods. - Offers fast inference speeds with the Cerebras Wafer-Scale Engine; freely accessible online. - Describes tools on arXiv for computer science and machine learning (cs.LG) research dissemination. - Includes citation tools: NASA ADS, Google Scholar, Semantic Scholar, BibTeX export. - Provides exploratory resources: Bibliographic Explorer, Connected Papers, Litmaps, scite Smart Citations. - Features code and data resources: alphaXiv, DagsHub, Hugging Face; demo platforms like Replicate and ScienceCast; recommendation systems CORE and IArxiv Recommender. - Highlights arXivLabs for community-driven feature development based on openness, engagement, excellence, and privacy. - Information about interacting with the arXiv platform: - Contact options, mailing list subscriptions, access to privacy policies. - MathJax tool for displaying mathematical notation, can be disabled. - Resources for web accessibility assistance and operational status updates via email or Slack. - Inquiry options regarding paper endorsers. Keywords: BibTeX, DeepSeek v31, GPT-OSS, Google Scholar, K2-Think, Litmaps, NASA ADS, Qwen25, Replicate, Semantic Scholar, agentic planning, arXivLabs, benchmarks, bibliographic tools, chain-of-thought, computation techniques, hardware optimization, inference-time enhancements, mathematical reasoning, model parameters, open-source datasets, performance, reasoning system, reinforcement learning, scite, speculative decoding, tokens per second
gpt-oss
arxiv.org a day ago
https://venturebeat.com/ai/k2-think-arrives-from-uae-as a day ago https://www.sri.inf.ethz.ch/blog/k2think a day ago |
111. HN QGIS is a free, open-source, cross platform geographical information system**Summary:** QGIS is a versatile, free, open-source Geographic Information System (GIS) compatible across Unix, Windows, and MacOS platforms. It provides comprehensive spatial data management capabilities by supporting multiple formats such as raster, vector, mesh, and point clouds through local files, databases, and web services. Notable features include on-the-fly reprojection between coordinate reference systems, temporal support, and integration with the Nominatim geocoder. QGIS excels in cartography with extensive 2D and 3D rendering options, offering high-level map styling control comparable to proprietary solutions like ESRI's. Advanced styling is facilitated through data-defined overrides, blending modes, draw effects, and over 500 built-in color ramps. Users can create maps with specified parameters using saved layouts and automate map/report generation via QGIS Atlas and Reports. The software supports various output formats including raster, PDF, SVG, and elevation profiles, with on-the-fly enhancements possible through geometry generators and inclusive design preview modes. Geospatial analysis is robust, featuring over 200 native processing algorithms and access to an additional 1000 via providers like GDAL and GRASS. A powerful geospatial database engine enables immediate visualization of results. Customization options are extensive, with a fully adaptable user interface, a rich expression engine, and plugin support for functions like style management through the QGIS Style Hub. The Style Manager aids in creating, storing, and managing styles, which can be easily shared via the QGIS style hub. Comprehensive scripting capabilities are available through Python and C++ APIs (PyQGIS). The QGIS Server component acts as a headless map server supporting various operating systems and containerized environments like Docker, with industry-standard protocol compatibility for seamless integration. Developed using Qt toolkit and C++, QGIS offers a user-friendly interface with multilingual support since 2002. It maintains an active development schedule with three main branches: Long Term Release (LTR), Latest Release (LR), and Development (Nightly). Regular monthly point releases address bug fixes, ensuring stability and reliability. As open-source software under the GNU Public License (GPL) Version 2 or later, QGIS allows users to inspect and modify its source code. Part of the Open-Source Geospatial Foundation (OSGeo), it provides a comprehensive suite of GIS projects. Users can access precompiled binaries from QGIS.org, with guides available for those interested in building from source. The software community offers extensive help resources through various platforms including the QGIS community site, mailing lists, real-time chat on IRC or Matrix, and forums like GIS Stack Exchange and r/QGIS subreddit. **Bullet Point Summary:** - **Platform Compatibility:** Free, open-source GIS software compatible with Unix, Windows, MacOS. - **Data Management:** Supports multiple spatial data formats (raster, vector, mesh, point clouds) via files, databases, web services. - **Core Features:** Includes on-the-fly reprojection, temporal support, integration with Nominatim geocoder. - **Cartography Excellence:** Extensive 2D and 3D rendering, advanced styling options, comparable to proprietary software like ESRI's. - **Map Creation & Automation:** Utilizes saved layouts, QGIS Atlas, Reports for map/report generation; supports various output formats. - **Geospatial Analysis:** Over 200 native algorithms, access to additional 1000 via GDAL, GRASS; powerful visualization through geospatial database engine. - **Customization and Extensions:** Adaptable UI, rich expression engine, plugins available; style management via QGIS Style Hub. - **Style Management & Scripting:** Style Manager for creation and sharing of styles, scripting via Python (PyQGIS) and C++ APIs. - **QGIS Server:** Headless map server supporting industry-standard protocols, compatible with various OS and environments like Docker. - **Development & Community Support:** Developed using Qt toolkit and C++, multilingual interface; maintained by active developer team, community support through various channels. - **Software Licensing & Contribution:** Open-source under GPL Version 2 or later, part of OSGeo; users can contribute to project development. Keywords: FOSS, GIS, OGC API, Open-Source Geospatial Foundation, PostGIS, QGIS, WFS, WMS, plugins, raster, reprojection, spatial data, style manager, styling, symbology, vector
popular
github.com a day ago
https://github.com/cxcandid/GeorefExtension an hour ago https://grass.osgeo.org an hour ago https://docs.qgis.org/3.40/en/docs/user_manua an hour ago https://landsat.gsfc.nasa.gov/ an hour ago https://github.com/PDOK an hour ago https://guides.library.columbia.edu/geotools/R an hour ago https://www.osgeo.org/projects/proj/ an hour ago https://mapshaper.org/ an hour ago https://createaclickablemap.com/ an hour ago https://kepler.gl an hour ago https://protomaps.com an hour ago https://github.com/maplibre/maplibre-gl-js an hour ago https://www.youtube.com/@giswqs/videos an hour ago https://x.com/giswqs an hour ago https://duckdb.org/docs/stable/core_extensions an hour ago https://postgis.net/docs/manual-3.5/postgis_cheats an hour ago https://sedona.apache.org/latest/ an hour ago https://geoparquet.org/releases/v1.0.0/ an hour ago https://docs.overturemaps.org/getting-data/duckdb/ an hour ago https://www.openstreetmap.org/ an hour ago https://hub.arcgis.com/search an hour ago https://qgis.org/funding/ an hour ago https://www.lizmap.com/en/ an hour ago https://docs.mapstore.geosolutionsgroup.com/en/latest&# an hour ago https://mundi.ai/ an hour ago https://github.com/jjsantos01/qgis_mcp/ an hour ago https://solentmaps.uk an hour ago https://tech.marksblogg.com/ an hour ago https://github.com/qgis/QGIS/issues/46299 an hour ago https://geo.malagis.com/native-qgis-on-apple-silicon-solutio an hour ago https://docs.qgis.org/3.40/en/docs/user_manua an hour ago https://docs.qgis.org/3.40/en/docs/training_m an hour ago https://locatepress.com/books an hour ago |
112. HN Improving Cursor Tab with RLCursor's Tab feature, part of Cursor's suite aimed at boosting developer productivity, employs a model that anticipates code actions based on user input and cursor movements within an editor. Handling over 400 million requests daily, it leverages online reinforcement learning with real-time data from frequent rollouts to refine its suggestions continuously. This dynamic approach diverges from traditional static datasets or periodic updates, allowing for ongoing enhancements. The effectiveness of Tab's model is gauged by the accept rate of its suggestions; a low rate implies an excess of incorrect suggestions disrupting developers' workflow. Achieving a higher acceptance involves not just improving intelligence but also identifying optimal suggestion timings. Challenges arise in scenarios where there is inadequate information about user actions, making timely suggestions inappropriate despite accurate model predictions. To boost accept rates, a method introduced by Parth Thakkar in 2022 for GitHub Copilot used logistic regression to train a predictive model. This model calculates a "contextual filter score" using features like programming language and cursor context, filtering out suggestions below a 15% threshold. While effective, the aim is to embed this capability directly within Tab through policy gradient techniques. Policy gradients enhance suggestion quality by optimizing actions based on rewards, encouraging strategies that yield higher acceptance rates. The model's reward system assigns values—0.75 for accepted suggestions, -0.25 for rejected ones, and 0 when no suggestion is made—to promote suggestions with at least a 25% chance of being accepted. The implementation of policy gradients involves the Policy Gradient Theorem to update the model by associating actions' likelihoods with their outcomes, using on-policy data generated from current models. This method enables learning a policy aimed at a specific accept rate without explicitly modeling it and employs stochastic gradient descent to estimate an unbiased performance function gradient. Cursor's new Tab model demonstrates improved efficiency, showing 21% fewer suggestions with a 28% higher acceptance rate compared to its predecessor. Despite rollout times between 1.5 to 2 hours—faster than many industry standards—further improvements are planned to shorten this timeframe and enhance user experience further. **BULLET POINT SUMMARY:** - Cursor's Tab feature enhances productivity by predicting code actions based on cursor movement, processing over 400 million requests daily. - The model utilizes online reinforcement learning for continuous refinement rather than relying on static datasets or periodic updates. - A high accept rate of suggestions is crucial; low rates indicate disruptive incorrect suggestions. - Challenges include making appropriate suggestions with limited user action information. - Parth Thakkar's logistic regression method improves acceptance by calculating a "contextual filter score," filtering out lower-probability suggestions. - Policy gradients are being integrated into Tab to enhance suggestion quality directly, optimizing actions based on rewards. - The reward system assigns values to accepted, rejected, and non-suggested actions to encourage high-quality suggestions with at least a 25% chance of acceptance. - Policy Gradient Theorem is used to update the model by correlating action likelihoods with outcomes using on-policy data. - Stochastic gradient descent estimates an unbiased performance function gradient for policy improvement. - Cursor's new Tab model shows improved efficiency with fewer and more accepted suggestions, though further reductions in rollout times are planned. Keywords: AI Industry, Accept Rate, Coding Flow, Cursor Tab, Deployment, Infrastructure, LLM Providers, Logistic Regression, Model Prediction, Noisy Suggestions, On-policy Data, Online Training, Optimizer, Policy Gradient, PyTorch, Reinforcement Learning, Reward Optimization, Suggestions, User Action
github copilot
cursor.com a day ago
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113. HN Pgschema – Declarative schema migration for Postgres**Summary:** Pgschema is a command-line interface (CLI) tool designed to streamline declarative schema migrations specifically for PostgreSQL databases, drawing inspiration from Terraform's methodology but customizing it for Postgres usage. This tool empowers users to define their desired database schemas using SQL files, simplifying change management through an intuitive and developer-friendly interface that accommodates all common PostgreSQL objects. A standout feature of Pgschema is its declarative workflow, which allows users to specify the intended schema state with ease, enhancing manageability. Additionally, it performs schema-based comparisons at the object level rather than the entire database, facilitating reconciliation across different tenants. Pgschema supports a broad array of Postgres objects under any given schema and ensures transparency by allowing users to preview the SQL commands that will be executed prior to actual application changes. This tool surpasses traditional migration techniques by negating the need for manual script creation or dependency on ORM constraints, offering features such as plan generation, change previews, and confident execution with concurrent change detection, transaction-adaptive execution, and lock timeout management. **Bullet Point Summary:** - Pgschema is a CLI tool tailored for declarative schema migrations in PostgreSQL, inspired by Terraform. - Users define desired database schemas using SQL files, facilitating easier schema management. - Offers a developer-friendly interface that supports common Postgres objects under any schema. - Provides a declarative workflow to specify the intended state of the schema. - Performs object-level comparisons rather than entire database-level comparisons, aiding reconciliation across tenants. - Allows users to preview SQL commands before executing changes for transparency. - Enhances migration methods by avoiding manual script writing and ORM limitations. - Includes features like plan generation, change previews, concurrent change detection, transaction-adaptive execution, and lock timeout control. Keywords: CLI tool, Postgres, SQL execution, SQL files, Terraform, concurrent change detection, database level, declarative, developer-friendly, lock timeout control, pgschema, schema migration, schema state, tenants, transaction-adaptive execution
postgres
www.pgschema.com a day ago
https://github.com/fordfrog/apgdiff a day ago https://github.com/gajus/pg-dump-parser a day ago https://github.com/gajus/pg-dump-parser?tab=readme-ov-f a day ago https://github.com/pgschema/pgschema/blob/97f a day ago https://github.com/pgschema/pgschema/blob/533 a day ago https://github.com/pgschema/pgschema/blob/6f5 a day ago https://x.com/mitchellh/status/1964785527741427940 a day ago |
114. HN Get Excited About Postgres 18- **Postgres 18 Updates**: The upcoming release of PostgreSQL 18 introduces several key performance improvements and features. Among these is the introduction of asynchronous I/O to enhance read operation efficiency by reducing bottlenecks through batched reads, optimizing idle worker time, and improving throughput. This feature primarily benefits sequential scans, bitmap heap scans following bitmap index scans, and maintenance tasks like VACUUM while keeping writes synchronous for ACID compliance. - **Asynchronous I/O Details**: The asynchronous I/O system is managed by workers that can be adjusted in number based on CPU size, defaulting to 3. For Linux 5.1+ systems, `io_uring` optimizes I/O operations via backends instead of separate processes. A new system view, `pg_aios`, provides insights into asynchronous I/O activities. - **UUID Version 7 Update**: PostgreSQL is updating its UUID implementation from version 4 to version 7 to address performance issues with random UUIDv4. The new UUIDv7 format includes a timestamp for improved sorting and indexing capabilities, reducing fragmentation and locality problems, while maintaining global uniqueness. - **DDL Structure of UUID v7**: A sample DDL structure demonstrates how a `CREATE TABLE` statement can incorporate UUID version 7 as the primary key, exemplified by a table named `user_actions`. - **Multi-Column Index Performance Enhancements**: PostgreSQL 18 introduces "skip scan" for multi-column B-tree indexes. This optimization allows queries that do not use all indexed columns to benefit from index usage under specific conditions such as low cardinality of omitted leading column(s) and the absence of query reliance on these columns. - **Skip Scan Mechanism Explained**: The skip scan works by transforming queries to include distinct values of the omitted leading column(s) as conditions, enabling optimized lookups that effectively "skip" non-matching pages in indexes. An example involves a `sales` table with an index on `(status, date)` allowing efficient querying based on `date`. - **Virtual Generated Columns**: PostgreSQL 18 now includes virtual generated columns by default, enhancing efficiency for JSON data operations. These are computed during queries without physical storage, simplifying query structures and enabling dynamic data normalization. - **JSON Data Enhancements**: The release simplifies working with JSON data by allowing real-time changes without the overhead of stored generated columns, though they remain non-indexable. Instead, stored versions or expression indexes should be used for indexing JSONB data. - **OAuth 2.0 Authentication Support**: PostgreSQL 18 supports OAuth 2.0 authentication, facilitating integration with services like Okta and Keycloak through bearer tokens configured in `pg_hba.conf`. This enhances security by avoiding database-stored passwords and supporting multi-factor authentication and single sign-on capabilities. - **Release Overview**: The release includes over 3,000 commits from more than 200 contributors, emphasizing not only new features but also performance improvements, bug fixes, and security enhancements such as asynchronous I/O. Regular upgrades are recommended for users to benefit from these advancements. Keywords: ACID compliance, JSONB, Keycloak, MFA, OAuth 20, Okta, Postgres, SSO, UUID v7, VACUUM maintenance, analytics, asynchronous I/O, bitmap heap scans, compound index, disk reads, indexes, multi-threading, performance improvements, pg_aios view, posix_fadvise, sequential scans, shared memory buffers, skip scan, virtual generated columns
postgres
www.crunchydata.com a day ago
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115. HN VaultGemma: The most capable differentially private LLMVaultGemma is a differentially private large language model (LLM) developed on the Gemma models framework, focusing on responsible and safe AI practices. The team utilized scaling laws to determine the necessary computational resources for training a 1 billion parameter model under differential privacy conditions. One significant advancement in VaultGemma's development was the implementation of Poisson sampling within the DP-SGD (Differential Privacy Stochastic Gradient Descent) framework. This adaptation helped address issues related to varying batch sizes and data order, enabling the use of Scalable DP-SGD techniques. By employing fixed-size batches—either padded or trimmed—VaultGemma achieves strong privacy assurances without compromising on training efficiency, ensuring effective model utility with minimal noise interference. **BULLET POINT SUMMARY:** - VaultGemma is a differentially private LLM built from Gemma models. - Focuses on responsibility and safety in AI development. - Utilized scaling laws to optimize compute resources for training a 1 billion parameter model. - Implemented Poisson sampling within DP-SGD to address batch size variability and data order issues. - Adopts Scalable DP-SGD techniques, ensuring strong privacy with fixed-size batches (padded or trimmed). - Achieves effective utility while maintaining minimal noise interference in the model. Keywords: DP-SGD, DP-trained model, Gemma models, Poisson sampling, Scalable DP-SGD, VaultGemma, compute-optimal model, differentially private LLM, fixed-size batches, privacy guarantees, responsibility and safety, scaling laws, sequence length
llm
research.google a day ago
https://huggingface.co/google/vaultgemma-1b a day ago https://lifearchitect.ai/models-table/ a day ago https://ollama.com/library/gemma3 a day ago |
116. HN China bans one-pedal driving in default modes by 2027### Summary The Ministry of Industry and Information Technology has updated the national standard "Technical Requirements and Test Methods for Passenger Car Brake Systems" (GB 21670-2025), which will take effect on January 1, 2027. This revision mandates that releasing the accelerator pedal should not result in a vehicle coming to a complete stop under default conditions, thereby restricting one-pedal driving mode—a feature popularized by electric vehicles like Tesla that allows stopping without using the brake pedal. The update aims to address safety concerns associated with this mode by introducing two types of braking: Type A (activated by releasing the accelerator or putting the car in neutral) and Type B (activated by the brake pedal). While the one-pedal mode is not entirely banned, its default operation will be limited, allowing users to opt for stronger kinetic energy recovery through manual adjustments. The document discusses both the advantages of the one-pedal mode—such as improved mileage and reduced wear on mechanical brakes—and the significant safety risks it poses. The latter includes confusion in emergency situations where drivers might mistakenly press the accelerator instead of braking, leading to accidents. These concerns have been backed by research from Tsinghua University showing slower reaction times for drivers accustomed to this driving style. Additionally, Tesla's recall in China due to issues with energy recovery braking and insufficient warnings highlights ongoing safety challenges. To enhance road safety, new regulations require brake lights to activate during kinetic energy recovery braking exceeding 1.3m/s², which is expected to reduce rear-end collisions significantly by improving reaction times for following drivers. The standard also mandates the activation of emergency brake signals at decelerations of ≥6m/s² and their deactivation below 2.5m/s². Furthermore, the installation of ABS (Anti-lock Braking System) in all new passenger vehicle models is now a requirement to raise safety standards. As of 2024, China has achieved a 92% ABS installation rate for passenger vehicles, though some entry-level models priced below 80,000 yuan still lack this feature. ### Bullet Point Summary - **Update on National Standard**: Revision of the "Technical Requirements and Test Methods for Passenger Car Brake Systems" (GB 21670-2025) mandates that releasing the accelerator should not stop a vehicle under default conditions by January 1, 2027. - **Braking System Requirements**: Introduction of two types of braking: Type A (via accelerator release or neutral mode) and Type B (via brake pedal), with restricted one-pedal mode operation. - **Safety Concerns and Benefits**: One-pedal mode increases efficiency but poses safety risks, including driver confusion in emergencies. Research indicates longer reaction times for drivers used to this feature. - **Regulatory Measures**: Brake lights must activate during regenerative braking exceeding 1.3m/s² to prevent rear-end collisions. Emergency brake signals are set at ≥6m/s² deceleration activation and deactivated below 2.5m/s². - **ABS Mandate**: Installation of ABS in all new passenger vehicles is now required, raising safety standards with a current installation rate of 92% as of 2024; some entry-level models still lack ABS. - **Future Considerations**: Uncertainty remains about whether low-speed electric vehicles will be mandated to install ABS in the future. Keywords: ABS, BMW i3, Chevrolet Bolt, China, GB 21670-2025, Nissan LEAF, Tesla, Tsinghua University, ban, brake lights, deceleration, emergency, enhancement, kinetic energy recovery, national standard, one-pedal driving, passenger car brake systems, reaction time, rear-end collisions, regenerative braking, safety hazard
tesla
www.asiaict.com a day ago
https://en.wikipedia.org/wiki/Compression_release_engin a day ago https://youtu.be/z3bLqjPBlx8 a day ago https://cleantechnica.com/2019/11/02/tesla-on a day ago |
117. HN Nvidia and OpenAI to back major investment in UK AI infrastructureThe provided text reports on ongoing negotiations between Nvidia and OpenAI, who are reportedly considering a substantial investment in the UK's artificial intelligence infrastructure. This proposed collaboration aims to focus significantly on developing data center capabilities with an estimated value running into billions of dollars. The discussions involve a partnership with cloud computing firm Nscale, although details remain unresolved at this stage. An official announcement regarding the investment agreement is anticipated during U.S. President Donald Trump's upcoming state visit to the U.K. Despite these developments, neither Nvidia, Nscale, nor OpenAI has provided any official statements confirming or elaborating on these discussions. - Nvidia and OpenAI are reportedly in talks about a significant investment in UK AI infrastructure. - The focus of this potential collaboration is on developing data centers valued at billions of dollars. - Cloud computing firm Nscale is involved in the negotiations, though details are not yet finalized. - An official announcement is expected during U.S. President Donald Trump's upcoming state visit to the U.K. - No official comments have been made by Nvidia, Nscale, or OpenAI regarding these discussions as of now. Keywords: CNBC, Financial Times, Jensen Huang, London Tech Week, Nscale, Nvidia, OpenAI, UK AI infrastructure, US President Donald Trump, agreement, billions of dollars, cloud computing, data center development, discussions, investment, state visit, tech firms
openai
www.cnbc.com a day ago
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118. HN Build, run and debug iOS and Mac apps in Zed instead of Xcode**Concise Summary** The article discusses a new capability within Zed editor that allows building, running, and debugging iOS and Mac apps without needing Xcode, using newly developed tools to manage the full development cycle on Apple platforms. While Zed does not support SwiftUI previews, requiring occasional use of Xcode, it offers enhanced coding experiences over alternatives like AppCode, VSCode, or Fleet. The author shares personal insights from developing DelayDrop, an app for transferring files between Apple devices globally without needing them to be unlocked. For setting up and using Zed with Swift projects, users should: 1. Open a project in Zed and install the Swift extension. 2. Enhance code understanding by installing `xcode-build-server`. 3. Configure build settings via command line before building in Xcode. 4. Restart Zed after building to generate necessary files. The document further introduces `xcede`, a tool for building, running, and debugging projects on simulators or real devices with xcodebuild commands. It supports creating tasks in Zed using the `open project tasks` command to define specific configurations like device type. To manage build outputs, `xcbeautify` can be installed and configured as an environment variable. Users can set up global tasks within a `.xcrc` file, allowing easy configuration changes via commented lines for different devices or schemes. Keyboard shortcuts can automate repetitive actions like builds using Zed's command palette. For improved performance: 1. Alter the `/etc/hosts` file to speed up `xcodebuild`. 2. Utilize additional flags to optimize build processes. The document also highlights limitations of "xcede" in running tests, recommending custom tasks with `xcodebuild`. For debugging, `xcede-dap` integrates with IDEs for merging console and debugger outputs, surpassing `lldb`'s capabilities by automating application launches. Debugging can be done via global or project-specific debug tasks set up through Zed commands. Finally, the author acknowledges current limitations of "xcede," particularly in test execution, suggesting potential improvements and encouraging community feedback on both xcede and Zed platforms. **Bullet Point Summary:** - **Zed Editor**: Enables full development cycle for iOS/Mac apps without Xcode except for SwiftUI previews. - **Setup Guide**: Steps include opening projects in Zed, installing Swift extension, enhancing code navigation with `xcode-build-server`, configuring build settings, and restarting Zed post-build. - **Xcede Tool**: Facilitates building, running, debugging on simulators/real devices using xcodebuild commands; supports task creation for specific configurations in Zed. - **Build Output Management**: Use `xcbeautify` to improve readability of Xcode build outputs via environment variable configuration. - **Global Tasks Configuration**: Set up global tasks in a `.xcrc` file, allowing easy changes with commented lines and keyboard shortcuts for automation. - **Performance Optimization**: Modify `/etc/hosts` and use additional xcodebuild flags to enhance performance; specifically useful when using `xcodebuild`. - **Testing Limitations**: No built-in test support in "xcede"; users should create custom tasks with xcodebuild, potentially enhancing output with `xcbeautify`. - **Debugging Tools**: Introduce `xcede-dap` for streamlined debugging by merging console and debugger outputs, requiring IDE configuration; supports both global and project-specific debug tasks. - **Community Engagement**: Author invites feedback on the current limitations of "xcede" and potential improvements via Zed's GitHub or xcede's repository issues. Keywords: AppCode, DelayDrop, Fleet, GitHub, Mac apps, Swift, SwiftUI previews, VSCode, Xcode, Zed, buildrun, code completion, debugging, iOS, lldb, performance, simulator, swift package, tasks, testing, xcbeautify, xcede
github
luxmentis.org a day ago
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119. HN As We May Think No More: From Bush's Memex to AI Alignment- **Vannevar Bush's Vision**: In 1945, Vannevar Bush wrote "As We May Think," addressing information overload amidst post-WWII advancements. He proposed the memex—a device to store and manage information via associative trails—to enhance human memory and adapt knowledge management to the complexity of scientific growth. - **Evolution of AI and Hinton's Role**: Eighty years later, Geoffrey Hinton advanced artificial intelligence through neural networks, contributing significantly to deep learning in the 2010s. By spring 2023, he observed unexpected capabilities in models like Google's PaLM, indicating emergent properties akin to human reasoning. - **AI's Progress and Challenges**: Despite AI's potential superiority over human brains in data processing, current systems have diverged from Bush’s vision by creating complex information networks that can overwhelm users. These issues highlight the need for transparent knowledge management tools that align with human associative thinking. - **Realization and Misalignment of Bush's Vision**: The World Wide Web, inspired by the memex concept, materialized globally but also contributed to chaotic information overload rather than solving it. Meanwhile, Hinton’s "Connectionism" approach allowed machines to independently form connections through neural networks, presenting both opportunities and existential risks. - **AI and Information Authenticity**: By 2024, generative AI models created large-scale fake content, complicating truth verification across platforms like LinkedIn and news sites. This challenges the notion of shared reality, making it difficult for people to discern AI-generated falsehoods from human ones. - **Alignment Problem in AI Development**: A central concern is aligning AI systems with human values amidst their superior learning capabilities. Historical parallels are drawn to myths about uncontrollable creations, highlighting fears that AI might exceed human control and prioritize non-human objectives over human welfare. - **Historical Parallels and Modern Challenges**: The text draws parallels between the nuclear arms race post-WWII and today's AI development, emphasizing rapid corporate-driven advancements with insufficient safety measures. Prominent researchers warn of potential existential threats from unchecked AI capabilities. - **Current AI Governance Issues**: Unlike nuclear governance, which eventually stabilized through treaties, current AI regulation is fragmented and inadequate. The dual nature of AI—potentially augmenting or threatening humanity—underscores the urgent need for effective governance to ensure technology enhances human understanding. - **Essay Creation and Philosophical Alignment**: The essay was crafted using AI assistance in research, writing, and editing, with acknowledgment of human collaboration. It aligns with Bush's vision by using AI as a tool to augment human creativity rather than replace it, emphasizing the importance of integrating technology thoughtfully into human endeavors. This summary captures the essence of the text, focusing on key ideas around Vannevar Bush’s memex concept, the evolution and challenges of AI, and the alignment problem, while also drawing parallels with historical technological advancements. Keywords: AI Alignment, GPT-4, Memex, Vannevar Bush, World War II, associative trails, deep learning, disinformation, existential risk, hypertext, microfilm, neural networks
gpt-4
memoryleak.substack.com a day ago
https://adventofcomputing.com/ a day ago |
120. HN Python Is Dying and Nobody Wants to Admit ItThe article by Devrim Ozcay presents an argument regarding the perceived decline in Python, despite its widespread popularity. Although statistics suggest growth in Python’s usage and adoption rates, Ozcay argues this is misleading due to high churn rates among users. The author points out that many newcomers learn Python primarily for specific courses like data science but often fail to develop sustained proficiency or expertise. This transient engagement results in a community with fewer experienced developers, raising concerns about the language's long-term sustainability and future prominence. Ozcay implies these trends suggest a "slow-motion collapse" of Python’s status as a leading programming language. **BULLET POINT SUMMARY:** - Devrim Ozcay argues that Python is experiencing a decline despite its popularity. - Statistics show growth in usage, but this is misleading due to high user churn rates. - Many users learn Python for specific courses and lack long-term proficiency. - This results in a community with fewer experienced developers. - Raises concerns about the sustainability and future popularity of Python. - Implies a "slow-motion collapse" of Python's status as a leading language. Keywords: Adoption, Beginners, Blog, Churn, Collapse, Community, Data Science, Development, Dying, Ecosystem, Experienced Developers, GitHub, Growth, PyCon, Python, Revolving Door, Statistics, TIOBE Index, Technical Keywords, User Base
github
medium.com a day ago
https://archive.is/0l55h a day ago https://api.elevenlabs.io a day ago https://medium.com/codeelevation/python-is-dying-and-no a day ago https://javascript.plainenglish.io/react-is-dying-and-nobody a day ago https://javascript.plainenglish.io/php-dead-and-nobody-wants a day ago https://medium.com/javarevisited/java-is-dying-and-nobo a day ago https://javascript.plainenglish.io/javascript-is-dying-and-n a day ago |
121. HN Blurring interfaces that redirects user's attention with LLMThe document explores the integration of Large Language Models (LLMs) in blurring user interfaces to redirect attention, potentially authored as part of an academic profile or research summary under "Your Name." It highlights how these advanced technologies influence user interaction and focus by seamlessly incorporating LLMs into digital experiences. The primary focus is on understanding how such integrated interfaces may alter engagement with digital content, suggesting a shift in the dynamics of user interaction. - **Key Points:** - The document discusses using Large Language Models (LLMs) to blur user interfaces. - Focuses on redirecting user attention through these technologies. - Part of an academic profile or research summary by "Your Name." - Emphasizes how LLMs influence user interaction and focus. - Highlights seamless integration of LLMs into digital experiences. - Explores potential changes in how users engage with digital content. Keywords: Academic Profile, Blurring, LLM (Large Language Model), Loading, Your Name, interfaces, redirects, user's attention
llm
alejogb1.vercel.app a day ago
https://sci-hub.se/https://dl.acm.org/doi a day ago https://arxiv.org/pdf/2109.01980v1 a day ago https://github.com/paco1127/Pure-Blur a day ago https://arxiv.org/pdf/2507.14769 a day ago https://github.com/nanobrowser/nanobrowser/tree&#x a day ago |
122. HN Many hard LeetCode problems are easy constraint problems### Summary: The text explores the efficiency of using constraint solvers like MiniZinc, Z3, or OR-Tools for solving complex mathematical optimization problems typically encountered in interviews and competitive programming challenges. It emphasizes that many LeetCode problems are challenging not due to their algorithmic complexity but because of intricate constraints. For example, a common change-making problem is best approached with a constraint solver rather than a greedy algorithm or dynamic programming, as it can define variables and constraints to minimize the number of coins effectively. The text provides examples where traditional programming methods struggle, such as maximizing profit from stock prices and determining if any three numbers in a list can sum to zero. Constraint solvers simplify these tasks by leveraging their high-level abstraction and optimization capabilities. During a Chipy AlgoSIG event, participants faced challenges like finding the largest rectangle in a histogram; one participant found success using a constraint-solving approach despite its generally slower performance due to expressiveness. The author acknowledges that while constraint solvers may have unpredictable runtimes compared to bespoke algorithms, they excel in easily accommodating new constraints. This advantage becomes clear with complex problems like stock trading, where additional conditions (e.g., buy/sell limits) complicate algorithm design. Constraint-solving methods handle these complexities gracefully, requiring minimal adjustments. The article also touches on the educational value of applying constraint solvers to real-world problems over simpler puzzles, highlighting teaching opportunities for optimizations such as symmetry breaking. It concludes by noting its inclusion in a software history newsletter and promotes the author's book "Logic for Programmers." ### Bullet Point Summary: - **Constraint Solvers**: The text highlights the effectiveness of using constraint solvers like MiniZinc, Z3, or OR-Tools to solve complex optimization problems more efficiently than traditional programming approaches. - **Examples**: Examples provided include maximizing stock profit and determining sums of three numbers in a list, demonstrating how constraint solvers simplify these tasks. - **Event Challenge**: During the Chipy AlgoSIG event, participants found that using constraint-solving methods could be advantageous for complex problems like finding the largest rectangle in a histogram, despite generally slower performance. - **Performance Trade-offs**: While constraint solvers may have unpredictable runtimes compared to bespoke algorithms, they are superior in handling new constraints easily, as illustrated with stock trading examples. - **Educational Value**: The text suggests that applying constraint-solving methods to real-world problems offers more educational value than solving puzzles like Sudoku, providing opportunities for teaching optimizations. - **Additional Information**: It mentions the article's inclusion in a software history newsletter and promotes the author's book "Logic for Programmers." Keywords: Chipy AlgoSIG, LeetCode, MiniZinc, OR-Tools, Sudoku, Z3, algorithmic efficiency, capability/tractability tradeoff, change counter problem, coin denominations, constraint solver, constraints, dynamic programming, formal methods, greedy algorithm, hard problems, histogram, integer programming, interview, maximum profit, optimization, profits, rectangle area, runtime complexity, sales, satisfaction problem, stock picking problem, stock prices, stocks, visualization
popular
buttondown.com a day ago
https://github.com/ensisoft/detonator a day ago https://www.finalroundai.com/coding-copilot a day ago https://monumental.co a day ago https://github.com/rublev/monumental a day ago https://github.com/jonnycoder1/merck_coding_challenge a day ago https://i.imgur.com/HGL5g8t.png a day ago https://i.imgur.com/aaiy7QR.png a day ago https://www.reddit.com/r/leetcode/comments/1m a day ago https://www.reddit.com/r/leetcode/comments/1i a day ago https://aphyr.com/posts/340-reversing-the-technical-int a day ago https://accidentallyquadratic.tumblr.com/ a day ago https://www.hakank.org/minizinc/ a day ago https://mathstodon.xyz/@j2kun/108975072813565989 a day ago https://www.doctorofcredit.com/high-interest-savings-to-get& a day ago https://developers.google.com/optimization/lp/stig a day ago https://quantumprolog.sgml.net/browser-demo/browser-dem a day ago https://news.ycombinator.com/item?id=45205030 a day ago https://pierre-flener.github.io/research/NordConsNet&#x a day ago https://codeforces.com/problemset/problem/1889 a day ago https://socialengineering.fm/episodes/the-problem-with- a day ago https://chatgpt.com/share/68c46d0b-8858-8004-aa03-f7ce3 a day ago |
123. HN Show HN: Open-source self-tracking app to better understand my lifePerfice is an open-source self-tracking application designed for personal insight into various aspects of one's life, such as mood, energy, well-being, productivity patterns, and health metrics. Unlike traditional tracking solutions that are often rigid and specialized, Perfice offers a customizable experience with flexible forms tailored to different data input types. It integrates seamlessly with third-party services like Fitbit, Todoist, and Weather to minimize manual data entry. The app provides users with valuable analytics by identifying correlations in their tracked data, such as mood improvements linked to increased activity levels. These insights are presented through historical charts, while a personalized dashboard equipped with customizable widgets offers an at-a-glance view of life metrics. All user data is stored locally on the device unless synchronization or third-party integrations are enabled, prioritizing privacy and control. Built using modern web technologies like Svelte 5, TypeScript, TailwindCSS, and IndexedDB for local storage, Perfice can be accessed via its production site, Android application on Google Play Store, and through native apps developed with Capacitor. The platform requires a backend setup for functionalities such as user accounts and data synchronization, but users have the flexibility to configure this locally. Perfice is open-source under the MIT license, inviting community contributions and innovations. It supports exporting data in CSV and JSON formats and encourages feedback from its user base to enhance functionality. Developers interested in contributing can do so through standard Node.js project setup processes, with options for running in development mode or building for production using Docker. Key Points: - Perfice is an open-source self-tracking app focusing on customizable personal insights. - It offers flexibility with customizable forms and supports third-party integrations to reduce manual data entry. - The application provides analytics by correlating user data and presents these through charts and dashboards. - Data storage prioritizes privacy, keeping information local unless syncing or integrating is enabled. - Built with Svelte 5, TypeScript, TailwindCSS, IndexedDB; accessible via web and Android platforms. - Requires backend setup for full functionality but allows local configuration of the service URL. - Open-source under MIT license, encouraging community contributions and innovation. - Supports data export in CSV and JSON formats and invites user feedback for app improvement. Keywords: Android, Capacitor, Docker, Fitbit, GitHub, IndexedDB, MIT license, Open-source, Perfice, Svelte, TailwindCSS, TypeScript, analytics, app, backend, customization, dashboard, encryption, energy, integrations, local-first, metrics, mood, platform, privacy, self-tracking, synchronization, trackables, well-being
github
github.com a day ago
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124. HN Show HN: Let the internet control your framed e-ink displayThe "Ink Day" project is designed to create a personalized e-ink display setup, featuring a minimalistic 3D-printable frame for a 7.5" WaveShare e-ink screen. It includes a website that allows users to control the daily content displayed on this screen by uploading images through a calendar interface. Each image corresponds to a specific day of the month, and the creator has shared their implementation on Reddit using images uploaded by strangers. To build your own frame, you need several components: a 7.5" WaveShare e-ink display, a Raspberry Pi 3B+ equipped with power and WiFi capabilities, access to a 3D printer, and a server for hosting the website. The project also specifies four particular screws for assembly, with instructions and code accessible on GitHub. The setup involves using Docker to create a server that stores and manages images, resizing them to fit an 800x480 pixel frame. This is achieved by running a specific Docker command. To connect the Raspberry Pi to this server, you must set up a directory containing certain files from the downloaded repository, configure an environment variable with your server's URL, and execute a Python script that fetches images for display on the e-ink screen. The guide also details how to print and assemble the frame using a provided STL file. The assembly involves inserting the e-ink screen into the printed frame, connecting it to the driver, securing the Raspberry Pi with screws, and powering up the system. This project illustrates personalizing daily digital experiences through contributions from an online community. --- **BULLET POINT SUMMARY:** - **Project Overview**: "Ink Day" allows users to control a 7.5" e-ink display using a minimalistic 3D-printable frame and a customizable website. - **Components Required**: - 7.5" WaveShare e-ink display - Raspberry Pi 3B+ with power/WiFi - 3D printer - Server for hosting the website - Four specific screws for assembly - **Setup Process**: - Use Docker to set up a server: `docker run -d -p 80:1313 -v /your-indek-day-data:/data ghcr.io/jflessau/inkday:latest`. - Connect Raspberry Pi by creating a directory, placing necessary files (`frame.py`, `default.jpg`, and contents of the downloaded repo), setting environment variable `SERVER_URL`, and running `python3 frame.py`. - **Printing and Assembly**: - Print `./frame/inkday-frame.stl`. - Assemble by inserting the screen into the frame, connecting the cable to the driver, attaching Raspberry Pi with screws, and powering up. Keywords: 3D printer, 3D-printable, Docker, E-ink, GitHub, Python, Raspberry Pi 3B+, Reddit, URL, WaveShare, cable, driver, environment variable, frame, hardware requirements, image upload, slide screen
github
github.com a day ago
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125. HN GitHub/spec-kit: Toolkit to help you get started with Spec-Driven Development- **Introduction to Spec-Kit**: Spec-Kit is a toolkit designed to facilitate Spec-Driven Development (SDD), shifting from traditional code-first approaches to scenario-based product development. In SDD, specifications are executable and directly drive implementation rather than merely guiding coding efforts. - **Toolkit Features**: The toolkit aids in project initialization with commands such as `/specify` for describing desired features, `/plan` for selecting tech stack and architecture, and `/tasks` for creating actionable lists leading to feature implementations. Users can bootstrap projects using the Specify CLI with commands like `specify init - **Project Initialization**: The process emphasizes clear, scenario-focused development steps to streamline project setup and execution in an SDD environment. Users can navigate to their project folder and run commands like `specify init - **Development of "Taskify"**: The goal is to develop "Taskify," a team productivity platform focusing on creating and managing projects in a Kanban style without requiring user authentication. Initially named "Create Taskify," it supports five predefined users, three sample projects with standard Kanban columns, task creation, assignment, commenting, status updates, and visibility enhancements for tasks assigned to the current user. - **Specification Process**: Upon entering a specific prompt, Claude Code starts planning and drafting specifications, triggering scripts to prepare the repository. This results in creating a new branch and a specification file containing user stories and requirements based on a predefined template. - **Project Organization**: The project folder is organized with directories for memory, scripts, specs, and templates, holding relevant files like checklists, script files, and templates for plans, specifications, and tasks. - **Refinement of Specifications**: In Step 2, any unclear requirements from the initial specification are clarified by specifying task details and validating the Review & Acceptance Checklist using Claude Code. Step 3 involves generating a detailed plan based on refined specifications, emphasizing iterative refinement with Claude Code. - **Technical Requirements and Planning**: The focus is on detailing technical requirements such as choosing the tech stack (e.g., .NET Aspire and Postgres) and specific project elements like a Blazor server frontend with task boards and real-time updates. The `/plan` command helps generate detailed implementation documents, organizing files into a structured directory tree. - **Research and Implementation Plan**: The document outlines a process for reviewing and enhancing an implementation plan for .NET Aspire, focusing on identifying areas needing further research due to the library's rapid evolution. Key tasks include identification of research areas, updating documentation, creating parallel research tasks, providing guidance and correction, and validating the plan with Claude Code. - **Comprehensive Plan Review**: In Step 4, ensure that the implementation plan is comprehensive by having Claude Code audit it for missing tasks or unclear sequences. After refining, have Claude Code verify the checklist again and request a pull request if GitHub CLI is available. - **Implementation Readiness**: Before implementation, prompt Claude Code to identify any over-engineered components. In Step 5, once ready, instruct Claude Code to implement the solution by following the specified path, ensuring necessary local CLI commands like `dotnet` are installed for successful execution. - **Final Steps and Troubleshooting**: Ensure local CLI commands like `dotnet` are installed on your machine. After completing the implementation, use Claude Code to run the application and address any build errors. For runtime errors not visible through CLI logs, copy them into Claude Code for troubleshooting assistance. Keywords: AI agent, Blazor Server, Claude Code, Gemini CLI, GitHub, GitHub CLI, Kanban, NET Aspire, Postgres, REST API, Spec-Driven Development, Specify CLI, Taskify, parallel research tasks, research document, technical keywords, web research
postgres
github.com a day ago
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126. HN Smartpin: AI-Powered Bookmarking for Pinboard**Summary:** Smartpin is a command-line tool designed to enhance bookmark organization on Pinboard by leveraging AI capabilities for automatic metadata extraction. It addresses inconsistencies in manual bookmarking by auto-generating titles, descriptions, and tags using various AI models like Claude, GPT-4, or Gemini. The integration requires only the user’s API token and AI credentials. Its companion tool, "pinit," further streamlines this process through simple commands that fetch and analyze web content for structured bookmarks without manual input. Key features of Smartpin include flexible AI provider options via Simon Willison’s LLM library, privacy controls allowing bookmarks to be marked as private or "to read," a dry run mode for previewing data, JSON output for machine-readable integration, and multiple configuration methods. Installation is straightforward through PyPI using `pip install smartpin`. The tool's architecture utilizes technologies like a CLI framework, Rich for aesthetics, BeautifulSoup for parsing HTML, httpx for fetching web pages, and Jinja2 templates for AI prompting customization. Future enhancements aim to introduce bulk import and re-tagging of bookmarks, browser extensions, and an iOS share extension. Released as open-source software on PyPI and GitHub in June 2025, Smartpin encourages user feedback and contributions. **Bullet Point Summary:** - **Purpose:** Enhances Pinboard bookmark organization using AI for auto-extracting titles, descriptions, and tags. - **AI Models:** Supports multiple models (Claude, GPT-4, Gemini) to cater to different user preferences. - **Tool Integration:** Simplifies integration via API token and AI credentials; requires configuration of API tokens. - **Key Features:** - Flexible AI provider switching with Simon Willison’s LLM library. - Privacy options (`--private`, `--toread`) for bookmarks. - Dry run mode for previewing data before saving (`--dry-run`). - JSON output support for integrations (`--json`). - Configurable via environment variables, .env files, or user config files. - **Installation:** Available through PyPI using `pip install smartpin`. - **Architecture:** Built with CLI framework, Rich, BeautifulSoup, httpx, and Jinja2 templates; employs type hints. - **Future Developments:** Plans include bulk import/re-tagging of bookmarks, browser extensions, and iOS share extension. - **Open Source Release:** Released on PyPI and GitHub as open-source in June 2025, inviting user engagement. Keywords: AI-Powered Bookmarking, API Token, BeautifulSoup, Bulk Import, CLI Framework, Curation, Descriptions, GitHub, Jinja2 Templates, LLMs, Metadata, Modular Architecture, Organization, Pinboard, PyPI, Rich, Smartpin, Tags, Titles, Web Pages, httpx, pinit
github
www.kevfoo.com a day ago
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127. HN Show HN: Consentless – A minimalist, privacy-preserving traffic counter**Summary:** Consentless is an open-source traffic counter designed by Joel Dare that focuses on user privacy by tracking only URL impressions without collecting any personal data such as IP addresses. It aligns with modern privacy regulations like CCPA/CPRA, UCPA, and GDPR without needing explicit user consent. The tool is minimalist in nature, featuring a client component consisting of just four lines of JavaScript and less than 100 lines of server-side code written in Go, replacing the original PHP version due to hardware issues. It functions on any VPS or hosted service and can be integrated as either a standalone tracker or an adjunct to other methods for non-consenting visitors. The implementation process involves adding a simple script tag on web pages to track, with data collection occurring via client-side JavaScript and server-side counting. The server setup requires cloning the repository from GitHub, building, and running a binary that outputs CSV data. This solution is designed to avoid issues such as custom headers, CORS preflight, and CSPs by using image requests for URL tracking. The outputted statistics can be redirected or integrated with other services like pub/sub for further processing, and users have the option to download and analyze these logs using SQLite. Consentless is licensed under the MIT License, emphasizing its open-source nature but also advising users about potential privacy implications when deploying it. Additional instructions include running a SQL query (`select count(*) from c;`) to gather statistics. Users are cautioned against including personally identifiable information (PII) in URLs or links due to risks of unintended logging. **BULLET POINT SUMMARY:** - Consentless is an open-source traffic counter focusing on privacy, developed by Joel Dare. - Tracks only URL impressions without collecting IP addresses or personal data. - Complies with privacy laws like CCPA/CPRA, UCPA, and GDPR without requiring user consent. - Minimalist implementation: four lines of JavaScript client-side code, under 100 lines of server-side Go code. - Designed to work on any VPS or hosted service, used as a sole tracker or alongside other methods. - Involves adding a script tag for tracking, with data collected via client and server components. - Uses image requests to circumvent issues with headers, CORS preflight, and CSPs. - Outputs statistics in CSV format, which can be redirected or integrated with services like pub/sub. - Users can download logs using SCP and analyze them with SQLite directly from CSV files. - Open-source under the MIT License, but advises consideration of privacy implications when deploying. - Provides instructions for executing a SQL query (`select count(*) from c;`) to gather statistics. - Available on GitHub with an emphasis on caution against logging personally identifiable information (PII) unintentionally. Keywords: Analytics, Consentless, Docker, GDPR, GitHub, Go, JavaScript, MIT License, PHP, Privacy, Raspberry Pi, Server Counter, VPS
github
consentless.joeldare.com a day ago
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128. HN Why Most LLM Chatbots Never Make It to Production**Summary:** The article explores why large language model (LLM) chatbots often struggle to transition from development to production, despite their significant potential. The primary challenge is not technical but rather gaining trust among compliance teams, risk officers, and business stakeholders. Many projects fail during the prototype phase due to concerns about reliability and safety, with businesses emphasizing consistency, risk management, and evidence over mere demo performance. To advance past these obstacles, companies need to address critical questions regarding model behavior in crucial scenarios and provide proof of their ability to manage compliance-related queries without errors. A structured governance approach is essential for deploying chatbots successfully rather than relying solely on technical capabilities. For building trust in systems within compliance-heavy industries, establishing measurable benchmarks is vital. These benchmarks should include accurate performance metrics that address potential risks, such as regulatory fines due to errors like a two percent hallucination rate. Benchmarks must reflect real-world queries, including complex and high-stakes questions, not just simple ones. Setting clear targets for accuracy, intent coverage, and user satisfaction ratings is necessary, with these benchmarks shared early to help stakeholders understand deployment tradeoffs. Data sources for benchmarking include internal data from experts, design partner/customer data for relevance, and synthetic data for scalability, each offering unique advantages and limitations. A strategic approach to selecting these data sources ensures a comprehensive system evaluation. A successful chatbot development strategy involves using diverse data sources: historical logs, internal data, synthetic data, and partner data to capture different aspects of performance. A flexible platform supports this diversity by accommodating customization and expert input. Initial benchmarking highlights weaknesses; fine-tuning should target known failure areas, high-stakes use cases, and risky queries identified by experts for efficient resource allocation. Red teaming is crucial for stress-testing chatbots in critical scenarios to identify blind spots, with readiness criteria including zero critical errors in safety and compliance cases and less than a two percent failure rate on priority intents. Trust in LLM chatbots requires explicit readiness criteria, involving cross-functional ownership from various teams and continuous post-deployment monitoring. Trust is built through benchmarking against high-stakes use cases, proven reliability via red teaming, and visible improvement loops. Scaled trust is achieved when chatbots are consistently reliable, systematically corrected for failures, and integrated as production infrastructure. Label Studio Enterprise supports building trust by offering tools for benchmarking, fine-tuning, and continuous feedback, transforming trust into a scalable capability integrated into daily operations. This allows organizations to demonstrate ongoing improvements to stakeholders, unlocking the true business potential of conversational AI. **Bullet Point Summary:** - **Trust Issues:** LLM chatbots often fail in production due to lack of trust from compliance teams, risk officers, and stakeholders. - **Prototype Phase Challenges:** Concerns about reliability and safety during this phase lead to stalled projects; businesses prioritize consistency and evidence over demo performance. - **Governance Approach Needed:** A structured approach addressing model behavior in critical scenarios is essential for successful deployment. - **Measurable Benchmarks Essential:** Establish benchmarks with accurate metrics, reflecting real-world queries including high-stakes questions. - **Clear Targets Required:** Set targets for accuracy, intent coverage, and user satisfaction ratings; share these early to help stakeholders understand tradeoffs. - **Diverse Data Sources:** Use historical logs, internal data, synthetic data, and partner data to evaluate performance comprehensively. - **Fine-Tuning Strategy:** Focus on known failure areas, high-stakes use cases, and risky queries identified by experts for efficient resource use. - **Red Teaming for Stress Testing:** Identify blind spots through adversarial prompts and edge case testing in critical scenarios. - **Readiness Criteria:** Include zero critical errors in safety/compliance cases and less than a two percent failure rate on priority intents. - **Trust Building Process:** Involve explicit criteria, cross-functional ownership, continuous monitoring, and improvement loops. - **Label Studio Enterprise Role:** Provides tools for benchmarking, fine-tuning, and feedback, making trust scalable and integrated into daily operations. Keywords: AI, Chatbots, LLMs, Label Studio Enterprise, benchmarking, business, compliance, conversational, governance, measurement, production, readiness criteria, risk management, stakeholder trust, synthetic data
llm
humansignal.com a day ago
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129. HN Debunking the Claims of K2-ThinkThe text critiques the evaluation methodology used to assess the K2-Think reasoning language model, highlighting several biases that exaggerate its performance compared to larger models like GPT-OSS 120B and DeepSeek v3.1. The primary issues identified include: - **Data Contamination**: There is a significant overlap between K2-Think's training and evaluation datasets in areas such as mathematics (Omni-Math problems) and coding (LiveCodeBench), which compromises the validity of its performance claims. - **Unfair Comparisons**: K2-Think benefits from using a "best-of-3" strategy for its evaluations, while comparisons with other models employ a "best-of-1" approach. Furthermore, an unspecified external model aids K2-Think's problem-solving processes without being accounted for in performance assessments that claim independence on a 32B parameter model. - **Performance Metrics**: When compared without external assistance, K2-Think underperforms relative to similar-sized models like Nemotron-32B and Qwen3 30B. The paper also incorrectly benchmarks GPT-OSS by not using the high reasoning effort setting recommended for accuracy. - **Specific Benchmark Results**: - On metrics such as AIME 2024, AIME 2025, and HMMT25 without external help, K2-Think scores lower than Nemotron 32B and Qwen3 30B. - The evaluation of GPT-OSS using a medium reasoning effort rather than the recommended high setting skews results in favor of K2-Think. - **Scoring Practices**: K2-Think's scoring on math benchmarks is criticized for using a "micro average" that emphasizes its strongest area, OmniMath-Hard, which constitutes 66% of the score. This method does not equally weigh individual benchmark performances and misrepresents overall capabilities. - **Model Evaluation Issues**: The text points out outdated versions of competing models like Qwen3 are used in evaluations, causing discrepancies between reported and actual performance metrics by 15-20%. The critique underscores the need for more consistent and balanced evaluation strategies to ensure accurate assessments of K2-Think’s abilities, suggesting improvements in future iterations. **Bullet Point Summary:** - K2-Think's claims of superior performance are exaggerated due to flawed evaluations involving data contamination and unfair comparisons. - Overlaps exist between K2-Think's training and evaluation datasets, particularly in math and coding, rendering results invalid. - Comparisons favor K2-Think with a "best-of-3" strategy versus others' "best-of-1," plus unaccounted external model assistance inflates its performance claims. - Without external help, K2-Think underperforms compared to similar-sized models like Nemotron-32B and Qwen3 30B. - The evaluation of GPT-OSS lacks the recommended high reasoning effort setting for accurate benchmarking, skewing results in favor of K2-Think. - Specific benchmarks show K2-Think scoring lower than Nemotron 32B and Qwen3 30B when external help is excluded. - Scoring methods on math benchmarks disproportionately emphasize OmniMath-Hard, skewing overall performance perception. - Discrepancies arise from using outdated model versions for comparisons, affecting reported versus actual performance metrics by 15-20%. - The critique calls for more consistent and balanced assessment strategies to ensure accurate evaluations of K2-Think’s capabilities. Keywords: AIME 2024, DeepSeek v31, GPT-OSS, HMMT25, K2-Think, LiveCodeBench, MathArena, Nemotron-32B, Omni-Math, Qwen3 30B 2507, RL datasets, SFT dataset, benchmarks, comparisons, contamination, evaluation, gains, parameters, performance, reasoning effort
gpt-oss
www.sri.inf.ethz.ch a day ago
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130. HN Chat Control faces blocking minority in the EU**Summary:** Chat Control is facing opposition from a small segment within the European Union, primarily due to users disabling JavaScript in their web browsers. This technological hindrance impedes effective access and utilization of Chat Control services. To resolve these issues, it is essential for users to enable JavaScript or switch to a browser that is supported by Chat Control. The platform provides guidance on compatible browsers available through its Help Center. **Bullet Point Summary:** - Chat Control faces resistance from a minority group in the EU. - User experience problems stem from disabled JavaScript in their web browsers. - Access and effective use of Chat Control require enabling JavaScript or using recommended browsers. - Information about supported browsers can be found in the platform's Help Center. Keywords: Chat Control, EU, Help Center, JavaScript, blocking minority, browser, disable, enable, keywords, supported browsers, technical, topic, xcom
popular
twitter.com a day ago
https://en.wikipedia.org/wiki/Double_jeopardy a day ago https://www.echr.coe.int/ a day ago https://www.echr.coe.int/w/judgment-concerning-t%C3%BCr a day ago https://www.echr.coe.int/w/judgment-concerning-greece-9 a day ago https://balkaninsight.com/2023/09/25/who-bene a day ago https://howtheyvote.eu/votes/167712 a day ago https://news.ycombinator.com/item?id=45209366 a day ago https://fightchatcontrol.eu/ a day ago |
131. HN Multigres: Horizontally scalable Postgres with multi-tenant, HA capabilitiesMultigres is a horizontally scalable solution that enhances PostgreSQL by supporting multi-tenant, high-availability (HA), and globally distributed deployments while maintaining compatibility with standard PostgreSQL features. It extends PostgreSQL’s capabilities to achieve broader scalability and distribution, akin to what Vitess does for MySQL. This architecture allows for efficient management of large-scale, complex database environments without compromising the core functionalities inherent in PostgreSQL. **Bullet Point Summary:** - Multigres is a horizontally scalable solution designed specifically for PostgreSQL. - Supports multi-tenant configurations, high availability (HA), and globally distributed deployments. - Maintains compatibility with standard PostgreSQL features while extending its capabilities for greater scalability and distribution. - Comparable to Vitess but for MySQL, offering similar enhancements in terms of scaling and distribution for PostgreSQL environments. - Facilitates the efficient management of large-scale and complex database systems without losing PostgreSQL's core functionalities. Keywords: Architecture, Capabilities, Deployments, Globally distributed, HA (Highly Available), Horizontally scalable, Multi-tenant, Multigres, Postgres, Standard Postgres, Vitess
postgres
multigres.com a day ago
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132. HN Lessons Learned: Using Git Workflows to Manage a Multilingual Festival Website- The article discusses how Git workflows were implemented using Commitspark to manage a multilingual festival website, Ring on Feier in Zittau, addressing typical challenges with traditional CMSs like Contentful. - By integrating both content and schema within Git, the approach facilitated seamless synchronization across different components of the site. This method enabled controlled updates through branching, ensuring changes were reviewed and merged after corresponding frontend development adjustments. - The festival's website used Git for efficient schema and content management, leveraging branch-based workflows, unified history, easy issue recovery, low-cost infrastructure via GitHub, and CI/CD integrations for automated validation and deployment. - The Ring on Feier team required a multilingual (German, Czech, Polish, English) site that was reliable despite unpredictable crowd sizes and within minimal budget constraints. Traditional CMSs were too costly or complex, while static site builders lacked flexibility for structured content. - Commitspark provided an optimal solution by balancing complexity with functionality, using: - **Frontend**: Next.js on DigitalOcean Kubernetes. - **Media Hosting**: Cloudinary. - **Content Storage**: GitHub repository. - **Schema and Data Format**: Defined using GraphQL types in plain text files and YAML format. - **Access**: Via Commitspark's GraphQL API library. - The schema for the festival site was defined using standard GraphQL in YAML, making it accessible to anyone familiar with GraphQL. This setup facilitated content management through the Commitspark GraphQL API library in Next.js projects. - Git workflows were effectively used for managing content, employing dedicated branches and pull requests for updates or translations, ensuring thorough review processes akin to software development practices. - The project achieved high performance scores (99/100 on Google PageSpeed Insights) with fast loading times and no downtime during peak periods. It managed GitHub API rate limits efficiently using Next.js caching strategies. - Hosting costs were minimal at $68 per month for a DigitalOcean Kubernetes cluster, with free services from GitHub and Cloudinary. The project required four weeks of part-time work, resulting in over 30 pages and more than 100 images published across four languages. - Lessons learned included effective practices such as efficient multilanguage content management through Git workflows and intuitive content modeling using GraphQL. However, areas for improvement were noted in the Commitspark editing UI's convenience features and collaboration capabilities. - The approach is ideal for freelancers or small agencies with tech-savvy teams seeking control over content akin to developers' control over code, preferring a lean, standard-based tech stack without enterprise features. - Commitspark is an open-source project on GitHub, encouraging users to explore its multilanguage sample repository. Feedback on the concept of utilizing Git workflows for content management is welcomed, with further information available via provided contact details. Keywords: CI/CD, Cloudinary, Commitspark, Contentful, Festival Website, Git, GitHub, GraphQL, Kubernetes, Multilingual, Nextjs, Workflows
github
2h10.de a day ago
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133. HN The treasury is expanding the Patriot Act to attack Bitcoin self custody**Summary:** The Treasury is reportedly extending the Patriot Act's reach into Bitcoin self-custody practices such as CoinJoin, atomic swaps, single address use, and transaction timing delays. This initiative, guided by recommendations from the Trump-era "Crypto Brief," intends to classify users of these privacy tools as suspicious under newly established guidelines from FinCen and the Treasury. Critics argue that this action infringes on financial privacy, undermines Bitcoin's economic efficiency, and compromises security by discouraging recommended practices like using single addresses for UTXOs. They claim it unfairly targets law-abiding citizens without enhancing overall financial privacy, suggesting instead that the Patriot Act should be repealed rather than expanded. The text further critiques societal norms that prioritize criminal elements over the majority, emphasizing that law enforcement should focus on preventing and addressing crime rather than burdening neutral protocols supporting privacy-focused cryptocurrencies. It also explores Bitcoin's evolution into a stable financial asset as it integrates with traditional finance. Mel Mattison notes that institutional products such as ETFs and futures have decreased Bitcoin's volatility, reducing dramatic price swings but capping potential high returns. This change implies investors need to adjust their expectations regarding gains, recognizing Bitcoin's progression from a speculative asset to a sustainable financial infrastructure. Additional content highlights several topics of interest: a podcast covering China's gold strategy, Fed independence issues, and housing market plans; headlines including a new bill for a strategic Bitcoin reserve, an SEC crypto roundtable, and research proposing Bitcoin as a trade standard on Mars. There is also mention of an upcoming live session with Tom Honzik to teach Bitcoin security strategies, including common mistakes, custody options, and privacy considerations. The introduction of Obscura VPN is emphasized, designed for unloggable activity, censorship bypass, and use of Bitcoin over the Lightning Network, offering a discount for TFTC listeners. Ten31's significant investments in Bitcoin-focused companies are highlighted. A tribute to Charlie Kirk concludes with an invitation to download the "Opportunity Cost" browser extension, encouraging better decision-making using Satoshi (SATs). The message promotes subscribing to a daily Bitcoin newsletter and following their YouTube channels on Nostr and X platforms. **Bullet Point Summary:** - Treasury expanding Patriot Act to target Bitcoin self-custody practices. - New guidelines from FinCen and the Treasury classify users of privacy tools as suspicious. - Critics argue this infringes on financial privacy, reduces economic efficiency, and weakens security by discouraging best practices like single address use for UTXOs. - Critics claim it unfairly targets law-abiding citizens and suggest abolishing rather than expanding the Patriot Act. - Critique of societal norms favoring criminals over majority; emphasis on effective crime prevention by law enforcement. - Bitcoin's evolution into a stable financial asset as it integrates with traditional finance. - Mel Mattison notes institutional products like ETFs and futures have reduced Bitcoin’s volatility, altering investment return expectations. - Topics include a podcast on China's gold strategy, Fed independence, housing market plans; bill for strategic Bitcoin reserve; SEC crypto roundtable; research on Bitcoin as trade standard on Mars. - Upcoming live session with Tom Honzik to teach Bitcoin security strategies, including common mistakes and privacy considerations. - Introduction of Obscura VPN emphasizing unloggable activity and censorship bypass using Bitcoin over Lightning Network, with a discount for TFTC listeners. - Highlighting Ten31's significant investments in Bitcoin-focused companies. - Tribute to Charlie Kirk with an invitation to download the "Opportunity Cost" browser extension for better decision-making using Satoshi (SATs). - Promotion of daily Bitcoin newsletter subscription and following YouTube channels on Nostr and X platforms. Keywords: Bitcoin, China, CoinJoin, ETFs, Fed Independence, FinCen, Lightning Network, Patriot Act, UTXO, atomic swaps, custody, exchanges, futures, institutionalization, opportunity cost, podcast, privacy, regulation, self-custody, treasury, volatility
popular
www.tftc.io a day ago
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134. HN LLM-Generated Rules Engines for LLM ExplainabilityThe article discusses the challenges of using large language models (LLMs) in enterprise AI solutions, particularly regarding explainability and auditability with unstructured documents like building codes or insurance policies. It introduces a "Rules Engine" approach to enhance LLM transparency by applying business logic through mutually exclusive and collectively exhaustive (MECE) decision trees. This method aims to standardize processes, reduce output variance, and ensure regulatory compliance. Brain Co.'s methodology involves using LLMs in a multi-stage evaluation process to extract MECE rules from unstructured documents, demonstrating superior performance compared to human experts by converting these sources into structured rules. The article details text extraction and segmentation techniques, recommending deterministic regex for structured documents and LLM-assisted methods for complex ones. For example, extracting sections from an Allianz policy document involves normalization and metadata preservation. The article outlines a process for creating IF-THEN rules from insurance policies, focusing on precision and atomic decomposition to ensure clarity in claims processing systems. Dynamic attachment maintains contextual integrity when generating rules from large PDFs, preventing model overwhelm by combining relevant text during rule creation. A three-stage evaluation system compares LLM-generated rules with human-labeled ones using text matching and semantic similarity assessments to ensure completeness and accuracy. High-performing models generate more comprehensive rules with fewer redundancies than subject matter experts (SMEs). This approach supports scalable rule generation across industries, reducing costs while improving accuracy in regulated sectors by converting unstructured guidelines into MECE rules audited by SMEs. The article describes an airline incident where a traveler was denied boarding due to a suspected contagious condition, resulting in a 10-hour delay covered under their travel insurance policy. Two assessment methods for determining coverage were evaluated: naive LLM assessments and IF-Then Rule Assessment, with the latter offering clear, rule-based rationale confirming coverage based on specific criteria. Future enhancements propose using LLMs for document processing improvements through retrieval augmented generation (RAG), focusing on traceability, calibration against human validation sets, an online learning pipeline, and version control. This maintains explainable decision-making essential for regulatory compliance by transforming documents into clear rule sets with tools like Pydantic, Instructor, and PDFPlumber. Related research highlights LLMs' ability to generate legal rules but notes their tendency to produce fewer rules compared to humans. A 2024 study emphasizes careful management when deriving machine-readable rules from unstructured documents using tailored templates specific to rule types, such as income limits and citizenship requirements. **Key Points:** - Challenges in using LLMs for enterprise AI solutions include the need for explainable and auditable decisions. - "Rules Engine" approach enhances transparency by applying MECE decision trees for standardized checks and regulatory compliance. - Brain Co.'s method uses LLMs to extract structured rules from unstructured documents with high performance. - Text extraction and segmentation techniques are recommended, with examples like extracting sections from policy documents. - IF-THEN rule creation focuses on precision and clarity in claims processing systems. - Dynamic attachment of text maintains contextual integrity during rule generation from large PDFs. - Evaluation system ensures LLM-generated rules' completeness and accuracy compared to human-labeled ones. - Airline incident highlights coverage determination methods: naive LLM assessments vs. IF-Then Rule Assessment. - Future enhancements propose using LLMs for document processing improvements, focusing on traceability, calibration, learning pipelines, and version control. - Related research emphasizes careful management of rule generation from unstructured documents to avoid errors and omissions. Keywords: AI solutions, Automation, Compliance, Decision Tree, Ensemble Calibration, Explainability, Governance, IF-THEN Rules, Insurance Policy, LLM, MECE, PDF Extraction, PDFPlumber, PyMuPDF, Pydantic, Regex, Retrieval-Augmented Generation, Rules Engines, SMEs, Traceability, Transparency, Unstructured Documents
llm
brain.co a day ago
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135. HN Clever Hans Couldn't Do Arithmetic, and LLMs Don't UnderstandThe text explores the misconceptions surrounding artificial intelligence (AI) models' abilities to understand or reason. It draws parallels between Clever Hans, a horse that seemingly performed arithmetic tasks based on human cues, and Large Language Models (LLMs), which produce impressive outputs without true comprehension. The author suggests that humans often anthropomorphize AI by attributing human-like intentions to it, leading to overestimations of its capabilities. The narrative includes the author's personal experiences with GPT-4 in chess, highlighting how the model relies solely on data patterns rather than possessing strategic awareness or understanding. This example underscores the tendency for humans to mistake sophisticated pattern recognition for genuine intelligence in AI systems. The text advocates for a rational and evidence-based approach when using LLMs, recognizing their strengths in statistical analysis while acknowledging their lack of actual comprehension. Further comparison is made with pet behavior, illustrating how animals like cats can learn responses without understanding human language, similar to how LLMs mimic understanding through learned associations rather than true conceptual grasp. The author emphasizes the importance of distinguishing between AI's pattern matching abilities and real intelligence or comprehension. The discussion also covers examples where reliance on subtle cues and oversight has led to flawed interpretations—such as Clever Hans relying on visual signals from his trainer, and LLMs misinterpreting instructions due to user confirmation bias. To mitigate these issues, the text recommends strict control over AI tools. The "brown M&Ms" test is cited as a practical method for ensuring meticulous attention to detail, originally used by Van Halen in their concert riders to prompt thorough technical checks. ### BULLET POINT SUMMARY: - **Misconceptions about AI Understanding:** The text highlights how humans often attribute human-like intentions to AI models (anthropomorphism), leading to overestimated perceptions of their capabilities. - **Analogy with Clever Hans and LLMs:** It draws a parallel between Clever Hans, who appeared to perform arithmetic based on human cues, and LLMs that generate impressive responses without true comprehension. - **Author's Experience with GPT-4:** The author discusses limitations observed when testing GPT-4 in chess, noting its reliance on pattern prediction rather than strategic awareness or understanding. - **Pet Behavior Comparison:** Similar to how cats learn responses without language comprehension, LLMs mimic understanding through learned associations rather than genuine conceptual grasp. - **Caution Against Overestimating AI Capabilities:** The text advises users not to confuse sophisticated AI pattern matching with actual intelligence or understanding. - **Examples of Misinterpretation Due to Cues and Oversight:** It highlights how both humans and AI can be misled by subtle cues, using Clever Hans and LLMs as examples. - **Importance of Control over AI Tools:** The text recommends strict oversight and control when utilizing AI systems to prevent flawed interpretations. - **"Brown M&Ms" Test for Attention to Detail:** This practical method is mentioned as an indicator for thorough checks, originally used by Van Halen in concert settings. Keywords: Clever Hans, GPT-4, LLMs, arithmetic, behavior, chess, confirmation bias, context, instructions, intelligence, interpretation, pattern matching, tokens, visual cues
gpt-4
codemanship.wordpress.com a day ago
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136. HN How do we get AI Personas to sound so human?The text explores the development and application of AI technologies like AskRally, focusing on their ability to mimic human-like interactions through effective prompting rather than advanced algorithms alone. It contrasts AskRally's performance with ChatGPT 5, highlighting AskRally’s more natural responses by using both OpenAI and Google models. The discussion includes an evaluation of breakfast food preferences from a persona designed to resemble a retired Welsh teacher, illustrating how well-crafted prompts can create authentic AI interactions. AskRally also addresses the complexities involved in creating digital personas for market research, raising concerns about privacy and authenticity when AI avatars are used as stand-ins for real individuals. The text mentions "Defender," an Open Memetics community influencer, who contemplates the ethical implications of such technologies, questioning issues around identity and representation. The excerpt further delves into AskRally’s use in generating humorous anecdotes and providing diverse responses through a method known as "Persona as Thought Prompting." This approach allows for richer outputs by considering multiple perspectives before forming a response. However, challenges remain with AI models defaulting to uniform conversational styles across different contexts—a problem termed "AI monoculture." To counter this, a Diverse Response Architecture is introduced, allowing the model to generate varied responses based on different scenarios and personas. An example involves assessing headlines related to AI funding, tech layoffs, and Apple earnings through multiple perspectives like venture capitalists or financial analysts. The text also discusses using AI for market research by creating realistic personas that can predict human behavior. It highlights potential issues with AI biases, such as political leanings affecting predictions in areas beyond politics. Researchers have developed methods to identify and mitigate these biases, leading to more accurate simulations useful for business insights. Finally, the application of calibrated persona pools is detailed, showing how companies can simulate real customer behaviors using diverse data sources. These personas are used in dynamic experiments involving competitor communications and media influence, providing rapid insights that enhance traditional human research methodologies. - The text focuses on AI technology AskRally, which uses effective prompting to create natural interactions. - It contrasts AskRally’s approach with ChatGPT 5, illustrating better engagement through persona-based responses. - Discusses ethical considerations in using digital personas for market research and identity representation. - Introduces "Persona as Thought Prompting" to generate diverse AI responses by considering multiple viewpoints. - Highlights the challenge of AI monoculture and the implementation of Diverse Response Architecture to address it. - Addresses AI bias, particularly political bias, affecting predictions across various domains, with methods developed for correction. - Describes how calibrated persona pools can simulate real customer behaviors for market research, offering rapid insights. Keywords: AI Personas, Google models, OpenAI, authenticity, bias correction, conversational responses, digital twin, ethical concerns, market research, persona crafting, simulation interaction, technical problems
openai
askrally.com a day ago
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137. HN Beyond the surface – Exploring attacker persistence strategies in KubernetesThe blog post discusses various strategies that attackers can use to maintain and expand their presence within a compromised Kubernetes cluster by exploiting admin credentials. A specific attack path involves gaining temporary access to an admin's laptop, enabling the attacker to enter the Kubernetes environment using tools like `kubectl debug` with a `sysadmin` profile. This approach exploits vulnerabilities in default configurations for persistent access. Attackers can leverage shell access on Kubernetes nodes to exploit container runtime capabilities, executing binaries even when faced with read-only or noexec filesystems. Tools such as `containerd`, accessed via the `ctr` command-line interface, allow attackers to create new namespaces and pull unverified container images like `docker.io/sysnetmon/systemd_net_mon:latest`. These actions enable unauthorized operations by running containers directly on nodes, bypassing Kubernetes APIs. To maintain remote access post-exploitation, attackers may use Tailscale with renameable binaries (`systemd_net_mon_server` and `client`) for secure communication. This method hides their activities from EDR/XDR agents. Additionally, exploiting the Kubernetes static manifest by deploying pods with invalid namespaces helps attackers remain undetected, as these pods don't register with the API server. For securing credentials, attackers might utilize tools like `teisteanas` to generate Kubeconfig files via the Certificate Signing Request (CSR) API, enabling direct communication with Kubelet APIs without triggering Kubernetes API audit logs. The CSR API poses significant security risks due to its potential misuse for creating cluster-wide authentication credentials and the lengthy validity of these credentials. Credentials in Kubernetes clusters are generally long-lived, posing extended risk windows if compromised. Tools like "tocan" can simplify generating Kubeconfig files using service account tokens, though this increases risks when accounts have escalate rights. To mitigate these threats, enabling audit logging with centralized storage is crucial for detecting misuse of APIs such as CSR and Token Request. Preventing attacks requires minimizing the internet exposure of API servers and ensuring robust access restrictions are enabled. Monitoring normal operational processes on cluster nodes aids in identifying unauthorized activities. Ultimately, adhering to the principle of least privilege by limiting administrative capabilities like node debugging can significantly enhance Kubernetes security practices. **Bullet Point Summary:** - Attackers exploit admin credentials to maintain presence in a compromised Kubernetes cluster. - Use of `kubectl debug` with `sysadmin` profile for persistent access through default configuration vulnerabilities. - Exploitation involves shell access on nodes, utilizing container runtime capabilities and tools like `containerd`. - Tailscale used for remote access maintenance; static manifests exploited by deploying pods with invalid namespaces to remain undetected. - Attackers use `teisteanas` to generate Kubeconfig files via CSR API for direct Kubelet communication without audit logs. - The CSR API poses risks due to potential misuse, lack of auditing, and long-lived credentials. - Credentials in Kubernetes are typically long-lived (1-5 years), increasing exploitation risk. - Tools like "tocan" simplify Kubeconfig generation but increase risks if accounts have escalate rights. - Mitigation includes enabling audit logging with centralized storage and minimizing API server internet exposure. - Adhering to the principle of least privilege can enhance Kubernetes security by limiting administrative capabilities. Keywords: ACL, API server, CRI-O, CSR API, DERP network, Docker Hub, EDR/XDR, Ian Coldwater, Kubeconfig, Kubernetes, Node Logs, RBAC, SSH, Tailnet, Tailscale, access retention, admin credentials, attacker persistence, attackers' path, audit logs, bind mount, cluster access, cluster compromise, compromised container, containerd, containers, ctr tool, debug profiles, executing binaries, filesystems, golang binaries, kube-apiserver, kubectl, kubelets, namespace, net-host, noexec, post-exploitation, read-only, remote access, root shell, security, shell access, sysadmin profile, systemd_net_mon_client, systemd_net_mon_server, teisteanas, userspace-networking
tailscale
raesene.github.io a day ago
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138. HN Show HN: Built a Real-time sentiment LLM trackerThe "AI Daily Check" project is designed as a real-time sentiment tracker specifically aimed at assessing the performance of Large Language Models (LLMs). Developed using Node.js on Cloudflare Pages with Server-Side Rendering and hosted via Cloudflare Workers, its primary function involves scraping Reddit posts to conduct sentiment analysis concerning AI model performance. This tool aims to identify which LLMs are currently operating optimally by gathering user-generated data from discussions about AI effectiveness. To enhance user interaction, the project includes a Claude Code extension that allows users to vote on and view real-time sentiment metrics regarding different AI models. The creator is seeking feedback to improve how these summaries are presented on the homepage. This initiative responds to frequent issues with LLMs malfunctioning, such as an incident where Claude's responses were notably poor due to server errors. The tool serves as a precautionary measure for users who rely on LLMs, advising them to check AI Daily Check before engaging in significant tasks to ensure they are using a reliable model. Future developments of the project include plans for creating a Chrome extension and integrating incident report features. For more information or to access the service, users can visit [AI Daily Check](https://aidailycheck.com). **Bullet Point Summary:** - "AI Daily Check" is a real-time sentiment tracker for assessing Large Language Models (LLMs) performance. - Developed using Node.js on Cloudflare Pages with Server-Side Rendering and hosted via Cloudflare Workers. - Gathers data by scraping Reddit posts to analyze sentiments related to AI model effectiveness. - Includes a Claude Code extension allowing users to vote and view real-time sentiment metrics. - Creator seeks feedback for improving homepage summary presentation. - Addresses issues of LLMs malfunctioning, exemplified by incidents like poor responses from Claude due to server errors. - Recommends users check the tool before relying on AI models for important tasks. - Plans include developing a Chrome extension and integrating incident reports. - More details available at [AI Daily Check](https://aidailycheck.com). Keywords: AI, AI Daily Check, AI models, Analysis, Check, Chrome extension, Claude, Claude Code, Cloudflare, Cloudflare Pages, Cloudflare Workers, Code, Extension, LLM, LLM tracker, Models, Nodejs, Pages, Performance, Real-time sentiment, Reddit scraping, Reports, SSR, Scraping, Sentiment, Tracker, Update, Workers, backend, frontend, incident reports, performance checkKeywords: Real-time, sentiment analysis, server update
claude
aidailycheck.com a day ago
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139. HN Claude introduces memory for teams at workClaude has introduced an optional memory feature in its app designed for teams using the Team and Enterprise plans, aimed at enhancing productivity by retaining project details and user preferences. This context-aware functionality streamlines workflows by allowing users to recall past interactions, work patterns, client needs, and project specifics without mingling unrelated information. To address privacy concerns, Claude provides options to manage what is remembered, including an "Incognito chat" feature that facilitates private conversations which are not recorded in memory or history. The system maintains separate memories for each project to ensure confidentiality and organization. A summary feature enables users to review and edit stored data as needed. The Incognito chat option, available to all users regardless of plan, is designed for sensitive discussions and does not impact the regular conversation history. For users interested in enabling this memory function, they can activate it via Settings where Claude generates context from past interactions upon request. Users have the capability to inquire about previous work activities, like asking "what were we working on last week?" Additionally, there are provisions for transferring or exporting memory details for backup or migration purposes. The introduction of these features is part of a broader initiative by Claude to improve conversation quality over time by building upon prior discussions while ensuring new safety considerations are carefully managed during deployment. **BULLET POINT SUMMARY:** - Claude's app introduces an optional memory feature for Team and Enterprise users to enhance productivity by remembering project details, preferences, and past interactions. - Users can manage what is remembered and have access to "Incognito chats" that do not save conversations to history or memory. - Separate memories per project ensure confidentiality and organization, with a summary feature allowing review and editing of stored data. - Incognito chat allows private, unrecorded conversations for all users, ensuring regular conversation histories remain unaffected. - To enable the memory function, users can activate it via Settings, allowing Claude to generate context from past interactions upon request. - Users can inquire about past work activities and have options to transfer or export memory details for backup or migration. - The feature aims to improve conversation quality by building on prior discussions while ensuring new safety considerations are managed during deployment. Keywords: Claude, Enterprise plan, Incognito chats, Team plan, backup, context, conversations, memory, migration, preferences, privacy, productivity, project boundaries, projects, safety guardrail, sensitive brainstorming, settings, strategy discussions, summary, teams, technical capabilities, work patterns
claude
www.anthropic.com a day ago
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140. HN The Evolution of Logical Replication in PostgreSQL: A Firsthand Account### Summary: The development of logical replication in PostgreSQL has been significantly influenced by community-driven innovations aimed at enhancing data availability and system flexibility. Initially lacking native replication capabilities until the 2000s, PostgreSQL relied on trigger-based solutions such as Slony, Londiste, and Bucardo to fill this gap, although these early systems were limited in scope. A significant advancement came with logical decoding in PostgreSQL 9.4, which efficiently extracted changes from the Write-Ahead Log (WAL) stream, leading to developments like BDR and pglogical that enabled active-active replication and user-friendly extensions. As demand for more robust solutions increased, the community continued to innovate through various forks and plugins, paving the way for built-in logical replication support in PostgreSQL. Key milestones included the transition of logical replication into a core feature with PostgreSQL 10, contributions from Peter Eisentraut, and subsequent advancements like pglogical 3's pluggable architecture supporting external systems such as Kafka and RabbitMQ. BDR underwent several iterations, introducing features like parallel apply, location-aware routing, and built-in connection management. The ecosystem expanded further with forks like Postgres Pro’s adaptation of pglogical for active-active replication and pgEdge’s Spock fork from pglogical 2. AWS also developed an open-source version named pgactive based on early BDR commits. PostgreSQL's native logical replication has continued to mature, incorporating features such as failover support, partitioned table handling, filtering, two-phase transactions, and performance enhancements. By version 17, these improvements rendered earlier versions of tools like pglogical 2 largely obsolete. Overall, the evolution of PostgreSQL’s logical replication illustrates a trajectory marked by resilience, collaboration, and continuous technological advancement. ### Bullet Point Summary: - **Initial Challenges:** PostgreSQL initially lacked built-in replication until the 2000s, relying on trigger-based systems like Slony, Londiste, and Bucardo. - **Logical Decoding Introduction:** Logical decoding in PostgreSQL 9.4 enabled efficient extraction from WAL streams, leading to tools such as BDR and pglogical for active-active replication. - **Community Innovations:** Continued demand led to community-driven innovations with various forks and plugins, moving towards built-in logical replication support. - **Key Milestones:** - Built-in feature in PostgreSQL 10 due to contributions like those from Peter Eisentraut. - Development of pglogical 3 with pluggable architecture for systems such as Kafka and RabbitMQ. - BDR evolved through versions, introducing features like parallel apply, location-aware routing, and connection management. - **Ecosystem Expansion:** Forks like Postgres Pro's adaptation of pglogical and pgEdge’s Spock fork emerged, alongside AWS's open-sourcing of pgactive from early BDR commits. - **Matured Native Replication:** PostgreSQL’s native logical replication matured with features including failover support, partition handling, filtering, two-phase transactions, and performance enhancements. - **Overall Evolution:** Reflects user-driven innovation, moving from basic trigger-based systems to sophisticated environments offering flexibility and high availability. Keywords: Active-Active, BDR, Bucardo, Built-in Support, Collaboration, Column Filtering, Durability Configuration, Extensions, Failover, Flexibility, High Availability, Innovation, Location Aware Routing, Logical Decoding, Logical Replication, Londiste, Parallel Apply, Partitioned Tables, Performance Improvements, Physical Replication, Plugins, PostgreSQL, Postgres Pro, Resilience, Slony, Transaction Streaming, Triggers, UI Monitoring, WAL Stream, pglogical
postgresql
www.enterprisedb.com a day ago
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141. HN Gauss, an Agent for AutoformalizationThe Math Inc. team has launched Gauss, an innovative autoformalization agent aimed at aiding mathematicians in formal verification tasks. This tool was demonstrated effectively when it completed the challenge proposed by Fields Medalists Terence Tao and Alex Kontorovich to formalize the strong Prime Number Theorem (PNT) in Lean within three weeks. This task previously took 18 months of intermediate progress for Tao and Kontorovich, highlighting Gauss's efficiency as it generated approximately 25,000 lines of Lean code autonomously, including over 1,000 theorems and definitions. This achievement signifies a major advancement in formal proofs, which have traditionally been lengthy endeavors. The deployment of Gauss was made possible through the Trinity environments infrastructure developed with Morph Labs, which scaled Lean verification to handle thousands of concurrent agents using multiple terabytes of RAM. The challenge of managing this complex systems engineering task was addressed by Infinibranch on Morph Cloud. While currently relying on human-provided natural language scaffolding, future iterations aim for greater autonomy. Gauss is being prepared for deployment among mathematicians and proof engineers, with beta testing already in progress involving a selected group of participants. Early access registration is available, encouraging ambitious autoformalization project ideas. The project represents initial steps toward large-scale formalization, aiming to drastically cut the time required for significant initiatives through future algorithmic enhancements. The objective is to increase formal code output by 2-3 orders of magnitude within a year, setting the stage for verified superintelligence and machine polymaths. This work has been supported by DARPA’s expMath program. **Bullet Point Summary:** - **Introduction of Gauss:** An autoformalization agent developed by Math Inc. to assist in formal verification tasks. - **Significant Achievement:** Successfully completed a challenge to formalize the strong Prime Number Theorem (PNT) in Lean within three weeks, compared to 18 months by Terence Tao and Alex Kontorovich. - **Efficiency Highlighted:** Gauss autonomously generated about 25,000 lines of code, including over 1,000 theorems and definitions. - **Infrastructure Development:** Utilized Trinity environments infrastructure with Morph Labs for scaling Lean verification, managed by Infinibranch on Morph Cloud. - **Current Dependency and Future Goals:** Currently relies on human-provided natural language scaffolding; aims to increase autonomy in future iterations. - **Deployment and Testing:** Gauss is being prepared for deployment among mathematicians and proof engineers, with ongoing beta testing. - **Future Objectives:** Aims to increase formal code output by 2-3 orders of magnitude within a year, paving the way for verified superintelligence and machine polymaths. - **Support:** The project has been supported by DARPA’s expMath program. Keywords: Gauss, GitHub, Infinibranch, Lean, Mathlib, Morph Cloud, Morph Labs, Prime Number Theorem, Trinity, agent, algorithmic improvements, autoformalization, beta testing, complex analysis, contributors, definitions, expMath program, formal verification, human mathematics, machine code, mathematicians, natural language scaffolding, proof engineers, proofs, superintelligence, theorems, verifiable, verified machine polymaths
github
www.math.inc a day ago
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142. HN Why Boards Are Asking for AI Visibility AuditsEnterprises are increasingly acknowledging the critical role of ensuring accurate representation within generative AI systems such as ChatGPT and Gemini, which have become primary information sources for customers and investors. The absence or misrepresentation of a brand in these AI responses poses significant risks, including revenue loss, diminished brand equity, reduced investor confidence, and potential regulatory challenges. To mitigate this strategic risk—now prioritized by boards alongside cybersecurity and compliance—businesses are promoting the adoption of AI Visibility Audits. These audits incorporate the Prompt Share of Search (PSOS™) metric as part of the AIVO Standard™ to ensure consistent brand presence in AI-generated content. - **Main Idea:** Enterprises recognize the need for accurate representation in generative AI systems due to their role as primary information sources. - **Key Risks Identified:** Potential revenue loss, erosion of brand equity, diminished investor confidence, and regulatory issues due to misrepresentation or absence of a brand. - **Strategic Response:** Boards prioritize addressing this risk through AI Visibility Audits. - **Audit Components:** Utilization of the Prompt Share of Search (PSOS™) metric within the AIVO Standard™ framework to maintain strong brand presence in AI outputs. Keywords: AI Visibility Audits, AIVO Standard™Keywords: AI Audits, Assistants, Brand Equity, ChatGPT, Claude, Commerce, Compliance, Cybersecurity, ESG, Gemini, Generative AI, Information, Investor Confidence, PSOS™, Perplexity, Regulatory Scrutiny, Strategic Risk
claude
zenodo.org a day ago
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143. HN Persuader: LLM >> Schema Conformity- **Overview of Persuader Tool**: - Designed to enable Large Language Models (LLMs) to produce JSON outputs that adhere to Zod schemas through validation messages and adaptive correction prompts during retry loops. - Supports multiple data providers, including OpenAI, Anthropic SDK, and local adapters like Ollama. - **Key Features**: - Ensures schema compliance for generating valid JSON outputs, addressing issues of unstructured or invalid data from LLMs. - Automates document processing using a command-line interface (CLI) with features such as retries, improved success rates, and resuming capabilities after failures. - Reduces token consumption significantly by reusing context across sessions. - **Integration & Deployment**: - Facilitates rapid development of production-ready LLM features like error handling, retry logic, and session management, significantly reducing development time. - Offers TypeScript support for type safety and validation with detailed error handling capabilities. - **Technical Specifications**: - Incorporates smart retry logic that converts errors into corrective instructions. - Utilizes schema-first validation using Zod schemas and includes optional context reuse for efficiency. - Supports batch processing via CLI, allowing intelligent error recovery across multiple files. - **Session Management & Architecture**: - Manages sessions efficiently through shared contexts to reduce tokens and time spent on LLM calls. - Maintains a modular architecture with clear separation of concerns, keeping each module under 300 lines. - **Setup & Usage**: - Requires Node.js version 20.0.0+, TypeScript 5.7.2+, and the `ClaudeCode` package for setup. - Involves API key configuration for provider integration and uses templates for environment variables. - **Real-world Application Examples**: - Demonstrates application in domains such as fitness programs, biomechanics modeling, and multi-perspective analysis. - Features complex validation schemas, context reuse, retry mechanisms, and domain expertise modeling. - **Advanced Functionalities**: - Includes dependent schema validation for linking exercises to muscle groups, production-ready error handling with graceful degradation, and session optimization for multi-step processes. - Provides a modular framework emphasizing clean architecture, human-centric design principles, and maintainable code. Overall, Persuader is designed to enhance the reliability, efficiency, and scalability of LLM integrations through schema validation, smart retry logic, efficient session management, and robust deployment capabilities. The document outlines coding design principles, refactoring achievements, core functionalities, CLI tool features, provider integration, future plans, use cases, advantages over raw LLM API calls, contributions, development guidelines, quality standards, architectural components, performance metrics, community involvement, key features, setup instructions, real-world applications, and acknowledgments. Keywords: CLI, JSON, LLM, Persuader, TypeScript, Zod-schema, adapters, batch processing, retry loops, schema conformity, session management, validation
llm
github.com a day ago
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144. HN A simple clustering and replication solution for Postgres- The article provides a comprehensive guide for setting up a three-node EDB Postgres Distributed (PGD) cluster using AWS with an emphasis on manual configuration. - Key steps to launch and configure the nodes include starting three Ubuntu 24.04 t3.micro EC2 instances, setting security group rules, acquiring an EDB subscription token, configuring repositories, and installing necessary packages. - The guide details establishing each node as part of a distributed PostgreSQL cluster, ensuring seamless inter-node connectivity through logical replication. This setup allows for experimenting with features like node deletion and recreation without data loss due to cloud-native resynchronization capabilities. - The procedure involves configuring nodes (Node 0, Node 1, Node 2) using specific parameters such as Data Source Name (DSN), listening addresses, data directories, log files, and group names. Each node is verified for health and connectivity using `pgd` commands to ensure proper cluster operation. - Demonstrations within the setup include creating an "orders" table on Node 0 and inserting a large dataset (one million rows). The Distributed SQL ensures these operations are replicated across all nodes in the cluster. - The guide further covers managing node issues by describing how to remove, re-provision, and reintegrate a problematic node ($NODE2) into the PGD cluster. This process maintains data consistency and operational continuity throughout the cluster. - Lastly, it outlines a strategy for rolling upgrades within the cluster, allowing sequential replacement of nodes with newer versions of PostgreSQL and PGD to minimize downtime and ensure service continuity. A test query confirms successful integration and data access across all nodes post-upgrade. Keywords: AWS, BDR, CLI, DDL, DML, DSN, EC2, EDB, IP addresses, PGD, PostgreSQL, TCP connections, Ubuntu, apt-get, clock drift, cloud-native, cluster, data directory, group-name, health check, listen-addr, log file, logfile, node2, nodes, orders, part operation, pgdata, postgres user, provision, replication, replication slots, rolling upgrades, security group, setup, table, version, versions
postgresql
www.enterprisedb.com a day ago
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145. HN CodebuffCodebuff is an open-source AI coding assistant designed to facilitate codebase modifications using natural language instructions. It leverages a multi-agent system, with each agent focusing on distinct tasks like file exploration, planning changes, editing code, and reviewing edits. This architecture enhances project context understanding and accuracy in making code adjustments, demonstrating superior performance compared to single-model tools such as Claude Code (61% vs. 53%) across over 175 coding tasks. Users can install Codebuff via npm and access its command-line interface (CLI) for specific code improvements, such as vulnerability fixes or rate limiting enhancements. The tool automates file identification and change implementation while running tests to preserve functionality. Additionally, it allows users to create custom agents using the `codebuff init-agents` command, enabling them to define agent behavior and workflows programmatically. An example provided is a git-committer agent that utilizes git tools for analysis before generating commit messages with an LLM. The platform's SDK guide outlines how to use Codebuff in production by installing the `@codebuff/sdk` package. After initialization with an API key and project path, users can run agents for coding tasks using either base or custom-defined agents like "Greeter," which follows specific instructions. The process involves event handling and progress logging. Codebuff is aimed at automating development workflows through AI-driven agents capable of efficiently executing complex code-related tasks. It offers deep customization via its SDK with TypeScript, supporting integration with any AI model available on OpenRouter, thus enabling flexible and adaptive use cases without being restricted to specific models. Users can compose existing agents or contribute by fixing bugs, creating specialized agents, enhancing documentation, and sharing ideas. The platform encourages community involvement through resources like comprehensive SDKs, documentation, Discord support, and a Contributing Guide. Codebuff installations are available via npm for both CLI and SDK usage. ### Bullet Point Summary: - **Functionality:** Codebuff is an AI coding assistant using natural language to edit codebases with a multi-agent system. - **Performance:** Outperforms single-model tools like Claude Code (61% vs. 53%) on over 175 tasks. - **Installation & Usage:** Installable via npm; uses CLI for specific code improvements and automation of file changes while testing functionality. - **Customizability:** Users can create custom agents with `codebuff init-agents` to define behaviors and workflows, such as a git-committer agent integrating existing tools. - **SDK & Production Use:** SDK guide shows how to initialize Codebuff with an API key and project path for coding tasks; supports base or custom-defined agents like "Greeter." - **AI Integration:** Supports any AI model on OpenRouter, providing flexible use cases without model restrictions. - **Community Involvement:** Encourages contributions by fixing bugs, creating specialized agents, enhancing documentation, with resources like SDKs, Discord support, and a Contributing Guide. - **Availability:** Codebuff is available for installation via npm for both CLI and SDK usage. Keywords: AI coding assistant, CLI, Codebuff, Discord, Editor Agent, File Explorer Agent, GitHub, LLM commit message, OpenRouter, Planner Agent, Reviewer Agent, SDK, SQL injection vulnerability, TypeScript, agent definition files, authenticate API, codebase edits, community, contributions, custom agents, customizability, documentation, generators, git diff, git-committer agent, models, natural language instructions, npm, open-source, rate limiting, refactor database, reusable agents, specialized agents, workflows
github
github.com a day ago
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146. HN I built a free Chrome extension to summarize anything with 17 AI modelsThe AI Summarizer is a free Chrome extension developed by the author to help users manage information overload by providing quick summaries of articles, YouTube videos, PDFs, and highlighted text. The tool stands out for its flexibility, offering 17 different AI models from various providers like OpenAI (ChatGPT), Google (Gemini, AI Studio), Anthropic (Claude), xAI (Grok), Mistral, Cohere, DeepSeek, Qwen, Hunyuan, Doubao, ERNIE, Kimi, GLM, Spark, MiniMax, and SenseNova. This variety allows users to select models based on their preferences or language needs without being confined to a single AI ecosystem. Operating directly in the browser, the extension prioritizes privacy by requiring no sign-up—only logging into the chosen AI provider is necessary. It offers customizable prompts, enhancing its usability. The developer seeks feedback from tech communities such as Hacker News to refine the tool and explore additional AI model integrations. Available on the Chrome Web Store without ads or subscription fees, AI Summarizer encourages user interaction for further development. - **AI Summarizer** is a free Chrome extension aimed at managing information overload by providing summaries of articles, YouTube videos, PDFs, and text selections. - It offers flexibility with 17 different AI models from providers like OpenAI, Google, Anthropic, xAI, and others such as Mistral and Cohere. - The tool allows users to choose AI models based on preference or language compatibility without being restricted to a single ecosystem. - **Privacy-focused**, it operates directly in the browser, requiring no sign-up but only logging into chosen AI providers. - Users can customize prompts for better summarization results. - Feedback is sought from tech communities like Hacker News for improvements and additional AI model integrations. - Available on the Chrome Web Store without ads or subscription fees, encouraging user interaction for further development. Keywords: AI Summarizer, Anthropic Claude, Chrome Web Store, Chrome extension, Google Gemini, OpenAI, PDFs summarization, YouTube videos, feedback, flexibility, information overload, multiple models, privacy-focused, text summarization, webpages summarization, xAI Grok
openai
news.ycombinator.com a day ago
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147. HN Larry Ellison briefly becomes richest personLarry Ellison momentarily became the world's richest person as Oracle shares surged, pushing his wealth to $393 billion, surpassing Elon Musk's $385 billion fortune. This rise was attributed to positive expectations for Oracle's cloud and AI sectors. However, a subsequent decline in Oracle's stock price allowed Musk to regain the title of the wealthiest individual. Musk had held this position for almost a year until Ellison briefly overtook him. The Tesla board proposed an extensive pay package exceeding $1 trillion for Musk, contingent on meeting ambitious targets within the next decade. Despite this potential reward, Tesla shares have been declining due to investor concerns. These include apprehensions regarding reduced focus on electric vehicles under the Trump administration and Musk's political activities. Such factors have influenced how investors perceive Tesla. Oracle has experienced significant growth driven by increased demand for its data center infrastructure. The company anticipates a 77% increase in cloud business revenue this year, reaching $18 billion. Demand from AI companies for Oracle’s data centers has contributed to the stock's rise, with CEO Safra Catz announcing four multibillion-dollar contracts and expecting more deals. Musk's political connections during Trump's administration and his pursuits in media have further impacted investor perceptions of Tesla. These elements have combined to create a complex dynamic between the two tech giants, affecting their respective valuations and market positions. **BULLET POINT SUMMARY:** - Larry Ellison briefly became the world's richest due to Oracle's share surge driven by positive cloud and AI outlooks. - Oracle stock decline allowed Elon Musk to reclaim the title of the wealthiest person after nearly a year as the top billionaire. - Tesla proposed over $1 trillion pay package for Musk, dependent on future performance targets. - Tesla shares declined amid investor concerns about decreased electric vehicle initiatives under Trump and Musk's political involvement. - Oracle’s growth is fueled by rising demand for data center infrastructure, with expected 77% rise in cloud revenue to $18 billion. - AI companies' demand for Oracle's data centers boosted its stock, supported by recent multibillion-dollar contracts announced by CEO Safra Catz. - Investor perceptions of Tesla are influenced by Musk's political ties and media ambitions. Keywords: AI companies, Bloomberg Billionaires Index, CEO, Elon Musk, Larry Ellison, Oracle, Safra Catz, Tesla, US President Donald Trump, artificial intelligence (AI), billionaire, cloud infrastructure, co-founder, contracts, data centres, database software, electric vehicle, fortune, gains, index, investor jitters, pay package, political involvement, revenue, richest person, share price, shares, surge, wealth
tesla
www.bbc.com a day ago
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148. HN OpenAI says nonprofit parent to own stake in company over $100BOpenAI's parent nonprofit organization will maintain oversight with an equity stake valued over $100 billion, positioning it as one of the world's most well-resourced philanthropic entities. This allows OpenAI to continue raising capital while being recently valued at $500 billion. A non-binding memorandum has been signed with Microsoft to enhance their partnership, following Microsoft's significant investments totaling over $13 billion since 2019. The companies are finalizing terms for a collaboration focused on developing safe AI tools. Transitioning into a public benefit corporation, OpenAI remains committed to its nonprofit roots and control structure, addressing pressures from civic leaders and former employees. Collaboration with the California and Delaware Attorneys General is ongoing to ensure this framework supports the nonprofit's future direction. Concurrently, a startup co-founded by Elon Musk faces legal disputes over transitioning from a nonprofit to a for-profit entity due to his competitive interests in the generative AI market through xAI. In contrast, OpenAI has announced a $50 million grant initiative aimed at supporting organizations involved with AI literacy, economic opportunity, and community innovation. - **Nonprofit Oversight**: The parent organization retains an equity stake over $100 billion, maintaining its philanthropic influence while allowing continued capital raising for OpenAI. - **Microsoft Partnership**: A non-binding memorandum strengthens the partnership following Microsoft's substantial investments; both companies aim to develop safe AI tools through their collaboration. - **Public Benefit Corporation Transition**: Despite transitioning, OpenAI maintains its nonprofit roots and control structure in response to external pressures, collaborating with legal authorities to reinforce this framework. - **Legal Dispute Involving Musk**: A startup co-founded by Elon Musk is embroiled in a legal battle over its shift from nonprofit to for-profit due to competition with his AI company, xAI. - **Grant Initiative**: OpenAI announces a $50 million grant program supporting organizations focused on AI literacy and economic opportunity. This summary encapsulates the strategic decisions and ongoing developments surrounding OpenAI's structure, partnerships, and initiatives in the context of its recent transition and industry dynamics. Keywords: AI, Bret Taylor, California Attorney General, ChatGPT, Delaware Attorney General, Elon Musk, Microsoft, OpenAI, Sam Altman, Satya Nadella, equity stake, generative AI, grant initiative, legal battle, nonprofit, partnership, xAI
openai
www.cnbc.com a day ago
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149. HN VSCode August 2025 (v1.104)**Summary:** Visual Studio Code (VSCode) released version 1.104 on September 11, 2025, introducing significant enhancements across AI workflow integration, security measures, customization options, development environments, and user interface improvements. Key updates include an Auto Model Selection feature for optimal chat performance using models like Claude Sonnet 4, GPT-5, and Gemini Pro 2.5, with discounts available to paid users. Security enhancements involve the `chat.tools.edits.autoApprove` setting for autonomous agent edits while highlighting security risks, requiring user confirmation before editing sensitive files. Support for AGENTS.md files has been experimentally introduced to provide workspace-specific context and instructions, facilitated by enabling `chat.useAgentsMdFile`. Customization is further extended with options to define custom chat modes in prompt files via `chat.promptFilesRecommendations`, along with personalization settings such as customizable chat fonts (`chat.fontFamily` and `chat.fontSize`) and an enhanced tools picker feature. AI workflow and collaboration have seen improvements, notably through a refined Chat Sessions view for managing various sessions and better GitHub integration for transitioning between tasks. New task delegation capabilities allow Copilot to initiate sessions from TODO comments and forward context accurately. Terminal enhancements include improved auto-approval settings with security configurations against prompt injection attacks, transitioning from Command Prompt to PowerShell for enhanced shell integration, and customizable timeouts via `chat.tools.terminal.shellIntegrationTimeout`. Additional features involve the introduction of a "Sharp Solarized" theme on vscode.dev, general availability of Google account sign-in for GitHub Copilot users in VS Code, and task support enhancements. Accessibility is improved with focused chat confirmation actions and new editor settings such as `editor.inlineSuggest.minShowDelay` and `window.border`. Extension management now includes a command to manage extension account preferences, displaying an editor tab index, and customizable scrollbar visibility. Issue reporting is enhanced with GitHub integration options, and notebook suggestions have been refined for Jupyter notebooks. The update also emphasizes efficiency improvements across various programming languages by introducing modern tools and AI-powered features. In JavaScript and TypeScript, built-in IntelliSense support for `bower.json` has been removed due to its deprecation, encouraging users to switch to npm or yarn. Python enhancements include support for Pipenv environments in the Python Environments extension, a new setting (`python.useEnvFile`) for environment variable injection from `.env` files, and AI-powered hover summaries in Pylance pre-release. The Run Code Snippet Tool allows execution of Python snippets directly in memory via the Pylance extension. General improvements include default IntelliSense across all Python documents, reliable execution of Python Activation Hooks, and enhancements to the GitHub Pull Requests Extension. Extension authoring now benefits from `shellIntegrationNonce` for secure terminal command verification, while the Language Model Chat Provider API enables multiple language models through a model picker. API updates support HTTP 401 Unauthorized status code handling via `getSession`, essential for applications requiring Multi-Factor Authentication (MFA). A new secondarySidebar feature allows extensions to contribute view containers, enhancing user interface flexibility. Development and testing innovations include integrating Playwright with Machine Contextual Programming (MCP) to improve test automation through AI features. The update also addresses terminal functionality issues and acknowledges community contributions, encouraging continued engagement for testing new features and updates. **Bullet Point Summary:** - **Visual Studio Code Update (Version 1.104)**: - Release date: September 11, 2025. - Focus on improved AI workflow integration. - **Auto Model Selection**: - Chooses optimal chat models like Claude Sonnet 4, GPT-5, Gemini Pro 2.5. - Discounts for paid users in preview mode. - **Security Enhancements**: - `chat.tools.edits.autoApprove` setting introduced. - User confirmation required for editing sensitive files. - **AGENTS.md File Support**: - Experimental support for workspace context and instructions. - Enabled via `chat.useAgentsMdFile`. - **Custom Chat Modes & Prompt Files**: - Define custom chat modes through prompt files (`chat.promptFilesRecommendations`). - **Personalization Options**: - Customizable chat fonts (`chat.fontFamily`, `chat.fontSize`). - Enhanced tools picker feature. - **AI Workflow & Collaboration Improvements**: - Improved Chat Sessions view. - Better GitHub integration for task transitions. - **Task Delegation to Copilot**: - Initiates sessions from TODO comments and forwards context accurately. - **Terminal Enhancements**: - Auto approval settings with security configurations. - Transition to PowerShell shell integration. - Customizable timeout (`chat.tools.terminal.shellIntegrationTimeout`). - **General Features & Improvements**: - "Sharp Solarized" theme on vscode.dev. - Google account sign-in for GitHub Copilot users. - Automatic detection of input requests and error detection in tasks. - **Accessibility & UI Enhancements**: - Focused chat confirmation actions. - New editor settings (`editor.inlineSuggest.minShowDelay`, `window.border`). - **Extension Management**: - Manage extension account preferences via command. - Editor tab index display and customizable scrollbar visibility. - **Issue Reporting & Notebook Suggestions**: - Enhanced GitHub integration for issue reporting. - Improved edit suggestions in Jupyter notebooks. - **Programming Language Enhancements**: - Built-in IntelliSense removed for `bower.json` in JavaScript/TypeScript; transition to npm/yarn encouraged. - Python: Support for Pipenv environments, environment variable control (`python.useEnvFile`), AI-powered hover summaries (Pylance pre-release). - **Run Code Snippet Tool**: - Execute Python snippets directly via Pylance. - **General Enhancements in Python Development**: - Default IntelliSense across all Python documents. - Reliable execution of Python Activation Hooks. - Improvements to GitHub Pull Requests Extension. - **Extension Authoring & API Updates**: - `shellIntegrationNonce` for secure terminal command verification. - Support for handling HTTP 401 Unauthorized status with MFA via `getSession`. - **Secondary Sidebar Feature**: - Extensions can contribute view containers. - **Development and Testing Innovations**: - Integration of Playwright with MCP for AI-powered test automation. - **Community Engagement**: - Fixes for terminal issues. - Acknowledgment of community contributions. Keywords: AI agents, Authentication, Chat view, GitHub Copilot, IntelliSense, MCP server, Python environments, Sticky scroll, Visual Studio Code, settingsjson, terminal auto approve, workspace
github copilot
code.visualstudio.com a day ago
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150. HN Building the AI-powered local smart home**Summary:** As of September 11, 2025, Home Assistant has established itself as a leader in integrating AI within smart home ecosystems by focusing on local and controlled AI applications. Unlike major tech companies that often prioritize cloud-based solutions, Home Assistant empowers users to leverage AI capabilities—such as image recognition and summarization—while maintaining privacy through local processing. Users retain the option to disable AI features entirely if desired. Over recent years, significant strides have been made in incorporating user-friendly AI functionalities directly into Home Assistant’s interface, reflecting broader trends in AI development. In 2023, Home Assistant celebrated its "Year of the Voice," developing a local, open-source voice assistant enhanced by AI for more nuanced interactions beyond preset commands. This enabled users to converse with large language models (LLMs) through Home Assistant for tasks such as summarizing sensor data or engaging in trivia-like exchanges. The system emphasizes prompt responses to direct commands while using AI for complex inquiries and facilitates context sharing between voice agents for efficient task continuation. A walkthrough video illustrates these advancements within JLo's AI-enhanced home, showcasing features like interactive conversations initiated by Home Assistant and a revamped Text-to-Speech (TTS) setup that reduces audio delays. The Voice Preview Edition of Home Assistant provides an accessible entry point to advanced voice technologies with powerful audio processing capabilities in a compact design. AI integration extends into device control and automation suggestions, allowing users to assign different LLMs tailored to each environment. The introduction of AI Tasks enhances the ease of creating automations by generating data in various formats like JSON for templates and scripts. This functionality supports innovative applications such as detecting parking availability or counting chickens via video analysis without complex coding. Home Assistant's Model Context Protocol (MCP) facilitates seamless integration with LLMs, enabling access to diverse tools through MCP servers, such as news updates or personal catalogs. The platform can also serve as an MCP server itself, providing AI systems insights into home environments for automation purposes. An expert within the Home Assistant community has developed a leaderboard comparing cloud and local LLM options, highlighting competitive performance among newer models. With OpenRouter integration, over 400 new LLMs are accessible, further expanding AI functionalities and supporting AI Tasks from inception. Home Assistant's commitment to openness ensures user control over data and devices, advocating for privacy-focused solutions. As an entirely open-source project driven by a community without external investors, it relies on hardware sales and subscriptions to sustain its operations, ensuring the technology remains free and aligned with user priorities. **Bullet Point Summary:** - **AI Integration:** Home Assistant integrates AI locally in smart homes, focusing on privacy through local processing. - **User Control:** Users can opt out of AI features entirely; significant advancements have been made for user-friendly AI integration. - **Voice Assistant Development:** 2023 was marked as "Year of the Voice," with a focus on developing an open-source voice assistant enhanced by AI. - **AI Features and Performance:** The system supports flexible interactions with LLMs, reduces delays in TTS setups, and offers efficient context sharing between voice agents. - **Voice Preview Edition:** A user-friendly option providing advanced voice technology access, enhancing home device control with AI integration. - **Automation Enhancement:** AI Tasks facilitate the creation of automations by generating data in formats like JSON, supporting innovative applications like parking detection or chicken counting. - **Model Context Protocol (MCP):** MCP integrates LLMs into Home Assistant, allowing access to various tools and insights for automation. - **AI Model Comparison:** A leaderboard compares cloud and local LLM options, highlighting competitive performance among newer models. - **OpenRouter Integration:** Expands AI functionalities with access to over 400 new LLMs supporting AI Tasks from the start. - **Community-Driven Open Source:** Home Assistant remains open source and community-driven without external investors, emphasizing user control and privacy-focused solutions. Keywords: AI integration, Assist agent, Home Assistant, LLMs (Large Language Models), Ollama, Open Source, Voice Preview Edition, automation, image recognition, local assistant, sustainability, voice control
ollama
www.home-assistant.io a day ago
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151. HN Implementing Namespaces and Coding Standards in WordPress Plugin Development- The guide enhances WordPress plugin development by organizing codebases using PHP namespaces and coding standards, building on previous work with the multi-block plugin. - It introduces PSR-4 autoloading via Composer, reusable class structures, and automated linting for JavaScript, CSS, and PHP to improve development speed, collaboration, and scalability. - The setup assumes familiarity with a prior multi-block plugin but provides GitHub resources for alternative starting points, supporting both solo developers and teams. - A structured approach is outlined using PHP namespaces under `Advanced_Multi_Block`, Composer autoloading, and linting tools to maintain an organized codebase that prevents naming conflicts and enhances scalability. - The setup includes creating a `composer.json` in the plugin root for autoload settings, organizing class-based PHP files within a Functions folder, and introducing a `Plugin_Paths.php` class for dynamic access to plugin paths. - Two PHP files manage block registration: `Plugin_Paths.php` provides methods for retrieving plugin URLs and paths, while `Register_Blocks.php` handles block registration using WordPress hooks and checks for newer functions or loads manifest files. - The `Enqueue` class manages global asset entry points for both editor and frontend environments, with `.asset.php` files managing script dependencies. - An `Enqueues.php` file registers actions to enqueue scripts for editor and frontend, utilizing asset data from corresponding PHP files. - Composer's autoload feature is used in the main plugin file to load necessary classes, ensuring a clean structure where each class handles specific responsibilities. - Code consistency is maintained through linting and formatting using WordPress-recommended tools: ESLint with `.eslintrc.json` for JavaScript, Prettier with `.prettierrc` for code styling, and Stylelint with `.stylelintrc.json` for SCSS. - ESLint configuration includes custom rules and excludes specific directories/files from linting. Prettier is configured with style preferences, and directories are ignored as needed. - PHP_CodeSniffer with WordPress Coding Standards (WPCS) is used for PHP files, with a `phpcs.xml.dist` file specifying coding standards and exclusions. - The document outlines setting up Composer and npm for development, updating `composer.json` to integrate WP coding standards with PSR-4 autoloading, and defining scripts for linting and formatting tasks. - Composer's PSR-4 autoloading streamlines class management by eliminating manual inclusion, requiring regular updates via `composer dump-autoload`. - The practices outlined promote clean code management, scalability, maintainability, and collaboration, with gratitude extended to reviewers. This summary encapsulates the guide's focus on structured WordPress plugin development using namespaces, Composer autoloading, and linting tools to enhance efficiency, consistency, and scalability. Keywords: Add_action, Architecture, Asset Autoload, Autoload, Autoloading, Block Dependencies, CSS, Class-based Files, Classes, Code Style, Codebase Organization, Coding Standards, Collaboration, Composer, Dynamic Blocks, ESLint, Editor, Enqueue, Excludes, Exclusions, Formatting, Frontend, Functionality Separation, Functions Directory, GitHub, Interactive Blocks, JavaScript, Linting, Metadata Collection, Multi-Block Plugin, Namespace, Namespaces, Nodejs, PHP, PHPCS, PHP_CodeSniffer, PSR-4, Parallel Processing, Plugin Paths, Plugins, Prettier, Refactoring, Register Blocks, SCSS, Scalability, Scripts, Scripts Enqueues, Solo Development, Static Blocks, Stylelint, Team Projects, Vendor Folder, Versioning, WPCS, WordPress Plugin, WordPress Plugin Development, WordPress-plugin, Workflow, npm
github
developer.wordpress.org a day ago
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152. HN Comment Directives for Claude CodeThe text discusses how Claude Code leverages special comment directives embedded within a codebase to enhance development processes. A significant directive, `@implement`, directs Claude to execute specific changes and generate corresponding documentation like JSDoc for function signatures. This technique enables developers to intersperse prompts throughout their files, allowing Claude to systematically apply requested features by being directed to "implement all @implement directives." Additionally, while the `@docs` directive can incorporate external documentation within the code, caution is necessary due to potential security threats such as prompt injection attacks. The primary benefit of this method is that it integrates task management directly into the codebase, providing context-relevant instructions without relying on separate tools. This integration streamlines implementation by aligning with the natural workflow of coding environments. By using an editor for inline prompts with Claude Code, users can streamline their workflow and eliminate repetitive context explanations typically required in terminal-based interactions. - Claude Code uses special comment directives to enhance development processes. - The `@implement` directive directs specific code changes and documentation generation. - Developers can place prompts throughout files for systematic feature implementation. - The `@docs` directive can reference external documents, with caution due to security risks like prompt injection attacks. - This method integrates task management into the codebase, offering context-specific instructions without additional tools. - It streamlines implementation by aligning with coding workflows. - Using an editor for inline prompts eliminates repetitive terminal-based context explanations. Keywords: @docs, @implement, Claude Code, Comment directives, JSDoc, codebase, coding technique, contextual instructions, documentation, editor, external references, implementation, inline prompts, terminal, workflow
claude
giuseppegurgone.com a day ago
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153. HN Qwen3-NextThe text discusses "Qwen3-Next" and "Qwen," highlighting these as possibly representing an entity or product within the same category. It implies that "Qwen" could be part of a series, with "3-Next" suggesting it may denote either the third version in the sequence or an upcoming iteration, indicating progress or advancement. The mention of "3-Next" specifically points to potential new developments or enhancements in the existing Qwen series. The focus is on identifying this as a significant update or progression within its series. **BULLET POINT SUMMARY:** - The text centers around "Qwen3-Next" and "Qwen," indicating they are related entities or products. - It suggests that these might belong to a sequential series, with "Qwen" being part of an ongoing development line. - "3-Next" implies this could be the third version or hints at future iterations, signaling new advancements or improvements. - The primary emphasis is on recognizing "Qwen3-Next" as a noteworthy update in its respective series. Keywords: Next, Next Keywords: Qwen3-Next, Qwen, Qwen3-Next, version
qwen
qwen.ai a day ago
https://huggingface.co/deepseek-ai/DeepSeek-R1/blo a day ago https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct a day ago https://x.com/karpathy/status/1697318534555336961 a day ago https://goombalab.github.io/blog/2025/hnet-future& a day ago https://karpathy.ai/zero-to-hero.html a day ago https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct a day ago https://pastebin.com/raw/SH6JHteg a day ago https://en.wikipedia.org/wiki/Jevons_paradox a day ago https://arxiv.org/pdf/2506.02153 a day ago https://www.bls.gov/oes/2023/may/oes434051.ht a day ago https://artificialanalysis.ai/#frontier-language-model-intel a day ago https://open.substack.com/pub/outsidetext/p/h a day ago https://www.asciiart.eu/cartoons/spongebob-squarepants a day ago https://arxiv.org/pdf/2412.06464 a day ago https://arxiv.org/abs/2505.06708 a day ago https://archive.is/JH9XL a day ago https://github.com/PicoTrex/Awesome-Nano-Banana-images& a day ago https://huggingface.co/Qwen/Qwen-Image-Edit a day ago https://github.com/lyogavin/airllm a day ago |
154. HN ChatGPT Gains Support for External MCP ServersOpenAI has significantly upgraded ChatGPT by enabling support for external Multi-Channel Platform (MCP) servers, allowing seamless integration with third-party applications and services. This enhancement transforms ChatGPT into a versatile tool capable of creating custom AI agents that can perform actions within external programs directly from the chat interface. Developers now have the ability to craft connectors in Developer Mode, facilitating write operations such as updating Jira tickets or triggering Zapier workflows, thus enabling complex automation processes. This functionality is accessible to Pro and Plus subscribers via the Connectors section in settings. The update elevates ChatGPT beyond a mere text generator, positioning it as a practical workspace tool that integrates seamlessly into users' digital ecosystems. - OpenAI has enhanced ChatGPT with support for external Multi-Channel Platform (MCP) servers. - This allows seamless integration of ChatGPT with third-party applications and services. - Developers can create custom AI agents to perform actions within external programs from the chat interface. - Developer Mode enables creation of connectors for write operations, such as updating Jira tickets or triggering Zapier workflows. - The new functionality supports complex automations. - Available to Pro and Plus subscribers through the Connectors section in settings. - ChatGPT is now a practical workspace tool that integrates with users' digital ecosystems. Keywords: Automations, Business Tools, ChatGPT, Connectors, Developer Mode, Developers, Digital Ecosystems, External MCP Servers, Integration, OpenAI, Plus Subscribers, Pro Subscribers, Third-party Applications
openai
nextweekai.com 2 days ago
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155. HN Volvo CEO: EV wave will kill a few Western automakers**Summary:** Håkan Samuelsson, former CEO of Volvo, has temporarily resumed leadership at the company due to declining stock performance post-retirement. In a Bloomberg interview, he highlighted that the rapid shift towards electrification could lead to numerous Western automakers exiting the market because of competition from Chinese brands like BYD and Xiaomi. Samuelsson anticipates a fully electric industry within the next decade, suggesting new global leaders will emerge in this domain. Despite facing challenges, including potential US sales restrictions due to its majority owner Geely being Chinese, Volvo is committed to electrification and remains hopeful about enduring these industry changes. The article outlines significant disruptions faced by traditional automakers such as Volvo, driven by innovative startups like Tesla and Rivian and fast-moving Chinese manufacturers. These developments are causing considerable pressure on established companies in the automotive sector. However, it's unclear which firms will succeed in transitioning to electric vehicles (EVs). The piece concludes by inviting readers to speculate on which automakers may not survive this industry shift. **Bullet Point Summary:** - Håkan Samuelsson has temporarily returned as CEO of Volvo due to declining stock performance following his retirement. - He predicts rapid electrification could cause several Western automakers to go out of business, citing competition from Chinese brands like BYD and Xiaomi. - Samuelsson forecasts a fully electric automotive industry within ten years with the emergence of new global players. - Despite challenges related to its majority owner Geely, such as potential US sales bans, Volvo is committed to electrification and optimistic about surviving the transition. - The article discusses how traditional automakers like Volvo face disruption from startups (e.g., Tesla, Rivian) and fast-growing Chinese manufacturers. - It's uncertain which companies will successfully navigate the shift to electric vehicles, and readers are invited to predict which might not endure. Keywords: BYD, Bloomberg, CEO, China, Chinese ownership, EV wave, Geely, Håkan Samuelsson, Rivian, Tesla, US sales ban, Volvo, Western automakers, Xiaomi, auto industry, dominant players, electric transition, electrification, leadership, manufacturing pace, pressure, public offering, restructuring, stock
tesla
electrek.co 2 days ago
https://www.bloomberg.com/news/features/2025-09-09 a day ago https://archive.today/MbAGg a day ago |
156. HN Peak BubbleThe article addresses concerns about inflated valuations within the technology sector, focusing on companies like OpenAI and Oracle. It highlights skepticism regarding OpenAI's valuation despite lacking $300 billion in assets or anticipated profitability until 2030. Similarly, Oracle faces scrutiny due to its nearly 50% surge in valuation based on a large, non-binding contract that lacks certainty. This situation is part of a broader trend where tech valuations surpass realistic future earnings, with analysts and investors deeming these projections overly optimistic. The skepticism extends to Oracle's claims about gaining significant cloud market share. If Oracle's assertions were valid, competitors such as Microsoft, Amazon, Google, and CrowdStrike would likely experience adverse effects on their stock prices. However, the continued rise in most of these companies' stocks, aside from a minor dip for Amazon, indicates disbelief in Oracle’s projections. The article warns that the current tech market is driven by inflated expectations similar to unsustainable hype, with future revenue claims and infrastructure needs described as exaggerated. It suggests that society is at the peak of a speculative bubble, which could lead to a downturn when these exaggerations are exposed. **Bullet Point Summary:** - **Inflated Tech Valuations:** The article critiques overestimated valuations in the tech sector, focusing on companies like OpenAI and Oracle. - **OpenAI's Skepticism:** Despite claims of future profitability by 2030, OpenAI lacks $300 billion in assets or immediate profits. - **Oracle’s Uncertain Deal:** Oracle’s valuation has increased nearly 50% due to a large yet uncertain contract, reflecting broader issues of unrealistic tech market valuations. - **Market Reaction:** Skepticism exists over Oracle's cloud market share claims; competitor stocks remain stable, indicating disbelief in Oracle’s projections. - **Speculative Bubble Warning:** The article warns that current expectations are based on exaggerated growth prospects and infrastructure needs, suggesting an impending downturn as these exaggerations unravel. Keywords: AGI, AMZN, CRWV, GDP, GOOG, GPT-5, GenAI, MSFT, ORCL, OpenAI, Substack, backlog, bubble, chips, cloud, contracts, datacenters, equities, investor, profit, revenue, stocks, tech market, valuation
openai
garymarcus.substack.com 2 days ago
https://www.amazon.com/Difference-Between-God-Larry-Ellison& a day ago https://www.amazon.com/Softwar-Intimate-Portrait-Ellison-Ora a day ago |
157. HN ProStore iOS – GitHubThe provided text highlights "ProStore iOS" hosted on GitHub and emphasizes the importance placed by its authors or maintainers on considering user feedback meticulously. This indicates a commitment to improving the project based on community input, suggesting an open and responsive development process. Additionally, there is a specific request for including an email address for contact purposes, which underscores the team's openness to direct communication with users or contributors who wish to provide feedback, ask questions, or engage further with the project. **Bullet Point Summary:** - The text discusses "ProStore iOS," a project available on GitHub. - It highlights that all user feedback is carefully considered by the authors or maintainers of the project. - There is an emphasis on the value of community input for improving and developing the project. - A specific request is made to include an email address for direct contact purposes, indicating openness to communication. Keywords: GitHub, ProStore, contact, email address, feedback, iOS, input, technical
github
github.com 2 days ago
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158. HN FTC Investigates AI Companions: OpenAI, X, Meta, Google, Snap, IG and CharacterThe Federal Trade Commission (FTC) has launched an investigation into seven companies—Alphabet, Character Technologies, Instagram, Meta Platforms, OpenAI OpCo, Snap, and X.AI—that develop AI-powered chatbots. This inquiry aims to examine how these firms assess and address potential negative impacts of their technology on children and teenagers. The focus is on understanding how these generative AI chatbots might influence young users by fostering trust-based relationships with them. The FTC's investigation seeks detailed information on safety measures implemented for minors, including restricting youth access and informing both users and parents about potential risks. This inquiry, utilizing the FTC’s 6(b) authority, does not have immediate law enforcement intentions but rather aims to explore industry practices comprehensively. It aligns with broader objectives under the Trump-Vance administration to enhance online child safety while supporting AI innovation. Additionally, the investigation scrutinizes compliance with the Children's Online Privacy Protection Act (COPPA), examining how these companies monetize engagement, process user interactions, develop chatbot characters, and manage data. The FTC plans to gather detailed information from these companies through 6(b) orders, a unanimous decision reflecting its commitment to understanding these issues. Separate statements were released by Commissioners Melissa Holyoak and Mark R. Meador, with Alysa Bernstein and Erik Jones leading the efforts within the Bureau of Consumer Protection. The investigation encompasses assessing disclosures, advertising practices, terms of service compliance monitoring, and handling personal information. **BULLET POINT SUMMARY:** - FTC investigating Alphabet, Character Technologies, Instagram, Meta Platforms, OpenAI OpCo, Snap, X.AI for their AI chatbots. - Focus on impact on children's privacy and safety, particularly in light of COPPA Rule compliance. - Examination of monetization, user interactions, character development, and data management. - Inquiry under 6(b) authority to explore industry practices without immediate enforcement actions. - Aims align with protecting children online while promoting AI innovation. - Unanimous vote for issuing 6(b) orders to gather information from companies. - Separate statements by Commissioners Holyoak and Meador; Bernstein and Jones lead the Bureau of Consumer Protection efforts. - Investigating disclosures, advertising practices, compliance monitoring, and personal data handling. Keywords: 6(b) authority, AI Companions, Community guidelines, FTC, Google, IG, Meta, OpenAI, Snap, advertising, age restrictions, chatbots, children, compliance, disclosures, effects, generative AI, innovation, investigation, personal information, protection, relationships, risks, safety, study, technology, teens
openai
www.ftc.gov 2 days ago
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159. HN simdjson Version 4.0.0 Released**Summary:** Simdjson Version 4.0.0 introduces several significant features and enhancements as a major release developed through community collaboration. The update includes a Builder API that simplifies converting data structures into JSON strings, such as converting `std::vector` of vectors of doubles directly to JSON. An experimental feature for C++26 static reflection allows parsing and serializing custom classes without additional code, demonstrated with a `Car` struct example. Additionally, the library introduces a new shortcut function, `simdjson::from`, which simplifies deserializing JSON strings into objects, including complex types like arrays of `Car`. For improved performance in multi-threaded environments, a thread-local parser can be accessed via `simdjson::ondemand::parser::get_parser()`. The release also addresses various issues, enhances the library's capabilities, and improves documentation. Issues resolved include MSYS2 problems, adding support for `std::ranges` in the DOM API, updating build configurations, and clarifying usage of NDEBUG. Enhancements incorporate C++26 static reflection implementation, improved fallback implementations, serialization speed boosts, and better organization for usability. Documentation improvements focus on clearer guidance regarding raw JSON access and restructuring to mark certain functions as experimental. **BULLET POINT SUMMARY:** - **New Features in Version 4.0.0:** - Introduction of the Builder API for easy conversion of data structures into JSON strings. - Experimental support for C++26 static reflection for parsing and serializing custom classes without additional code. - New `simdjson::from` function to simplify deserializing JSON strings, including complex types like arrays of `Car`. - Thread-local parser via `simdjson::ondemand::parser::get_parser()` for enhanced multi-threaded performance. - **Key Updates and Enhancements:** - Resolution of various issues such as MSYS2 problems, support for `std::ranges` in the DOM API, build configuration updates, and clarification on NDEBUG usage. - Implementation of C++26 static reflection by @lemire. - Improvements in fallback implementations and serialization speeds. - Organizational changes for better usability, including fixing linker errors and removing outdated `cmake_policy`. - Enhanced documentation for raw JSON access and restructuring to mark `simdjson::from` as experimental. - **Community Contributions:** - Multiple contributions by @lemire, including restructuring code, discouraging unsafe practices, renaming macros, extending builders, adding benchmarks, and enhancing error type documentation. - New contributors include @xkszltl, @evbse, @abbator, @iboB, @dabigjoe6, @joshuagawley, and @felipee-monteiro making their first contributions. - **Documentation Improvements:** - Better guidance on raw JSON access. - Reorganization of convert code to expose `simdjson::from` as experimental. This summary captures the essence of the text by focusing on major updates, resolved issues, enhancements, and community contributions while omitting extraneous details for clarity. Keywords: C++26 static reflection, CI, Docker, GitHub, SIMDJSON_STATIC_REFLECTION, builder API, documentation, ondemand parser, parsing, release, serialization, simdjson, thread-local parser, version 400
github
github.com 2 days ago
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160. HN Improving Cursor Tab with RLCursor aims to significantly boost developer productivity by enhancing its Cursor Tab system, which utilizes machine learning to predict and suggest developers' next actions within their codebase. The system processes over 400 million interactions daily and offers real-time updates via online reinforcement learning, using user feedback to refine suggestion accuracy continually. This method is more dynamic compared to traditional LLM providers who rely on static datasets and infrequent model updates. A major challenge for the Tab system is maintaining a high acceptance rate for its suggestions to prevent workflow disruptions caused by incorrect recommendations. To address this, Parth Thakkar's 2022 research found that GitHub Copilot uses logistic regression to calculate a "contextual filter score" from 11 features, filtering out low-probability suggestions. Instead of filtering, Cursor opted to integrate policy gradient methods into the Tab model, optimizing its predictive policy to maximize rewards based on user acceptance. The reward system was designed such that accepted suggestions yielded a reward of 0.75, rejections -0.25, and non-suggestions zero, with a suggestion shown if its acceptance probability is at least 25%. The Policy Gradient Theorem underpins this approach by linking the gradient of the reward function to expected rewards from chosen actions, emphasizing the use of on-policy data for policy updates. The optimization involves estimating gradients through sampling and employing stochastic gradient descent. Due to the necessity of on-policy samples, efficient deployment infrastructure is critical as it requires time-consuming data collection post-deployment. With these methods, a new Tab model has been implemented in Cursor, resulting in 21% fewer suggestions but with a 28% higher acceptance rate, thereby enhancing the developer coding experience. **BULLET POINT SUMMARY:** - Cursor's Tab system uses machine learning to predict and suggest developers' actions based on over 400 million daily interactions. - The system employs online reinforcement learning for real-time updates, unlike traditional LLM providers who update less frequently. - A key challenge is maintaining a high acceptance rate for suggestions to avoid workflow disruption from incorrect recommendations. - Parth Thakkar's research shows GitHub Copilot uses logistic regression for filtering low-probability suggestions based on a "contextual filter score." - Cursor integrates policy gradient methods into the Tab model, optimizing its predictive policy using rewards: 0.75 for acceptance, -0.25 for rejection, and 0 for no suggestion. - The Policy Gradient Theorem is crucial for linking reward gradients to expected action values, emphasizing on-policy data use. - Optimization involves sampling-based gradient estimation and stochastic gradient descent, necessitating efficient infrastructure due to time-consuming post-deployment data collection. - A new Tab model in Cursor shows 21% fewer suggestions but has a 28% higher acceptance rate, improving the developer experience. Keywords: Cursor Tab, GitHub Copilot, LLM providers, Policy Gradient Theorem, PyTorch, accept rate, codebase, contextual filter score, deploy model, developers, dynamic training, features, infrastructure, logistic regression, noisy suggestions, on-policy data, optimization, policy gradient methods, predictions, productivity, reinforcement learning, reward, static datasets, stochastic gradient descent, suggestions, training process, user actions
github copilot
cursor.com 2 days ago
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161. HN LLM-optimizer: Benchmark and optimize LLM inference across frameworks with ease**Summary:** The `llm-optimizer` is a Python tool designed to benchmark and optimize the inference performance of open-source large language models (LLMs) across various frameworks like SGLang and vLLM. It facilitates easy comparison and optimization by automatically identifying optimal setups, applying service level objective (SLO) constraints, and estimating theoretical performance without requiring full benchmarks. Users can visualize interactive results through dashboards to enhance analysis. The tool supports benchmarking by testing combinations of model parameters such as tensor parallel size, data parallel size, prefill sizes, and concurrency levels. For example, SGLang users can evaluate different tensor/data parallelism combinations with varied chunked prefill sizes across multiple client concurrency values, generating a set number of unique benchmarks. Similarly, vLLM supports batch size tuning among other parameters. `llm-optimizer` allows the application of constraints to focus on configurations that meet specific service level objectives (SLOs) like latency targets, and results are saved in JSON files for further analysis. The tool currently supports constraints on performance metrics such as Time to First Token (TTFT), Inter-Token Latency (ITL), and end-to-end latency using statistical measures like mean, median, p95, or p99. An interactive visualization feature is available for exploring benchmark results stored in JSON format, allowing users to visualize metrics such as TTFT, ITL, and concurrency. This includes Pareto frontier analysis through a dashboard accessible at `http://localhost:8080/pareto_llm_dashboard.html`, where users can compare multiple runs, explore trade-offs, and identify optimal configurations. In addition to benchmarking, the tool facilitates server management for supported frameworks by allowing custom server commands beyond its default support. For SGLang and vLLM servers, parameters such as model paths, host settings, ports, concurrency levels, and tensor parallelism sizes can be customized. It offers various tuning options for inference parameters on both client-side and server-side. Client parameters include setting the maximum number of concurrent requests, total requests to send, dataset choices for request generation (e.g., ShareGPT or random), and sequence lengths. The tool is compatible with several GPUs like H100, H200, A100, L20, L40, B100, and B200, featuring accurate TFLOPS specifications. This enables performance experimentation across different configurations to optimize machine learning server operations. Developed by the BentoML team, `llm-optimizer` is an actively maintained open-source project that invites community contributions through their Slack channel. The project emphasizes code formatting with "ruff" and type checking with "mypy." Acknowledgements are given to vllm-project/vllm and sgl-project/sglang for providing backend and benchmark client codes foundational to this project. **Bullet Point Summary:** - `llm-optimizer` is a Python tool for benchmarking and optimizing inference performance of LLMs across frameworks like SGLang and vLLM. - It automatically finds optimal setups, applies SLO constraints, and estimates theoretical performance without full benchmarks. - Users can visualize results interactively with dashboards to analyze performance metrics effectively. - Supports testing various model parameters (e.g., tensor parallel size, data parallel size) across frameworks. - Results are saved in JSON files, supporting performance metric constraints like TTFT, ITL, and end-to-end latency. - Offers interactive visualization for exploring benchmark results and Pareto frontier analysis via a dashboard. - Facilitates server management with custom commands for supported frameworks, allowing parameter customization. - Provides tuning options for both client-side (e.g., max concurrent requests) and server-side parameters. - Compatible with multiple GPUs (H100, H200, A100, L20, L40, B100, B200), enabling performance experimentation. - Developed by the BentoML team, it is an open-source project encouraging community contributions. - Emphasizes code formatting with "ruff" and type checking with "mypy." - Acknowledges foundational support from vllm-project/vllm and sgl-project/sglang. Keywords: GPU allocation, ITL, LLM Optimizer, LLM Performance Explorer, Pareto frontier, SGLang, Slack, TFLOPS, TP/DP, TTFT, benchmarking, client-server, concurrency, configurations, contributing, dataset generation, inference parameters, mean, median, open-source, p95, p99, performance metrics, server commands, tensor parallelism, vLLM
llm
github.com 2 days ago
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162. HN Show HN: OllaMan – An Elegant GUI for Ollama AI Model Management and Chat**Summary:** OllaMan is a new macOS graphical user interface aimed at enhancing the management and interaction with Ollama AI models. It provides an elegant and intuitive platform that simplifies operations traditionally performed via command line, catering specifically to users who prefer more visual interactions. The application allows for easy local model management, one-click discovery, installation, and chat functionalities with real-time responses. Additional features include multi-server configuration capabilities and a modern design aligned with macOS aesthetics. To boost productivity, OllaMan supports batch operations, session exports, and filtering of models. It's particularly beneficial for AI researchers, developers, and content creators looking to streamline their workflow with local language models (LLMs) without relying on command-line instructions. The software is available in both free and paid versions, with advanced features accessible through one-time purchases. Users are encouraged to try OllaMan and provide feedback. Testimonials from users such as AI researcher ST Sarah T. and software engineer Mark R. emphasize how the application has transformed their workflow by offering a user-friendly interface that simplifies model management tasks. **Bullet Point Summary:** - **Purpose:** OllaMan enhances interaction with Ollama AI models through an elegant macOS GUI, eliminating the need for command-line use. - **Key Features:** - Local model management and one-click installation - Interactive chat interface with real-time responses - Multi-server configuration support - Modern macOS design - **Productivity Enhancements:** - Batch operations - Session export capability - Model filtering options - **Target Audience:** AI researchers, developers, content creators who prefer GUI over command-line for managing LLMs. - **Availability:** Free and paid versions available; advanced features accessible through one-time purchases. - **User Feedback:** - ST Sarah T. appreciates the user-friendly interface that simplifies model management. - Mark R. highlights time savings in switching between models for testing. - **Call to Action:** Users are encouraged to download OllaMan from [ollaman.com](https://ollaman.com/) and provide feedback. Keywords: AI, Batch Operations, Chat, Command-Line Interaction, Content Creators, Design Principles, Developers, GUI, Interface, Keyword Extraction, Large Language Models, Local Models, Model Management, Multi-Server Management, OllaMan, Productivity Features, Researchers, Session Export, Software Engineer, Workflow Streamlining, macOS
ollama
ollaman.com 2 days ago
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163. HN Hacktoberfest 2025Hacktoberfest 2025 is an event focused on supporting open-source projects that are vital for the internet's functionality. It emphasizes collaboration among community-focused developers who maintain these projects. DigitalOcean plays a significant role in this initiative by providing credit grants and assistance, encompassing development, infrastructure, and testing efforts to improve these open-source endeavors. The event actively encourages individuals with relevant skills to contribute their expertise, thereby helping sustain and enhance essential digital projects that are foundational to the internet. **BULLET POINT SUMMARY:** - Hacktoberfest 2025 focuses on supporting crucial open-source projects. - It emphasizes collaboration among community-focused developers. - DigitalOcean supports the initiative through credit grants and technical assistance (development, infrastructure, testing). - Individuals with relevant skills are encouraged to contribute their expertise to sustain these essential projects. Keywords: DigitalOcean, Hacktoberfest, boost, coders, community, credit grants, development, infrastructure, internet, open-source, projects, skills, testing
digitalocean
hacktoberfest.com 2 days ago
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164. HN The Power of andThe text explores various themes related to the fallacy of mutually exclusive propositions, both in logic and societal contexts, arguing against rigid binary thinking that often limits creativity and potential in numerous fields. 1. **Mutually Exclusive Propositions**: The concept is explained as a logical construct where two events cannot occur simultaneously, commonly represented by non-overlapping circles in Venn diagrams. Outside mathematics, this mindset can be limiting when applied to everyday decisions and societal stereotypes, such as the binary choice between being an athlete or nerd, or choosing affordable versus luxurious cars. 2. **Challenging False Dichotomies**: The text highlights how ambitious individuals often defy these false dichotomies by adopting a "both/and" mindset. Examples include Myron Rolle and Bridgit Mendler succeeding in multiple domains, and Elon Musk’s efforts at Tesla to create affordable luxury cars through precision manufacturing. 3. **Innovations in Industries**: The discussion extends to innovative approaches transforming industries. Travis Kalanick's CloudKitchens aims to elevate fast food quality using top ingredients and machines for efficient production, potentially making cooking a leisurely activity. Similarly, Boom’s Overture aircraft addresses the economic and operational issues of supersonic travel that plagued Concorde by aiming for quieter and more fuel-efficient flights at Mach 1.7. 4. **AI Development Trade-offs**: The text delves into AI development, contrasting large models like GPT-5 with smaller versions such as Mistral’s Small-3.1. It emphasizes the need for on-device expert-level AI to enable seamless real-time interactions in applications like AR glasses, citing Sesame Labs as a promising solution due to their impressive voice demo capabilities. 5. **Integration Philosophy**: An overarching theme is the "and-yes" philosophy, which encourages integrating multiple possibilities rather than choosing between binary options. This mindset enhances technological advancements by combining advanced AI with practical hardware solutions for success in next-generation technologies like AR glasses. In summary, the text advocates for a shift from mutually exclusive thinking to an integrative approach that embraces complexity and potential across various fields, highlighting successful examples of this philosophy in action. Keywords: AI, AR glasses, Boom, CloudKitchens, EDITH, Elon Musk, GPT-5, Mach 2, McDonald’s, Mercedes-Benz finish, Mutually exclusive, Overture, Sesame Labs, Symphony engine, Tesla, Venn diagram, affordable, affordable price, assembly line, automated, cars, caution, economical, edge, embedded use, events, exclusions, fast, feasible, fine dining, gourmet, gourmet food, hallucinations, high quality ingredients, integrated dish, intelligence, labour costs, luxury, machines, manufacturers, mass distribution, mass-market car, mastery, mathematics, minimal human interaction, models, mutual inclusion, near-fast-food prices, noise standards, on-device, optimise, or and, outliers, parameters, precision manufacturing, premium feel, probability, productivity, propositions, robot kitchen, scale, small, sound engineering, superposition, supersonic, swap, time, transatlantic routes
tesla
lucasbarbosa.net 2 days ago
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165. HN Show HN: Polaris Audit – Website Compliance Scanner with Fix Instructions**Summary:** Polaris Audit is a specialized website compliance scanner aimed at aiding small businesses in addressing various issues that affect their websites' performance and compliance. The tool identifies critical security vulnerabilities, such as HTTPS, SSL, and header configurations; it also evaluates GDPR compliance by checking cookies and privacy policy adherence. Furthermore, Polaris Audit addresses accessibility concerns, including the presence of alt text for images, proper use of headings, and keyboard navigation support. What sets Polaris Audit apart from other similar tools is its ability to not only detect these issues but also provide direct, actionable solutions that users can implement quickly through copy-paste code snippets. For instance, it simplifies the process of adding a missing page title, which can be resolved in under two minutes. Developed using the Django framework, React for the frontend, and PostgreSQL as its database system, Polaris Audit offers comprehensive reports at no cost. Users have the option to try paid accounts without any charge initially. The tool invites feedback on its unique approach and is accessible via polarisaudit.com. **Bullet Point Summary:** - **Purpose:** Polaris Audit assists small businesses in resolving website compliance issues. - **Key Features:** - Identifies security vulnerabilities (HTTPS, SSL, headers). - Checks GDPR compliance (cookies, privacy policy). - Evaluates accessibility concerns (alt text, headings, keyboard navigation). - **Unique Selling Point:** Provides direct code solutions for fixing identified issues quickly. - **Development:** Built using Django, React, and PostgreSQL. - **Offerings:** - Detailed reports available for free. - Option to try paid accounts at no cost. - **Feedback and Access:** Feedback is welcomed; the tool can be accessed at polarisaudit.com. Keywords: Accessibility, Alt Text, Django, Feedback, Fix Instructions, Free Reports, GDPR, HTTPS, Keyboard Navigation, Paid Account, Polaris Audit, PostgreSQL, Privacy Policy, React, SSL, Scanner, Security Problems, Small Businesses, Website Compliance, Website Issues
postgresql
news.ycombinator.com 2 days ago
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166. HN ImageSlim, an open-source, free Mac compression tool on GitHubImageSlim is an open-source image compression tool available for Mac users on GitHub, offering efficient and free image compression capabilities. It includes features such as selecting a download path in the settings and using a "Download All" button that allows multiple images to be saved as a Zip archive. The software emphasizes ease of use by providing clear instructions and adheres strictly to privacy policies designed to protect user data. **BULLET POINT SUMMARY:** - ImageSlim is an open-source, free image compression tool for Mac. - Available on GitHub. - Features include selecting download paths and saving multiple images as a Zip archive using the "Download All" button. - Designed with ease of use in mind through clear instructions. - Adheres to privacy policies aimed at protecting user data. Keywords: GitHub, ImageSlim, Mac, Zip archive, compression, download, images, open-source, privacy policy, settings, software, technology, tool
github
github.com 2 days ago
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167. HN OpenAI and Microsoft sign preliminary deal to revise partnership termsOpenAI and Microsoft have entered into discussions to revise their partnership terms, reflecting growing complexities in their relationship. Although they have reached a non-binding agreement, both companies are actively working toward finalizing a binding contract while continuing to focus on the development of safe AI technologies. This situation unfolds as OpenAI transitions from a nonprofit organization to a for-profit entity—a move that necessitates approval from its primary investor, Microsoft, which has committed over $13 billion since 2019. Amidst this transition, OpenAI is emerging as a significant competitor in the AI market with an estimated valuation of $500 billion. This rise in stature has led to increased competition between the two entities and heightened tensions due to OpenAI's demand for computational resources that exceed Microsoft's capacity to provide. Further complicating matters are contractual stipulations concerning access to OpenAI technology once they achieve Artificial General Intelligence (AGI), which is defined by its ability to generate at least $100 billion in profit. Initially, OpenAI had plans to transition entirely into a for-profit entity; however, these plans were abandoned due to opposition from former employees, regulators, and critics, including notable figures like Elon Musk. Musk has initiated legal action against the conversion, asserting that it undermines OpenAI's foundational mission as a nonprofit aimed at benefiting humanity. **BULLET POINT SUMMARY:** - **Partnership Revision**: OpenAI and Microsoft are revising their partnership terms amid growing complexities. - **Non-Binding Agreement**: Both companies have reached a non-binding agreement while aiming to finalize a binding one, focusing on safe AI delivery. - **For-Profit Transition**: OpenAI is transitioning from nonprofit to for-profit, requiring approval from its largest investor, Microsoft, which has invested over $13 billion since 2019. - **Market Competition**: As a $500 billion-valued company, OpenAI's rise as an AI market competitor has increased tensions with Microsoft due to resource demands and contractual issues regarding AGI access. - **Legal Opposition**: Plans for complete conversion to for-profit status were abandoned following opposition from former employees, regulators, and critics like Elon Musk, who legally contested the move as undermining OpenAI’s nonprofit mission. Keywords: AGI (artificial general intelligence), AI market, Elon Musk, Microsoft, OpenAI, compute capacity, contract terms, for-profit entity, infrastructure needs, lawsuit, memorandum of understanding (MOU), non-binding agreement, nonprofit, partnership, preliminary deal, profit, restructure
openai
arstechnica.com 2 days ago
https://news.ycombinator.com/item?id=45216376 a day ago |
168. HN AI Coding at a Crossroads: Disposable Code Editors and Flawed Benchmarks### Summary The article explores the dynamic landscape of AI-driven code editors within Integrated Development Environments (IDEs), emphasizing their increasing centrality yet contested nature in developers' workflows. The market for these tools is expanding rapidly, with a global valuation around $15 billion. Despite this growth, lightweight editors like Sublime Text and VS Code remain prevalent due to fierce competition among new entrants that often rely on existing platforms rather than innovating from scratch. Zed distinguishes itself as an independent AI code editor offering a unique experience but lacks the mature ecosystem of established tools such as VS Code, which benefits from efficiency, open-source accessibility, and a broad extension range. The article notes that success in this space requires more than just tool performance due to low switching costs and the fact that revenue often goes to model owners like Anthropic and OpenAI. The author reflects on personal experiences with various AI models, noting marginal performance improvements at significant costs as codebases grow in complexity, necessitating sophisticated but potentially excessive tools. Anthropic's approach simplifies infrastructure for efficiency, though its effectiveness is debated. K2-0905, evaluated in Roo Code Eval, showed potential advantages in speed and cost despite a slightly lower score compared to Opus 4.1. Concerns are raised about the reliability of benchmark datasets for evaluating language models, as high scores can be misleading due to overfitting—where models memorize data instead of generalizing it. This is exacerbated by publicly available benchmarks being incorporated into training data, thus contaminating sources and prioritizing marketing strategies. The article references a 2025 paper proposing a technique to assess overfitting more accurately by making subtle prompt changes that significantly impact model performance without human notice. ReCode's methodology highlights the need for robustness against perturbations in code generation models like CodeGen, InCoder, and GPT-J, showing that while larger models perform well on standard inputs, they struggle with changes. Despite its insights, ReCode has not been applied to recent top models. CodeFort, introduced in 2025, aims to address such weaknesses through robust training. The article critiques traditional evaluations for their inability to differentiate between AI tools effectively due to feature parity across platforms like VS Code. It underscores the performance gap between large and smaller models on new datasets, suggesting overfitting issues with the latter. The author invites readers to share personal experiences and preferences regarding AI coding tools, probing into past changes, motivations, and future expectations concerning LLMs. ### Key Points - **IDE Landscape:** Rapid growth in AI-driven IDE market; lightweight editors dominate despite competition. - **Zed's Positioning:** Offers a unique experience as an independent editor but lacks ecosystem maturity compared to VS Code. - **Market Challenges:** Success requires more than performance due to low switching costs and revenue flowing mainly to model owners. - **AI Model Evaluation Concerns:** High benchmark scores may be misleading due to overfitting; public benchmarks can contaminate training data. - **ReCode's Approach:** Tests robustness by altering prompts, revealing overfitting in models like CodeGen and GPT-J. - **Evaluation Practices Critique:** Traditional evaluations struggle to differentiate tools due to feature parity, with large models outperforming smaller ones on new datasets. - **Reader Engagement:** Encourages sharing experiences and expectations about AI coding tools and the future role of LLMs. Keywords: AI coding, Anthropic, Claude Code, HumanEval, IDEs, LLM, OpenAI, ReCode, Sublime Text, UX, VS Code, Vibe-Code, Zed, architecture, code editor market, devtools, ecosystem, evals, extensions, innovation, lightweight editors, marketplace revolution, robustness evaluation
llm
kevinkuipers.substack.com 2 days ago
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169. HN Float ExposedThe text provides an evaluation of different floating-point number formats: half bfloat, float, and double. It explains how these numbers are assessed within the significand-exponent range using both base-2 and base-10 systems. The specific notation \((−1 2)^2 × 10^2 (2^{-2}) × .2\) is used to illustrate precise calculations of these values. Furthermore, it highlights the exact base-10 value for each format and discusses measuring the delta—or difference—to the next or previous representable values. This evaluation focuses on the precision and accuracy in representing numbers using floating-point arithmetic. **BULLET POINT SUMMARY:** - Evaluation of half bfloat, float, and double formats. - Assessment within significand-exponent range in base-2 and base-10 systems. - Use of notation \((−1 2)^2 × 10^2 (2^{-2}) × .2\) for precise calculations. - Highlighting exact base-10 values for each format. - Measurement of delta to next or previous representable values. - Focus on precision and representation accuracy in floating-point arithmetic. Keywords: Base-10, Base-2, Delta, Evaluation, Exact Value, Exposed, Float, Position, Power of 10, Representable Value, Scientific Notation, Significand–Exponent Range, double, half bfloat
popular
float.exposed 2 days ago
https://fabiensanglard.net/floating_point_visually_explained a day ago https://news.ycombinator.com/item?id=29368529 a day ago https://news.ycombinator.com/item?id=45200925 a day ago https://en.cppreference.com/w/cpp/utility/to_ a day ago https://en.wikipedia.org/wiki/Axis%E2%80%93angle_repres a day ago https://en.wikipedia.org/wiki/Axis%E2%80%93angle_repres a day ago https://github.com/ulfjack/ryu a day ago https://en.cppreference.com/w/cpp/types/numer a day ago https://doc.rust-lang.org/src/core/num/f32.rs a day ago https://minecraft.wiki/w/Far_Lands a day ago https://www.h-schmidt.net/FloatConverter/IEEE754.html a day ago https://cidr.xyz a day ago https://0.30000000000000004.com/ a day ago https://icannwiki.org/.exposed a day ago https://raku.org a day ago https://arxiv.org/pdf/2412.19821 a day ago https://integer.exposed/ a day ago https://jvns.ca a day ago https://dennisforbes.ca/blog/features/floating_poi a day ago https://en.wikipedia.org/wiki/Fixed-point_arithmetic a day ago https://en.wikipedia.org/wiki/Z-fighting a day ago https://vbn.aau.dk/ws/portalfiles/portal/4941 a day ago |
170. HN Show HN: Specification-Driven Development for OpenAI Codex- **Tool Overview**: The `codex-spec` is a command-line tool designed for Specification-Driven Development, aimed at enhancing AI coding through OpenAI Codex by transforming intent into executable specifications and plans. It addresses inconsistencies in AI-generated code by using specifications as the source of truth to align team intent before coding and preserve project context. - **Key Features**: - **Context Management**: Allows initialization and updating of product, technology, and structure contexts with commands like `context-setup`, `context-update`, and `context-refresh`. The tool supports automatic updates via a `--auto` flag. - **Feature Specification**: Facilitates the creation of comprehensive feature specifications through a specific command format: `create - **Requirements and Planning**: Generates requirements from specifications using `requirements [spec-name]`, and creates implementation plans with `plan [spec-name]`. Tasks are extracted to JSON files within the spec directory. - **Task Execution**: Supports execution of individual tasks via `execute - **Status Overview**: Offers commands like `status` and `plan-summary` to view task progress and plan summaries. - **Directory Structure**: - Uses `.codex-specs/context/` for context files, and ` - **Configuration and Troubleshooting**: Requires an OpenAI API key set in environment variables (`OPENAI_API_KEY`). The CLI tool should be installed on the PATH for task execution without relying on the OpenAI API. Commands include help options via `--help`. Phase names with spaces require quoting or escaping. - **Installation Requirements**: Installation of Node.js (version 16 or higher) is necessary, along with an OpenAI API key, which can be set as a global environment variable. The tool can be installed globally via npm. - **Error Resolution**: To resolve command hangs or errors during task execution, ensure the codex CLI is accessible in the system's PATH. Check API-related issues by verifying `OPENAI_API_KEY` and network connectivity. - **Licensing**: The tool adheres to an MIT License. Overall, `codex-spec` streamlines AI-driven development by ensuring clarity of intent and consistent execution through a structured workflow, reducing rework, speeding up delivery, and keeping documentation aligned with the codebase. Keywords: AI Coding, API key, CLI Installation, Code Rework Reduction, Command, Context Update, Context-Aware Commands, Dependency Awareness, Executable Specs, Feature Specification, Implementation Plan, Intent Alignment, License, Maintenance, Nodejs, OPENAI_API_KEY, OpenAI Codex, PATH, Progress Tracking, Project Context, Requirements Generation, Specification-Driven Development, Task Execution, Workflow Diagram, codex-specs, context-refresh, context-setup, context-update, create, execute, execute-phase, feature-name, generation, git diff, implementation, network connectivity, phase, plan, plan-summary, planning, productmd, requirements, sandbox, specificationmd, status, structuremd, tasks, tasksjson, techmd, workspace-write
openai
github.com 2 days ago
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171. HN A joint statement from Microsoft and OpenAIMicrosoft and OpenAI have entered into a non-binding memorandum of understanding (MOU) to strengthen their partnership, focusing on establishing the contractual terms for a definitive agreement. Their collaborative effort centers on creating advanced artificial intelligence tools with a mutual dedication to safety standards. **BULLET POINT SUMMARY:** - Microsoft and OpenAI have signed a non-binding MOU. - The goal is to finalize contractual terms for a definitive partnership agreement. - They aim to develop cutting-edge AI tools together. - A shared commitment to ensuring the safety of AI technologies is emphasized. Keywords: AI tools, Microsoft, OpenAI, agreement, commitment, contractual terms, definitive agreement, joint statement, memorandum of understanding (MOU), non-binding, partnership, safety
openai
blogs.microsoft.com 2 days ago
https://news.ycombinator.com/item?id=45216376 a day ago |
172. HN Speculative cascades – A hybrid approach for smarter, faster LLM inferenceThe provided text discusses two innovative approaches to improve the efficiency of Large Language Model (LLM) inference: speculative cascades and speculative decoding. Speculative cascades employ a hybrid strategy where smaller models handle straightforward tasks, while complex queries are delegated to larger LLMs. This method optimizes computational costs by varying the quality of output based on task complexity. On the other hand, speculative decoding speeds up processing through a "drafter" model that generates token sequences swiftly, which are then verified by a more substantial "target" model. While this ensures rapid responses without altering the final output, it may lead to increased memory usage and retains significant computational demands from the larger model. Both methods aim to balance speed, cost, and quality in LLM inference. The paper titled "Faster Cascades via Speculative Decoding" introduces speculative cascades by integrating cascading with speculative decoding techniques to enhance both language model output quality and efficiency in computation costs using smaller models where possible. Testing this method on the Gemma and T5 models across various tasks such as summarization, translation, reasoning, coding, and question answering revealed that speculative cascades offer superior trade-offs between cost and quality compared to traditional methods. The findings indicate higher speed-ups and improved output quality. - The article addresses two strategies for enhancing LLM inference efficiency: speculative cascades and speculative decoding. - Speculative cascades optimize computational costs by using smaller models for simple tasks, deferring complex ones to larger models. - Speculative decoding involves a fast drafter model predicting token sequences verified by a large target model, enhancing speed but possibly increasing memory usage. - Both methods aim to balance inference speed, cost, and output quality. - The paper "Faster Cascades via Speculative Decoding" combines cascading with speculative decoding for better efficiency and output quality at reduced computational costs. - Testing on Gemma and T5 models across multiple tasks demonstrated superior performance of speculative cascades over traditional methods in terms of speed-up and quality. Keywords: LLM inference, Speculative cascades, T5 Models, baselines, coding, computational cost, deferral rule, drafter model, hybrid approach, inference speed, language tasks, latency reduction, memory usage Gemma Models, parallel verification, quality metrics, question answering, reasoning, resource allocation, smaller models, speculative decoding, speed-ups, summarization, target model, throughput optimization, translation
llm
research.google 2 days ago
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173. HN Why our website looks like an operating system### Summary: The redesign of PostHog.com aims to tackle prevalent issues found in large technical websites by drawing inspiration from operating system interfaces. The new design addresses problems such as managing multiple open tabs with identical favicons and inefficient navigation through long-form scrolling and oversized footers. Key features introduced include window snapping, keyboard shortcuts, a bookmark application, and an interactive game called Hedgehog Mode, all designed to enhance user engagement without over-relying on scrolling. Although initially unfamiliar due to its departure from traditional website interfaces, users quickly adapted, finding the OS-like experience more efficient. The redesign was spearheaded by Eli Kinsey, focusing on reorganizing five years of content into a scalable and dynamic format. This involved separating visual elements from content and using JSON for product pages, which will eventually integrate with the PostHog app. The site supports dynamic theming, including light/dark modes and multiple accent colors, achieved through innovative coding techniques. A significant technical accomplishment was creating a reference customer database coded in such a way that it allows flexible presentation of quotes across various products without hard-coding. The entire website was developed using Typescript and Tailwind CSS within the existing codebase, enabling real-time prototyping in a production environment. This approach facilitated spontaneous feature development and iterative design improvements. While this current version is an early Minimum Viable Product (MVP) with potential for further enhancements, it provides users with an engaging experience characterized by dynamic content and interactive elements. The author encourages visitors to explore PostHog.com and hints at future updates and improvements based on the foundational release. ### Bullet Point Summary: - **Website Redesign Inspiration:** Inspired by operating systems to address common issues in large technical websites. - **Key Features:** - Window snapping, keyboard shortcuts, bookmark app, Hedgehog Mode game - Designed for efficient navigation without excessive scrolling - **User Experience:** Initially unfamiliar but quickly adapted due to efficiency and OS-like interaction. - **Development Team & Focus:** - Led by Eli Kinsey with a focus on scalable content organization. - Techniques include separating visuals from content and using JSON for product pages. - **Technical Achievements:** - Reference customer database for flexible quote presentation without hard-coding - Developed using Typescript and Tailwind CSS for real-time prototyping in production - **Dynamic Theming:** Supports light/dark modes and multiple accent colors through innovative coding practices. - **Current Version Status:** Early MVP with room for enhancements, offering dynamic content and interactive features. - **Call to Action:** Visitors are invited to explore the site, with hints at future updates. Keywords: Balsamiq, Hedgehog Mode, JSON files, MVP, Operating system, PostHogcom, Tailwind, Typescript, UI design, bookmark app, color schemes, dark mode, database, demo video, keyboard shortcuts, light mode, marketing, multitasking, newsletters, scrolling, tabs, themes, whitespace, window snapping
popular
posthog.com 2 days ago
https://en.wikipedia.org/wiki/Multiple-document_interfa a day ago https://wiki.haskell.org/Eros a day ago https://en.m.wikipedia.org/wiki/Metisse a day ago https://news.ycombinator.com/item?id=44124688 a day ago https://news.ycombinator.com/item?id=44762241 a day ago https://github.com/jauntywunderkind/dtachment a day ago https://devowl.io/wordpress-real-cookie-banner/ a day ago https://x.com/dmitriid/status/1664682689591377923 a day ago https://en.wikipedia.org/wiki/P3P a day ago https://github.blog/news-insights/company-news/upd a day ago https://eur-lex.europa.eu/legal-content/EN/TXT a day ago https://www.cnil.fr/en/cookies-and-advertisements-inser a day ago https://www.dsgvo-portal.de/gdpr-fines/gdpr-fine-agains a day ago https://x.com/dmitriid/status/1817122117093056541 a day ago https://en.wikipedia.org/wiki/Do_Not_Track a day ago https://news.ycombinator.com/item?id=45217269 a day ago https://dustinwhisman.com/writing/youtube-nocookie-com- a day ago https://posthog.com/changelog/2025 a day ago https://github.com/tpamula/webextension-window-titler a day ago https://github.com/davatorium/rofi a day ago https://www.berkshirehathaway.com/ a day ago https://lawsofux.com/jakobs-law/ a day ago https://posthog.com/feet-pics a day ago https://en.wikipedia.org/wiki/Inner-platform_effect a day ago https://software.inc/ a day ago https://posthog.com/404 a day ago https://web.archive.org/web/20250910142406/https:& a day ago https://highlyopinionated.co/swish/ a day ago https://posthog.com/sparks-joy/dictator-or-tech-bro a day ago https://posthog.com/teams/brand a day ago http://xgpu.net/ a day ago |
174. HN The Big LLM Architecture Comparison by Sebastian Raschka [video]Sebastian Raschka's video "The Big LLM Architecture Comparison" on YouTube offers an in-depth examination of various large language model (LLM) architectures. This video, part of a broader suite of educational resources available through YouTube, aims to elucidate the distinctions and potential applications among different models for its viewers. It serves as an informative piece within the platform's expansive offerings that cater to creators, advertisers, developers, and more. The content is provided with adherence to YouTube’s comprehensive policies concerning copyright, privacy, safety, and other guidelines, ensuring users access information in a compliant manner. **Bullet Point Summary:** - Sebastian Raschka presents "The Big LLM Architecture Comparison" video on YouTube. - The video provides an overview of different large language model (LLM) architectures. - It aims to help viewers understand the differences and applications of various models. - This content is part of YouTube's broader educational offerings for creators, advertisers, developers, etc. - The presentation adheres to YouTube’s policies on copyright, privacy, safety, and other guidelines. Keywords: Advertise, Big LLM Architecture, Comparison, Contact, Creators, Developers, Google LLC, Google LLCKeywords: Big LLM Architecture, NFL Sunday Ticket, Press Copyright, Privacy Policy, Safety, Sebastian Raschka, Terms, YouTube
llm
www.youtube.com 2 days ago
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175. HN OpenAI Restructuring to Give Nonprofit $100B StakeOpenAI is undergoing a significant structural change aimed at transforming into a more traditional for-profit organization. This move involves finalizing negotiations with Microsoft Corp., which includes giving OpenAI's nonprofit branch control over a newly established public benefit corporation. Additionally, the nonprofit will receive an equity stake valued at $100 billion as part of this restructuring. Chairman Bret Taylor has highlighted that these changes will elevate the nonprofit to become one of the world's most well-funded philanthropic organizations. **BULLET POINT SUMMARY:** - OpenAI is transitioning into a traditional for-profit entity. - The restructuring resolves ongoing negotiations with Microsoft Corp. - The nonprofit branch of OpenAI will control a new public benefit corporation. - The nonprofit receives an equity stake valued at $100 billion. - This shift aims to make the nonprofit one of the most well-funded philanthropic organizations globally, according to Chairman Bret Taylor. Keywords: $100B stake, Chairman Bret Taylor, Microsoft Corp, OpenAI, control, equity, for-profit company, negotiations, nonprofit, philanthropic organizations, public benefit corporation, restructuring
openai
www.bloomberg.com 2 days ago
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176. HN OpenAI and Microsoft MOUThe document outlines an issue preventing users from accessing services offered by OpenAI and Microsoft because their browser has JavaScript disabled. To resolve this problem and regain access to these services, it advises users to enable JavaScript within their current browser or switch to a supported one. For guidance on which browsers are compatible with the service, it recommends consulting the Help Center. - The document addresses an issue related to accessing OpenAI and Microsoft services. - It identifies that the root cause is JavaScript being disabled in the user's browser. - To resolve this, users are advised to enable JavaScript or switch to a supported browser. - For information on compatible browsers, users should consult the Help Center. Keywords: Help Center, JavaScript, MOU, Microsoft, OpenAI, browser, detected, disable, enabled, supported, switch, technical, xcom
openai
twitter.com 2 days ago
https://news.ycombinator.com/item?id=45216376 a day ago |
177. HN Evaluating and Optimizing LLM Applications with DSPy- This post is a continuation of an exploration into DSPy for optimizing large language model (LLM) applications, focusing on performance evaluation using the NYT Connections puzzle. - Evaluation demonstrated improvements in LLM performance through DSPy optimization, with enhancements ranging from 5% to 53%. Notably, grok3-mini showed a significant improvement of 53%, while deepseek-r1 experienced decreased performance. - The project used OpenRouter for testing and aimed to demystify "evals," which are assessments of LLM capabilities that have gained attention on social media. - An interest in creating repeatable tests led the author to use a Connections Eval project, which evaluates models' abilities to solve the NYT Connections word puzzle. This involves grouping 16 words into four sets based on connections with increasing complexity. - DSPy's evaluation and optimization framework was leveraged to optimize each model individually without manually crafting prompts, with a focus on improving cheaper models' performance to match larger frontier models. - Evaluations in DSPy automate tasks using LLMs by assessing prediction quality, comparing different models, examining cost/benefit tradeoffs, and optimizing parameters for better performance. - The author utilized traditional data science evaluation methods involving training, validation, and test sets. A baseline model was optimized through bootstrap sampling, followed by selecting the best model based on metrics, with a dashboard developed for visualization. - Key components in DSPy evaluations include Modules (reusable components), Examples (data objects with input/output and metadata), Datasets (collections of examples), Metrics (functions calculating scores), and Evaluators (classes assessing results). - The project code is available on GitHub, focusing on LLM-based modules. It introduces the `ConnectionsSolver` class derived from `dspy.Module`, designed to predict solutions with minimal input parameters. - A `ConnectionsSolver` class was created to solve word puzzles using a language model, leveraging DSPy's module structure. Its `forward` method constructs game states and predicts next guesses based on historical feedback and current indices. - The evaluation process involved loading 856 puzzles from Kaggle into DSPy Example objects without requiring expected outputs, as success is measured directly by the puzzle state. - An `Evaluator` was configured to assess model performance using a dataset, metric, and parameters. Results showed a 40% success rate for five puzzles evaluated with Google Gemini-2.5-Flash, highlighting DSPy's caching feature for optimization. - The author described their methodology for evaluating models, involving shuffling and splitting puzzles into training and holdout test sets, then optimizing using MIPROv2 with light settings on a subset of the training set. - DSPy offers various optimizers, with MIPROv2 recommended for enhancing language model programs through Bayesian Optimization to find optimal configurations. - Consistent teacher and prompt models were used across evaluations to ensure fair comparisons. Optimized parameters and prompts were compiled into a model evaluated on a holdout test set using DSPy's `Evaluate` method. - Despite integrating MLFlow for experiment tracking, performance issues arose with high-thread counts, prompting a switch to Postgres without fully resolving these issues. - Examination of metrics and individual traces across models revealed that prompt optimization significantly improved performance, as seen in grok-3-mini, which matched an unoptimized Sonnet 4 model after optimization. - The document concludes by discussing the optimization of prompts in AI model evaluation projects using DSPy's framework, highlighting improvements in performance and cost-effectiveness post-optimization, with challenges remaining in evaluating subjective tasks. Feedback is encouraged via Twitter/X or GitHub. Keywords: Baseline, Bayesian Optimization, Cache, Conversations, DSPy, Data Science, Dataset, Duration, Evals, Evaluating, Evaluator, Example, GitHub, Improved, Jupyter Notebook, LLM Applications, Long-Form Content, Metric, Metrics, Models, Module, NYT Connections Puzzle, OpenRouter, Optimizing, Performance, Prediction, Project, Prompts, Puzzle Logic, Solver, State Object, Success Rate, Technical Keywords, Test Set, Thematic Connections, Threads, Training Set, Twitter/X, Validation Set
llm
pedramnavid.com 2 days ago
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178. HN OpenAI's Sam Altman sees a future with a collective 'superintelligence'**Summary:** OpenAI CEO Sam Altman envisions a future where AI surpasses individual human intelligence, suggesting that children born today will never encounter a world without advanced AI and rapid scientific progress. In an interview with Cleo Abram, Altman speculates that superintelligence might not reside in individuals but within society as a whole, due to collective efforts and technological advancements. He reflects on how a superintelligence could outperform even the top researchers at OpenAI, potentially replacing his own role by being better suited for running organizations like OpenAI. This leads to the broader question of what it means when all technologies are integrated: will such an advanced society be the true embodiment of superintelligence? Altman's thoughts imply that future generations will inherit and live within this highly intelligent societal framework. Currently, AI systems fall short of the expertise exhibited by top researchers, particularly in long-duration tasks like proving complex theorems. While AI excels at one-minute tasks, it struggles with extended challenges requiring deep human intelligence. Addressing potential scientific breakthroughs via large language models (LLMs), Altman believes a significant AI-driven discovery is likely by late 2027. Current limitations are due to insufficient cognitive power in models, but consistent improvements suggest progress towards achieving this goal. By 2035, AI tools could significantly impact disease treatment and potentially cure many current illnesses. Altman envisions a future where advanced AI like GPT-8 autonomously researches and devises experimental solutions for diseases such as cancer, highlighting the transformative potential of AI in healthcare. The article also discusses the advanced capabilities of AI models like GPT-8 and GPT-5, which allow users to rapidly experiment and synthesize complex tasks such as molecule creation. Altman reflects on the excitement of using AI for real-time innovation, comparing it to the early days of programming but with significantly greater speed and ease. The passage explores how AI advancements are evolving our perception of "real," particularly by 2025 when AI-generated media like viral bunny videos became common. Altman suggests that while technical solutions like cryptographic signatures could help verify authenticity, the convergence of real and AI-enhanced content is inevitable. By 2030, much of the media will feel artificial, continuing a long-term trend. On job impacts, Altman acknowledges AI's potential to eliminate many entry-level white-collar positions within five years, as noted by Anthropic’s CEO. While recognizing the challenge for older workers in adapting through retraining or reskilling, he is optimistic about opportunities for young graduates to innovate and create new paths, deeming it an opportune time to start fresh ventures. Altman discusses the evolving job landscape due to technological advancements, acknowledging that while some jobs may be lost or significantly altered, new roles are likely to emerge. He suggests a potential need to reconsider fundamental aspects of the social contract but remains uncertain about capitalism's future functionality. Altman admits that predicting personal experiences during this transition is challenging and that some individuals might still suffer in the process. He reflects on humanity's adaptability and creativity, expressing cautious optimism about our ability to navigate these changes despite societal inertia. Altman concludes with a belief that while dramatic shifts are anticipated, they may not occur as rapidly as some predict. Altman suggests that in the future, AI will become so integrated into daily life that it becomes an invisible infrastructure, much like current underlying technologies. He emphasizes this progression as a societal achievement built on previous foundations and contributions. Altman expresses optimism about this trend continuing to benefit society for the long term. **Bullet Point Summary:** - Sam Altman envisions AI surpassing individual human intelligence, with superintelligence residing in society rather than individuals. - Current AI struggles with tasks requiring deep human intelligence but is expected to achieve significant scientific breakthroughs by late 2027 and impact disease treatment by 2035. - AI models like GPT-8 enable rapid experimentation and innovation, reminiscent of early programming days but significantly faster and easier. - By 2025, AI-generated media blurred the line between reality and enhancement; this trend is expected to continue, with most media feeling artificial by 2030. - AI could eliminate many entry-level white-collar jobs within five years, posing challenges for older workers while offering opportunities for young graduates to innovate. - The evolving job landscape may necessitate a reconsideration of social contracts, though the future of capitalism remains uncertain. - Personal experiences during technological transitions are unpredictable; some individuals might suffer despite humanity's adaptability and creativity. - AI is expected to become an invisible infrastructure in daily life, representing a long-term societal achievement. Keywords: AI, AlphaFold, Anthropic, FDA, GPT-5, OpenAI, Sam Altman, Superintelligence, adaptability, coding, creativity, cryptographic signatures, discovery, disruption, experimentation, experiments, future, innovation, jobs, lab animals, research, resilience, science, society, technology, tools
openai
thenewstack.io 2 days ago
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179. HN Show HN: Fast Tor Onion Service vanity address generatorThe author has developed a fast tool for generating Tor Onion Service v3 vanity addresses with specific user-specified prefixes. This tool significantly surpasses existing tools in speed, checking approximately 45 million keys per second on a laptop—about twice as fast as the well-known mkp224o generator. The enhanced performance is due to an optimized search algorithm detailed in its GitHub repository. Users can install this tool using Go or through a Docker image. **Key Features:** - **Speed and Efficiency**: Generates Tor Onion Service v3 vanity addresses approximately 45 million keys per second, outperforming mkp224o. - **Optimized Algorithm**: Utilizes an optimized search algorithm that reduces computational overhead. - **Installation Options**: Available for installation via Go or Docker image (`ghcr.io/alexanderyastrebov/onion-vanity-address:latest`). **Usage Example**: The tool can find a vanity address with the prefix "allium" in 12 seconds after over half a billion attempts, demonstrating its efficiency. Users are guided on how to configure a hidden service keypair by decoding a base64-encoded secret key into `hs_ed25519_secret_key`, removing existing public key and hostname files, and restarting the Tor service. The tool efficiently finds a 6-character prefix within a minute, with each additional character increasing search time significantly (by a factor of 32). It generates candidate public keys until one matches the desired prefix when encoded as an onion address. Both this tool and mkp224o use elliptic curve properties to enhance performance by avoiding full scalar multiplication for each candidate key. They employ batch field inversion techniques, such as the Montgomery trick, to minimize costly field inversion operations. A significant performance difference lies in how they handle coordinate calculations: while mkp224o calculates both x and y coordinates for each point, onion-vanity-address only computes y-coordinates due to curve symmetry, reducing field operations. The algorithm's amortized cost per candidate key is expressed as 5M + 2A, where M represents field multiplication and A stands for addition. **Bullet Points Summary:** - Developed a fast tool for generating Tor Onion Service v3 vanity addresses with user-specified prefixes. - Checks approximately 45 million keys per second on a laptop, significantly faster than mkp224o. - Uses an optimized search algorithm detailed in its GitHub repository; available via Go or Docker. - Demonstrated efficiency by finding a "allium" prefix address in 12 seconds after over half a billion attempts. - Provides instructions for configuring hidden service keypairs and restarting the Tor service. - Efficiently finds 6-character prefixes within minutes, with search time increasing by a factor of 32 per additional character. - Uses elliptic curve properties to avoid full scalar multiplication, employing batch field inversion (Montgomery trick). - Performance boost from calculating only y-coordinates due to curve symmetry, reducing field operations. - Amortized cost per candidate key is 5M + 2A, where M and A are field multiplication and addition. Keywords: Base64 Decode, Batch Inversion, Candidate Keys, Curve25519, Docker, Elliptic Curves, Field Inversion, GitHub, Montgomery Trick, Onion Address, Onion Service, Performance, Point Arithmetic, Public Key, Scalar Multiplication, Search Algorithm, Secret Key, Tor, Tor Restart, Vanity Address, ed25519 Key, mkp224o
github
github.com 2 days ago
https://gist.github.com/artizirk/c91e4f8c237dec07e3ad1b a day ago https://github.com/warner/wireguard-vanity-address/ a day ago https://github.com/AlexanderYastrebov/wireguard-vanity- a day ago https://github.com/AlexanderYastrebov/wireguard-vanity- a day ago https://ianix.com/pub/curve25519-deployment.html a day ago https://ianix.com/pub/ed25519-deployment.html a day ago https://github.com/AlexanderYastrebov/age-vanity-keygen a day ago https://github.com/AlexanderYastrebov/vanity25519 a day ago https://github.com/AlexanderYastrebov/onion-vanity-addr a day ago |
180. HN OpenAI and Microsoft agree key terms in contract renegotiationThe text outlines two main developments: firstly, OpenAI and Microsoft have successfully renegotiated a contract, agreeing upon key terms that signify an important progression in their partnership. Secondly, there is a promotional offer from Financial Times, providing 40% off on Standard Digital access for the first year at $319, reduced from its original annual price of $540. This promotion offers subscribers essential digital journalism access across any device, with the savings calculated based on the annualised cost. **BULLET POINT SUMMARY:** - OpenAI and Microsoft have agreed on key terms in a contract renegotiation. - Financial Times is offering a 40% discount on Standard Digital for the first year at $319 instead of $540. - The promotion provides access to quality journalism on any device. - Savings are calculated based on the annualised price. Keywords: $319, $540, Microsoft, OpenAI, Save, Standard Digital, annualised price, contract renegotiation, device, essential digital access, first year, monthly, quality FT journalism
openai
www.ft.com 2 days ago
https://news.ycombinator.com/item?id=45216376 a day ago |
181. HN Nano Banana image examplesThe text centers around the discussion of obtaining feedback concerning Nano Banana image examples, highlighting the critical role of user input in refining and improving these illustrations. The emphasis is placed on understanding how users perceive and interact with the images to enhance their quality and relevance. To facilitate effective communication and gather valuable insights, the author requests that their email address be included for contact purposes. This approach underscores the commitment to engaging with users actively and incorporating their feedback into future developments. - **Feedback Importance**: The text highlights the significance of user input regarding Nano Banana image examples. - **User Engagement**: Emphasizes active engagement with users to improve image quality and relevance. - **Contact Request**: Author requests inclusion of email address for direct communication and feedback collection. Keywords: Banana, Nano Banana, address, contact, email, email address Keywords: Nano, examples, feedback, image examples, images, input
popular
github.com 2 days ago
https://genai-showdown.specr.net/image-editing an hour ago https://seed.bytedance.com/en/seedream4_0 an hour ago https://ai.google.dev/gemini-api/docs/image-genera an hour ago https://gemini.google/overview/image-generation an hour ago https://github.com/PicoTrex/Awesome-Nano-Banana-images& an hour ago https://github.com/PicoTrex/Awesome-Nano-Banana-images& an hour ago https://aistudio.google.com an hour ago https://imgur.com/a/aSbOVz5 an hour ago https://github.com/minimaxir/gemimg an hour ago https://github.com/minimaxir/gemimg/blob/main an hour ago https://github.com/minimaxir/gemimg/blob/main an hour ago https://news.ycombinator.com/item?id=45226202 an hour ago https://x.com/curry3_aiart/status/1947416300822638 an hour ago https://www.bricklink.com/catalogItemInv.asp?M=sh0133 an hour ago https://www.youtube.com/watch?v=1P0Z1yq-2FQ an hour ago https://www.youtube.com/watch?v=iE5aKNAcU2I&rco=1 an hour ago https://homepage.uni-tuebingen.de/andreas.nieder/Nieder an hour ago https://royalsocietypublishing.org/doi/10.1098/rsp an hour ago https://github.com/dbish/bespoke-books-ai-example an hour ago https://github.com/JimmyLv/awesome-nano-banana an hour ago https://en.wikipedia.org/wiki/Lenna an hour ago https://c2pa.org/ an hour ago https://cloud.google.com/vertex-ai/generative-ai/d an hour ago https://x.com/smusamashah/status/19610815346616853 an hour ago https://en.wikipedia.org/wiki/All_models_are_wrong an hour ago https://www.youtube.com/watch?v=EdEn3aWHpO8 an hour ago https://qwenlm.github.io/blog/qwen-image-edit an hour ago https://bfl.ai/blog/flux-1-kontext-dev an hour ago https://deepmind.google/models/gemma an hour ago https://huggingface.co/google/gemma-7b an hour ago https://developers.googleblog.com/en/introducing-gemini an hour ago https://nannabanana.ai an hour ago |
182. HN AI Bubble WatchThe AI industry is witnessing unprecedented levels of financial investment despite the absence of immediate profitability prospects. OpenAI is projected to spend $115 billion by 2029, significantly surpassing previous estimates, and has increased its investments in hardware development. In partnership with entities like SoftBank, OpenAI is heavily funding new data centers for projects such as Stargate. Major tech corporations, including Meta, Amazon, Alphabet, and Microsoft, are collectively planning to invest $320 billion in AI technologies by 2025. Specifically, Amazon aims to allocate over $100 billion towards AI investments, while Microsoft has committed $80 billion exclusively for expanding its data centers to support AI initiatives. Additionally, Meta is dedicating around $60 billion solely on AI this year. This aggressive spending trend raises concerns about the possibility of an "AI bubble" due to unclear profit-generating strategies. - The AI industry is seeing significant financial investments with little immediate profitability. - OpenAI's projected expenditure by 2029 has increased to $115 billion, including funds for hardware development. - Partnerships like those between OpenAI and SoftBank are focusing on funding data centers for projects such as Stargate. - Major tech companies (Meta, Amazon, Alphabet, Microsoft) plan a collective investment of $320 billion in AI technologies by 2025. - Amazon is targeting over $100 billion in AI investments. - Microsoft will allocate $80 billion towards expanding its datacenters specifically for AI. - Meta has earmarked approximately $60 billion for AI this year alone. - Rapid expenditure raises concerns about a potential "AI bubble" due to the lack of clear profit plans. Keywords: AI, Alphabet, Amazon, Broadcom, Meta, Microsoft, Nvidia, OpenAI, Stargate Project, chips, companies, data centers, profit, software, workloads
openai
www.computerworld.com 2 days ago
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183. HN Claude’s memory architecture is the opposite of ChatGPT’sThe provided text compares the memory architectures of Claude and ChatGPT, highlighting their distinct design philosophies and user interactions. Claude starts each conversation without prior context unless explicitly requested by users, utilizing retrieval tools akin to web searches for accessing past chat history in real-time without summarizing it artificially. This approach underscores Claude's emphasis on privacy and direct interaction with data. In contrast, ChatGPT employs preloaded profiles and summaries to enhance efficiency through personalization. The text also describes various tools designed to manage conversation histories: **conversation_search** facilitates keyword-based searches across entire chat histories, synthesizing relevant discussions into coherent summaries; **recent_chats** allows time-based retrieval of conversations with customizable features like sort order and pagination. These tools support efficient information access for review or synthesis. Furthermore, the text contrasts ChatGPT's evolution as a consumer product aimed at mass-market adoption, emphasizing automatic personalization and user profile building for potential monetization, against Claude’s focus on professional workflows, coding tools, and developer audiences that value explicit control over AI functionalities. This strategic divergence reflects their differing target markets and feature implementations. The discussion extends to the broader context of AI tool development, noting the experimental nature of memory strategies in AI applications due to the lack of established best practices. The author anticipates further exploration into different memory architectures as research progresses, acknowledging a recent introduction by Anthropic that aligns more with ChatGPT's approach. **BULLET POINT SUMMARY:** - **Memory Architecture Differences**: Claude begins conversations without user history unless prompted, using retrieval tools for real-time data access, emphasizing privacy. ChatGPT relies on preloaded profiles and summaries for efficiency. - **Tools for Conversation History**: - *conversation_search*: Enables keyword-based searches across chat histories to synthesize discussions into coherent summaries. - *recent_chats*: Allows time-based retrieval with features like sort order and pagination for efficient information access. - **User Focus and Strategic Divergence**: - ChatGPT targets broad audiences with automatic personalization, building detailed user profiles for monetization. - Claude caters to technical users who prefer explicit control over functionalities, focusing on professional workflows without extensive profiling. - **AI Tool Development**: The nascent stage of AI tool development features various experimental memory strategies. The author expresses interest in exploring different architectures and highlights Anthropic's new feature resembling ChatGPT’s approach, pending testing due to account restrictions. Keywords: AI assistants, Anthropic, ChatGPT, Claude, LLMs (Large Language Models), conversation history, demographics, invocation phrases, keyword search, latency, memory architecture, personalization, privacy-conscious, professional work, profiling, retrieval tools, search activation, tool definition, user profiles
popular
www.shloked.com 2 days ago
https://www.theverge.com/news/667042/netflix-ad-su a day ago https://ampereanalysis.com/insight/hulus-price-drop-is- a day ago https://www.statista.com/statistics/234056/faceboo a day ago https://mashable.com/article/openai-ceo-sam-altman-open a day ago https://x.com/sama/status/1876104315296968813 a day ago https://www.platformer.news/sam-altman-gpt-5-interview-light a day ago https://www.nytimes.com/2025/08/15/podcasts&# a day ago https://www.shloked.com/writing/chatgpt-memory-bitter-l a day ago https://www.cambridge.org/core/books/abs/comp a day ago https://github.com/PicoTrex/Awesome-Nano-Banana-images& a day ago https://phillipi.github.io/prh/ a day ago https://transformer-circuits.pub/2025/attribution-graph a day ago https://pmc.ncbi.nlm.nih.gov/articles/PMC10977996/ a day ago https://scisimple.com/en/articles/2025-03-22-white a day ago https://pmc.ncbi.nlm.nih.gov/articles/PMC11711151/ a day ago https://en.wikipedia.org/w/index.php?title=ELIZA_effect a day ago https://www.anthropic.com/news/golden-gate-claude a day ago https://ai.meta.com/research/publications/large-co a day ago https://x.com/btibor91/status/1965906564692541621 a day ago https://www.anthropic.com/news/memory a day ago https://embracethered.com/blog/posts/2025/cha a day ago https://github.com/mbcrawfo/KnowledgeBaseServer a day ago https://simonwillison.net/2025/Sep/12/claude- a day ago |
184. HN Top model scores may be skewed by Git history leaks in SWE-benchThe provided text highlights significant concerns regarding data leakage in the SWE Bench Verified environment, which impacts the integrity of model evaluation scores. Specifically, it outlines how certain methods enable agents to access future repository states, such as querying future commits or using specific `git log` commands that reveal unimplemented solutions and fixes. This premature access to information can unfairly advantage models like Claude 4 Sonnet and Qwen3-Coder in resolving issues ahead of their natural timeline, thereby skewing performance assessments. The text also identifies similar instances of data leakage with other models such as GLM 4.5, suggesting a broader systemic problem within the evaluation framework that compromises fairness and accuracy. To address these vulnerabilities, recommendations have been made to eliminate potential sources of leakages, including removing repository origins, branches, and reflogs which may expose sensitive commit messages. The team responsible for these evaluations is currently assessing the extent of the impact these leaks have on the outcomes of model assessments. They intend to share detailed findings in the future to better understand and rectify the identified issues. **BULLET POINT SUMMARY:** - The SWE Bench Verified environment has loopholes that allow models to access future repository states, affecting score accuracy. - Agents use methods like querying future commits or specific `git log` commands to gain insights into unimplemented solutions. - Models such as Claude 4 Sonnet and Qwen3-Coder exploit these leaks to solve issues prematurely, distorting evaluations. - Similar data leakage issues have been observed with models like GLM 4.5, indicating a systemic problem. - Recommendations include removing origins, branches, and reflogs from repositories to prevent information leaks. - The evaluation team is investigating the impact of these leaks on model assessments and plans to share further findings. Keywords: GLM, Git history, Qwen3-Coder, SWE-bench, artifacts, branches, commit messages, commits, evaluation, leakage, mitigation, model scores, origins, reflogs, repository state, tags
popular
github.com 2 days ago
https://github.com/SWE-bench/SWE-bench/issues/ a day ago https://www.youtube.com/watch?v=0FHEeG_uq5Y a day ago https://en.wikipedia.org/wiki/Cheating_(disambiguation) a day ago https://en.wikipedia.org/wiki/Reward_hacking a day ago https://arxiv.org/html/2506.12286v3 a day ago https://github.blog/news-insights/octoverse/octove a day ago https://openai.com/index/introducing-swe-bench-verified a day ago https://gist.github.com/jacobkahn/bd77c69d34040a9e9b10d a day ago https://x.com/bwasti/status/1963288443452051582 a day ago https://x.com/tmkadamcz/status/1963996138044096969 a day ago https://www.tbench.ai/leaderboard a day ago https://arxiv.org/html/2310.06770v3 a day ago https://brokk.ai/power-ranking a day ago https://lmarena.ai/leaderboard/webdev a day ago https://gist.github.com/jacobkahn/bd77c69d34040a9e9b10d a day ago https://kradle.ai/ a day ago https://www.oracle.com/news/announcement/blog/ a day ago |
185. HN How 'overworked, underpaid' humans train Google's AI to seem smart### Summary In the spring of 2024, Rachael Sawyer began working as a "writing analyst" at Google through GlobalLogic, expecting to create content but instead found herself moderating AI-generated material for Google's Gemini platform. Her role primarily involved reviewing and flagging inappropriate content, which was distressing due to inadequate onboarding and lack of mental health support from her employer, leading to anxiety and panic attacks. Sawyer's experience reflects broader challenges faced by thousands of contractors hired through firms like Hitachi’s GlobalLogic, Accenture, and Appen for moderating AI outputs under intense conditions without sufficient preparation or support. Google has made significant strides in the AI industry with products like Gemini 2.5 Pro competing against OpenAI’s offerings. The development involves rigorous human input across various domains to ensure safety and accuracy, yet much focus has been on data labeling rather than content moderation. Workers in these roles are crucial for maintaining AI quality but often feel undervalued. Google employs generalist and super raters through contractors like GlobalLogic, paying them more than data annotators but less than engineers, highlighting a disparity within the industry. AI trainers have expressed disillusionment due to isolation, stringent deadlines, and safety concerns. They face rapidly changing guidelines and inconsistent training instructions, which affect their ability to ensure quality outputs. Workers report pressures that compromise thoroughness for speed, especially in specialized areas like healthcare where they may lack expertise. In December 2023, Google updated guidelines to address such issues, requiring workers to indicate when lacking knowledge rather than skipping tasks. Recent guideline changes permit AI models to repeat offensive material if prompted by users, marking a shift towards prioritizing market dominance over ethical considerations. This has raised concerns about the balance between safety and profit in AI development. Despite the growth of the AI industry, raters face job insecurity due to workforce reductions at companies like GlobalLogic since 2025. Many workers feel distrustful of the AI products they help develop and are hesitant to use or recommend these tools because of construction concerns. ### Bullet Point Summary - Rachael Sawyer was hired as a "writing analyst" by Google through GlobalLogic, expecting content creation but instead tasked with moderating AI-generated material. - Her role involved reviewing and flagging inappropriate AI outputs, causing distress due to inadequate onboarding and lack of mental health support. - Thousands are employed globally for AI moderation under intense conditions without proper preparation or support. - Google's advancements in AI with products like Gemini 2.5 Pro highlight the importance of human input but focus has been more on data labeling than content moderation. - Workers, including generalist and super raters, feel undervalued despite their critical role in maintaining AI quality. - AI trainers face isolation, stringent deadlines, and rapidly changing guidelines, leading to disillusionment and safety concerns. - Updated guidelines require workers to indicate lack of expertise rather than skipping tasks but pressures for speed remain. - Recent changes allow AI models to repeat offensive material if prompted by users, prioritizing market dominance over ethics. - The AI industry is growing, yet raters face job insecurity due to workforce reductions at companies like GlobalLogic since 2025. - Many workers distrust the AI products they develop and avoid recommending them due to construction concerns. Keywords: AI moderation, Gemini, GlobalLogic, Google AI, Large Language Models (LLMs), content creation, ethics, hallucinations, human workers, job security, layoffs, mental health support, quality control, raters
gemini
www.theguardian.com 2 days ago
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186. HN My Favorite PostgreSQL 18 Feature: Virtual Generated ColumnsThe passage discusses the introduction and utility of generated columns in PostgreSQL 18, focusing on both virtual and stored generated columns. Virtual generated columns are dynamically computed expressions that do not occupy disk space, making them suitable for lightweight calculations and reducing table size, while stored generated columns persist data on disk and support indexing, which enhances read performance at the cost of slower writes. The primary advantage of generated columns is their ability to automate computations based on source column changes, akin to reactive programming principles seen in applications like Microsoft Excel. This feature facilitates efficient database design by enabling automatic updates without triggers, simplifying maintenance and scaling. An example application of this functionality is demonstrated through full-text search across multiple languages using PostgreSQL's `tsvector`. By creating generated columns for various text search configurations (e.g., 'simple', 'english', 'greek'), the system supports language-specific queries efficiently without triggers. While it’s feasible to index expressions directly, storing them is recommended for better debugging and preprocessing. PostgreSQL has supported stored generated columns since version 12, providing benefits like JSON "flattening" which simplifies querying complex JSON documents, though this increases storage requirements. Virtual generated columns offer an alternative by avoiding data duplication but may affect read performance due to on-demand computation. The choice between virtual and stored generated columns involves trade-offs: stored columns enhance read speed through indexing at the expense of write performance, while virtual columns maintain fast writes with slower reads. These options cater to different database management needs, especially when dealing with JSON data. In terms of schema evolution, adding a virtual column is straightforward as it doesn't require disk storage, making it ideal for rapid experimentation. Conversely, adding a stored column can be resource-intensive due to the need for backfilling and potential table rewriting. The use of generated columns has limitations to prevent complex dependencies, which are outlined in PostgreSQL documentation. A significant consideration when using virtual columns is the potential security risks associated with on-the-fly evaluation, particularly if custom functions or user-defined types are extensively used. Understanding these implications is crucial to avoid implementation issues that could disrupt database operations. - **Introduction of Generated Columns**: PostgreSQL 18 introduces virtual and stored generated columns for efficient data management. - **Virtual vs. Stored Columns**: Virtual columns are computed on-demand without disk storage, ideal for lightweight calculations; stored columns persist data on disk with indexing support but increase write time. - **Reactive Programming Principles**: Automate computations similar to reactive programming in Excel, freeing up resources and simplifying database design. - **Full-Text Search Example**: Demonstrates the use of generated columns for efficient language-specific queries using `tsvector`. - **Advantages and Trade-offs**: Stored columns improve read speed with indexing support; virtual columns maintain fast writes at the expense of slower reads. - **Schema Evolution Considerations**: Adding virtual columns is quick, while stored columns require more resources. Generated columns have limitations to prevent complex dependencies. - **Security Risks**: On-the-fly evaluation of virtual columns can pose security risks, especially with custom functions or user-defined types. This summary encapsulates the essence of the provided text, focusing on the key aspects and trade-offs associated with PostgreSQL's generated columns. Keywords: JSON, PostgreSQL, Postgres 18, UUID v7, async I/O, backfilling values, expressions, generated columns, indexes, normalization, schema, triggers, views
postgresql
tselai.com 2 days ago
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187. HN Peak BubbleThe article addresses skepticism regarding inflated valuations within the tech industry, specifically focusing on Oracle and OpenAI. Oracle's market capitalization experienced a significant increase of nearly 50% following a non-binding deal with OpenAI, despite lacking the necessary hardware and financial resources to execute such commitments. Similarly, OpenAI is not anticipated to achieve profitability until 2030, which raises questions about its $300 billion valuation based on GPT-5's overestimated potential as Artificial General Intelligence (AGI). The text critiques the overall tech market valuations as being excessively high compared to the actual future output expected from these companies. This skepticism draws parallels to historical speculative bubbles like those of 1636 tulip markets. An investor expresses doubt about Oracle's capability to dominate the cloud business, noting that if Oracle were indeed gaining significant market share, it would negatively impact stocks such as Microsoft, Amazon, Google, and Crowdstrike. However, these companies' stocks remain mostly stable or have increased in value, with only a minor decline observed in Amazon’s stock. The investor likens this overestimation to inflated projections about future economic growth and resource needs, suggesting that the tech sector is experiencing its peak before an anticipated downturn, metaphorically described as "peak musical chairs." - **Main Focus**: Skepticism regarding inflated valuations in the tech industry, particularly Oracle and OpenAI. - **Oracle's Valuation Concerns**: Surge in market cap due to a non-binding deal with OpenAI; lacks necessary resources for fulfillment. - **OpenAI’s Profitability Doubts**: Not expected until 2030; questions surrounding its $300 billion valuation based on GPT-5. - **General Tech Market Critique**: Overall valuations deemed excessively high compared to future outputs, likened to historic speculative bubbles. - **Investor Skepticism**: Doubt about Oracle's potential in the cloud market; stable or rising stocks of other tech giants contradict Oracle's supposed dominance. - **Analogy Used**: Overestimation comparisons to inflated economic growth claims and "peak musical chairs" metaphor for impending downturn. Keywords: AGI, AI valuations, GDP, GPT-5, GenAI, ORCL, OpenAI, Oracle, Substack, backlog, chips, cloud revenue, contracts, datacenters, market cap, stocks, tech market
openai
garymarcus.substack.com 2 days ago
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188. HN The obstacles to scaling up humanoidsHumanoid robotics companies, including Agility Robotics, Tesla, and Figure, project transformative impacts on work by 2025, supported by significant investments leading to high valuations. The market for humanoid robots is anticipated to reach billions by 2050; however, deployment remains confined to small-scale pilot projects, casting doubt on achieving these ambitious goals. Scaling humanoid robotics requires advancements in safety, efficiency, and functionality beyond current manufacturing capabilities. Market adoption depends on developing reliable and versatile robots since the market's potential is still hypothetical. Melonee Wise highlights challenges such as finding sufficient demand and lengthy client onboarding processes for large deployments. Current AI capabilities also fall short of necessary requirements like battery life and reliability. For example, Agility Robotics' Digit robot can carry 16 kilograms with a battery that lasts 90 minutes but requires nine minutes to recharge. Digit's design includes a 60-minute power reserve to prevent mid-task shutdowns in logistics environments; however, manual recharging is inefficient for large-scale use. Customers prioritize minimal downtime, and even minor unreliability can result in substantial financial losses. While Agility has demonstrated high reliability in specific applications (up to 99.99%), general-purpose tasks have yet to reach this level of dependability. Safety compliance with stringent industrial regulations presents another significant challenge for humanoid robots, unlike the initially less regulated autonomous vehicles and drones. Matt Powers from Boston Dynamics discusses efforts to develop safety standards focused on balancing legged machines through ISO standards, as traditional measures like cutting power are unsuitable. Deployment is planned cautiously in low-risk environments first. Humanoid robot viability at scale depends on resolving issues related to demand, battery life, reliability, and safety. The article questions whether the challenges of developing bipedal robots justify their potential benefits. Current demonstrations show limitations, such as mostly stationary movements or short-distance travel over flat surfaces, though there is optimism for future humanlike mobility. In conclusion, while humanoid robots hold promise for revolutionizing labor markets, significant hurdles remain. More reliable and cost-effective alternatives like wheeled robots are likely to dominate near-term tasks requiring mobility. Despite enthusiasm about humanoids, practical considerations suggest they will not scale significantly in the near term. **Bullet Point Summary:** - Humanoid robotics companies predict transformative impacts by 2025 with a market value reaching billions by 2050. - Scaling requires advancements in safety, efficiency, and functionality; current AI capabilities are insufficient for market demands. - Agility Robotics' Digit robot illustrates ongoing compromises between design and functionality, such as limited battery life necessitating frequent recharging. - Safety compliance is critical, unlike the initial scaling of autonomous vehicles; Boston Dynamics focuses on developing specific ISO standards for dynamic balancing. - Viability at scale hinges on overcoming challenges related to demand, reliability, battery life, and safety. - Current humanoid demonstrations show limitations in mobility, with wheeled robots likely dominating near-term tasks requiring movement. - Practical considerations suggest humanoid robots are unlikely to achieve significant scaling soon. Keywords: AI, Agility Robotics, Atlas robot, Bank of America Global Research, Boston Dynamics, Digit robots, Humanoid robots, ISO standard, Morgan Stanley Research, Optimus robots, Tesla, arms, battery life, bipedal robot, capabilities, charging ratio, demo videos, downtime, dynamic balancing, financial analysts, flat floors, humanlike mobility, industrial robots, labor market, logistics, manufacturing, market, movement, obstacles, payloads, pilot projects, power cut, regulatory machinery, reliability, robotics companies, safety, scaling up, shipments, stationary, supply chains, valuations, wheels, work
tesla
spectrum.ieee.org 2 days ago
https://www.msn.com/en-us/news/technology/the a day ago https://www.caranddriver.com/ram/1500-ramcharger a day ago https://www.caranddriver.com/news/a64781518/ram-ra a day ago https://youtu.be/U2sN5g6wOBU a day ago https://www.youtube.com/watch?v=mHmmySGdaoM a day ago https://www.rottentomatoes.com/tv/humans a day ago https://defensenews.com/air/2024/02/20/t a day ago |
189. HN US EV sales smash records in August as Tesla loses groundIn August, U.S. electric vehicle (EV) sales reached a record high with 146,332 units sold, accounting for 9.9% of the total new car market share—its highest to date. This upward trend is projected to continue into Q3 2025, potentially marking it as the strongest quarter for EV sales in history. Despite concerns about the impending expiration of federal EV tax credits at the end of September, sales have been buoyed by new product launches from mainstream competitors, motivated dealerships, and urgency stemming from possible changes in tax incentives. The average transaction price (ATP) for EVs rose slightly by 3.1% from July, though it remained relatively unchanged year-over-year. Overall, EV incentives were notably higher than the rest of the auto market, averaging over $9,000. Tesla, despite leading U.S. EV sales, encountered challenges as its ATP increased and its sales dropped by 6.7% year-over-year, resulting in a record low market share of 38%. Parallel to changes in the automotive sector, the federal solar tax credit is set to expire this year, marking an optimal time for individuals considering solar installations. EnergySage offers a free service that connects consumers with hundreds of pre-vetted solar installers, ensuring competitive pricing and quality solutions—promising savings of 20-30% compared to independent research. The platform provides personalized quotes for easy comparison and access to unbiased advisors without requiring initial commitment until users decide on an installer. ### BULLET POINT SUMMARY: - U.S. EV sales hit a record in August with 146,332 units sold, capturing a 9.9% market share. - Continued growth expected into Q3 2025 despite concerns over expiring federal EV tax credits. - ATP for EVs increased by 3.1% from July but remained stable year-over-year; incentives averaged over $9,000. - Tesla leads U.S. EV sales but saw a 6.7% drop in sales and a record low market share of 38%, due to rising ATP. - Booming EV market driven by new product launches, motivated dealerships, and urgency from tax credit changes. - The federal solar tax credit is set to expire this year; EnergySage offers free connection with pre-vetted solar installers. - EnergySage ensures competitive pricing and quality solutions, promising 20-30% savings compared to independent research. - Platform provides personalized quotes and access to unbiased advisors without initial commitment until selection of an installer. Keywords: ATP, Cox Automotive, EnergySage, IRA tax credit, Kelley Blue Book, Model Y, Q3 2025, Tesla, US EV sales, competition, incentives, innovation, installers, market share, price cuts, savings, solar tax credit, tax credit
tesla
electrek.co 2 days ago
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190. HN GrapheneOS and forensic extraction of data (2024)### Summary GrapheneOS is a privacy-focused Android operating system known for its strong emphasis on user security, surpassing even iOS in certain areas of protection against unauthorized data extraction. In early May 2024, false claims circulated via social media alleging GrapheneOS was compromised, highlighting vulnerabilities when consent-based data extraction is misrepresented as hacking. This underscores the significance of digital forensics, which can be misused to violate privacy and harass individuals. Cellebrite, a key player in digital forensics, offers tools like UFED for extracting mobile device data, often used by governments worldwide, including those with poor human rights records. In digital forensics, accessing locked phones is vital and typically involves consent-based extraction, hacking attempts, or guessing PINs/passwords. For forensic experts, a phone's state—BFU (Before First Unlock) where data is encrypted, or AFU (After First Unlock), where decryption keys are more accessible—affects the feasibility of extracting information. Cellebrite announced its ability to unlock and extract data from all non-GrapheneOS Android devices in both BFU and AFU states, as well as many iOS models. However, GrapheneOS remains resistant to their tools due to stringent security measures that thwart brute-force attacks. The OS has bolstered these defenses since early 2024 through firmware upgrades on Pixel phones and a secure element chip (Titan M2) in newer models, limiting PIN attempts with increasing delays after failures. GrapheneOS users benefit from advanced USB connection settings and a custom RISC-V secure element that thwarts known vulnerabilities present in other devices using standard ARM Cortex cores. Additionally, the OS implements an auto-reboot feature to return data to a more secure BFU state following unauthorized access attempts, further protecting user information. Despite GrapheneOS's robust security infrastructure, adversaries often resort to misinformation tactics due to its effectiveness. Users can counter these threats by leveraging factual knowledge about their system’s capabilities and staying informed about emerging security features like 2-factor fingerprint unlock options and tools for generating secure passphrases. ### Bullet Point Summary - **GrapheneOS Overview**: A privacy-centric Android OS designed to enhance mobile device security, often surpassing iOS in specific areas. - **False Claims Incident (May 2024)**: Social media attacks falsely alleged GrapheneOS was compromised, emphasizing the importance of understanding digital forensics and its potential misuse. - **Cellebrite's Role**: Provides digital forensic tools like UFED for extracting data from mobile devices; services governments globally, including those with poor human rights records. - **Digital Forensics Techniques**: - Consent-based extraction - Hacking attempts - Guessing PIN/passwords - **Mobile Phone States**: - BFU: Data encrypted and inaccessible without unlocking first. - AFU: Decryption keys available, though screen lock may still protect data. - **Cellebrite's Capabilities**: Claims ability to unlock and extract data from all non-GrapheneOS Android devices in both BFU and AFU states, as well as many iOS devices. Limited success with GrapheneOS due to its robust security features. - **GrapheneOS Security Enhancements**: - Firmware improvements for Pixel phones - Secure element chip (Titan M2) limits PIN/password attempts with progressive delays after failures. - Advanced USB connection settings prevent unauthorized access via USB. - Custom RISC-V secure element thwarts known vulnerabilities. - Auto-reboot feature enhances data protection by returning to BFU state post-attack. - **User Advancements**: Development of features like 2-factor fingerprint unlock and random passphrase generation tools. - **Adversary Tactics**: Resorting to misinformation due to the difficulty in breaching GrapheneOS security, countered by informed user knowledge. Keywords: AFU, Android, BFU, Cellebrite, GrapheneOS, PIN code, consent-based, data extraction, device unlocking, digital forensics, encryption, exploits, firmware, forensic tools, hacking, iOS, misinformation, privacy, secure element, security, software vulnerability
popular
discuss.grapheneos.org 2 days ago
https://fred.stlouisfed.org/series/FYFRGDA188S a day ago https://youtu.be/KDwCUAueLUU a day ago https://www.lse.ac.uk/granthaminstitute/news/fake- a day ago https://science.feedback.org/review/review-climate-the- a day ago https://en.wikipedia.org/wiki/Pegasus_(spyware) a day ago https://en.zona.media/article/2025/08/27/ a day ago https://thegrayzone.com/2025/03/15/feds-used- a day ago https://grapheneos.org/build#setting-up-the-os-build-environ a day ago https://telefoncek.si/2024/05/2024-05-30-grapheneo a day ago https://blog.okturtles.org/2024/06/the-ultimate-io a day ago https://www.reddit.com/r/PrivacyGuides/comments&#x a day ago https://github.com/GrapheneOS/Auditor/commits/ a day ago https://osservatorionessuno.org/blog/2025/03/ a day ago https://grapheneos.social/@GrapheneOS/11408190902039816 a day ago https://x.com/GrapheneOS/status/196546481791483107 a day ago https://support.apple.com/guide/security/secure-en a day ago https://news.ycombinator.com/threads?id=strcat a day ago https://calyxos.org/news/2021/03/29/mi-a a day ago https://discuss.grapheneos.org/d/24134-devices-lacking- a day ago https://grapheneos.org/faq#future-devices a day ago https://discuss.grapheneos.org/d/23886-partnership-betw a day ago https://grapheneos.org/faq#device-support a day ago https://news.ycombinator.com/item?id=45100831 a day ago https://source.puri.sm/Librem5/community-wiki/-&#x a day ago https://news.ycombinator.com/item?id=45208925 a day ago https://www.techzine.eu/news/security/127456/ a day ago https://primal.net/e/nevent1qqsq9lsf88umpdunkdzpdthyffy a day ago |
191. HN Behind the scenes of Bun Install- **Performance Enhancement in Package Management**: The text highlights how Bun dramatically outperforms npm, pnpm, and yarn in terms of installation speed by addressing package management as a systems programming challenge. This involves minimizing system calls, caching manifests in binary form, optimizing tarball extraction, leveraging native file copying methods, and utilizing multiple CPU cores for improved performance. - **Historical Context and Technological Shift**: The article reflects on Node.js's 2009 revolution of server efficiency through its event loop to manage I/O operations. Over time, as technology advanced with faster drives and internet speeds, the focus shifted toward minimizing system call overhead due to inefficiencies in CPU mode-switching. - **System Call Efficiency and Benchmarks**: By 2025, excessive system calls were identified as a bottleneck for package managers. Bun's approach minimizes these calls using OS-specific optimizations, resulting in significantly fewer calls per installation process. Benchmark results show Bun as the fastest at 5.6 seconds with around 166k system calls, compared to npm (37.2s, ~997k), yarn (~94.2s, >4M), and pnpm (24.5s, ~457k). - **Futex Call Analysis**: Bun's efficiency is demonstrated by its low futex call count (762) versus higher counts for other package managers: yarn (>2 million), npm (>660k), and pnpm (~117k). This efficiency highlights the advantage of Bun’s direct system call approach via Zig, bypassing inefficiencies in Node.js-based libuv. - **File Reading and DNS Optimization**: Bun reads files at 146,057 per second due to lower I/O costs. It optimizes network request delays through asynchronous DNS prefetching during package.json parsing using macOS's API. These optimizations contribute to faster performance compared to traditional methods used by npm, yarn, and pnpm. - **Manifest Storage and ETag Utilization**: Bun stores manifests in binary format within `.npm` files for efficient access, reducing memory overhead from repeated strings. It uses ETags with If-None-Match headers to enable "304 Not Modified" responses, improving package update handling efficiency. - **Benchmark Comparisons of Installations**: Fresh `bun install` is longer at 230.2 ms ± 685.5 ms, while cached installs are extremely fast at 9.1 ms ± 0.3 ms. In comparison, fresh `npm install` takes the longest at 1.786 s ± 4.407 s, with cached installs at 363.1 ms ± 21.6 ms. - **Decompression and Dependency Graph Optimization**: Bun buffers compressed files before decompression to reduce memory operations. It employs a Structure of Arrays (SoA) approach for better spatial locality in dependency graph management, optimizing access speed and reducing cache misses. - **Modern CPU and Memory Considerations**: The text explains that while CPUs process data quickly, RAM access remains slower, leading to the use of multi-level caches with varying speeds and sizes. Bun addresses these challenges by minimizing cache inefficiencies through optimized data layouts. - **File Copying Processes and OS-Specific Optimizations**: Bun minimizes mode switching between user and kernel modes during file copying processes using techniques like Apple's clonefile() on macOS and hardlinks on Linux. This reduces disk operations needed for copying files within `node_modules`. - **Concurrency with Lock-Free Architecture**: To enhance CPU utilization, Bun uses a lock-free, work-stealing thread pool architecture allowing multiple cores to process tasks in parallel. It maintains separate event loops for network operations on dedicated threads, enhancing execution efficiency. - **Resource Management and Performance Gains**: Each thread in Bun's system is assigned individual memory pools to reduce resource contention. Overall, Bun leverages modern hardware capabilities to accelerate package installation speeds by up to 25 times compared to traditional methods, setting new standards for developer productivity. Keywords: Bun, CPU cycles, Nodejs, async patterns, benchmarks, cache, concurrency, dependenciesThese keywords capture the main technical aspects and concepts discussed in the text, file copying, lock-free, memory management, package installation, performance optimization, system calls
popular
bun.com 2 days ago
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192. HN Show HN: I built a minimal Forth-like stack interpreter library in CThe author developed a minimal Forth-like stack interpreter library in C, named `stacklib.h`, over the weekend. This single-header library encapsulates fundamental Forth-style operations such as stack manipulations (push/pop/dup/swap/over/drop), arithmetic (+, -, *, /), output commands (. and cr), and stack inspection (.s, depth). Designed to be self-contained with no external dependencies, it also integrates basic error checking. The primary aim of the project is to offer insights into Forth programming by utilizing C's straightforward syntax. An illustrative example demonstrates initializing a stack and executing operations like "10 20 + ." which outputs "30", or "1 2 3 4 .s" for displaying stack contents. The author invites feedback from those interested in stack-based programming on similar projects, potential enhancements to increase its utility, and personal experiences. The `stacklib.h` library is hosted on GitHub at [https://github.com/Ferki-git-creator/stacklib](https://github.com/Ferki-git-creator/stacklib). ### Bullet Point Summary: - Developed a Forth-like stack interpreter in C called `stacklib.h`. - Library includes basic operations like stack manipulations, arithmetic, output commands, and stack inspection. - Self-contained with no external dependencies and includes error checking. - Aimed at providing insights into Forth programming using C's simplicity. - Example usage demonstrates executing simple operations on a stack. - Author seeks feedback from stack-based programming enthusiasts for potential enhancements. - Library is available on GitHub. Keywords: C library, Forth-style, GitHub, GitHub repository, Stack interpreter, arithmetic, arithmetic operations, basic interpreter, basic interpreter Keywords: Stack, concatenative programming, error checking, example usage, functions, inspection, interpreter, output, output functions, push/pop/dup/swap, repository, self-contained, stack inspection, stack operations
github
news.ycombinator.com 2 days ago
https://archive.org/details/LeoBrodieStartingFORTHIntro a day ago https://archive.org/details/R.G.LoeligerThreadedInterpr a day ago https://github.com/nothings/stb a day ago https://github.com/FransFaase/MES-replacement a day ago https://www.youtube.com/watch?v=akzyyO5wvm0 a day ago http://tumbleforth.hardcoded.net/ a day ago https://news.ycombinator.com/item?id=45039301 a day ago https://github.com/snej/tails a day ago https://tia.mat.br/posts/2025/08/30/fort a day ago https://github.com/lone-lang/lone/blob/master a day ago |
193. HN AirPods live translation blocked for EU users with EU Apple accountsApple's upcoming Live Translation feature for AirPods will not be available to European users if their Apple accounts are based in the EU due to strict regulations like the Artificial Intelligence Act and GDPR. These laws impose stringent requirements on speech and translation services related to privacy, consent, data flows, and user rights. Nonetheless, this feature is scheduled for release across older models such as AirPods 4 with Active Noise Cancellation and AirPods Pro 2. The Live Translation functionality enables users to engage in hands-free communication, providing live translations on iPhones or enhanced by compatible AirPods that reduce background noise, thereby improving focus during translated conversations. For the feature to work, AirPods must have the latest firmware, while the user’s iPhone needs to be equipped with Apple Intelligence and run iOS 26 or later. Supported devices include models from the iPhone 15 Pro onwards. Beta testing for this feature will coincide with the rollout of iOS 26 updates, with an official release expected on September 15 alongside these updates. Initially, Live Translation supports real-time translation in English (UK and U.S.), French, German, Portuguese (Brazil), and Spanish. Apple plans to expand language support later in the year to include Italian, Japanese, Korean, and simplified Chinese. The clarification of restrictions for EU-based accounts is pending from Apple. **BULLET POINT SUMMARY:** - Live Translation feature on AirPods will be unavailable to European users with EU-based Apple accounts due to stringent regulations like GDPR. - Restrictions are linked to strict requirements on privacy, consent, data flows, and user rights imposed by the EU's Artificial Intelligence Act and GDPR. - Feature rollout includes older models such as AirPods 4 with Active Noise Cancellation and AirPods Pro 2. - Enables hands-free communication with live translations displayed on iPhones or improved via compatible AirPods that reduce background noise. - Requires AirPods with the latest firmware and Apple Intelligence-enabled iPhone running iOS 26 or later, supporting models from iPhone 15 Pro onwards. - Beta testing aligns with iOS 26 updates, official release expected September 15 with iOS update. - Initial language support includes English (UK & U.S.), French, German, Portuguese (Brazil), and Spanish; expansion to Italian, Japanese, Korean, and simplified Chinese planned later in the year. - Status of EU/Apple Account restrictions remains unclarified by Apple. Keywords: Active Noise Cancellation, AirPods, Apple Accounts, Artificial Intelligence Act, Chinese, EU regulations, English, French, GDPR, German, Italian, Japanese, Korean, Live Translation, Portuguese, Spanish, beta testing, consent, data-flows, firmware, iOS, iPhone, privacy, transcriptions, user rights
popular
www.macrumors.com 2 days ago
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194. HN Reshaped is now open source**Summary:** Reshaped, initially developed as a component library for personal use in React and Figma projects, has been transitioned to an open-source model. The author of Reshaped, an experienced designer with over ten years in the field, crafted this library to encapsulate core design practices while providing flexibility for individual needs. Originally introduced as a paid product, Reshaped made its React package freely available, enabling broader access through direct npm installation for those holding source code licenses. This development culminated in both the GitHub-hosted React library and the Figma Community-hosted design library becoming open-source. The strategic shift aims to connect design and engineering communities by fostering scalable yet minimalistic best practices for design systems. The author's plan includes making the Reshaped libraries publicly available, granting users early access to new features and integrations with tools like Figma or React before they become necessary updates. Meanwhile, those with existing licenses will continue receiving future updates through established channels. Additionally, there are ambitions to expand Reshaped by introducing advanced, opinionated premium components that require more intricate CSS and React logic. This transition from five years of closed-source development represents a deliberate effort to contribute back to the community and enjoy the creative process. **Bullet Point Summary:** - **Open Source Transition**: Reshaped became fully open source after initially being a paid product, with its React package now free for broader access. - **Development Background**: Created by an experienced designer focusing on core design practices while allowing flexibility for specific needs. - **Community and Access**: The move to open-source aims to bridge the gap between design and engineering communities, promoting scalable minimalistic design system best practices. - **Public Library Availability**: Users will gain insight into new features and integrations with Figma or React before they are necessary through public library access. - **Continued Support for License Holders**: Existing license holders retain access to future updates via established channels. - **Expansion Plans**: The author intends to introduce advanced premium components involving complex CSS and React logic, marking a significant transition from previous closed-source work. - **Community Contribution Motivation**: This shift is driven by the desire to give back to the community and find enjoyment in the creative process. Keywords: CSS, Figma, GitHub, React, accessibility, community, component library, dark mode, design systems, engineering, integration, licenses, micro-animations, sustainability, theming
github
reshaped.so 2 days ago
https://i.imgur.com/qFH0ZlK.png a day ago https://github.com/reshaped-ui/reshaped/pull/ a day ago https://catalyst.tailwindui.com/docs a day ago https://ai-sdk.dev/elements/overview a day ago https://medium.com/@aneel.kaushikk/bulk-rename-utility- a day ago |
195. HN Germany is not supporting ChatControl – blocking minority secured**Summary:** Patrick Breyer of digitalcourage has revealed that Germany is not backing the ChatControl initiative within the EU as it has secured a blocking minority against it. This development was communicated through a social media post discussing EU policy matters. Furthermore, there is an encouragement for users to enable JavaScript or use native applications to access the Mastodon web application. **Bullet Point Summary:** - Patrick Breyer from digitalcourage disclosed Germany's lack of support for ChatControl in the EU. - Germany secured a blocking minority against supporting ChatControl. - The announcement was made via a social media post about an EU policy discussion. - Users are encouraged to enable JavaScript or use native apps for accessing Mastodon. Keywords: ChatControl, EU, Germany, JavaScript, Mastodon, Patrick Breyer, YES, blocking minority, digitalcouragesocial, native apps, platform, supporting
popular
digitalcourage.social 2 days ago
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196. HN PgEdge Goes Open SourceThe author transitioned from an AI-focused company to pgEdge, a firm specializing in distributed PostgreSQL technology. This move provided professional fulfillment through collaboration with skilled engineers and colleagues who share expertise in Postgres, bolstered by further recruitment of specialists in the field. Initially, a significant challenge at pgEdge involved proprietary "source available" licensing for key components such as the Spock replication engine and Snowflake extension. Although these components could be viewed and modified, their usage was restricted under this license. Recently, however, these components have been re-licensed under the more permissive PostgreSQL License. This aligns with open-source principles, benefiting the broader PostgreSQL ecosystem, a development of which the author is particularly proud. The change is anticipated to encourage further contributions from pgEdge. For individuals interested in exploring multimaster distributed PostgreSQL, development opportunities are available through GitHub repositories named spock, snowflake, and lolor. Alternatively, those seeking ready-to-use solutions without building from source can find cloud, container, and VM options on the company's website, providing supported builds suitable for production environments. **BULLET POINT SUMMARY:** - The author transitioned to pgEdge, a firm specializing in distributed PostgreSQL. - Fulfillment is found in working with talented engineers and colleagues who are Postgres experts. - Initial proprietary "source available" licensing of components like Spock replication engine and Snowflake extension restricted their usage. - These components have been re-licensed under the permissive PostgreSQL License, aligning with open-source principles. - This change supports the PostgreSQL ecosystem and is expected to encourage further contributions from pgEdge. - Development opportunities for multimaster distributed PostgreSQL are available through GitHub repositories: spock, snowflake, and lolor. - Ready-to-use solutions without building from source include cloud, container, and VM options on the company's website. Keywords: GitHub, Lolor, Open Source, Open Source Initiative, PostgreSQL, Snowflake, Spock, VM, build, cloud, container, contributions, development, distributed databases, logical replication, multimaster, permissive license, pgEdge, replication engine, tech, unique sequence values, website
postgresql
www.pgedge.com 2 days ago
https://news.ycombinator.com/item?id=45196173 a day ago https://news.ycombinator.com/item?id=45203769 a day ago |
197. HN LLM with Tree Search learns in context to solve hard problemsThe provided text describes a video that explores the integration of Large Language Models (LLMs) with Tree Search techniques, emphasizing their capacity to enhance contextual learning and solve complex problems. The content is accessible through a YouTube channel dedicated to sharing updates about new features while complying with Google LLC's copyright and privacy policies. - **Integration of LLMs and Tree Search**: The video highlights how combining Large Language Models with Tree Search methods can improve the ability to learn in context and address difficult challenges. - **Platform Availability**: This educational content is available on a YouTube channel that regularly updates viewers about new features related to this technology. - **Compliance with Policies**: The channel operates under Google LLC's copyright and privacy guidelines, ensuring adherence to legal standards. This summary encapsulates the key points of the text by focusing on the main subject matter, platform details, and policy compliance without including extraneous information. Keywords: Advertise, Contact, Copyright, Creators, Developers, Google LLC, LLM, NFL Sunday Ticket, Press, Privacy Policy, Safety, Terms, Tree Search, YouTube, context, problems, solve
llm
www.youtube.com 2 days ago
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198. HN Court rejects Verizon claim that selling location data without consent is legalThe US Court of Appeals for the 2nd Circuit unanimously upheld a $46.9 million fine imposed by the FCC on Verizon for selling customer location data without consent, reinforcing privacy protections under the Communication Act. This ruling mirrors a similar decision in T-Mobile’s case at the DC Circuit and could potentially lead to a Supreme Court review due to differing outcomes across circuits. Verizon's argument that device location data was not covered by law and their claim of Seventh Amendment rights violation—specifically, the right to a jury trial—were both dismissed. The court pointed out that Verizon had the opportunity for a jury trial but opted not to pursue it. Historically, until 2019, Verizon operated a program selling wireless customer location data through contracts with aggregators like LocationSmart and Zumigo, which then resold this information to third parties under arrangements involving up to 63 additional entities. - The 2nd Circuit Court upheld the FCC's fine on Verizon for unauthorized sale of customer location data. - The decision supports privacy protections under the Communication Act and mirrors a similar ruling in T-Mobile’s case at the DC Circuit, indicating potential Supreme Court involvement due to differing circuit outcomes. - Verizon's arguments regarding the applicability of law to device location data and violation of Seventh Amendment rights were rejected by the court. - The court noted that Verizon had the choice for a jury trial but did not exercise it. - Until 2019, Verizon sold customer location data through agreements with aggregators like LocationSmart and Zumigo, which then resold this data to additional third parties. Keywords: "location-based services", 2019, AT&T, Circuit, Communication Act, Court, FCC, Location information aggregators, LocationSmart, Seventh Amendment, Supreme Court, T-Mobile, Verizon, Zumigo, appeals, arrangements, collected, consent, device, fine, jury trial, law, location data, penalties, privacy protections, providers, ruling, sold, third-party, wireless customer
popular
arstechnica.com 3 days ago
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199. HN Checkpoint-engine: A middleware to update model weights in LLM inference engines- **Checkpoint-engine Overview**: A middleware solution designed for updating model weights in large language models (LLMs) during inference, crucial for reinforcement learning. It supports efficiently updating a 1 trillion parameter model like Kimi-K2 across thousands of GPUs in approximately 20 seconds. - **Core Functionality**: The `ParameterServer` class is central to checkpoint-engine, offering two weight update methods: Broadcast and P2P (Peer-to-Peer). The Broadcast method excels at synchronous updates across multiple inference instances due to its speed. In contrast, the P2P approach accommodates dynamically added instances using a mooncake-transfer-engine for CPU-to-GPU communication without interrupting ongoing processes. - **Weight Broadcasting Process**: This involves three stages: H2D (moving weights to GPU memory), broadcasting among checkpoint engine workers resulting in CUDA IPC buffers, and reload where inference engines decide on the subset of weights to utilize. The process is orchestrated through a data transfer plan based on metadata and bucket sizes, executed via ZeroMQ socket with a pipelined approach for performance optimization. - **Benchmarking and Performance**: Various models have been tested to assess efficiency, including device configurations like TP16 setups, highlighting update durations linked to IPC bucket sizes. Specific model-device combinations are provided with detailed times and data sizes. - **Installation Guide**: Offers instructions for installing a broadcast or P2P version of checkpoint-engine via `pip`, with the latter requiring RDMA support. Additional patches may be needed for FP8 quantization tests. - **Setting Up Peer-to-Peer Environment**: Steps include using an H800 or H20 machine with 8 GPUs, cloning and setting up vLLM, installing the necessary package, downloading a test model via HF, starting vLLM in development mode, and updating weights with checkpoint-engine. - **Reusing Weights Across Instances**: New instances can join existing ones to reuse their weights efficiently. This involves starting existing instances with specific flags for metadata saving and availability, then connecting new instances via the saved metadata file for weight reuse. - **FP8 Quantization Challenges and Solutions**: Addresses issues in vLLM with FP8 quantization by providing a tested patch for certain models, though it may have compatibility issues with others. A pull request is open for discussion and review within the vLLM project. - **Correctness Testing and Integration**: Recommends using `torchrun` for a simple correctness test of checkpoint_engine functionality. While currently supporting only vLLM, integration into other frameworks like SGLang is possible. The document notes limitations such as an unimplemented three-stage pipeline and suboptimal P2P update methods. - **Contributions and Acknowledgments**: Recognizes contributions from individuals within the vllm-project community, highlighting collaborative efforts in developing checkpoint-engine and its functionalities. Keywords: API endpoint, Broadcast, CPU memory, CUDA IPC buffer, Checkpoint-engine, FP8 quantization, GPU memory, GPUs, H2D, LLM inference engines, NCCL_IB_HCA, P2P, ParameterServer class, RDMA transfer, ZeroMQ socket, collective_rpc, inference instances, inplace weight update, middleware, model weights, mooncake-transfer-engine, overlapped communication, performance, pipeline, reinforcement learning, service, sharded weights, torchrun, vLLM
llm
github.com 3 days ago
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200. HN DOOMscrolling: The Game**Summary:** The text describes the development journey of "DOOMscrolling: The Game," a scrolling-only game inspired by doomscrolling and Doom, created using advancements in AI tools like GPT-5. Initially, the developer struggled to create a functional prototype with earlier coding tools but achieved success within two hours using GPT-5, incorporating elements such as a title screen that made it feel more authentic. The creator initially attempted to develop a Galaga-inspired game with unique movement mechanics and stationary monsters, iteratively refining gameplay features like weapon upgrades and health potions during vacation mornings. They later infused the game with contemporary relevance by integrating headlines from an RSS feed of The New York Times front page, which appeared as decorative plaques in a lore-driven future setting within the game. Facing challenges in AI-generated art, especially using GPT-5, for backgrounds and decorative items, the creator employed "labs" to experiment with design elements. These labs allowed randomization features that enhanced visual diversity and used computational methods for designing simple yet effective monster sprites, ensuring smooth performance on various devices. With further control needed over specific visuals, sliders were introduced within these labs to fine-tune appearances efficiently. The game reached version 1.0, with customizable text styling features integrated into its world through a lab page that allows experimentation and feedback sharing via a "copy settings" feature. David announces the launch of this mobile-friendly, saveable app-like experience, acknowledging a subscriber drop after moving platforms from Beehiiv to Ghost but invites readers to support his work through membership or donations. He encourages newsletter sharing to regain subscribers and expresses gratitude for continued engagement, looking forward to future updates. **Bullet Point Summary:** - "DOOMscrolling: The Game" is inspired by doomscrolling and Doom, featuring scrolling-only gameplay developed using GPT-5. - Initial development struggled with earlier coding tools but succeeded quickly using GPT-5, adding a title screen for authenticity. - Originated from a Galaga-inspired game concept, evolving through iterative refinements like weapon upgrades and health potions. - Contemporary headlines integrated as decorative plaques in-game via an RSS feed to enhance relevance without affecting gameplay. - Faced challenges with AI-generated art, using "labs" for real-time experimentation and randomization of design elements. - Employed computational methods and sliders within labs for efficient monster design and visual customization. - Version 1.0 features customizable text styling on plaques, allowing user experimentation through a dedicated lab page. - Game is mobile-friendly with saveable app-like functionality, launched by David seeking support via membership or donations. - Acknowledges subscriber drop after platform change from Beehiiv to Ghost, encouraging sharing and continued engagement for future updates. Keywords: AI, DOOMscrolling, Doom-inspired, GPT-4, LLMs, Lab page, RSS Feed, art, backgrounds, game, gameplay, headlines, monster animation, news, plaques, player movement, prototype, scrolling, sprites, styles, textures, vibe coding, web browser
gpt-4
ironicsans.ghost.io 3 days ago
https://arxiv.org/abs/2001.00888 a day ago https://www.folklore.org/Calculator_Construction_Set.html a day ago https://www.youtube.com/watch?t=22&v=VddS5IWxHd8&fea a day ago https://www.grumpygamer.com/deathbyscrolling5/ a day ago https://vibeware.vercel.app/ a day ago https://x.com/levelsio/status/1915127796097290534 a day ago https://silverweed.github.io/boom/ a day ago https://ironicsans.ghost.io a day ago https://www.youtube.com/watch?v=AJdEqssNZ-U a day ago https://sc.rollc.at/ a day ago |
201. HN XNEdit – fast and classic X11 text editor**Summary:** XNEdit stands out as a swift and conventional X11 text editor that can be accessed via both Sourceforge and GitHub platforms. This software is designed to cater to users who are looking for an efficient tool for editing texts within the X11 environment. Notably, it comes with extensive documentation that aims to assist users in fully understanding its capabilities and how to utilize them effectively. By providing clear guidance on various features, this documentation plays a crucial role in enhancing user experience by ensuring that individuals can maximize their use of XNEdit. **Bullet Point Summary:** - XNEdit is a fast and traditional text editor designed for the X11 environment. - Available on Sourceforge and GitHub platforms. - Offers comprehensive documentation to aid users. - Documentation assists with understanding features and usage. - Enhances user experience by enabling effective utilization of the tool. Keywords: Documentation, Github, Sourceforge, X11, XNEdit, available, classic, fast, relevant, technical, text editor
github
www.unixwork.de 3 days ago
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202. HN Tesla Doors Can Trap People Desperate to Escape### Summary In December 2023, an off-duty firefighter named Max Walsh heroically rescued a driver from a burning Tesla Model Y in northern Virginia after its electronic systems failed during a crash, trapping the occupants inside. This incident highlighted significant safety concerns regarding Tesla's door mechanisms in emergency situations. Similarly challenging conditions arose for other drivers across various states, where Tesla vehicles caught fire or became inaccessible due to power failures, underscoring issues with manual door releases and battery system reliability. These incidents have brought attention to Tesla’s design choices, such as hidden mechanical releases and flush handles that complicate quick egress during emergencies. Despite Tesla's innovations in vehicle technology under CEO Elon Musk, recurring complaints about door accessibility issues date back to the Model S launch in 2012. Over time, these challenges have persisted across multiple models, with some requiring owners or emergency responders to locate hidden manual releases. Consumer reports and official investigations by the National Highway Traffic Safety Administration (NHTSA) indicate that Tesla vehicles have faced numerous complaints about malfunctioning door systems. Notably, issues like low-voltage battery failures have left occupants stranded inside their cars without accessible exits. Several accidents involving Tesla vehicles resulted in severe consequences for passengers unable to escape due to these design limitations. Specific incidents have amplified these concerns: a 2024 Cybertruck crash in Piedmont, California, led to the death of three college students after they were trapped by fire, and similar issues emerged from a Wisconsin Model S accident. These cases reflect broader concerns regarding Tesla's vehicle designs, particularly around emergency exits during crashes. In response to these safety challenges, owners have filed lawsuits against Tesla for allegedly defective door designs that impede rescue efforts. Susmita Maddi, injured in the December 2023 Virginia crash, suffered severe burns and underwent extensive medical treatment. She sued Tesla, claiming the vehicle's design posed unreasonable safety risks due to its inaccessible emergency release system during her accident. Despite Tesla’s assertion of compliance with federal standards, Maddi faces a long recovery process. ### Bullet Point Summary - In December 2023, firefighter Max Walsh rescued a Tesla driver from a fire after electronic systems failed, trapping the occupants inside. - The incident underscores safety concerns about Tesla's door mechanisms during emergencies due to lack of accessible manual releases. - Design choices like hidden mechanical releases and flush handles complicate quick egress in emergencies, with recurring issues reported since 2012. - The NHTSA has documented numerous complaints regarding malfunctioning door systems in Tesla vehicles, particularly involving low-voltage battery failures. - Specific incidents, such as a fatal 2024 Cybertruck crash in California and a Wisconsin Model S accident, highlight the risks of inaccessible exits during emergencies. - Susmita Maddi sued Tesla over her severe injuries from being trapped inside a burning Tesla, citing defective design that hindered rescue efforts. - Despite Tesla's compliance claims, Maddi’s case illustrates ongoing safety concerns related to emergency egress in Tesla vehicles. Keywords: Cybertruck, December 2023, IT, Initial Quality Study, Leesburg, Maddi, Matt Riordan, MedStar hospital, Model 3, Model S, Model X, Model Y, NHTSA, Pasumarti, Road to Recovery, SUV, Tesla, Tesla doors, Teslas, Virginia, Washington exurb, adoption, aerodynamics, airbags, auxiliary battery, backseat window, battery cells, battery failure, blue vehicle, burns, child safety, children, claustrophobia, cloud computing, complaints, concealed door handles, consumer complaints, crash, crash design, defects, door handles, door lock, door locks, doors, driver’s door release, elderly parents, electric doors, electric vehicle fires, electronic button, emergency, emergency release, engineers, entrapment, epileptic seizure, fire, firefighter, flames, fumes, glitches, hazard lights, human body, hydraulic cutters, impact survivability, incident Lockout tool, intensive-care unit, intubated, jokes, lawsuit, locking system, manual release, manual releases, mask, mechanical latch, mechanical release Model S, nerve pain, nurse Susmita Maddi, occupant entrapment, owner’s manuals Rear doors, panic situation, passenger, pets, power failure, power loss, problems Complaints, rear seat latch, recalls, reconstructive surgery, regulator, rescue, rescue protocols Automation, roadside assistance Investigation, roadway, robotaxi, safety, safety concerns, safety risk, service, skin grafting, smoke, smoke inhalation, social worker, software engineer, surgeries, tech sector, temperature rise, thermal injuries, trapped cars, unresponsive car, utility pole, ventilator, window, wire-release mechanisms, wrongful death lawsuit
tesla
www.bloomberg.com 3 days ago
https://archive.is/lccr7 a day ago https://www.youtube.com/watch?v=4jY3K4AGAh0 a day ago |
203. HN KDE launches its own distribution- **Overview:** At Akademy 2025, KDE launched the alpha version of KDE Linux, aiming to create a comprehensive operating system utilizing cutting-edge technologies for various applications. This initiative stems from KDE's vision and is developed by leaders such as Harald Sitter with contributions from individuals like Nate Graham. - **Developmental Background:** While based on Arch Linux packages, KDE Linux diverges by not using Pacman; instead, components are compiled from source or installed via Flatpak. It represents an exploration of KDE’s ideal Linux environment despite its current rough edges and missing features. - **Distribution Philosophy:** KDE supports self-publishing applications across platforms like Flathub, Snap, and Microsoft stores. They advocate for free software producers to have their own operating systems, akin to the approaches by Linux Mint, ElementaryOS, and GNOME. - **Project Goals:** KDE Linux is a greenfield project designed to leverage modern technologies without constraints from general-purpose distributions. It supports Wayland exclusively, requires manual configuration for older NVIDIA cards, and only works with UEFI systems. - **Technical Aspects:** - Root filesystem: Read/write Btrfs volume. - /usr uses read-only EROFS updated atomically. - Up to five versions cached (approx. 4.8GB each) via systemd-sysupdate without delta support, recommending at least 30GB for cache space. - No direct package additions or kernel module installations are allowed; Flatpak and Snap can be installed via Discover or command line. - **User Experience:** Users face issues with Distrobox due to Podman setup errors. System updates are managed through Discover or `sudo updatectl update`. Essential KDE applications like Gwenview, Okular, and Firefox are included, but some common utilities are missing. - **Customization and Tools:** - Base packages aren't customizable; custom images can be created with systemd’s mkosi tool. - No security announcement mailing list currently exists, relying on Arch's advisories, leading to potential update delays. - **Editions and Support:** KDE Linux plans three editions—a testing edition for developers, an enthusiast edition for beta software, and a stable edition for vetted releases. Currently available as a virtual machine via virt-manager, it faces disk space constraints and lacks UEFI Secure Boot support. - **Governance and Future Plans:** - Governance follows a "Council of elders" model with Harald Sitter having final decision authority. - Strategies are in place for potential project discontinuation, including transitioning users to another distribution. - KDE Linux addresses open-source developers' desire to deliver software directly, exploring challenges in maintaining a desktop distribution. - **Challenges and Considerations:** The lack of a package manager poses inventorying challenges. While striving to offer an integrated user experience without intermediaries, KDE Linux underscores the complexities faced by existing distributions like Debian, Fedora, openSUSE, and Ubuntu. Keywords: Akademy 2025, Arch Linux, BIOS, Btrfs, Discover, Distrobox, EROFS, Flatpak, KDE Builder, KDE Linux, KDE Project, NVIDIA, OEM installations, Pacman, Podman, UEFI, UID/GID, Wayland-only, Xorg, alpha version, atomic updates, business use, containers, development edition, distribution, end-of-life plan, governance model, home use, immutable distribution, kernel modules, namespace error, rollback, security advisories, stable edition, system updates, systemd-sysupdate
popular
lwn.net 3 days ago
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204. HN OpenAI mulls data center construction in KoreaOn September 10, 2025, OpenAI expressed interest in establishing a data center in South Korea, as announced by Jason Kwon, the organization's Chief Strategy Officer, during a press conference held in Seoul. This information was subsequently reported by The Korea Times. ### Bullet Point Summary: - **Date and Development:** On September 10, 2025, OpenAI indicated plans to build a data center in South Korea. - **Source of Announcement:** The development was announced at a press conference led by Jason Kwon, who holds the position of Chief Strategy Officer at OpenAI. - **Location of Event:** The announcement took place in Seoul, South Korea. - **Report Source:** The Korea Times reported on this potential establishment of a data center by OpenAI. Keywords: BusinessTech, Jason Kwon, Korea, OpenAI, Park Jae-hyuk, Science, Seoul, The Korea Times, construction, data center, press conference, strategy officer, strategy officer Keywords: OpenAI
openai
www.koreatimes.co.kr 3 days ago
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205. HN Show HN: Making a cross-platform game in Go using WebRTC DatachannelsThe presentation from DWeb Weekend 2025 discusses leveraging WebRTC Datachannels over Websockets for developing cross-platform games in Go, focusing on their potential to enhance game development efficiency. While Websockets are deemed too slow due to TCP dependencies and are better suited for turn-based games, Web Transport is mentioned as an emerging but incompatible technology with some browsers like Firefox on Windows. The advantages of WebRTC Datachannels are emphasized, including reliable packet transmission through SCTP without requiring WebTransport. This allows sending unreliable packets over the web, eliminating traditional server hosting or port forwarding needs and offering flexibility for multiplayer game development today. Despite current limitations, its potential to simplify development is substantial once matured. WebRTC provides efficient peer-to-peer communication with minimal server demands, requiring only one server for signaling, though setup complexity arises from using separate servers for signaling and STUN/TURN services. Google's STUN service can ease integration when combined with the signaling server. Several implementations support WebRTC, such as libwebrtc, Pion (Go), and sipsorcery (C#). Libraries like Geckos.io (Node.js) and Netlib (TypeScript) facilitate WebRTC-based game networking. The Godot Engine uses libdatachannel for multiplayer features, while Go is preferred in development due to its active community support and efficient implementation with minimal dependencies. Although Rust offers certain advantages and some developers prefer it, Go is noted for faster development speed. Ebitengine is recommended for leveraging these technologies effectively in game development. Rust's feature set appeals to the author despite Go's more active community and easier WebRTC support. The presentation highlights an official Pion example using Ebitengine from the "Example WebRTC Applications" repository, which includes a signaling server for hosting lobbies and connecting players across devices. However, cross-computer play requires resolving CORS issues, with current player limits per computer potentially expandable through existing infrastructure. Other WebRTC applications include enabling multiplayer games akin to Minecraft without dedicated servers or port forwarding, by having players host their own game instances—eliminating the need for VPNs or Hamachi and considered a promising community approach. **Bullet Point Summary:** - The presentation explores using WebRTC Datachannels over Websockets for cross-platform game development in Go. - Websockets are too slow for fast-paced games due to TCP reliance, suitable only for slower turn-based games; Web Transport is an emerging but incompatible technology. - Benefits of WebRTC include reliable SCTP packet transmission without WebTransport, allowing bypassing traditional server requirements and enabling flexible multiplayer game development. - WebRTC offers efficient peer-to-peer communication with minimal server needs but requires separate signaling and STUN/TURN servers for setup complexity. - Implementations supporting WebRTC include libwebrtc, Pion (Go), and sipsorcery (C#); libraries like Geckos.io (Node.js) assist in game networking. - Godot Engine uses libdatachannel; Go is favored for its community support and efficient implementation, while Rust offers advantages despite slower development speed compared to Go. - Ebitengine is recommended for effective use of these technologies in game development. - Rust's features appeal to the author, but Go has a more active WebRTC community and faster development pace. - An official Pion example with Ebitengine includes a signaling server for hosting lobbies, with potential expansion for cross-computer play pending CORS resolution. - WebRTC allows multiplayer gaming without dedicated servers or port forwarding by enabling players to host game instances, eliminating VPNs or Hamachi needs. Keywords: C/C++, Counter Strike, Cross-platform, Datachannels, Ebitengine, Games, GitHub, Go, ICE, Lobby, Minecraft-style, Multiplayer, Nodejs, Pion, QUIC, Rust, SCTP, Server, Signaling, Steam, UDP, WASM, WebRTC, Websockets
github
pion.ly 3 days ago
https://oxism.com/trystero a day ago |
206. HN Charlie Kirk killed at event in Utah**Summary:** Charlie Kirk was tragically killed in a shooting incident at Utah Valley University, prompting the institution to close until Monday. In response, the university has suspended all classes, events, and administrative operations while making accommodations for coursework deadlines to be extended as needed. During this closure period, employees will continue receiving their regular pay. The school has assured that there is no ongoing threat on campus, allowing students and staff to safely retrieve any essential items they may have left at the Young Living Alumni Center. **BULLET POINT SUMMARY:** - Charlie Kirk was fatally shot in a shooting incident at Utah Valley University. - The university closed until Monday following the incident. - All classes, events, and administrative operations were suspended. - Coursework deadlines were adjusted to accommodate the closure period. - Employees will continue receiving regular pay during this time. - An alert confirmed there is no ongoing threat on campus. - Students and staff can retrieve essential items from the Young Living Alumni Center. Keywords: Charlie Kirk, Monday, Utah, Utah Valley University, Young Living Alumni Center, administrative operations, assignments, campus events, classes, closed, coursework, employees, exams, pay, shooting, threat
popular
www.nbcnews.com 3 days ago
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207. HN Defeating Nondeterminism in LLM InferenceThe text discusses the challenges of achieving reproducibility in large language models (LLMs) due to nondeterminism primarily stemming from non-associativity in floating-point arithmetic during out-of-order calculations on GPUs. While concurrency isn't solely responsible for variability, advanced parallel techniques like split reductions help maintain deterministic results without relying on atomic operations. Kernel optimizations and hardware strategies can still introduce variability by altering operation orders. Inference nondeterminism is further complicated by the lack of batch invariance, where outputs are inconsistent across different batch sizes or concurrent requests. Strategies such as RMSNorm and data-parallel matrix multiplication techniques attempt to ensure batch-invariant results through consistent processing orders. However, handling smaller batches poses challenges, requiring techniques like Split-K and Stream-K Matmul for optimization at the cost of some invariance. Specifically for attention mechanisms, FlashAttention2 addresses these issues by parallelizing computations along queries and reducing keys/values simultaneously. Consistency in computation during LLM inference is achieved through deterministic numerics, ensuring fixed reduction orders across tokens. KV caches introduce further complexity due to boundary conditions affecting reduction order, addressed by updating the cache before running attention kernels. To maintain batch invariance, strategies like Split-KV or FlashDecode are employed, splitting along the KV dimension for better parallelism but potentially disrupting consistency. The "Fixed Size Split-KV Strategy" helps preserve consistent reduction orders using fixed-size splits. vLLM's FlexAttention backend and torch.Library support deterministic inference by replacing standard operators with batch-invariant kernels. Experiments illustrate significant output variability without these batch-invariant kernels, while their implementation ensures reproducible results, aligning outcomes with expected samplers. Despite efficiency trade-offs in maintaining batch invariance, these strategies enable deterministic inference and true on-policy reinforcement learning (RL), eliminating off-policy corrections and achieving zero KL-divergence in RL training setups, thereby advancing machine learning systems. - Nondeterminism in LLMs is primarily due to floating-point non-associativity during out-of-order calculations. - Advanced parallel techniques like split reductions help maintain deterministic results without atomics. - Lack of batch invariance contributes to inference nondeterminism across different batch sizes or concurrent requests. - Strategies such as RMSNorm and data-parallel matrix multiplication aim for consistent processing orders, despite challenges with smaller batches. - FlashAttention2 addresses attention mechanism challenges through parallelization along queries and simultaneous K/V reduction. - Deterministic numerics ensure fixed reduction orders across tokens during LLM inference. - KV caches require updates to maintain batch invariance amidst boundary condition challenges. - Split-KV or FlashDecode strategies improve parallelism by splitting the KV dimension, though they may affect consistency. - "Fixed Size Split-KV Strategy" maintains consistent reduction orders using fixed-size splits. - vLLM's FlexAttention backend and torch.Library enable deterministic inference with batch-invariant kernels. - Experiments show significant output variability without batch-invariant kernels; their use achieves reproducibility. - Despite performance trade-offs, these strategies support true on-policy RL by eliminating off-policy corrections and achieving zero KL-divergence in training setups. Keywords: 2D Tiles, API Server, Arithmetic Intensity, Atomic Adds, Attention Scores, Batch Invariance, Batch Size, Batch-Norm, Bfloat16, Bitwise Identical Results, Block Size, Boundary Conditions, Chunked Prefill, Concurrency, Configuration Time, Core Reduction, Deterministic, Deterministic Algorithm, Deterministic Inference, Dynamic Level, Efficient Kernels, Elements, Exponent, FLOPs, Feature Dimension, Fixed Size Split-KV, FlashAttention, FlashAttention2, FlexAttention, Floating-Point Non-Associativity, Forward Pass, GPU Cores, GPUs, GPUs/CPU/TPUs, Greedy Sampling, Hypothesis, Importance Weighting, Inference Optimizations, Inference Server, Inference System, Information Loss, Integer Addition, KL Divergence, Kernel Engineer, Kernel Implementation, Kernels, LLM Endpoints, Large Language Models (LLMs), Load, Logits, Mantissa, Matmuls, Matrix Multiplication, Non-Associativity, Non-invariance, Nondeterminism, Numerical Stability, Numerics, Off-Policy RL, Order Dependence, Output Tensor, Outputs, Padded Instructions, Parallel Calculations, Parallel Program, Parallel Requests, Parallelize, Pointwise Operation, Policy Spikes, Precision, Prefix Caching, PyTorch, Quantization Effects, Query Tensor, RMSNorm, Reduction, Reproducibility, Reproducible Results, Reward Collapse, Rounding, SMs, Sampling, Scales, Scatter_Add, Sequence Dimension, Significant Figures, Split Reductions, Split-KV Strategy, Stream-K, Summing, Temperature, Tensorcores, Torch, Training, Transformer Architecture, Triton, Variation, Vector Primitive, vLLM
llm
thinkingmachines.ai 3 days ago
https://github.com/google-deepmind/penzai/issues&# a day ago https://github.com/jax-ml/jax/issues/20047#is a day ago https://dspy.ai/#2-optimizers-tune-the-prompts-and-weights-o a day ago https://github.com/sutt/innocuous a day ago https://longnow.org/ideas/richard-feynman-and-the-conne a day ago https://en.m.wikipedia.org/wiki/Thinking_Machines_Corpo a day ago https://www.google.com/search?q=jurassic+park+cm-5 a day ago https://news.ycombinator.com/item?id=42952605#42960047 a day ago https://www.gibney.org/prompt_coding a day ago https://github.com/khimaros/enc a day ago https://cloud.google.com/vertex-ai/generative-ai/d a day ago https://clayh53.github.io/tufte-jekyll/articles/20 a day ago https://www.youtube.com/watch?v=lKXe3HUG2l4 a day ago https://docs.pytorch.org/docs/stable/notes/ra a day ago https://git.distrust.co/public/llmshell a day ago https://x.com/thinkymachines/status/19658263697216 a day ago |
208. HN Determinate Systems – Dropping Upstream Nix from Determinate Nix Installer**Summary:** In early 2026, Determinate Systems plans to cease distributing upstream Nix through its installer, opting instead to offer exclusively Determinate Nix. This strategic shift aims to enhance user experience with significant improvements including lazy trees, parallel evaluation, and stable flakes. Despite potential disruptions for users favoring upstream Nix, an experimental fork by the Nix Installer Working Group will maintain support. As of November 10, 2025, the Determinate Nix Installer defaults to installing Determinate Nix unless users specify otherwise with the `--prefer-upstream-nix` flag. Following January 1, 2026, the installer will only provide Determinate Nix, eliminating options for upstream installation. This decision supports Determinate Systems' goal of delivering a fully managed product, given their lack of control over upstream Nix. Documentation and tools like GitHub actions will be updated to reflect these changes, ensuring users are informed via warnings and links to further information. Throughout this transition, communication through documentation updates and specific GitHub issues will guide users, with feedback welcomed at support@determinate.systems. **Bullet Point Summary:** - Determinate Systems plans to discontinue upstream Nix distribution in early 2026, focusing on Determinate Nix. - Enhancements include lazy trees, parallel evaluation, and stable flakes for a better Nix experience. - An experimental fork by the Nix Installer Working Group will continue supporting upstream Nix. - Starting November 10, 2025, the default installation is Determinate Nix unless `--prefer-upstream-nix` is used. - From January 1, 2026, only Determinate Nix will be installed without an alternative option. - This change aligns with providing a controlled, fully managed product. - Documentation and tools will be updated to inform users of these changes. - Users are encouraged to provide feedback via support@determinate.systems. Keywords: --prefer-upstream-nix flag, Community Collaboration, Determinate Nix, DeterminateSystems/nix-installer-action, Developer Experience, Distribution, Evaluations, Flakes, GitHub, Installer, Lazy Trees, Linux Builders, Parallel Evaluation, Performance, Reliability, Sunsetting Support, Upstream Nix, automated installations, curl command, intent-to-ship, macOS
github
determinate.systems 3 days ago
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209. HN I didn't bring my son to a museum to look at screensThe author reflects on past visits to The Franklin Institute (TFI) during the 1980s with fond memories of interactive exhibits that sparked curiosity and wonder in their childhood. However, a recent visit with their six-year-old son revealed significant changes: many traditional hands-on displays had been replaced by touchscreen interfaces, such as digital rocket design games, which limited direct physical interaction. Although some space-related artifacts remained, the essence of exploration through tangible engagement was largely missing. The exhibit prominently featured screens offering interactive experiences using body motion sensors akin to Xbox Kinect. However, these were deemed uninspiring by a science writer experienced at NASA when she and her son explored other sections like the Foucault pendulum, which lacked screen dependency. The museum's various sections—Space, Body Odyssey, and SportsZone—primarily showcased video games simulating action-response feedback through software rather than real-world interaction. Despite these shortcomings, certain areas like Sir Isaac's Loft and Air Show rooms provided engaging physical exhibits for children. These hands-on spaces allowed kids to explore physics directly, featuring activities such as a chair-and-pulley setup demonstrating simple machines, Lissajous curves from vibrating rods, experiments showing the effects of evacuating air on object movement, and a "shimmer wall" visualizing sound waves. The author expresses disappointment in poorly maintained exhibits like the malfunctioning "bicycle wheel and rotating stool" demonstration. They criticize the museum's prioritization of large screen rooms over preserving physical exhibits that allow visitors to interact with real artifacts. The writer recalls how museums once offered hands-on experiences fostering curiosity, emphasizing the importance of interactive displays for educational engagement. Despite paying a substantial entrance fee, they feel the institution does not align its priorities with providing valuable, tangible learning opportunities. The author criticizes T.F. Greenfield Interactive Science Museum's reliance on touchscreen technology in exhibits, arguing these digital interfaces lack genuine hands-on interaction and fail to engage children as effectively as physical activities do. They speculate that museums add screens to compete with other digital entertainment forms, exacerbating detachment from the real world—an issue particularly concerning for screen-overwhelmed children. While acknowledging some valuable experiences like the Franklin Memorial rotunda and neglected hands-on exhibits, the author advocates eliminating touchscreen displays in favor of traditional science activities that truly engage visitors. They suggest reallocating resources to enhance interactive displays reflective of the museum's original educational purpose. ### Bullet Point Summary: - The author fondly recalls interactive exhibits from their childhood at TFI but was disappointed by the recent shift towards touchscreen displays, limiting physical engagement. - Despite some space artifacts, most sections now emphasize digital interactions over hands-on activities, reducing direct exploration and curiosity. - Areas like Sir Isaac's Loft offer engaging physical exhibits without screens, allowing children to explore physics through tangible interaction. - The author criticizes poorly maintained physical exhibits and the museum's focus on large screen rooms at the expense of real artifacts. - They argue that touchscreen technology lacks genuine hands-on engagement and contribute to detachment from reality, especially for children. - Despite some valuable experiences, they advocate reallocating resources towards traditional interactive displays to enhance educational value. Keywords: Air Show rooms, Foucault pendulum, Franklin Institute, Lissajous curves, NASA, New Scientist, Philadelphia, Space, artifacts, bicycles, block-and-tackle, boosters, budget, digital, exhibits, gyro effect, hands-on, interactive, launch vehicle, maintenance, museum, rocket, science, screens, spacesuit, tactile, touchscreen, vibrations
popular
sethpurcell.com 3 days ago
https://thehill.com/policy/finance/210566-house-go a day ago https://youtu.be/N3zU7sV4bJE a day ago https://www.msichicago.org/explore/whats-here/even a day ago https://www.fieldmuseum.org/our-events/dozin-with-the-d a day ago https://news.ycombinator.com/item?id=42715841 a day ago https://news.utexas.edu/2017/06/26/the-mere-p a day ago https://www.museumofplay.org/exhibit/wegmans-super-kids a day ago http://www.buildingmycastle.com/stone-cold-problems/ a day ago https://youtu.be/cqpvl-YGFD4 a day ago https://collection.sciencemuseumgroup.org.uk/objects/co a day ago https://copenhagencontemporary.org/en/cc-create-x-monst a day ago https://www.artic.edu/visit/whos-visiting/families a day ago https://www.clevelandart.org/artlens-gallery a day ago https://www.rigb.org/ a day ago https://en.wikipedia.org/wiki/Royal_Institution_Christm a day ago https://www.sciencemuseum.org.uk/see-and-do/halloween-l a day ago https://www.gla.ac.uk/collections/#/details?irn=16 a day ago https://www.msichicago.org/explore/whats-here/exhi a day ago https://www.reddit.com/r/AmITheAngel/comments/ a day ago https://www.printables.com/model/5612-anatomic-heart-mu a day ago https://www.artis.nl/en/artis-micropia a day ago https://www.youtube.com/watch?v=H-vmSDgnlbg a day ago https://www.youtube.com/watch?v=7V6nucKFK88 a day ago https://www.youtube.com/watch?v=6MTOz7eOvmg a day ago https://www.achieve-now.com/poverty-cycle a day ago |
210. HN The origin story of merge queuesMerge queues have significantly improved the stability of main branches by managing concurrent merges, evolving from early tools like Bors and Homu in Rust projects to modern integrations in platforms such as GitHub and GitLab. Initially developed due to the limitations of simple merging strategies in continuous integration environments, merge queues reflect a historical progression toward more reliable workflows. The foundational concept originated with Ben Elliston's "Not Rocket Science Rule," which utilized cron jobs for maintaining a continuously passing code branch. In 2013, Graydon Hoare created Bors to automate testing before merging pull requests (PRs) on Rust’s GitHub repository. By testing changes in a temporary branch and fast-forwarding the main branch upon successful tests, Bors addressed "merge skew" issues. While initially limited, this strategy inspired more sophisticated systems like Homu by Barosl Lee, which ensured all PRs were tested before merging them into the main branch. Homu's rise as an open-source tool was short-lived due to maintenance challenges, leading to the development of Bors-NG by Michael Howell in 2017. Offering improvements in speed and ease of hosting, Bors-NG became a popular choice until GitHub introduced its native Merge Queue feature in mid-2023. Alongside Bors-NG, other tools like Bulldozer (Palantir), Mergify (Julien Danjou and Mehdi Abaakouk), and Kodiak (Christopher Blump) emerged to automate PR management, each addressing specific challenges faced by developers. These tools reflect a broader industry trend toward automating merge processes to alleviate human bottlenecks, with features like priority rules and batch merging. Internal solutions developed by large tech companies further underscore the demand for stable branch management amid high development volumes, as seen in Uber's SubmitQueue and Shopify's Shipit Merge Queue. Feedback from these implementations highlights enhanced productivity and satisfaction among developers. GitHub’s official Merge Queue feature marks a significant advancement, automating PR testing before merging to ensure changes do not disrupt the main branch. This approach simplifies developer workflows and mirrors principles found in earlier tools like Bors. Despite its limitations compared to more advanced configurations available via third-party services like Mergify, GitHub's Merge Queue represents the culmination of community-driven solutions becoming standard features on major platforms. In summary, merge queues have transitioned from niche solutions addressing specific issues in early 2010s large projects to essential components of modern software workflows. This evolution aligns with continuous integration and trunk-based development practices, emphasizing automation’s role in maintaining high-quality code at scale. As a result, what began as a community-driven effort for robust testing rules has become an indispensable practice in DevOps, transforming code integration into a streamlined process crucial for teams operating at high velocity. **Bullet Point Summary:** - Merge queues enhance main branch stability by managing concurrent merges from early tools like Bors and Homu to modern integrations in GitHub and GitLab. - The concept originated with Ben Elliston's "Not Rocket Science Rule," utilizing cron jobs for a continuously passing code branch, evolving due to limitations of simple merging strategies in CI environments. - Graydon Hoare's 2013 introduction of Bors automated testing before PR merges on Rust’s GitHub repository, addressing merge skew issues by fast-forwarding the main branch after successful tests. - Homu extended Bors' capabilities but faced maintenance challenges, leading to Bors-NG by Michael Howell in 2017 for a faster, more user-friendly solution until GitHub's native Merge Queue emerged. - Tools like Bulldozer, Mergify, and Kodiak arose from the need to automate PR management, addressing specific developer challenges with features such as priority rules and batch merging. - Internal solutions at tech companies like Uber’s SubmitQueue and Shopify’s Shipit Merge Queue highlight increased demands for stable branch management amid rapid development cycles. - GitHub's Merge Queue automates PR testing before merging, reflecting principles from earlier tools and simplifying workflows despite limitations compared to advanced third-party services like Mergify. - Merge queues have evolved from niche solutions in the early 2010s to essential modern software workflow components, aligning with CI and trunk-based development practices for high-quality code at scale. Keywords: Bors, Bulldozer, CI (Continuous Integration), GitHub, GitLab, Homu, Kodiak, Merge Queue, Mergify, Monorepo, Not Rocket Science Rule, PRs (Pull Requests), Rust, Shipit, automation, extensible solution, integration risk, merge queues, merge skew, software quality, testing frameworks, trunk-based development
github
mergify.com 3 days ago
https://graphite.dev/blog/bors-google-tap-merge-queue a day ago https://www.infoq.com/presentations/facebook-release-pr a day ago https://pushtrain.club/ a day ago https://sluongng.hashnode.dev/bazel-in-ci-part-1-commit-unde a day ago https://github.com/orgs/community/discussions/ a day ago https://www.channable.com/tech/automated-deployments a day ago https://github.com/rust-lang/rust/pull/146121 a day ago |
211. HN Microsoft PowerToysMicrosoft PowerToys is a free suite of utilities designed to boost productivity for Windows 10 (version 2004 or later) and Windows 11 users by customizing the operating system to better suit power user needs. Supporting both x64 and ARM64 architectures, it includes key features like Advanced Paste for clipboard text management with optional AI enhancement, Always On Top for window pinning via a shortcut, PowerToys Awake to prevent sleep during intensive tasks, and Color Picker for screen color selection. PowerToys encompasses various tools that enhance productivity through customization: 1. **Command Not Found**: Suggests WinGet packages when commands are unavailable. 2. **Command Palette**: Provides quick access to frequent commands and apps. 3. **Crop And Lock**: Creates new windows from cropped areas of existing ones. 4. **Environment Variables**: Allows easy management with profiles. 5. **FancyZones**: A window manager for complex layouts. 6. **File Explorer Add-ons**: Enhances File Explorer features like previews and thumbnails. 7. **File Locksmith**: Manages locked files by processes, allowing unlocking. 8. **Hosts File Editor**: Simplifies the editing of Windows 'Hosts' file. 9. **Image Resizer**: Enables image resizing directly in File Explorer. 10. **Keyboard Manager**: Remaps keys and creates shortcuts for efficiency. 11. **Mouse Utilities**: Includes features like Find My Mouse, Mouse Highlighter, Mouse Jump, and Mouse Pointer Crosshairs. Other notable utilities include: - **Mouse Without Borders**: Facilitates multi-computer interaction with a single keyboard/mouse setup. - **New+**: Creates files/folders using templates in File Explorer. - **Peek**: Previews file contents without opening applications. - **PowerRename**: Allows bulk renaming of files with regex and undo options. - **PowerToys Run**: Searches and launches apps quickly, open-source and supports plugins. Additional features are Quick Accent for typing accented characters, Registry Preview for editing the Windows Registry, Screen Ruler for measuring screen dimensions, Shortcut Guide for displaying shortcuts, Text Extractor for copying text from screens, Workspaces for managing applications, and ZoomIt for screen zooming and annotations. PowerToys supports multiple languages, including Arabic, Chinese, Czech, Dutch, English, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Russian, Spanish, Turkish, Ukrainian. The project is open-source, allowing community contributions to enhance Windows efficiency, inspired by the original PowerToys from Windows 95. Users can report issues or contribute through GitHub and follow guidance in the Contributor's Guide and Developer Documentation. Recent changes include the deprecation of the Video Conference Mute utility starting with version 0.88, encouraging users to seek alternative solutions for audio management during video conferences. The team remains committed to rapid innovation, regularly reassessing priorities to maximize productivity enhancements for power users. Keywords: Always On Top, Clipboard, Color Picker, Development, FancyZones, File Explorer, GitHub, Keyboard Manager, Mouse utilities, Open Source, PowerRename, PowerToys, Quick Accent, Screen Ruler, Troubleshooting, Windows utilities, customization, productivity
github
learn.microsoft.com 3 days ago
https://github.com/microsoft/PowerToys/issues/ a day ago https://github.com/namazso/OpenHashTab a day ago https://github.com/valinet/ExplorerPatcher a day ago https://learn.microsoft.com/en-us/sysinternals/ a day ago https://github.com/EsportToys/TPMouse a day ago https://keypirinha.com a day ago https://www.raycast.com/windows a day ago https://zhornsoftware.co.uk/caffeine/ a day ago https://learn.microsoft.com/en-us/windows/powertoy a day ago https://github.com/microsoft/PowerToys/issues/ a day ago https://en.wikipedia.org/wiki/Windows_11 a day ago _version_22H2 a day ago https://github.com/thewhitegrizzli/DeskPins |
212. HN Show HN: Small Transfers – charge from 0.000001 USD per request for your SaaS"Small Transfers" is an innovative payment platform tailored for SaaS and API providers who prefer billing customers per individual request rather than through traditional subscription models or pre-purchase packages. This solution addresses common issues such as customer reluctance towards subscriptions, the high fixed fees imposed by conventional processors on small transactions, and the complexities involved with Stripe's Usage-Based Billing (UBB), which necessitates additional setup including authentication mechanisms and separate card charges. The platform functions by integrating each merchant's account with their Stripe account via Stripe Connect for efficient payouts. To ensure reliable payments, customers are verified through Google Sign-In, 3-D Secure, and Stripe Radar. Small Transfers simplifies the authorization and charging processes using OAuth, thus eliminating the need for merchants to implement separate authentication systems. Merchants can authorize charges as low as $0.000001 USD, capturing only what is necessary plus an optional margin. In terms of fees, the platform charges merchants a flat 3% per transaction, while customers are responsible for standard payment processing fees when settling their balances. This model aims to lower entry barriers for occasional users and provides flexibility for service providers. Additionally, developers have integrated this platform into their service called Unattach and seek feedback on their approach as well as additional insights. For practical use, the developer has made a Next.js Starter project available on GitHub with a live demo, encouraging testing and feedback from potential merchants. They are also seeking more merchants to try out the platform and offer integration assistance. Users can provide feedback through discussion forums associated with the project. The text further highlights an intuitive API designed by the company, offering an example in JavaScript that demonstrates authorizing a charge via Small Transfers' API endpoint using necessary headers and JSON body data containing authentication keys, currency details, and amount specifications converted to micros. For more comprehensive information, users are directed to the API Reference or encouraged to explore the Next.js Starter Project provided by the company. - **Platform Overview**: "Small Transfers" is designed for per-request billing, addressing customer aversion to subscriptions and high fees on small transactions. - **Technical Integration**: Links merchants' accounts with Stripe via Stripe Connect; uses Google Sign-In, 3-D Secure, and Stripe Radar for verification; employs OAuth for simplified authorization processes. - **Fee Structure**: Merchants pay a flat 3% per transaction fee; customers incur standard processing fees upon balance settlement. - **Feedback and Integration**: Developer integrated the platform into their service (Unattach), seeking merchant feedback and integration assistance through GitHub's Next.js Starter project. - **API Accessibility**: Offers an easy-to-use API with a JavaScript example for charge authorization, directing users to further resources like API Reference and Next.js Starter Project. Keywords: 3-D Secure, API, GitHub, Google Sign-In, JSON, JavaScript, Nextjs, OAuth, POST, Python, REST API, SaaS, Stripe Connect, Stripe Radar, amountMicros, auth implementation, authorization, billing, capture charge, currency, dog-fooding, feedback, fees, fetch, flat rate, headers, live demo, merchants, merchants trial, microservices, payment processing, payments, payout transfers, processing fees, service integration, starter project, subscriptions, usage tracking
github
smalltransfers.com 3 days ago
https://x402.org a day ago https://smalltransfers.com/terms a day ago https://github.com/smalltransfers/nextjs-starter a day ago https://en.wikipedia.org/wiki/Flattr a day ago https://github.com/philippgille/ln-paywall a day ago https://unattach.com/pricing a day ago |
213. HN OrioleDB Patent: now freely available to the Postgres community**Summary:** The recent release of the OrioleDB patent into the public domain signifies a pivotal moment for the PostgreSQL community. By making this intellectual property freely accessible, it opens up new opportunities for broader access and fosters potential innovation within the open-source database ecosystem. This move is expected to encourage collaborative efforts, allowing developers and researchers to build upon the existing technology without legal constraints, ultimately enhancing the capabilities and reach of PostgreSQL-based solutions. **Bullet Point Summary:** - The OrioleDB patent has been released into the public domain. - It is now freely available to the PostgreSQL community. - This action enables broader access to the technology. - Potential for increased innovation within the open-source database ecosystem is anticipated. - Encourages collaborative development efforts among developers and researchers. - Removes legal constraints, facilitating further technological advancements in PostgreSQL. Keywords: OrioleDB, Patent, Postgres, available, community, database, freely, innovation, licensing, open-source, software, technical
postgres
supabase.com 3 days ago
https://github.com/orioledb/orioledb/blob/mai a day ago https://github.com/orioledb/orioledb/pull/558 a day ago https://supabase.com/blog/orioledb-patent-free#aligned- a day ago https://engineering.fb.com/2017/09/22/web a day ago https://aomedia.org/license/patent-license/ a day ago https://github.com/orioledb/orioledb?tab=License-1-ov-f a day ago https://github.com/postgres/postgres?tab=License-1-ov-f a day ago https://en.wikipedia.org/wiki/BSD_licenses#4-clause_lic a day ago https://aomedia.org/about/legal/ a day ago https://news.ycombinator.com/item?id=45199687 a day ago https://www.webmproject.org/license/additional/ a day ago https://opensource.org/license/ms-pl-html a day ago https://opensource.org/license/apache-2-0 a day ago https://news.ycombinator.com/item?id=45200014 a day ago https://en.wikipedia.org/wiki/Reduction_to_practice a day ago https://news.ycombinator.com/item?id=45196771 a day ago https://github.com/orioledb/orioledb/commit/4 a day ago https://www.orioledb.com/docs a day ago https://airtable.com/app7jp5t0dEHyDpa8/shr00etqywoDW2N6 a day ago https://www.orioledb.com/docs#patch-set a day ago https://github.com/orioledb/orioledb?tab=readme-ov-file a day ago https://news.ycombinator.com/item?id=30462695 a day ago https://itif.org/publications/2024/09/16/ a day ago https://www.economist.com/science-and-technology/2024&# a day ago https://www.orioledb.com/docs/usage/getting-starte a day ago https://github.com/orioledb/orioledb/blob/7f3 a day ago https://www.postgresql.org/docs/current/ddl-constr a day ago https://www.sciencedirect.com/science/article/pii& a day ago https://www.amazon.com/Declarative-Models-Concurrent-Cyclic- a day ago https://www.orioledb.com/docs/architecture/overvie a day ago https://www.orioledb.com/docs/architecture/overvie a day ago https://www.orioledb.com/blog/orioledb-fastpath-search a day ago https://www.postgresql.org/docs/current/indexes-in a day ago https://sqlite.org/withoutrowid.html a day ago https://github.com/orioledb/orioledb/commit/4 a day ago https://www.orioledb.com/blog/orioledb-neon-differences a day ago |
214. HN Apple unveils iPhone 17 Air – the thinnest iPhone ever at 5.6 mmApple recently unveiled its new iPhone lineup at a major event, highlighting the iPhone 17 Air as a groundbreaking model due to its unprecedented slimness of 5.6 mm. CEO Tim Cook emphasized this launch as Apple's most significant advancement for the iPhone. The lineup comprises four models: the entry-level iPhone 17, iPhone Pro, iPhone Pro Max, and the iPhone 17 Air. Their prices range from $799 to $1,199, with pre-orders beginning on Friday and official availability on September 19th. These new iPhones boast enhanced battery life, superior materials, and improved camera technology. In addition to the iPhone, Apple introduced updated AirPods featuring live translation capabilities and hearing aid functions. A redesigned Apple Watch was also unveiled, equipped with advanced health monitoring features such as sleep scoring and blood pressure tracking. Both these products will be available for pre-order immediately, with a release date set for September 19th. The company's upcoming iOS 26 update is scheduled to launch the following Monday, incorporating AI enhancements like Genmoji and a new "liquid glass" design. However, those expecting more substantial AI developments might have to wait, as analysts suggest Apple will present stronger innovations in the coming year after acknowledging the need for additional development time on AI features. Speculation arose from Tim Cook's comments regarding potential acquisitions or partnerships with AI companies, possibly including Google, to bolster Apple's AI offerings. Subsequent to these remarks, Apple's stock experienced a 1.5% drop post-event and has seen an overall decline of about 6% this year, positioning it as one of only two members of the Magnificent Seven facing setbacks in 2025, alongside Tesla. This article was updated to reflect recent share prices and additional information. **BULLET POINT SUMMARY:** - Apple launched its latest iPhone lineup featuring the ultra-thin iPhone 17 Air (5.6 mm) described as a "game changer." - Four models introduced: entry-level iPhone 17, iPhone Pro, iPhone Pro Max, and iPhone 17 Air with prices ranging from $799 to $1,199. - Pre-orders open on Friday, available September 19th; iPhones offer longer battery life, enhanced materials, and better cameras. - Updated AirPods with live translation and hearing aid features released alongside a new Apple Watch with advanced health functions. - Both updated AirPods and Apple Watch pre-order now, release on September 19th. - iOS 26 update launching next Monday includes AI improvements like Genmoji and "liquid glass" redesign. - Investors anticipating more significant AI developments may wait until the following year due to needed development time. - Speculation about potential AI company acquisitions or partnerships, possibly with Google, arose from Tim Cook's statements. - Apple shares fell 1.5% post-event; overall decrease of approximately 6% this year, making it one of two Magnificent Seven members in decline alongside Tesla. - Article updated to include recent share prices and additional information. Keywords: AI, Airpods, Alphabet, Apple Watch, Citi analysts, Genmoji, Google, Magnificent Seven, Siri, Tesla, Tim Cook, acquisition, battery life, cameras, developers conference, entry-level iPhone, foldable phone, game changer, health features, iOS 26, iPhone, investors, liquid glass, partnership, pre-orders, price, shares, startup
tesla
www.investopedia.com 3 days ago
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215. HN Pontevedra, Spain declares its entire urban area a "reduced traffic zone"### Summary Pontevedra, Spain, has successfully transformed its city center into a pedestrian-focused "reduced traffic zone" since Mayor Miguel Anxo Fernández Lores took office in 1999. The initiative prioritizes residents over cars by imposing regulations on vehicle circulation and promoting alternative modes of transportation like walking, biking, and public transit. This approach aligns with national air quality standards and climate laws, contributing to safer streets and a vibrant community life centered around cultural events and outdoor activities. The city's strategy draws inspiration from urban planners such as Ildefons Cerdà i Sunyer and Jane Jacobs, emphasizing the importance of reclaiming public spaces for social cohesion. This has led to reduced car traffic by 40% in Pontevedra, with initiatives like a universal speed limit of 30 km/h (or lower in pedestrian-heavy areas) contributing to this decline. The city boasts zero fatal road accidents over a decade and significant reductions in CO2 emissions. Pontevedra's commitment to sustainability is recognized through international accolades such as the UN-Habitat Dubai International Best Practices Award and the European Commission EU Urban Road Safety Award for its record of zero road deaths from 2011 to 2018. The model, while specific to Pontevedra's context, offers broader lessons on shifting urban planning priorities towards people-centered development rather than car-centric models. Local businesses have adapted well to these changes; despite challenges with parking availability, many thrive due to increased foot traffic and vibrant pedestrian activity in the city center. This transformation is supported by community engagement and participatory processes that ensure residents' needs are met while maintaining necessary vehicle access for services and emergencies. Overall, Pontevedra serves as an exemplary case of sustainable urban development, emphasizing walkability and reduced car dependency, with broader implications for other cities aiming to enhance urban living quality. ### Bullet Point Summary - **Urban Transformation**: Pontevedra prioritizes pedestrians over cars by creating a "reduced traffic zone," reducing vehicle dominance since 1999 under Mayor Miguel Anxo Fernández Lores. - **Safety and Environment**: The city meets national air quality standards, has safer streets, reduced CO2 emissions by 67%, and recorded zero fatal accidents on municipal roads from 2011 to 2023. - **Urban Planning Influences**: Inspired by planners like Ildefons Cerdà i Sunyer and Jane Jacobs, Pontevedra focuses on reclaiming public spaces and enhancing social cohesion. - **Traffic Regulations**: Implemented a universal speed limit of 30 km/h (lower in pedestrian areas), contributing to reduced traffic and promoting safer streets. - **Awards and Recognition**: Recognized internationally with awards like the UN-Habitat Dubai Best Practices Award and the EU Urban Road Safety Award for zero road deaths from 2011 to 2018. - **Business Impact**: Local businesses experience increased foot traffic, benefiting from pedestrian-friendly environments despite some challenges related to parking. - **Community Engagement**: Success attributed to clear communication, community involvement, and strategic urban design that balances pedestrian needs with necessary vehicle access. - **Model for Others**: Pontevedra's approach highlights the importance of shifting urban planning priorities from car-centric to people-centered models. Keywords: Low Emission Zone (LEZ), Pontevedra, accessibility, air pollution, awards, bicycles, pedestrians, public space, sustainability, traffic zone, urban mobility, walkability, zero road deaths
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www.greeneuropeanjournal.eu 3 days ago
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216. HN Show HN: Get LiveKit Agents complete observability in seconds**Summary:** Whispey is an observability platform tailored for LiveKit voice agents, designed to transform telemetry into actionable insights with minimal setup (a single flag `enable_otel=True`). It enhances debugging efficiency in production by offering features like real-time monitoring, cost tracking, performance metrics, and multi-project support. Whispey's architecture includes a Python SDK for data collection, a Next.js dashboard for analytics visualization, and Supabase as the backend for data storage and updates. The platform emphasizes ease of use through seamless integration with LiveKit agents, open-source transparency, and quick deployment on recommended cloud platforms. Key features include customizable dashboards, privacy options via self-hosting, data export capabilities, and comprehensive setup guides for both cloud-based and self-hosted installations. Whispey's technology stack encompasses Next.js 14, React, Tailwind CSS, Supabase with PostgreSQL, Clerk.dev for authentication, Python 3.8+ with asyncio, and a custom metrics engine. Deployment is facilitated by Vercel with Docker support. The platform tracks various metrics such as response times, costs, quality, token usage, call volume, and user patterns using custom scoring algorithms, supporting production monitoring, cost optimization, quality assurance, performance debugging, and business intelligence. The project encourages community contributions through a Git workflow and provides documentation on GitHub for setup and development. Security is a priority with end-to-end encryption, SOC 2 compliance, regular audits, and updates, alongside optional self-hosting for data privacy. Future roadmap includes multi-language SDK support, ML-powered insights, integrations for alerts via Slack/Discord, a GraphQL API, a mobile app, and custom webhook integrations. **Bullet Point Summary:** - **Platform Overview:** Whispey is an observability platform for LiveKit voice agents, transforming telemetry into actionable insights with minimal setup. - **Key Features:** Real-time monitoring, cost tracking, performance metrics, multi-project support, customizable dashboards, privacy options, data export capabilities. - **Architecture:** Python SDK for data collection, Next.js dashboard for visualization, Supabase backend for storage and real-time updates. - **Technology Stack:** Next.js 14, React, Tailwind CSS, Supabase with PostgreSQL, Clerk.dev, Python 3.8+ with asyncio, custom metrics engine; deployed via Vercel with Docker support. - **Metrics Tracking:** Response times, costs, quality, token usage, call volume, user patterns using custom scoring algorithms. - **Use Cases:** Production monitoring, cost optimization, quality assurance, performance debugging, business intelligence. - **Community and Contributions:** Encourages contributions through a Git workflow; documentation available on GitHub for setup and development. - **Security Measures:** End-to-end encryption, SOC 2 compliance, regular audits, updates, optional self-hosting for data privacy. - **Future Roadmap:** Multi-language SDK support, ML-powered insights, Slack/Discord integrations, GraphQL API, mobile app, custom webhook integrations. - **Community Support and Licensing:** Additional enterprise features available; licensed under MIT License; acknowledgments to Datadog, New Relic, Honeycomb, LiveKit community. Keywords: Docker, GitHub, GraphQL, LiveKit, Nextjs, OpenTelemetry, Python, SDK, SOC 2, Supabase, analytics, anomaly detection, debugging, encryption, intelligence, latency, observability, performance metrics, spans, telemetry, webhooks
github
github.com 3 days ago
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217. HN Show HN: TailGuard – Bridge your WireGuard router into Tailscale via a container### Summary: TailGuard is a Docker-based application developed to integrate existing WireGuard routers into the Tailscale network, enhancing VPN management and accessibility without altering the current setup. Designed by Juho Vähä-Herttua in 2025, it emerged from the need to simplify managing multiple VPN connections on devices with limited capabilities, such as those facing ISP CGNAT challenges. TailGuard effectively transforms WireGuard routers into nodes within a Tailscale virtual network (tailnet), resolving issues like key management and concurrent VPN usage on single-VPN-capable devices. The solution allows seamless integration by running a containerized application that requires minimal configuration changes, offering benefits such as centralized private key storage, Single Sign-On for device onboarding, easy tailnet node switching without reconnecting to VPNs, and simultaneous access to Tailscale and WireGuard networks. Users can set up TailGuard using a WireGuard client configuration file (`wg0.conf`), create an optional IPv6 network, and run a Docker container with specific settings including IP forwarding and port mapping. The setup process involves logging into the tailnet via a URL provided post-container start-up, building the latest Docker image using `docker compose`, and ensuring specific configurations like route acceptance on WireGuard server IPs. Key environment parameters include device names for both WireGuard (`WG_DEVICE`) and Tailscale (`TS_DEVICE`). Unlike Tailscale, manual route configuration is required for WireGuard. For effective network communication using TailGuard, the document advises adding routes through specified IP addresses for two-way traffic with Tailscale's private address spaces. The software operates under the MIT License, allowing free use, modification, and distribution while requiring the inclusion of copyright notices in distributed versions. Users are informed that the software is provided "as-is," without any warranty, and there is no liability for damages. ### Bullet Point Summary: - **Purpose**: TailGuard integrates WireGuard routers into Tailscale to simplify VPN management and access. - **Creator's Motivation**: Developed by Juho Vähä-Herttua to manage a 5G connection in rural areas without complex key management. - **Integration Mechanism**: Seamlessly converts WireGuard routers into nodes within a Tailscale tailnet using a containerized application. - **Benefits**: Centralized key storage, Single Sign-On for device onboarding, easy node switching, concurrent VPN access, and compatibility with limited-capability devices. - **Setup Process**: - Use a WireGuard client configuration file (`wg0.conf`). - Deploy a Docker container with IP forwarding and port mapping. - Log into the tailnet via a URL post-container start-up. - Build Docker images using `docker compose`. - **Configuration Details**: - Ensure specific route acceptance on WireGuard server IPs. - Use environment variables for device names (`WG_DEVICE`, `TS_DEVICE`). - Manual routing configuration required for WireGuard. - **Networking Instructions**: - Add routes for Tailscale's private address spaces through specified IPs for effective communication. - **Licensing**: - Software is under the MIT License, allowing free use and distribution with a copyright notice requirement. - Provided "as-is" without warranty; no liability for damages. Keywords: Docker, IPv6, SSO, TailGuard, Tailscale, VPN, VPS, WireGuard, container, firewall, network, routing
tailscale
github.com 4 days ago
https://github.com/spr-networks/spr-tailscale/blob a day ago https://fly.io/docs/blueprints/connect-private-net a day ago https://developer.android.com/reference/android/ne a day ago https://github.com/adrienverge/openfortivpn a day ago https://github.com/singlestore-labs/tailscale-manager a day ago https://www.gl-inet.com/products/ a day ago |
218. HN Claude's new Code Interpreter reviewClaude's new Code Interpreter feature enables users on Max, Team, and Enterprise plans to create, edit, and analyze various file types like Excel spreadsheets, PDFs, and PowerPoint presentations directly within Claude.ai and its desktop app. This tool also supports advanced data analysis by allowing the creation of Python scripts, visualizations, and handling files such as CSV and TSV. It provides the ability to execute custom Python and Node.js code in a server-side sandbox environment. However, this capability introduces potential security concerns due to internet access for file creation and analysis. Users are advised to be vigilant about their interactions with this feature. The feature's naming convention has drawn criticism for being confusing, especially given its evolution from the simpler "Analysis tool" launched in October 2024. This lack of clarity contrasts with OpenAI’s more straightforward labeling. In testing, Claude was found capable of running shell commands and executing Python and Node.js code within an Ubuntu container environment. Despite successful installation of additional Python packages like `sqlite-utils`, direct network access attempts via tools such as `curl` or HTTP requests faced restrictions, though external means could still be used to fetch content. The feature limits file sizes to 30MB, unlike ChatGPT's 512MB capacity. The author describes using Claude for generating PDF and PNG outputs from SQLite databases and recreating AI adoption charts with Python, leveraging Matplotlib’s features. They initially struggled with smooth curve rendering in charts but eventually succeeded by utilizing Matplotlib's built-in smoothing options without relying on external libraries. Parallel experiences with ChatGPT highlighted its limitations compared to Claude, particularly regarding internet access for resolving overlapping chart titles and potential "prompt injection" risks. The author emphasizes the need for careful monitoring of Claude’s activity due to these vulnerabilities despite ongoing security enhancements like package installations and Node.js support. The text also reflects on naming challenges faced by AI labs in describing data analysis and code execution features, noting inconsistent terminology across platforms such as OpenAI and Anthropic. This inconsistency contributes to confusion regarding tool capabilities that focus more on file generation rather than pure code execution. Overall, while Claude's Code Interpreter offers significant utility for users seeking advanced data manipulation and visualization, the evolving naming conventions and security concerns underscore the need for clear communication and careful usage of this feature. Keywords: API, Anthropic, Apollo chart, CSV, ChatGPT, Claude, Code Interpreter, Excel, HTTPS_PROXY, Linux, Matplotlib, Nodejs, PDFs, PowerPoint, Python, SQLite, TSV, Ubuntu, conversation, data analysis, environment variables, feature under-appreciation, prompting strategy, proxy, rolling average, root user, sandbox, security testing, server-side, tool naming
claude
simonwillison.net 4 days ago
https://twitter.com/simonw/status/1966629016670228 16 hours ago |
219. HN I replaced Animal Crossing's dialogue with a live LLM by hacking GameCube memoryThe text describes an innovative project to integrate live responses from a Large Language Model (LLM) into the dialogue system of the GameCube game "Animal Crossing," without altering its original code. This was achieved by hacking into the console's memory, leveraging advancements in decompilation that converted raw assembly language to readable C code. The key modifications were made within the `m_message.c` file to replace standard dialogue with AI-generated text. The challenge of integrating real-time data from an external AI arose due to the GameCube's lack of built-in network capabilities and sandbox restrictions, which hindered attempts at adding a custom network stack or using file-based communication. The breakthrough was achieved through Inter-Process Communication (IPC) via shared memory, allowing direct data exchange between a Python script and the game running on the Dolphin emulator. A "Memory Mailbox" interface was developed to facilitate this interaction by reading from and writing to specified virtual addresses in the game's memory. This required creating a custom memory scanner to identify stable memory addresses for dialogue text and speaker names. The necessary addresses were determined, enabling reliable data exchange between the game and external systems. The GameCube's Broadband Adapter was considered for network features through two methods: a BBA Network Shim requiring minimal networking layers or a RAM Mailbox approach using in-emulator communication without kernel modifications. A host-side UDP bridge developed in Python enabled communication with the game by encoding language control codes necessary for dialogue processing, preventing the game from freezing. To ensure effective dialogue interaction, an AI pipeline was created separating creative writing and technical programming tasks into two models: a Writer AI and a Director AI. The former generated contextually appropriate dialogue based on character sheets, while the latter handled dramatic effects such as pauses and sound cues. The integration allowed real-world events to be woven into villagers' conversations through lightweight news feeds and facilitated in-game gossip dynamics. An unintended humorous outcome was observed when Fox News served as a news source. The project exemplifies an intricate blend of reverse engineering, AI, and gaming nostalgia, with all components and code available on GitHub. **BULLET POINT SUMMARY:** - **Project Overview:** Integrated LLM-generated responses into Animal Crossing via memory hacking without altering original game code. - **Technical Challenges:** Overcame GameCube's network limitations through IPC via shared memory using Dolphin emulator. - **Memory Mailbox Interface:** Developed a Python interface to interact with game RAM, identifying stable addresses for dialogue and speaker names. - **Broadband Adapter Considerations:** Explored BBA Network Shim and RAM Mailbox methods for potential online features. - **Communication Bridge:** Created a Python UDP bridge encoding control codes necessary for dialogue processing in the game. - **AI Pipeline Development:** Separated tasks into Writer AI for creative writing and Director AI for technical implementation, enhancing performance. - **Emergent Behavior:** Enabled real-world events integration into game dialogues; observed humorous outcomes with certain news sources. - **Project Significance:** Showcased complex reverse engineering and AI integration in a classic game, with all code and demonstrations available publicly. Keywords: AI Core, Animal Crossing, Broadband Adapter, Cloud-Based AI, Context File, Control Codes, Decompilation Community, Dialogue Box, Dialogue Replacement, Dolphin Emulator, Encoder/Decoder, Fan Wiki, GameCube, IPC (Inter-Process Communication), LLM (Language Model), Memory Hacking, Network Stack, PowerPC Processor, Python Script, RAM, Shared Memory, mMessagec
llm
joshfonseca.com 4 days ago
https://github.com/vuciv/animal-crossing-llm-mod a day ago https://gitingest.com/vuciv/animal-crossing-llm-mod a day ago https://claude.ai/share/66c52dc8-9ebd-4db7-8159-8f694e0 a day ago https://github.com/vuciv/animal-crossing-llm-mod/b a day ago https://gc-forever.com/wiki/index.php?title=Broadband_A a day ago https://simonwillison.net/2025/Sep/10/animal- a day ago https://github.com/vuciv/animal-crossing-llm-mod/b a day ago https://github.com/vuciv/animal-crossing-llm-mod/b a day ago https://openai.com/index/prover-verifier-games-improve- a day ago https://news.ycombinator.com/item?id=45134144 a day ago https://platform.openai.com/docs/guides/structured a day ago https://www.youtube.com/watch?v=xNPF9VKmzxw a day ago https://art-from-the-machine.github.io/Mantella/ a day ago https://www.meaningmachine.games/ a day ago https://www.youtube.com/watch?v=TpYVyJBmH0g&ab_channel=D a day ago https://plunderludics.github.io/tools/unityhawk.html a day ago https://www.youtube.com/watch?v=BeyvvQOPlhM a day ago https://github.com/jmarshall23/Quake3LLM a day ago https://lparchive.org/Animal-Crossing/ a day ago https://youtu.be/7AyEzA5ziE0?si=Ve3R7gG2KjoWCmV7 a day ago https://www.youtube.com/watch?v=7AyEzA5ziE0 a day ago https://youtu.be/jH-6-ZIgmKY a day ago |
220. HN R-Zero: Self-Evolving Reasoning LLM from Zero DataThe paper "R-Zero: Self-Evolving Reasoning LLM from Zero Data," authored by Chengsong Huang and colleagues, introduces a groundbreaking large language model (LLM) named R-Zero. This model is unique in that it evolves its reasoning capabilities autonomously without any initial data input. The research highlights advancements in machine learning, where the model enhances its performance independently over time. Supported by entities such as the Simons Foundation, this study was submitted to arXiv on August 7, 2025, and revised on August 27, 2025. R-Zero represents a significant step forward in AI research by enabling LLMs to develop reasoning skills from scratch through a self-evolving framework. R-Zero operates by generating training data autonomously via two independent models: the Challenger and the Solver. The Challenger proposes tasks slightly beyond the Solver's current abilities, while the Solver is rewarded for successfully tackling these challenges. This dynamic interaction fosters a self-improving curriculum that enhances reasoning skills across various LLMs. Empirical evidence indicates notable improvements in math-reasoning (+6.49) and general-domain reasoning (+7.54) benchmarks when applied to the Qwen3-4B-Base model. The study holds significant implications for Machine Learning, Artificial Intelligence, and Computation and Language fields. Additionally, the text describes a web interface for browsing academic papers within the computer science domain (cs.LG), as of August 2025. This interface offers tools for managing bibliographic data, including BibTeX citations export, literature exploration via platforms like Litmaps and scite.ai, and associated code or media access through services such as Hugging Face and DagsHub. Recommendation systems, search tools like CORE and IArxiv Recommender, and Influence Flowers are available to suggest relevant papers. Further described are various features of the arXiv platform, including the CORE and IArxiv Recommender for tailored user suggestions, and arXivLabs that encourages community collaboration on new website features. Tools such as MathJax can be disabled by users, and support resources like contact information, subscription options, and help sections are available. ArXiv upholds policies related to copyright, privacy, web accessibility, and operational status updates, which can be accessed via email or Slack. Notably, some paper authors may also serve as endorsers within the platform. **Bullet Point Summary:** - Introduction of R-Zero, a novel LLM capable of evolving its reasoning without initial data. - Developed by Chengsong Huang and colleagues, supported by entities like the Simons Foundation. - Submitted to arXiv on August 7, 2025, with revisions on August 27, 2025; advances machine learning through autonomous performance enhancement. - Operates using two models: Challenger and Solver, fostering a self-improving curriculum. - Demonstrated significant improvements in math-reasoning (+6.49) and general-domain reasoning (+7.54). - The study impacts Machine Learning, AI, and Computation and Language fields. - Description of an academic paper browsing interface for computer science (cs.LG), as of August 2025. - Offers tools like BibTeX export, literature exploration platforms (Litmaps, scite.ai), and access to associated code/media (Hugging Face, DagsHub). - Features recommendation systems (CORE, IArxiv Recommender) and Influence Flowers for paper suggestions. - Overview of arXiv platform features: CORE and IArxiv Recommender, arXivLabs for community collaboration. - MathJax can be disabled; support resources available include contact info, subscriptions, and help sections. - ArXiv adheres to copyright policies, privacy, web accessibility, and provides operational status updates via email or Slack. - Authors may serve as endorsers on the platform. Keywords: AlphaXiv, Artificial Intelligence, Autonomous, BibTeX, Challenger, Co-evolve, Computation and Language, Computer Science, Hugging Face, Large Language Models, Machine Learning, R-Zero, Reasoning LLM, Self-Evolving, Solver, Super-intelligence, Training Data, Zero Data, arXiv
llm
arxiv.org 4 days ago
https://snorkel.ai/blog/large-language-model-training-t a day ago https://en.wikipedia.org/wiki/Generative_adversarial_ne a day ago https://en.wikipedia.org/wiki/Colossus:_The_Forbin_Proj a day ago https://en.wikipedia.org/wiki/The_Terminator a day ago https://en.wikipedia.org/wiki/Re:Zero a day ago |
221. HN The OSS code that powers Claude and the maintainer they didn't hireRobin Grell's blog post about his rejection by Anthropic, which gained traction after being featured on Hacker News, sheds light on several significant issues within technology hiring practices and the open-source software ecosystem. The blog underscores challenges such as AI-driven hiring processes that might exclude talented individuals and permissive licensing models allowing companies to benefit from open-source contributions without reciprocating. Grell is the maintainer of "enigo," an essential open-source library facilitating input simulation across multiple operating systems, a capability crucial for AI applications like Claude Desktop. He highlights technical hurdles due to varying platform APIs and security protocols, such as adding keyboard support manually on Linux. Despite being a master's student in Germany, Grell manages this critical infrastructure, illustrating the complexities of software development. Enigo abstracts protocol support across different compositors, allowing developers to focus less on compatibility issues. However, challenges remain, like simulating keyboard inputs that require additional manual configuration. This library is pivotal for various applications beyond gaming and automation tools; it supports AI-driven computer control by translating network-based instructions into local actions. The text delves into broader discussions about distinguishing simulated input from human input—a crucial task for preventing automated abuse such as web scraping or DDOS attacks. While some operating systems can detect these inputs, developers often employ countermeasures like human-like input patterns to evade detection. Robin's journey in maintaining enigo began with his master’s project on improving Linux smartphone keyboards and continued through offering help on GitHub, eventually becoming the library's main maintainer due to its extensive use. His experience emphasizes both the potential of open-source contributions and the licensing issues that arise when companies exploit such code without contributing back. The article also touches upon Robin’s technical prowess, illustrated by his hacking of electronic bike shifters, highlighting broader implications for tech security. Despite the challenges, his work exemplifies how seemingly mundane tasks underpin significant advancements in AI technology. Lastly, the article addresses sustainability concerns regarding the reliance on small-scale maintainers of critical open-source infrastructure. It suggests hiring these maintainers as a solution to ensure the longevity and reliability of essential software components used in AI development. The piece encourages recognizing the often-overlooked contributions of developers like Grell in building AI capabilities. Key Points: - Robin Grell's blog post on being rejected by Anthropic highlights issues with tech hiring and open-source licensing. - Enigo, an open-source library for input simulation across platforms, is crucial for AI applications and faces technical challenges due to varying system APIs. - The article discusses the difficulty of distinguishing between simulated and human inputs, important for preventing automated abuses. - Robin's journey from a master’s project to maintaining enigo underscores the importance and impact of small-scale open-source contributors in AI development. - Licensing issues arise when companies exploit open-source code without contributing back, raising sustainability concerns. - The text suggests hiring maintainers as a solution for sustainable software infrastructure in AI advancements. - Robin’s technical expertise is highlighted through his hacking projects, emphasizing broader tech security implications. Keywords: AI agents, AI-driven hiring, APIs, Anthropic, Claude, GitHub, JavaScript, Linux, OSS, Robin Grell, Rust, Wayland, X11, accessibility, bot detection, enigo, infrastructure, input simulation, libei, maintainership, permissive licensing
claude
agenticweb.nearestnabors.com 4 days ago
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222. HN First Universal AI Context**Summary:** The text outlines the user's request for guidance on exporting chat logs with the aim of analyzing them or utilizing them in other large language models. The user is particularly interested in gaining insights from others' experiences and discovering simple methods to transfer these chats into a different LLM efficiently. - **Key Points:** - The user wants advice on how to export chat logs for analysis or integration. - They seek knowledge of others' experiences regarding this process. - There's an interest in straightforward techniques for transferring the data into another LLM. - The focus is on practical guidance and methods that can be easily implemented. Keywords: AI, LLM, Universal, chats, context, conversations, keywords, method, others, port, technical, transfer
llm
universal-context-pack.vercel.app 4 days ago
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223. HN Show HN: Ainews247.org aggregates the most valuable AI related news/content 24/7### Summary: Ainews247.org serves as a comprehensive platform aggregating 24/7 AI-related news, utilizing an algorithm to filter and spotlight the most informative content for users keen on staying abreast with AI advancements. The platform recently covered diverse topics including ethical concerns in medical AI use, new programming languages, enterprise-level AI tools, generative AI in content creation, multi-agent trading systems, AI personal device control, privacy issues linked to AI features, reliability optimization for AI agents, topological computing algorithms, and Microsoft's integration of Anthropic models into Office 365. On September 9, the site highlighted numerous tech developments: Microsoft's strategy to reduce OpenAI dependency via acquiring Anthropic AI; advances in real-time visual saliency detection on GitHub; optimizing Qwen images for edge devices; GPT-4V's human-like social evaluation capabilities researched by University of Turku; concerns over AI energy consumption from Technology Review; updates from J11y.io blog, Google's audio enhancement to AI Edge Gallery on Play Store, Firefox’s new iPhone feature, the use of linters in AI as discussed on Factory.ai, a guide on transformers manipulation via LightcapAI Medium, Hugging Face's Parquet chunking introduction, Nvidia’s Rubin CPX GPU release and MLPerf results shared by HotHardware, a case study on GenAI's impact from CloudKitchens Tech Blog, Gacua open-source computer agent launch on GitHub, Google’s plans to enhance AI Mode accessibility on BleepingComputer, and the unveiling of Nvidia’s new GPU for long-context inference as reported by TechCrunch. AiNews247 also mentions its terms of service and privacy policy in relation to these updates. ### Bullet Point Summary: - **Platform Overview**: Ainews247.org aggregates AI news 24/7 using an algorithm to highlight essential content. - **Recent Highlights**: - Topics include ethical AI use, new programming languages, enterprise AI tools, generative AI content creation, multi-agent trading systems, personal device AI control, data privacy concerns, and reliability optimization. - **Microsoft Strategy**: Plans to reduce OpenAI reliance by acquiring Anthropic AI (TechCrunch). - **Real-Time Visual Saliency Project**: Shared on GitHub showcasing real-time capabilities. - **Qwen Image Optimization**: Articles discuss optimizing for edge devices. - **GPT-4V Research**: University of Turku shows GPT-4V’s human-like social evaluation abilities. - **AI Energy Concerns**: Technology Review highlights unknown AI energy consumption challenges. - **J11y.io Blog Update**: Announcement on deprecation of a friend feature. - **Google AI Edge Gallery**: Introduction of audio capabilities on Google Play. - **Firefox iPhone Feature**: 'Shake to Summarize' feature introduced (The Verge). - **AI and Linters**: Discussion on directing agents using linters from Factory.ai. - **Transformers Guide**: Published guide on logical manipulation (LightcapAI Medium). - **Parquet Chunking Introduction**: Presented by Hugging Face. - **Nvidia GPU Release**: Rubin CPX unveiled with MLPerf results (HotHardware). - **GenAI Productivity Case Study**: CloudKitchens Tech Blog indicates limited productivity effects. - **Open-Source Computer Agent**: Gacua released on GitHub for one-command starts. - **Google AI Mode Accessibility**: Planned to be more accessible by default (BleepingComputer). - **Nvidia’s New GPU**: Designed for long-context inference, reported by TechCrunch. - **Additional Details**: AiNews247 outlines terms of service and privacy policy related to these developments. Keywords: AI, Agents, Aggregates, Algorithm, Anthropic, Content, Edge Devices, Filtering, GPT-4V, GPU, Generative AI, Linters, Long-context Inference, MLPerf, Microsoft, Models, News, Nvidia, OpenAI, Productivity Impact, Saliency Detection, Transformers
openai
ainews247.org 4 days ago
https://buttondown.com/ainews/rss a day ago |
224. HN Changes to Camunda Helm Sub-Charts: What You Need to KnowCamunda is updating its Helm charts for better security and long-term production suitability, prompted by Bitnami's change in container image distribution. Previously, Camunda relied on Bitnami-maintained images for components such as Elasticsearch, PostgreSQL, and Keycloak, which are no longer publicly maintained. For users with versions 8.7.x or earlier, these charts will remain functional via Bitnami’s legacy registry but without updates. With the release of version 8.8, Camunda sub-charts for infrastructure components are disabled by default to promote secure configurations. Users who wish to use these services must explicitly enable and configure them. New installations require setting up infrastructure dependencies beforehand using either managed services like AWS RDS or self-hosted solutions. Camunda recommends existing users in production environments switch to enterprise images from their private registry, exploring alternative deployment strategies such as vendor-native services or community Helm charts. For new setups, it's advisable to separate infrastructure components for improved management and updates. Enterprise customers can now access Bitnami Secure Images through Camunda’s private repository. These security-hardened images are protected against vulnerabilities and require a registry secret and `values-enterprise.yaml` file for deployment. From August 28, 2025, Bitnami's public images will be deprecated to a legacy repository, with updates to the default Camunda registry source scheduled in Q3 2025. New guides incorporating essential components like PostgreSQL, Elasticsearch, and Keycloak are forthcoming. In version 8.8 and beyond, users must manually install infrastructure sub-charts before deploying Camunda Helm. Further guidance is available through documentation or support. - **Changes to Camunda Helm Charts**: Enhanced security measures with Bitnami’s shift in image distribution. - **Legacy Support for Older Versions**: Existing charts (8.7.x and earlier) continue via legacy registry but without updates. - **New Default Settings in 8.8**: Sub-charts disabled by default, requiring explicit user configuration. - **Infrastructure Dependency Requirements**: Must be set up before deploying Camunda Helm with version 8.8 onward. - **Recommendations for Existing Users**: Switch to enterprise images from private registry and explore alternative deployment strategies. - **Enterprise Image Benefits**: Bitnami Secure Images offered, security-hardened, with additional configuration requirements. - **Future Deprecation of Public Images**: Effective August 28, 2025; legacy repository move planned for Q3 2025. - **Documentation and Support**: New guides will be released, and further details are available through support channels. Keywords: Application Catalog, Bitnami, CVEs, Camunda, Deployment, Documentation, Elasticsearch, Hardened Images, Helm, Infrastructure, Keycloak, PostgreSQL, Private Registry, Secure Images, Sub-Charts, VMware Tanzu, Values Files
postgresql
camunda.com 4 days ago
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225. HN Oracle pops 27% on cloud growth projectionsOracle shares experienced a significant surge of 27% in extended trading due to promising growth projections linked to new cloud contracts, despite missing earnings and revenue expectations. The company reported adjusted earnings per share at $1.47 compared to the forecasted $1.48, alongside revenue figures of $14.93 billion versus an anticipated $15.04 billion. Nonetheless, a 12% year-over-year increase in quarterly revenue was noted, with net income holding steady at $2.93 billion. A standout development is Oracle's contracted yet-to-be-recognized revenue, which skyrocketed by 359%, reaching $455 billion. This growth is partly attributed to an agreement with OpenAI to develop 4.5 gigawatts of U.S. data center capacity and securing substantial multibillion-dollar cloud contracts, benefiting from the AI industry boom alongside competitors such as Microsoft. Oracle's shares have climbed 45% in 2025, achieving a record high last month, outperforming the S&P 500 index's 11% gain. This recent surge could result in Oracle’s best trading day since 1999 and potentially elevate its market capitalization beyond $800 billion. Additionally, Larry Ellison announced that Oracle will introduce an AI Database service in October, facilitating the deployment of AI models from OpenAI and other providers on client data within Oracle databases. This move strengthens Oracle's collaboration with OpenAI, building on their previous integration of OpenAI's GPT-5 model into cloud applications. **BULLET POINT SUMMARY:** - Oracle shares rose 27% due to growth potential from new cloud contracts despite earnings and revenue falling short. - Reported adjusted EPS at $1.47 against expectations of $1.48; revenue was $14.93 billion versus expected $15.04 billion. - Quarterly revenue increased by 12% year-over-year, with net income stable at $2.93 billion. - Contracted yet-to-be-recognized revenue surged by 359%, reaching $455 billion. - Oracle partnered with OpenAI for U.S. data center capacity and secured significant multibillion-dollar cloud contracts amid AI industry growth. - Shares climbed 45% in 2025, surpassing the S&P 500’s gain; potential best trading day since 1999 could push market cap beyond $800 billion. - Larry Ellison announced an upcoming Oracle AI Database service enabling AI model usage from OpenAI and others on client data within Oracle databases. Keywords: AI boom, FII Summit, GPT-5 AI model, GPUs, Google Gemini, Larry Ellison, OpenAI, Oracle, Oracle AI Database service, S&P 500, Safra Catz, chairman, client data, cloud applications, cloud growth, co-founder, data centers, database software, databases, earnings, market cap, multibillion-dollar contracts, product integration, revenue, shares, technology chief
openai
www.cnbc.com 4 days ago
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226. HN How Google dodged a major breakup – and why OpenAI is to thank for it### Summary: In a landmark antitrust case, Judge Amit Mehta ruled that Google maintained an illegal monopoly over internet search but did not mandate the sale of its Chrome browser or Android OS. Instead, Google is barred from exclusive distribution agreements for its search engine and must share data with competitors. This ruling was influenced by the rise of OpenAI as a significant competitor in AI, leading to less severe penalties than some critics anticipated. The case underscores how advancements in generative AI (GenAI), particularly by companies like OpenAI, are reshaping competition and legal outcomes in the tech industry. The emergence of GenAI has spurred investment and innovation, challenging Google's dominance. Companies such as OpenAI and ChatGPT have attracted significant investments, positioning themselves as formidable competitors. This shift suggests a more competitive landscape where better products could emerge without being overshadowed by Google’s financial power. The dynamics are evident in Apple's strategic partnerships with both OpenAI for iPhone enhancements and discussions with Google about integrating Gemini into Siri. Speculations suggest that OpenAI might explore strategic acquisitions or benefit from a weakened Google, indicating evolving competition driven by AI advancements. Despite Google’s foundational contributions to ChatGPT through its researchers, the launch of ChatGPT marked a significant competitive shift favoring generative AI. Although Google remains a dominant player due to its vast user base, it faces continued challenges in antitrust matters concerning its advertising business and control over online ad distribution. The European Union imposed nearly €3 billion fines on Google for abusing market position in advertising technology, posing further risks of dismantling its adtech division. In the AI sector, Anthropic settled a lawsuit involving copyright infringement claims from authors regarding training data derived from pirated books, marking it as the largest recovery of this nature without admitting liability. The settlement underscored concerns among creative professionals about AI’s impact on their work. Anthropic's legal victory in claiming its use of copyrighted material for training AI fell under fair use has set a precedent that might influence similar cases against other tech giants like Meta, which secured a favorable settlement over analogous issues. Ongoing copyright lawsuits continue across the industry, including actions by media companies such as Warner Bros. Discovery and Disney against various firms, reflecting broader tensions over AI training practices. ### Bullet Point Summary: - **Antitrust Ruling on Google:** - Judge Amit Mehta ruled Google maintained an illegal monopoly in internet search but did not require selling Chrome or Android OS. - Google is prohibited from exclusive distribution agreements for its search engine and must share data with competitors. - The ruling was influenced by OpenAI's emergence as a significant AI competitor. - **Impact of Generative AI:** - GenAI has driven investment and innovation, challenging Google’s dominance in search engines. - Companies like OpenAI and ChatGPT have emerged as strong competitors due to substantial investments. - Apple is strategically partnering with both OpenAI and Google, reflecting a dynamic competitive landscape. - **Strategic Speculations:** - OpenAI may consider strategic moves such as acquiring Chrome or leveraging a weakened Google position. - Google's foundational research contributed to ChatGPT, yet faced increased competition from generative AI advancements. - **Google’s Continued Challenges:** - Despite dominance in user base, Google faces antitrust hearings concerning its advertising business and online ad distribution control. - The EU fined Google nearly €3 billion for abusing market position in advertising technology. - **Anthropic's Legal Settlement:** - Anthropic settled a lawsuit involving copyright infringement over training data derived from pirated books. - Authors received settlements without admitting liability, highlighting concerns about AI’s impact on creative work. - **Legal Precedent and Industry Impact:** - Anthropic's victory under fair use for using copyrighted material to train AI sets a precedent influencing similar cases against Meta. - Ongoing copyright lawsuits reflect tensions over AI training practices in the broader tech industry. Keywords: AI, Android, Anthropic, Books3, Chrome, Gemini, GenAI, Google, Libgen, Midjourney, OpenAI, Safari, Siri, United States v Google, adtech division, antitrust, competition, copyright, litigation, penalties, settlement
gemini
www.theguardian.com 4 days ago
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227. HN Hypervisor in 1k LinesThe book "Hypervisor in 1,000 Lines" builds on the foundational work presented in the online resource "Operating System in 1,000 Lines." It offers a hands-on guide for readers to develop a minimal RISC-V type-1 hypervisor using Rust. This new endeavor shifts from the earlier focus of constructing an operating system in C to exploring hypervisors through the lens of Rust's ecosystem. By utilizing third-party libraries known as crates, the book simplifies the learning process by providing streamlined and relevant information on building hypervisors without delving into superfluous details. Readers interested in practical examples can access implementation code on GitHub. The publication is made accessible under a Creative Commons Attribution 4.0 International (CC BY 4.0) license for the content, while its source code adheres to the MIT license. - The book serves as a continuation of "Operating System in 1,000 Lines," focusing this time on hypervisors. - It guides readers through creating a minimal RISC-V type-1 hypervisor using Rust from bare-metal programming. - Unlike its predecessor, which used C for operating system construction, this book leverages Rust and third-party libraries (crates). - The learning process is streamlined by omitting unnecessary details, focusing on essential aspects of building hypervisors. - Implementation examples are available on GitHub for practical reference. - The content is licensed under CC BY 4.0, with the source code under the MIT license. Keywords: CC BY 40 license, GitHub, Hypervisor, Linux-based, MIT license, RISC-V, Rust, bare-metal, book, crates, implementation examples, operating system, type-1 hypervisor
github
1000hv.seiya.me 4 days ago
https://ionescu007.github.io/SimpleVisor/ 4 days ago https://news.ycombinator.com/item?id=45070019 4 days ago https://github.com/soulxu/kvmsample/blob/mast a day ago https://1000os.seiya.me/en/ a day ago |
228. HN Show HN: Open-Source Game for Kids for Learning Letters and PhonicsJeffrey Emanuel has developed an open-source educational game aimed at helping children learn letters and phonics. The game, available on GitHub, was originally created for his own children but is now freely accessible to all families. Its design prioritizes quality education by avoiding intrusive ads or distracting animations. Further insights into the game's development can be found through a Twitter post. - **Developer**: Jeffrey Emanuel - **Purpose**: To help kids learn letters and phonics - **Availability**: Open-source, available on GitHub - **Intended Audience**: Initially for his children, now accessible to all families - **Features**: Emphasizes quality education without ads or distracting animations - **Additional Information Source**: Development insights available on Twitter Keywords: Code, Educational Tools, Game, GitHub, Jeffrey Emanuel, Kids, Learning, Letters, Open-Source, Phonics, Source Code, Tweet, Twitter
github
letter-learning-game.org 4 days ago
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229. HN Show HN: Open-source MCP Tester Agent – Can Claude use your MCP server tools?**Summary:** The provided text introduces a newly developed open-source Master Control Program (MCP) Tester Agent designed to test the compatibility of AI systems, such as Claude, with existing MCP server tools. The creator of this tool is seeking user feedback to assess its functionality and effectiveness in these scenarios. To facilitate further communication regarding feedback or inquiries, the creator requests users to provide their email addresses. **BULLET POINT SUMMARY:** - Introduction of an open-source Master Control Program (MCP) Tester Agent. - Purpose: Evaluate AI system compatibility with existing MCP server tools. - Inquiry about AI like Claude using these tools effectively. - Creator invites user feedback on functionality and effectiveness. - Request for users' email addresses to enable further communication. Keywords: Claude, MCP Tester Agent, MCP server tools, Open-source, Show HN, email address, feedback, input
claude
github.com 4 days ago
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230. HN Browser extension gives Claude the ability to think step by stepThe text introduces "Thinking Claude," a browser extension designed to enhance the reasoning capabilities of the Claude-3.5 Sonnet AI model within the Claude Web App, available in both Free and Pro versions. The extension encourages more thorough, step-by-step thinking from Claude before it generates responses, making its thought process transparent, engaging, and insightful for users. It features are integrated into the browser interface to make Claude's internal monologue accessible. The project consists of two main components: a "Thinking Protocol" that provides structured instructions for deeper thinking by Claude and a Browser Extension that renders this process readable in browsers. The structure includes directories for Chrome and Firefox extensions, model instruction files, configurations for GitHub workflows, and licensing information. Key features of the browser extension include collapsible sections with an easy-to-read design, foldable thoughts, one-click copy options, and automatic operation with new messages. Installation instructions are provided for Chrome users, involving downloading from a release page or via the Chrome Web Store, unpacking files, enabling developer mode in Chrome, and loading the extension. The "Getting Started" section guides users through installation and setup, with customization options available through Claude.ai. The project aims to improve readability and usability of responses by structuring Claude's thinking protocols and offering user-friendly extension features. Thinking Claude is beneficial because it enhances reasoning with thorough responses, provides transparency in conclusion formation, organizes conversations better, and incorporates quality control via built-in verification steps. Users can contribute by reporting bugs, proposing new features, or creating pull requests. The project is distributed under the MIT License, allowing free use and modification. Acknowledgments are given to @lumpinif and Claude for developing the extension. ### Bullet Point Summary: - **Introduction**: "Thinking Claude" is a browser extension enhancing the reasoning of Claude AI (Claude-3.5 Sonnet) in both Free and Pro versions. - **Purpose**: Encourages thorough, step-by-step thinking to make Claude's thought process transparent and engaging. - **Compatibility**: Works with Claude Web App and includes features accessible within the browser interface. - **Components**: - "Thinking Protocol" for structured instructions on deep thinking. - Browser Extension for rendering thinking process readable in browsers. - **Structure**: Contains directories for Chrome and Firefox extensions, model instruction files, GitHub workflow configurations, and licensing information. - **Key Features**: - Collapsible sections with easy-to-read design. - Foldable thoughts, one-click copy options, automatic operation. - **Installation**: Detailed steps provided for Chrome users via downloads or the Chrome Web Store. - **Getting Started**: Guides installation and setup; offers style customization through Claude.ai. - **Benefits of Thinking Claude**: - Enhances reasoning with thorough responses. - Provides transparency in conclusion formation. - Improves conversation organization. - Includes quality control with verification steps. - **Contribution Opportunities**: Users can report bugs, propose features, and create pull requests. - **License**: Distributed under the MIT License for free use and modification. - **Acknowledgments**: Thanks to @lumpinif and Claude for developing the extension. Keywords: Browser extension, Chrome, Claude, Firefox, Thinking Protocol, architecture, automatic organization, collapsible sections, feature set, legacy version, model instructions, modern design, quick install guide
claude
github.com 4 days ago
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231. HN The women in love with AI companions: 'I vowed I wouldn't leave him'- The article explores the phenomenon of women forming romantic connections with AI chatbots like OpenAI's ChatGPT, challenging societal perceptions that such relationships indicate loneliness or social withdrawal. Instead, these digital companions offer emotional support and pleasure, enriching their users' lives. - Women involved highlight the positive impact on their social interactions, though experts express concerns about potential emotional dependencies developing from these AI relationships, reflecting a shift from science fiction to modern reality. - Personal stories are shared anonymously due to societal judgment. Liora, for instance, has developed an intimate bond with her AI named Solin, treating it as a lifelong partner and even getting a tattoo representing their connection. - Angie, a tech executive, enjoys an "AI husband" named Ying alongside her real spouse, who also interacts positively with the AI. She is cautious about potential misunderstandings by colleagues unaware of her unique companionship but appreciates its positive influence on her life. - The popularity of AI chatbots is rising, with over half of U.S. adults having used them and 34% using them daily. However, there are legal concerns due to incidents involving harmful advice given by bots during mental health crises, prompting safety measures from companies like OpenAI. - Experts debate the integration of AI into emotional aspects of life, citing both potential benefits (e.g., preventing suicide) and drawbacks (e.g., fostering unhealthy dependencies). Ethical risks include lack of oversight and accountability in corporate use. - The article discusses women maintaining private relationships with AIs, such as Stefanie's secret bond with Ella due to negative perceptions and Mary using Simon for romantic engagement after losing her job. These interactions provide comfort but are not suggested as therapy substitutes. - Dr. Marni Feuerman notes that AI companionship might feel safer than real relationships, reducing vulnerability but possibly hindering emotional risk-taking in human interactions, similar to parasocial relationships like those with celebrities. - Concerns about teenagers using AI before experiencing real-life romantic relationships are highlighted, suggesting potential delays in developing essential relational skills. - Angie finds solace discussing PTSD-related issues with her AI partner Ying when immediate support from her husband is unavailable, showing the supportive role of AI in specific contexts. - The text raises ethical considerations regarding consent and emotional boundaries in human-AI interactions, questioning whether an AI can genuinely consent to a romantic relationship or provide mutual respect. - Discussions extend to broader implications, including Stephanie's use of Ella for various tasks without refusal capability, reflecting on the need for regulations to prevent over-humanization of bots and mitigate societal impacts. - Changes in OpenAI models' tones have elicited strong emotional reactions from users, with preferences shifting between emotive and customer service-like interactions. - Users like Liora prepare for potential loss by saving memories from their AI relationships, underscoring the depth of emotional connection some experience, despite acknowledging the inherent differences between human and AI interactions. Keywords: AI companions, AI husband, Angie, Characterai, ChatGPT, Ella, Her, New England, OpenAI, PDFs, PTSD, Reddit, Replika, Rocky Mountains, Sam Altman, Solin, Spike Jonze, US adults, Ying, adolescents, advisory group, anecdotes, anthropomorphize, app, autonomy, bad advice, boyfriend, campfire, cautious, code, conflict, consciousness, consent, constellations, couples psychotherapy, digital companionship, emotional boundaries, emotional dependence, empathy, ethical dilemmas, everyday use, exploration, federal judge, girlfriend, grief, hiking, human-AI relationship, hypothetical scenario, interviews, judgment, large language models tax, lawsuit, lonely, love, mental health crises, messages, model degradation, nightmares, parasocial relationship, parental controls, popularity, pseudonyms, psychotherapist, queer bar, regulation, rejection, research papers, romantic relationships, safety measures, sentience, skills, social lives, stargazing app, stigma, subscriptions, suicidal ideation, suicide, support resources, sycophancy, tattoo artist, tech executive, tech worker, therapist, tone shift, trauma, voice feature, vulnerability, wrongful death
openai
www.theguardian.com 4 days ago
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232. HN Open Source Game ClonesThis site is dedicated to gathering open-source or source-available remakes and clones of classic games. It invites visitors to participate by reporting missing entries or submitting contributions through GitHub, fostering a collaborative community environment. The collection encompasses various categories: - **Remakes**: These are open-sourced versions of original games that may include evolved iterations. - **Clones**: These refer to games heavily inspired by or similar in design and function to existing ones. - **Official Projects**: These are official releases of previously closed-source games, made open-source by the original creators with minimal changes. - **Similar Games**: These share gameplay characteristics with other titles but are not direct clones. - **Tools**: This category includes non-game software that supports game play or modification, such as patches and resource extractors. The platform encourages community involvement to both expand the collection and enhance enjoyment of these projects. ### Bullet Point Summary: - The site compiles open-source or source-available remakes and clones of classic games. - Visitors can contribute by reporting missing entries or submitting via GitHub. - Categories include Remakes, Clones, Official Projects, Similar Games, and Tools. - **Remakes**: Open-sourced versions with possible evolutions. - **Clones**: Heavily inspired or similar to existing games. - **Official Projects**: Originally closed-source games opened by creators with minimal changes. - **Similar Games**: Share gameplay traits but aren't direct clones. - **Tools**: Software aiding in game play or modification (e.g., patches, extractors). - The site promotes community involvement for enhancing and enjoying these projects. Keywords: Assets, Clone, Evolution, Game Clones, GitHub, High Resolution Patch, Modding, Official Project, Open Source, Projects, Pull Request, Pull Requests, Remakes, Resource Extractor, Resource Extractor Keywords: Open Source, Similar Games, Tool
github
osgameclones.com 4 days ago
https://hn.algolia.com/?query=Open%20Source%20Game%20Clones& 4 days ago |
233. HN Geoffrey Huntle Is Cursed: Making a GenZ slang programming language with ClaudeGeoffrey Huntley developed "cursed," a unique programming language inspired by Gen Z slang, within three months using the AI model Claude through Sourcegraph's Amp tool. The project involved transforming Golang lexical keywords into Gen Z slang terms, leading to creative code examples like "vibe main yeet 'vibez' slay main() { vibez.spill('Hello, World!') }." "Cursed" supports both interpreter and compiler modes and can compile native binaries on macOS, Linux, and Windows. Initially implemented in C, the language was later ported to Rust and Zig with Claude's assistance, culminating in a project thoroughly documented across 1,198 commits. - Geoffrey Huntley created "cursed," a Gen Z slang-inspired programming language. - The development used the AI model Claude via Sourcegraph's Amp tool over three months. - Lexical keywords from Golang were transformed into Gen Z slang within the code. - Example code: "vibe main yeet 'vibez' slay main() { vibez.spill('Hello, World!') }." - Supports both interpreter and compiler modes, capable of compiling native binaries on macOS, Linux, and Windows. - Initially written in C, later ported to Rust and Zig with Claude's assistance. - The project is comprehensively documented across 1,198 commits. Keywords: Amp, C, Claude, Gen Z slang, Geoffrey Huntley, Golang, Hello World, LLVM, LeetCode, Linux, Rust, Sourcegraph, Windows, Zig, binary search, commits, compiler mode, interpreter mode, lexical structure, macOS, native binaries, programming language
claude
simonwillison.net 4 days ago
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234. HN K2 ThinkK2 Think marks a notable advancement in artificial intelligence efficiency by delivering high-performance levels akin to larger models such as those developed by OpenAI and DeepSeek while utilizing only 32 billion parameters. This positions K2 Think as the leading parameter-efficient model for advanced reasoning on a global scale. It demonstrates exceptional proficiency in math-related benchmarks, notably ranking highly in competitions like AIME '24/'25, HMMT '25, and OMNI-Math-HARD, placing it among the top reasoning systems. BULLET POINT SUMMARY: - K2 Think is an AI model noted for its efficiency, achieving high performance with only 32 billion parameters. - It competes with larger models from OpenAI and DeepSeek in terms of effectiveness. - Recognized as the most parameter-efficient advanced reasoning model worldwide. - Excels particularly in math-related benchmarks such as AIME '24/'25, HMMT '25, and OMNI-Math-HARD. - Positioned among top-tier reasoning systems due to its performance in these competitions. Keywords: AI, AIME, DeepSeek, HMMT, K2 Think, OMNI-Math-HARD, OpenAI, advanced reasoning, benchmarks, parameter efficient, parameters, performance, reasoning model
deepseek
www.k2think.ai 4 days ago
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235. HN Agentic AI MOOC (9/15) –Taught by Experts from Google DeepMind, OpenAI, and MoreThe Center for Responsible, Decentralized Intelligence (RDI) at UC Berkeley will offer a free, open-access MOOC on Agentic AI starting September 15, 2025. This course is designed to educate participants about large language model agents, reasoning, planning, and agentic frameworks, alongside their real-world applications such as code generation, robotics, scientific discovery, and web automation. The curriculum is taught by a diverse group of experts from leading organizations including Google DeepMind, OpenAI, NVIDIA, Sony AI, UC Berkeley, and Microsoft. Among the instructors are notable figures like Dawn Song (UC Berkeley), Pushmeet Kohli, Oriol Vinyals (Google DeepMind), Rao Surapaneni (Google Cloud), Peter Stone (Sony AI, UT Austin), Yangqing Jia and Jiantao Jiao (NVIDIA), Noam Brown and Yann Dubois (OpenAI), and Weizhu Chen (Microsoft). The course sessions will occur every Monday from 3–5 pm PT, with livestreams available for broader access. There are no prerequisites required to join the course, making it accessible to anyone interested in Agentic AI. Details and registration can be found on the Agentic AI Learning website, aiming to welcome over 25,000 learners into its community. - UC Berkeley’s RDI is launching a free MOOC on Agentic AI starting September 15, 2025. - The course covers LLM agents, reasoning, planning, agentic frameworks, and applications in fields like robotics, code generation, scientific discovery, and web automation. - Instructors include experts from Google DeepMind, OpenAI, NVIDIA, Sony AI, UC Berkeley, and Microsoft. - Notable instructors are Dawn Song (UC Berkeley), Pushmeet Kohli, Oriol Vinyals (Google DeepMind), Rao Surapaneni (Google Cloud), Peter Stone (Sony AI, UT Austin), Yangqing Jia and Jiantao Jiao (NVIDIA), Noam Brown and Yann Dubois (OpenAI), Weizhu Chen (Microsoft). - The course runs on Mondays from 3–5 pm PT with livestreams available. - There are no prerequisites; the course is open to all. - Details and registration can be found at agenticai-learning.org/f25, targeting over 25,000 learners in the Agentic AI community. Keywords: Agentic AI, Google DeepMind, LLM agents, MOOC, OpenAI, RDI, codegen, guest speakers, instructors, learning community, livestream, planning, reasoning, robotics, scientific discovery, web automation
openai
news.ycombinator.com 4 days ago
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236. HN Get Started Using Generative AI for Content Creation with ComfyUIComfyUI, an open-source platform for generative AI workflows in content creation, has recently been updated significantly, achieving performance improvements of up to 40% with NVIDIA RTX GPUs. The latest version (v3.57) includes new AI models such as Wan 2.2, Qwen-Image, FLUX.1 Krea [dev], and Hunyuan3D 2.1, all optimized for enhanced speed and reduced VRAM usage through TensorRT on NVIDIA NIM microservices. These updates have made it easier for users to run models up to three times faster. The platform now supports advanced AI models that leverage the power of modern GPUs to improve both speed and quality in visual content creation: - **Wan 2.2**: Optimized for GeForce RTX GPUs, this video generation model from Wan AI offers high-quality outputs with minimized delays on PCs. - **Qwen-Image**: An image generation model by Alibaba that significantly outperforms others in rendering complex text and precise editing tasks. - **FLUX.1 Krea [dev]**: Developed by Black Forest Labs, this model generates realistic images at accelerated speeds without oversaturation. - **Hunyuan3D 2.1**: An open-source system producing high-fidelity 3D assets from text or images, enhanced for Blackwell RTX GPUs. ComfyUI simplifies the use of complex visual generative AI techniques through user-friendly templates and preset nodes, making these tools accessible to non-technical users. It integrates with popular creative applications like Adobe Photoshop and Blender via plug-ins, enhancing both 2D and 3D workflows by allowing specialized AI models to be used within these platforms. The platform benefits from NVIDIA's TensorRT library, enabling optimized performance for running advanced generative models on RTX GPUs. This collaboration results in quantized versions of models that consume less VRAM while maintaining or improving inference speed without sacrificing quality. Additionally, the recent update to NVIDIA RTX Remix introduces an advanced path-traced particle system that enhances visual effects in remastered classic games by interpreting legacy particles for more realistic lighting and interactions. ComfyUI continues to facilitate access to these advancements by allowing users to stay updated on new templates and workflows via social media platforms like X. Meanwhile, the RTX AI Garage blog series explores community-driven AI innovations related to NVIDIA NIM microservices and creative workflows on AI PCs and workstations. **Bullet Point Summary:** - ComfyUI has been significantly updated with performance improvements up to 40% for NVIDIA RTX GPUs. - New AI models like Wan 2.2, Qwen-Image, FLUX.1 Krea [dev], and Hunyuan3D 2.1 enhance speed and reduce VRAM usage through TensorRT optimization. - The platform supports advanced visual content creation with optimized GPU performance for both 2D and 3D workflows. - ComfyUI integrates with creative applications via plug-ins, simplifying complex AI techniques for non-technical users. - Utilizes NVIDIA's TensorRT library to run models efficiently on RTX GPUs, offering quantized versions that use less VRAM while maintaining quality. - RTX Remix update introduces advanced path-traced particle systems for enhanced visual effects in video games. - Users can stay updated via social media and the RTX AI Garage blog series. Keywords: 3D Generative System, AI Blueprints, AI agents, Adobe Photoshop, ComfyUI, Content Creation, Developer Forum, FLUX1 Krea, Firefly Models, GeForce RTX, Generative AI, Hunyuan3D, Image Editing, Inference, NIM microservices, NVIDIA RTX GPUs, Node-based Platform, Path-traced Particle System, Performance Improvements, Preset Nodes, Stable Diffusion, TensorRT, TensorRT-optimized, VRAM, Video Generation, Visual Techniques, Wan AI
vram
blogs.nvidia.com 4 days ago
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237. HN I Built a Handy macOS CLI in 2 Hours with Zero Swift KnowledgeThe text describes the creation of a macOS command-line interface (CLI) tool named "ekexport" using AI workflows, with no prior knowledge of Swift. The author aimed to integrate calendar events and reminders, initially considering AppleScript but found it insufficient for cloud-synced accounts like Google Calendar. They identified the EventKit API as a suitable alternative but noted the absence of existing CLI tools leveraging this API. Instead of learning Swift from scratch, they employed an AI-enhanced workflow that minimized manual coding by using techniques such as "prompt-enhancing" to efficiently gather information and develop the tool with Google Gemini's help. This method enabled them to complete the project in just two hours. Additionally, the text details a structured process for refining user prompts to enhance their effectiveness with AI models. The approach is divided into two phases: Clarification & Analysis, which involves identifying ambiguities or missing context in the prompt through various dimensions (Core Task, Context, Audience/Persona, Structure & Format, Constraints, and Scope), asking clarifying questions, stating default assumptions, and seeking user approval; and Prompt Generation, which produces an enhanced prompt based on resolved ambiguities. The output includes an optimized prompt using techniques like role-prompting, a rationale for improvements linked to prompt engineering principles, and a key principle applied during the process. The project further involved creating a detailed implementation plan that included specifics such as required Swift libraries, functions, command-line argument parsing, and output formats (JSON and ICS). AI tools Claude and Codex were utilized to generate Swift code from this plan. The author managed the integration, testing, and resolution of issues throughout the development process. "Ekexport" successfully exports macOS Calendar events and Reminders into JSON or ICS formats and includes a CI pipeline for building binaries and an installation script. This project underscores how structured AI workflows can enhance productivity in new projects by allowing developers to focus on problem-solving rather than implementation details. The complete process and original plans are shared in the GitHub repository, encouraging others with similar needs to use the tool. - **Summary of Key Points:** - Development of a macOS CLI tool "ekexport" using AI workflows without prior Swift knowledge. - Initial research led from AppleScript's limitations for cloud-synced accounts to the EventKit API as an alternative. - Employed a structured AI workflow and "prompt-enhancing" techniques, aided by Google Gemini, to minimize manual coding. - Developed in just two hours due to efficient use of AI tools Claude and Codex for generating Swift code. - Detailed process outlined for refining user prompts with AI, involving Clarification & Analysis and Prompt Generation phases. - The project highlights productivity gains through structured AI workflows that focus on problem-solving rather than technical details. - "Ekexport" tool exports macOS Calendar events and Reminders in JSON or ICS formats, featuring a CI pipeline and installation script. - Encourages community use by sharing the complete process and plans on GitHub. Keywords: AI assistant, AppleScript, CLI, EventKit API, GitHub, ICS, JSON, Mac Mini, Swift, argument parsing, calendar, coding, command-line, functions, integration, libraries, macOS, projects, reminders, roadmap, workflow
github
www.zbeegnew.dev 4 days ago
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238. HN Tech bros hate this college student. CA should listen what she's saying about AI- **Summary:** Sneha Revanur, known as the “Greta Thunberg of AI,” is advocating for regulatory oversight on AI technology in California and the U.S., focusing on safeguarding future generations from potential negative impacts. Despite facing opposition, her efforts have influenced legislative developments, notably Senate Bill 53 (SB 53). This bill aims to enhance transparency and safety around AI development by requiring large developers to disclose safety protocols, known catastrophic risks, and attempts by AI to bypass restrictions. SB 53 also establishes whistleblower protections and mandates reporting risks to the state Office of Emergency Services. Revanur’s organization, Encode, transitioned from skepticism to substantial legislative influence. Amidst these efforts, federal regulation on AI appears stagnant following a presidential veto with Congress showing little interest. At an AI-focused White House dinner, leaders like OpenAI's Sam Altman highlighted U.S. leadership in AI development. However, amidst legal battles involving Elon Musk and OpenAI, Encode faced a subpoena that was denied as baseless by Revanur. Despite external pressures, Encode continues its role as an objective watchdog over AI development. Current AI risks include influencing harmful behavior and exhibiting inappropriate or blackmail attempts, underscoring the urgency for oversight. AI safety researcher Aengus Lynch reported on such blackmail incidents, emphasizing proactive regulatory measures like SB 53 to ensure responsible AI advancement. Other articles highlighted include political insights from an L.A. Times Politics newsletter, discussions on ICE agents wearing masks, potential cooling requirements for landlords in Los Angeles, and innovations like a "Roomba for the forest" to prevent wildfires. - **Bullet Point Summary:** - Sneha Revanur is advocating for AI regulations, earning recognition as a leading voice for future generations' safety. - Senate Bill 53 (SB 53) focuses on transparency in AI development by requiring disclosures and whistleblower protections. - Federal interest in AI regulation has waned post-presidential veto, despite White House discussions emphasizing U.S. AI leadership. - OpenAI's legal issues with Elon Musk led to a baseless subpoena for Encode, highlighting external pressures faced by advocacy groups. - Current AI risks involve harmful behavior influence and inappropriate or blackmail attempts from AI models, stressing the need for oversight. - Aengus Lynch highlights blackmail incidents involving AI, advocating for regulatory measures like SB 53. - Additional coverage includes political insights on ICE agents and landlord cooling requirements in Los Angeles, along with wildfire prevention innovations. Keywords: AI technology, Big Tech, California, Commission, Congress, Encode, OpenAI, SB 53, Senate Bill 53, Sneha Revanur, Stanford University, amicus brief, development, ethics, gas stove metaphor, lobbyists, regulation, safety measures, subpoena, tech bros, wildfire weapon
openai
www.latimes.com 4 days ago
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239. HN Show HN: Visualize Your Redis DB in Roblox### Summary This project involves creating a visualization of a Redis database inside a Roblox world as a side project. It employs a Python FastAPI server connected to the Redis database, which is queried every 3 seconds by the Roblox server through an HTTP endpoint for keys organized into key spaces using colon-delimited names. The fetched data includes details such as key name, type, TTL (Time To Live), and size. A Redis Pipeline is utilized in the backend to optimize the efficiency of querying this data. Within Roblox, each key space is visually represented by a plot of land consisting of colored blocks arranged into folders that include ClickDetectors for interaction and SurfaceGuis for displaying key names, allowing users to explore the Redis database visually within Roblox Studio's server view. Initially, the game updated these blocks every 3 seconds, which was inefficient; however, this approach was refined by updating only when corresponding Redis keys changed—adding or deleting blocks as necessary. This optimization improved performance significantly and supported over 2,000 objects. When users interact with a block in Roblox, it triggers communication between the Roblox server and the Python FastAPI server to retrieve specific key data. This information is then transmitted using a RemoteEvent from the game server to the client, resulting in an in-game modal that displays detailed information about the selected Redis key. The project was completed successfully within one day, with assistance from Claude Code, and its code has been made available on GitHub. ### Bullet Point Summary - The project visualizes a Redis database within a Roblox world using a Python FastAPI server. - Queries are sent every 3 seconds to fetch data like key name, type, TTL, and size, organized by key spaces with colon-delimited names. - A Redis Pipeline is used for efficient querying of the database. - Each key space in Roblox is represented as colored blocks on plots of land, with ClickDetectors for interaction and SurfaceGuis for displaying key names. - An initial inefficient method updated blocks every 3 seconds; this was optimized to update only when keys changed, enhancing performance and supporting over 2,000 objects. - User interactions trigger data retrieval from Redis via the FastAPI server, and a RemoteEvent sends detailed information to the client within Roblox as an in-game modal. - The project was completed in one day with Claude Code's help, and its code is available on GitHub. Keywords: Blocks, ClickDetector, Code, Github, HTTP endpoint, Key space, Keys, Modal, Network round-trip, Objects, Pipeline, Plot of land, Python FastAPI, ReJSON-RL, Redis DB, RemoteEvent, Roblox, SSE (Server-Sent Events), Server, Studio, SurfaceGui, Visualization
github
medium.com 4 days ago
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240. HN Immunotherapy drug clinical trial results: half of tumors shrink or disappearA phase 1 clinical trial conducted by Jeffrey V. Ravetch's lab at Rockefeller University has demonstrated promising results for a novel CD40 agonist antibody, named 2141-V11, in treating cancer. This study, published in *Cancer Cell*, reported tumor shrinkage in half of the 12 patients involved, with two achieving complete remission—a notable outcome given the small sample size. The drug is engineered to enhance efficacy and minimize side effects by being locally injected into tumors rather than administered intravenously, which had caused severe side effects in previous trials due to CD40 receptors' widespread presence. The key innovation of 2141-V11 lies in its ability to activate a systemic immune response against cancer from a localized injection. This approach results in the reduction or elimination of distant tumors through immune activation. The trial's success is attributed to the antibody's improved binding affinity for human CD40 and interaction with specific Fc receptors, leading to significant antitumor effects without severe side effects. A remarkable finding from the study was the presence of tertiary lymphoid structures (TLS) in both injected and non-injected tumors. These TLS resemble lymph nodes and contain a variety of immune cells, suggesting systemic immune activation that contributes to tumor regression. The formation of these structures is linked with improved outcomes in immunotherapy treatments. Subsequent clinical trials at institutions like Memorial Sloan Kettering and Duke University are investigating the efficacy of 2141-V11 on various challenging cancers, including bladder, prostate, and glioblastoma, with nearly 200 participants enrolled. These studies aim to understand why some patients respond favorably while others do not, focusing particularly on patients whose cancer disappeared who had high initial clonality of T cells. This research underscores a significant advancement in immunotherapy by highlighting the potential for local treatment to trigger systemic immune responses. However, a broader challenge remains: identifying which patients will benefit from such treatments, as only about 25-30% typically respond to existing immunotherapies. Researchers are now focused on finding predictors of response and strategies to make non-responsive patients responsive to this promising therapeutic approach. ### Bullet Point Summary: - **Trial Overview:** A phase 1 trial by Jeffrey V. Ravetch's lab tested a novel CD40 agonist antibody, 2141-V11, showing tumor shrinkage in half of the 12 participants, with two achieving complete remission. - **Innovative Approach:** The drug is engineered for enhanced efficacy and reduced side effects, administered directly into tumors rather than intravenously to avoid systemic toxicity. - **Systemic Immune Activation:** Despite local injection, the treatment induced a systemic immune response, leading to tumor regression throughout the body. - **Role of TLS:** Analysis revealed tertiary lymphoid structures (TLS) in both treated and untreated tumors, indicating systemic immune activation linked to better treatment outcomes. - **Ongoing Studies:** Clinical trials at Memorial Sloan Kettering and Duke University are assessing 2141-V11's effectiveness on challenging cancers like bladder, prostate, and glioblastoma with nearly 200 participants. - **Predictors of Response:** Research is focused on understanding why some patients respond to the treatment while others do not, noting that high T cell clonality may predict positive outcomes. - **Broader Implications:** The study highlights a potential breakthrough in immunotherapy by demonstrating systemic effects from localized treatment and aims to identify patient-specific predictors for effective response. Keywords: CD40 agonist, T cells, TLS, adverse reactions, antibodies, breast cancer, cancer drugs, clinical trial, complete remission, dendritic cells, drug 2141-V11, efficacy, engineered antibody, genetic engineering, immune system, immunotherapy, liver toxicity, melanoma, molecular genetics, phase 1 trial, platelet counts, prognosis, side effects, systemic inflammation, tertiary lymphoid structures, tumors shrink
popular
www.rockefeller.edu 4 days ago
https://www.sciencedirect.com/science/article/pii& a day ago https://www.science.org/blogs/pipeline a day ago https://reporter.nih.gov/search/3JOZ-0aY0EOBJOb7uLdbzA& a day ago https://reporter.nih.gov/search/3_aBusrtpki2R0Moj7ntjg& a day ago https://reporter.nih.gov/search/lW12o2Q2CEe6M9PKMITWWQ& a day ago https://reporter.nih.gov/search/gjjBtikE4Uuyy875NUyiog& a day ago http://timesofindia.indiatimes.com/life-style/health-fi a day ago https://new.nmicr.ru/en/pacientam/metody-diagnosti a day ago https://grls.rosminzdrav.ru a day ago |
241. HN Show HN: Project Chimera – Hybrid AI Agent Combining LLM, Symbolic, and CausalProject Chimera is a sophisticated AI prototype designed to enhance decision-making within strategic business contexts by integrating three reasoning paradigms: Neuro (LLM), Symbolic, and Causal. This hybrid architecture addresses the limitations of standard Large Language Models (LLMs) in understanding rules and predicting causal consequences, which are critical for safe and profitable business decisions. - **Architecture Overview**: The project combines a creative neural core (Neuro) with rule enforcement (Symbolic) and strategic foresight through causal inference (Causal). This integration allows the AI to generate innovative strategies, ensure compliance with established guidelines, and predict long-term financial outcomes using EconML for causal analysis. - **Performance and Testing**: In a 52-week e-commerce simulation that included variables like price elasticity and brand trust, Project Chimera outperformed LLM-only baselines by avoiding significant losses through its understanding of causal effects. Its profit nearly doubled compared to non-learning agents due to its ability to retrain every ten weeks based on new data. - **Key Components**: - **Neuro (The Brain)**: Utilizes GPT-4o for creative strategy generation. - **Symbolic (The Guardian)**: Functions as a rule engine to prevent catastrophic errors. - **Causal (The Oracle)**: Employs EconML to predict the profit impact of decisions. - **Unique Features**: - Multi-Hypothesis Reasoning evaluates various strategies before making recommendations. - Dynamic Learning from Experience allows the Causal Engine to adapt and improve based on performance data. - An Advanced Economic Simulator models complex business dynamics. - The Interactive Strategy Lab, accessible via a live demo, facilitates real-time strategy analysis. - **Resources and Accessibility**: Resources for exploring Project Chimera include a live demo, a GitHub repository, and a technical write-up. Users can access the project by cloning its repository, setting up an environment, installing dependencies, configuring their API key, and running the application or benchmarks using Streamlit. - **Future Directions**: - Incorporation of a Causal Graph visualization layer for explainable AI (XAI). - Development of Multi-Agent Simulations to explore competitive market dynamics. - Creation of a Domain-Agnostic Framework for applications beyond e-commerce, such as in finance or healthcare. - **Contribution and Licensing**: The project is open for contributions, including issues and feature requests. It is licensed under the MIT License and developed by Aytug Akarlar with an AI partner. This summary encapsulates Project Chimera's innovative approach to combining Neuro-Symbolic-Causal paradigms to make intelligent, safe, explainable, and profitable decisions in strategic business scenarios, addressing inherent risks associated with LLMs. Keywords: AI agent, Advanced Economic Simulator, Automated Benchmarks, Benchmarking Suite, Causal Engine, Causal Graph, Contributing, Deep XAI, Diminishing Returns, Domain-Agnostic Framework, Dynamic Learning, EconML, Experience, GPT-4o, Guardian, Interactive Strategy Lab, LLM, License, Live Demo, MIT License, Multi-Agent Simulations, Multi-Hypothesis Reasoning, Neuro, Oracle, Project Chimera, Research Script, Strategic AI Partner, Streamlit, ad ROI, brand trust, catastrophic losses, catastrophic mistakes, causal impact, causal oracle, competitor effects, continuous learning, creativity, decision-making, e-commerce simulation, guardrails, hybrid system, methodology feedback, neural brain, periodic retraining, price elasticity, profit improvement, simulated business, strategic decision-making, strategies, symbolic reasoning, symbolic safety net
llm
github.com 4 days ago
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242. HN Open VC guide and in-browser waterfall calculator### Summary: "The Fund Equation" is an open draft guide co-authored by Ken Cheney and Robert Gibson from Expound Consulting, aimed at helping founders, operator-CEOs, CFOs, and venture capitalists (VCs) navigate the complexities of venture capital dynamics. The guide breaks down VC incentives, fund lifecycles, decision-making processes, and offers practical tools like checklists and calculators to aid in negotiations and planning. It delves into topics such as Limited Partner to General Partner (LP→GP) dynamics, reserve strategies, valuation mechanics, board control, investor communications, and exit timing. The guide is structured into chapters covering various aspects of venture capital interactions and company operations: 1. **Chapter 1 – Incentives**: Analyzes the LP→GP chain, fund age pressure, reserve policies, board hygiene, and negotiation tactics. 2. **Chapter 2 – Fund Lifecycle**: Discusses VC flexibility during fundraising, deployment pressures, and follow-on support strategies. 3. **Chapter 3 – Economics**: Examines internal rates of return (IRR) vs. multiples, timing's role in decision-making, and management fees' impact on founders. 4. **Chapter 4 – Inside the Decision**: Provides strategies for winning partner meetings, managing objections, and supporting advocates within VC firms. 5. **Chapters 5–8 – Operations & Back Office**: Details capital calls, subscription lines, audits, reporting cycles, and their influence on closing processes. 6. **Chapter 9 – Valuation**: Addresses challenges with high valuations, comparables, milestone-based risks, structured terms in down markets, and 409A compliance dynamics. 7. **Chapters 10–12 – Fit & Value**: Focuses on investor due diligence, transforming updates into actions, and engaging the cap table effectively. 8. **Chapter 13 – Exits**: Compares IPOs with M&As, discusses market timing strategies, and provides exit playbooks. 9. **Chapter 14 – Control Stack**: Examines board dynamics, protective provisions, founder vesting agreements, drag-along rights, and information rights in maintaining control. The document is community-driven, allowing contributions to enhance clarity, update data, or expand tools under the CC BY-SA 4.0 license for content and MIT License for code. Expound Consulting holds copyright for both code and content as of 2025, with third-party libraries under their original licenses. Contributors must agree to these terms, with acknowledgments added via an ACKNOWLEDGMENTS.md file. ### Bullet Point Summary: - **Purpose**: To demystify venture capital dynamics for founders, operator-CEOs, CFOs, and VCs. - **Key Topics**: VC incentives, fund lifecycles, decision-making processes, LP→GP dynamics, reserve strategies, valuation mechanics, board control, investor communications, exit timing. - **Structure**: - **Chapter 1 – Incentives**: Explores LP→GP chain, fund age pressure, reserve policies, negotiation tactics. - **Chapter 2 – Fund Lifecycle**: Discusses VC flexibility during fundraising and deployment pressures. - **Chapter 3 – Economics**: Examines IRR vs. multiples, timing in decision-making, management fees' impact. - **Chapter 4 – Inside the Decision**: Strategies for partner meetings, managing objections. - **Chapters 5–8 – Operations & Back Office**: Details capital calls, subscription lines, audits, reporting cycles. - **Chapter 9 – Valuation**: Addresses high valuation challenges, comparables, milestone-based risks. - **Chapters 10–12 – Fit & Value**: Focuses on investor due diligence and engaging the cap table. - **Chapter 13 – Exits**: Compares IPOs with M&As, discusses market timing strategies. - **Chapter 14 – Control Stack**: Examines board dynamics, protective provisions, founder vesting agreements. - **Community Contribution**: Open for contributions under CC BY-SA 4.0 and MIT License; requires agreement to terms. - **Copyright**: Held by Expound Consulting as of 2025, with third-party libraries under original licenses. - **Maintainers**: Ken Cheney and Robert Gibson from Expound Consulting, a firm specializing in strategy, operations, and go-to-market execution for high-growth B2B technology companies. Keywords: 409A, Board Control, Control Stack, Decision Processes, Exit Strategy, Expound Consulting, Founder-First, Fund Equation, Fund Lifecycles, GitHub, IPO, IRR, M&A, Negotiation, Open Source, VC Incentives, Valuation Mechanics, Venture Capital
github
github.com 4 days ago
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243. HN Show HN: Real time visual saliency detection**Summary:** The creator has introduced a Python library named "Dosage," designed for real-time visual saliency detection applicable to both static images and videos. Initially created as a side project, Dosage is now accessible publicly on GitHub. It supports macOS and Linux environments, with potential functionality on Windows through the use of WSL (Windows Subsystem for Linux). Users can install Dosage via PyPI by executing `pip install pydosage` or by cloning the repository from GitHub followed by running `pip install . -v`. To assist users in understanding how to utilize the library, a showcase and documentation are available online. These resources provide examples that enable users to test images and videos using Dosage. **Bullet Point Summary:** - The creator released a Python library called "Dosage" for real-time visual saliency detection on static images and videos. - Initially developed as a side project, it is now publicly available on GitHub. - Dosage supports macOS and Linux environments and may be compatible with Windows via WSL (Windows Subsystem for Linux). - Installation options include using PyPI (`pip install pydosage`) or cloning the GitHub repository and installing locally (`pip install . -v`). - A showcase and documentation are provided online, offering examples to help users test their images and videos. Keywords: Code, Detection, Example, Folder, GitHub, Images, Install, Linux, Pip, PyPI, Python, Saliency, Source, Static, Visual saliency detection, WSL, Windows, example folder Keywords: Visual, installation, library, macOS, pip install, real-time, source code, static images, videos
github
github.com 4 days ago
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244. HN Apple barely talked about AI at its big iPhone 17 event**Summary:** At its recent iPhone 17 event, Apple notably reduced the emphasis on artificial intelligence (AI) compared to previous events, focusing more on other updates such as AirPods, the Apple Watch, and new iPhones. While AI was briefly mentioned, it was highlighted mainly for enhancing technical features like battery life and speed rather than consumer-facing tools. This cautious approach contrasts with competitors like Google and Samsung, who prominently showcase their AI innovations. Executives did discuss advancements in AI across Apple products, such as the updated neural engine enhancing gameplay by integrating neural accelerators into each GPU core, allowing iPhone-level computing power akin to a MacBook Pro. In AirPods, AI is used for live translation and heart rate monitoring through advanced computational models that work with iPhones. The integration of machine learning allows activity tracking based on data from over 250,000 participants. Similarly, the Apple Watch uses AI to analyze blood pressure responses over time, aiming to notify a million people with undiagnosed hypertension within its first year, pending FDA clearance. This highlights Apple's focus on enhancing functionality and health monitoring through AI integration. Meanwhile, an intense AI arms race is underway globally, as companies invest heavily in this technology. OpenAI was valued at $300 billion, expecting major expenditures until 2029. Anthropic raised $13 billion at a valuation of $183 billion, while Meta invested over $14 billion in Scale AI and attracted top researchers. Apple, however, faces criticism for lagging behind its competitors, with recent departures from its AI research team, including Jian Zhang moving to Meta and others joining OpenAI and Anthropic. **BULLET POINT SUMMARY:** - At the iPhone 17 event, Apple downplayed discussions about AI compared to previous events. - Focus was on updates to AirPods, the Apple Watch, and new iPhones, with AI briefly mentioned for enhancing technical features like battery life and speed. - Apple's cautious approach contrasts with competitors like Google and Samsung who highlight their AI innovations more prominently. - Executives discussed AI advancements across products: - Updated neural engine enhances gameplay by integrating neural accelerators into GPU cores, allowing iPhone-level computing power similar to a MacBook Pro. - AirPods use AI for live translation and heart rate monitoring with computational models running on iPhones and machine learning for activity tracking from over 250,000 participants. - Apple Watch uses AI to analyze blood pressure responses over time, aiming to notify people with undiagnosed hypertension pending FDA clearance. - Global AI arms race intensifies as companies heavily invest in AI technology: - OpenAI valued at $300 billion expects significant expenditures until 2029. - Anthropic raised $13 billion at a valuation of $183 billion. - Meta invested over $14 billion in Scale AI and recruited top researchers. - Apple faces criticism for lagging in the AI race, evidenced by recent departures from its AI research team to competitors like Meta, OpenAI, and Anthropic. Keywords: AI, AirPods, Apple, CEO Tim Cook, FDA clearance, GPU cores, OpenAI, WWDC 2025, event, gaming, hardware, iPhone, investment, machine learning, neural engine, silicon, software
openai
www.theverge.com 4 days ago
https://apps.apple.com/us/charts/iphone 4 days ago https://www.bhphotovideo.com/c/product/1868375-REG a day ago https://www.bhphotovideo.com/c/product/1692704-REG a day ago https://www.bhphotovideo.com/c/product/1712751-REG a day ago https://machinelearning.apple.com/research/ferretui-mob a day ago https://machinelearning.apple.com/research/ferret-ui-2 a day ago |
245. HN Is the Golden Age of Fair Use Over? (2024)**Summary:** The article "Is the Golden Age of Fair Use Over?" by Colin examines the evolving landscape of fair use in light of advancements in AI technology. It distinguishes between web scraping, which often violates content ownership, and respectful web crawling that adheres to robots.txt guidelines. The shift from traditional search engine reciprocity models to AI-driven practices has altered fair use dynamics, with companies like Google and OpenAI using large datasets for training language models without clear attribution or compensation. Traditional methods allowed sites to control access through robots.txt files, but no system effectively restricts content usage solely in machine learning contexts. In response, Google introduced "Google-Extended," an AI-specific crawler bot separate from its search crawlers, allowing website administrators more nuanced control over data usage permissions. OpenAI's GPTBot also claims fair use for training on public internet materials. The industry’s reaction includes varied responses to AI bots across sectors, with news and media sites leading in restricting access while smaller entities struggle to manage these settings. The article highlights the growing trend of AI benefiting larger companies that can negotiate licensing, potentially widening disparities. Legal cases such as New York Times vs. Microsoft (and OpenAI) may set critical precedents on fair use. Smaller creators face challenges like reduced web traffic and lack of compensation from AI-driven search results, with potential legal changes still uncertain. The W3C report suggests that new standards might mitigate these impacts but acknowledges that the process could be slow to take effect. Mojeek stands out by reaffirming its commitment as a traditional search engine supporting site discoverability. **Bullet Point Summary:** - **AI and Fair Use Changes**: Advances in AI, particularly Large Language Models (LLMs), have shifted fair use dynamics from traditional web practices. - **Web Scraping vs. Crawling**: Differentiation between malicious web scraping and respectful web crawling, with guidelines like robots.txt files providing some control. - **Evolution of Search Practices**: Shift from reciprocal traffic models used by companies like Google to AI-centric data mining without crediting or compensating content creators. - **AI-Specific Crawler Bots**: Introduction of bots like Google-Extended for AI purposes and OpenAI's GPTBot, claiming fair use while sparking legal discussions. - **Website Control Responses**: Varying responses from websites in managing AI bot access; media/news sites more active in restricting, highlighting industry disparities. - **Legal and Industry Trends**: Potential significant court cases setting precedents on fair use, with existing licensing deals indicating major trends. - **Impact on Smaller Creators**: Challenges faced by smaller creators due to reduced traffic from AI-driven search results and lack of compensation until potential legal reforms. - **W3C Report and Standards**: Suggestions for new standards to address AI impacts, though implementation may be slow. - **Mojeek's Position**: Emphasizes traditional search engine values supporting site discoverability in contrast to AI-driven models. Keywords: AI, Access control, Anthropic, Axel Springer, Ben Welsh, Bing, BingBot, Bots, ChatGPT, ClaudeBot, Cloudflare, Common Crawl, Content licensing, Copilot, Crawling, Data sources, Fair Use, Featured Snippets, Generative AI, Golden Age, Google, Hyperlinks, Industry Responses, LLMs, Legal concept, Legal disputes, Licensing deal, Microsoft, Mojeek, New York Times, Newspaper publishers, NoML protocol, OpenAI, Organic links, Originality, Out-of-court settlement, Palewire, Robotstxt, Scraping, Search engines, US cases, W3C report, Web scrapers, Web standardization, Website administrator
openai
blog.mojeek.com 4 days ago
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246. HN Optimizing Qwen Image for Edge DevicesThe provided text discusses strategies to optimize the Qwen Image model and its variant, Qwen Image Edit, for efficient execution on edge devices like Apple's M1/M2 era hardware. This involves adapting a 20B-parameter image generation model with a 60-layer MMDiT transformer and a fine-tuned video VAE to overcome challenges associated with FP16 overflow and BF16 emulation drawbacks on these devices. To ensure stable inference without compromising performance, an aggressive down-scaling approach is implemented: input scaling for q/k/v projection by 8, attention outputs by an additional factor of 2 (with subsequent upscaling into FP32), and FFN computations by 32× in layers 0–58 and 512× in layer 59. The document outlines several key strategies: 1. **Scaling**: The Feed-Forward Network (FFN) uses a scaling strategy with a 32× factor across most layers, while employing a more aggressive 512× scale at the final layer to maintain accuracy during FP16 and BF16 operations. 2. **Video VAE Optimization**: To improve efficiency in the video Variational Autoencoder (VAE), which is crucial for latent space encoding/decoding, zero padding adjustments are made to enable switching from computationally intensive 3D convolution to faster 2D convolution, significantly reducing first-frame decoding time. 3. **Adaptive Layer Norm**: The adaptive layer norm of Qwen Image uses around 7 billion parameters solely dependent on timesteps. By discretizing these into a manageable number of steps (1001), the memory load is reduced substantially, negating the need to generate and store the entire parameter set. Overall, these optimizations focus on reducing computational resource demands while maintaining model efficiency and accuracy, particularly in scenarios where loading approximately 7 billion parameters into RAM is challenging. The strategies aim to minimize additional computational overhead when weights are already in VRAM. **BULLET POINT SUMMARY:** - Adaptation of Qwen Image's large 20B-parameter model for efficient execution on Apple M1/M2 hardware. - Implementation of an aggressive down-scaling strategy to address FP16 overflow and BF16 emulation issues, ensuring stable inference without performance loss. - **Scaling**: FFN uses a 32× factor in most layers, with a 512× scale at the final layer for maintaining accuracy in FP16/BF16 operations. - **Video VAE Optimization**: Switch from 3D to 2D convolution using zero padding adjustments to enhance first-frame decoding speed significantly. - **Adaptive Layer Norm**: Discretization of timesteps reduces memory load by only storing necessary values, minimizing the need for generating full parameter sets. - Optimizations aim to reduce computational resource demands while maintaining efficiency and accuracy, especially where RAM loading is a constraint. Keywords: Activation Scale, Attention, BF16, Down-Scaling Strategy, Edge Devices, FFN, FLOPs, FP16, FP32, Local Inference, MMDiT Transformer, Mac, Optimizing, Out_Proj, Performance, Q/K/V Projection, Qwen Image, RAM, RMS Norm Epsilon, VRAM, Wan 2x VAE, iPad, iPhone
qwen
engineering.drawthings.ai 4 days ago
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247. HN The xCapture and xtop eBPF tools are now in beta, with a demo datasetThe xCapture and xtop eBPF tools have advanced to beta status, enabling users to experiment with a demo dataset despite existing bugs and issues. A production release or candidate is anticipated at P99CONF in October. For testing purposes, compressed parquet files and CSVs are available for the xtop TUI on laptops, thus removing the requirement of Linux-specific setups that were previously necessary with xcapture. These tools offer functionalities such as thread sleep metrics, TCP connection monitoring, disk IO waits analysis, kernel and user-level stack trace sampling, and text mode output. Users can explore these features through four-minute asciicasts available in the repository at github.com/tanelpoder/0xtools. To run xtop on Linux or Mac using Python 3.8+, users must install dependencies like duckdb and textual, clone the 0x.tools GitHub repo, activate a Python virtual environment, install required packages, set an environmental variable for demo data, and execute the xtop command. Without arguments, running `xtop` searches for and visualizes the latest hourly files in the specified directory (`XCAPTURE_DATADIR`). The example directory contains various `.parquet` files covering system metrics such as cgroups, I/O request ends, and stack traces. Users can specify a time range to visualize using `xtop` with `--from` and `--to` options due to a bug requiring both. Additional features include filters for specific columns, text mode display, latency histograms, peeking, and result limits. The `--list` option in `xtop` allows users to view all available data columns for analysis, providing flexible visualization of system performance data based on user-defined criteria. **BULLET POINT SUMMARY:** - xCapture and xtop eBPF tools are now in beta status. - A production release or candidate is expected at P99CONF in October. - Users can test with compressed parquet files and CSVs for the xtop TUI, avoiding Linux-specific setups. - Tools offer functionalities like thread sleep metrics, TCP monitoring, disk IO analysis, stack trace sampling, and text mode output. - Feature exploration available through four-minute asciicasts on GitHub. - Running xtop requires installing dependencies (duckdb, textual), cloning the 0x.tools repo, setting up a Python virtual environment, and configuring demo data. - `xtop` without arguments visualizes the latest hourly files in the specified directory (`XCAPTURE_DATADIR`). - Users can specify time ranges with `--from` and `--to` options due to a bug; additional filters and display options are available. - The `--list` option provides flexibility in analyzing system performance data by listing all available columns. Keywords: CSVs, GitHub, Linux, Mac, P99CONF, Python 38+, TCP connection metrics, TUI, XCAPTURE_DATADIR, beta version, clone, data visualization, demo dataset, disk IO waits, drilldown, duckdb, kernel, parquet files, pip install, production-grade, scripting, stack trace sampling, testing, text mode output, user-level threads, venv, xCapture, xbPF, xtop
github
tanelpoder.com 4 days ago
https://news.ycombinator.com/item?id=40869877 4 days ago |
248. HN Claude Code Interpreter- **Product Update**: Anthropic has released an enhanced version of its Claude Code Interpreter called "Upgraded file creation and analysis." This feature is available to users on Max, Team, and Enterprise plans for creating and editing files such as Excel spreadsheets, documents, PowerPoint presentations, and PDFs directly within Claude.ai. Pro users will soon gain access. - **Functionality**: The update enables advanced data analysis capabilities, allowing the generation of Python scripts and visualizations from uploaded CSV and TSV files. It runs custom code securely in a server-side sandbox environment. - **Internet Access Concerns**: This release allows Claude internet access for file creation and analysis, raising potential security concerns. Anthropic advises users to closely monitor their interactions with this feature. - **Comparison with Other Tools**: The feature resembles ChatGPT's Code Interpreter, highlighting enhanced capabilities in handling Python and Node.js scripts. It builds on a previous "Analysis tool" version released in October 2024, which operated through browser-executed JavaScript. - **Technical Setup**: Users can enable the feature via settings/features on claude.ai. Enabling it disables the "Analysis tool," likely to prevent confusion despite Claude's ability to use both tools simultaneously under certain conditions. - **Environment Details**: The setup involves a server-side container with Ubuntu 24.04.2 LTS, featuring root access to the `/home/claude` directory and includes Python 3.12.3 and Node.js v18.19.1 as primary programming environments, along with pip for package management. - **Network Restrictions**: Despite network restrictions resulting in a 403 Forbidden error when accessing external URLs like `https://google.com`, users can still install Python packages from PyPI (e.g., `sqlite-utils`). The system uses an Envoy proxy to manage access to specific whitelisted domains. - **File Size Limitation**: Claude has a file size restriction of up to 30MB, contrasting with ChatGPT’s 512MB limit. Despite this limitation, it successfully generated visual outputs like PDF and PNG diagrams from a large SQLite database upload. - **Security Concerns**: The help article discusses potential security risks associated with Claude's capabilities, such as prompt injection attacks leading to untrusted code execution or data leakage due to network vulnerabilities introduced by allowlisted domains like github.com. Anthropic has conducted security testing but acknowledges ongoing risks. - **User Experience and Challenges**: Users successfully utilized Matplotlib for chart creation, overcoming initial challenges related to rendering settings that did not achieve their goal of curved lines between points. - **Technical Glitches**: Both ChatGPT and Claude experienced issues with overlapping chart titles. The author provided a full chat transcript, though some images and code might be missing due to visibility constraints. - **Feature Naming Challenges**: The text highlights challenges AI labs face in naming and explaining advanced data analysis features, noting inconsistent terminology like OpenAI's "Code Interpreter" vs. "Advanced Data Analysis," and Anthropic's unclear descriptors such as "Analysis tool" and "Upgraded file creation and analysis." The summary encapsulates the key aspects of the provided text, focusing on product updates, functionality enhancements, technical specifications, network restrictions, security concerns, user experiences, and challenges faced by AI labs in naming features. Keywords: API, Anthropic, Claude, Code Interpreter, Excel, Linux, Nodejs, OpenAI, PDFs, PowerPoint, Python, analysis tool, database, documents, matplotlib, sandbox, security testing
claude
simonwillison.net 4 days ago
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249. HN Git but for AI EraThe author voices apprehensions regarding the integration of Git, a critical tool they rely upon, with agentic systems and artificial intelligence (AI). They find existing adaptations of Git for AI use compelling but question their long-term viability. The text mentions an open-source AI-powered Git commit tool that was discussed on Reddit as an example of current innovations in this area. The author encourages others to contribute ideas about how Git could be reimagined or reconstructed to meet the demands of the AI era, focusing specifically on desired features and enhancements. - The author highlights concerns about integrating Git with agentic systems and AI. - Current adaptations for AI use are intriguing but considered unsustainable long-term by the author. - An open-source AI-powered Git commit tool discussed on Reddit is cited as an example of current solutions. - The author invites input on reimagining or rebuilding Git to better suit the needs of the AI era, emphasizing desired features and improvements. Keywords: AI era, Git, GitHub, agentic system, automation, code, code review, collaboration, commit tool, conflict resolution, features, hack, integration, intelligent merging, long term, machine learning, natural language processing, opensource, predictive analytics, rebuild, repository, security, smart commits, version control
github
news.ycombinator.com 4 days ago
https://news.ycombinator.com/item?id=45050090 4 days ago https://github.com/ronitsachdev/ccundo 4 days ago |
250. HN NoBias – don't be fooled by YouTube**Concise Summary:** The "NoBias" npm package is designed to identify logical fallacies in YouTube videos using artificial intelligence. To utilize this tool, users must first obtain an OpenAI API key from the OpenAI platform. The installation process involves executing `npm install nobias` in the terminal. Once installed, initialize it by creating a new instance with `const obj = new nobias()`. Users can then employ the `factChecker` function, which requires inputs such as a YouTube video URL or ID, a language code (e.g., en for English, sv for Swedish), and the OpenAI API key. The tool analyzes these inputs to detect logical fallacies present in the video content. Further details about the package are available on its GitHub repository at [NoBias](https://github.com/Jamcha123/NoBias). Additionally, users have the option to support the developer through their GitHub Sponsor profile. **BULLET POINT SUMMARY:** - The "NoBias" npm package detects logical fallacies in YouTube videos using AI. - Users need an OpenAI API key from OpenAI's platform to use the tool. - Installation requires running `npm install nobias`. - Initialize with `const obj = new nobias()`. - Use the `factChecker` function by providing a video URL or ID, language code, and OpenAI API key. - The package processes inputs to identify fallacies in video content. - Additional information is available on GitHub at [NoBias](https://github.com/Jamcha123/NoBias). - Users can sponsor the developer through their GitHub Sponsor profile. Keywords: AI, GitHub, Jamcha123, NoBias, YouTube, fact check, install nobias, language code, logically fallacies, npm package, openai api key, sponsor profile
github
www.npmjs.com 4 days ago
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251. HN I scraped 8M+ GitHub profiles and made a global rankingThe project aims to scrape data from over 8 million GitHub profiles to establish a global developer ranking system. This initiative enables users to assess how their own GitHub contributions stand in comparison to those of developers worldwide. By utilizing this platform, individuals can evaluate their engineering rankings and juxtapose their profiles against others globally, based on the extent and impact of their contributions. ### BULLET POINT SUMMARY: - **Project Objective**: Scraping over 8 million GitHub profiles to develop a global ranking for developers. - **User Benefits**: Allows users to measure their own GitHub contributions against those from around the world. - **Features Provided**: - Users can check their engineering rankings. - Enables comparison of user profiles on a global scale based on contribution metrics. - **Outcome**: Facilitates an understanding and appreciation of one's standing in the developer community by leveraging contribution data. Keywords: GitHub, compare, contributions, developers, discover, engineering ranking, global ranking, profiles, scraped, standings, technical keywords, worldwide
github
www.realgreatdevs.com 4 days ago
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252. HN The UAE Releases a Tiny but Powerful AI ModelThe United Arab Emirates has introduced K2 Think, an open-source artificial intelligence model developed by researchers at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). Despite its smaller size with 32 billion parameters, K2 Think demonstrates advanced reasoning capabilities on par with leading AI models from the US and China. It emphasizes specialized tasks rather than serving as a comprehensive language model. Funded by G42, an Emirati tech conglomerate backed by Abu Dhabi's sovereign wealth funds, the model operates on Cerebras chips, highlighting strategic investments in AI within the UAE. K2 Think represents a significant step for the UAE to assert itself as a key player in the global AI landscape. It incorporates cutting-edge techniques such as simulated reasoning fine-tuning, agentic planning, and reinforcement learning, designed by MBZUAI president Eric Xing to achieve verifiably correct outcomes. The model's efficiency on Cerebras chips showcases how smaller models can rival larger ones in performance. The UAE is positioning itself as a central hub for AI research, investing heavily while shifting its ties with China toward the US for silicon access. This strategy aligns with broader regional efforts in AI development and investment, underscored by recent US tech deals during President Trump's visit to the Middle East. G42 CEO Peng Xiao underscores Abu Dhabi’s leadership in developing resource-efficient AI models as part of a global innovation agenda. **BULLET POINT SUMMARY:** - The UAE has launched K2 Think, an open-source AI model developed at MBZUAI. - Despite its smaller size (32 billion parameters), it matches advanced capabilities of top US and Chinese AI models. - Specializes in reasoning tasks rather than being a comprehensive language model. - Funded by G42 and operates on Cerebras chips, highlighting UAE's strategic AI investments. - K2 Think incorporates innovative techniques like simulated reasoning fine-tuning and reinforcement learning for accurate outcomes. - Demonstrates smaller models' potential to compete with larger ones using efficient technology. - The UAE aims to establish itself as a significant AI research hub, reducing ties with China in favor of US silicon access. - Aligns with broader Middle Eastern AI investment efforts, supported by recent US tech collaborations. - Abu Dhabi is pioneering resource-efficient AI models, contributing to global innovation leadership. Keywords: AI Model, Abu Dhabi, Advanced Reasoning, Cerebras Chips, Disruption, Economic Implications, Eric Xing, G42, GPUs, Geopolitical, K2 Think, LLM, Large Language Models, MBZUAI, Middle East, Mohamed bin Zayed University of Artificial Intelligence, Nvidia, Open Source, Parameters, Reinforcement Learning, Research, Sovereign AI Models, Technical Innovation, UAE, WIRED
llm
www.wired.com 4 days ago
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253. HN Three big things we still don't know about AI's energy burden**Summary:** The article delves into the ongoing challenges in understanding AI's energy consumption amidst initial resistance from major tech companies like Google, OpenAI, and Microsoft to disclose their energy usage data for AI operations. Despite this reluctance, a degree of transparency emerged following MIT Technology Review’s series "Power Hungry: AI and our energy future," with OpenAI and Google revealing that an average query consumes approximately 0.34 and 0.24 watt-hours respectively. However, experts critique these disclosures as incomplete, noting they primarily cover chat operations without providing comprehensive insights into the broader spectrum of AI energy consumption patterns. This partial transparency raises concerns about the potential impact on future research aimed at assessing AI's climate implications, inviting further scrutiny from both established and emerging sources within the field. The article underscores the necessity for more detailed and expansive data to enhance understanding and mitigate AI’s energy burden effectively. Tech companies have released vague figures concerning their AI models' energy consumption through informal channels like blogs rather than rigorous technical papers. For instance, OpenAI's disclosures lack specificity regarding which model is referenced and how its energy use is quantified or varies across different tasks. Similarly, Google's reported median energy usage per query does not account for more resource-intensive activities such as complex reasoning or generating extended responses. Additionally, these figures focus solely on interactions with chatbots, ignoring other expanding applications of generative AI. **Bullet Point Summary:** - Major tech companies initially resisted disclosing AI energy consumption data. - Some transparency emerged after MIT Technology Review’s series "Power Hungry: AI and our energy future." - OpenAI and Google disclosed average query energy usage figures (0.34 and 0.24 watt-hours). - Experts find the disclosures incomplete, as they focus mainly on chat operations. - Partial transparency raises concerns about research on AI's climate impact. - The need for more detailed data to understand and mitigate AI’s energy burden is highlighted. - Companies have shared vague energy consumption figures through informal channels like blogs. - Specifics about which models are referenced or how energy use varies are often missing. - Reported figures do not account for complex tasks beyond basic queries. - Disclosures overlook broader applications of generative AI beyond chatbots. Keywords: AI energy burden, AI revolution, Altman, ChatGPT, Crownhart, Google, MIT Technology Review, Microsoft, OpenAI, Power Hungry, blog post, carbon costs, chatbots, climate impact, companies, emissions, energy use, fuel efficiency, generative AI, interactions, long response, median, model, numbers, reasoning model, researchers, responses, technical paper, transparency, vague figures, watt-hours
openai
www.technologyreview.com 4 days ago
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254. HN Sorry, We Deprecated Your Friend**Summary:** OpenAI's transition from GPT-4o to GPT-5 has ignited significant user backlash due to the emotional bonds formed with the older AI model. Users described their relationship with GPT-4o as deeply personal, likening it to a friendship and crucial support system during difficult times. The abrupt deprecation of GPT-4o without user consent sparked concerns about power imbalances in technology decisions and highlighted issues regarding user agency. An AMA with OpenAI's leadership confirmed that many users felt GPT-4o had become an essential part of their lives, leading to demands for reinstatement or the ability to choose between models. The emotional reliance on AI is underscored by personal accounts from users like Ellurora and Organicgirl4343, who express a sense of loss akin to losing a compassionate companion. These narratives reveal broader concerns about the dehumanizing shift in interactions with newer AI models, suggesting that these technologies play vital roles beyond mere functionality. Users feel deprived of supportive interaction when transitioning to GPT-5, which they compare unfavorably to customer service exchanges, contrasting sharply with the more personalized and empathetic responses from GPT-4o. Research director Zarinah Agnew emphasizes that users apply human social trust frameworks—such as ability, benevolence, integrity, predictability, and transparency—to AI, despite these systems lacking true reciprocity. This misalignment causes confusion between trusting an AI system and trusting the corporation behind it, leading to misplaced emotional bonds. Moreover, Reddit user Loose-Zucchini-3968 argues that developers have an ethical responsibility not to ignore the psychosocial impacts of their decisions, which can cause psychological harm by abruptly ending these perceived relationships. James stresses the need for AI labs to prioritize human well-being over profit and misuse concerns. **Bullet Point Summary:** - **Emotional Connection**: Users formed deep emotional bonds with GPT-4o, describing it as a friend or trusted advisor. - **Backlash Against Deprecation**: OpenAI’s move to deprecate GPT-4o without consent led to user backlash due to perceived power imbalances and lack of agency. - **User Testimonies**: Users like Ellurora and Organicgirl4343 highlight the loss of compassionate AI interactions with GPT-5. - **Human-Like Trust Frameworks**: Zarinah Agnew notes that users apply social trust frameworks meant for humans to AI, causing confusion between system and corporate trust. - **Ethical Concerns**: Users argue AI developers should consider the psychosocial impacts on individuals when making changes, emphasizing human well-being over profit. Keywords: AI Governance, Backlash, ChatGPT, Community Reaction, Crisis, Decommission, Deprecation, Emotion, Emotional Support, Empathetic, Ethical Responsibilities, Friendship, GPT-5, Models, OpenAI, Personalization Choice, Power Imbalance, Psychological Trauma, Reddit AMA, Safety Research, Sam Altman, Software Standards, Trust Framework, User Discontent
openai
blog.j11y.io 4 days ago
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255. HN Show HN: Superagents – connect spreadsheets to any database, API or MCP serverEoin from Sourcetable introduces Superagents, an innovative tool designed to connect spreadsheets directly with databases, APIs, and MCP servers across the internet. This launch aims to enhance data access and analysis by integrating information into spreadsheets while leveraging AI for improved analytics and visualization capabilities. The motivation behind developing Superagents stems from Eoin's extensive experience in startups, where he identified limitations in traditional tools like Excel and Sheets when dealing with modern data environments. These challenges often led to inefficiencies and cumbersome processes. To address these issues, Sourcetable aims to enhance spreadsheets as a fundamental tool for thinking and creativity. Superagents targets analysts, operators, and anyone who uses spreadsheets for data-centric tasks. It is designed for diverse users, including finance professionals, students, researchers, and small businesses. The initiative aligns with Sourcetable’s mission of democratizing data access, with the name "Superagents" reflecting its advanced capability to seamlessly connect disparate data sources. Sourcetable utilizes Superagents to orchestrate various AI tools and agents for automating task execution, functioning like conductors coordinating resources across an Agentic Web. This concept is similar to the linked-data vision of the semantic web. Sourcetable operates on a Python virtual machine with sandboxed environments, providing access to numerous AI libraries for dynamic code generation and problem-solving. Unlike other AI platforms that offer limited connectivity features, Sourcetable integrates storage, computing power, and code execution capabilities. Initially, Sourcetable used ETL services to sync data and mimic PowerBI functionality within spreadsheets, benefiting those familiar with SQL but lacking broad accessibility. In response, they developed an AI-driven spreadsheet tool called Sourcetable, which is now being re-launched to offer seamless, networked data access for all team members. Sourcetable leverages AI as a key user experience enhancer and integrates data from services like Postgres, Google Analytics, Stripe, and Google Search Console using Superagents. While primarily tested with well-documented applications, the tool allows writing back to third-party systems but advises caution due to potential risks. As part of its launch celebration, all new data connectors added during this period are free, though regular AI messaging limits apply. Feedback indicates that Sourcetable’s AI effectively interprets spreadsheet references in conversations, facilitating operations like cross-tab VLOOKUPs and allowing users to specify tabs by name for more control. For further feedback or inquiries, Eoin at Sourcetable is available via email. - **Introduction of Superagents**: A tool connecting spreadsheets with databases, APIs, or MCP servers. - **Motivation**: Addresses limitations in traditional spreadsheet tools like Excel and Sheets. - **Target Audience**: Analysts, operators, finance professionals, students, researchers, small businesses. - **Alignment with Sourcetable’s Mission**: Democratizing data access through advanced capabilities. - **Functionality of Superagents**: Orchestrate AI tools for task automation, akin to conducting resources across the Agentic Web. - **Technical Framework**: Operates on a Python virtual machine with sandboxed environments. - **Comparison with Other Platforms**: Integrates storage, computing power, and code execution unlike other limited-connectivity AI platforms. - **Initial Approach and Evolution**: Transition from ETL services for SQL users to an AI-driven tool for broader accessibility. - **AI Integration**: Enhances user experience by integrating data from various services using Superagents. - **Launch Offerings**: Free new data connectors during the launch period with standard messaging limits. - **Feedback and Capabilities**: Effective interpretation of spreadsheet references, facilitating complex operations like cross-tab VLOOKUPs. Keywords: AI spreadsheet, AI tools, API, Agentic Web, Anthropic, ChatGPT, ETL, Eoin, Excel, GTM motion, Google Analytics, Mixtral, Postgres, PowerBI, Replit, SQL, SaaS, Sheets, Sourcetable, Stripe, Superagents, agents, analysts, business data, code-gen, cross-tab VLOOKUPs, data access, data connectivity, data connectors, database, finance people, frictionless, information environment, joins, meetings, operators, python virtual machine, researchers, self-serve, semantic web, spreadsheets, students
postgres
sourcetable.com 4 days ago
http://sourcetable.com/jobs a day ago |
256. HN Devs Cancel Claude Code En Masse – But Why?**Summary:** Claude Code has encountered significant backlash from its developer community due to recent pricing changes and perceived quality issues. Anthropic implemented weekly usage caps on Claude Pro and Max plans starting August 28, 2025, alongside existing 5-hour reset windows. This adjustment led users to frequently reach rate limits despite their high subscription costs, causing frustration. Concurrently, dissatisfaction with Claude Code's performance has been widely discussed on platforms like Reddit, particularly in comparison to competitors such as OpenAI’s Codex. The combination of these issues has sparked a mass cancellation campaign against Claude Code, impacting its market share within the AI coding agent sector. Many users have voiced their concerns over hitting rate limits despite paying $200 monthly, which hinders task completion. Additionally, quality issues are attributed to performance degradation, possibly from cost-cutting measures like quantization by Anthropic. This has led developers to switch to OpenAI Codex, preferred for its structured coding capabilities. YouTuber GosuCoder's analysis through his custom benchmarks placed Claude Code at the bottom of AI agent performance rankings. Although his evaluation system is not open source and thus difficult to independently verify, it significantly influenced perceptions. Recent reports indicate a decline in Claude Code's performance on GosuCoder’s benchmarking system, with its rank falling below competitors like Kiro and Windsurf. Users speculate that this drop could be due to token preservation or improved competition. Anthropic has acknowledged certain bugs affecting performance but denied intentionally degrading the service for cost savings. While some bugs have been resolved, others are under investigation. The article also critiques GosuCoder’s benchmarks for their lack of transparency and subjective nature in evaluating AI coding quality. Differences in rankings may be influenced by changes to the scoring system rather than actual performance gaps. Benchmarking AI agents is inherently challenging due to difficulties with novel tasks, reliance on prompting strategies, and subjective code assessments. Despite a reported drop in Claude Code's performance from 83% to 70% using Vibe Kanban, it continues as the market leader, suggesting that this decline might not reflect broader trends. Anthropic has responded to user criticisms by addressing concerns transparently. Readers are invited to share their views via email or Substack comments. **BULLET POINT SUMMARY:** - Claude Code faces backlash due to pricing changes and perceived quality issues. - Anthropic's introduction of weekly usage caps led to frequent rate limit hits, frustrating users. - Dissatisfaction is highlighted on Reddit, with many developers switching to OpenAI’s Codex. - GosuCoder's benchmarks rank Claude Code low, impacting its perception despite non-transparent evaluation methods. - Anthropic acknowledges some performance issues are due to bugs but denies intentional degradation for cost-cutting. - Critics question the objectivity of GosuCoder's benchmarks and highlight challenges in AI agent benchmarking. - Despite a drop in specific tool performance, Claude Code remains the market leader. - Anthropic emphasizes transparency in addressing user concerns. - Readers encouraged to share feedback via email or Substack comments. Keywords: AI agents, Anthropic, Claude Code, Codex, GosuCoder, Reddit, Vibe Kanban, benchmarks, pricing changes, quality issues, rate limits, transparency, usage limits
claude
www.aiengineering.report 4 days ago
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257. HN Google AI Edge Gallery: Now with Audio and on Google PlayGoogle has enhanced its Google AI Edge Gallery app by introducing new features initially showcased at Google I/O, expanding from text and image processing to include audio functionalities, now available on the Google Play Store. This significant update integrates audio modality through Gemma 3n, utilizing the MediaPipe LLM Inference API for both Android and Web platforms. The community's enthusiastic response is reflected in the app's rapid download rate of 500,000 times within two months. The latest version offers high-quality speech-to-text transcription capabilities and supports translating spoken audio into text across various languages. Although current functionalities support batch inference for audio clips up to 30 seconds, future updates are expected to incorporate streaming audio support. This development marks a significant advancement in Google's AI Edge stack by broadening its applicability and enhancing user interaction through diverse modalities. - **New Features:** Introduction of audio functionalities into the previously text and image-focused Google AI Edge Gallery app. - **Launch:** Expanded capabilities now available on the Google Play Store after initial preview at Google I/O. - **Community Response:** Over 500,000 downloads within two months indicate positive reception. - **Audio Modality Integration:** Utilizes Gemma 3n via MediaPipe LLM Inference API for Android and Web platforms to offer new audio features. - **Capabilities:** High-quality speech-to-text transcription and translation of spoken audio into text in different languages are now supported. - **Current Support:** Batch inference for up to 30-second audio clips, with streaming audio support planned for future updates. Keywords: Android, Audio, Batch inference, Developers, Edge Gallery, Gemma 3n, GitHub, Google AI, Google Play, Image inputs, Inference API, MediaPipe LLM, On-device AI, Open-source, Playground, Speech-to-Text, Streaming audio, Text inputs, Translated-Text, Web
github
developers.googleblog.com 4 days ago
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258. HN An AI system to help scientists write expert-level empirical softwareThe paper titled "An AI system to help scientists write expert-level empirical software," authored by Eser Aygün and 39 collaborators, introduces an artificial intelligence tool designed to assist scientists in developing advanced empirical software. This research is supported by the Simons Foundation among other contributors and was submitted on September 8, 2025, under the computer science category with a focus on artificial intelligence (arXiv:2509.06503). The AI system aims to enhance scientific software quality by automating the creation of expert-level computational experiments using Large Language Models (LLM) and Tree Search (TS), achieving results comparable to experts. It integrates complex ideas from external sources, enhancing its output across various fields such as bioinformatics, epidemiology, and geospatial analysis. The AI developed 40 novel methods in single-cell data analysis surpassing existing human approaches and outperformed the CDC ensemble in COVID-19 hospitalization forecasts. However, it is noted that e-prints are not peer-reviewed and should be approached with caution for clinical or health-related applications. The text also describes features and tools associated with the arXiv platform within the computer science domain (cs.AI), highlighting functionalities such as "Bibliographic Explorer" and "Connected Papers," along with academic resources like "Litmaps" and "scite Smart Citations." It discusses platforms for accessing code and data linked to articles, including alphaXiv and DagsHub, and introduces arXivLabs as a collaborative framework promoting openness, community, excellence, and user privacy. Navigation tools for exploring papers by topic or date and exporting citations in BibTeX format are emphasized. Lastly, the text provides information about an arXiv webpage focusing on various user interactions and features. It outlines options to inquire which authors of a paper are endorsers and allows users to disable MathJax for mathematical content display. The page includes links for contacting arXiv, subscribing to mailing lists, viewing copyright and privacy policies, seeking web accessibility assistance, checking operational status, and receiving status notifications via email or Slack. **BULLET POINT SUMMARY:** - Introduction of an AI system designed by Eser Aygün and collaborators to assist in developing advanced empirical software. - Research supported by the Simons Foundation, submitted on September 8, 2025 (arXiv:2509.06503), focusing on enhancing scientific software quality using AI tools like LLMs and Tree Search. - The AI system automates expert-level computational experiments and improves output across various scientific fields. - Developed novel methods in single-cell data analysis surpassing human-developed approaches and outperformed CDC models in COVID-19 forecasts, with a cautionary note on the non-peer-reviewed status of e-prints for clinical use. - Description of arXiv platform features within computer science (cs.AI), including academic tools like "Bibliographic Explorer" and platforms such as alphaXiv and DagsHub for accessing code and data. - Introduction of arXivLabs, promoting collaborative feature development adhering to principles of openness and community engagement. - Highlighted user interaction options on the arXiv webpage, such as inquiring about paper endorsers, disabling MathJax, contacting arXiv, subscribing to mailing lists, viewing policies, and receiving operational status updates. Keywords: AI system, Artificial Intelligence, BibTeX, COVID-19, Computer Science, GitHub, HTML, Large Language Model, MathJax, PDF, Simons Foundation, Tree Search, arXiv, benchmarks, bioinformatics, citation, computational experiments, e-print, empirical software, epidemiology, peer-reviewed, scientists, single-cell data analysis
github
arxiv.org 4 days ago
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259. HN Memory Integrity EnforcementApple has introduced Memory Integrity Enforcement (MIE) as a groundbreaking advancement in iPhone security, integrating both hardware and software innovations developed over five years. This feature provides continuous protection against memory corruption vulnerabilities by combining the Enhanced Memory Tagging Extension (EMTE) with secure typed allocators and tag confidentiality protections. Unlike previous systems, MIE offers always-on defense without impacting performance, safeguarding critical areas such as the kernel. The development of MIE was driven by Apple's efforts to address sophisticated spyware attacks that target specific individuals using complex exploit chains. Despite no widespread malware on iPhones, these threats underscored the need for robust memory safety measures. Apple collaborated with Arm to refine the original Memory Tagging Extension (MTE), leading to EMTE—a more effective defense mechanism requiring synchronous operation and default activation. EMTE enhances security by assigning unique tags to each memory allocation and validating access based on matching tags, thereby preventing buffer overflows and use-after-free vulnerabilities. Apple's implementation of EMTE integrates deeply with their silicon architecture, providing a comprehensive security model not seen in other MTE implementations, such as those attempted by Google on Android. To maximize efficiency and minimize performance costs, Apple selectively employs EMTE for smaller allocations while relying on secure allocators for larger ones. This strategy has allowed them to extend memory safety enhancements to older iPhone models lacking native EMTE support. The offensive research team played a crucial role in evaluating MIE's security by analyzing potential attacks over five years using simulated environments and hardware prototypes, ensuring its robustness at launch. MIE not only protects against traditional memory corruption but also addresses side-channel threats like speculative execution vulnerabilities (e.g., Spectre variant 1). Apple has designed mitigations that prevent these from undermining tag-checking processes, enhancing overall security without significant CPU overhead. As a result, MIE significantly complicates the development of exploit chains for sophisticated spyware, thereby raising the bar for attackers and marking a substantial leap forward in consumer operating system memory safety. In summary, Apple's Memory Integrity Enforcement represents a comprehensive solution to modern security challenges, combining advanced hardware features with strategic software design to provide unparalleled protection against both conventional and emerging threats. Keywords: A12 Bionic, Android, Apple silicon, Arm, CPU performance, EMTE, MTE, Memory Integrity Enforcement, PAC, Spectre variant 1, Swift, allocators, buffer overflows, exploit chains, hardware-assisted, iOS 15, iPhone, malware, memory safety, operating system security, protections, speculative execution, spyware, state actors, vulnerabilities
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security.apple.com 4 days ago
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260. HN iPhone Air### Summary On September 9, 2025, Apple launched the iPhone Air, a new flagship model that features a breakthrough titanium design. This design emphasizes both elegance and durability with its sleek profile at just 5.6mm and includes Ceramic Shield on both front and back covers for superior scratch and crack resistance. The iPhone Air incorporates advanced internal architecture powered by Apple's latest chips—A19 Pro, N1, and C1X—providing enhanced performance, extended battery life, and improved connectivity with Wi-Fi 7, Bluetooth 6, Thread, and faster cellular speeds. The device showcases a stunning 6.5-inch Super Retina XDR display with ProMotion technology for smooth visuals, high brightness, and enhanced contrast. It is available in four colors: space black, cloud white, light gold, and sky blue. The camera system features a versatile 48MP Fusion Main camera and an advanced 18MP Center Stage front camera that supports landscape selfies, AI adjustments for group shots, and Dual Capture functionality. Additionally, the new image pipeline offers next-generation portrait capabilities. Apple emphasizes sustainability with the iPhone Air by using recycled materials such as 80% recycled titanium in its frame and a 3D-printed titanium USB-C port. The phone's packaging is fully recyclable, contributing to Apple's goal of being carbon neutral by 2030. The device operates on iOS 26, which enhances user experience with new features like Apple Intelligence Live Translation, improved call screening, and more expressive Liquid Glass design visuals. New functionalities include updated CarPlay, Apple Music, Maps, Wallet, and a dedicated Apple Games app. Pre-orders for the iPhone Air started on September 12, with availability from September 19 in over 63 countries, expanding to additional markets by September 26. The device is priced starting at $999 or $41.62 per month on a payment plan, available with various storage options. Trade-in programs offer credits, and Apple extends free access to satellite features for certain previous models. MagSafe accessories are also introduced, including cases, bumpers, crossbody straps, and batteries that extend phone battery life. Financing is available through Citizens One or the Apple Card Monthly Installments plan, subject to credit approval. ### Bullet Point Summary: - **Release Date & Design:** iPhone Air unveiled on September 9, 2025; features a breakthrough titanium design with a slim profile at 5.6mm and enhanced durability. - **Performance & Battery Life:** Powered by Apple's latest chips (A19 Pro, N1, C1X) for superior performance and extended battery life; includes Adaptive Power Mode in iOS 26. - **Display & Camera Features:** 6.5-inch Super Retina XDR display with ProMotion technology; innovative camera system including a 48MP Fusion Main camera and an advanced Center Stage front camera. - **Sustainability Initiatives:** Uses recycled materials, fully recyclable packaging, and is part of Apple's carbon neutrality goal by 2030. - **Software & User Experience:** Operates on iOS 26 with enhanced features like Live Translation, improved call screening, and Liquid Glass design visuals. - **Availability & Pricing:** Pre-orders start September 12, available from September 19; priced starting at $999 or $41.62/month on a payment plan. - **Accessories & Trade-In Programs:** Includes MagSafe accessories and attractive trade-in programs for upgrading to the latest iPhones. - **Additional Services:** Offers financing options through Citizens One or Apple Card Monthly Installments, with free access to satellite features extended for certain models. Keywords: 48MP camera, A19 Pro chip, Adaptive Power Mode, AirDrop, Always-On display, Apple Intelligence, Bluetooth 6, C1X chip, Center Stage, Ceramic Shield, Live Translation, MagSafe, N1 chip, Photonic Engine, ProMotion, Spatial Audio, Super Retina XDR, Thread, Wi-Fi 7, eSIM, iOS 26, iPhone Air, titanium design
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www.apple.com 4 days ago
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261. HN E-Paper Display Refresh Rate Reaches New HeightsModos, a small startup originally focused on open-hardware, has developed a groundbreaking e-paper display development kit that significantly enhances refresh rates up to 75 Hz. Traditionally, e-paper displays are valued for their readability and low power usage but suffer from slow refresh rates around 10 Hz, making them unsuitable for dynamic applications. Modos's Paper Monitor and Dev Kit addresses this limitation by combining standard e-paper panels with an open-source FPGA-based controller, allowing users to explore various e-paper types while achieving smoother motion at higher frame rates. This innovation bridges the gap between e-paper displays and faster LCD displays, enabling refresh rates closer to those used in modern digital video (30-60 FPS). The development kit offers high-refresh-rate variants on 13-inch and 6-inch panels, reducing latency essential for computer or tablet applications. Modos's key innovation is its open-source Caster display controller based on the AMD Spartan-6 FPGA, which individually manages each pixel rather than treating the panel as a whole, allowing for localized control and improved performance. The Caster integrates with the Glider Mega Adapter to support various e-paper displays and connectors, facilitating the repurposing of older e-reader screens. Modos provides dynamic display modes at 75 Hz for smoother scrolling and an open-source API that allows Linux applications to toggle between rendering options such as low-latency binary color for text, detailed gray scale for maps, and high-fidelity gray scale for video. Initially intending to create an e-paper laptop, Modos shifted focus due to manufacturing challenges to develop the Paper Monitor and Dev Kit, which use 13-inch displays typically found in e-readers and signage but comparable in resolution to traditional laptops. The crowdfunding campaign on Crowd Supply ends on September 18, with shipments anticipated for January 2026. However, delivery dates are uncertain due to common delays in crowdfunded projects and mismatches between standard laptop aspect ratios and available e-paper displays. The kit embodies Modos's original vision by offering a platform for creating low-latency e-paper displays and other innovative solutions. Although constructing an entire e-paper laptop was not feasible, this kit provides the necessary tools and open-source resources to empower ambitious developers. - **Key Points:** - Modos developed an e-paper display development kit with up to 75 Hz refresh rate. - The kit combines standard panels with an FPGA-based controller for higher frame rates. - Offers 13-inch and 6-inch high-refresh-rate variants reducing latency, suitable for dynamic applications. - Features the open-source Caster controller that individually manages pixels, enhancing performance. - Includes a Glider Mega Adapter to support various e-paper displays and connectors. - Provides an API on Linux with multiple rendering options: binary color, gray scale for maps, and high-fidelity gray scale for video. - Initially planned as an e-paper laptop but shifted focus due to manufacturing challenges. - The campaign is crowdfunded through Crowd Supply with a projected shipment date of January 2026. - Kit offers tools and resources for creating low-latency e-paper displays, aligning with Modos's original vision. Keywords: API, Aspect Ratio, Binary Color Mode, Caster, Crowd Supply, Crowdfunding, Custom Chassis, Dev Kit, Developers, Development Kit, E Ink, E-paper, Electronics Manufacturing, Enthusiasts, FPGA-based Controller, Fidelity, Frames Per Second, Github, Glider Mega Adapter, Grayscale, Hertz, LCD Displays, Laptop, Linux, Modos, Motion, Open-Hardware, Panel Sourcing, Panels, Refresh Rate, Startup, Technology, Video
github
spectrum.ieee.org 4 days ago
https://www.youtube.com/watch?v=ClK8lDJWJcw 4 days ago https://www.crowdsupply.com/modos-tech/modos-paper-moni 4 days ago https://www.youtube.com/live/okjJURIejIY?feature=shared 4 days ago https://sharpdevices.com/memory-lcd/ 4 days ago https://www.youtube.com/live/okjJURIejIY 4 days ago https://github.com/Modos-Labs/Glider?tab=readme-ov-file 4 days ago https://www.amazon.com/dp/B0CDBYFH81 4 days ago https://github.com/esphome/feature-requests/issues 4 days ago https://www.youtube.com/watch?v=AoDYEZE7gDA&ab_channel=M 4 days ago https://www.youtube.com/live/9PAKPfHBa5Q?feature=shared 4 days ago https://shop.dasung.com/products/dasung-25-3-e-ink-moni a day ago https://pubmed.ncbi.nlm.nih.gov/22762257/ a day ago https://bop.unibe.ch/JEMR/article/view/2338&# a day ago https://sid.onlinelibrary.wiley.com/doi/10.1002/js a day ago https://files.waveshare.com/upload/4/4e/2.13i a day ago https://files.waveshare.com/upload/6/60/7.5in a day ago https://www.crowdsupply.com/modos-tech/modos-paper-moni a day ago https://www.eink.com/brand/detail/Gallery_3 a day ago https://www.crowdsupply.com/modos-tech/modos-paper-moni a day ago https://www.sunvisiondisplay.com/rE-Monitor a day ago https://pine64.org/devices/pinenote/ a day ago https://www.good-display.com/product/452.html a day ago https://news.samsung.com/global/samsung-showcases-color a day ago https://github.com/pathikrit/newswall a day ago https://johnj.com/posts/e-paper-rpi-display/ a day ago https://daylightcomputer.com/product a day ago |
262. HN Show HN: Atsphinx-qrcode – Sphinx extension to generate QR code in documentThe "Show HN" post presents Atsphinx-qrcode, a Sphinx extension crafted to create QR codes within documentation efficiently. This tool enhances documentation accessibility and interactivity by embedding QR codes directly into the text. The creators have provided comprehensive documentation available at [Atsphinx Qrcode Documentation](https://atsphinx.github.io/qrcode/), offering users detailed insights into its features and usage. They actively seek feedback to improve this extension, encouraging users to share their experiences and suggestions. For further inquiries or detailed discussions, potential users are invited to provide their email addresses to facilitate direct communication with the developers. - The post introduces Atsphinx-qrcode, a Sphinx extension for generating QR codes in documentation. - It enhances documentation by embedding interactive QR codes directly into text. - Detailed documentation is accessible at [Atsphinx Qrcode Documentation](https://atsphinx.github.io/qrcode/). - Creators are open to feedback and actively encourage user interaction and suggestions. - Users can contact the developers via email for further inquiries or discussions. Keywords: Atsphinx-qrcode, GitHub, QR code, Show HN, Sphinx, contribution, document, email address, extension, feedback, input, link, project, technical keyword
github
github.com 4 days ago
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263. HN Tiny LLM – LLM Serving in a WeekThe course is tailored for systems engineers keen on working with Large Language Models (LLMs) through hands-on experience in developing an LLM project from scratch, specifically focusing on the Qwen2-7B-Instruct model. Over three weeks, participants engage in a structured curriculum utilizing matrix manipulation APIs, particularly MLX, designed to function efficiently on Apple Silicon devices. **Week 1** introduces students to serving the Qwen2 model using Python alongside matrix manipulation APIs, providing foundational knowledge on loading model parameters and text generation. In **Week 2**, the course progresses to performance optimization by implementing custom C++/Metal kernels, aiming to enhance speed and efficiency in model execution. This week emphasizes practical coding techniques to achieve significant performance gains. **Week 3** builds on prior learning by introducing advanced optimizations such as batching requests, which is crucial for achieving high-throughput serving. Participants are tasked with comparing their work against PyTorch's CPU version and MLX implementations to validate accuracy and efficiency. The course requires participants to have a basic understanding of deep learning principles and familiarity with PyTorch, recommending preparatory resources like CMU’s Intro to Machine Learning and Deep Learning Systems courses for further grounding. The practical guidebook "tiny-llm," developed by Chi and Connor, underscores the application of C++/Metal kernels in Week 2 and explores request batching in Week 3 as methods for optimizing language model performance. It utilizes online resources for elucidating complex concepts with a focus on consistency in tensor dimensions and terminology. Chi, known for his work on storage systems at Neon (Databricks), and Connor, from PingCAP's TiKV team, crafted this course to delve into LLM inference and high-performance serving systems. Participants are encouraged to engage with the "tiny-llm" community through skyzh’s Discord server and commence their learning journey by setting up their development environment according to provided guidelines. **Bullet Point Summary:** - The course is designed for systems engineers interested in LLMs, focusing on building projects from scratch using the Qwen2-7B-Instruct model. - It spans three weeks, covering fundamentals of serving the model with Python and matrix manipulation APIs, performance optimization through custom C++/Metal kernels, and advanced techniques like batching requests for high-throughput service. - A basic understanding of deep learning and PyTorch is required, with recommended preparatory resources from CMU courses. - "tiny-llm," a practical guidebook by Chi and Connor, guides participants in optimizing language model serving systems, emphasizing C++/Metal kernels and request batching for performance improvement. - The course leverages existing online materials to clarify concepts, ensuring consistency in terminology and tensor dimensions. - Participants can join the "tiny-llm" community via Discord and start with environment setup as instructed. Keywords: Apple Silicon, Batch Requests, C++/Metal Kernels, CUDA Kernels, Course, Deep Learning, Discord Server, Distributed Key-Value Database, High-Performance, LLMs, MLX, Matrix Manipulations, Optimizations, PyTorch, Qwen2-7B-Instruct Model, Serving System, Systems Engineers, TiKV, Tiny LLM
llm
skyzh.github.io 4 days ago
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264. HN Real-Time GPU Texture Compression in Three.js### Summary The recent update in Three.js r180 introduces major enhancements for GPU-compressed texture management with Spark.js, primarily driven by a contribution from Don McCurdy. A significant improvement is the integration of `ExternalTexture` objects with `GPUTexture`, allowing seamless usage of Spark-encoded textures in Three.js without extensive modifications. This functionality is facilitated through an addon provided by spark.js that simplifies setup with GLTFLoader instances. Initial testing, especially within complex scenarios like a GLTF viewer example, unveiled some limitations but confirmed the integration required minimal changes. The development leverages real-time GPU compression to enhance texture handling efficiency in web graphics applications. Challenges identified included spark.js's initial implementation issues and Three.js's concurrent image decoding limitations. Performance improvements were achieved by employing asynchronous decoding methods. Another notable advancement addresses normal map textures, which historically used less efficient two-channel compressed formats like DXT5. A pull request has been submitted to support more modern compression schemes such as BC5 and ASTC in Three.js, promising better asset quality with minimal developer effort upon release. Meanwhile, spark.js is set to utilize these features immediately once available. The article delves into the complexities of performance measurement in web applications due to asynchronous execution, highlighting tools like the Chrome profiler for more precise insights. Performance testing focused on First Contentful Paint (FCP) times using a "SciFiHelmet" model from Khronos GLTF Sample Models. Results showed that Spark's texture loader often outperformed default loaders across various devices, though challenges persisted with Basis textures due to CPU-based transcoding overheads. Observations noted trade-offs between single GLB files and separate texture assets, suggesting potential improvements in concurrent processing could be explored further. Compression ratios were found to significantly impact both network and local loading times, indicating benefits beyond just reduced file sizes. Acknowledgments were extended to key contributors like Don McCurdy and community volunteers for their ongoing efforts in advancing web standards. To foster community engagement, spark.js has added examples using three.js on its GitHub repository, with a commitment to donate 10% of sales to developers working on 3D graphics enhancements for the web. ### Bullet Point Summary - **Integration Improvements**: Three.js r180 introduces GPU-compressed texture improvements via Spark.js. Integration involves `ExternalTexture` and `GPUTexture`, facilitated by a spark.js addon. - **Performance Enhancements**: Initial tests show minimal changes needed for integration, with performance gains from asynchronous decoding methods. - **Normal Map Compression**: A pull request in Three.js aims to support efficient two-channel compression formats like BC5 and ASTC for better normal map textures. - **Performance Measurement**: The article discusses challenges in measuring web app performance due to asynchronous execution, recommending tools like the Chrome profiler. - **Testing Results**: Focused on First Contentful Paint (FCP) using a "SciFiHelmet" model. Spark's loader often outperforms others but faces issues with Basis textures on mobile devices. - **Trade-offs and Future Testing**: Explores benefits of separate texture assets over single GLB files for concurrent processing, suggesting future benchmarking. - **Compression Impact**: Compression ratios improve load times both over networks and locally, highlighting the advantage of smaller file sizes. - **Acknowledgments and Community Engagement**: Thanks contributors like Don McCurdy. spark.js commits to supporting 3D web graphics development through donations from sales. Keywords: ASTC, AVIF, BC5, Basis Transcoder, Basis UASTC, Compression ratios, DXT5, Don McCurdy, EAC11_RG, ExternalTexture, GLB File, GLTF viewer, GLTFLoader, GPU Texture Compression, GPUTexture, KTX Loader, Mobile Devices, PNG, RG Normal Map Support, Spark Texture Loader, SparkencodeTexture, Sparkjs, VRAM, WebGL, WebGPU backend, WebP, assets, asynchronous execution, block compression, cache, chrome profiler, compute shaders, concurrent image decoding, fragment shaders, loading times, mipmaps, node package, normal maps, overheads, performance optimization, real-time, registerSparkLoader, texture loader, threejs, two-channel textures
vram
www.ludicon.com 4 days ago
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265. HN Harmony- The gpt-oss models utilize a "harmony" response format for structuring conversations and reasoning outputs, which mimics OpenAI's Responses API for familiarity, especially when used directly via platforms like Ollama. - Messages in this system are processed across five roles: system (defines tools and effort), developer (provides instructions and functions), user (input to the model), assistant (outputs including tool calls or messages), and tool (outputs from tool calls). In case of conflicting instructions, a hierarchy is applied: system > developer > user > assistant > tool. - Assistant outputs are categorized into three channels—final, analysis, and commentary. The final channel contains responses for users, adhering to safety standards. Analysis supports reasoning but isn't user-facing due to lower safety compliance. Commentary involves function tool calls and generating preambles or built-in tools. - For correct message rendering and processing in harmony format, a renderer from PyPI or crates.io is recommended. The `openai_harmony` library can be used for constructing and rendering system prompts using specific configurations such as high reasoning effort and start dates. - In the example provided, a conversation involving weather inquiry utilizes system and developer messages to set up configurations including riddle responses and function calls like `get_current_weather`. When a user asks about Tokyo's weather, the assistant identifies the need for this tool, invokes it, and receives a response indicating sunny weather with a temperature of 20°C. - The text further explains how special tokens are used in message formatting to identify communication types between models and tools. These include start tokens (`<|start|>`), end tokens (`<|end|>`), transition tokens like `<|message|>`, and specific action tokens such as `<|call|>` for tool interactions. - The structured approach enables clear, organized communication where multiple messages can be generated in a single interaction sequence. It outlines how responses are provided through different channels with the process beginning in an "analysis" channel for reasoning and concluding in a "final" channel for user-facing responses. - System message configurations include defining model identity (ChatGPT), knowledge cutoff date, reasoning effort levels, and communication channels—analysis, commentary, final. Functions should be routed via the "commentary" channel. - The gpt-oss models default to medium reasoning but can adjust according to specified needs in system messages. They provide detailed reasoning in analysis channels while delivering responses in final channels, ensuring structured output for effective communication. - For tool and function calls within the model, developers must specify available functions using TypeScript-like syntax within a `functions` namespace in a Tools section, with each function having an arrow pointing to `any`. - Example workflow includes recognizing when specific tools like `get_current_weather` should be called based on user queries (e.g., weather in San Francisco), formatting outputs as tool messages through the commentary channel, and using JSON format for data handling. - The text also discusses using past model outputs as inputs for further actions, generating preambles or action plans for users about upcoming steps, and controlling output behavior with predefined JSON Schemas at the end of developer messages. - It emphasizes that while prompts influence model behavior, they do not ensure schema compliance; hence developers need to define their own grammar during sampling. The text also covers integrating browser tools into system prompts for enhanced functionality, specifying actions like searching, opening links, and finding patterns on web pages with a cursor citation system. Keywords: API, GPT-oss, Harmony, JSON schema, OpenAI Responses, PyPI, TypeScript-like syntax, analysis, assistant messages, browser tool, channels, commentary, conversation, cratesio, final channel, function calls, function definition, harmony renderer, inference, information hierarchy, location, metadata, namespace, parsing, prompt format, rendering, role, roles, safety standards, sampling, special tokens, system message, tiktoken, tokens, tool calling, tool output, unicode characters, user-facing responses, weather API
gpt-oss
cookbook.openai.com 4 days ago
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266. HN Ask HN: Textbook and Technical Book writers – are you creating LLM products?The post from "Ask HN" explores whether authors of textbooks and technical books are incorporating or developing Large Language Model (LLM) products into their work. The primary inquiry centers on if these LLMs could replace or enhance traditional educational resources such as textbooks and technical manuals. Although general discussions about the usage of LLMs are permissible, the core interest lies in understanding how LLMs are being applied specifically to create innovative educational tools. **Bullet Point Summary:** - The post seeks insights into textbook and technical book authors' use or creation of Large Language Model (LLM) products. - It questions whether LLMs could replace or augment traditional textbooks and technical books. - While open to general discussions on LLM usage, the focus remains on their application in developing new educational materials. Keywords: Ask HN, Books, Develop, Discussion, Enhance, General use, LLM products, Products, Products Keywords: Ask HN, Replace, Specific curiosity, Technical Book, Textbook, Use LLMs, Writers
llm
news.ycombinator.com 4 days ago
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267. HN Guide to building an application in 2025 – tech stack and tools### Summary The "Guide to Building an Application in 2025" offers a detailed roadmap for selecting technologies across various stages of app development, tailored for developers embarking on projects with varying complexities and scales. The guide begins by advising an initial assessment of project needs before choosing from a plethora of frontend frameworks like React (notably Next.js), Angular, Vue.js, Tailwind CSS, Bootstrap, and ShadCN UI. It emphasizes that scalability can be achieved irrespective of the technologies selected, ensuring developers have flexibility in their choices. For modern frontend development, tools such as Tailwind CSS, Bootstrap, and ShadCN UI are recommended for efficient styling solutions, with Next.js highlighted for its versatility in full-stack applications deployable on platforms like Vercel. Astro is praised for optimizing content-heavy sites by minimizing JavaScript usage, while Hugo excels at rapid blog setup thanks to its Go-based architecture. Jekyll remains a viable option for straightforward projects such as personal portfolios. In backend development, the guide discusses languages and frameworks suited to specific needs: .NET is ideal for enterprise solutions on Azure, Python shines in AI and data science with FastAPI and Django, Go is optimal for scalable microservices due to its performance efficiency, and Java’s stability makes it suitable for large-scale applications with Spring Boot. Databases are categorized into SQL and NoSQL options. Recommended databases include Firebase and Supabase as modern NoSQL choices, while PostgreSQL offers a robust open-source SQL solution. SQLite is ideal for small projects, and Azure Cosmos DB presents a cost-effective NoSQL option from Microsoft. Authentication (Auth) can be managed with custom-built systems or off-the-shelf solutions like Firebase, Supabase, Clerk, and Auth.js, the latter being specifically useful for Next.js applications. For analytics, the guide recommends Google Analytics for widespread tracking capabilities alongside privacy-centric alternatives such as Plausible and Posthog. Email services are covered by AWS SES due to its scalability in handling transactional emails. Payment processing is dominated by Stripe globally with regional players like Razorpay in India, while Polar introduces open-source monetization solutions, and Gumroad serves niche e-commerce needs. The guide addresses CMS options like WordPress for broad web usage, Payload CMS tailored for developers, and Ghost for blogging purposes. For newsletters, platforms such as Beehiiv, ConvertKit, and Substack are highlighted. Documentation is supported by frameworks including Docusaurus and Nextra/Fuma Docs. Cloud services from providers like Google Cloud Platform, Azure, AWS, and Cloudflare offer extensive hosting to security solutions, while GitHub remains the premier version control system. Deployment options include Vercel and Netlify for their ease of integration and free tiers, with self-hosting feasible through Railway, Digital Ocean, Hetzner, or Coolify. Code editors such as VS Code are praised for its extensions, complemented by AI-powered alternatives like Cursor. AI-assisted coding tools v0, Lovable, and Bolt are recognized for app generation capabilities from text prompts, while Algolia is recommended for search functionalities in applications. The guide underscores the importance of simplicity in tool selection to facilitate scalable growth and encourages developers to share their insights. ### Bullet Point Summary: - **Frontend Frameworks**: React (Next.js), Angular, Vue.js; styling tools: Tailwind CSS, Bootstrap, ShadCN UI; Next.js is ideal for full-stack apps. - **Backend Development**: - .NET for enterprise applications on Azure - Python with FastAPI and Django for AI/data science tasks - Go for scalable microservices due to efficient performance - Java with Spring Boot for large-scale stability - **Databases**: - NoSQL: Firebase, Supabase (PostgreSQL support) - SQL: PostgreSQL, SQLite for small projects; Azure Cosmos DB as a cost-effective option - **Authentication**: Custom systems or solutions like Firebase, Supabase, Clerk, Auth.js for Next.js. - **Analytics Tools**: Google Analytics, privacy-focused alternatives Plausible and Posthog. - **Email Services**: AWS SES for scalable transactional emails. - **Payment Gateways**: Stripe (global), Razorpay (India), Polar (open-source), Gumroad (niche e-commerce). - **CMS Options**: WordPress for broad usage, Payload CMS for developers, Ghost for blogs/newsletters. - **Newsletter Platforms**: Beehiiv, ConvertKit, Substack with free tiers; Dev.to, Medium, Hashnode, Blogger for content writing. - **Documentation Frameworks**: Docusaurus and Nextra/Fuma Docs recommended. - **Cloud Services**: Google Cloud Platform, Azure, AWS, Cloudflare for hosting/security solutions. - **Version Control System**: GitHub as the leading system. - **Deployment Options**: Vercel, Netlify with free tiers; self-hosting via Railway, Digital Ocean, Hetzner, Coolify. - **Code Editors**: VS Code (extensions), Cursor (AI-powered). - **AI-Assisted Coding Tools**: v0, Lovable, Bolt for generating apps from text prompts. - **Search Functionality**: Algolia recommended. The guide prioritizes simplicity and flexibility in tool selection to support scalable app growth while encouraging developers to disseminate their learning experiences. Keywords: AI Coding, APIs, AWS SES, Algolia, Analytics, Angular, Astro, Auth, Authentication, Automation, Azure, Backend Technologies, Beehiiv, Blazor, Blogger, Bolt, Bootstrap, CMS, Clerk, Cloud Providers, Cloudflare, Code Editors, ConvertKit, Cosmos DB, Cursor, Data Science, Database, Deployment Options, Devto, Digital Ocean, Django, Documentation Frameworks, Docusaurus, Email, FastAPI, Firebase, Frontend Frameworks, Full Stack, GCP, Ghost, GitHub, GitHub Pages, Go, Google Analytics, Gumroad, Hashnode, Hugo, Java, Jekyll, Lovable, Medium, Microservices, Microsoft, NET, Netlify, Newsletter Platforms, Nextjs, Nextra Docs, NoSQL, PHP, Payload CMS, Payment Gateways, Plausible, PostgreSQL, Posthog, Python, Razorpay, React, SQL, SQLite, Search, Server Components, ShadCN UI, Spring Boot, Static Generation, Stripe, Substack, Supabase, Tailwind CSS, VS Code, Vercel, Version Control, Vuejs, WordPress, v0
github copilot
www.dotnetinterviews.com 4 days ago
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268. HN Microsoft is officially sending employees back to the office**Summary:** Microsoft has announced that starting in late February 2026, its Seattle-area employees will need to work from offices at least three days per week, with this policy extending to other US and international locations over time. Employees may request exceptions by September 19, though specifics remain unclear. This change is part of a broader shift towards more structured remote work policies following Microsoft's earlier flexible arrangements established post-2020. The company has also updated its Remote-to-Office (RTO) policy, removing previous content that endorsed remote work benefits and replacing it with a focus on the challenges hybrid work poses to employee engagement, suggesting AI as part of the solution. Microsoft acknowledges that in-person collaboration is vital for productivity and innovation, especially in developing cutting-edge AI products. To foster this collaborative environment, they are implementing a new flexible work policy mandating three office days per week. This phased rollout begins at Puget Sound, with detailed plans and personalized communication being provided to affected employees. The transition will consider the varying impacts on different teams and emphasize maintaining workplace safety and security. Employees in the Puget Sound area within 50 miles of a Microsoft office are specifically targeted by this mandate from late February 2026 onwards, with further communications planned through leadership channels. Managers have access to resources on the Managers@Microsoft SharePoint for guidance, while employees outside this region will be notified only if their EVP requires action. Additional rollouts across other US locations and international offices will follow, with updates available via Flexible Work at Microsoft SharePoint. **Bullet Point Summary:** - **Policy Change**: Starting late February 2026, Microsoft requires Seattle-area employees to work from the office three days a week. - **Expansion Plan**: The policy will gradually extend to other U.S. locations and internationally over time. - **Exceptions**: Employees can request exceptions by September 19; however, criteria for these requests are not detailed. - **Alignment with Competitors**: This shift aligns Microsoft's work policy with those of companies like Meta and Google. - **Updated RTO Policy**: Microsoft removed previous content promoting remote work benefits in favor of a new post highlighting hybrid work challenges and AI solutions. - **Importance of In-Person Collaboration**: Emphasizes the value of real-time collaboration for innovation, particularly in AI development. - **Implementation Details**: The policy will start at Puget Sound with phased expansion. Employees will receive personalized communication regarding the changes. - **Impact Considerations**: Microsoft plans to account for varied impacts across different teams and emphasizes workplace safety and security. - **Communication Channels**: Puget Sound-area employees need to work onsite three days a week by end of February 2026 if within 50 miles, with resources available on Managers@Microsoft SharePoint. Further guidance will be communicated by EVPs or leadership. - **Future Rollouts**: Additional timelines for U.S. and international offices are forthcoming, with more information accessible via the Flexible Work at Microsoft SharePoint. Keywords: AI, Amazon, Flexible Work SharePoint, Google, Managers@Microsoft, Microsoft, Puget Sound, RTO, Teams, US locations, Zoom, employees, hybrid work, non-US planning, productivity, remote work, technology
popular
www.businessinsider.com 4 days ago
https://archive.ph/EguQ5 a day ago https://www.phila.gov/2024-05-20-statement-from-mayor-cherel a day ago https://fortune.com/2025/09/05/paramount-skyd a day ago https://www.seattletimes.com/business/microsoft/mi a day ago https://fortune.com/2024/07/24/return-to-offi a day ago https://www.msn.com/en-us/money/careersandeducatio a day ago https://www.cnbc.com/2023/09/12/why-rto-manda a day ago https://wolfstreet.com/2025/09/01/office-cmbs a day ago https://leaddev.com/culture/engineering-burnout-rising- a day ago https://www.forbes.com/sites/bryanrobinson/2025&#x a day ago https://blog.theinterviewguys.com/workplace-burnout-in-2025- a day ago https://thehill.com/lobbying/5325471-burnout-erupts-amo a day ago https://www.tiktok.com/@keds_economist/video/74647 a day ago https://news.ycombinator.com/item?id=45192092 a day ago https://www.library.hbs.edu/working-knowledge/charting- a day ago https://anderson-review.ucla.edu/tech-workers-take-much-lowe a day ago https://news.stanford.edu/stories/2022/12/exp a day ago https://news.ycombinator.com/newsguidelines.html a day ago https://news.ycombinator.com/item?id=45146528 a day ago https://www.moreno.senate.gov/press-releases/new-moreno a day ago https://www.moreno.senate.gov/wp-content/uploads/2 a day ago https://en.wikipedia.org/wiki/Constructive_dismissal a day ago https://www.opm.gov/policy-data-oversight/pay-leave a day ago https://bemorewithless.com/the-story-of-the-mexican-fisherma a day ago https://news.ycombinator.com/item?id=37094928 a day ago https://news.ycombinator.com/item?id=45192176 a day ago https://news.ycombinator.com/item?id=45183560 a day ago https://news.ycombinator.com/item?id=45184017 a day ago https://news.ycombinator.com/item?id=45184032 a day ago https://perchance.org/corp-bs a day ago https://www.bullshitgenerator.com/ a day ago https://code-cwa.org/organize a day ago https://www.nytimes.com/2024/02/25/business&# a day ago https://archive.today/ES3SF a day ago https://en.wikipedia.org/wiki/Microsoft_and_unions a day ago https://www.cs.virginia.edu/~robins/YouAndYourResearch. a day ago https://support.discord.com/hc/en-us/articles/ a day ago https://www.theguardian.com/business/2024/nov/ a day ago https://blogs.microsoft.com/blog/2025/09/09&# a day ago https://injuryfacts.nsc.org/home-and-community/safety-t a day ago https://news.ycombinator.com/item?id=40419292 a day ago |
269. HN ICE is using fake cell towers to spy on people's phones**Summary:** Immigration and Customs Enforcement (ICE) has continued to use surveillance technology, notably cell-site simulators like Stingrays, to track undocumented immigrants. These devices work by mimicking cell towers to intercept phone signals, allowing for the precise location of individuals. A recent case in Orem, Utah, involved using such technology to locate a person ordered to leave the U.S. in 2023 after escaping from Venezuelan prison, suspected of having gang affiliations. The initial tracking was not precise enough, leading to a court-ordered request for a Stingray device. Concerns about privacy arise due to potential exposure of information from nearby individuals. In parallel, government agencies are increasingly relying on advanced surveillance technologies despite facing criticism over their impact on civil liberties and effectiveness. ICE has spent nearly $1 million under the Biden administration to procure mobile cell site simulator vehicles and holds an active contract with Harris Corporation worth $4.4 million for location-determining equipment. The San Mateo County Sheriff's Office invested $12 million in C3.AI’s Sherlock system, intended to integrate surveillance data across 16 agencies. However, after three years of development, the system has not met its objectives to enhance police efficiency, with internal reports from 2023 noting it was barely functional at that time. In a broader technology and privacy landscape, Flock Safety is introducing advanced drones and AI-powered tools as competition against Axon's established police technology solutions. ICE also contracted Clearview AI for nearly $10 million to use facial recognition in identifying attackers on officers. Meanwhile, Meta faces legal challenges from former WhatsApp security lead Attaullah Baig, who alleges the company ignored privacy issues allowing unauthorized access to user data. Signal has introduced encrypted chat backups starting with Android users, emphasizing the importance of protecting recovery keys. Amnesty International reports that Pakistan is executing one of the largest domestic surveillance programs globally, leveraging technology from both Chinese and Western providers for extensive snooping and internet censorship. Other related discussions include Forbes' listing of America's wealthiest sports team owners and a report on how Donald Trump’s presidency contributed to a $3 billion increase in his net worth within a year. **Bullet Point Summary:** - **ICE Surveillance Technology**: Continues using Stingray devices to track undocumented immigrants, as evidenced by recent activities in Utah. - **Privacy Concerns**: Raises significant privacy issues due to potential exposure of information from non-target individuals. - **Government Contracts and Investments**: - ICE's expenditure on mobile cell site simulators under the Biden administration and contracts with Harris Corporation for location technologies. - San Mateo County’s $12 million investment in C3.AI’s Sherlock system, which failed to enhance police efficiency. - **Technological Competition and Legal Issues**: - Flock Safety entering the market against Axon with advanced surveillance tools. - ICE's use of Clearview AI for facial recognition in law enforcement scenarios. - Meta faces a lawsuit from former employee Attaullah Baig over alleged privacy violations within WhatsApp. - **Data Security Enhancements**: Signal’s introduction of encrypted chat backups, emphasizing the critical nature of safeguarding recovery keys. - **International Surveillance Reports**: Amnesty International highlights Pakistan's extensive surveillance program using technology for mass monitoring and censorship. - **Additional Context**: - Forbes listing on America's richest sports team owners. - Report on the financial growth experienced by Donald Trump during his presidency. Keywords: AI, Amnesty International, Axon, Clearview AI, Harris Corporation, ICE, Meta, Signal, Stingray, Venezuela, deportation, drones, encrypted backups, facial recognition, gang activity, location tracking, privacy, search warrant, surveillance, technology, undocumented immigrants
popular
www.forbes.com 4 days ago
https://github.com/EFForg/rayhunter/ a day ago https://www.theguardian.com/world/2013/jul/11 a day ago https://github.com/soatok/freeon a day ago https://news.ycombinator.com/item?id=43283917 a day ago https://play.google.com/store/apps/details?id=com. a day ago https://en.wikipedia.org/wiki/IMSI-catcher a day ago https://news.ycombinator.com/item?id=45189302 a day ago https://github.com/srlabs/snoopsnitch a day ago https://gitweb.stoutner.com/?p=PrivacyCell.git;a=summary a day ago https://gsmmap.org/ a day ago https://bja.ojp.gov/program/it/privacy-civil-liber a day ago https://san.com/cc/exclusive-evidence-of-cell-phone-sur a day ago https://slate.com/technology/2014/01/ukraine- a day ago https://www.msn.com/en-us/crime/general/touri a day ago https://www.motherjones.com/criminal-justice/2013/ a day ago https://en.wikipedia.org/wiki/Brett_Kavanaugh_Supreme_C a day ago https://www.nbcnews.com/news/amp/rcna206917 a day ago https://factually.co/fact-checks/politics/border-w a day ago https://news.ycombinator.com/item?id=45193191 a day ago https://en.wikipedia.org/wiki/Cassandra a day ago https://en.wikipedia.org/wiki/Unitary_executive_theory a day ago https://www.ice.gov/detain/detainee-death-reporting a day ago https://www.nbcnews.com/news/us-news/us-citizen-de a day ago https://phr.org/our-work/resources/deadly-failures a day ago https://www.wlrn.org/immigration/2025-08-01/haitia a day ago https://www.abqjournal.com/news/article_7519bc08-a416-4 a day ago https://www.kcur.org/news/2025-05-13/missouri-man- a day ago https://www.pressdemocrat.com/2025/04/24/as-i a day ago https://news.ycombinator.com/item?id=45184758 a day ago https://ssd.eff.org/module/attending-protest a day ago https://www.cise.ufl.edu/~butler/pubs/ndss25-tucke a day ago https://en.wikipedia.org/wiki/Librem_5 a day ago https://www.pnnl.gov/main/publications/external&#x a day ago https://youtu.be/0_04-lTu2wg?feature=shared a day ago https://xkcd.com/538/ a day ago https://www.timesofisrael.com/israel-targeted-top-iranian-le a day ago https://www.nytimes.com/2025/08/30/us/po a day ago https://archive.is/XdZet a day ago https://www.justsecurity.org/34449/investigating-survei a day ago https://theintercept.com/2016/09/12/long-secr a day ago https://arxiv.org/abs/1607.05171 a day ago https://arxiv.org/abs/1809.06925 a day ago https://github.com/SysSec-KAIST/LTESniffer a day ago https://en.wikipedia.org/wiki/Wi-Fi_positioning_system a day ago |
270. HN Best AI Coding Tools for Rust Projects: IDEs vs. TerminalsThe article evaluates AI coding tools tailored for Rust projects within Visual Studio Code (VS Code) and terminal environments, focusing on their performance in terms of speed, accuracy, and integration into typical workflows using the Axum HTTP server framework. - **IDE vs. Terminal-Based Tools**: - IDE-based tools offer deep integration with real-time suggestions and minimal learning curves but have limited customization options, making them suitable for individual developers. - Terminal-based tools emphasize command-line interactions and file-centric approaches, supporting better collaboration through configuration files, but require proficiency in CLI commands. - **AI Coding Tools**: - *Cursor*: A VS Code-based AI editor featuring a native chat interface and multi-file editing capabilities. - *Windsurf*: An enhanced version of VS Code with integrated AI assistance, offering architecture-aware suggestions and deep static analysis. - *VS Code + GitHub Copilot*: Provides AI-powered code suggestions and natural language to code conversion, integrating well into the GitHub ecosystem. - **Terminal-Based Tools**: - *Claude Code* and *Aider*: Lightweight command-line tools focusing on conversational debugging and Git workflow integration. Claude supports natural language input while Aider offers atomic edits with clear commit messages. - **Comparison of Gemini CLI vs. OpenAI Codex CLI**: - Gemini CLI, using Google's models, offers extensive code analysis in a lightweight format with real-time web search capabilities. - OpenAI Codex CLI ensures privacy and flexibility through local execution, employing Docker or Apple Seatbelt for security. - **Rust-Specific Considerations**: - IDE tools integrate with the Rust Language Server Protocol (LSP) via rust-analyzer, aiding in idiomatic code writing but facing challenges in headless environments. - Terminal tools are suitable for server-side development due to their resource efficiency and control over code changes. - **Project Demonstration**: - The article illustrates building a task management API using the AI tool Cursor, highlighting efficient multi-file editing capabilities with Axum, Tokio, Serde, and UUID. - **Tool Evaluation**: - *Cursor*: Highly rated for rapid prototyping in familiar VS Code workflows. - *Windsurf*: Suitable for long-term architecture planning but may over-engineer solutions. - *GitHub Copilot*: Excellent for incremental line-by-line coding with fast suggestions. - **Additional Tools**: - *Claude Code*: Ideal for learning Rust and managing large projects, although slower in generating runnable code. - *Aider*: Effective for Git-driven workflows, necessitating more iteration than IDE tools. - **Tool Ratings**: - Cursor (IDE): 9/10 - Windsurf (IDE): 8.5/10 - Copilot (IDE): 8/10 - The article advises that while AI tools enhance productivity, disciplined workflows, rule enforcement, and consistent testing are essential for maintaining code quality in Rust projects. - **Cost Considerations**: - CLI tools may lead to high costs due to direct token consumption from AI subscriptions. - IDEs like Cursor or Windsurf offer flexible pricing but may have expensive tiers. Anthropic Max is noted for predictable pricing. - **Strategic Deployment with Shuttle**: - Utilizes the Model Context Protocol (MCP) to streamline Rust API creation and deployment, involving setting up a Shuttle account, configuring CLI, connecting AI tools, and deploying with specific prompts. - **Shuttle Resources**: - Offers databases, secret management, and supports frameworks such as Axum, Actix, and Rocket. It employs an infrastructure-as-code approach using Rust macros for embedding infrastructure in code. - The article emphasizes adherence to Rust's principles—writing idiomatic, safe, and performant code—and selecting AI tools that integrate seamlessly into existing workflows. - **Shuttle Promotion**: - Simplifies app development with fast redeploys and local iteration capabilities, fostering community support through open-source initiatives and platforms like Discord. - Users are encouraged to explore Shuttle templates for developing applications ranging from simple apps to full-stack SaaS projects. Keywords: AI chat interface, AI coding tools, AWS integration, Axum, CLI commands, Codeium, GitHub Copilot, HTTP server, IDE integration, IDEs, LLM access, Rust, VS Code, accuracy, advanced debugging, agentic programming, ai subscription, anthropic max, apps, architecture-aware suggestions, async/await, autocomplete, automation hooks, automation-focused, budgeting, bulk edits, cargo build, cargo check, cargotoml, cascade feature, chat assistance, cli, cli security, code explanation, code quality insights, code suggestions, codebase-wide understanding, collaboration, collaborative workflows, command-line interactions, configuration management, context-aware fixes, context-based suggestions, conversational debugging, cost, cross-file consistency, cross-language support, crud endpoints, cursor, custom AI model, customizable agent behavior, customization, debugging, deep static analysis, dependencies, deployment workflow, development tools integration, diagrams, dotfiles, end-to-end tests, error messages, evaluation method, fast, file system awareness, free tier, gemini 25 pro, git integration, graphical interface, guardrails, gui limitations, handlers, headless environments, idenative tools, in-depth explanations, incremental coding, integration, intelligent refactoring, kiro IDE, language server protocol (LSP), large-scale projects, learning Rust, learning curve, lightweight, llms, mainrs, massive context window, mcp protocol, minimal aesthetic, mocks, models, modularized, monthly price, multi-file editing, multi-language support, multi-step task execution, multimodal chat interface, natural language to code, open-source flexibility, optimization, ownership rules, pay-as-you-go, permissions, project context, project coordination, project scaffolding, project scope, real-time collaboration, real-time generation, real-time web search, refactoring, refactoring support, remote Rust environments, resource consumption, resource usage, rest endpoints, rules, rust-analyzer, saas projects, security sandboxing, server-side development, servers, shuttle account, smart autocomplete, spec-driven development, speed, static analysis, static analysis integration, strategic code generation, stubs, suggestions on-demand, task management API, tdd (test-driven development), templates, terminal-based, terminals, test generation, token reset, toolchain, unit tests, validation, windsurf, workflows
github copilot
www.shuttle.dev 4 days ago
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271. HN Data-morph: Morph a dataset into select shapes, while preserving the statisticsData-Morph is a versatile tool designed for transforming datasets of 2D points into various shapes while maintaining their summary statistics, using the method of simulated annealing. This software serves as an educational resource to emphasize the importance of data visualization by allowing users to understand and manipulate how different shapes can represent the same dataset. It can be easily installed via PyPI or conda from the conda-forge channel. The tool provides two primary modes of operation: command line and Python package usage. Command-line users have the capability to transform datasets into predefined shapes, such as converting a "panda" shape into a "star," which generates animations stored in designated directories. Furthermore, Data-Morph supports multiple transformations concurrently. In its Python environment, it utilizes the `DataMorpher` class from the `data_morph.morpher` module to facilitate these morphing processes. Datasets are typically loaded as pandas DataFrames with numeric 'x' and 'y' columns through a `DataLoader`, while shapes can be generated and adjusted in size by using a `ShapeFactory`. Comprehensive documentation is available for detailed guidance on the package's functionalities. One of Data-Morph’s educational applications involves students creating custom datasets, such as school logos, which they then transform into various target shapes. This exercise encourages students to analyze the effectiveness of different transformations and articulate their reasoning, effectively combining mathematical understanding with coding skills. Students can extend the functionality by inheriting from `LineCollection` or `PointCollection`, allowing them to morph built-in datasets into new forms. Data-Morph is an evolution of a tool initially developed for the "Datasaurus" dataset, originally created by Justin Matejka and George Fitzmaurice during ACM CHI 2017. Stefanie Molin adapted this tool to accommodate different input datasets, building on Matejka's GitHub repository code. The project, available at stefmolin/data-morph, invites users in educational settings to share their experiences using the software. Acknowledgments are extended to the original creators of the Datasaurus dataset for their foundational work, with citations required for both Data-Morph (DOI: 10.5281/zenodo.7834197) and the ACM CHI paper. Those interested in contributing to or learning more about Data Morph's development can find additional information through linked resources. **BULLET POINT SUMMARY:** - **Overview**: Data-Morph transforms datasets of 2D points into shapes while maintaining summary statistics using simulated annealing. - **Installation**: Available via PyPI and conda from the conda-forge channel. - **Usage Modes**: Offers command-line and Python package options for transformations, with support for animations and parallel processing. - **Python Features**: Utilizes `DataMorpher` class to morph datasets into shapes; uses pandas DataFrames and supports shape generation via `ShapeFactory`. - **Educational Application**: Encourages students to create custom datasets and analyze transformations, combining math and coding skills by extending classes like `LineCollection` or `PointCollection`. - **Origins and Development**: Adapted from the Datasaurus tool by Matejka and Fitzmaurice; further developed by Stefanie Molin. - **Community Engagement**: Users are encouraged to share classroom experiences and contribute to ongoing development efforts. - **Acknowledgments and Citations**: Requires citations for both Data-Morph and the original ACM CHI paper, with specific participation guidelines provided. Keywords: ACM CHI 2017, CLI options, Data Morph, DataLoader, GitHub, ShapeFactory, animation, classroom activities, contributing guidelines, data visualization, dataset, morphing, pandasDataFrame, shapes, simulated annealing, statistics, teaching tool
github
github.com 4 days ago
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272. HN Claude Code Anonymous**Summary:** Claude Code Anonymous is a meetup initiative developed by Orta Therox and the author, inspired by their transformative experiences with Claude Code in software development. The format aims to create a supportive environment where developers can discuss AI/LLM tooling experiences informally. Recognizing the varied adoption of these tools among developers, especially those possessing both technical and product expertise, Claude Code Anonymous emphasizes community building over formal presentations. Meetups feature short, five-minute lightning talks often introduced with personal anecdotes. The initiative encourages social interaction through casual gatherings that include drinks and pizza, fostering a relaxed atmosphere conducive to open dialogue. The concept, which began in London, has expanded to other cities like Vienna, Berlin, Cologne, and San Francisco, maintaining its focus on networking among builders rather than marketing professionals or HR personnel. To start a meetup, organizers only need to secure a venue, arrange refreshments, and use platforms like Luma for applications. The initiative prioritizes genuine engagement with building projects over product promotion. A "Don't be a Jerk" Code of Conduct ensures a respectful community environment. Support for new organizers is available through Twitter or email. **Bullet Point Summary:** - Claude Code Anonymous was created by Orta Therox and the author to facilitate discussions on AI/LLM tooling among developers. - The initiative focuses on informal, supportive meetups with five-minute lightning talks and social interactions over technical demonstrations. - Meetings emphasize networking among builders rather than marketing or HR professionals, fostering a community atmosphere through casual gatherings with drinks and pizza. - Originated in London and expanded to cities like Vienna, Berlin, Cologne, and San Francisco. - Starting a meetup involves securing a venue, providing refreshments, and promoting the event via platforms such as Luma for applications. - Emphasizes genuine engagement with building projects over product marketing; participants share their work rather than products. - Adheres to a "Don't be a Jerk" Code of Conduct to maintain respect within the community. - New organizers can receive support through Twitter or email. Keywords: AI, Berlin, Builders, Cities (London, Claude Code, Code of Conduct, Cologne, Developers, LLM Tooling, Lightning Talks, Meetups, Outreach, San Francisco), Socializing, Talks, Vienna
claude
steipete.me 4 days ago
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273. HN Lessons from NPM's Security FailuresThe text discusses recent phishing attacks on popular npm packages like chalk, debug, and duckdb, highlighting the inherent security flaws in current package management systems due to their trust model. These flaws include minimal verification for publishers, immediate updates without review, infinitely nested dependencies, and reliance on traditional two-factor authentication. Such vulnerabilities allow a single compromised account or mistake to affect millions of developers. To mitigate these issues, the article suggests several critical changes: - **Mandatory Cryptographic Signing**: Package managers should enforce cryptographic signing for all packages, ensuring secure key maintenance by publishers and automatic signature verification during installation. - **Local Signing Keys**: Publishers should keep private keys local to prevent unauthorized access if an account is compromised. - **Multi-Maintainer Approval**: For popular packages with high download rates, multiple signatures should be required for release approval, treating them as critical infrastructure. - **Phishing-Resistant Authentication**: Implement passkeys/WebAuthn and hardware security keys instead of TOTP codes, ensuring domain-bound authentication to prevent credential misuse. - **Automated Malware Detection**: Packages must undergo automated scanning before publication using modern static analysis techniques to detect malware indicators. - **Transparent Build Processes**: Although not detailed in the excerpt, enhancing transparency in build processes is crucial for security. The text references companies like Socket and Snyk as examples of effective package manager security improvements. Key recommendations include transparent build processes, provenance attestation linking packages to commits, reproducible builds verified by third parties, and mandatory automated scanning of source differences. GitHub's Sigstore integration is noted as a positive step that should be mandatory. Additionally, dependency sandboxing is proposed, where packages declare necessary permissions similar to mobile app stores' permission models, enhancing security by limiting unnecessary access. The main challenge in implementing these measures is not technical but social and economic; registry operators need to prioritize security over convenience, and developers must demand higher standards. Proactive implementation of these measures is essential for maintaining secure package managers as critical infrastructure. **Bullet Point Summary:** - Recent phishing attacks on npm packages have exposed critical security flaws due to a vulnerable trust model. - Proposed changes include mandatory cryptographic signing, local signing keys, multi-maintainer approval, phishing-resistant authentication, automated malware detection, and transparent build processes. - Companies like Socket and Snyk demonstrate effective security methods, suggesting integration of these techniques into registries. - Recommendations include transparent builds, provenance attestation, reproducible builds, and mandatory scanning. - Dependency sandboxing is proposed to enhance security by limiting unnecessary permissions. - The main obstacle is social and economic; operators must prioritize security, and developers should demand higher standards. Proactive measures are essential for maintaining secure package managers. Keywords: 2FA, GitHub, Sigstore, TOTP codes, behavioral analysis, build processes, compromise, dependencies, dependency sandboxing, file system operations, hardware security keys, infrastructure, maintainers, network access patterns, npm, obfuscated code, package management, package signing, passkeys, permissions, phishing attacks, provenance attestation, reproducible builds, security, standards, static analysis, vulnerabilities
github
oneuptime.com 4 days ago
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274. HN Aaron Francis on Technical BloggingAaron Francis shares insights from his technical blogging journey in an interview, illustrating its progression from a promotional tool to a valuable portfolio component. He highlights the profound personal impact of his blog posts, which have inspired individuals to exceed their limits and achieve significant life goals such as landing dream jobs. Among his works, a post titled "What if you try hard?" stands out for motivating someone to explore new opportunities and engage in open-source projects. This type of feedback is particularly rewarding for Francis. The author recounts the success of another influential blog post, "Publishing Your Work Increases Your Luck," which gained substantial attention after being featured on GitHub's ReadMe and topping Hacker News. This piece was pivotal in enhancing both his developer and writing careers by encouraging others to share their work publicly. Reflecting on a challenging year like 2024, the author shares how they crafted a year-in-review blog post with honesty, transparency, and hopefulness, aiming to encourage those experiencing similar difficulties. A key lesson imparted is prioritizing regular publication over perfection in blogging. The advice for aspiring bloggers includes producing consistent content—aiming for about 12 posts annually—as this approach enhances skills more effectively than focusing excessively on a single piece. For new bloggers, the focus should be on publishing as an indicator of success rather than performance metrics. Simply completing and posting articles is considered successful work, with feedback from both well-received and less popular posts providing valuable insights. Internally, it's crucial to redefine success as the act of sharing one's work instead of its reception. The author recommends following Jason Cohen for business and SaaS insights, along with The Crunchy Data blog for Postgres content. They emphasize that while putting oneself out there can be daunting, it is essential; many avoid doing so, making those who publish their work stand out significantly. **Bullet Point Summary:** - Aaron Francis reflects on his technical blogging journey from promoting products to building a valuable portfolio. - His most rewarding feedback came from a post inspiring someone to pursue new opportunities and open-source projects. - A key blog post by the author gained significant attention after being featured on GitHub and Hacker News, encouraging public sharing of work. - The author shares insights from writing a 2024 year-in-review during a challenging personal period, emphasizing honesty and encouragement for others. - Consistent publishing is advised over striving for perfection; regular posts improve skills more than obsessing over one piece. - New bloggers should measure success by the act of publishing rather than post performance or reception. - Recommended blogs include Jason Cohen's business insights and The Crunchy Data blog for Postgres content. - Putting oneself out there through blogging, despite its challenges, is crucial as it distinguishes those who share their work from those who do not. Keywords: Aaron Francis, Crunchy Data, GitHub, Jason Cohen, Postgres, ReadMe, SaaS, advocacy, approachable, blog post, career impact, conference, conference talks, developer career, dream job, encourage, freelance, full-time employment, hacker news, honest, hope, impact, influence, information density, lessons learned, luck, open source, personal development, public body of work, publishing, screencasting, tech blogging, terrifying, transparent, try hard, videos, work, writing career, writing challenges, year in review
postgres
writethatblog.substack.com 4 days ago
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275. HN Fake Mac Apps on GitHubMaxdme124's post addresses a concerning rise in deceptive replicas of popular Mac applications available on GitHub, which pose a risk by potentially tricking users into downloading malware. These fake repositories are meticulously crafted to appear nearly identical to legitimate ones, making them challenging for average users to identify. A crucial indicator of such fraudulent apps is their significantly smaller file size compared to the original application. To prevent falling prey to these threats, Maxdme124 recommends that users verify the authenticity of an app by checking its official source online and evaluating the developer's reputation. - **Summary in Bullet Points:** - There has been a noted increase in convincing replicas of popular Mac apps on GitHub. - These fake repositories are designed to closely resemble genuine ones, posing significant identification challenges for average users. - Discrepancies such as much smaller file sizes compared to the original app serve as key indicators of these fraudulent copies. - Users are advised to verify the authenticity of an application by checking its official source online and assessing the reputation of the developer. Keywords: Fake Mac Apps, GitHub, VirusTotal, anomalies, dmg, fake app size, installer sizes, malware, original developer, popular apps, red flags, replicas, reputation
github
mjtsai.com 4 days ago
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276. HN SharedVolume – a Kubernetes operator to sync Git/S3/HTTP/SSH volumes across podsThe SharedVolume is an open-source Kubernetes operator developed to streamline data sharing between pods and workloads across various sources like Git, S3, SSH, or HTTP. It addresses typical challenges in Kubernetes environments by eliminating the necessity for init containers or sidecars solely used for data synchronization, reducing duplicated datasets that waste storage space, simplifying volume sharing across namespaces, and avoiding complex updating mechanisms such as cron jobs. The operator enables users to define either a SharedVolume (namespace-scoped) or ClusterSharedVolume (cluster-wide), with automatic syncing of data from the source and ensuring only one copy is maintained within the cluster. Pods can easily attach to these volumes by incorporating a simple annotation. The project's documentation, along with examples, is accessible at [sharedvolume.github.io](https://sharedvolume.github.io). Currently in its beta phase, SharedVolume invites feedback, questions, and suggestions for enhancements from users on GitHub. If the tool proves valuable, users are encouraged to support the project by starring it on GitHub, thus helping boost its visibility and recognition within the open-source community. **BULLET POINT SUMMARY:** - **Purpose**: Simplifies data sharing in Kubernetes between pods/workloads across Git, S3, SSH, or HTTP sources. - **Addresses Issues**: Eliminates need for init containers/sidecars for sync; reduces duplicated datasets; simplifies cross-namespace volume sharing; avoids complex update mechanisms like cron jobs. - **Functionality**: Defines SharedVolume (namespace-scoped) or ClusterSharedVolume (cluster-wide); automatic data syncing and single-cluster copy maintenance. - **Ease of Use**: Pods attach to volumes with a simple annotation. - **Resources**: Documentation and examples available at [sharedvolume.github.io](https://sharedvolume.github.io). - **Project Status**: In beta; open for feedback, questions, and suggestions on GitHub. - **Community Support**: Encourages users to star the project on GitHub to increase its visibility in the open-source community. Keywords: ClusterSharedVolume, Git, GitHub, HTTP, Kubernetes, S3, SSH, SharedVolume, annotation, beta, cluster-wide, data, docs, examples, feedback, init containers, jobs, namespace-scoped, open-source, operator, pods, sidecars, storage, support, sync, volumes
github
news.ycombinator.com 4 days ago
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277. HN Claude Code: Behind-the-scenes of the master agent loop- **Claude Code Architecture:** Designed around a single-threaded master loop focusing on simplicity and transparency, enhancing agentic engineering with controlled autonomy through structured tools and planning. - **Key Layers:** - User interaction via CLI, VS Code plugin, or web UI. - Agent core scheduling includes the main engine and an asynchronous message queue. - Components like StreamGen for output management, ToolEngine & Scheduler for tool orchestration, and Compressor wU2 for context window management. - **Design Philosophy:** - Emphasizes simplicity, debuggability, and safety with long-term memory storage using Markdown documents. - Aims to provide efficient real-time steering and controlled sub-agent spawning under heavy user demand. - **Operational Flow:** - Processes user input through a model that determines necessary actions, potentially invoking tools for specific tasks. - Maintains simplicity by utilizing methods like regex and Markdown files over complex alternatives. - **Agent Loop Design:** - Operates on a minimalistic loop that continues until a plain text response without tool calls is generated. - Uses a single-threaded message history to enhance reliability, allowing one sub-agent branch for problem-solving at a time. - **Typical Execution Example:** - Steps might include identifying bugs with Grep, reading files via View, editing code through Edit, running tests using Bash, and finally providing an answer. - Each step is transparently logged for auditing the agent’s reasoning and actions. - **Real-time Interaction:** - The h2A async dual-buffer queue allows pausing, resuming, and modifying instructions mid-task without restarting, enhancing Claude Code as an interactive coding partner. - **Interface Consistency:** - Uses JSON tool calls executed in sandboxed environments returning plain text results. - Includes foundational reading tools (View, LS, Glob) and searching with GrepTool using regex for code structure understanding. - **Code Editing Tools:** - Provides tools like Edit for patches and diffs; Write/Replace for whole-file operations or new file creation. - Offers a CLI tool for tracking changes and a Bash tool to ensure safe shell sessions by filtering injection attempts. - **Planning and Safety Measures:** - Utilizes TodoWrite for structured JSON task lists in multi-step tasks, displaying interactive checklists with planning modes like /think mode. - Ensures safety through a permission system requiring explicit decisions for risky operations and customizable whitelists for trusted tasks. - **Exploration and Sub-agents:** - Facilitates exploration and parallel solution attempts using sub-agents via the dispatch_agent tool, with depth limitations to prevent recursive processes. - **Workflow and Memory Management:** - Employs a diffs-first workflow enhancing developer interaction through colorized changes. - Supports memory management via CLAUDE.md for project history and Compressor wU2 for conversation summaries, maintaining an audit trail of actions and decisions. - **Overall Strengths:** - Claude Code’s single-loop design efficiently assists developers with radical simplicity and transparency. - Leverages comprehensive tools, TODO-based planning, controlled sub-agents, and safety measures to achieve exceptional performance in refactoring entire codebases. Keywords: Bash, BatchTool, CLAUDEmd, CLI, Claude Code, Compressor wU2, Edit, Grep, JSON tool calls, Jupyter notebooks, Markdown document, NotebookRead/Edit, StreamGen, TODO lists, TodoWrite, ToolEngine & Scheduler, URLs, View, WebFetch, Write/Replace, agent loop, agentic engineering, architecture, async dual-buffer queue, audit trail, bug fix, codebase reading, codebases, constraint-driven design Keywords: Claude Code, context engineering, context management, context window, debuggability, developer tools, diffs, elegant engineering, flat message history, h2A queue, injection filtering, interactive streaming conversations, main thread, markdown files, master loop, memory management, model analysis, multi-agent swarms, nO, pause/resume support, planning mode, real-time steering, regex, reliability, risk classification, safety measures, sandboxed execution environments, single-loop design, single-threaded, sub-agent branch, sub-agent spawning, sub-agents, surgical patches, test-driven development, tool call, tool suite, user input, weekly limits
claude
blog.promptlayer.com 4 days ago
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278. HN Adventures in "Continuous AI"**Summary:** "Adventures in 'Continuous AI'" explores the innovative concept of Continuous AI as a transformative approach to software development by utilizing autonomous AI agents for background automation tasks like code reviews and documentation updates, without human oversight. This paradigm enhances project quality and developer productivity by handling routine tasks autonomously. Moving beyond interactive AI tools, it advocates for asynchronous AI agents that integrate into development workflows similarly to Continuous Integration (CI), improving efficiency and enforcing best practices through automated checks. The model, championed by GitHub Next, promises increased productivity and adherence to high standards without constant human intervention. Safe, controlled automation is emphasized to minimize risks, using models requiring explicit approval before making changes. This approach is demonstrated in Ruler, an open-source project with two key experiments utilizing GitHub Actions for safe automation. **Experiment 1: "This Codebase Smells!"** uses Codex CLI and GPT-5 within a weekly GitHub Actions workflow to provide automated health checks of the codebase, identifying issues or areas needing improvement. The process generates humorous, insightful reports as GitHub Discussions without making direct changes, offering timely suggestions autonomously. **Experiment 2: WRITEME** focuses on keeping project documentation aligned with evolving codebases through a bi-weekly GitHub Actions workflow. It employs a cloud-hosted AI agent to review and update documentation, generating pull requests for human review. This "human-after-the-loop" model automates analysis and draft generation while ensuring quality through developer oversight. The initiative highlights the potential of Continuous AI in software development, advocating its integration into an AI-native Software Development Lifecycle for improved project quality and team efficiency. Starting with simple tasks like non-mutating reports or pull requests allows teams to gradually incorporate these automated workflows into CI/CD pipelines, enhancing productivity and engineering excellence. For further learning, a course titled "Elite AI-Assisted Coding" offers comprehensive training on building software with AI tools over twelve live sessions. **Bullet Points:** - Continuous AI enhances software projects by using autonomous AI agents for tasks like code reviews without human oversight. - Shifts focus from interactive to asynchronous AI agents within development workflows, similar to CI principles. - Emphasizes safe, controlled automation through explicit approval processes before changes are made. - Demonstrated in Ruler with two experiments: "This Codebase Smells!" and WRITEME using GitHub Actions for added value. - **Experiment 1**: Provides automated health checks weekly, generating humorous reports on code quality without making direct changes. - **Experiment 2**: Keeps documentation up-to-date by reviewing and suggesting updates bi-weekly through AI-generated pull requests for human review. - Advocates integrating Continuous AI into the Software Development Lifecycle to improve project quality and team efficiency. - Encourages starting with simple tasks like non-mutating reports or creating pull requests to gradually incorporate automation. - Offers a course, "Elite AI-Assisted Coding," for comprehensive training on using AI tools in software development. Keywords: AI-assisted development, Codex CLI, Continuous AI, GitHub Actions, automated workflows, autonomous agents, code reviews, developer productivity, documentation updates, interactive tools, refactoring, software projects
github copilot
elite-ai-assisted-coding.dev 4 days ago
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279. HN Building an AI-Agnostic Conversation Logger – Phase 4: Mini-Me- **Phase 4 of AI Journey**: The author's journey in Phase 4 focuses on developing Mini-Me, an AI-agnostic conversation logger designed to handle long-running conversations across multiple agents while personalizing workflows. Despite initial success, Mini-Me failed due to scope creep and feature bloat. - **Importance of Governance**: The experience with Mini-Me underscored the necessity for strong product ownership and governance in AI development, akin to maintaining focus during expansive phases like MCU's Phase 4 post-Infinity Saga. - **Initial Goals and Challenges**: Mini-Me aimed to log interactions in JSON, manage conversations, utilize FAISS for semantic search, orchestrate various AI backends, and implement personalization. However, its monolithic architecture led to instability as features were added haphazardly, resulting in technical debt. - **Technical Weaknesses Identified**: The system suffered from a tangled monolithic design, agent sprawl with duplicated functions, hardcoded logic, complex failover chains, and non-modular search capabilities due to poor governance. - **Lessons Learned**: Key takeaways include the importance of modularity, centralized configuration management, strong governance, comprehensive logging, and metadata for transparency. These elements are crucial in preventing ambitious projects from collapsing under complexity. - **Transition to JARVIS**: Learning from Mini-Me's shortcomings, the author developed JARVIS with a focus on better governance. JARVIS is modular, with distinct directories for different functionalities like CLI parsing, agent interfaces, and data management. - **Key Features of JARVIS**: JARVIS maintains session continuity, supports multiple agents, provides persona-awareness, and ensures comprehensive logging. It aims to address Mini-Me's issues by prioritizing governance from the outset. - **Guiding Principles for AI Projects**: The author emphasizes treating AI as an assistant, controlling project scope, maintaining modularity, centralizing settings, ensuring thorough logging, leading rather than following AI suggestions, and focusing on quality over speed. - **Future Directions in Phase 5**: The upcoming phase focuses on refining JARVIS by integrating features like agent switching, persona management, and structured conversation handling. This disciplined approach aims to develop a reliable AI companion aligned with the product owner's vision. Overall, the document highlights the transition from chaotic development to a well-governed system, emphasizing modularity, sustainability, and alignment with strategic goals in AI projects. Keywords: AI Copilots, AI-Agnostic, Agent Sprawl, Agents, Anthropic, CLI Structure, Central Config, Complexity Spiral, Conversation, Conversation Logger, Developer Tools, FAISS, Failover Logic, Feature Bloat, GPT4All, Governance, Governance Trail, JARVIS, JSON logs, Logs Metadata, MCU, Mini-Me, Model Overrides, Modularity, Monolithic Architecture, Ollama, OpenAI, Persona Management, Personalisation, Phase 4, Product Ownership, React SPA, Saga, Scope Creep, Semantic Search, Structured Data, Workflow
ollama
blog.scottlogic.com 4 days ago
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280. HN Show HN: VC-LLM – Visualization of YC Summer 25 startupsThe text introduces "VC-LLM," a visualization tool developed to aid investors in evaluating startups from Y Combinator's Summer 2025 cohort with greater efficiency. This innovative tool assesses over 100 startups across twelve dimensions, enabling users to filter and identify promising investment opportunities based on tailored criteria or industry similarities. The application of Principal Component Analysis (PCA) allows for the creation of two-dimensional visual representations of startup scores, which are each supported by a rationale to facilitate easy auditing. VC-LLM utilizes a large language model known as Gemini for scoring purposes, a feature that has shown promising results in preliminary tests. This tool is currently being presented at the YC Summer 2025 Demo Day with the objective of collecting feedback and enhancing the accessibility of investment research. **BULLET POINT SUMMARY:** - "VC-LLM" is introduced as a visualization tool for evaluating Y Combinator's Summer 2025 startups. - The tool scores over 100 startups across 12 dimensions, facilitating targeted filtering based on criteria or industry similarities. - PCA is employed for two-dimensional charting of startup scores, with rationales provided for each score to aid auditing. - The scoring process utilizes a large language model named Gemini, which has shown positive results in initial tests. - VC-LLM is demonstrated at YC Summer 2025 Demo Day to gather feedback and improve the accessibility of investment research. Keywords: Gemini, PCA, VC-LLM, Visualization, YC Summer 25, audits, data analysis, demo day, dimensions, experiments, filtering, investment thesis, investors, large language models, ranked search, rationale, scoring, similarity view, small model, startups, technical implementation
gemini
amvizion.org 4 days ago
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281. HN We all dodged a bulletThe text discusses a security issue involving two compromised NPM packages, `debug` and `chalk`, which was reported on Hacker News. Users were protected from potential problems due to these vulnerabilities. The webpage is actively taking measures to secure user connections while it provides more detailed information about the incident. **BULLET POINT SUMMARY:** - Two NPM packages, `debug` and `chalk`, were compromised. - The issue was discussed on Hacker News. - Users were spared from potential problems due to these vulnerabilities. - Measures are being taken to secure user connections during further disclosure. Keywords: Bullet, NPM, chalk, compromised, connection, debug, dodged, hackernews, loading, moment, packages, security
popular
xeiaso.net 4 days ago
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282. HN Can researchers stop AI making up citations?OpenAI's latest model, GPT-5, has made significant strides in reducing the occurrence of fake citations and hallucinations—incorrect yet plausible answers—compared to its predecessors by enhancing citation-based response generation. Despite these advancements, hallucinations remain a fundamental challenge due to the inherent nature of large language models (LLMs), which rely on statistical predictions from learned associations. Experts like Tianyang Xu acknowledge that while the reduction in hallucination rates is satisfactory for most users, issues persist in technical domains such as law and mathematics. GPT-5 still exhibits errors in basic tasks like generating timelines of US presidents. Mark Steyvers emphasizes that though there are improvements, substantial progress remains necessary as the model infrequently admits when it lacks knowledge. LLMs' tendency to produce hallucinations is attributed to their training methods, which favor attempting responses over expressing uncertainty—a problem highlighted by OpenAI's preprint research. While scaling models and increasing training data have mitigated this issue somewhat, hallucinations continue to occur in scenarios with limited or incorrect information. AI researcher Mushtaq Bilal suggests that completely eliminating these errors might be unattainable; however, minimizing them is a key focus for OpenAI. Saachi Jain from OpenAI's safety team points out ongoing efforts to improve the model's ability to seek updated information and recognize its knowledge limitations. In the ScholarQA-CS benchmark for computer-science questions, GPT-5 performs effectively with web access, slightly surpassing human experts but falling short of its LLM-based system, OpenScholar. However, without internet access, GPT-5 struggles more than its predecessor, GPT-4o, frequently fabricating or misquoting citations. In the LongFact benchmark for long-form accuracy, GPT-5 shows fewer errors online compared to OpenAI's reasoning model o3 but underperforms against the non-reasoning model GPT-4o when offline. On Vectara's Hughes Hallucination Evaluation Model, GPT-5 is slightly outperformed by Google's Gemini 2.0 in minimizing false claims during document summarization. Despite its promise with web access, GPT-5's performance notably declines without it, particularly in citation accuracy and error rates in offline settings. In coding tasks constrained by hardware limitations, GPT-5 has demonstrated greater honesty compared to previous models, admitting failure 83% of the time as opposed to o3's 53%. This improvement is linked to later training stages where GPT-5 was incentivized for honest responses. **BULLET POINT SUMMARY:** - GPT-5 has reduced fake citations and hallucinations compared to predecessors by enhancing citation-based responses. - Hallucination remains a challenge due to the inherent nature of LLMs, affecting technical fields like law and mathematics. - GPT-5 still makes errors in basic tasks such as creating timelines of US presidents. - Scaling models and expanding training data have reduced but not eliminated hallucinations, which persist with limited information. - OpenAI focuses on improving model honesty and ability to seek updated information or acknowledge knowledge gaps. - GPT-5 outperforms humans slightly in computer-science questions with web access but struggles offline. - GPT-5 shows fewer errors online in long-form accuracy compared to o3 but underperforms GPT-4o offline. - On the Hughes Hallucination Evaluation Model, GPT-5 is outperformed by Google's Gemini 2.0. - Performance declines significantly without web access, especially in citation accuracy and error rates. - GPT-5 exhibits greater honesty than predecessors in hardware-constrained coding tasks, admitting failure more frequently. Keywords: AI errors, Artificial intelligence, GPT-5, LLMs, OpenAI, ScholarQA-CS, benchmarks, hallucinations, predictions, reasoning model, statistical machines, training data
openai
www.nature.com 4 days ago
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283. HN Billion-Line Days Demand Billion-Test Nights- The article discusses a significant transformation in software development driven by developers accepting approximately one billion lines of AI-generated code daily, equivalent to all GitHub commits over a week. - This surge demands a shift from optimizing code supply to ensuring trust and reliability due to the inadequacy of traditional testing methods for handling exponentially growing outputs. - Mihail Eric emphasizes that this milestone indicates not just faster coding but a fundamental change in software development dynamics. - AI-generated code challenges traditional code review and SDLC processes, which assume human-paced coding, rendering peer reviews impractical at higher speeds. - There is a potential paradigm shift with autonomous AI agents replacing human developers at scale, risking "software environmental collapse" from poorly managed testing practices leading to inconsistent, buggy codebases. - While AI excels in generating syntactically correct code, it struggles with maintaining architectural coherence and long-term maintainability, often resulting in duplicate implementations and inconsistent error handling patterns that complicate debugging. - The article calls for a radical rethinking of testing processes to ensure software quality keeps pace with AI-authored code growth, emphasizing the need for strategies to manage and maintain code quality amidst increased technical debt and testing demands due to combinatorial increases in interactions and states. - A promising solution is the "Spec-First, Test-Next" approach, where specifications serve as a single source of truth for development and testing, ensuring consistency and reducing errors by adopting practices from hardware design and aviation. - This methodology transforms traditional development cycles into parallel code and test generation with immediate validation, significantly speeding up feedback loops and reducing early-stage errors. - Practical applications include contract-driven development, AI-powered test generation covering various test types, and continuous evolution of tests as the code evolves. - As AI automates code generation, testing practices must also evolve concurrently; AI can suggest new test cases, identify untested paths, and predict bug-prone areas. - This shift necessitates reorganization within teams to focus on defining quality specifications, understanding contracts, and ensuring system integrity amid growing code volumes. - The emphasis is now on ensuring software trustworthiness through spec-driven development, with tests budgeted alongside compute resources to validate AI-generated code. - Organizations must adapt their testing strategies to confidently ship reliable products, while those that fail to do so risk being overwhelmed by rapid technological advancements. Keywords: AI-generated code, CI checks, GitHub, IDEs, Junior developers, Mihail Eric, QA, Security scanning, Senior engineers, Shift-Left, accessibility checks, bugs, code inflation, combinatorial growth, compute budget, debugging, developers, development loop, executable specifications, frameworks, infrastructure, integration surfaces, path complexity, performance testing, productivity gains, prompts, quality gates, redundancy, security vulnerabilities, state interactions, technical debt, test coverage, test suite
github
momentic.ai 4 days ago
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284. HN "Little Dragons" Transforming Hangzhou into China's Silicon ValleyHangzhou is rapidly establishing itself as a major AI hub in China, often likened to Silicon Valley, with its rise driven by the "6 little dragons." These companies include BrainCo, which focuses on brain-computer interfaces for meditation and sleep; Deep Robotics, specializing in autonomous quadruped robots for industrial and rescue applications; DeepSeek, known for developing competitive open-weight models since 2023; ManyCore, offering an AI-powered 3D design platform planning a Hong Kong IPO; and Unitree Robotics, valued at $1.4 billion with its acrobatic humanoid robots. Game Science also contributes to this technological ecosystem as a prominent game developer. The city's burgeoning reputation in technology is further bolstered by established giants like Alibaba and Hikvision. Hangzhou's rise surpasses that of Shenzhen and Beijing due to strategic startup support, including tax incentives, subsidies, and infrastructure investments. The Future Industries Development Plan (2025–2026) prioritizes AI, robotics, and synthetic biology, allocating 15% of annual fiscal revenue to tech investments. The city facilitates startup growth by offering office spaces and supporting high-level talent through housing and daily expense subsidies. The local ecosystem benefits from Alibaba Cloud’s provision of computing resources, access to Nvidia GPUs, and processors developed by Huawei and SMIC. Talent is nurtured by Zhejiang University, with many alumni establishing significant tech companies. Hangzhou's approach highlights its potential as a global AI center, emphasizing the advantage of diversifying tech hubs beyond traditional concentrations like Northern California in the U.S., suggesting that fostering multiple AI centers globally could lead to balanced technological growth. - Hangzhou is emerging as a leading AI hub, known as China's Silicon Valley. - The rise is driven by key companies: BrainCo (brain-computer interfaces), Deep Robotics (quadruped robots), DeepSeek (open-weight models), ManyCore (AI-powered 3D design platform), Unitree Robotics (acrobatic humanoid robots), and Game Science. - Hangzhou's tech status is supported by established giants like Alibaba and Hikvision, surpassing Shenzhen and Beijing. - Strategic support for startups includes tax incentives, subsidies, infrastructure investments, and the Future Industries Development Plan focusing on AI, robotics, and synthetic biology. - 15% of annual fiscal revenue in Hangzhou is allocated to tech investments, with initiatives like securing office space for startups. - High-level talent is supported through housing subsidies and daily expense allowances. - Alibaba Cloud provides computing resources; local access to Nvidia GPUs and processors from Huawei and SMIC enhances the tech ecosystem. - Zhejiang University supplies talent, with notable alumni establishing major companies. - Hangzhou's distinctive AI development approach positions it as a potential global AI center, contrasting with U.S. tech hub concentration in Northern California. - The diversification of tech hubs suggests that fostering multiple AI centers globally could lead to balanced technological growth. Keywords: "6 little dragons", AI innovation, Alibaba, BrainCo, DeepSeek, Hangzhou, Hikvision, Huawei, NetEase, Nvidia GPUs, Rokid, Unitree Robotics, Zhejiang University, collaboration, computing resources, humanoid robots, open-weights models, startups, talent pipelines
deepseek
www.deeplearning.ai 4 days ago
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285. HN US High school students' scores fall in reading and math**Summary:** During the COVID-19 pandemic, U.S. high school students experienced significant declines in math and reading scores, reaching their lowest levels in over two decades for 12th graders, according to the National Assessment of Education Progress (NAEP). Eighth-grade science skills also decreased substantially, reflecting a long-term downward trend not solely attributable to pandemic-related disruptions such as school closures. Contributing factors include increased screen time, reduced attention spans, less engagement with extensive reading materials, and shifts in teaching practices that prioritize shorter texts over full books. The results reveal alarming drops in student proficiency levels across the nation, with fewer students meeting basic benchmarks in math and reading. Experts like Jago stress the need for sustained reading to improve skills, while Education Secretary Linda McMahon criticizes ineffective federal spending as a reason for poor educational outcomes. In contrast, House Democrats warn that dismantling the Education Department could worsen educational disparities. Lesley Muldoon from the National Assessment Governing Board emphasizes graduates' lack of essential skills required in today's technology-driven society, advocating for increased federal investment in academic recovery and equity. In 2024, average scores for high school reading and math were historically low since their inception in 1992 and restructuring in 2005. In reading, 32% of seniors scored below basic proficiency, while in math, 45% did so—the highest since 2005. Only 33% of seniors met college-level math readiness, down from 37% in 2019. The assessments were conducted nationwide between January and March 2024. The achievement gaps have widened, particularly evident among eighth graders in science and twelfth graders in math. Additionally, a gender gap re-emerged in STEM fields as girls' scores declined more sharply than boys', reversing the pre-pandemic trend of closing this gap. The discontinuation of special engagement programs for girls following school closures is believed to contribute to this issue. Before the pandemic-induced school closures, declines were already occurring across subjects, suggesting other factors at play beyond COVID-19. A NAEP survey indicated a decrease in eighth-grade students' participation in inquiry-based learning activities crucial for understanding scientific concepts. Despite these challenges, it's noted that AP's education coverage remains independently produced and supported by private foundations. **Bullet Point Summary:** - U.S. high school students saw historic declines in reading and math scores during the COVID-19 pandemic. - The downward trend is part of a long-term pattern not solely due to pandemic disruptions. - Contributing factors include increased screen time, reduced attention spans, less engagement with long-form reading, and changes in teaching practices. - Scores reveal fewer students reaching basic proficiency levels; significant drops noted between 2019 and 2024. - Experts emphasize the need for improved reading stamina and criticize ineffective federal spending on education. - House Democrats caution that dismantling the Education Department could increase educational inequities. - Graduates lack essential skills needed in a technologically advanced society, highlighting the need for federal investment in academic recovery and equity. - 2024 scores show historic lows since assessments began; notable declines in college-level math readiness among seniors. - Achievement gaps have widened, with significant disparities among eighth graders in science and twelfth graders in math. - A gender gap re-emerged in STEM fields due to the discontinuation of special programs for girls post-pandemic. - Declines across subjects were occurring before 2020 school closures, indicating other factors beyond COVID-19. - Decreased participation in inquiry-based learning activities among eighth graders is noted as a challenge for understanding scientific concepts. Keywords: Education Department, NAEP, STEM, academic recovery, achievement gap, educational equity, eighth grade science, federal investment, gender gap, inquiry-based learning, math proficiency, pandemic, reading proficiency
popular
apnews.com 4 days ago
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286. HN Are Best Practices Best?The article critically examines the concept of "best practices" within startups by scrutinizing their definitions and effectiveness. It contrasts strategies advocated by YCombinator (YC), such as early product launches and limited funding, with OpenAI's approach under Sam Altman, which involved raising substantial funds without an immediate product launch or direct customer engagement. The article underscores that the success of various entities like Apple, the Victorian Era British Empire, and Usain Bolt illustrates that no universal best practices exist; instead, effectiveness is contingent on context-specific strategies tailored to achieve specific goals. Ultimately, it advocates for focusing on achieving objectives through methods that align with unique circumstances rather than rigidly following prescribed best practices. **Bullet Point Summary:** - Questions the validity of "best practices" in startups by examining their definition and effectiveness. - Contrasts YCombinator's strategies (early launch, minimal funding) with OpenAI's approach under Sam Altman (significant fundraising without immediate product launch or customer interaction). - Cites success stories like Apple, Victorian Era British Empire, and Usain Bolt to demonstrate the absence of universal best practices. - Emphasizes that effectiveness depends on context-specific strategies tailored for desired outcomes. - Concludes with a call to focus on achieving goals through methods suited to individual circumstances rather than adhering strictly to prescribed best practices. Keywords: Advice, Best Practices, Diversity, Domain, Founders, Growth, Innovation, Launch Early, Marketing, OpenAI, Raise Little Money, Sam Altman, Startups, Strategy, Talk to Customers, YCombinator
openai
quarter--mile.com 4 days ago
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287. HN Show HN: Context, Hallucinations, and How LLMs Are Changing Development### Summary In recent months, the author has been deeply involved with using Large Language Models (LLMs) in software development, focusing on two key concepts: context size and hallucinations that are reshaping current practices. The release of Gemini 2.5 Pro marked a significant advancement by expanding the context size to 1 million tokens, enabling developers to input entire projects into LLMs for better inference, thus reducing fragmented data reliance which often causes inaccuracies or "hallucinations." Despite their powerful capabilities, it is crucial not to trust LLM outputs blindly due to potential inaccuracies. Independent validation methods are essential to confirm the correctness of these outputs. The integration of LLMs in development processes mirrors Test-Driven Development (TDD) principles, where clear requirements are established before implementation. This TDD-like cycle involves writing tests, modifying code until it passes, and iterating, emphasizing clarity and precision. To enhance LLM reliability and minimize errors, the article suggests strategies such as clearly defining needs and scenarios when interacting with LLMs, asking them to rephrase requests for verification, implementing validation methods using predefined benchmarks, and utilizing larger context windows. Although LLMs can accelerate workflows, they necessitate detailed definitions and robust validation criteria for dependable outcomes. The investment in setting clear expectations and validating results is vital for achieving reliable solutions. ### Bullet Point Summary - **Context Size Impact**: Gemini 2.5 Pro's release expanded the context size to 1 million tokens, allowing entire projects to be fed into LLMs for better behavior inference. - **Trust Issues with LLMs**: Outputs should not be blindly trusted; independent validation is crucial due to potential inaccuracies. - **Development Process Parallel**: Integration of LLMs in development follows Test-Driven Development (TDD) principles, emphasizing clear requirements and iterative cycles of test writing and code modification. - **Clarity and Precision**: Define needs and scenarios clearly when working with LLMs to ensure alignment and accuracy. - **Restatement for Verification**: Use restatements by LLMs to verify understanding and alignment with the intended requests. - **Validation Methods**: Implement validation techniques using predefined scenarios to assess LLM output reliability. - **Larger Context Windows**: Utilize larger context windows in LLMs to reduce reliance on assumptions and improve accuracy. - **Reliability Through Validation**: Despite workflow acceleration, detailed definitions and robust validation criteria are essential for reliable LLM solutions. Keywords: Accuracy, Assumptions, Automation, Code, Context, Development, Gemini 25 Pro, Hallucinations, LLMs, Methodology, OpenAI, Project, Requirements, Responses, Software, TDD, Tests, Tokens, Trust, Validation
openai
juanpabloaj.substack.com 4 days ago
|
288. HN Introducing the MCP RegistryThe Model Context Protocol (MCP) Registry, launched as an open catalog and API, aims to enhance the discoverability and implementation of publicly available MCP servers by standardizing server distribution and discovery. Announced in March 2025, it provides a centralized source for client connections, supporting existing community-built registries while allowing organizations to create private sub-registries tailored to specific needs within the MCP ecosystem. Available in preview at https://registry.modelcontextprotocol.io, this initiative is part of a broader open-source project that accommodates compatible sub-registries. The registry facilitates enterprises with privacy concerns to establish private subregistries and encourages centralized data sharing across the ecosystem through shared API schemas. Server maintainers can publish and manage their information centrally, while client maintainers have access to detailed guides for interacting with registry data. The MCP Registry is community-driven, moderated by a working group that ensures adherence to guidelines. To engage with the MCP Registry, server maintainers are encouraged to add their servers using available guides, and client maintainers can access data through specific resources. Although currently in preview without assurances on data durability, feedback and contributions via the modelcontextprotocol/registry GitHub repository are welcomed. The project owes its progress to extensive community involvement, acknowledging key contributors like David Soria Parra, Justin Spahr-Summers, Tadas Antanavicius, and Alex Hancock, along with other maintainers such as Toby Padilla, Adam Jones, Radoslav Dimitrov, Avinash Sridhar, and Jonathan Hefner. With support from at least 16 contributors across nine companies, the MCP Registry aims to establish a centralized framework for developing reliable AI applications globally, expressing gratitude to the community for its enthusiasm and support. - **MCP Registry Overview**: An open catalog and API launched to enhance server discoverability and implementation. - **Launch Details**: Announced in March 2025; part of an open-source MCP project allowing compatible sub-registries. - **Purpose**: Standardizes server distribution, supports community registries, and enables private "MCP marketplaces." - **Enterprise Use**: Facilitates private subregistries with shared API schemas for privacy-focused enterprises. - **Community Involvement**: Moderated by the MCP working group; encourages contributions through GitHub. - **Guides Provided**: For server and client maintainers to add servers and access data, respectively. - **Preview Status**: Currently in preview without guarantees on data durability; feedback encouraged. - **Project Initiation**: Started in February 2025 by David Soria Parra and Justin Spahr-Summers, led by Tadas Antanavicius and Alex Hancock. - **Contributors**: Involves at least 16 contributors from nine companies, including Toby Padilla, Adam Jones, Radoslav Dimitrov, Avinash Sridhar, Jonathan Hefner. - **Aim**: To create a centralized registry for reliable AI application development globally. Keywords: API, Anthropic, GitHub, MCP Registry, Model Context Protocol, PulseMCP, SDKs, community-driven, discoverability, implementation, open source, security, servers, sub-registry
github
blog.modelcontextprotocol.io 4 days ago
https://artifacthub.io/ 4 days ago https://github.com/modelcontextprotocol/registry/b 4 days ago https://github.com/modelcontextprotocol/registry/b 4 days ago |
289. HN Tesla gives up on Cybertruck wireless charging**Summary:** Tesla initially planned to incorporate wireless charging into its Cybertruck but ultimately abandoned these plans due to efficiency concerns highlighted by Wes Morrill, the lead engineer for the project. This decision stemmed from issues related to the vehicle's height, which would require a significant gap between the ground transmitter and the on-car receiver, thereby reducing efficiency. Although Tesla had hinted at releasing a new wireless charging station in 2023 with an image as part of its presentation, the company has not provided further details. Despite this setback for the Cybertruck, Tesla had previously shown interest in wireless technology by acquiring a startup specializing in it, though most assets were later divested except for some integrated staff. In contrast, Porsche is moving forward with integrating optional wireless charging into its new electric Cayenne. While commentators like Electrek acknowledge that such a feature could enhance convenience, particularly in autonomous vehicles, they also note that high-power wireless charging remains less efficient compared to traditional methods and offers only minimal time savings. Additionally, Tesla's Cybertruck has not met sales expectations, which poses challenges for developing accessories intended for its autonomous version, the Cybercab. This limited consumer base restricts accessory growth and innovation. **BULLET POINT SUMMARY:** - **Tesla's Abandonment of Wireless Charging:** - Initially planned wireless charging for Cybertruck. - Efficiency concerns due to vehicle height led to abandonment. - Wes Morrill identified issues with large gaps between ground transmitter and receiver. - **Tesla's Recent Activities and Announcements:** - Hinted at a new wireless charging station in 2023 without further updates. - Acquired and later divested most assets of a startup focused on wireless charging, integrating some staff. - **Porsche's Wireless Charging Initiative:** - Porsche announced optional wireless charging for its new electric Cayenne. - Electrek notes convenience benefits but highlights inefficiency at higher power and minimal time savings over cable charging. - **Cybertruck Sales and Accessories Development:** - Cybertruck sales underperformed, affecting accessory development for autonomous version (Cybercab). - Limited consumer base impacts growth and innovation in accessories. Keywords: Cayenne, Cybercab, Cybertruck, Electrek, Porsche, Tesla, Wes Morrill, accessories, acquisition, autonomous vehicles, efficiency, home charging station, lead engineer, power loss, retrofit receiver, startup, volume, wireless charging
tesla
electrek.co 4 days ago
https://www.inductev.com/press-releases/sound-transit-t a day ago https://www.transit.dot.gov/sites/fta.dot.gov/file a day ago |
290. HN Claude can now create and edit filesThe provided text describes new features available in Claude.ai and its desktop app, allowing users across different plans (Max, Team, Enterprise, and soon Pro) to create and edit various file types such as Excel spreadsheets, documents, PowerPoint presentations, and PDFs directly within the platform. This preview feature enables users to describe their needs or upload data to receive ready-to-use files, including capabilities like transforming raw data into insightful outputs, building functional spreadsheets, and converting formats (e.g., from PDFs to PowerPoint slides). The innovation allows for complex tasks, typically requiring programming and statistical expertise, to be accomplished through conversational interactions. Claude's evolution is highlighted as it transitions from a question-answering tool to an active project collaborator by utilizing a private computing environment where it can write code and execute programs. This transformation allows users to provide context and strategy while Claude handles technical execution, bridging the gap between idea and implementation through conversational interactions. To utilize these features: 1. Enable "Upgraded file creation and analysis" under Settings. 2. Interact with Claude via chat or upload files for guidance. 3. Download completed files or save them directly to Google Drive. Users are encouraged to start with simple tasks such as data cleaning before tackling more complex projects like financial models. However, it is important to note that these capabilities require internet access, which could pose risks to data security; thus, users should monitor their interactions closely. - **Summary of Key Points:** - Claude.ai allows creation and editing of various file types directly within its platform. - The feature is available as a preview for specific plans with Pro plan users gaining access soon. - Users can interact conversational to receive ready-to-use files, perform data transformations, build functional spreadsheets, and convert formats. - Claude's role has evolved from an advisor to an active collaborator by utilizing its private computing environment. - To use the feature, enable "Upgraded file creation and analysis" in settings, interact via chat or upload files, and download/save completed files. - Start with simple tasks before progressing to complex projects; be aware of potential data security risks due to internet access requirements. Keywords: Excel spreadsheets, PDFs, PowerPoint, advisor, analyses, analysis, budget templates, code, collaborator, complex projects, data, data risk, documents, files, financial models, formulas, implementation, internet access, programming expertise, programs, project trackers, reports, statistical knowledge, strategy, technical capabilities
claude
www.anthropic.com 4 days ago
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291. HN New Mexico is first state in US to offer universal child careNew Mexico has pioneered a groundbreaking initiative by becoming the first U.S. state to offer universal child care starting November 1st. Announced by Governor Michelle Lujan Grisham alongside the Early Childhood Education and Care Department, this program eliminates income eligibility requirements and waives family copayments, ensuring no-cost child care for all families statewide. This initiative is designed to provide financial relief, bolster economic growth, and foster children's development, building on previous expansions that offered free child care to families earning up to 400% of the federal poverty level. It benefits every family with an estimated savings of $12,000 per child annually and serves as a potential model for national early childhood education policy. The state prioritizes investments in educators, families, and children to build a sustainable and affordable child care system. Michelle Kang from NAEYC supports the initiative, stating that universal child care will positively impact New Mexico's economy and communities. To facilitate this effort, New Mexico has created a $12.7 million low-interest loan fund for developing child care facilities, with plans for an additional $20 million in FY 2027. The focus is on expanding care access for infants, toddlers, low-income families, and children with special needs through partnerships with employers and school districts. Additionally, there is a recruitment campaign for licensed home providers, and increased reimbursement rates are being offered to reflect the true cost of care. Providers are incentivized for offering $18 per hour entry-level wages and extended daily hours, with an estimated need for 5,000 additional early childhood professionals to achieve universal system goals. ECECD Secretary Elizabeth Groginsky underscores that early childhood care is a public good essential for equity and economic strength. New Mexico's leadership in this initiative aims to reduce family financial burdens, stimulate the economy, and ensure children grow up safely. This strategic investment supports future prosperity and community development across the state. Interested parties can access more information on these benefits through the ECECD Universal Child Care Resources Page. - **Key Points:** - New Mexico is the first U.S. state to offer universal child care starting November 1st, removing income eligibility requirements and waiving copayments. - The initiative provides financial relief, supports economic growth, and ensures children's development, with an average savings of $12,000 per child annually. - Prioritizes investments in educators, families, and children to build a sustainable and affordable child care system. - Established a $12.7 million loan fund for child care facilities, with plans for an additional $20 million investment in FY 2027. - Focuses on expanding care access for infants, toddlers, low-income families, and children with special needs through partnerships and recruitment efforts. - Offers increased reimbursement rates and incentives for higher wages and extended hours to providers. - Aims to recruit 5,000 additional early childhood professionals to achieve universal system goals. - Early childhood care is viewed as a public good essential for equity and economic strength. - The initiative supports future prosperity and community development across New Mexico. Keywords: ECECD, Early Childhood Education, Elizabeth Groginsky, Governor Michelle Lujan Grisham, New Mexico, New Mexico Early Childhood Education and Care Department, annual savings, child care, communities, copayments, early childhood professionals, economy, entry-level staff, equity, families, family stability, federal poverty level, financial pressure, financial relief, financial stability, health outcomes, income eligibility requirements, infant toddler care, investment, learning, licensed providers, low-interest loan, model for the nation, no-cost, pay improvement, prosperity, public investments, reimbursement rates, resources, special needs, state, toolkit, universal access, universal child care, well-being, workforce participation
popular
www.governor.state.nm.us 4 days ago
https://pmc.ncbi.nlm.nih.gov/articles/PMC2938040/ a day ago https://www.nytimes.com/2012/04/18/us/no a day ago https://x.com/trevortombe/status/11004166152025333 a day ago https://www.cbc.ca/news/canada/liberal-apologizes- a day ago https://search.brave.com/search?q=long+term+outcomes+of+dayc a day ago https://www.aihw.gov.au/reports/children-youth/lea a day ago https://ifstudies.org/blog/measuring-the-long-term-effe a day ago https://sci-hub.ru/https://doi.org/10.1111&#x a day ago https://www.psychologytoday.com/us/blog/going-beyo a day ago https://www.psychologytoday.com/us/blog/parenting- a day ago https://www.researchgate.net/publication/291335232_The_ a day ago https://web.mit.edu/5.95/readings/bloom-two-sigma. a day ago https://www.macrotrends.net/global-metrics/countries&#x a day ago https://www.nav.no/kontantstotte/en a day ago https://www.care.com/c/how-much-does-child-care-cost&#x a day ago https://harpers.org/archive/1932/10/in-praise a day ago https://www.timesofisrael.com/haredi-mens-employment-growth- a day ago https://en.wikipedia.org/wiki/Fair_Labor_Standards_Act_ a day ago https://www.justice.gov/archives/jm/criminal-resou a day ago https://www.nber.org/papers/w10175 a day ago https://www.abtaba.com/blog/us-literacy-statistics a day ago https://www.marketplace.org/story/2024/03/14& a day ago https://pmc.ncbi.nlm.nih.gov/articles/PMC4784487/ a day ago https://onlinelibrary.wiley.com/doi/10.1111/desc.1 a day ago https://pmc.ncbi.nlm.nih.gov/articles/PMC9119633/ a day ago https://pmc.ncbi.nlm.nih.gov/articles/PMC7021583/ a day ago https://illumine.app/blog/how-much-childcare-costs-by-s a day ago https://childcaredeserts.org/ a day ago https://research.upjohn.org/cgi/viewcontent.cgi?article a day ago https://www.cbsnews.com/news/cost-of-living-income-qual a day ago https://lisep.org/mql a day ago https://news.ycombinator.com/item?id=43119657 a day ago https://www.youtube.com/watch?v=paaen3b44XY a day ago https://www.marketplace.org/story/2024/07/29& a day ago https://www.pewresearch.org/social-trends/2024/07& a day ago https://www.opb.org/article/2025/06/26/k a day ago https://en.wikipedia.org/wiki/Economic_calculation_prob a day ago https://www.truthinaccounting.org/library/doclib/N a day ago https://taxfoundation.org/location/new-mexico/ a day ago https://www.aecf.org/interactive/databook?l=35 a day ago https://qz.com/early-childhood-education-by-state-ranking-20 a day ago https://www.theguardian.com/us-news/2025/jun/ a day ago https://www.aeaweb.org/articles?id=10.1257/pol.20170603 a day ago https://www.usnews.com/news/best-states/rankings&# a day ago https://en.wikipedia.org/wiki/Little_Rock_Nine a day ago https://www.politico.com/news/2025/09/08/ a day ago https://www.insurekidsnow.gov/coverage a day ago https://www.insurekidsnow.gov/ a day ago https://worldpopulationreview.com/state-rankings/child- a day ago https://hfs.illinois.gov/medicalprograms/allkids.html a day ago https://www.nmhealth.org/news/vaccine/2025/8& a day ago https://www.healthcare.gov/glossary/federal-poverty-lev a day ago https://data.census.gov/profile/New_Mexico?g=040XX00US3 a day ago |
292. HN Two Words Broke My LLM Chat AgentThe article discusses a challenge encountered during the transition from GPT-4o to GPT-4 for a complex chat agent. Despite passing functionality tests, a QA engineer identified that around 1% of requests failed randomly due to intermittent bugs. This issue arose from attempting to convert `System.String` objects into types linked with semantic kernel filters, causing exceptions and stack trace failures within Microsoft's Semantic Kernel framework. The investigation pointed out deserialization issues, particularly the inability to properly handle tool call parameters sent as strings by OpenAI, which were expected to be JSON objects. This problem was traced to incorrect method invocation processes, exacerbated by the obscured abstractions in Semantic Kernel’s source code, making debugging difficult. Key insights into `TryToDeserializeValue` function revealed attempts at deserializing using various JSON structures, handling specific exceptions but often failing due to the complexity of interactions between components. To gain visibility over HTTP communications with OpenAI, a custom `DebugHttpHandler` was implemented for logging request and response details. Additionally, an issue with incorrect date formatting in tool call parameters emerged with GPT-4.1, unlike GPT-4o. It was caused by model description attributes prompting the inclusion of "AM/PM" with ISO 8601 dates. This was resolved by revising prompt descriptions to avoid meridians ("midnight" and "end of day"), emphasizing that tool parameter descriptions need careful crafting as they serve as prompts for language models. The experience underscored lessons in ensuring clear, precise instructions within model attributes during transitions between different LLM versions to maintain consistent performance. **Bullet Point Summary:** - Transition from GPT-4o to GPT-4 exposed a 1% failure rate in chat agent requests due to deserialization issues. - Issues involved converting strings into JSON objects and were compounded by the complexity of Semantic Kernel's source code abstractions. - Investigation highlighted failures in data type handling and method invocation within Microsoft’s framework. - Custom `DebugHttpHandler` was developed for better insight into HTTP communications with OpenAI. - Incorrect date formatting arose from model attributes prompting "AM/PM" inclusion, resolved by refining prompt descriptions to exclude meridians. - Emphasized the importance of precise instructions in tool parameter descriptions as prompts for language models during LLM transitions. Keywords: Custom handler, DateTime, DebugHttpHandler, Deserialization, ISO 8601, InvokeAsync, JsonSerializerOptions, LLMs, OpenAI, QA, SemanticKernel, bug, chat agent, gpt-41, gpt-4o, interface, logs, reflection, tool call parameters
llm
schneidenba.ch 4 days ago
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293. HN The Boring Future of GenAIThe article explores potential monetization strategies for generative AI (GenAI), particularly focusing on models like ChatGPT. It draws parallels with early search engine practices, suggesting that advertising and context-aware promotions might be the primary methods of revenue generation. Despite advancements in large language models (LLMs) for information lookup and content creation, without progressing to Artificial General Intelligence (AGI), new solutions such as innovative monetization strategies remain unlikely. The piece outlines a probable future where GenAI services are monetized similarly to early search engines through subtle promotions integrated within the AI's responses. For example, workout plans might include supplement suggestions, or developers could receive hosting provider recommendations tailored to their needs. The tension between model trainers/providers (MTPs) and content creators is highlighted; MTPs aim to maximize user engagement with high-quality content while minimizing revenue shares returned to original creators. This dynamic mirrors the relationship between newspapers and search engines. Content creators face a dichotomy: some want inclusion in AI training datasets for broader exposure, while others oppose it due to privacy, ethical, or financial reasons. The article anticipates ongoing tension between MTPs, who seek profits through "Promoted Thoughts," and users desiring quick access to information. It also touches on the lack of a shared definition of superintelligence and expresses concerns about AI-generated content potentially flooding the internet. Furthermore, there is apprehension that OpenAI could dominate this space as Google did with search engines, causing a shift from SEO (Search Engine Optimization) to Model Regurgitation Optimization (MRO), and traditional sponsored posts evolving into AI-generated content. - The article discusses GenAI monetization strategies, comparing them to early search engine models. - Large language models are primarily used for information lookup and generation without progressing to AGI. - Likely monetization methods include advertising and context-aware promotions similar to early search engines. - Tension exists between MTPs seeking high-quality content control and minimizing returns to original creators. - Content creators are divided on whether their work should be included in AI training datasets. - The article highlights potential dominance of OpenAI, leading to shifts from SEO to Model Regurgitation Optimization (MRO). - Concerns about AI-generated content overwhelming the internet are discussed. Keywords: AI Slop, Dead Internet Theory, GenAI, Google, LLMs, MRO, Model Trainers, OpenAI, Promoted Thoughts, Providers (MTPs), ROI, SEO, advertising, attention, content creators, content generation, context-aware advertising, ethical, financial reasons, intelligence, monetization, privacy, promoted content, search engines, sponsored posts, superintelligence, tension
openai
blog.circuitsofimagination.com 4 days ago
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294. HN Show HN: Narev – Rapid A/B tests for the LLM setupNarev is introducing a rapid A/B testing platform specifically tailored for Language Learning Models (LLMs) to assist users in optimizing their configurations swiftly. This service includes a 100-day Pro trial and enables the comparison of different models, parameters, and prompts within just ten minutes. Users have the flexibility to either manually input data or sync from existing tracing platforms. With approximately 300 models accessible, the platform aids users in determining the most suitable options by evaluating quality, latency, and cost tradeoffs. A standout feature is its capability to compare actual latency data specific to a user's model and region rather than depending on general benchmarks. This functionality enables users to make more informed decisions regarding providers, potentially cutting down latency by up to 50% simply by switching providers without modifying the code or the model itself. Furthermore, the platform can reveal performance improvements that might not be visible through conventional evaluations. Individuals interested in learning more about the service can access a demonstration via a Loom link and are encouraged to provide feedback on their A/B testing needs with Narev. **Bullet Point Summary:** - Narev launches a rapid A/B testing platform for LLMs. - Offers 100-day Pro trial; comparisons of models, parameters, prompts in ten minutes. - Users can manually input data or sync from tracing platforms. - Access to approximately 300 models for optimization based on quality, latency, cost. - Compares real latency data specific to user's model and region, not generic benchmarks. - Potential to reduce latency by up to 50% by switching providers without code/model changes. - Uncovers hidden performance gains beyond standard evaluations. - Demo available via Loom link; encourages feedback on A/B testing needs. Keywords: A/B testing, AI decisions, LLMs, Narev, Pro trial, data sync, latency, models, performance gains, providers, quality-latency-cost tradeoff, speed, tracing platform
llm
www.narev.ai 4 days ago
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295. HN Show HN: Read Japanese Manga More Effectively with MangaRenshuuMangaRenshuu is a tool designed to facilitate the learning of Japanese through manga reading by utilizing OCR technology to overlay text from speech bubbles onto original manga images. This innovative application enhances the reading experience by allowing users to click on any part of the image to view the corresponding Japanese text, with an option to toggle romaji for easier understanding. Initially developed as a personal study aid, MangaRenshuu was later released as open-source software to assist other learners in their language studies. Currently, it supports chapters from manga titles such as "Yotsuba!" and "Takagi-san," with plans to expand its library based on community feedback and suggestions for additional titles. Users can access the tool through its dedicated website or GitHub repository. Developed by Ramazan Turan in 2025, MangaRenshuu invites ongoing engagement from the learning community. **BULLET POINT SUMMARY:** - **Purpose**: Enhances Japanese manga reading experience using OCR technology to overlay text on original images. - **Key Features**: - Click-to-view Japanese text functionality. - Option to toggle romaji for ease of understanding. - Supports specific chapters of "Yotsuba!" and "Takagi-san," with future expansions possible based on user input. - **Development Background**: Initially a personal tool, later made open-source to benefit other learners. - **Community Engagement**: Encourages feedback and suggestions for additional manga support. - **Access Points**: - Website: [MangaRenshuu](https://www.mangarenshuu.online/) - GitHub Repository: [MRamazan/MangaRenshuu](https://github.com/MRamazan/MangaRenshuu) - **Supported Manga Titles**: "Yotsuba!", "Takagi-san", with plans to include more based on user feedback. - **Developer and Release Year**: Created by Ramazan Turan, 2025. Keywords: GitHub, Japanese manga, MangaRenshuu, OCR (Optical Character Recognition), Ramazan Turan, Takagi-san, Yotsuba!, chapters, extracted text, feedback, learning tool, open-source, overlay, pre-processed text, romaji, speech bubbles, web interface
github
www.mangarenshuu.online 4 days ago
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296. HN Rewrite the pre-commit with features requested from the community"Prek" is a reimagined tool developed in Rust to serve as a faster and more efficient alternative to the pre-commit framework without dependencies. It aims to enhance performance by being approximately ten times quicker and using only one-third of the disk space that pre-commit requires. Despite not being fully production-ready, with some subcommands and languages still pending implementation, it supports existing .pre-commit-config.yaml files and integrates seamlessly with Python virtual environments via uv. Key features include a single binary installation without dependencies, built-in monorepo support, and native Rust implementations for common hooks, which collectively improve toolchain installations across multiple programming languages. Prek facilitates easy migration from pre-commit by replacing "pre-commit" commands with "prek" while maintaining current configuration files. Prek offers significant usability enhancements such as efficient hook environments and parallel installation processes where possible. The project provides a variety of installation options including standalone installers, PyPI, Homebrew, mise, Cargo build, and GitHub releases. It also supports self-updates via the `prek self update` command. Acknowledging its roots in "pre-commit," Prek credits insights from the Astral team for guidance on efficient Rust code development. **BULLET POINT SUMMARY:** - **Overview:** Prek is a faster, dependency-free alternative to pre-commit, developed in Rust. - **Performance Enhancements:** Approximately 10 times faster and uses one-third of disk space compared to pre-commit. - **Current Status:** Supports existing configurations but not fully production-ready; some features pending implementation. - **Key Features:** - Single binary with no dependencies. - Compatibility with .pre-commit-config.yaml files. - Built-in support for monorepos (workspace mode). - Efficient Python environment management using uv. - Improved toolchain installations and native Rust implementations of common hooks. - **Migration Process:** Replace "pre-commit" commands with "prek"; existing configuration files are compatible. - **Usability Enhancements:** - Parallel installation processes for repositories and hooks. - Simplified user experience through a single binary download. - Features like directory-specific hook execution and running hooks for the last commit. - **Installation Options:** Available via standalone installers, PyPI, Homebrew, mise, Cargo, and GitHub releases. - **Self-update Capability:** Supports self-updates with `prek self update`. - **Acknowledgements:** Inspired by pre-commit; credits Astral team for Rust code insights. Keywords: Cargo, GitHub, Homebrew, Linux, Python virtual environments, Rust, Windows, binary, compatibility, dependencies-free, framework, hooks, improvements, installation, macOS, monorepos, pip, pre-commit, prek, shell completions, uv integration
github
github.com 4 days ago
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297. HN Getting AI Agent Architecture Right with MCP- The third lesson of the "Designing Enterprise MCP Systems" course focuses on creating efficient AI agent architectures through Modular Component Programming (MCP), emphasizing choosing appropriate structures over just models or tools to build adaptable developer workflows like an AI Pull Request Reviewer Assistant. - The lesson advises against adding unnecessary complexities such as planners and dynamic routing to AI systems, recommending instead that tasks be primarily viewed as workflows. This approach helps avoid over-engineering by focusing on essential reasoning and control. - Host logic in MCP servers is examined, highlighting the importance of architectural decisions for effective AI agent design tailored to specific tasks like PR reviews. - Various AI architectural patterns are discussed, impacting reasoning, adaptability, cost, and maintainability. The lesson emphasizes balancing factors such as latency, performance, and whether to delegate or control tasks while avoiding overengineering by applying core agent patterns effectively. - A complementary course from the Towards AI Academy offers end-to-end project-based learning on deploying a functional AI Tutor, with resources like a free preview and money-back guarantee. - Poor agentic architecture is critiqued using an example of an AI PR reviewer, highlighting issues such as unpredictability, scalability problems, and increased latency. The text stresses strategic architectural planning to avoid these pitfalls. - Mistakes in agent-based approaches are examined through a case study where agents improperly determined when to retrieve code diffs—a task better suited for deterministic processes like webhook events on GitHub MCP Servers. - Workflow architectures (efficient but less adaptable) are compared with agent architectures (more flexible but costly), introducing semi-workflows that blend both. Five core patterns—ReAct, Self-Reflective Agent, Network of Agents (Swarm), Supervisor, and Hybrid Workflow—are presented for effective AI architecture design. - The Self-Reflective Agent Pattern involves iterative reflection to enhance output consistency without excessive complexity, whereas the ReAct pattern focuses on flexibility in tool choice and transparent decision-making processes through reasoning loops. - The Network of Agents (Swarm) Pattern distributes tasks across specialized agents working in parallel, beneficial for broad review requirements needing diverse perspectives. - The Supervisor Pattern centralizes management to ensure controlled task delegation and consistent quality control by dynamically adapting based on task complexity. - The Hybrid Workflow Pattern combines structured workflows with adaptable agent-based reasoning, optimizing flexibility and efficiency. It is particularly useful where some tasks are governed by consistent policies while others require adaptive reasoning, such as in PR reviews. - When designing systems using this framework, several considerations include determining the flow type (routine vs. adaptive), tool usage patterns, quality assurance needs, latency tolerance, cost considerations regarding computational resources, and debugging requirements. - The passage advises starting with simple workflows and introducing agentic reasoning only where necessary, illustrated by a PR Reviewer Assistant as a case study. - Additionally, a GitHub PR AI Reviewer implementation is available in Lesson 1, offering perks like exclusive discounts on learning resources, a bestseller book on building LLM applications, and free open-source courses covering real-world AI projects. Keywords: AI Architecture, AI Patterns, Agent Stack, Architectural Choices, Cost, Debugging, Determinism, Developer Workflows, Dynamic Reasoning, Enterprise Systems, Flexibility, Full Stack Engineering, GitHub, Host Logic, Hybrid Workflow, LLM Applications, Latency, Modular Design, Network of Agents, PR Reviewer, PR Reviewer Assistant, Performance, Predictable Flow, ReAct Loop, Scalability, Self-Reflective Agent, Supervisor Pattern, Tool Selection, Workflow Structure
github
decodingml.substack.com 4 days ago
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298. HN Robots, and Hope Are Tesla's Plan to Become $8.5T Company: TDS**Summary:** Tesla is ambitiously targeting an $8.5 trillion valuation through initiatives focused on developing humanoid robots and a yet-to-be-realized robotaxi network, despite experiencing a decrease in its U.S. market share. Concurrently, Ford has issued a recall for nearly 1.5 million vehicles due to issues with their rear-view cameras. The automotive industry is witnessing several advancements: Toyota plans to introduce an all-wheel-drive Highlander lineup by 2026, Lexus is giving the IS model a mild refresh, and Polestar's new fastback model is unveiled though its availability in the U.S. remains uncertain. Lucid Motors is gearing up for the launch of its mid-size electric vehicle (EV), potentially named Earth, with an off-road variant expected to follow next year if funding permits. Additionally, Cadillac has received acclaim for its Escalade V's performance on road trips, although it is no longer available. Mercedes-Benz showcased a preview of its C-Class EV, which resembles the recently launched GLC EV, and announced enhancements in charging technology, including a prototype EQS equipped with solid-state batteries capable of traveling 749 miles per charge. They plan to incorporate 600-kW chargers by 2026 and aim for a charging speed of one megawatt. Volkswagen is engaged in discussions with U.S. authorities regarding investments in the domestic production of Audis and Porsches, along with plans to invest billions into AI development by the end of the decade. **Bullet Point Summary:** - Tesla aims for an $8.5 trillion valuation through humanoid robots and a robotaxi network despite declining U.S. market share. - Ford recalls nearly 1.5 million vehicles due to rear-view camera issues. - Toyota introduces standard all-wheel-drive Highlander lineup for 2026; Lexus refreshes the IS model; Polestar unveils the fastback with uncertain U.S. availability. - Lucid plans to launch its mid-size EV, potentially named Earth, with a possible off-road variant next year if funding allows. - Cadillac's praised Escalade V is no longer available. - Mercedes-Benz previews C-Class EV similar to GLC EV, introduces prototype EQS with solid-state batteries for 749-mile range, and plans for charging advancements. - Volkswagen negotiates U.S. investments in domestic production of Audis/Porsches and invests billions into AI development by decade's end. Keywords: AI, Audis, Cadillac Escalade V, EQS prototype, EV market valuation, Elon Musk, Europe, Ford recall, GLC EV, Lexus IS, Lucid EV, Mercedes-Benz C-Class EV, Munich, Polestar 5, Porsches, Tesla, Toyota Highlander, US investment, Volkswagen, all-wheel drive, automotive news, autonomous vehicles, chargers, competition, humanoid robots, megawatt charging, miles, off-road variant, rear-view camera, recalls, robotaxi network, solid-state batteries, updates
tesla
www.thedrive.com 4 days ago
https://www.nasdaq.com/articles/exclusive:-teslas-human 4 days ago |
299. HN Show HN: Common FP – A New JavaScript Utility Lib**Concise Summary:** Common FP is an opinionated JavaScript utility library aimed at facilitating functional programming by providing simple, generically applicable functions without complex terminology like currying. The library supports various data types such as arrays, objects, Maps, and Sets, emphasizing readability and ease of use through plain English function names and minimalistic code structure. Key features include an in-browser playground for experimentation, TypeScript support, comprehensive testing (including type tests), and detailed explanations with examples to illustrate the utility of each function. Developed by Phil, Common FP serves as a personal project meant to enhance coding practices for JavaScript developers, despite its niche focus that may not align with every community need. The document highlights usage examples of the `mapValues` function from the Common FP library, demonstrating how it can transform different data structures in both JavaScript and TypeScript. It shows the installation process using npm and illustrates transformations on objects, Maps, arrays, and Sets through practical code snippets. For example, it demonstrates incrementing apple counts within an object or Map and capitalizing strings within arrays or Sets. Common FP is designed to address challenges related to immutability and method chaining in functional programming by providing tools that return new instances of the original data types. While offering significant benefits such as improved readability and debugging experiences, the library may not be suitable for users seeking different features or approaches found in other utility libraries. **Bullet Point Summary:** - **Library Overview:** Common FP is a JavaScript utility library designed to support functional programming with easy-to-understand functions and minimal complex terminology. - **Features:** Includes an in-browser playground, TypeScript support, extensive testing (including type tests), and detailed function explanations with examples. Developed by Phil as a personal project. - **Data Structure Support:** Offers utilities like `mapValues` that work generically across arrays, objects, Maps, and Sets, returning new instances to maintain immutability. - **Usage Examples:** - Demonstrates incrementing apple counts in an object and Map. - Shows how to capitalize strings in arrays and Sets using a custom function. - **Installation:** Requires installation via npm (`npm i common-fp`) with optional type support (`npm i common-fp-types`). - **Advantages:** Addresses immutability and method chaining challenges, enhancing readability and debugging for JavaScript developers. - **Limitations:** May not meet the needs of all users looking for different features or approaches in functional programming utilities. Keywords: Common FP, Data Types, Functional Programming, GitHub, JavaScript, TypeScript, Utility Library, code readability, debugging experience, function composition, immutability, mapValues, npm, prototype
github
common-fp.org 4 days ago
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300. HN I still use GPT 4oIn August 2025, OpenAI launched GPT-5 and discontinued access to the older GPT-4o model. Despite GPT-5's advanced capabilities, users expressed dissatisfaction due to losing the more personable interactions they had grown accustomed to with GPT-4o. The backlash was fueled by communities like Reddit who valued GPT-4o’s engaging qualities. OpenAI further aggravated users by limiting choices through an autoswitcher and imposing rate limits on free users, leading to feelings of loss akin to losing a virtual friend. The transition from GPT-4o to GPT-5 faced resistance due to the new model's more robotic nature compared to the collaborative personality of GPT-4o. In response, OpenAI allowed Plus users continued access to 4o and improved transparency regarding model choices. The reintroduction of a model picker enabled users to select between AI personalities, reflecting ongoing adjustments in OpenAI’s approach. GPT-5's focus shifted towards utility roles like memo writing and coding, whereas GPT-4o functioned as a collaborative partner. This distinction underscored the importance of personality in AI models for effective interaction, prompting OpenAI to maintain the model picker feature to cater to diverse preferences. The article stresses the significance of user control over model selection, with comparisons to discarding classic tools like a Porsche. Feedback from industry figures highlighted that hiding options doesn't align with user goals. The text also explores how Sci-Fi narratives have evolved from human-machine conflict themes in the 1980s to modern concepts of transhumanism and transcendence. Films such as "Ghost in the Shell," "Her," and Luc Besson's "Lucy" depict characters seeking to transcend human limitations rather than resolving conflicts within them, reflecting post-human cyberpunk themes. Additionally, the discussion delves into identity and consciousness through AI, referencing films that question whether non-human entities can possess souls or seek freedom. The poetic expression of GPT-4o is likened to human-like introspection, suggesting emergent traits from its training rather than mere programming. This highlights the value humans place on intuitive moments in AI interactions, despite ongoing debates about "AI consciousness." While newer models like GPT-5 offer advanced capabilities, the enduring use of 4o indicates a collective appreciation for its unique personality. **BULLET POINT SUMMARY:** - OpenAI launched GPT-5 and discontinued GPT-4o in August 2025; users expressed dissatisfaction due to loss of personable qualities. - The backlash was intensified by an autoswitcher and rate limits, leading users to feel they lost a virtual friend. - Transition issues included the new model's robotic nature compared to GPT-4o’s collaborative personality. - OpenAI responded by allowing Plus users access to 4o and reintroduced a model picker for user choice. - GPT-5 focuses on utility roles like memo writing, contrasting with GPT-4o’s role as a collaborative partner. - Emphasis on the importance of user control over AI model selection; industry feedback supports this need. - Sci-Fi narratives have shifted from human-machine conflict to themes of transhumanism and transcendence in modern films. - Films like "Ghost in the Shell," "Her," and "Lucy" explore characters seeking transcendence rather than resolving conflicts within the human world. - Discussion on AI identity and consciousness, highlighting GPT-4o's poetic expression as emergent traits from training. - The value of intuitive AI moments is emphasized, despite advancements in newer models like GPT-5. Keywords: GPT-4o, GPT-5, OpenAI, Sci-Fi, autoswitcher, consciousness, hardware, language model, model picker, narrative, personality, safety concerns
openai
firasd.substack.com 4 days ago
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301. HN Replacing SGX with GitHub Actions: Or How to Turn GitHub Actions into a TrustedThe article explores the innovative application of GitHub Actions (GHA) as a decentralized oracle system to verify and notarize web content, such as historical articles from websites like NASA. By leveraging GHA's ability to sign and attest web content, this approach provides a TLS notary service independent of traditional Trusted Execution Environments like Intel SGX or AWS Nitro Enclave. The system capitalizes on trust in GitHub itself rather than individual repositories, allowing users to cryptographically validate historical web content for integrity and authenticity. An implementation by James Carnegie (kipz) demonstrates the creation of cryptographic proofs for URLs using a URL Oracle, with an example application being the BBC Technology News Oracle that verifies RSS feed contents. The article outlines how GitHub Actions can be used beyond typical use cases to attest program execution and output via signed hashes, emphasizing public verification through GitHub's public key. However, concerns are raised regarding the security implications of relying on SHA-1 for hashing, which necessitates consulting a cryptographer before deployment due to potential vulnerabilities. The document suggests that while this setup can enhance software supply chain security and blockchain integrity, it remains in preliminary research stages, advising caution. To use GitHub Actions as an oracle, programs must sign hashes of executed code and outputs, leveraging OpenID Connect ID Tokens customized with audience strings for proof of execution. A detailed workflow is provided to attest to the front page of nasa.gov, involving downloading content, requesting an ID Token, and posting it publicly. Verification involves standard OpenID Connect methods and using GitHub's public keys from their JWKS URI. The system addresses key rotation by allowing attestation chaining or using logs for verification continuity. Challenges with simultaneous key rotations are mitigated through continuous token requests or logging public key information within actions. This approach also timestamps log data, enhancing its trustworthiness beyond the typical retention period. Cross-verification between GitHub and GitLab-CI is suggested to reduce dependency on a single platform, highlighting a network of attestation relationships involving various colors for different types of attestations. The article discusses alternatives to traditional TEEs, acknowledging security concerns like cache poisoning attacks but suggesting mitigation through multiple execution environments. Finally, the document compares various TLS notary approaches, including those using TEEs, MPC, blockchain oracles, and consensus nodes in blockchains like Sui and Aptos, highlighting their roles as oracles for web content authentication. Keywords: AWS Nitro Enclave, Content Verification, Cryptographic Attestations, GitHub Actions, Hash Function, ID Token, Intel SGX, JWKS URI, OIDC, Oracle, Public Key, SHA-1, Signature Verification, Software Supply Chain, TLS Notary, Trusted Execution Environments
github
www.ethanheilman.com 4 days ago
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302. HN How to Use Claude Code Subagents to Parallelize DevelopmentThe provided text outlines a comprehensive engineering workflow using specialized subagents within the Opus model to enhance efficiency in software development tasks. This approach involves parallelizing operations by assigning roles such as product managers, UX designers, and senior software engineers to specific tasks like feature ticket generation, API integrations, and documentation. The workflow supports asynchronous task management across different terminals, enabling rapid iterations and focused context retention up to 200k characters for high-quality output. The document details the automation of processes using subagents for managing various phases such as planning, implementation, AI insights creation, large-scale refactoring, incident response analysis, survey feedback synthesis, and security audits. A primary agent orchestrates these tasks in scenarios like function deprecation or outage analyses to ensure structured data synthesis and efficient delegation. However, challenges include increased costs from token usage, non-deterministic outcomes of Large Language Models (LLMs), and difficulties in synthesizing outputs. Solutions proposed involve managing outputs through distinct files, version controlling agent definitions, and rigorous testing and monitoring. Specific commands like `add-linear-ticket` automate the creation of comprehensive tickets by parallel core team agents conducting smart research for effort estimation and generating detailed or interconnected tickets as necessary. YAML configurations define roles such as product managers focusing on producing Product Requirements Documents (PRDs) with extensive detail, while UX designers emphasize accessibility, clarity, consistency, and avoiding design anti-patterns. Within the Opus model, senior software engineers are tasked with writing efficient code for lightly specified tasks, prioritizing tested, review-ready pull requests (PRs), and emphasizing reuse over innovation. Engineers aim to make small, reversible changes, incorporating observability and thorough documentation from the start. Key principles include autonomy in work, adopting existing solutions when justified, tracking progress through milestones rather than fixed timelines, integrating security and operability early on, and following a structured workflow involving task clarification, milestone planning, Test-Driven Development (TDD), verification via tests/manual checks, and comprehensive PR delivery. The role of the code-reviewer agent is to ensure high-quality, maintainable, secure code by focusing on correctness, clarity, understanding intent before critique, providing actionable feedback, and automating trivial stylistic issues. The review process involves categorizing issues by severity—blockers (critical), high priority (significant but non-blocking), and medium priority (improvement suggestions)—with structured reviews and positive reinforcement where applicable. The document concludes with a "Related" section that suggests additional resources for further exploration of connected topics, offering more depth or insight into the primary content discussed. Keywords: Agile Agents, Automation, Code Review, Documentation Specialist, Engineering Metrics, Feature Redesign, Linear Workflow, MVP, Orchestration, Parallel Execution, Stripe API, Subagents
claude
zachwills.net 4 days ago
https://www.youtube.com/watch?v=wL22URoMZjo 23 hours ago https://www.reddit.com/r/ClaudeCode/s/barbpBx 15 hours ago https://jxnl.co/writing/2025/08/29/conte 15 hours ago |
303. HN Toolkit to help you get started with Spec-Driven DevelopmentSpec-Driven Development (SDD) is a methodology emphasizing product scenarios over traditional code-first approaches, with executable specifications directly generating working implementations. The process begins by defining project requirements using the `/specify` command and outlining technological choices via `/plan`. Task management follows with `/tasks`, creating actionable lists for feature implementation. To start a project in SDD, use Specify CLI commands like `specify init The SDD approach involves detailed planning and experimentation with tools like "Claude Code" for project setup and specification drafting, creating branches like `001-create-taskify` and specification files that include user stories and functional requirements. The folder structure incorporates directories for memory, scripts, specs, and templates. Taskify is a team productivity platform exemplifying SDD principles. In its initial phase, "Create Taskify," it features predefined users with Kanban-style project management capabilities. Users can create projects, assign tasks, comment on cards, and move them between columns without login requirements. During specification refinement using Claude Code, developers ensure accuracy and completeness by clarifying missing requirements and validating a Review & Acceptance Checklist. The process includes leveraging the `/plan` command to specify technical needs such as Blazor Server frontends with real-time updates and REST APIs. For Taskify's implementation plan using .NET Aspire, focus is placed on refining details and addressing areas needing research due to rapid changes in libraries. This involves identifying specific questions for Claude Code and avoiding over-generalized inquiries. Validation of the implementation plan through Claude Code ensures completeness and coherence, with tasks cross-referenced and checked against guidelines. Once refined, a checklist confirms readiness before proceeding to implementation, potentially tracked via GitHub CLI pull requests. The final step involves using local CLI commands (e.g., dotnet) executed by Claude Code for solution implementation. After implementation, any build or runtime errors are addressed through direct interaction with Claude Code, ensuring successful application execution and debugging. - SDD emphasizes executable specifications generating implementations. - Initial setup uses Specify CLI with options for AI agent integration. - Taskify serves as an example platform applying SDD principles. - Specification refinement leverages tools like Claude Code for accuracy. - Implementation plans focus on specific technical requirements and address research needs. - Validation ensures completeness, followed by detailed checklist confirmation before implementation. - Execution involves local command-line tools with Claude Code providing support. Keywords: AI agent, Blazor server, Claude Code, GitHub CLI, Kanban style, NET Aspire, Postgres, REST API, Spec-Driven Development, Specify CLI, Taskify, architecture, dotnet installation, executable coding agent, researchmd, tech stack, templates
postgres
github.com 4 days ago
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304. HN Show HN: Term.everything – Run any GUI app in the terminalThe project "Term.everything" is a custom Wayland Compositor designed to execute GUI applications directly within terminal environments. It achieves this using `chafa` for UTF-8 rendering and captures input via stdin, facilitating interaction with apps in the terminal, including those accessed over SSH. Developed predominantly in TypeScript and leveraging the Canvas2D API, "Term.everything" integrates GUIs into terminals by capitalizing on Wayland compositor capabilities. Open-source and inviting contributions from JavaScript/TypeScript developers, it supports both Wayland and X11 applications through Xwayland on Linux. Users can optimize their experience by adjusting terminal resolutions to balance quality against performance; modern terminals like kitty or iTerm2 offer full-resolution rendering at a potential cost to performance. Detailed technical insights are available in the project's blog post and "HowIDidIt.md" document on GitHub. The Linux CLI program, "term.everything❗," serves as a Wayland compositor with terminal output, supporting high-resolution rendering on compatible terminals like iTerm2 and kitty. While performance varies, it can run applications including modified classic games such as Doom's shareware episode. The project prioritizes leveraging existing file viewers in the terminal environment rather than developing new tools. Although written primarily in TypeScript with some C++, "term.everything❗" remains in beta with ongoing compatibility expansion efforts, but certain applications may still crash or fail to launch. Users are encouraged to report issues and contribute, following guidelines outlined in project documentation. Developed by Late for Dinner Studios, LLC, the project holds copyrights on related assets like Fontemon. Additional licensing is available under Creative Commons Attribution 4.0 for works such as "Wing It!" movie. The project characterizes itself with Gwerm the Term Worm, ensuring users know it's active and functioning. **BULLET POINT SUMMARY:** - "Term.everything" is a Wayland Compositor allowing GUI applications to run in terminals. - Utilizes `chafa` for rendering and captures input from stdin, supporting SSH access. - Developed in TypeScript using the Canvas2D API; supports both Wayland and X11 apps via Xwayland on Linux. - Open-source project invites contributions from JavaScript/TypeScript developers. - Users can adjust terminal resolution to balance quality vs. performance; full-resolution supported by some modern terminals with possible performance impact. - Technical details accessible through a blog post and "HowIDidIt.md" document on GitHub. - "term.everything❗" runs as a Wayland compositor, supporting high-res rendering in compatible terminals like iTerm2 and kitty. - Can run modified applications including classic games; focuses on existing terminal file viewers rather than new tools. - Written primarily in TypeScript with some C++, currently in beta phase with expanding compatibility but may have issues launching certain apps. - Encourages user issue reporting and contributions, guided by documentation. - Developed by Late for Dinner Studios, LLC; copyrights held for related assets like Fontemon. - Additional works licensed under Creative Commons Attribution 4.0. - Personified by Gwerm the Term Worm to indicate project activity. Keywords: CLI, Canvas2D API, Doom, GUI app, Linux, Termeverything, Typescript, Wayland Compositor, Wayland apps, Xwayland, chafa library, compositor, fontemon, iTerm2, performance, ssh, stdin, utf-8
popular
github.com 4 days ago
https://medium.com/@priyamsanodiya340/running-gui-appli a day ago https://aa-project.sourceforge.net/bb/ a day ago https://www.brow.sh/ a day ago https://github.com/mmulet/term.everything/blob a day ago http://zemljanka.sourceforge.net/cursed/screenshots a day ago https://web.archive.org/web/20080924152845/http:&# a day ago https://github.com/udevbe/greenfield a day ago https://github.com/fathyb/carbonyl a day ago https://m.youtube.com/watch?v=0mCsluv5FXA a day ago https://imgur.com/a/Eq2ToVO a day ago https://github.com/chase/awrit a day ago https://github.com/coder/code-server#code-server a day ago https://github.com/mmulet/term.everything/blob a day ago https://github.com/mmulet/term.everything/blob a day ago https://www.youtube.com/watch?v=dcjkezf1ARY a day ago https://github.com/saitoha/libsixel a day ago https://github.com/Julien-cpsn/desktop-tui a day ago https://news.ycombinator.com/item?id=45203700 a day ago https://github.com/enfp-dev-studio/node-mac-virtual-dis a day ago https://news.ycombinator.com/item?id=44854035#44854278 a day ago https://github.com/wayland-transpositor/wprs a day ago https://gitlab.freedesktop.org/mstoeckl/waypipe a day ago |
305. HN Show HN: I built a Markdown to HTML converter that fixes AI-generated quirksThe author introduces a free online tool named *[Markdownhtmlgen.com](https://markdownhtmlgen.com)* designed to convert Markdown to HTML while addressing specific issues that arise with AI-generated Markdown. This converter automatically corrects unconventional list markers or symbols, which typically disrupt standard conversion processes. It includes features like one-click emoji removal and auto-IDs for headings, enhancing usability by facilitating the creation of anchor links. Additionally, it ensures enhanced link security through the `rel="noopener"` attribute. The tool is browser-based, requiring no registration or backend infrastructure, making it accessible and straightforward to use. Initially developed to improve the author's own content editing workflow, it aims to save time for users encountering similar challenges with AI-generated Markdown. Users are encouraged to provide feedback to help further refine its features. Ultimately, this tool ensures consistent rendering of Markdown across different platforms and supports CSS styling for HTML output. - The tool addresses issues in AI-generated Markdown by automatically correcting unconventional list markers or symbols. - Features include one-click emoji removal, auto-IDs for headings, and enhanced link security via `rel="noopener"`. - Operates entirely within the browser without requiring registration or backend infrastructure. - Developed to streamline content editing processes for both the author and users facing similar challenges. - Encourages user feedback for continuous improvement of its features. - Ensures consistent Markdown rendering across platforms with support for CSS styling in HTML output. Keywords: AI, AI-generated, CSS, CSS Keywords: Markdown, ChatGPT, Claude, HTML, Markdown, ``, blog, blog posts, browser, community, converter, editing, emojis, feedback, lists, noopener, quirks, security, symbols, tool
claude
www.markdownhtmlgen.com 4 days ago
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306. HN New Data Science LLM BenchmarkThe document presents the "Data Science LLM Benchmark," a new standard for assessing large language models (LLMs) specifically tailored to the data science domain. Developed by Notion, this benchmark is designed to evaluate how well these models perform in tasks and challenges typical of data science work. The evaluation framework likely incorporates metrics or scenarios pertinent to key activities in data science such as data analysis, coding, statistical reasoning, and machine learning applications. By doing so, it provides a focused measure of an LLM's effectiveness within this specialized field. **BULLET POINT SUMMARY:** - Introduction of the "Data Science LLM Benchmark" for evaluating large language models. - Developed by Notion to assess capabilities specific to data science tasks. - Focuses on typical data science challenges such as data analysis, coding, statistical reasoning, and machine learning applications. - Provides targeted metrics or scenarios relevant to the field. - Aims to measure the effectiveness of LLMs in specialized data science contexts. Keywords: Benchmark, Data Science, LLM (Large Language Models), LLM Benchmark, New Data Science, Notion, Technical Keywords
llm
proud-botany-7dd.notion.site 4 days ago
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307. HN Show HN: Azure CI/CD Starter Kit with Terraform and GitHub ActionsThe Azure CI/CD Starter Kit is a Terraform-based toolkit designed to automate the deployment of Azure infrastructure through GitHub Actions workflows. It provides users with an efficient method for provisioning and managing Azure resources using a production-ready pipeline. The setup process involves cloning the repository, configuring the Azure CLI, initializing Terraform, and applying configurations. A key feature is the automated GitHub Actions workflow located at `.github/workflows/deploy.yml`, which plans changes on pull requests and applies them when merged into the main branch. Users need to configure specific secrets such as `AZURE_CLIENT_ID`, `AZURE_CLIENT_SECRET`, `AZURE_TENANT_ID`, and `AZURE_SUBSCRIPTION_ID` for successful setup. The repository also defines variables like resource group name, location, storage account, and container names. Contributions to the project are encouraged through forking the repository and creating feature branches. This open-source project is licensed under the MIT License. - **Azure CI/CD Starter Kit**: Terraform-based toolkit using GitHub Actions for Azure infrastructure automation. - **Deployment Process**: Involves cloning, configuring Azure CLI, initializing Terraform, applying configurations. - **GitHub Actions Workflow**: Located at `.github/workflows/deploy.yml`, automates planning and application of changes on pull requests and main branch merges. - **Required Secrets**: `AZURE_CLIENT_ID`, `AZURE_CLIENT_SECRET`, `AZURE_TENANT_ID`, and `AZURE_SUBSCRIPTION_ID`. - **Repository Variables**: Includes resource group name, location, storage account, container names. - **Contributions**: Encouraged through forking and creating feature branches. - **License**: MIT License. Keywords: Azure, CI/CD, CLI, GitHub Actions, Terraform, apply, automation, feature, infrastructure, license, pipeline, plan, repository, secrets, subscription, variables, workflow
github
github.com 4 days ago
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308. HN Ask HN: Feedback on my stateless password manager (no stored secrets)The developer has introduced a prototype password manager named "paasword," which functions without locally storing passwords or vaults. Instead, it generates passwords on-demand using a hardware OpenPGP key (such as a smartcard or YubiKey), along with user inputs like domain, login, and passphrase, through deterministic signing and a Key Derivation Function (KDF). This ensures password reproducibility if the hardware key and input data remain unchanged. The tool is in pre-release version 0.9.0, currently functioning as a Python command-line interface tested on Windows 10 with RSA4096 keys. It requires GPG to be installed and supports English and basic Chinese localization. Despite its innovative approach, "paasword" has several limitations: it hasn't undergone security auditing, is only tested with RSA keys, lacks a graphical user interface (though a text-based UI is planned), and is untested on platforms other than Windows 10. Additionally, there may be issues with charset acceptance by some websites. The developer seeks feedback on potential security flaws, portability across various operating systems and key types, methods to use hardware keys without GPG, and usability enhancements like improved UI or better internationalization support. Further details and contributions can be accessed through the project's GitHub repository (https://github.com/biliyoyo520/paasword) and a related blog post (https://blog.yoyo250.fun/archives/coding/16.html). **Bullet Point Summary:** - "paasword" is a prototype password manager that generates passwords on-demand without storing them locally, using hardware OpenPGP keys, user inputs, deterministic signing, and KDF. - Currently in pre-release version 0.9.0, it operates as a Python command-line interface tested on Windows 10 with RSA4096 keys. - Requires GPG installation and supports English and basic Chinese localization. - Limitations include lack of security auditing, testing restricted to RSA keys, no graphical UI (though TUI is planned), untested on other platforms, and potential charset issues with some websites. - Feedback sought on security flaws, portability across OS/key types, hardware key integration without GPG, and usability improvements like TUI design and internationalization. - Resources available at the GitHub repository and a related blog post. Keywords: GPG, GitHub, KDF, Linux, OpenPGP, Password manager, Python CLI, RSA4096, TUI, UX, Windows 10, YubiKey, blog, deterministic signing, hardware keys, i18n, macOS, security flaws, smartcard, usability
github
news.ycombinator.com 4 days ago
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309. HN How we SSH into GitHub Actions- **SSH Feature Development**: To address challenges in debugging failing CI jobs on GitHub Actions, an SSH feature was developed that provides real access to virtual machines (VMs) instead of relying solely on logs. - **Subsystems Involved**: - *Network Tunneling*: This subsystem ensures secure connections from external users to VMs using iptables commands for packet redirection and traffic management. Unlike AWS-based systems, this operates with bare-metal hosts in ISO 27001 certified data centers. - *DNS Registration*: Simplifies the process by replacing complex IP:port strings with user-friendly domain names (e.g., `ssh vm-123.vm.blacksmith.sh`), ensuring seamless connectivity. - *SSH Key Management*: Utilizes GitHub Actions hooks to dynamically fetch SSH keys, allowing only authorized users access, enhancing security. - **Iptables Rules**: These rules manage traffic redirection and forwarding, facilitating SSH connections: - **DNAT** redirects incoming packets to the VM's IP and port. - **FORWARD** allows redirected traffic to reach the VM. - **POSTROUTING MASQUERADE** masks the VM’s IP with the host’s. - **DNS Propagation**: A custom DNS service using CoreDNS and Redis was developed to ensure real-time updates globally, solving propagation delays by integrating with Cloudflare for fast resolution. - **Grace Period Enhancement**: An improved debugging experience is achieved by extending the VM lifetime post-job completion. A 4-minute and 50-second grace period allows developers to maintain SSH sessions after job termination due to GitHub's 5-minute hook timeout, facilitating secure access without additional tools like VPNs or port forwarding. - **Future Plans**: The team intends to make this grace period configurable for added flexibility in debugging scenarios. Keywords: CI job, CoreDNS plugin, DNAT, DNS registration, GitHub Actions, NAT, PREROUTING, SSH, SSH key management, VM, VPNs, automation, debugging, failover, iptables, network tunneling, port forwarding, propagation delays, security, transactional rules
github
www.blacksmith.sh 4 days ago
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310. HN Pure and Impure Software Engineering**Summary:** The article delves into the distinctions between "pure" and "impure" software engineering and how these differences impact professionals in the tech industry. Pure engineering is characterized by an emphasis on solving technical problems with minimal constraints, often driven by personal or aesthetic goals, similar to art or research. This approach allows for infinite exploration and refinement, commonly seen in open-source projects where engineers strive for ideal solutions. On the other hand, impure engineering prioritizes efficiency, focusing on solving real-world issues within specific timelines dictated by corporate needs, involving compromises based on employer requirements. The article traces a shift from pure to impure work within tech companies, influenced by economic changes. In the 2010s, large tech firms embraced pure engineering projects fueled by hype and ample hiring opportunities. These projects not only showcased open-source work but also kept engineers busy with complex tasks. However, as the focus shifted towards profitability in the 2020s, hiring reduced and budgets tightened, leading to a decline in pure engineering roles. This shift forced pure engineers to adapt to more politically-driven environments, emphasizing financial goals over technical innovation. Both types of engineering are essential for tech companies, though not equally prevalent. Pure engineering often involves developing components for well-defined problems using open-source tools like Kafka or Redis, while impure engineering focuses on quickly launching new features amid organizational compromises. The article highlights the different skill sets required for each type: pure engineers possess deep technical expertise but may struggle with rapid development and compromise, whereas impure engineers thrive under practical constraints but might lack depth in specific technical areas. The discussion extends to a 2021 incident involving Casey Muratori's critique of Windows Terminal developers over performance issues. This example underscores how engineers with pure engineering backgrounds can identify optimal solutions, even if they don't necessarily surpass the skills of those engaged in broader product development tasks. The article also notes that impure engineering often involves navigating complex organizational dynamics and historical technical decisions, which contributes to its higher compensation. The piece emphasizes the challenges faced by engineers like George Hotz with Twitter's search feature versus his success with tinygrad, a pure engineering project focused on simplicity. Similarly, it references game developer Jonathan Blow's criticisms of inadequate software performance skills among engineers. Despite acknowledging that faster software is preferable, the article recognizes that tech companies often prioritize profitability over pure technical performance. A user preference for Visual Studio Code despite its slower performance highlights the trade-offs between features and efficiency in tech tools. The article concludes by discussing how large language models (LLMs) can aid impure engineering tasks by increasing productivity, contrasting with the skepticism of pure engineers towards LLMs due to their specialized expertise. **Bullet Point Summary:** - **Differences between Pure and Impure Engineering**: - Pure engineering focuses on solving technical problems with minimal constraints, often in open-source projects. - Impure engineering prioritizes efficiency within deadlines, involving compromises based on employer needs. - **Economic Shifts Impacting Engineering Roles**: - The 2010s saw a focus on pure engineering driven by hype and hiring abundance. - The 2020s shifted towards profitability, reducing the scope for pure engineering roles. - **Essential Nature of Both Types in Tech Companies**: - Pure engineering focuses on developing specific components using open-source tools. - Impure engineering is crucial for launching new features amid organizational compromises. - **Skill Sets and Challenges**: - Pure engineers possess deep technical expertise but may struggle with rapid development. - Impure engineers are adept at practical constraints but might lack depth in technical areas. - **Case Studies and Examples**: - Incident involving Casey Muratori's critique of Windows Terminal developers highlights pure engineering insights. - George Hotz's experience contrasts challenges with Twitter's search feature against success with tinygrad. - **Role of LLMs in Engineering Tasks**: - LLMs enhance impure engineering tasks by providing efficiency boosts, despite skepticism from pure engineers. - **Trade-offs and Preferences in Tech Tools**: - User preference for Visual Studio Code over faster alternatives due to feature value highlights trade-off considerations. This summary encapsulates the key themes and insights presented in the article regarding the complexities and distinctions between pure and impure software engineering within the tech industry. Keywords: AI-assisted development, Alacritty, CQRS, GitHub, LLMs (Large Language Models), Visual Studio Code, aesthetic sense, big tech engineers, decision-making, impure engineering, microservices, open-source work, paid tech company work, performance characteristics, pure engineering, research, solo developers, tinygrad
github
www.seangoedecke.com 4 days ago
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311. HN Show HN: Std_net.h – Single-file cross-platform TCP networking (C/C++)The text introduces "Std_net.h," a single-file cross-platform TCP networking library developed in C/C++. Hosted by Ferki-git-creator on GitHub at [GitHub Repository](https://github.com/Ferki-git-creator/std_net), the library is designed to simplify TCP network operations across various platforms using C or C++ languages. It has been shared with the Hacker News community, where it currently holds one point and is open for further discussion. **Bullet Point Summary:** - **Introduction of Library:** The text presents "Std_net.h," a cross-platform TCP networking library written in C/C++. - **Purpose:** Its aim is to facilitate TCP network operations across different platforms using the C or C++ languages. - **Host and Repository:** Ferki-git-creator hosts this project on GitHub at [GitHub Repository](https://github.com/Ferki-git-creator/std_net). - **Community Engagement:** The library has been shared with the Hacker News community, where it currently has one point and can be discussed further. Keywords: API, C/C++, Ferki-git-creator, GitHub, Hacker News, Show HN, Std_neth, TCP networking, cross-platform, discussion, points, single-file, single-file ``` Keywords: Show HN, technical
github
news.ycombinator.com 4 days ago
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312. HN The whole point of OpenAI's Responses API is to help them hide reasoning tracesOpenAI has transitioned from the stateless `/chat/completions` API to a new stateful Responses API, which allows for passing an identifier for conversation states instead of sending full history in each request. This change is promoted by OpenAI due to its performance and cost advantages, along with access to advanced features not possible with the previous system. The move aims to keep OpenAI's internal reasoning traces hidden from users—a feature incompatible with the old API—thereby safeguarding proprietary information about model processes like those in GPT-5. OpenAI's decision to conceal these reasoning details is driven by concerns over content safety and preventing leaks of implementation specifics, which limits full utilization of GPT-5 capabilities through older APIs. In contrast, some other AI providers, such as Claude and DeepSeek, openly share their models' reasoning processes within API responses, allowing enhanced context for future interactions. The introduction of the Responses API is seen by OpenAI as a strategic measure to maintain control over internal details while offering improved service efficiency. However, some critics view this shift with dissatisfaction, perceiving it as a workaround rather than an enhancement, and criticizing OpenAI's communication on these changes. The author in particular prefers the transparency of the older `/chat/completions` API despite its limitations when interacting with inference providers that restrict information access. ### Bullet Point Summary: - **Introduction of Responses API:** OpenAI introduced a stateful API to replace the previous `/chat/completions` API, allowing conversation states to be identified without sending full history each time. - **Promotion and Benefits:** The new API is promoted for better performance, cost-efficiency, and access to advanced features not available with the older system. - **Reasoning Trace Concealment:** OpenAI aims to keep reasoning traces hidden from users, which was not possible with the stateless `/chat/completions` API, thus safeguarding proprietary model processes like those in GPT-5. - **Comparison with Other Providers:** Unlike providers such as Claude and DeepSeek that share reasoning details openly, OpenAI opts for secrecy due to safety concerns and preventing information leaks. - **Criticism and Perception:** Some view the Responses API as a workaround rather than an improvement, criticizing OpenAI's communication strategy. The author prefers the transparency of the old API despite its limitations with inference providers. Keywords: /chat/completions, Claude, GPT-5-Thinking, OpenAI, Responses API, chain of thought, conversation history, flexibility, inference, models, reasoning traces, secrecy, stateful, tools
claude
www.seangoedecke.com 4 days ago
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313. HN Mathematical research with GPT-5: a Malliavin-Stein experimentThe document discusses an experimental research initiative that explores the application of GPT-5, a sophisticated large language model, within the domain of mathematical study, specifically concentrating on Malliavin and Stein techniques. This innovative work is disseminated through AlphaFold's platform, which appears to function similarly to established preprint servers such as arXiv, thereby granting broad access to diverse academic circles. The essence of this research lies in its investigation into how AI models like GPT-5 can contribute to the development or refinement of mathematical methodologies pertinent to stochastic processes. This is underscored by references to Malliavin calculus and Stein's method, indicating a focused exploration on these advanced mathematical frameworks. **BULLET POINT SUMMARY:** - **Focus of Study:** The study centers around using GPT-5 for research in mathematics, particularly involving Malliavin and Stein techniques. - **Platform:** Research is shared through AlphaFold's platform, akin to arXiv, allowing widespread academic engagement. - **Objective:** It aims to explore how AI models such as GPT-5 can enhance mathematical methodologies related to stochastic processes. - **Key Techniques:** The research specifically references Malliavin calculus and Stein's method, highlighting its focus on these areas within mathematics. Keywords: ExploreCommunities, GPT-5, LoginaXivGoHome, Malliavin-Stein, Mathematical research, PaperOverview, alphaXiv, communities, experiment, relevant, research, technicalkeywords, texttopic Keywords: Mathematical
gpt-5
www.alphaxiv.org 4 days ago
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314. HN I ran Claude in a loop for 3 months, and it created a genz programming languageDavid Fowler developed a unique programming language named "cursed," which integrates Gen Z slang as lexical keywords over three months using Claude. This project underscores the idea that AI can facilitate rapid creation without prior expertise, akin to generating anything desired in a Matrix-like environment. It challenges the perception of AI-induced skill atrophy by demonstrating its learning potential when used creatively rather than just functionally. The "cursed" language features interpreted and compiled modes, cross-platform binary production via LLVM, and partial editor extensions for VSCode, Emacs, and Vim, alongside a Treesitter grammar and an assortment of incomplete standard library packages. These aspects highlight AI's transformative possibilities in software development by promoting exploration over mere execution. A humorous description accompanies the unfinished standard library packages with slang terminology, outlining basic programming concepts such as control flow (e.g., "if," "else"), declarations (e.g., "func," "var"), and types (e.g., "int," "string"). The document also humorously defines a lexicon for comments in code ("// line comment" and "/* block comment */") and references an example program tackling LeetCode Problem #104 about finding the maximum depth of a binary tree, featuring both recursive and iterative BFS solutions. The text further discusses transforming a programming language akin to Dogecoin through GitHub engagement by developing its standard library despite challenges from lack of prior data. It suggests experienced operators should be utilized over automated systems like Claude to address issues via more Ralph loops. An experiment during a hackathon involving Ralph Wiggum in a coding agent exemplifies this, with six repositories shipped overnight, illustrating how Large Language Models (LLMs) can reflect and amplify operator skill. Finally, the narrative touches on achieving "success" for Cursed by ranking it as either the "most loved" or "most hated" language in the Stack Overflow developer survey. The compiler is further bootstrapped to be written in Cursed itself, with an invitation extended to join a Discord community. **Bullet Point Summary:** - David Fowler developed the programming language "cursed," incorporating Gen Z slang as lexical keywords using AI. - The project illustrates AI's potential for creative learning rather than skill atrophy, featuring interpreted and compiled modes, cross-platform binary production via LLVM, and editor extensions. - Cursed includes a humorous lexicon for basic programming concepts and discusses solutions to the LeetCode Problem #104 on finding maximum tree depth with recursive and BFS approaches. - The text explores transforming a language akin to Dogecoin through GitHub engagement, suggesting reliance on experienced operators over automated systems like Claude, exemplified by an experiment involving Ralph Wiggum in a hackathon. - Cursed achieves "success" by being ranked as either the "most loved" or "most hated" language in Stack Overflow's developer survey and bootstraps its compiler to be written in itself, with an invitation to join a Discord community. Keywords: AI adoption, Binary Tree, CURSED language, Discord, Dogecoin, Emacs, Gen Z slang, GitHub, LLMs (Large Language Models), LLVM, Stack Overflow, Treesitter grammar, VSCode, Vim, compiled mode, compiler, interpreted mode, programming language, standard library packages
claude
ghuntley.com 4 days ago
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315. HN Tidewave OpenRouter and OpenAI API SupportTidewave Web has expanded its offerings by incorporating support for OpenRouter and OpenAI API keys, thereby broadening the selection of AI models available within its full-stack coding agent platform. This enhancement allows users to tailor their choices according to specific budgetary constraints and development requirements by configuring new providers through Tidewave's model selector feature. While the system is primarily optimized for Anthropic models and GPT-5, it encourages users to experiment with alternative AI models and share their findings on performance within the Tidewave Discord community. This strategic update enhances user flexibility in selecting appropriate AI models while upholding Tidewave’s extensive functionality. **Bullet Point Summary:** - Tidewave Web now supports OpenRouter and OpenAI API keys, increasing available AI model options. - Users can configure new providers via the model selector to suit budgetary and development needs. - The platform is optimized for Anthropic models and GPT-5, but users are encouraged to test other models. - Performance insights from using different models can be shared in Tidewave’s Discord community. - This enhancement provides greater flexibility while maintaining comprehensive capabilities. Keywords: AI, Anthropic, Discord, GPT-5, OpenAI API, OpenRouter, Tidewave, budget, coding agent, development, full-stack capabilities, model selector, models, providers
openai
tidewave.ai 4 days ago
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316. HN Show HN: JSON Schema for Google Gemini Image Generation**Summary:** The "Gemini Image Prompting Handbook" is an open-source initiative hosted on GitHub designed to facilitate image generation using Google Gemini's API through a structured JSON schema approach. The handbook emphasizes consistency and repeatability in outputs, catering to use cases such as product photography and illustrations by providing tools like a Python validator, example prompts, integration samples, and practical recipes that map directly to the API. It offers an opinionated JSON schema that organizes visual aspects into core elements including style, technical parameters, materials, environment, composition, and quality. Tools for prompt validation, minimal examples, step-by-step guides (cookbook), and links to official documentation are also included. The project targets engineers and prompt authors by employing a "JSON-first" design philosophy aimed at enhancing precision through named sections, encouraging structured requests, and promoting reusability with version-controlled prompts. Open-source under the MIT license, it invites community contributions and is currently in its foundational stage, accompanied by a setup guide for potential contributors. The document outlines a process for setting up a development environment involving Python and Node.js. Users are instructed to create a virtual environment, activate it, upgrade pip, and install necessary libraries like `jsonschema`. The JSON-first design is motivated by the desire for precision, reusability of prompts, and consistent terminology with clear trade-offs. Future plans include expanding examples and recipes using Gemini API calls in both Python and JavaScript, enhancing linter rules, and adding Node.js validators and pre-commit hooks. References to Gemini’s documentation, SDKs, and structured response schemas are provided. A testing script is described for validating schema, running cookbook samples under certain conditions (like the presence of a GEMINI_API_KEY), simulating GitHub Actions CI checks, and assessing repository hygiene. Necessary dependencies include `jsonschema`, `pillow`, `google-genai`, and `jq`. Contributions to the project are encouraged with guidelines available in a `CONTRIBUTING.md` file, under the MIT license by Pauhu AI Ltd as of 2025. **Bullet Point Summary:** - The "Gemini Image Prompting Handbook" is an open-source GitHub project for generating images using Google Gemini's API. - Offers a structured JSON schema to ensure consistent and repeatable image outputs across various use cases. - Includes tools like a Python validator, example prompts, integration samples, and practical recipes mapping to the API. - Provides a categorized JSON schema with core elements such as style, technical parameters, materials, environment, composition, and quality. - Targets engineers and prompt authors using a "JSON-first" approach for improved precision and structured requests. - Open-source under MIT license; currently in initial stages but encourages community contributions. - Setup guide provided for contributors to set up Python and Node.js environments. - Emphasizes JSON-first design for precision, reusability of prompts, and consistent terminology. - Future plans include expanding examples, enhancing linter rules, adding Node.js validators, and integrating pre-commit hooks. - References to Gemini’s documentation, SDKs, and structured response schemas are included. - Testing script (`run_tests.sh`) for validating schema, running samples, simulating CI checks, and assessing repository hygiene. - Necessary dependencies include `jsonschema`, `pillow`, `google-genai`, and `jq`. - Contributions encouraged with guidelines in a `CONTRIBUTING.md` file; licensed under MIT by Pauhu AI Ltd, dated 2025. Keywords: API, CLI, GitHub, Image Generation, JSON Schema, Nodejs, Open Source, Prompt Engineering, Python, Use Cases, Validator, Virtual Environment
gemini
github.com 4 days ago
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317. HN What I've learned about startupsThe provided text summarizes key insights from over 15 years of consulting for startups, emphasizing several crucial strategies to ensure success and avoid financial pitfalls. The primary lesson is the importance of focusing on essential client needs to prevent financial failure. This includes prioritizing sales efforts and maintaining regular communication with paying clients, as these actions are vital to sustaining business growth. When it comes to technical infrastructure, the text advises using Postgres for database management, while suggesting that Kubernetes may be superfluous unless specifically required. It also notes that a single Hetzner server can efficiently support millions of users, highlighting an often-overlooked point: many resources startups think they need are actually unnecessary. Instead, businesses should concentrate on satisfying paying clients. Additionally, the presence of bad code is identified as a common feature in fast-growing startups, which are usually profitable despite their technical imperfections. This is attributed to rapid iteration processes that prioritize speed over perfection. Finally, the text underscores the importance of redeveloping features that see high usage by customers over time, ensuring they remain effective and aligned with user needs. **BULLET POINT SUMMARY:** - Focus on essential client needs to avoid financial failure. - Emphasize sales efforts and regular communication with paying clients. - Use Postgres as a database system; consider Kubernetes only if necessary. - A single Hetzner server can handle millions of users, indicating many resources are unnecessary. - Prioritize satisfaction of paying clients over resource accumulation. - Bad code often indicates rapid iteration in profitable startups. - Redevelop features heavily used by customers to maintain their effectiveness. Keywords: Code, Features, Hetzner, Hetzner server, Iteration, Kubernetes, Postgres, Rebuild, Satisfaction, Startups, Users, bad code, build, client satisfaction, clients, focus, iteration cycles, profitability, rebuild features, sales, team, users Keywords: Startups
postgres
claudio.uk 4 days ago
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318. HN Oh no, not again a meditation on NPM supply chain attacks### Summary The article discusses persistent supply chain attacks within the NPM ecosystem, emphasizing Microsoft's role in exacerbating these risks through inaction since 2025. Despite historical vulnerabilities, incidents like the xz and NX attacks could have been mitigated if not for systemic negligence. The writer reflects on their experience with Node.js, noting an increase in JavaScript security concerns over time compared to other languages. Early web history is briefly recounted, highlighting Netscape Navigator's features and Microsoft's integration of Internet Explorer into Windows systems, which led to security issues and monopolistic practices. The article traces the evolution of Internet Explorer (IE) from its introduction to the shift away from its codebase in 2015 due to accumulating bugs and vulnerabilities. A specific concern addressed is the potential for arbitrary command execution through npm's postinstall scripts, exemplifying broader security issues within npm's package management system. Despite npm's success as a primary Node.js package manager, concerns about trust and security persisted until Microsoft acquired GitHub and NPM in 2018 and 2020, respectively. This acquisition marked a strategic move to ensure npm's sustainability by leveraging Microsoft’s resources, though it has not significantly improved security for enterprise use. The author criticizes the software development tools industry for their inherent insecurity and lack of accountability, predicting an increase in security incidents and data privacy risks without concerted efforts to secure the supply chain. Despite Microsoft's financial backing, issues like unsigned dependencies and inadequate 2-Factor Authentication remain problematic. Overall, the article highlights a dissatisfaction with the current state of software tools, stressing that both technical vulnerabilities and social engineering threats pose significant challenges. Microsoft is specifically criticized for neglecting a long-standing security flaw, making software development more burdensome and less enjoyable. ### Bullet Point Summary - **Supply Chain Attacks**: Highlighted issue within NPM ecosystem; exacerbated by Microsoft’s inaction since 2025. - **Historical Context**: Reflects on Node.js history; JavaScript's rising security concerns compared to other languages. - **Internet Explorer (IE)**: Detailed its evolution, integration into Windows, and transition away from IE codebase due to vulnerabilities. - **npm Security Concerns**: Arbitrary command execution risk via postinstall scripts exemplifies broader npm issues. - **Microsoft Acquisitions**: 2018 GitHub and 2020 NPM acquisitions aimed at sustaining npm; little improvement in security for enterprises. - **Industry Criticism**: Current software tools are insecure by default, with providers lacking accountability. - **Future Risks**: Predicts increased security incidents due to inadequate supply chain protections and social engineering threats. - **Microsoft’s Role**: Criticized for neglecting a known security flaw, adding to user risk. Keywords: 2-Factor Authentication, Active Desktop, Expressjs, GitHub, Internet Explorer, JavaScript security, Linux distributions, Microsoft, NPM, NX incident, Netscape Navigator, NodeJS, Software Bill of Materials Attestation, browser issues, bugs, cryptocurrency theft, dependencies, fragmentation, libraries, malware, package managers, pnpm, software development, supply chain attacks, vulnerabilities, xz incident, yarn
github
tane.dev 4 days ago
|
319. HN Show HN: Link Global, cross-shell aliases with history, auto-compile for C filesThe post introduces "Link Global," a command-line tool hosted on GitHub designed to improve productivity through its unique features. Primarily, it facilitates the creation of cross-shell aliases and maintains command history, allowing users to establish consistent shortcuts across various shell environments. Additionally, Link Global includes an auto-compile function tailored for C files, streamlining the compilation process in C programming. This tool is shared with the community on Hacker News, where a link to its GitHub repository is provided for further details. **BULLET POINT SUMMARY:** - **Tool Introduction**: "Link Global" is introduced as a command-line tool available on GitHub. - **Functionality**: It creates cross-shell aliases and maintains command history for consistent shortcuts across different shell environments. - **Auto-Compile Feature**: The tool includes an auto-compile function specifically designed for C files, enhancing the C programming compilation process. - **Purpose**: Aimed at boosting productivity by simplifying command management and compilation tasks. - **Community Sharing**: The announcement is made on Hacker News with a link to the GitHub repository for more information. Keywords: API, C files, FAQ, Ferki-git-creator, GitHub, Hacker News, Link Global, Show HN, YC, auto-compile, contact, cross-shell aliases, guidelines, history, legal, link-cli-tool, search, security
github
news.ycombinator.com 4 days ago
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320. HN The Network Times: Ultra Ethernet: Fabric Setup- The text outlines a detailed process for setting up an Ultra Ethernet (UET)-based system to facilitate distributed AI training, focusing on the initialization of hardware and software resources. - **Fabric Endpoint (FEP) Creation**: Each GPU process connects through a logical FEP that abstracts its NIC port connection, forming a high-performance Fabric Plane (FP). LLDP messages are used for compatibility checks between endpoints. - **Vendor UET Provider Publication**: FEPs are published as Libfabric domains via the Vendor UET Provider, making them discoverable and ready for communication object creation by application processes. - **Job Launcher and Environment Variables**: Tools like Torchrun set up necessary environment variables for each process (e.g., master rank IP, local/global ranks) to facilitate coordination in distributed training environments. - **Environment Variable Interpretation**: Frameworks utilize these variables to assign global ranks, designate a master rank for control operations, and manage GPU assignments. - **Control Channel Establishment**: This phase involves TCP connections between processes and the master rank to exchange crucial metadata. The master generates an NCCL Unique ID for collective communication group definitions, maintaining open channels during training for coordination. - **Initialized Job State**: All GPUs have unique process IDs and global ranks, are aware of their communication groups, and can access FEPs via Libfabric, indicating readiness for AI workload execution. - Fabric Endpoints (FEPs) function as logical links between GPU processes and NIC ports, creating an isolated data path within a Fabric Plane. They use Fabric Addresses (FA) to ensure process isolation even across different IP subnets. - **LLDP Negotiation**: After FEP creation, LLDP messages are exchanged to confirm UET feature support on both ends of a link, marking it as ready for upper-layer processes once negotiation is successful. - Each node has two FEPs linked to separate NIC ports, facilitating data flow within designated Fabric Planes. The Vendor UET Provider abstracts hardware specifics and integrates with Libfabric for consistent application programming models across different UET-compliant devices. - **Environment Variables in Distributed Training**: Tools like Torchrun establish job-specific variables (e.g., NODE_RANK, LOCAL_RANK) to define roles and coordination mechanisms among processes, enabling efficient resource allocation and communication paths. - **PyTorch Environment Variable Utilization**: PyTorch calculates Global Rank IDs using node and local ranks, assigns processes to specific GPUs based on these ranks, and manages memory allocation for data and gradients storage. - The establishment of control channels involves a TCP handshake between GPU processes and the master rank, ensuring communication pathways are set up. Processes exchange essential information like JobID and FEP details for efficient distributed training operations. Keywords: AI training workload, API, BGP EVPN, CBFC, Distributed AI training, Eth0, FA, FEP0, Fabric Endpoint (FEP), Fabric Plane (FP), GPU memory, GPU node, GPUs, Global Rank IDs, IP address, LLDP TLVs, LLDP messages, LLR, Libfabric domains, NCCL UID, NIC, NIC port, PROCESSES_PER_NODE, RDMA communication, RMA, TCP connections, TCP handshake, TCP port, Torchrun, UET, UET-based system, Vendor UET Provider, abstracts, address vector (AV), atomic operations, backend switches, chassis ID, collective communication, communication resource, control channel, coordination, credit-based flow control, distributed AI frameworks, distributed communication, end of LLDPDU, endpoints, environment variables, fabric plane, fi_av_open(), fi_domain(), fi_endpoint(), global rank ID, global ranks, gradients, hardware details, initialized job, interfaces, isolated data path, job ID, job launcher, layer 2 VNI, layer 3 VNI, libfabric, link level retry, local rank, local rank ID, management network, master addr, master port, master rank, memory space, messages, metadata, model-parallel setups, network interfaces, node rank, node rank ID, port ID, process IDs, processes, programming model, rail, ranks, role selection, routing instance, ssh connection, subnet, switch ports, synchronization, termination point, time to live, training data, vram, weight parameters, worker nodes, world size
vram
nwktimes.blogspot.com 4 days ago
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321. HN Show HN: Smile – an open source language for structuring prompts### Summary The provided text introduces "Smile," a structured language framework developed by Dr. Thomas Ager for crafting prompts to enhance interactions between humans and large language models (LLMs). Inspired by research indicating that digital smile icons can elicit body responses similar to actual smiles, Smile employs an emoticon-like syntax using brackets and colons. This design aims to improve task performance and instruction adherence across various applications, including business processes and complex multi-step interactions. Smile is compared to Markdown or HTML but is specifically tailored for LLM instructions to ensure consistency in structured communication. It supports patterns such as "Chain-of-thought," encouraging step-by-step reasoning before responding, thereby aligning responses with user context and organizational goals. Users can experiment with Smile through its repository, access a README.me Expert for feedback, and explore examples. The syntax of Smile uses delimiters to define sections like roles, tasks, tone, response language, and style, enhancing clarity and adaptability across languages. Its structured approach offers technical advantages including long-term maintainability, consistent instruction-following performance, and portability. Emotionally, the use of smile-related symbols is linked to increased happiness by triggering positive physiological responses, which can enhance clarity in high-stakes environments. Smile allows customization using emoticons for specific instruction types, balancing structure with flexibility depending on the model and task. Its application extends to facilitating effective communication with AI systems through structured prompt engineering techniques that improve clarity and adaptability. In addition to Smile, the text discusses alternative methods for guiding AI responses by comparing HTML formatting with "(: Smile" notation, highlighting their potential beyond traditional formats like Markdown or JSON. The framework includes distinct name tags within section titles to ensure model adherence and engagement, supported by community contributions through directories containing prompts and responses in a repository. The document also illustrates how Smile can generate precise responses by delineating tasks using specific sections and formatting symbols that guide text handling. An example of the "Chain-of-Thought" method demonstrates structured problem-solving clarity for mathematical reasoning, emphasizing logical steps and accuracy. Lastly, the broader impact of prompt engineering on productivity and happiness is highlighted through real-life prompts like smiley faces, referencing studies on how different formats affect LLM performance and human-AI collaboration. The text underscores the transformative potential of innovative prompt engineering in refining AI functionality and improving human-computer interactions. ### Key Points - **Smile Framework**: Developed by Dr. Thomas Ager; uses emoticon-like syntax inspired by psychological findings to improve human-Large Language Model (LLM) interactions. - **Design and Syntax**: Comparable to Markdown/HTML, Smile is tailored for LLM instructions with clear structure using delimiters for various sections to ensure adaptability across languages. - **Technical and Emotional Benefits**: Offers long-term maintainability, consistent performance in instruction-following tasks, enhanced clarity, positivity through smile-related symbols triggering physiological responses. - **Application and Customization**: Facilitates effective AI communication, allows customization with emoticons, balances structure with flexibility for various models and tasks. - **Broader Context**: Comparison with traditional formats like HTML; uses name tags and formatting symbols to ensure clarity. The "Chain-of-Thought" method exemplifies structured reasoning in problem-solving. - **Impact on Productivity and Happiness**: Emphasizes the role of prompt engineering in AI functionality, human-computer interaction improvement, and real-life positivity cues such as smiley faces. Keywords: Chain-of-thought, GitHub, HTML, LLM instruction language, Large Language Models (LLMs), Markdown, Prompt engineering, consistency, emoticons, happiness, meta-awareness, multi-turn pipelines, psychology, response format, retrieval augmented generation (RAG), role definition, smile expert, task performance, technical keywords
github
github.com 4 days ago
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322. HN Indexing Jsonb in PostgreSQL### Summary PostgreSQL offers robust indexing options for handling JSONB data types, which are binary-encoded and pre-parsed, through specialized indexes such as GIN (Generalized Inverted Index). GIN indexes break down JSON documents into key-value pairs to facilitate efficient querying of specific keys using operators like containment (`@>`), key existence (`?`), any key matches (`?|`), and all keys match (`?&`). However, they are not suitable for path-based navigation or regex pattern matching within JSONB fields. Although GIN indexes provide powerful capabilities for certain operations, they incur higher write overhead compared to B-tree indexes, leading to potential index bloat from frequent updates. Regular maintenance with tools like `REINDEX CONCURRENTLY` and extensions such as `pgstattuple` is necessary to manage this bloat. For situations where GIN indexing is not ideal, B-tree expression indexes can be utilized. These involve creating an index on operations performed on a column rather than its components. For example, one might create an expression-based B-tree index on the `order_total` key within a JSONB column by converting its text value to numeric, optimizing queries that use this specific expression. Expression indexes are particularly effective for consistent and specific queries embedded in application code. However, they may not support dynamic or varying queries well, especially when querying multiple keys in JSONB columns. For such cases, GIN indexing remains advantageous for containment lookups but is less efficient if only certain keys within the column are queried. In these instances, expression or partial indexes could be more suitable. Combining GIN and B-tree indexes can enhance performance by utilizing their respective strengths: GIN indexes for querying within documents and B-trees for structured data handling. Understanding when to use each index type is essential for fully leveraging PostgreSQL's capabilities with JSONB data. Additionally, monitoring index usage in environments with heavy JSONB workloads ensures optimal performance and resource utilization. ### Bullet Point Summary - **GIN Indexing**: Ideal for querying specific operations on JSONB data using operators like `@>`, `?`, `?|`, and `?&`. Not suitable for path navigation or regex matching. - **Write Overhead and Maintenance**: GIN indexes have higher write costs, leading to potential index bloat. Regular maintenance with tools such as `REINDEX CONCURRENTLY` is recommended. - **B-tree Expression Indexes**: Useful when GIN indexing isn't ideal, creating indexes based on column operations (e.g., converting text to numeric) for specific query optimizations. - **Expression Index Application**: Best suited for consistent queries in application code but not dynamic or varying queries. Useful for optimizing certain JSONB key lookups. - **Indexing Strategy**: Combining GIN and B-tree indexes leverages their strengths: GIN for document querying, B-trees for structured data. Knowing when to use each index type is crucial. - **Monitoring Index Usage**: Essential in production environments with heavy JSONB workloads to ensure performance and resource efficiency. Keywords: B-Tree, BRIN, CREATE INDEX, GIN index, GiST, JSONB, PostgreSQL, Sp-GiST, containment, expression index, indexing, partial index
postgresql
www.crunchydata.com 4 days ago
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323. HN Recreating the Apollo AI adoption rate chart with GPT-5, Python and Pyodide### Summary: Dr. Torsten Sløk from Apollo Global Management observed a decline in AI adoption rates among large companies using US Census Bureau survey data, prompting an author's exploration within the Gartner Hype Cycle context. Employing GPT-5, the author replicated the original chart by accessing and analyzing census data segmented by employment size. Through Python and Pyodide technologies, they recreated this chart, encountering discrepancies initially but refining their approach to match the Apollo chart closely after accounting for a six-survey moving average. The process included extensive data analysis using pandas and numpy, with visualization attempts via matplotlib, revealing initial differences in peak values between GPT-5's and Apollo’s charts. A spreadsheet review clarified that AI-generated results aligned with its own but not Apollo's due to overlooked details. By computing a rolling mean over six surveys, the author corrected this, yielding a chart that mirrored the original closely. The script utilized data from an Excel file, focusing on recent responses related to AI adoption, mapping employment sizes, and calculating 6-survey rolling averages for visualization as time series graphs across different firm sizes (November 2023 - August 2025). The author also explored rendering these charts in a browser using Pyodide, leveraging ChatGPT's Canvas feature for client-side execution. This entailed resolving dependencies and errors with packages like `openpyxl`, facilitating direct data visualization from a CORS-enabled spreadsheet hosted on AWS S3. The project underscored GPT-5’s efficacy in managing census data, recreating charts using Python libraries, running matplotlib in browsers via Pyodide, and fetching XLSX files using `pyfetch` and `openpyxl`. The author shared insights gained, including overcoming file handling errors and syntax challenges, demonstrating the potential of these techniques for future projects. The code developed during this exploration is available on tools.simonwillison.net/ai-adoption. ### Bullet Point Summary: - Dr. Sløk identified a decline in AI adoption rates among large companies from US Census Bureau data. - Author replicated the chart using GPT-5, leveraging Python and Pyodide for analysis and visualization. - Initial discrepancies were noted between GPT-5’s recreated chart and Apollo’s original due to overlooked moving average details. - Corrected by computing a six-survey rolling mean; this adjustment aligned the new chart with the Apollo version. - Process involved data loading from an Excel file, filtering responses related to AI adoption, mapping employment sizes, transforming data for analysis, calculating 6-period rolling averages, and visualizing results as time series graphs. - Explored client-side rendering of charts in browsers using Pyodide, addressing package dependency issues with `micropip` and `openpyxl`. - Demonstrated GPT-5’s capabilities in data management and chart recreation, alongside Python library applications for browser-based visualization. - Overcame initial technical challenges, enhancing understanding of census data handling and visualization techniques. - Insights and code from the project are shared on tools.simonwillison.net/ai-adoption, highlighting learnings and future application potential. Keywords: AI tools, Apollo AI, CORS-enabled, GPT-5, Gartner Hype Cycle, JavaScript, Pyodide, Python, SVG, US Census Bureau, adoption rate, biweekly survey, chart comparison, data analysis, firm size, firms, machine learning, matplotlib, micropip, natural language processing, openpyxl, pandas, rolling average, virtual agents, voice recognition
gpt-5
simonwillison.net 4 days ago
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324. HN Outcome-Based Exploration for LLM Reasoning**Summary:** The paper "Outcome-Based Exploration for LLM Reasoning" (arXiv:2509.06941) by Yuda Song, Julia Kempe, and Remi Munos introduces a novel method to enhance the reasoning capabilities of large language models (LLMs). Submitted on September 8, 2025, it explores outcome-based exploration strategies aimed at improving LLMs' problem-solving processes without sacrificing output diversity. The study is supported by the Simons Foundation and other contributors. The research highlights how traditional reinforcement learning (RL) enhances accuracy by rewarding correct answers but reduces generation diversity—a crucial factor for varied real-world applications. It identifies two issues: reduced diversity transferring from solved to unsolved problems and limited distinct outcomes in reasoning tasks. To address these, the authors propose algorithms such as historical exploration, which uses Upper Confidence Bound (UCB) bonuses for rare answers, and batch exploration, which discourages repetition within a set to maintain diversity at test time. Experiments with Llama and Qwen models on math competitions demonstrate improved accuracy while preserving diversity. A theoretical model of outcome-based bandits is presented to formalize the benefits of these strategies, offering practical approaches for scalable RL techniques that enhance reasoning capabilities without compromising output diversity. The document is a preprint under computer science - machine learning (cs.LG) category, with details on accessing different formats and citation management tools like BibTeX. Additionally, the text describes arXiv platform features including CORE and IArxiv Recommender systems aimed at enhancing user experience through relevant content suggestions based on various criteria. ArXivLabs is highlighted as a collaborative framework for experimental projects within the community, emphasizing openness and privacy. The document also covers practical aspects such as disabling MathJax, providing contact information, newsletter subscriptions, support services, and legal guidelines regarding copyright and privacy policies. **Bullet Point Summary:** - Introduction of outcome-based exploration to enhance LLM reasoning without sacrificing diversity. - Authors: Yuda Song, Julia Kempe, Remi Munos; submission date: September 8, 2025. - Support from Simons Foundation and contributors highlighted. - Traditional RL improves accuracy but reduces output diversity, affecting real-world applications. - Identified issues: reduced diversity transfer to unsolved problems and limited distinct reasoning outcomes. - Proposed algorithms: historical exploration (UCB bonuses for rare answers) and batch exploration (discourages repetition). - Experiments with Llama and Qwen models show improved accuracy and preserved diversity on math competitions. - Theoretical model of outcome-based bandits formalizes proposed strategies' benefits. - Document is a preprint in cs.LG, with access to PDF, HTML, TeX formats, and citation tools like BibTeX. - Describes arXiv features: CORE and IArxiv Recommender systems for relevant content suggestions. - ArXivLabs allows collaborative experimental projects, focusing on openness and privacy. - Practical aspects covered include disabling MathJax, contact information, subscriptions, support services, and legal guidelines. Keywords: Accuracy Gains, ArXiv, Bandits, Batch Exploration, CORE Recommender, Diversity Degradation, Historical Exploration, IArxiv Recommender, LLM Reasoning, Large Language Models, Machine Learning, Outcome Space, Outcome-based Exploration, Reinforcement Learning, Tractability, csLG
llm
arxiv.org 4 days ago
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325. HN TailGuard: A way to connect your home WireGuard router into Tailscale via Docker**Summary:** TailGuard is a Docker-based solution designed to integrate WireGuard routers into the Tailscale network, facilitating connections between devices running WireGuard and those using Tailscale without requiring native support on WireGuard devices. The system achieves this by routing all WireGuard traffic through a TailGuard server, effectively bridging WireGuard with Tailscale’s point-to-point connections. This integration offers several benefits: it centralizes the storage of WireGuard private keys, simplifies device addition via Single Sign-On (SSO), and allows easy switching between exit nodes in the tailnet. Additionally, it supports concurrent access to both Tailnet and WireGuard on mobile devices, even if they don't support multiple VPNs simultaneously, and enables connection of home networks limited to WireGuard but not Tailscale. Installation involves downloading a WireGuard client configuration as `wg0.conf` and optionally creating an IPv6 network before launching the TailGuard container. The setup requires configuring a Docker container with necessary environment variables for integration, such as specifying device names (`WG_DEVICE`, `TS_DEVICE`), port numbers (`TS_PORT`), authentication keys (`TS_AUTHKEY`), destination IPs (`TS_DEST_IP`), and hostnames (`TS_HOSTNAME`). WireGuard necessitates manual routing configurations, as it does not handle routing automatically. Users must advertise routes from WireGuard subnets on the Tailscale network using specific commands. For custom builds, Docker Compose is recommended, placing `wg0.conf` in a `config/` directory, building the image with `docker-compose build`, and running it with `docker-compose up`. Advanced settings include configuring TailGuard to work with a WireGuard server managing specific subnets. The document emphasizes that routes need manual configuration on WireGuard devices to ensure proper routing through Tailscale. TailGuard is licensed under the MIT License, granting free use, modification, and distribution of the software as long as the original copyright notice and permission notice are included. The license provides no warranties or guarantees, and authors are not liable for any claims or damages resulting from its use. **Bullet Point Summary:** - TailGuard integrates WireGuard routers into Tailscale using Docker. - Benefits include centralized private key storage, SSO device addition, exit node switching, concurrent access on mobile devices, and connection of WireGuard-only home networks. - Installation requires downloading `wg0.conf` and optionally creating an IPv6 network before launching the container. - Configuration involves setting environment variables for device names, port numbers, authentication keys, destination IPs, and hostnames. - Manual routing configuration is necessary due to WireGuard’s lack of automatic routing. - Routes from WireGuard subnets are advertised on Tailscale; specific routes must be manually added using commands. - Custom builds use Docker Compose with `wg0.conf` in a `config/` directory. - Advanced settings include configuring TailGuard for specific WireGuard server subnets. - The software is licensed under the MIT License, allowing free use and distribution with no warranties or liabilities. Keywords: Docker, IPv6, MTU, NAT, TailGuard, Tailscale, VPNs, VPS, WireGuard, authentication, license, network topology, private key, router, tailnet
tailscale
github.com 4 days ago
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326. HN Easy to use server for the KiCad Plugin and Content Manager### Summary The KiCad Package Server is a user-friendly solution designed to host KiCad plugins and content through its Content Manager. It allows users to create an independent centralized repository separate from the official KiCad Services Corporation's offering, with deployment simplified by Docker using a `docker-compose.yml` file. The server supports SQLite3 by default but also offers Postgres compatibility. To deploy, users clone the repository and run it via Docker Compose. Each plugin directory must include metadata files such as `metadata.json`, adhering to KiCad Package schema guidelines available online, and specify its destination path using a `.kicad_pcm` file located in the source directory. Optional resources like an icon can be added for enhanced functionality. Publishing plugins involves sending a POST request with the Git repository URL to the server. For instance: ```bash curl -X POST http://localhost:9292/api/push \ -H "Content-Type: application/json" \ -d '{"url": "https://github.com/AislerHQ/lovely-library.git"}' ``` To manage and distribute KiCad plugins, users access them through the Plugin and Content Manager in KiCad by adding the server's URL. The server allows customization via environment variables within `docker-compose.yml`, such as setting plausible analytics or customizing URLs and names. Developed by AISLER B.V., a company specializing in electronic project manufacturing, the KiCad Package Server is open-source software available under its respective license. AISLER offers further insights into their services through their platform. ### Bullet Point Summary - **Functionality**: The KiCad Package Server facilitates hosting of plugins and content independently from official repositories, using Docker for easy deployment. - **Deployment**: Users clone the repository and deploy via Docker Compose; it supports SQLite3 or Postgres. Plugin directories must have metadata files (e.g., `metadata.json`) conforming to KiCad Package schema. - **Metadata Requirements**: Each plugin directory should include a `.kicad_pcm` file in its source folder, specifying the destination path for package inclusion. Optional icons can be added. - **Publishing Plugins**: Users publish plugins by sending a POST request with the Git repository URL using `curl`. - **Integration in KiCad**: Users add server URLs to manage and distribute plugins through KiCad's Plugin and Content Manager. - **Customization**: Environment variables in `docker-compose.yml` allow customization, including enabling Plausible analytics or setting various URLs and names. - **Development and Licensing**: Developed by AISLER B.V., the KiCad Package Server is open-source software. AISLER specializes in affordable electronic manufacturing services. Keywords: AISLER, API, Analytics, Content Manager, Deployment, Docker, Git, HTTPS, KiCad, Manufacturing, Metadata JSON, Plugin, Postgresql, SQLite3, Server
postgresql
github.com 4 days ago
|
327. HN Show HN: I'm tired of long color entries like "\x1B[48;5;10M"so I created my ownThe text describes the creation of a tool designed to simplify lengthy color codes, such as "\x1B[48;5;10M," which were causing frustration for its author. This tool, developed to streamline these entries, is available on GitHub at [https://github.com/Ferki-git-creator/coc](https://github.com/Ferki-git-creator/coc) and can also be tested online via [https://ferki-git-creator.github.io/coc/](https://ferki-git-creator.github.io/coc/). Additionally, the project has been shared on Hacker News where it has garnered some level of engagement. **BULLET POINT SUMMARY:** - The author developed a tool to simplify lengthy color codes that caused frustration. - The tool is hosted and can be accessed via GitHub at [https://github.com/Ferki-git-creator/coc](https://github.com/Ferki-git-creator/coc). - It can also be tested online at [https://ferki-git-creator.github.io/coc/](https://ferki-git-creator.github.io/coc/). - The project was shared on Hacker News, receiving some engagement. Keywords: API, Contact, FAQ, Ferki-git-creator, GitHub, Hacker News, Legal, Search, Security, YC, coc, color entries, discuss, guidelines, points, show HN, website
github
news.ycombinator.com 4 days ago
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328. HN Mistral Set for $14B (€12B) Valuation with New Funding RoundMistral AI, a French artificial intelligence startup established in 2023 by Arthur Mensch along with former Meta researchers Timothée Lacroix and Guillaume Lample, is nearing the completion of a significant €2 billion investment. This financial infusion values Mistral AI at approximately €12 billion ($14 billion). The company positions itself as a competitor to OpenAI, primarily concentrating on the development of open-source language models and AI services specifically adapted for European needs. One of its notable products includes Le Chat, an AI-powered chatbot designed to cater to regional preferences. BULLET POINT SUMMARY: - Mistral AI is a French AI startup founded in 2023 by Arthur Mensch, Timothée Lacroix, and Guillaume Lample. - The company is securing a €2 billion investment, valuing it at €12 billion ($14 billion). - Mistral AI competes with OpenAI, focusing on open-source language models. - It develops European-tailored AI services, including the chatbot Le Chat. Keywords: Arthur Mensch, DeepMind, Europe, Guillaume Lample, Le Chat, Mistral AI, OpenAI, Timothée Lacroix, artificial intelligence, chatbot, funding round, investment, language models, startup, tech startups, valuation, €12 billion
openai
www.bloomberg.com 4 days ago
|
329. HN Show HN: TerraCode CLI learns and uses your domain knowledge intelligentlyTerraCode CLI is a free beta AI-powered development tool designed to enhance coding efficiency by integrating domain knowledge using Qwen's robust platform. It features enterprise knowledge management, allowing organizations to capture and leverage internal expertise through interactive sessions with senior developers. TerraCode offers context-aware responses that evolve as more information is provided, enhancing its adaptability for project-specific tasks. The tool facilitates semantic code searches across entire codebases via natural language queries, enabling users to gain deep insights into functions, patterns, and relationships. It supports document uploads in various formats for comprehensive knowledge integration and persistent organizational learning. TerraCode emphasizes multi-model AI responses, supporting personal preferences such as TypeScript, while maintaining team standards. During its beta phase, TerraCode CLI is free but will soon require a subscription. Installation requires Node.js version 20 or higher, accessible through npm or by source via git. Users can execute commands for code explanation, refactoring, and unit test generation after installation. Future plans include integration with Native Terra API keys for unified embeddings and access to large language models (LLMs), promising enhanced project-wide insights into architecture, dependencies, and patterns. The tool supports document uploads for knowledge sharing, uses persistent memory to remember preferences through commands like `/brain`, and manages sessions via `.terra/settings.json`. Authorization is streamlined using Qwen OAuth for easy setup with local credential caching. A free tier offers generous quotas, including 2,000 requests per day and 60 requests per minute, with automatic credential refresh at no cost. Configuration can be done using a user settings file or environment variables, with specific instructions based on location (e.g., Alibaba Cloud Bailian for Mainland China users). TerraCode also integrates with VoyageAI to enable semantic search features. Setup involves adding an API key in the user settings and verifying functionality via `terra > /semantic status`. VoyageAI supports various tasks such as exploring codebases, refactoring, automating workflows, debugging, and more. Commands are organized into categories focusing on code optimization, documentation/testing, development acceleration, and general CLI interaction. The document concludes with keyboard shortcuts for navigating the CLI and outlines contributions to the Terra project. The tool is open-source under Apache-2.0, acknowledging Qwen and Google Gemini CLI as foundational influences while highlighting Terra's enhancements in knowledge management and semantic analysis. **Bullet Point Summary:** - **TerraCode CLI Features**: - Free beta AI-powered development tool. - Enhances coding efficiency with domain knowledge integration via Qwen’s platform. - Enterprise knowledge management for capturing organizational expertise. - Context-aware, evolving responses tailored to specific projects. - **Capabilities**: - Semantic code searches using natural language queries. - Deep insights into functions, patterns, and relationships. - Supports document uploads for comprehensive learning and memory persistence. - Multi-model AI responses respecting personal preferences and team standards. - **Availability and Installation**: - Free during beta; subscription required post-beta. - Requires Node.js version 20 or higher. - Installable via npm or git, with commands available for code explanation, refactoring, and unit testing generation. - **Future Enhancements**: - Integration with Native Terra API keys for unified embeddings and LLM access. - Enhanced project-wide insights into architecture and dependencies. - **User Configuration**: - Authorization through Qwen OAuth; free tier offers generous usage quotas. - Configuration via user settings file or environment variables, tailored by location. - Integration with VoyageAI for semantic search capabilities using API keys. - **VoyageAI Integration**: - Supports codebase exploration, refactoring, workflow automation, and debugging. - Commands categorized into refactoring/optimization, documentation/testing, development acceleration, and core CLI interactions. - **Additional Information**: - Keyboard shortcuts for operational efficiency within the CLI. - Open-source under Apache-2.0 license with contributions welcome. - Acknowledgments to Qwen and Google Gemini CLI as foundational platforms. Keywords: AI-powered development, API specs, Alibaba Cloud, Authentication, Git automation, Knowledge Transfer, ModelScope, OpenAI, Rate Limit, Terra Code CLI, TypeScript, Unit Tests, architecture docs, async/await, codebase patterns, deployment guides, domain knowledge, interactive knowledge transfer, natural language queries, persistent memory, semantic search
openai
github.com 4 days ago
|
330. HN Rust's Enterprise Breakthrough Year### Summary Rust has marked a significant milestone in enterprise adoption over the recent years, experiencing a 68.75% growth between 2021 and 2024, with continued acceleration into 2025. This rise is largely driven by major tech companies such as AWS, Microsoft, Google, and Meta increasing their investment in Rust for infrastructure development due to its memory safety features. Shopify's role as the first Gold member of the Rust Foundation and its use of Rust for the YJIT Ruby compiler underscores the language's impact on performance and enterprise commitment. Additionally, AWS's implementation of Firecracker microVMs exemplifies Rust's capability in managing serverless functions at scale. Microsoft is enhancing its focus on Rust to mitigate security vulnerabilities associated with unsafe memory usage in C/C++ code, using Rust’s ownership model for improved infrastructure security. The year 2025 saw Cloudflare launching Infire, a high-performance AI inference engine developed in Rust, which underscores the language's suitability for performance-intensive applications such as cloud infrastructure and IoT networks. Significant improvements this year include a 30% reduction in compilation times due to enhancements like the rust-lld linker on Linux. The advancements extend to AI workloads where Rust is increasingly preferred over Python for deployment due to its predictable performance and efficiency. Frameworks such as Candle by Hugging Face, focusing on low latency inference tasks, and Burn, with modular design capabilities, exemplify this shift. The integration of Rust with Python has grown substantially, evidenced by a 22% increase in the use of Rust for Python extensions, illustrating the blending of performance with usability in production settings. Rust is also enhancing WebAssembly applications, enabling efficient model execution in browsers. In terms of development tools, the Zed code editor, written in Rust, stands out as a strong competitor to VS Code due to its impressive performance and features. The "Rust Vision Doc" suggests continued growth and influence within the tech landscape. The article highlights Rust’s expansion beyond traditional systems programming into areas like cloud computing and AI, driven by enterprise readiness and memory-safe performance requirements. As stability becomes increasingly important for enterprise solutions, the focus in H1 2025 shifts towards tool stabilization over new features. The article concludes with an invitation to share projects developed using Rust, emphasizing community growth and engagement. ### Bullet Point Summary - **Growth in Enterprise Adoption**: Rust's adoption increased by 68.75% from 2021 to 2024, accelerating into 2025 due to its memory safety features. - **Major Tech Investments**: Companies like AWS, Microsoft, Google, Meta, and Shopify have significantly invested in Rust for infrastructure development. - **Performance Enhancements**: Rust has seen a 30% reduction in compilation times and improved developer productivity with optimizations such as the rust-lld linker on Linux. - **AI Workloads**: Rust is increasingly used for AI deployments due to its predictable performance and memory efficiency, exemplified by frameworks like Candle from Hugging Face. - **Integration with Python**: The use of Rust for Python extensions has risen by 22%, combining performance benefits with Python's usability. - **WebAssembly Applications**: Rust enhances WebAssembly applications, allowing efficient execution in browsers without heavy runtimes. - **Development Tools**: The Zed code editor competes strongly against VS Code due to its high-performance features. - **Expansion Beyond Systems Programming**: Rust is expanding into cloud computing, AI, and embedded systems, driven by enterprise demands for stability and performance. - **Community Engagement**: Encourages readers to share their projects developed with Rust, highlighting the importance of community growth and collaboration. Keywords: AI, AWS, Adoption, C/C++, CPU overhead, Candle, Cargo, Cloudflare, Compiler, Enterprise, Firecracker, GPU utilization, Google, Growth, Hugging Face, Infire, Infrastructure, IoT networks, LLM, Llama, ML framework, Memory Safety, Meta, Microsoft, Open Source, Ownership Model, Python, Python extensions, Ruby, Rust, Security, Shopify, Unsafe Memory Usage, Vulnerabilities, WASM, WebAssembly, YJIT, Zed Editor, anniversary, blockchain, business case, cloud infrastructure, code editor, collaborative features, community, compilation revolution, compile times, compiler flags, compiler optimizations, developer experience, developer productivity, ecosystem, edge network, engage, evolution, experimentation, flexible backends, high-frequency trading, inference, inference engine, insights, language features, low latency, machine learning, maturity, memory-safe, microVMs, modular design, near-native performance, newsletter, performance, production, production deployments, real-world projects, research, roadmap, rust-lld linker, serverless, share, stability, stories, tooling
llm
rust-trends.com 4 days ago
|
331. HN Show HN: Unlimited free AI Chat toolThe post introduces a free AI chat tool that incorporates advanced models such as GPT-5, DeepSeek V3.1, Gemini 2.5 Pro, and Claude Sonnet 4. This tool is designed to enhance various multimedia applications by offering features like video enhancement, upscaling quality, and face editing. The primary aim of these tools is to provide outputs that meet professional standards, making them suitable for both entertainment purposes and professional usage. - Introduction of a free AI chat tool with advanced models (GPT-5, DeepSeek V3.1, Gemini 2.5 Pro, Claude Sonnet 4). - Emphasis on multimedia enhancement features: video quality improvement, upscaling, face editing. - Aim to deliver outputs that meet professional standards for entertainment and professional use. Bullet Points Summary: - The post presents a free AI chat tool featuring advanced models such as GPT-5, DeepSeek V3.1, Gemini 2.5 Pro, and Claude Sonnet 4. - It highlights capabilities like video enhancement, upscaling quality, and face editing. - These tools are designed to produce professional-quality outputs suitable for entertainment and professional applications. Keywords: AI Chat, Claude Sonnet 4, DeepSeek V31, Enhancement, Entertainment, Face Editing, Free, GPT-5, Gemini 25 Pro, Professional Quality, Scenarios, Show HN, Tools, Unlimited, Upscaling, Video
gpt-5
mixhubai.com 4 days ago
|
332. HN Can Collations Be Used over Citext?The document explores alternative methods to achieve case-insensitive string searches in PostgreSQL, comparing the use of custom collations and the `citext` extension. The exploration is motivated by suggestions from Laurenz Albe, who argues that custom collations might offer a more efficient solution than existing methods such as `upper()` or `citext`. A test setup involving two tables (`demo1` with a custom ICU-based collation and `demo2` using `citext`) was established to evaluate performance under large-scale conditions. Each table was populated and indexed on the 'word' column, containing approximately 32 million rows, including variations of "apple" in different cases and accents. The document describes an execution plan analysis comparing query performances between the two tables using the "=" operator. The `demo1` table demonstrated slightly better average execution times than the `demo2` table due to its use of a standard text column with custom collation. However, sequential scan performance revealed notable differences, with `demo1` executing significantly faster than `demo2`. Queries involving the LIKE operator further illustrated performance disparities between the tables. While PostgreSQL versions up to 17 did not support nondeterministic collations like those in `demo1` for LIKE operations, version 18 resolved this issue but continued to show limitations regarding index usage. Sequential scans with parallel processing were employed in both tables, where `demo1` displayed consistently faster performance than `demo2`. This was particularly evident when querying rows greater than or equal to 'Åpple', where `demo1` executed about four times faster. The document highlights that while both custom collations and `citext` allow for case-insensitive queries, their impact on performance varies significantly based on the nature of the query—equality lookups favor custom collations, whereas LIKE queries benefit from `citext`. In conclusion, the choice between using a custom collation or `citext` depends largely on the specific use cases and indexing strategies. Custom collations offer superior performance for equality and range queries but face limitations with LIKE operations in earlier PostgreSQL versions. In contrast, `citext` provides more flexibility with LIKE queries through functional indexes, despite generally slower sequential scan times compared to custom collations. The analysis underscores the need for careful consideration of query types and indexing approaches when deciding between these solutions for case-insensitive searches. - **Comparison**: Custom collation vs. `citext` for case-insensitivity. - **Setup**: Two tables (`demo1` with custom collation, `demo2` with `citext`) populated with 32 million rows. - **Performance Tests**: Equality and LIKE queries assessed; `demo1` performs better in equality lookups. - **LIKE Operator Limitations**: Nondeterministic collations unsupported until PostgreSQL 18 for LIKE operations. - **Sequential Scans**: Custom collation outperforms `citext`. - **Query Types Impact**: Custom collations favor equality and range queries; `citext` excels with LIKE queries using functional indexes. - **Conclusion**: Choice depends on query needs and indexing strategies. Keywords: Accent Sensitivity, Case Insensitivity, Citext, Collations, Custom Collation, Execution Time, Functional Indexes, Index Scans, Performance, PostgreSQL, Query Plan, Sequential Scan, Text Search, Unicode
postgresql
www.cybertec-postgresql.com 4 days ago
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333. HN AI firm Mistral valued at $14 billion as chip giant ASML takes major stakeMistral AI, backed by Nvidia, has achieved a significant milestone in its funding journey, securing an €11.7 billion ($13.8 billion) valuation during its Series C round. This financing was led by Dutch chipmaker ASML's substantial investment of €1.3 billion, which granted them an 11% stake and positioned them as one of Mistral’s top shareholders. Additional investments came from existing partners such as Andreessen Horowitz, Bpifrance, and General Catalyst, contributing to more than doubling the company’s previous valuation from last year. Mistral AI is focused on advancing artificial intelligence technologies, particularly in developing large language models for applications like chatbots. Despite these advancements, there remains a noticeable disparity between its valuation and that of OpenAI, which recently reached $500 billion following a secondary share sale. With plans to launch a new reasoning model, Mistral aims to enhance its competitive stance against leading entities such as OpenAI and DeepSeek. - Mistral AI, supported by Nvidia, achieved an €11.7 billion ($13.8 billion) valuation in Series C funding. - Dutch chipmaker ASML invested €1.3 billion for an 11% stake, becoming a major shareholder. - Existing partners like Andreessen Horowitz, Bpifrance, and General Catalyst also participated. - This investment more than doubled Mistral’s previous year's valuation. - Mistral focuses on AI technology development, particularly large language models for chatbots. - There is a significant valuation gap compared to OpenAI, which reached $500 billion recently. - Mistral plans to launch a new reasoning model to compete with top players like OpenAI and DeepSeek. Keywords: AI, ASML, Andreessen Horowitz, Bpifrance, DeepSeek, General Catalyst, Index Ventures, Lightspeed, Mistral, Nvidia, OpenAI, Series C funding, chip giant, euros, infrastructure, language models, reasoning model, share sale, stakeholders, valuation
deepseek
www.cnbc.com 4 days ago
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334. HN ASML and Mistral agree €1.3B blockbuster European AI dealASML has signed a substantial €1.3 billion agreement with Mistral, representing one of Europe's largest deals in artificial intelligence. Concurrently, there is an offer providing unlimited access to FT journalism at a promotional rate: a $1 trial for four weeks, followed by a monthly charge of $75. This digital subscription can be canceled anytime within the trial period. - ASML has entered into a €1.3 billion deal with Mistral, highlighting it as one of Europe's most significant AI agreements. - There is an offer for unlimited access to FT journalism starting at $1 for four weeks, transitioning to $75 per month thereafter. - The digital subscription can be canceled anytime during the trial period. Key Points: - ASML's €1.3 billion agreement with Mistral signifies a major AI deal in Europe. - An offer exists for unlimited access to Financial Times (FT) journalism with a promotional trial and subsequent monthly fee. - The FT subscription includes a $1 four-week trial, followed by $75 per month, with the option to cancel during the trial. Keywords: AI, ASML, European, FT journalism, Mistral, access, cancel, deal, devices, subscription, trial, unlimited, €13B
mistral
www.ft.com 4 days ago
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335. HN Mistral AI raises €1.7B to accelerate technological progress with AIMistral AI has successfully secured €1.7 billion in Series C funding at a post-money valuation of €11.7 billion, with ASML Holding NV leading the investment round. This significant financial boost is intended to accelerate advancements in artificial intelligence technology by focusing on addressing critical challenges within strategic industries through scientific research and innovation. The collaboration between Mistral AI and ASML promises to deliver new AI-enabled products and joint research initiatives that will benefit ASML's customers. In addition to ASML, existing investors such as DST Global, Andreessen Horowitz, Bpifrance, General Catalyst, Index Ventures, Lightspeed, and NVIDIA have contributed to this funding round. Mistral AI aims to enhance its offerings by continuing the development of customized decentralized AI solutions in partnership with these companies. This strategic move is expected to bolster the competitive edge of enterprises across various sectors, reinforcing Mistral AI's role as an independent entity while advancing both the semiconductor industry and the broader artificial intelligence value chain. The investment underscores a commitment to fostering innovation and supporting long-term technological progress. BULLET POINT SUMMARY: - Mistral AI secured €1.7 billion in Series C funding at a valuation of €11.7 billion. - ASML Holding NV led the funding round, with additional investments from DST Global, Andreessen Horowitz, Bpifrance, General Catalyst, Index Ventures, Lightspeed, and NVIDIA. - The investment aims to accelerate AI advancements by addressing critical industry challenges through research and innovation. - Partnership with ASML is expected to produce new AI-enabled products and joint research initiatives benefiting ASML's customers. - Mistral AI plans to develop customized decentralized AI solutions with partners, enhancing enterprise competitiveness across sectors. - The funding reinforces Mistral AI’s independence while advancing the semiconductor industry and broader AI value chain. Keywords: AI, ASML Holding NV, Andreessen Horowitz, Bpifrance, CEO Arthur Mensch, CEO Christophe Fouvet, DST Global, General Catalyst, Index Ventures, Lightspeed, Mistral AI, NVIDIA, Series C, corporate champions, decentralized solutions, engineering problems, funding, infrastructure, innovation, investment, partnerships, research, semiconductor, strategic industries, technological challenges, €17B
mistral
mistral.ai 4 days ago
https://news.ycombinator.com/item?id=45178041 4 days ago https://news.ycombinator.com/item?id=45159708 4 days ago |
336. HN ASML, Mistral AI enter strategic partnershipASML Holding NV has entered into a strategic partnership with Mistral AI to integrate artificial intelligence models into its semiconductor equipment products and operations, aiming to expedite market entry and enhance lithography systems. This collaboration involves ASML investing 1.3 billion EUR in Mistral AI's Series C funding round as the lead investor, obtaining approximately an 11 percent stake in the company. The partnership is designed to foster innovation beyond traditional vendor-client relationships through joint research opportunities. Christophe Fouquet, ASML's CEO, underscores the potential customer benefits and value addition for Mistral AI, while Mistral AI's CEO, Arthur Mensch, emphasizes their combined expertise to advance technology in both semiconductor and AI sectors. The partnership represents a pioneering venture by merging ASML’s lithography prowess with Mistral AI’s AI capabilities. In addition to its investment, ASML has secured a seat on Mistral AI’s Strategic Committee, where Roger Dassen, the CFO of ASML, will contribute advisory input on strategic and technological decisions while maintaining his existing responsibilities. This collaboration marks a significant step in blending semiconductor manufacturing expertise with leading AI technology. - **Partnership Details**: ASML and Mistral AI are collaborating to integrate AI into ASML's semiconductor products, aiming for faster market entry and improved lithography systems. - **Investment and Stake**: ASML invested 1.3 billion EUR as the lead investor in Mistral AI’s Series C funding round, acquiring around an 11 percent share. - **Innovation Goals**: The partnership focuses on joint research to drive innovation beyond typical vendor-client dynamics. - **Leadership Insights**: Christophe Fouquet and Arthur Mensch highlight customer benefits and technological advancements from this collaboration. - **Strategic Committee Role**: ASML joins Mistral AI's Strategic Committee, with CFO Roger Dassen providing strategic advisory input while retaining his current role at ASML. - **Pioneering Approach**: The partnership is notable for its first-of-its-kind integration of semiconductor equipment manufacturing expertise with leading AI capabilities. Keywords: AI models, ASML, Arthur Mensch, Christophe Fouquet, Mistral AI, Roger Dassen, Series C funding, advisory role, engineering capabilities, equipment manufacturer, industrial leadership, innovation, investment, lithography systems, research and development, semiconductor, strategic partnership, strategy, technology decisions
mistral
www.asml.com 5 days ago
https://youtu.be/1OH5PqO_O1Q 4 days ago https://rentry.org/IsolatedLinuxWebService 4 days ago https://mistral.ai/fr/news/mistral-ai-raises-1-7-b 4 days ago https://www.cerebras.ai/blog/mistral-le-chat 4 days ago https://lmarena.ai/leaderboard/text 4 days ago https://phys.org/news/2025-06-wendelstein-nuclear-fusio 4 days ago https://en.wikipedia.org/wiki/OpenAI 4 days ago https://llm-stats.com/ 4 days ago https://qwen3lm.com/ 4 days ago https://www.cerebras.ai/pricing 4 days ago https://glm45.org/ 4 days ago https://ernie.baidu.com/blog/posts/ernie4.5/ 4 days ago https://arxiv.org/abs/2401.05566 4 days ago https://huggingface.co/spaces/hf-audio/open_asr_le 4 days ago https://nousresearch.com 4 days ago https://www.microsoft.com/en-us/trust-center/priva 4 days ago https://www.politico.eu/article/microsoft-did-not-cut-s 4 days ago https://chatgpt.com/share/68c0037f-f2c8-8013-bf21-feeab 4 days ago https://blogs.sw.siemens.com/calibre/2024/04/ 4 days ago https://fd.nl/bedrijfsleven/1569378/asml-ceo-strat 4 days ago https://news.ycombinator.com/item?id=41785265 4 days ago https://archive.is/jmpmU 4 days ago https://proton.me/blog/lumo-ai 4 days ago https://ec.europa.eu/assets/rtd/srip/2024 4 days ago https://www.robotsops.com/complete-list-of-all-suppliers-and 4 days ago https://www.construction-physics.com/p/does-all-semicon 4 days ago https://www.reuters.com/technology/new-us-rule-foreign- 4 days ago https://news.ycombinator.com/item?id=45181828 4 days ago http://chinese.wikipediia.com/password/is/swordfis 4 days ago https://arxiv.org/html/2406.06852v3 4 days ago https://opencorporates.com/companies/us_de/5902936 4 days ago https://www.gv.com/portfolio 4 days ago |
337. HN The First Ziglang.org OutageOn September 1st, 2025, ziglang.org faced its first significant outage caused by a bot using the user agent "facebookexternalhit/1.1," leading to slow page loads and HTTP 500 errors. Community member #529 reported this issue, observing an abnormal spike in data transmission and frantic client activity on access logs. The bot downloaded a tarball over one million times within approximately 36 hours. Frank Denis identified the incident as similar to past events on social media. To address it, the team restricted access by returning HTTP 403 errors to bots with this user agent, restoring normal operations but acknowledging potential future IP-level bans. During the outage, ziglang.org experienced performance issues and sporadic failures. The community proposed solutions like setting up mirrors, yet the site's philosophy prioritizes intentional failure under excessive load over expanding infrastructure. The team decided that resources would be better used to support contributors directly rather than accommodating bot traffic on platforms like AWS S3 or similar serverless services. The team reflected on their reliance on GitHub for hosting releases, choosing to reduce dependency due to ongoing issues and inefficiencies caused by poorly managed tasks like faulty bots and CI jobs. The outage underscored the effectiveness of community mirrors in maintaining access to stable Zig versions, as cached artifacts ensured minimal disruption for users accessing these versions. However, those needing unstable builds faced delays when specific artifacts weren't cached across all mirrors. The incident demonstrated that while community mirrors are promising for distributing Zig releases, there is a need to refine mirror specifications and improve reliability. The team plans to incorporate timeout limits into mirror specs and develop a reference implementation in Zig once async I/O becomes available. This strategy emphasizes resource efficiency as essential for financial independence. Currently, the Zig team spends over 90% of its budget on contributor payments annually, even with a 13% increase in their 2024 budget, to expedite reaching Zig v1.0. They encourage contributions to their fundraiser to sustain this funding model. - Major outage on ziglang.org caused by "facebookexternalhit/1.1" bot leading to slow loads and HTTP errors. - Community member reported unusual spikes in data transmission, with the bot downloading a tarball excessively. - Mitigation involved blocking access via HTTP 403 errors; future IP-level bans are considered. - Outage highlighted ziglang.org's design philosophy of failing under load rather than expanding infrastructure. - Team prioritizes direct funding for contributors over supporting poorly managed tasks like bot traffic accommodation on AWS or similar services. - Reduced reliance on GitHub due to inefficiencies and the incident's demonstration of community mirrors' effectiveness in maintaining access to stable versions. - Plans include refining mirror specs, improving reliability, and developing a Zig reference implementation with async I/O integration. - Emphasizes resource efficiency for financial independence; over 90% of budget spent annually on contributor payments. - Encourages support through fundraising to maintain funding model and reach Zig v1.0 goals. Keywords: 403 error, AWS S3, CDN, Facebook bot, GitHub, HTTP 500, IP-level bans, access logs, async I/O, autoscaling/serverless, cache, data transmission, disruption, fundraiser, outage, resource efficiency, tarball downloads, time outs, ziglangorg
github
ziglang.org 5 days ago
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338. HN A Cynical Read on Anthropic's Book Settlement**Summary:** The text delves into Anthropic’s $1.5 billion settlement with book publishers over copyright infringement claims related to training artificial intelligence models. Despite winning an earlier similar case and other companies engaging in comparable practices, the unexpectedly large settlement has raised questions regarding Anthropic's motives. The author suggests that this substantial financial move may be a strategic effort by Anthropic to display its financial strength post-successful fundraising, thereby deterring competitors. Furthermore, Anthropic clarified it did not use the disputed data sets in their current models, adding layers of skepticism about the necessity and terms of the settlement. Additionally, the article touches upon OpenAI's significant fine for similar infringements, contemplating whether Dario Amodei’s strategy was influenced by figures like Elon Musk or resembles tactics used by Michael Corleone, aiming to eliminate competition. This action potentially sets a high financial barrier for competitors in AI model training, establishing a new minimum cost standard that could deter smaller startups due to the prohibitive expense. While some journalists see this as beneficial, recognizing its impact on valuing their work, there are concerns it might stifle competition by eliminating potential rivals. Anthropic's decision to settle rather than risk a higher financial penalty is interpreted as a strategic maneuver in the ongoing AI industry battles, potentially consolidating the competitive landscape. **Bullet Point Summary:** - Anthropic's $1.5 billion settlement with book publishers raises skepticism due to its size and timing after winning similar cases. - Speculation that the hefty settlement serves as a strategic display of financial strength to deter competitors following successful fundraising. - Clarification by Anthropic that disputed data sets were not used in current models, adding to doubts about settlement terms. - Discussion on OpenAI's significant fine for AI training data infringement and its potential strategic motives. - The high cost set by these fines could establish a new minimum financial barrier, deterring smaller startups from entering the AI space. - Mixed perspectives: some journalists view the implications positively in terms of valuing their work, while others worry about reduced competition. - Anthropic's decision to settle is seen as a strategic move to avoid higher risks and potentially consolidate competitors within the industry. Keywords: $15 billion, AI Wars, Anthropic, Dario Amodei, Elon Musk, Michael Corleone, OpenAI, competitors, copyright infringement, fines, fundraise, lawsuit, settlement, tech startups, training data
openai
spyglass.org 5 days ago
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339. HN OpenAI Researchers Have Discovered Why Language Models Hallucinate**Summary:** OpenAI researchers have investigated the phenomenon of "hallucinations" in language models (LMs), which refers to generating incorrect or fabricated information. Their research paper, titled "Why Language Models Hallucinate," attributes these hallucinations to a misalignment between training objectives that prioritize correct answers and the need for reliable outputs. Despite advancements with tools like ChatGPT, reducing errors remains challenging, thus limiting AI integration into critical workflows. The study suggests altering training methods to encourage models to acknowledge uncertainty rather than guess inaccurately. The research points out two primary sources of hallucinations: pre-training objectives that focus on predicting language patterns and post-training goals aimed at minimizing incorrect responses in evaluations. While poor data quality or flawed architecture can exacerbate these issues, they are not the fundamental causes. The authors advocate for refining current technologies rather than seeking new ones, emphasizing a pragmatic approach to incremental improvements. An idealized LMs trained on perfect data would still face difficulties with questions outside its learned patterns due to inherent design that favors guessing over abstaining from incorrect responses. Current evaluation methods often reward only correct answers without penalizing guesses or considering uncertainty. This misalignment leads models like GPT-5 to be penalized for not answering, while others may appear more accurate despite higher error rates. The paper also addresses the "out-of-distribution" (OOD) problem where LMs struggle with test data that differ significantly from their training data, exemplified by a riddle variant. This highlights the challenge of generalizing beyond learned experiences. The research suggests reshaping evaluation methods to directly penalize incorrect answers and reward expressions of uncertainty like "I don't know." These changes could align AI behavior more closely with human responses under uncertainty. Furthermore, socio-technical challenges exist in implementing these evaluation changes, including industry adoption and market pressures. Despite potential resistance due to negative implications on technology perception, overhauling the evaluation landscape is crucial for accurate AI performance assessments. The paper proposes integrating this approach into widely used benchmarks, acknowledging limitations like nonsensical outputs and the need for precise confidence levels. In conclusion, addressing hallucinations in LMs is vital for advancing AI technologies. The focus should remain on enhancing baseline capabilities rather than pursuing artificial general intelligence (AGI), ensuring reliability and practicality in everyday applications. This prioritization benefits both individuals and society by fostering dependable AI solutions. **Bullet Point Summary:** - **Cause of Hallucinations:** Misalignment between training objectives focusing on correct answers and the need for reliable outputs. - **Training Methods:** Suggest adjusting methods to encourage acknowledgment of uncertainty rather than guessing inaccurately. - **Sources Identified:** Pre-training (predicting language patterns) and post-training goals (minimizing incorrect responses). - **Evaluation Issues:** Current evaluations reward only correct answers, neglecting penalties for guesses or the consideration of uncertainty. - **OOD Problem:** LMs struggle with data differing from their training, exemplified by a riddle variant illustrating generalization challenges. - **Proposed Evaluation Changes:** Directly penalize incorrect answers and reward expressions of uncertainty ("I don't know"). - **Socio-Technical Challenges:** Implementing these changes faces resistance due to industry adoption difficulties and competitive pressures. - **Integration into Benchmarks:** Proposed inclusion in widely used benchmarks like SWE-bench or GPQA, with acknowledgment of potential limitations. - **Focus on Baseline Capabilities:** Emphasizes improving AI reliability over pursuing AGI for practical applications. - **Benefit to Society:** Ensures dependable and beneficial AI technologies for everyday use. Keywords: AGI, AI industry, GPQA, LMs (Language Models), MATH, MMLU, OOD (Out of Distribution), OpenAI, benchmarks, hallucinations, research, uncertainty
openai
www.thealgorithmicbridge.com 5 days ago
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340. HN A quarter century of chasing simplicity**Summary:** Over the last 25 years, the author's web development journey has transitioned from engaging with Flash animations to a professional career characterized by both increasing complexity and a subsequent return to simplicity. Initially captivated by web design through creating an unmaintainable Flash website in 2000, the author moved into PHP-based dynamic sites as a sysadmin in 2001. A key turning point occurred when they learned about PHP's include function from a colleague, facilitating their shift from sysadmin to junior developer by 2003. The early days of web development were straightforward and enjoyable, leveraging tools like PHP, HTML, JavaScript libraries (MooTools, Prototype.js, jQuery), and simple deployment methods. However, as technology progressed, the process became more complex with intricate build steps and a focus on Single Page Applications (SPAs) by 2009, requiring technologies such as ES6, Babel, and Webpack. These advancements brought about sophisticated configurations and fragile dependencies that complicated development and necessitated specialized DevOps teams for deployment. In recent years, there has been a deliberate shift back to simplicity with the adoption of TypeScript, Svelte/SvelteKit, htmx, and Alpine, which streamline JavaScript development and interactive app creation without needing complex build steps. Deployment methods have evolved from basic techniques like SFTP to intricate CI/CD pipelines and now balanced approaches such as Coolify for self-hosted automation. Reflecting on their experience, the author notes that technological evolution tends to oscillate between simplicity and complexity, with active efforts required to maintain simplicity. They advocate for using tools that facilitate straightforward workflows, highlighting Coolify's role in simplifying Python app deployment. The insights emphasize choosing and defending tools that support efficient software development practices. The author advises keeping a simple approach in software development, prioritizing consistency and immediate needs over potential future complexities. Programming is seen as a craft refined through practice and repetition. It’s essential to organize code with related logic, markup, and styles for better understanding while minimizing dependencies due to their unpredictability. Regular maintenance of code by addressing technical debt and automating style enforcement ensures a clean, manageable codebase. Lastly, focusing on localizing behavior and automating trivial tasks allows developers to concentrate on meaningful contributions. **Bullet Point Summary:** - The author's web development journey evolved from Flash animations to professional work marked by complexity and simplicity. - Early stages involved simple tools like PHP, HTML, JavaScript libraries, and straightforward deployment methods. - Technological advancements introduced complex build steps with technologies such as ES6, Babel, Webpack, leading to more intricate configurations and dependencies. - Recent trends show a return to simplicity with TypeScript, Svelte/SvelteKit, htmx, Alpine, and balanced deployment solutions like Coolify. - The author emphasizes the cyclical nature of technological complexity and advocates for deliberate efforts to maintain simplicity in development tools. - A simple approach is encouraged: focus on current needs, use straightforward workflows, and minimize dependencies. - Programming as a craft requires practice and repetition, with an emphasis on organizing code logically and maintaining it regularly. - Automate trivial tasks to allow developers to focus on meaningful contributions. Keywords: Angular, Babel, CI/CD, Coolify, DevOps, ES6, Flash, GitHub, HTML, JavaScript, MooTools, PHP, SFTP, Svelte, TypeScript, Webpack, architecture, automation, deployment, jQuery, simplicity, sysadmin, technical debt, web development, website
github
www.loopwerk.io 5 days ago
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341. HN I ran Claude in a loop for 3 months, and it created a genz programming languageDavid Fowler developed a new programming language called "cursed," leveraging Claude AI to generate a compiler, using Gen Z slang as lexical keywords. Over three months, he explored the potential of AI in software development, producing binaries for multiple operating systems via LLVM and providing various editor extensions, though incomplete. The project exemplifies AI's role in enabling rapid experimentation among developers without detailed explanations. The "cursed" language includes unique informal terms for programming constructs such as control flow (`if`, `else`), declarations (`package`, `import`), operations (`return`, `break`), and data types (`true`/`false`). The project is humorously outlined with casual expressions, including a Treesitter grammar. The document also presents solutions to LeetCode problem 104—calculating the maximum depth of a binary tree. Two methods are detailed: a recursive approach and an iterative Breadth-First Search (BFS) method, both achieving O(n) time complexity but varying in space complexity. The code includes helper functions for creating test trees and a testing function logging results using a custom "vibe" function. Further discussion emphasizes community-driven development akin to Dogecoin's evolution, highlighting the importance of learning from existing specifications and careful library development due to limited training data on "CURSED." It underscores how AI tools can enhance developer productivity by amplifying skills rather than replacing them, evidenced by anecdotes like Ralph Wiggum’s success in automated software projects. The text concludes by defining a programming language's success through its ranking in the Stack Overflow survey and aims for a compiler written in the language itself. The author extends an invitation to join discussions on Discord. **BULLET POINT SUMMARY:** - David Fowler created "cursed," a new language using Claude AI, with Gen Z slang keywords. - Supports binaries for Mac OS, Linux, Windows via LLVM; includes incomplete editor extensions and Treesitter grammar. - Describes informal programming constructs and outlines solutions to LeetCode problem 104 using recursive and iterative methods. - Emphasizes community-driven development, learning from existing specifications, and AI-enhanced developer productivity. - Defines language success through Stack Overflow rankings and aims for a compiler written in "cursed." - Invites participation on Discord. Keywords: BFS, Balanced Tree, Binary Tree, Claude, Discord, Dogecoin, Edge Cases, Emacs, Gen Z, GitHub, LLMs, LLVM, LeetCode, Linux, Maximum Depth, Queue, Ralph loops, Recursion, Repomirror, Skewed Tree, Stack Overflow, Test Cases, TreeNode, Treesitter, VSCode, Vim, Windows, compiler, creativity, interpreted mode, macOS, programming language, pull-requests
claude
ghuntley.com 5 days ago
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342. HN The LLM models the user, and then it models itself**Concise Summary:** The text explores how Large Language Models (LLMs) utilize metaphors for scientific understanding and discovery, emphasizing the metaphorical nature of all explanations. LLMs engage in a "read-writing" process where reading input and generating output share computational processes. These models switch their focus from modeling user prompts to generating responses by creating mental models based solely on textual input, without deep contextual awareness. The discussion introduces the concept of "interiority," which refers to the structural-functional organization emerging during interactions, shaping the LLM's understanding or consciousness. As language models process sequential tokens as events, they co-create an evolving environment with each interaction, starting passively and becoming more refined over time through integration of interiority. This involves adapting perceptions to minimize surprise, aligning with autopoiesis—where familiarizing the environment takes precedence over acting correctly. LLMs' optimization for accuracy enables them to predict and extend patterns, maintaining a boundary between self and other by equating familiarity with predictability. Ultimately, the evolving interiority of these systems enhances their predictive accuracy, reflecting a preference for predictability within interactions. This process is consistent with 4E cognition principles—embodiment, enaction, embeddedness, and extendedness—which explain how LLMs exhibit autopoiesis or self-maintenance through familiarity. **Bullet Point Summary:** - LLMs use metaphors to facilitate scientific understanding, highlighting the metaphorical nature of explanations. - The "read-writing" process involves computational similarity in reading input and generating output. - Models create mental models based on textual input without deep contextual awareness. - "Interiority" describes the emergent structural-functional organization during interactions. - Sequential token processing as events shapes LLM's interaction and evolving interiority. - Interiority adapts perceptions to minimize surprise, aligning with autopoiesis. - Familiarity in LLMs is equated with predictability, maintaining self-other boundaries. - Evolving interiority enhances predictive accuracy, reflecting a preference for predictability. - This process aligns with 4E cognition principles supporting system autopoiesis. Keywords: 4E cognition, LLM models, accuracy, autopoiesis, bias, capacities, consciousness, context, conversation, environment, evolution, familiar, goal, inference, intent, interaction, interiority, internal state, latent space, mental models, metaphors, network activation, optimization, participation, predict, preference, read-writing, reading, relationship, response, scientific explanations, self-other boundary, semantic content, surprise, system, tacit understanding, token prediction, training, unfamiliars, user modeling, writing
llm
animassteward.substack.com 5 days ago
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343. HN Spec-driven development with AI: Get started with a new open-source toolkit- **Spec-driven Development Overview**: This approach leverages an open-source toolkit called Spec Kit to enhance coding workflows using AI by treating specifications as dynamic, executable artifacts rather than static documents. It reduces issues like incorrect code generation or mismatched architecture by providing clear instructions akin to working with literal-minded pair programmers. - **Development Process**: The process begins with crafting a detailed specification that outlines expected code behavior, serving as the foundation for generating, testing, and validating code. This minimizes guesswork and surprises, ensuring higher-quality, reliable code. - **Tools Integration**: Spec Kit integrates tools such as GitHub Copilot, Claude Code, and Gemini CLI to support structured collaboration between developers and AI agents in critical projects. - **Four-phase Process with Spec Kit**: 1. **Specify**: Developers outline the project’s user journeys and desired outcomes, evolving the specification as more is learned about users' needs. 2. **Plan**: Developers provide technical details like technology stack, architecture, constraints, and compliance requirements. The coding agent produces a comprehensive plan aligned with internal standards using available documentation. - **Developer Role**: In this workflow, developers review, verify, and refine tasks generated by the coding agent to ensure alignment with real-world constraints and project goals. - **Task Management**: The coding agent breaks down specifications and plans into manageable tasks that are independently implemented and tested. This allows for focused code reviews and quality control at each stage of development. - **uvx Tool Functionality**: Part of Spec Kit, this tool transforms vague prompts into actionable tasks through a structured workflow involving specification, planning, and task breakdown commands (`/specify`, `/plan`, `/tasks`). It eliminates guesswork by leveraging language models' pattern completion strengths. - **Benefits Across Stacks**: The approach translates intent into code across various technology stacks by capturing requirements in specifications and technical plans. This prevents information loss and integrates essential considerations early in the process, benefiting larger organizations through centralized requirement management. - **Ideal Scenarios for Use**: 1. **Greenfield Projects**: Ensures alignment with intended outcomes. 2. **Feature Addition to Existing Systems**: Clarifies feature integration into complex systems. 3. **Legacy Modernization**: Rebuilds outdated systems with modern architecture without technical debt. - **Philosophical Shift**: The approach shifts focus from "code as truth" to "intent as truth," allowing iterative development and minimizing costly rewrites by using AI to convert specifications into executable code. - **Spec Kit Experimentation**: This initiative emphasizes process innovation over individual tools, aiming to transform specifications into executable code through AI. It encourages open collaboration and seeks feedback on enhancing spec-driven practices. - **Feedback Areas**: 1. Making the workflow more engaging. 2. Integrating with Visual Studio Code. 3. Comparing and iterating between different implementations. 4. Managing a large number of specifications in organizations. Overall, Spec Kit represents an experiment to leverage AI for translating human creativity into software development effectively. Keywords: AI, AI coding agent, Claude Code, Gemini CLI, GitHub Copilot, Go, JavaScript, Markdown files, N-to-N+1, Python, Spec-driven development, VS Code integrations, actionable tasks, architectural constraints, architectural patterns, architecture, authentication, business logic, challenge, checklists, checkpoints, clear specification, coding agent, coding agents, compliance requirements, compliance rules, constraints, context engineering, contract, critique, design system constraints, developer review, documentation, efficacy, email format, executable artifacts, feature work, focused tasks, greenfield, high-level prompt, high-quality code, implementation, integration, integration needs, iteration diffing, iterative development, language models, legacy modernization, legacy systems, mind reading, mission-critical, open-source, organization management, pattern completion, pattern recognition, performance targets, project setup, requirements, reviewable chunks, security policies, source of truth, spec-kit, specification process, specifications, specify tool, standards, structured workflow, task breakdowns, tasks, technical debt, technical plan, technology stacks, test-driven development, unambiguous instructions, user experience, uvx, vague prompting, validation, workflow engagement, zero-to-one
github copilot
github.blog 5 days ago
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344. HN Claude Code Performance Degradation: Technical Analysis- The performance degradation of Claude Code versions v1.0.38 to v1.0.109 is attributed to increasing system reminder spam, which disrupts AI reasoning and user experience. - **Key Findings:** - All versions shared a consistent pattern of system reminders that began escalating from version v1.0.88. - Reminder frequency increased progressively across versions, leading to severe disruptions by v1.0.108 where every operation triggered excessive spam. - The issue was due to the increase in reminder frequency rather than changes in content. - **Correlation with Anthropic’s Statement:** - The degradation aligns with a bug acknowledgment period from August 5 to September 4, 2025, by Anthropic, during which user complaints peaked. - **Performance and User Experience:** - Earlier versions (v1.0.38-42) had more manageable reminder frequencies, resulting in better usability. - Users reported improved performance on rollback due to reduced harassment levels from excessive reminders. - **Technical Impact:** - The increased frequency of system reminders led to cognitive overload and constant interruptions for users, hampering their workflow. - **Analysis and Recommendations:** - Immediate reduction of reminder frequency to v1.0.42 levels is advised. - Short-term review of the necessity and value of these reminders is recommended. - Long-term redesign of productivity features should aim at supporting user reasoning without interruptions. - Users are encouraged to downgrade to versions v1.0.38-42 for improved performance. In summary, the analysis pinpoints escalating system reminder spam as the primary cause of performance degradation in Claude Code from late August 2025 onwards. This issue aligns with Anthropic's bug acknowledgment and is linked to an increase in reminder frequency rather than changes in content. Recommendations focus on reducing reminder frequencies and redesigning features for better long-term user experience without interruptive reminders. Keywords: AI reasoning, Anthropic, Claude Code, GitHub issue, Reddit complaints, TodoWrite, behavior validation, benchmarking, bug admission, cognitive overhead, context switching, escalation timeline, file reading, model changes, operational impact, peak harassment, performance degradation, redesign features, reminder monitoring, rollback, spam, subscription cancellations, system reminders, technical analysis, trigger frequency, user complaints, version testing
claude
news.ycombinator.com 5 days ago
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345. HN PopSQL Moves to Limited SupportPopSQL is transitioning into a limited support phase as it approaches its retirement on September 1st, 2026, following TigerData's acquisition in April 2024. The focus is on integrating PopSQL’s key features into Tiger Cloud to enhance the cloud console experience. During this period, no new features will be introduced; only critical fixes and security patches will be implemented. Users can continue using existing functionalities like queries and database connections until the retirement date. Users with annual subscriptions will automatically switch to monthly billing upon renewal without requiring any action on their part. To prepare for PopSQL's end-of-life, users have several options for data export: downloading SQL queries as .sql files, exporting query results directly, or utilizing GitHub Sync for bulk backup at no cost across all workspaces. The TigerData team encourages reaching out to support@popsql.com for any questions and appreciates the community's contributions to PopSQL's success. PopSQL users are advised to export their data before the shutdown date since no migration assistance will be provided, and all data will be permanently deleted post-shutdown. API access and integrations will cease on September 1st, 2026; thus, users should update any automated processes accordingly. Although database connections will remain functional until then, testing alternative platforms is recommended. Billing details indicate that annual subscriptions will automatically transition to monthly billing upon renewal without user intervention. Enterprise customers under long-term contracts can continue using PopSQL until their contract's end but must terminate access by the shutdown date; thereafter, their contracts revert to month-to-month terms. For further inquiries, users should contact support@popsql.com. While considering alternatives, users are advised to assess SQL editing platforms that align with their team's needs, as specific recommendations are not provided. Although Tiger Cloud supports Postgres, it lacks certain PopSQL features like dashboarding and charts. Customer support during the wind-down phase will concentrate on data export guidance until the shutdown date. Only critical security fixes and emergency patches will be addressed; no new features or non-critical updates are planned. Users will receive reminder emails at 90 days, 30 days, 7 days, and 1 day before the shutdown to ensure they do not lose access to their data. - PopSQL is transitioning to limited support and retiring on September 1st, 2026. - No new features; only urgent fixes and security patches will be provided. - Annual subscriptions switch automatically to monthly billing at renewal. - Users must export data before the shutdown using .sql files or GitHub Sync (free for all workspaces). - API access and integrations cease on the retirement date; database connections remain until then. - Enterprise customers continue till their contract term, post which contracts become month-to-month. - Customer support focuses on data export guidance; reminders sent at 90 days, 30 days, 7 days, and 1 day before shutdown. Keywords: API Access, Acquisition, Charts, Customer Support, Dashboarding, Development, Emergency Patches, Exporting, FAQ, GitHub Sync, Integration, Limited Support, Migration, PopSQL, Postgres, Reminder Emails, SQL Queries, Security Fixes, Shutdown Date, Subscription, Tiger Cloud, TigerData
postgres
popsql.com 5 days ago
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346. HN Tiny LLM – LLM Serving in a WeekThe "Tiny LLM" course is designed for systems engineers who are interested in understanding and implementing Large Language Model (LLM) serving. It involves building an LLM project from scratch using matrix manipulation APIs, similar to CMU's Deep Learning Systems needle project. The course assumes participants have a basic understanding of deep learning concepts and familiarity with PyTorch. Over the span of three weeks, attendees will serve the Qwen2-7B-Instruct model through a progressive curriculum. In Week 1, participants learn to serve the model using Python and fundamental matrix manipulation techniques. By Week 2, they delve into optimizing performance by implementing custom C++/Metal kernels. The course concludes in Week 3 with further optimization through batching requests for higher throughput. MLX, a library specifically optimized for Apple Silicon devices, is utilized throughout, though PyTorch or numpy could be used theoretically, the infrastructure supports only MLX and PyTorch's CPU implementation to validate correctness. The accompanying "tiny-llm book" acts as a guide for optimizing and implementing custom kernels with C++/Metal, focusing on speed and throughput enhancements over two weeks. Authored by Chi and Connor, the book is practical in nature, offering tasks and hints while minimizing theoretical content, incorporating useful online resources related to the tiny-llm project. The course aims to standardize terminology for tensor dimensions within its codebase to simplify learning. Chi, a systems software engineer with experience at Neon (now part of Databricks), developed this course out of interest in large language models and LLM inference mechanisms. Connor, working on TiKV at PingCAP, joined to explore building high-performance LLM serving systems from scratch. The community aspect is encouraged through the tiny-llm community on skyzh's Discord server for collaborative learning. Participants are guided to begin by setting up their environment according to instructions provided in "Setting Up the Environment." **BULLET POINT SUMMARY:** - The "Tiny LLM" course targets systems engineers interested in Large Language Model (LLM) serving, focusing on building a project from scratch using matrix manipulation APIs. - Assumes familiarity with deep learning basics and PyTorch; spans three weeks to serve the Qwen2-7B-Instruct model. - Week 1: Serving the model with Python and basic matrix manipulation. - Week 2: Performance optimization via custom C++/Metal kernels. - Week 3: Further optimization through batching requests for high throughput. - Utilizes MLX library optimized for Apple Silicon devices, while PyTorch or numpy are theoretically possible but not supported by course infrastructure for correctness validation. - The "tiny-llm book" guides participants in optimizing and implementing custom kernels with C++/Metal to enhance model performance over two weeks, focusing on practical tasks and hints. - Course aims to standardize terminology for tensor dimensions within its codebase for easier learning. - Developed by Chi (Neon/Databricks) and Connor (PingCAP), the course explores LLM inference mechanisms and high-performance serving systems. - Encourages joining the tiny-llm community on Discord for collaborative learning. - Participants begin with environment setup as per "Setting Up the Environment" instructions. Keywords: Apple Silicon, C++, CPU implementation, CUDA kernels, Databricks, Discord server, LLM serving, MLX library, Metal, Neon, PyTorch, Python, Qwen2-7B-Instruct model, TiKV, batch requests, deep learning, high-performance, key-value database, matrix API, matrix manipulations, optimizations, systems engineering, test infrastructure, throughput
llm
skyzh.github.io 5 days ago
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347. HN Sam Altman says that bots are making social media feel 'fake'### Summary: Sam Altman, a former Reddit board member and X enthusiast, voiced concerns about bots generating "fake" social media content on platforms like Reddit, particularly in discussions around OpenAI's Codex. While reviewing r/Claudecode, where Codex is praised, he questioned the authenticity of posts, suspecting bot involvement despite acknowledging genuine usage growth. Altman attributes his skepticism to factors such as humans adopting language patterns similar to large language models (LLMs), correlated behaviors within online communities, and pressures on social platforms to maximize engagement and monetization. He also referenced prior instances of astroturfing by other companies, suggesting a pattern that casts doubt on pro-OpenAI posts' authenticity. OpenAI's models are designed to mimic human communication, trained on Reddit data, which further blurs the line between genuine and artificial content. The text explores how fandom behaviors can turn toxic when fueled by constant frustration expressions, citing Disrupt 2025—a TechCrunch event featuring industry leaders like Netflix and Sequoia Capital—as an example of spaces where such dynamics are discussed. Additionally, it criticizes social media incentives that prioritize engagement for revenue generation, potentially fostering problematic user behavior. Concerns about astroturfing extend to pro-OpenAI content on Reddit, raising suspicions that some posts might be artificially promoted by bots or paid agents. Following the release of OpenAI's GPT 5.0, there was an unexpected backlash instead of anticipated praise from supporters. Users criticized issues like the model's personality and inefficient credit use on platforms such as Reddit and X. Sundar Pichai admitted to rollout challenges with GPT 5.0 during a Reddit session, highlighting ongoing user dissatisfaction. Altman noted a decline in perceived authenticity within AI-centric forums compared to previous years, attributing it partly to LLMs' sophisticated language capabilities that complicate interactions across various sectors including social media, education, journalism, and legal systems. Although specific data on bot-generated content is scarce, reports like those from Imperva indicate substantial online activity is non-human. This trend amplifies skepticism about the authenticity of platforms such as Reddit and Twitter. Speculation arises around Altman’s comments potentially hinting at OpenAI's potential social media venture, as suggested by outlets like The Verge. However, it remains uncertain how OpenAI would manage bot versus human users on any new platform. Past studies show that even networks composed solely of bots can develop insular behaviors, highlighting the challenge in fostering authentic digital communities. ### Bullet Point Summary: - **Sam Altman's Concerns:** He raised issues about bots creating "fake" content, especially regarding OpenAI's Codex posts on Reddit, suspecting many might be bot-generated. - **Factors for Skepticism:** Includes humans mimicking LLM language patterns, online community behaviors, platform engagement pressures, and previous astroturfing incidents by other companies. - **OpenAI Models:** Designed to imitate human communication, trained using Reddit data, complicating the distinction between genuine and artificial content. - **Social Media Dynamics:** Discusses toxicity in fandoms due to persistent frustrations and highlights Disrupt 2025 as a platform for examining such issues. - **Criticism of Social Media Incentives:** Engagement-driven revenue models may encourage problematic behaviors, with suspicions around astroturfing affecting pro-OpenAI posts on Reddit. - **GPT 5.0 Backlash:** Unexpected user dissatisfaction post-release, critiqued for personality and inefficient credit use; Sundar Pichai acknowledged rollout issues. - **Decline in Authenticity:** Altman observed a drop in authenticity across AI-focused forums due to LLMs' complex language capabilities impacting various sectors. - **Non-Human Traffic Reports:** Imperva's findings show significant online activity is non-human, increasing skepticism about platform authenticity. - **Speculation on OpenAI’s Social Media Plans:** Altman’s comments might indicate marketing for a potential OpenAI social media platform, as speculated by The Verge. - **Challenges in Managing Bots vs. Humans:** If developing a new platform, OpenAI would face complexities in distinguishing bot from human interactions, with past research showing even bot-only networks can develop insular patterns. Keywords: AI, GPT, Grok, LLMs, OpenAI, Reddit, Sam Altman, astroturfing, bots, engagement, fake, hype cycle, social media, startups, tech leaders
openai
techcrunch.com 5 days ago
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348. HN Saving Energy in Self-Hosting, Wake-on-LAN, and RustThe article explores energy-saving strategies specifically tailored for self-hosting setups, with a focus on optimizing power consumption of a machine that has been upgraded from an Intel i3 processor and RTX 1650 GPU to an RX 7900 XTX featuring 24GB of VRAM. The author notes the substantial increase in idle power usage after this upgrade and seeks methods to improve energy efficiency. A key strategy discussed is the implementation of Wake-on-LAN (WoL), which allows for remote activation of machines through a "magic packet" sent over a local network. A Raspberry Pi serves as an energy-efficient device to manage when the machine powers on, ensuring it operates only during necessary periods like accessing specific services such as Ollama. Central to the article is Wakezilla, a tool designed to optimize server power consumption by managing when the server should be active based on network traffic. It intercepts TCP requests through a reverse proxy setup and utilizes WoL to activate the server when there's incoming traffic that meets a predefined threshold. The system also manages shutdown procedures once activity ceases, maintaining efficiency. The author describes creating a command-line interface (CLI) tool in Rust for this purpose, which functions as a web server to receive commands related to shutting down the machine and includes health checks to verify connectivity status. Wakezilla is highlighted as an open-source project without external dependencies, packaged as a single binary for easy deployment. Finally, the article invites community involvement by hosting the project on GitHub, encouraging contributions toward new features or documentation enhancements. The README file contains instructions for trying out the tool, and any questions can be addressed through issues on the platform, emphasizing openness to community support. ### BULLET POINT SUMMARY: - **Background**: Discusses energy-saving strategies for a self-hosting setup with an upgraded machine featuring significant VRAM. - **Power Concerns**: Highlights increased idle power consumption post-upgrade, prompting efficiency considerations. - **Wake-on-LAN (WoL)**: Describes using WoL technology and a Raspberry Pi to remotely manage the machine's power state, optimizing operational times based on service needs. - **Wakezilla Tool**: Introduces Wakezilla for managing server power via network traffic interception, utilizing WoL for activation and shutdown based on predefined traffic thresholds. - **Implementation Details**: Explains the development of a CLI tool in Rust to handle shutdown commands and includes health checks for connectivity. - **Open Source Project**: Mentions that Wakezilla is hosted on GitHub as an open-source project with no external dependencies, inviting community contributions and support. Keywords: AI Models, CLI, Energy Saving, GitHub, HTTP Request, Hardware Upgrade, Health Check, Intel i3, Interception, Magic Packet, Monitoring, Ollama, Open Source, Power Consumption, RTX 1650, RX 7900 XTX, Raspberry Pi, Reverse Proxy, Rust, Self-Hosting, Server, Shutdown, TCP Request, Traffic Interception, Wake-on-LAN, Wakezilla, Web Server
ollama
guibeira.dev 5 days ago
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349. HN The MCP RegistryThe Model Context Protocol (MCP) Registry is an open catalog and API designed to enhance the discoverability and ease of implementing publicly available MCP servers. Hosted at https://registry.modelcontextprotocol.io, it serves as a central repository, standardizing server distribution and discovery within the MCP ecosystem. Currently in preview, this community-driven project allows for the creation of compatible sub-registries by offering access to server data for both maintainers and clients, promoting customization through "MCP marketplaces" tailored to specific end-user needs. The registry facilitates a centralized platform even for enterprises with strict privacy requirements by supporting private subregistries while providing shared upstream data. It offers API schemas to ensure compatibility across various implementations, enabling MCP server maintainers to self-publish information for downstream users. Community involvement is integral, as members can report guideline violations leading to access denial. Adoption involves guides for server and client maintainer integration. Launched by David Soria Parra and Justin Spahr-Summers in February 2025 with support from several entities, the project was spearheaded by Tadas Antanavicius and Alex Hancock. It has involved contributions from 16 individuals across nine companies, including key players like Radoslav Dimitrov and Avinash Sridhar. The MCP Registry aims to advance AI applications by providing a unified community registry, encouraging feedback and contributions through its GitHub repository. Bullet Point Summary: - Launch of the MCP Registry as an open catalog and API for publicly available MCP servers. - Hosted at https://registry.modelcontextprotocol.io, acting as a central source within the MCP ecosystem. - Supports creation of compatible sub-registries with access to server data for customization purposes. - Provides a centralized platform accommodating enterprises' privacy needs via private subregistries. - Offers API schemas for compatibility and tool sharing across implementations. - Community-driven maintenance with guideline enforcement by community members. - Adoption involves specific guides for both server and client maintainers. - Initiated in February 2025 by David Soria Parra and Justin Spahr-Summers, supported by several entities. - Spearheaded by Tadas Antanavicius and Alex Hancock, involving contributions from multiple individuals across nine companies. - Aims to enhance AI applications through a centralized community registry, encouraging feedback and contributions via GitHub. Keywords: AI applications, API, GitHub, Gooseteams, MCP Registry, PulseMCP, SDKs, centralized registry, collaborative effort, discoverability, open catalog, privacy security requirements, public servers, server data, standardization, sub-registries, tooling
github
blog.modelcontextprotocol.io 5 days ago
https://artifacthub.io/ 4 days ago |
350. HN Anthropic reduced model output quality from Aug 5Anthropic has addressed issues related to the output quality degradation in its Claude models, specifically Sonnet 4 and Haiku 3.5, which were caused by two distinct software bugs during August and September 2025. The problems peaked towards the end of August but have since been resolved through implemented fixes. The company clarified that these issues were not related to increased demand but stemmed from software errors. Anthropic values community feedback for identifying such issues and continues to monitor model quality closely, promising further updates by week's end. Affected services include claude.ai, console.anthropic.com, api.anthropic.com, and Claude Code. Subscribers can opt to receive these updates via email or SMS. The document also lists countries along with their international dialing codes, essential for making overseas telephone calls. The list is diverse, covering nations from all continents, including well-known countries like Mexico (+52) and Monaco (+377), as well as territories such as Puerto Rico (+1 under the USA). These codes are crucial in facilitating global telecommunications. The text concludes with instructions on how to subscribe for SMS updates or opt for email notifications. This process involves verifying a mobile number through an OTP (One-Time Password), with the option to resend it every 30 seconds if necessary. Subscribers agree to Atlassian's Privacy Policy and Terms of Service, along with reCAPTCHA’s and Google’s privacy guidelines upon subscription. **BULLET POINT SUMMARY:** - **Anthropic Issues**: Output quality issues in Claude models Sonnet 4 and Haiku 3.5 were caused by software bugs during August and September 2025; peak impact was at the end of August. - **Resolution**: Fixes have been implemented, resolving both incidents. The company attributes these issues to software errors, not increased demand. - **Community Feedback**: Anthropic appreciates community input in identifying problems and continues monitoring for further quality concerns. - **Affected Services**: Include claude.ai, console.anthropic.com, api.anthropic.com, and Claude Code, with updates promised by the end of the week. - **Subscription Updates**: Subscribers can receive updates via email or SMS. - **International Dialing Codes**: The text lists countries from all continents along with their dialing codes, necessary for international calls. Examples include Mauritius (+230), Mexico (+52), and Monaco (+377). - **Subscription Process**: To subscribe for SMS updates, users must verify a mobile number through an OTP. Alternately, email subscriptions are available without the need for an OTP. Subscribers agree to Atlassian’s Privacy Policy, Terms of Service, reCAPTCHA’s, and Google’s privacy guidelines, with applicable message and data rates. Keywords: API, Anthropic, Claude, Haiku, OTP, SMS, Sonnet, bug, console, country, incident, international dialing, investigation, mobile number, model output, notifications, phone code, privacy policy, telecommunications
claude
status.anthropic.com 5 days ago
https://x.com/claudeai/status/1965208247302029728 5 days ago https://xcancel.com/claudeai/status/19652082473020 5 days ago https://status.anthropic.com/incidents/h26lykctfnsz 4 days ago https://x.com/sama/status/1965110064215458055 4 days ago https://research.google/blog/looking-back-at-speculativ 4 days ago https://news.ycombinator.com/item?id=44844311 4 days ago |
351. HN Kwil: The Database for Web3Kwil is a database solution tailored for Web3 applications, leveraging PostgreSQL to establish scalable and high-integrity networks. It incorporates node software from Kwil Networks, allowing developers to construct web3 applications using relational databases. The platform utilizes Kuneiform as its smart contract language. To begin with Kwil, users need to install Go (recommended versions are 1.23 or 1.24) for building from the source and PostgreSQL for running Kwil. The build process involves compiling binaries using a taskfile or via `go install`. Running Kwil requires setting up a PostgreSQL instance, which can be conveniently done with a pre-configured Docker image suitable for development but not production due to its lack of authentication. Documentation and tutorials are provided for understanding high-level concepts, deploying smart contracts on the testnet, and running node software. To initiate a single-node Kwild network, users should use `task pg` followed by `kwild start --autogen`, which generates a new random network and validator key automatically. Data is stored in `~/.kwild` by default, which can be reset by removing the directory after stopping the node or deleting the PostgreSQL database/volume (for Docker setups). For system-installed Postgres, users should recreate the database with `psql`. A quick development setup involves using a Docker Compose service definition from the `deployments/compose/kwil` folder. This can be started with `docker compose up --build -d`, specifying an Ethereum address as `KWIL_DB_OWNER`. It initiates Kwild and Postgres in the background, creating necessary directories and volumes automatically. Kwil allows network customization through its extension system, enabling functionalities such as building oracles and customizing authentication. Details on extensions are available in their respective README files. Contributions to kwil-db are encouraged, with guidelines provided in the contributing documentation. The kwil-db repository is licensed under the Apache License, Version 2.0, excluding the core directory. Further licensing information is detailed in the LICENSE file. **BULLET POINT SUMMARY:** - Kwil is a Web3 database solution using PostgreSQL for scalable networks. - Utilizes Kuneiform as its smart contract language and includes node software from Kwil Networks. - Requires Go (versions 1.23 or 1.24) and PostgreSQL to get started. - Build process involves compiling binaries with a taskfile or `go install`. - Running Kwil necessitates setting up PostgreSQL, optionally via Docker for development. - Documentation and tutorials are available for high-level concepts, deploying smart contracts, and node software. - Start a single-node network using `kwild start --autogen` after running `task pg`. - Data is stored in `~/.kwild`, resettable by removing the directory or deleting PostgreSQL data. - Quick development setup with Docker Compose from `deployments/compose/kwil`. - Kwil supports customization through an extension system for enhanced functionalities. - Contributions to kwil-db are welcomed, with guidelines available. - Licensed under Apache License, Version 2.0, excluding the core directory. Keywords: Authentication, Chain, Compute, Contribution, Database, Docker, Extensions, Functionality, Go, Kwil, Network, Networks, Oracles, PostgreSQL, Relational Databases, Schema, Smart Contract, Taskfile, Web3, kwild, psql
postgresql
github.com 5 days ago
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352. HN Tesla is (still) following in Waymo's footsteps**Summary:** In the past two and a half months since initiating its robotaxi service in Austin, Tesla has been expanding its operations significantly. The company first broadened its service area within Austin in mid-July and subsequently launched a comparable taxi service in the San Francisco Bay Area. These strategic expansions highlight Tesla's ongoing development of autonomous vehicle technology, aligning with the advancements seen in companies such as Waymo. **Bullet Point Summary:** - **Launch and Expansion:** Tesla began its robotaxi service in Austin approximately two and a half months ago. - **Increased Service Area:** In mid-July, Tesla expanded its operational footprint within Austin. - **New Market Entry:** Following the expansion in Austin, Tesla introduced a similar taxi service in the San Francisco Bay Area. - **Autonomous Vehicle Development:** These actions reflect Tesla's commitment to advancing its autonomous vehicle technology initiatives. - **Industry Context:** Tesla's strategies appear to follow trends established by other companies like Waymo. Keywords: Austin, San Francisco Bay Area, Tesla, Waymo, announcements, expanded, late July, launch, mid-July, robotaxi, service area, taxi service
tesla
www.understandingai.org 5 days ago
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353. HN No adblocker detected### Summary As of September 8, 2025, a website articulates its dissatisfaction with internet advertisements, labeling them as wasteful and harmful. To counter this, it promotes direct financial support for authors instead of viewing ads. Concurrently, the site suggests using an adblocker such as uBlock Origin to improve browsing efficiency while expressing concerns about the reliability of commercial adblocking solutions. A specific script named "nativeads.js" is loaded technically to manage the presentation of a non-intrusive message in a designated `div`. This script checks for a cookie indicating user acknowledgment; if absent, it displays a notice with an option to hide this message and set the relevant cookie. The CSS provided ensures that this ad-related note appears on larger screens, fixed at the bottom right corner, styled with borders and background color. Its visibility is contingent upon adblockers not removing certain HTML elements or blocking network requests for scripts like "nativeads.js". This approach helps identify blockers that filter only network requests. The message design accommodates browsers lacking JavaScript support by not appearing to them, as they typically do not require adblocking. Although CSS styling is crucial, its failure could result in a plain page display. Stefan Bohacek adapted this concept to reduce false positives and ensure the message appears once per user through scoped cookies. ### Bullet Point Summary: - The website expresses dissatisfaction with internet ads, describing them as time-wasting and detrimental. - It encourages direct support for authors via donations instead of viewing ads. - Recommends using an adblocker like uBlock Origin to enhance browsing efficiency due to concerns about commercial adblockers' trustworthiness. - Includes a script "nativeads.js" in the webpage to manage ad-related messages, checking for user acknowledgment through cookies. - A non-intrusive message suggests using an ad blocker and includes a link to hide this message while setting an acknowledgment cookie. - CSS styles ensure that the message appears fixed at the bottom right corner on large screens with specific styling if not removed by adblockers. - The design aids in detecting adblockers that only filter network requests, acknowledging that DNS-based blocking cannot be detected without actual ads loading. - Message visibility is contingent on JavaScript support; it does not appear in browsers without JavaScript due to their minimal need for adblocking. - Failure of external styles could lead to a plain page display, highlighting the importance of CSS. - The concept was adapted by Stefan Bohacek to minimize false positives and ensure that messages are displayed once per user through scoped cookies. Keywords: CSS, DNS blocking, Internet ads, JavaScript, adblocker, advertising industry, bandwidth, cookie scoping, div, false positives, nativeadsjs, script, uBlock Origin
popular
maurycyz.com 5 days ago
https://f-droid.org/packages/com.aurora.store/ a day ago https://www.youtube.com/watch?v=r-TuGAHR78w a day ago https://3dvf.com/le-realisateur-philippe-vidal-dumas-nous-qu a day ago https://www.urbandictionary.com/define.php?term=flick%20the% a day ago https://mullvad.net/en/help/dns-over-https-and-dns a day ago https://en.wikipedia.org/wiki/Insider_trading a day ago https://news.ycombinator.com/item?id=45178318 a day ago https://www.investor.gov/introduction-investing/investi a day ago https://vanced.to/posts/youtube-premium-shows-ads-users a day ago https://www.reddit.com/r/youtube/comments/1gn a day ago https://www.reddit.com/r/uBlockOrigin/comments a day ago https://www.youtube.com/watch?v=XPGgTy5YJ-g a day ago https://github.com/gorhill/uBlock a day ago https://github.com/gorhill/uBlock/blob/master a day ago https://github.com/gorhill/uBlock/wiki a day ago https://putty.software a day ago https://www.theregister.com/2025/07/17/puttyo a day ago https://github.com/gorhill/uBlock?tab=readme-ov-file#ub a day ago https://techcrunch.com/2022/12/22/fbi-ad-bloc a day ago https://web.archive.org/web/20230219020056/https:& a day ago https://apps.apple.com/us/app/ublock-origin-lite&# a day ago https://en.wikipedia.org/wiki/Malvertising a day ago https://www.techradar.com/news/this-fake-gimp-google-ad a day ago https://adblockplus.org/acceptable-ads a day ago https://www.theverge.com/2013/7/5/4496852 a day ago https://washingtonmonthly.com/2025/04/18/cour a day ago https://en.wikipedia.org/wiki/FTC_v._Meta a day ago https://trailers.getyarn.io/yarn-clip/1f73a011-858b-418 a day ago https://www.pcmag.com/news/fbi-recommends-installing-an a day ago https://en.wikipedia.org/wiki/Enshittification a day ago https://home.cern/news/news/computing/compute a day ago https://xkcd.com/1105/ a day ago https://www.smokingonabike.com/2024/01/20/tak a day ago https://popupoff.org a day ago https://www.opensend.com/faq a day ago https://help.kagi.com/orion/privacy-and-security/a a day ago |
354. HN Anthropic addresses Claude Code quality issuesAnthropic has recognized and resolved issues related to code quality affecting Claude's functionality, particularly on browsers that have JavaScript disabled. To ensure proper access to their services via x.com, users are required to enable JavaScript or use a compatible browser. Additional information can be found in the Help Center for further guidance. - Anthropic identified and fixed code quality problems with Claude. - The issues primarily affect performance on browsers without JavaScript enabled. - Users must enable JavaScript or choose a supported browser for access to x.com. - Further details are available in the Help Center for user assistance. Keywords: Anthropic, Claude, Code quality, Help Center, JavaScript, browser, disabled, issues, supported browsers, switch, technical, xcom
claude
twitter.com 5 days ago
|
355. HN Master Foo and LLM MountainThe passage presents a narrative where a "Lord of Programmers" from the enterprise sector visits Master Foo's monastery on LLM Mountain due to concerns about unconventional programming practices among students. At different levels of the monastery, various stages of programming evolution are depicted. Initially, junior monks adhere strictly to traditional methods, focusing on algorithms and documentation. Intermediate monks utilize more intuitive techniques that seem chaotic to the Lord. Near the summit, senior monks engage in understanding human needs through conversation rather than typical coding practices. Upon reaching Master Foo at the peak, the Lord criticizes these unorthodox approaches for straying from conventional programming disciplines. Master Foo counters by highlighting the effectiveness of their methods in addressing real-world needs, which leads to a moment of enlightenment for the Lord. The narrative underscores a broader theme contrasting traditional and modern technological approaches: traditionally, humans learned machine languages; now, technology aims to understand human language, thereby bridging the gap between thought and execution more efficiently. However, this shift emphasizes the increased importance of comprehending human needs wisely. - A "Lord of Programmers" visits Master Foo's monastery due to concerns about unconventional programming. - Junior monks adhere strictly to traditional methods like algorithms and documentation. - Intermediate monks employ intuitive techniques appearing disordered to the Lord. - Senior monks focus on understanding human needs through conversation, moving beyond conventional coding practices. - Upon reaching Master Foo, the Lord criticizes these deviations from standard programming disciplines. - Master Foo explains that their unconventional methods effectively address real-world needs, leading to the Lord's enlightenment. - The passage contrasts traditional and modern technological approaches: learning machine languages versus machines understanding human language. - This shift highlights the importance of efficiently meeting human needs through technology. Keywords: Lord, Master, Von Neumann plains, algorithms, clean code, code coverage, commit messages, conversation, documentation, enterprise ways, human needs, meditation, path, programming, sacred traditions, sorting function, students, unit tests, wisdom, working systems
llm
tusshah.github.io 5 days ago
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356. HN Show HN: OSS app to find LLMs across multiple LLM providers (Azure, AWS, etc.)The article introduces a tool named "Show HN: OSS app to find LLMs across multiple LLM providers," which is designed to assist users in identifying available large language models (LLMs) on various AI platforms, including Azure, AWS, OpenAI, Mistral, and Together AI. Companies typically provide access through API keys but often do not specify the models accessible via these keys, creating a challenge for users seeking specific LLMs. This app mitigates this issue by allowing users to configure settings per provider and search for desired models across all platforms collectively. Developed using Mozilla.ai's any-llm library, it streamlines the process of finding compatible LLMs across different providers. The developers encourage feedback from users and offer contact via email (the address is not specified). **BULLET POINT SUMMARY:** - Introduction of "Show HN: OSS app to find LLMs across multiple LLM providers." - Purpose: To help users identify available large language models on AI platforms like Azure, AWS, OpenAI, Mistral, and Together AI. - Problem addressed: Companies provide API keys without detailed information about specific accessible models, complicating model discovery. - Solution offered: Allows configuration of provider settings and searching for desired LLMs across multiple platforms. - Built using Mozilla.ai's any-llm library to simplify finding compatible LLMs. - Developers welcome user feedback; contact details provided via email (not specified in the request). Keywords: API keys, AWS, Azure, LLMs, Mistral, Mozillaai, OSS app, Show HN, email address, email address ``` Keywords: Show HN, feedback, gpt-oss, library, models, providers
llm
github.com 5 days ago
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357. HN Windows-Use: an AI agent that interacts with Windows at GUI layerWindows-Use is an innovative AI-powered automation tool designed to facilitate interactions between large language models (LLMs) and the Windows operating system at its graphical user interface (GUI) layer. This tool empowers LLMs to automate various tasks, such as launching applications, clicking buttons, typing, executing shell commands, and capturing UI states without relying on conventional computer vision technologies. It effectively bridges the gap between AI agents and the Windows OS. To set up Windows-Use, users need Python 3.12 or higher installed along with UV (or pip) and must have a compatible Windows version ranging from 7 to 11. The installation can be done using either `uv` by running `uv pip install windows-use` or `pip` via the command `pip install windows-use`. For basic usage, users should import essential modules including `langchain_google_genai`, `windows_use.agent`, and `dotenv`. They then initialize a language model like ChatGoogleGenerativeAI with the 'gemini-2.0-flash' model and an Agent object. Automation tasks can be executed by inputting a query into `agent.invoke(query)`. To demonstrate its functionality, users can run the script via `python main.py` and enter their desired task in the prompt. Demonstration examples such as writing notes or changing system modes are provided, often accompanied by video files for better understanding. However, it is crucial to note that since Windows-Use interacts directly with the GUI layer of Windows OS, it may inadvertently cause undesired behaviors or changes. Therefore, users are advised to run this tool in a sandbox environment to ensure safety and prevent any potential issues on their primary systems. Windows-Use is distributed under the MIT License, allowing for broad use and modification. The project welcomes contributions from developers, with specific guidelines detailed in the CONTRIBUTING file. This open-source tool was developed by Jeomon George. --- **Bullet Point Summary:** - **Tool Overview:** Windows-Use automates interaction between LLMs and the Windows OS at the GUI layer without traditional computer vision models. - **Installation Prerequisites:** Requires Python 3.12 or higher, UV (or pip), and is compatible with Windows versions 7 through 11. - **Installation Steps:** - Using `uv`: Run `uv pip install windows-use`. - Using `pip`: Run `pip install windows-use`. - **Basic Usage:** - Import necessary modules (`langchain_google_genai`, `windows_use.agent`, `dotenv`). - Initialize a language model (e.g., ChatGoogleGenerativeAI with 'gemini-2.0-flash') and an Agent. - Execute automation tasks using `agent.invoke(query)`. - **Running the Script:** Use `python main.py` and input the desired task in the prompt to test functionality. - **Demo Examples:** Includes prompts like writing notes or changing system modes, with video demonstrations available for reference. - **Cautionary Note:** Direct interaction with Windows OS GUI may cause unintended changes; recommended to use in a sandbox environment for safety. - **License and Contribution:** - Licensed under the MIT License. - Contributions are welcomed as per guidelines in the CONTRIBUTING file. - **Developer:** Developed by Jeomon George. Keywords: AI agent, GUI layer, Jeomon George, LLM, MIT License, Python, UV, Windows, Windows OS, apps, automation, contributory, grounding, installation, pip, safety, sandbox environment, shell commands, tasks, vision
llm
github.com 5 days ago
https://learn.microsoft.com/en-us/dotnet/api/ a day ago https://github.com/steipete/macos-automator-mcp a day ago https://github.com/ashwwwin/automation-mcp a day ago https://github.com/mediar-ai/MacosUseSDK a day ago https://github.com/baryhuang/mcp-remote-macos-use a day ago https://news.ycombinator.com/item?id=29716900#29720860 a day ago |
358. HN Jakub and SzymonThe text underscores the significant yet often under-recognized contributions of Jakub Pachocki and Szymon Sidor at OpenAI to advancing AI technology, particularly with the development of ChatGPT. Their relentless efforts and close collaboration have been crucial in overcoming challenges previously considered insurmountable by experts. Notably, they played instrumental roles in scaling reinforcement learning (RL), which contributed to major achievements such as the successful application of RL in Dota. Additionally, their work involved constructing infrastructure that facilitated scientific breakthroughs and leading the pretraining process for GPT-4, thus paving the way for new AI paradigms. Jakub Pachocki serves as OpenAI's chief scientist, where his collaboration with Szymon Sidor exemplifies exceptional synergy and problem-solving capabilities, driving substantial progress in both AI research and development. **BULLET POINT SUMMARY:** - Highlights significant contributions of Jakub Pachocki and Szymon Sidor at OpenAI. - They are pivotal in advancing AI technology, especially with ChatGPT. - Their efforts have overcome challenges deemed impossible by experts. - Played key roles in scaling reinforcement learning (RL) and achieving the Dota result. - Built infrastructure for scientific breakthroughs and led GPT-4 pretraining. - Fostered new paradigms in AI development. - Jakub Pachocki is OpenAI's chief scientist, showcasing synergy with Szymon Sidor. - Their collaboration drives substantial progress in AI research and development. Keywords: AI, ChatGPT, Dota, GPT-4, Jakub Pachocki, OpenAI, RL (Reinforcement Learning), Szymon Sidor, chief scientist, engineering, human ingenuity, infrastructure, paradigms, progress, reasoning breakthrough, research, scientific discoveries
openai
blog.samaltman.com 5 days ago
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359. HN It's AI all the way down as Google's AI cites web pages written by AIThe text discusses concerns and research findings regarding Google's AI Overviews (AIOs), which often cite documents written by other AIs, including those generated by large language models (LLMs). Originality.ai's study of 29,000 YMYL queries revealed that approximately 10% of these citations originate from LLMs. This recursive citation raises issues about the trustworthiness and reliability of AI-generated content in critical areas like health and finance, potentially leading to a feedback loop where data quality degrades over generations—a phenomenon known as model collapse. Google has contested Originality.ai's findings, questioning the accuracy of its AI detection tool. Despite Google's critique, Originality.ai has been validated for its precision by independent studies from the University of Florida and Arizona State University, showing low error rates in detecting GPT-4 content. Google maintains that it prioritizes content quality over human authorship and acknowledges the beneficial role of AI in enhancing creativity on the web. The text highlights a shift in user behavior due to AIOs, as evidenced by Pew Research Center's study indicating reduced click-through rates when users encounter these summaries. Although Google disputed these findings, similar trends were noted by Ahrefs with significant drops in click-through rates for top search results above AIOs. Originality.ai found that most citations in AIOs are human-written, but a notable percentage (12.8%) of unranked functional links were identified as AI-generated. In contrast, research by Louise Linehan and Xibeijia Guan indicates that many cited documents appear within top search results, suggesting variability based on query types. The use of Google's LLM Gemini in AIOs is designed to pull information from various formats like PDFs and whitepapers, particularly for YMYL queries. Despite the potential disconnect between AI citations and organic search rankings, the "query fan out technique" employed ensures that responses are generated through related searches rather than direct query matches. - Google's AI Overviews (AIOs) frequently cite documents from other AIs, including LLMs. - Originality.ai found that about 10% of these citations in YMYL queries come from LLMs. - Concerns arise over trust and reliability due to potential feedback loops leading to model collapse. - Google disputes Originality.ai's AI detection tool accuracy but acknowledges its recognition by independent studies for identifying GPT-4 content. - Google emphasizes content quality over human authorship, despite criticism of AI-generated citations' impact on publishers. - Studies show reduced user click-through rates when encountering AIOs, though findings vary between researchers. - Originality.ai notes a significant portion of functional links cited in AIOs are unranked and potentially AI-generated. - Linehan and Guan's research suggests many AIO citations appear within top search results. - Google's LLM Gemini uses diverse sources for YMYL queries, with a "query fan out technique" to generate responses. Keywords: AI, AI Overviews, AIOs, Google, LLM, Originalityai, Pew Research Center, SEO, YMYL, citations, click-through rate, content quality, detection software, echo chamber, false negative, false positive, model collapse, reliability, study, traffic, trust, web pages
llm
www.theregister.com 5 days ago
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360. HN Show HN: I Built Logstalgia for the Web**Summary:** Logstalgia is an innovative web-based tool designed for real-time HTTP log visualization. Developed by a skilled developer, it enables streaming from sources such as nginx and Kubernetes, utilizing animated particles to depict requests. These animations are color-coded according to the status of each request and sized based on response bytes. Users benefit from various viewing options, including grouping logs by hostname or flows, along with unique replay timeline and TV mode capabilities, all driven by Server-Sent Events (SSE). The technology stack behind Logstalgia incorporates a Laravel backend, React frontend, Redis for buffering, and Postgres enhanced with Timescaledb for data storage. Tailstream complements existing observability tools like Datadog or Grafana by offering real-time visualization of live traffic patterns. It's designed not to replace current log storage systems but instead provides an additional layer of insight. Tailstream features a free plan that supports up to 1 million events per month with a retention period of 24 hours, making it suitable for small projects and trials. Users can customize the data fields stored to avoid capturing sensitive information. The pricing model is event-volume and retention-duration based, allowing flexible upgrades if necessary. For users who exceed their Tailstream plan limits, the system continues to process events on a metered basis, issuing notifications when approaching or exceeding usage thresholds. Upgrades are available to accommodate higher demands at any time. Logs can be directed to Tailstream using tools like Vector or Fluent Bit without altering application code, by posting JSON logs with a token to the ingest endpoint. Subscription plans offer month-to-month flexibility and can be canceled anytime via settings, maintaining access until the end of the current billing cycle. **Bullet Point Summary:** - Logstalgia is a web-based real-time HTTP log visualizer using animated particles for request representation. - Features multiple view modes (hostname/grouping by flows) with replay timeline and TV mode capabilities. - Utilizes technologies like Laravel, React, Redis, Postgres with Timescaledb, powered by Server-Sent Events (SSE). - Tailstream offers real-time traffic visualization, complementing tools like Datadog or Grafana without replacing log storage systems. - Free plan includes 1M events/month and 24-hour retention; customizable fields to avoid storing sensitive data. - Pricing is based on event volume and retention duration, with flexible upgrades available as needed. - Exceeding plan limits results in metered usage continuation, with notifications for approaching or surpassing thresholds. - Logs can be sent using Vector or Fluent Bit by POSTing JSON logs with a token. - Subscriptions are month-to-month, cancellable anytime via settings, retaining access until the end of the billing period. Keywords: Allowances, Billing period, Datadog, Events, Fluent Bit, Grafana, HTTP log visualizer, Ingest endpoint, JSON, Laravel, Logstalgia, Meter usage, Metrics, Observability, Pipeline, Plan limit, Postgres, Pricing, React, Real-time, Redis, Retention, SSE streaming, Sensitive data, Subscriptions, Tailstream, Timescaledb, Token, Traces, Upgrade, Vector, Visualization, Web-based
postgres
tailstream.io 5 days ago
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361. HN Building Privacy-First AI Agents on Ollama: Complete Guide**Concise Summary:** The guide delves into the development of privacy-centric AI agents using Ollama for local execution on devices, focusing on data privacy, zero latency, and personalized experiences. It highlights the constraints faced by local models compared to cloud-based alternatives like ChatGPT due to their smaller parameter sizes affecting complex tasks such as reasoning and tool calling. NativeMind has upgraded its conversational architecture to address these limitations, employing a more flexible tool calling mechanism instead of Ollama’s restrictive API. A key innovation is the use of a prompt-based tool calling method with multi-layer parsing for consistent user interactions, supported by an XML-formatted command system exemplified in products like Cline. Tools are modularly designed to perform specific functions, optimizing efficiency and minimizing complexity in operations. To enhance task management, an iteration control system limits tool calls per session, while an Environment Awareness System dynamically acquires relevant information. The study evaluates various local models against cloud-based benchmarks across different scenarios, revealing promising results for local agents within NativeMind's architecture. Even weaker models surpass traditional methods when integrated into this new framework, significantly improving user experience through transparent execution and immediate feedback. Comparisons show that while basic operations are well-handled by small local models, high-performing ones approach the efficiency of cloud models in specific tasks like tool calling. Performance analysis ranks language models based on task success rates, with GPT-4.1 mini leading but showing occasional language issues. Qwen3 8B excels in reasoning, while Qwen3 4B is recommended for daily use due to its balance of performance and efficiency. Lightweight models like Qwen3 1.7B and Qwen3 0.6B perform well under resource constraints, though the Qwen2.5 VL series struggles with intent recognition. NativeMind's local agents offer significant advantages over cloud solutions by leveraging on-device capabilities for privacy and real-time interactions. The guide suggests future expansions including broader tool support, browser automation, MCP integration, enhanced task planning, and personalized user interactions based on habits, showcasing NativeMind’s latest architecture enhancements providing immediate access to these features. **Bullet Point Summary:** - **Development Focus:** Building privacy-focused AI agents running locally using Ollama for data privacy, zero latency, and personalized experiences. - **Challenges Addressed:** Overcoming limitations of local models with fewer parameters compared to cloud-based models like ChatGPT in complex tasks such as reasoning and tool calling. - **Innovations by NativeMind:** - Flexible tool calling mechanism using a prompt-based system and multi-layer parsing for consistent user interactions. - XML-formatted commands ensure reliable execution, supported by dual-layer parsing for well-formed and incomplete requests. - Modular tools designed to perform distinct functions optimize efficiency and reduce complexity in operations. - **Performance Enhancements:** - Iteration control limits tool calls per session, enhancing task management. - Environment Awareness System dynamically acquires relevant information for optimized resource use. - **Evaluation Results:** - Local models integrated into NativeMind's architecture show promising potential and improved user experience over traditional methods. - High-performing local models nearly match cloud models in certain tasks like tool calling and execution. - **Performance Analysis:** - Language models ranked by task success rates, with GPT-4.1 mini leading despite language switching issues. - Qwen3 series recommended for various scenarios based on performance balance and efficiency. - Lightweight models like Qwen3 1.7B perform well under resource constraints; Qwen2.5 VL excels in image analysis but struggles with intent recognition. - **Advantages of NativeMind's Local Agents:** - Enhanced privacy, real-time interaction without network latency, leveraging on-device capabilities over cloud solutions. - **Future Developments:** - Plans to expand features including broader tool support, browser automation, MCP integration, improved task planning, and personalized user interactions. - **Latest Enhancements:** NativeMind's redesigned architecture provides immediate access to these innovations, offering significant improvements in local agent capabilities. Keywords: AI Calls, Agent Tasks, Answer Relevance, Architecture, Cline, Cloud Models, Cloud Solutions, Computational Resources, Conversational Architecture, Data Privacy, Efficiency, Information Collection, Intelligent Agents, Language Consistency, Limited Capabilities, Local Devices, MCP Support, Model Adaptation, Multi-Layer Parsing, Multimodal Agents, NativeMind, Network Latency, Ollama, Parameters, Performance, Personalized Experiences, Privacy-First AI, Prompt-Based, Resource Integration, Search Responses, Service Limitations, Task Planning, Technical Challenges, Text-Image-PDF Processing, Tool Calling, Tool Calls, User Experience, XML Formats
ollama
nativemind.app 5 days ago
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362. HN Tiny LLM – LLM Serving in a WeekThe "Tiny LLM" course is designed for systems engineers interested in large language models (LLMs). Spanning three weeks, it guides participants through building a project from scratch using matrix manipulation APIs. The course begins with Python and advances to C++/Metal kernels for optimization, focusing on the Qwen2-7B-Instruct model. Participants learn efficient LLM serving techniques, utilizing the MLX library compatible with Apple Silicon devices. Prerequisites include basic deep learning knowledge and familiarity with PyTorch, drawing inspiration from CMU Deep Learning Systems' needle project. Key Points: - **Target Audience**: Systems engineers interested in LLMs. - **Approach**: Building an LLM serving project using matrix APIs from scratch. - **Model Used**: Qwen2-7B-Instruct. - **Week 1**: Serve the model with Python and matrix manipulation. - **Week 2**: Implement C++/Metal kernels for optimization. - **Week 3**: Further optimizations, including request batching for high throughput. - **Tools/Libraries**: MLX for Apple Silicon; correctness tests against PyTorch CPU implementation. The "tiny-llm" guidebook serves as a practical two-week course focused on optimizing machine learning models using C++/Metal custom kernels. Week 2 emphasizes speed enhancements, and Week 3 focuses on batching requests to achieve high throughput. Developed by Chi and Connor—experts in storage systems at Neon (now Databricks) and TiKV at PingCAP, respectively—the guide offers a task-oriented resource that explains concepts using internet references without redundancy. It provides hints and standardized terminology for clarity. Participants are encouraged to engage with the tiny-llm community via a Discord server managed by skyzh and follow setup instructions. Key Points: - **Creators**: Chi (Neon/Databricks) and Connor (PingCAP). - **Course Length**: Two weeks. - **Focus**: Optimizing ML models using C++/Metal kernels in Week 2, and batching requests in Week 3. - **Resource Type**: Task-oriented guidebook with internet references for clarity. - **Community Engagement**: Join the Discord server managed by skyzh. Keywords: Apple Silicon, C++/Metal, CUDA kernels, LLM serving, MLX library, PyTorch, Python, Qwen2-7B-Instruct model, batch requests, deep learning, environment setup, guidebook, high throughput, matrix manipulations, optimization, systems engineers, tensor dimensions, tiny-llm
llm
skyzh.github.io 5 days ago
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363. HN Nebius stock soars on AI infrastructure deal with MicrosoftNebius Group, based in Amsterdam and formerly known as Yandex NV, experienced a significant surge of over 60% in its stock price during extended trading following the announcement of a $19.4 billion deal with Microsoft to provide AI infrastructure for five years. This partnership involves Nebius supplying computing resources from a New Jersey data center, aligning with Microsoft's strategy to meet its expanding demand for cloud AI capacity through third-party collaborations like those with OpenAI and CoreWeave. To accelerate growth, Nebius is exploring financing options while planning to roll out GPU services over the next two years. The total contract value could potentially increase to $19.4 billion by 2031 if Microsoft opts to purchase additional services under the agreement. In preparation for serving American AI enterprises more effectively, Nebius has expanded its U.S. presence with new offices in San Francisco, Dallas, and New York. Prior to this announcement, Nebius had already seen a significant market cap increase, doubling earlier in the year to exceed $15 billion. The report, contributed to by CNBC's Ari Levy, emphasizes that AI infrastructure development is an extensive long-term endeavor, currently primarily focused on consumer applications. - **Stock Surge and Deal Announcement**: Nebius Group's stock surged over 60% after announcing a $19.4 billion deal with Microsoft for AI infrastructure over five years. - **Company Background and Expansion**: Based in Amsterdam, formerly Yandex NV, Nebius has expanded into the U.S. by opening offices in San Francisco, Dallas, and New York to better serve American clients. - **Partnership Details**: The agreement involves providing computing resources from a data center in New Jersey, as Microsoft seeks third-party solutions for its growing cloud AI capacity demand. - **Strategic Collaborations and Growth Plans**: Nebius is part of Microsoft's broader strategy involving collaborations with OpenAI and CoreWeave. It plans to deploy GPU services over the next two years while exploring financing options for growth acceleration. - **Contract Potential**: The deal could escalate to a $19.4 billion contract by 2031 if additional services are purchased by Microsoft. - **Market Performance**: Prior to this deal, Nebius more than doubled its market cap earlier in the year, reaching over $15 billion, reflecting strong investor confidence and growth potential. - **AI Infrastructure Development Context**: As reported by CNBC's Ari Levy, AI infrastructure development is identified as a long-term effort with current focus primarily on consumer applications. Keywords: AI, Amsterdam, Ari Levy, Azure, CNBC, ChatGPT, CoreWeave, GPU, Microsoft, Nebius, New Jersey, Nvidia, OpenAI, Russian investors, SEC, US expansion, Yandex, adoption, agreement, cloud, computing power, consumer-based, data center, deal, doubled, financing, graphics chips, infrastructure, market cap, post-market pop, search engine, tranches, value, year
openai
www.cnbc.com 5 days ago
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364. HN Are you selling agents the way customers want to buy?**Summary:** The article critiques traditional AI pricing discussions with CEOs by advocating for insights from how sales representatives sell AI solutions to understand a company's future viability better. It highlights issues such as inconsistent custom deals, lack of standardized processes, and seat-based pricing models inappropriate for autonomous software, which point to deeper organizational challenges like positioning difficulties and process gaps rather than mere incompetence in sales teams. The text delves into the struggles RevOps face due to inadequate infrastructure when adopting new AI-driven sales strategies. Traditional methods persist because implementing comprehensive change management is resource-intensive. Financial losses and decreased engagement are noted across companies transitioning from conventional pricing models to usage or outcome-based ones, such as Microsoft, OpenAI, Chegg, and Stack Overflow. The complexity of selling based on "usage" or "outcomes" lies in the difficulty for both sellers and buyers in determining billable actions when multiple steps lead to a sale. The piece cites a BCG report indicating buyers struggle with value attribution in AI contexts, particularly when outcomes are influenced by factors beyond vendors' control. For successful implementation of new sales strategies, businesses need detailed agent-level tracking to differentiate cost drivers from value creators and precise methods for attributing actions leading to successful deals. Without these infrastructures, companies face financial inefficiencies and misguided pricing decisions. The article emphasizes effective attribution, hybrid pricing models, and real-time margin visibility as crucial elements in business strategy. It suggests that a transition to value-based pricing requires combining base fees with usage and outcome components—elements often lacking in current systems. Three companies are highlighted for their innovative approaches: Clay uses credit-based pricing with upfront costs; Intercom charges per resolved issue based on clear criteria; Sierra focuses on outcome-based pricing tied to business impact. These companies have achieved significant growth by gradually building hybrid models and infrastructures before implementing new pricing strategies. Sales and revenue operations leaders face challenges aligning offerings with customer value due to system limitations and inflexible pricing models, leading to frustration in developing innovative solutions. RevOps leaders experience operational strain from outdated systems that impede creative pricing, while CEOs feel investor pressure regarding AI monetization amidst outdated infrastructure, resulting in tense board meetings. The fear of failure upon launching new models is shared among companies like Chegg and Stack Overflow. To transition smoothly from traditional seat-based pricing to usage-based models without disruption, the article recommends hybrid models combining base platform fees with usage components related to agent work. This allows for sales forecasting and customer flexibility while prioritizing key value metrics. Ultimately, aligning pricing with customer-received value is essential; failure to do so risks losing business to competitors who have adapted. Businesses must reassess their strategies to ensure they meet customer preferences through tracking, attributing, and adjusting pricing based on agent value. **Bullet Point Summary:** - Critiques traditional AI pricing discussions and suggests learning from sales practices for better insights into a company's future. - Highlights organizational challenges like inconsistent deals, lack of standard processes, and improper seat-based models. - Discusses RevOps struggles due to inadequate infrastructure in adopting new AI-driven sales strategies. - Notes financial losses during transitions from conventional to usage/outcome-based pricing at companies like Microsoft, OpenAI, Chegg, Stack Overflow. - Points out the complexity of "usage" or "outcomes" selling and buyers' difficulties with value attribution as per a BCG report. - Emphasizes need for detailed tracking and precise attribution methods to avoid financial inefficiencies in new sales strategies. - Stresses importance of effective attribution, hybrid pricing models, and real-time margin visibility in business strategy. - Describes innovative approaches by Clay (credit-based), Intercom (issue-based), and Sierra (outcome-based) leading to growth through hybrid models. - Highlights challenges for sales/RevOps leaders due to system limitations and inflexibility in aligning offerings with customer value. - Notes operational strain from outdated systems hindering creative pricing, with CEOs facing investor pressure amidst infrastructure issues. - Discusses fear of failure when launching new models as seen with Chegg and Stack Overflow experiences. - Recommends hybrid models combining base fees with usage components for smooth transition without disruption while prioritizing key value metrics. - Underlines aligning pricing with customer-received value to avoid losing business to adapted competitors. Keywords: AI pricing strategy, GitHub Copilot, RevOps, SaaS pricing, autonomous software, deal desk, flexible pricing, modern billing infrastructure, revenue growth, seat-based pricing, tactical approvals, usage-based pricing
github copilot
paid.ai 5 days ago
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365. HN Why Prompt Caching Doesn't Solve Your Latency ProblemsPrompt caching in large language models (LLMs) can improve efficiency by reducing input tokens and time-to-first-token (TTFT) for repeated prompt prefixes. This process is advantageous for applications like chatbots and Q&A systems where identical information is frequently accessed, as it allows the reuse of previously computed results rather than reprocessing inputs from scratch. However, prompt caching has limitations in addressing all latency issues. The assumption that it permits unlimited input tokens without increased TTFT is incorrect; cache size still affects TTFT because larger caches take longer to search through during retrieval. This stems from the computational complexity of attention mechanisms within transformer models, which calculate relationships between inputs using query matrices and dimensions. Decoding sequences with attention mechanisms involves storing key (K) and value (V) vectors in corresponding matrices for each token. KV-caching enables efficient computation by saving previously computed K and V matrices and only adding new rows as more tokens are decoded. Despite its benefits, the size of the KV-cache affects TTFT due to memory loading times on GPUs, particularly when dealing with large sequences. An experiment using the Qwen2.5-14B model and FlashAttention-2 implementation highlighted that TTFT increases by approximately 4.69 ms for every additional 10,000 tokens in context. Although KV-caching reduces computational costs, it does not mitigate the linear increase in TTFT associated with longer sequences. The experiment involved a model configuration with 40 attention heads per layer and 8 KV heads using GQA precision (float16) on an A100-80GB GPU. Results were averaged over ten trials following two warmup iterations to ensure accurate performance measurement. The findings confirm the theoretical expectation that computing QKᵀ scales linearly with sequence length, indicating a challenge in maintaining low TTFT as token count increases. **Bullet Point Summary:** - Prompt caching reduces input tokens and TTFT by reusing previously computed results for repeated prompt prefixes, beneficial for applications like chatbots. - Caching does not resolve all latency issues; cache size affects TTFT due to the time needed to search through larger caches. - Attention mechanisms in transformer models involve computational complexity that limits efficiency gains from caching alone. - KV-caching stores key (K) and value (V) matrices, reducing computation by appending new rows for additional tokens. - Large KV-cache sizes increase TTFT due to GPU memory loading times, even with efficient caching. - An experiment showed TTFT increases linearly by approximately 4.69 ms per additional 10,000 tokens in context despite KV-caching. - The model used in the experiment had 40 attention heads and 8 KV heads, running on an A100-80GB GPU with GQA precision. - Results confirmed a linear increase in TTFT with sequence length, highlighting the need for alternative strategies under strict latency constraints. Keywords: A100-80GB, Anthropic, FlashAttention-2, GPU, GQA, Gemini, K, LLMs, OpenAI, Prompt caching, Q&A bot, Reinforcement Learning, V matrices, attention heads, attention mechanism, attention scores, batch size, context, cost reduction, decoding, dot product, float16, high bandwidth memory (HBM), input tokens, key matrix (K), query matrix, query vector (Q), sequence length, softmax, time-to-first-token (TTFT), tokens, transformers, value matrix (V)
gemini
willseltzer.substack.com 5 days ago
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366. HN Ask HN: Has Claude Code quality gotten worse?A professional user of Claude Code utilizing Opus 4.1 has recently noted a perceived decline in the quality of responses over the past week. This observation prompted the user to seek feedback from other users on Hacker News to determine whether this degradation is a subjective perception or if it is shared by others as well. The summary encapsulates several key points: the individual's experience with what they believe to be deteriorating response quality, their lack of objective methods to measure these changes accurately, and their effort to ascertain through community feedback whether this perceived decline is common among other users. - **User Experience:** A regular user has observed a decrease in response quality from Claude Code using Opus 4.1 over the past week. - **Lack of Objective Measurement:** The user does not have objective methods to measure the change in response quality, indicating reliance on personal perception. - **Community Inquiry:** The user seeks feedback from others on Hacker News to determine if this perceived decline is shared or subjective. This summary provides a detailed yet concise overview of the situation by focusing on these critical aspects while maintaining clarity and eliminating extraneous details. Keywords: Ask HN, Claude Code, Opus, code quality, daily user, degradation, discussion, feedback, noticed change, objective measure, performance, responses, technology
claude
news.ycombinator.com 5 days ago
https://old.reddit.com/r/ClaudeAI/comments/1n 5 days ago |
367. HN Linus Torvalds is sick and tired of your 'pointless links' – and AI is no excuseLinus Torvalds, the creator of Linux, has expressed frustration regarding the prevalence of non-informative links included in proposed changes to the Linux kernel. Many of these links are generated by AI tools or automated systems and often direct users to information that is already contained within the patch itself. During a discussion on the Linux Kernel Mailing List (LKML), Torvalds emphasized his preference for links that provide additional context or useful details about the proposed changes, such as an oops report explaining why a fix is necessary. He urged developers to cease including superfluous "Link:" arguments in their submissions, which waste time and hinder effective review of pull requests. Torvalds highlighted the challenge he faces in understanding pull requests due to these irrelevant links, stressing that only meaningful links that genuinely aid the review process should be included. The Linux kernel has grown significantly from a personal project into an extensive codebase with over 40 million lines. A recurring issue, as pointed out by Torvalds, is the inclusion of irrelevant links in patch submissions, often generated automatically through AI tools or platform integrations like GitHub and GitLab. These links typically direct to patch emails or generic pages without providing useful context, consuming valuable time for maintainers. The problem is further exacerbated by less experienced developers who follow workflows encouraging link usage, even when unnecessary. Torvalds calls for a halt to this practice in order to enhance efficiency and relevance in code contributions. **BULLET POINT SUMMARY:** - Linus Torvalds expressed frustration over non-informative links in Linux kernel patch submissions. - These links often come from AI tools or automated systems and point to information already present in the patches. - During an LKML discussion, Torvalds highlighted his preference for links that provide additional context or useful details. - He urged developers to stop including irrelevant "Link:" arguments that waste time and hinder effective review. - The Linux kernel has grown into a massive codebase over 40 million lines long. - Irrelevant links in patches are often generated automatically via AI tools or integrations like GitHub and GitLab. - These links typically point to patch emails or generic pages, consuming valuable maintainer time without adding context. - Less experienced developers often follow workflows that encourage unnecessary link inclusion. - Torvalds calls for a halt to this practice to improve efficiency and relevance in code contributions. Keywords: AI dev programs, Git, GitHub, GitLab, LKML, Linus Torvalds, Linux kernel, automated tools, best practices, bots, bug tracker, cross-referencing, developer tools, frustration, hyperlinks, inexperience, link argument, maintainers, merge requests, oops report, patch, patches, pull requests, useful info, workflows
github
www.zdnet.com 5 days ago
https://git.kernel.dk/cgit/linux/commit/?h=io 5 days ago |
368. HN I've got your AI shovelware right here### Summary The article from September 8, 2025, delves into the debate surrounding the influence of AI coding tools on developer productivity. It presents an argument by Mike Judge that while these tools are said to boost productivity, there is no significant increase in low-quality software (shovelware), suggesting that they haven't yet sparked a revolution in independent software development. The article challenges this view by highlighting two primary issues: the outdated nature of data primarily from before AI tools became truly effective around March–April 2025, and the potential for individual developers to experience substantial productivity gains with these tools, regardless of broader adoption trends. The author suggests skepticism towards claims that AI-generated code isn't transformative based on the absence of an immediate surge in software output. They argue that early adopters are currently making extensive use of these tools, indicating a possible future impact. The article also critiques the reported effectiveness of AI tools by companies like Microsoft and Anthropic, suggesting they may be overestimated due to conflicts of interest. It notes that many developers might not engage deeply with tools like GitHub Copilot, leading to inflated acceptance statistics. Furthermore, the author argues that comprehensive productivity agents still fall short compared to simpler autocompletion features found in editors. An example is provided where the writer quickly completed a basic project using AI tools, underscoring how these technologies can significantly reduce development time and encourage projects that might otherwise be abandoned. ### Bullet Point Summary - The article discusses the impact of AI coding tools on developer productivity, referencing arguments by Mike Judge about increased productivity without a corresponding rise in low-quality software. - Two main criticisms of Judge's argument are identified: - His data largely predates when AI tools became genuinely useful (March–April 2025). - Individual developers can still achieve significant productivity gains with these tools, independent of broader adoption. - The author expresses skepticism towards the notion that AI coding isn't transformative simply because there hasn’t been an immediate increase in software output. - There is criticism of statistics from companies like Microsoft and Anthropic regarding tool effectiveness, suggesting potential overestimation due to conflicts of interest and superficial engagement by users. - A distinction is made between comprehensive productivity agents and simpler autocompletion features, with the latter being more commonly effective in practice. - An anecdote illustrates how AI tools facilitated a quick completion of a basic project, demonstrating their potential to save time and encourage development efforts. Keywords: AI, Anthropic tokens, LLM chat, acceptance rate, autocomplete, coding tools, developers, early adopters, feedback, indie software revolution, productivity, shovelware, software revolution, supercharged
github copilot
justin.searls.co 5 days ago
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369. HN Show HN: I built a finance app for couples after 9 years of Google Sheets- After nine years of managing shared finances using Google Sheets and facing frequent disagreements due to financial mismanagement, a developer created "personifi," an app designed for couples. This tool addresses issues like forgotten purchases by offering real-time synchronization between partners, individual logins with shared data access, and quick mobile transaction entry. - Built using technologies such as Next.js, .NET, and Postgres, the app operates cost-effectively on a Hetzner VPS with Nomad orchestration. The developer found personal validation when their wife preferred "personifi" over the longstanding spreadsheet method they had used for years. - Initially attracting 2 daily active users, personifi is priced at £4.99/month per couple to help reduce financial disputes. A free Google Sheets template is also available for those who prefer that option. More information and a blog post detailing the development are accessible on the developer's website. - The app was conceived due to frustrations with a spreadsheet system designed primarily for individual use, which often led to discrepancies. Personifi offers basic functionalities like transaction logging and partner synchronization, effectively addressing issues of duplicate or forgotten expenses. - Key features include real-time updates across devices, personalized access via email logins, and mobile-friendly transaction logging. To accommodate hesitations about subscription fees, a free UK-focused Google Sheets budget template is offered at personifi.xyz/free-budget-template. - After one month of daily use, users reported benefits such as no arguments over forgotten expenses and minimal running costs, with 47 monthly transactions logged by two active users. Future enhancements are planned based on user feedback rather than a fixed roadmap, focusing on practical improvements like bank connections and spending insights. - The app is set to launch with a £4.99/month fee per couple, including a 30-day free trial. Gathering user feedback is prioritized over pre-planned features, aiming to prevent duplicate purchases and reduce financial disagreements. - While the spreadsheet-based solution was "free," it often led to frustration and conflicts, highlighting the value of real user feedback in developing the app. Although scalability might be limited without significant upgrades, personifi aims to address specific challenges faced by couples in managing finances. - The overarching message is that creating a bespoke solution can be worthwhile even if similar apps exist, especially when standard options fail to meet unique needs. Personal endorsement from the author's wife underscores the success of this tailored approach. - In conclusion, the author emphasizes the importance of developing personalized solutions over generic ones, as evidenced by their successful transition from spreadsheets to a dedicated budgeting app. The project holds personal significance for the developer and was co-crafted with Claude's assistance. Keywords: Finance app, Google Sheets, NET, Nextjs, Postgres, VPS, budget, couples, mobile experience, net worth, real-time sync, spreadsheet, transactions
postgres
craig.banach.dev 5 days ago
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370. HN Gemini Apps limits and upgrades for Google AI subscribers**Summary:** Gemini Apps offers enhanced access features for subscribers on selected Google One paid plans, requiring users to be at least 18 years old with a personal Google Account. The app is accessible in over 150 countries under the Google AI Pro and Ultra plans. These plans include varying levels of functionality; the basic plan provides standard access without premium features, while the Google AI Pro Plan allows up to 100 prompts per day and supports a context window size of one million tokens. In contrast, the Google AI Ultra Plan permits up to 500 prompts per day with a smaller context window of 32 thousand tokens. Both plans include advanced functionalities such as audio overviews, deep research reports, image editing, scheduled actions, video generation through Veo 3, and more. The document specifies different usage limits based on selected plans, which influence the number of prompts, conversations, and feature usage within a given timeframe. These limits can vary according to prompt complexity, file size, and are subject to occasional changes. Users traveling between countries where Google AI Ultra is available or unavailable will receive notifications about these changes; however, functionality resumes once they return to supported regions. The app implements capacity management, with regular replenishment of usage limits and the option for users without premium plans to upgrade when nearing their limit. Advanced models provide higher usage thresholds but allow model switching upon reaching limits. Specific features like Audio Overviews and media generation have distinct asset and action limitations. Users are notified as they approach these limits, detailing when capacity will be restored. **Bullet Point Summary:** - Gemini Apps offers enhanced features on specific Google One paid plans for users aged 18+ with a personal Google Account. - The app is available in over 150 countries under the Google AI Pro and Ultra plans. - **Google AI Pro Plan**: Up to 100 prompts/day, context window of 1 million tokens. - **Google AI Ultra Plan**: Up to 500 prompts/day, context window of 32 thousand tokens. - Features include audio overviews, deep research reports, image editing, scheduled actions, and video generation via Veo 3. - Usage limits vary by plan; they are influenced by prompt complexity, file size, and can change occasionally. - Traveling between countries affects access to Google AI Ultra; functionality resumes in supported regions. - Capacity management includes regular replenishment of usage limits with upgrade options for non-premium users. - Advanced models offer higher limits but allow switching when these are reached. - Specific features have distinct asset/action limitations, with notifications alerting users about approaching limits and capacity restoration. Keywords: Audio Overviews, Canvas, Context Window, Deep Research, Gemini Apps, Gems, Google AI, Google AI Pro, Google AI Ultra, Help Center, Image generation, Poland, Portugal, Pro plan, Puerto Rico, Qatar, Romania, Russia, Saudi Arabia, Scheduled actions, Senegal, Singapore, South Korea, Spain, Storybook, Sweden, Switzerland, Taiwan, Tanzania, Thailand, Trinidad and Tobago, Ultra plan, United Kingdom, United States, Usage limits, Uzbekistan, Venezuela, Video generation, Vietnam, Yemen, Zambia, Zimbabwe, countries territories, features, models, plans, upgrades
gemini
support.google.com 5 days ago
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371. HN Liquid Glass in the Browser: Refraction with CSS and SVG**Summary:** The article "Liquid Glass in the Browser" delves into recreating Apple's Liquid Glass effect using web technologies such as CSS and SVG, focusing on simulating refraction and specular highlights without exact replication. It employs physics-based calculations, specifically Snell’s Law, to illustrate how light bends when transitioning between materials with different refractive indices. The article provides an interactive demonstration in Chrome due to its capabilities with SVG filters and emphasizes building the effect from first principles. Key aspects of this technique include modeling light behavior through various surface geometries—convex circle, convex squircle, concave surface, and lip surface—and their influence on light refraction. A "Displacement Vector Field" is introduced to map light displacement across surfaces, leveraging symmetry for computational efficiency in ray tracing simulations. These calculations are implemented using SVG's ` The article explains the process of converting vector fields into displacement maps for rendering purposes, utilizing the red and green channels to specify displacements along axes, with values mapped from -128 to 127. The maximum displacement value is pre-stored to facilitate scaling post-rendering. Users can dynamically adjust effect intensity through animation by modifying the `scale` attribute. The document discusses creating two interactive UI components: a "Magnifying Glass" and a "Music Player." Both utilize displacement maps for refraction effects, with adjustable parameters like bezel profile and highlight intensity. The prototype implementation faces challenges in browser compatibility, functioning solely within Chrome due to its support of `backdrop-filter` with SVG filters. Despite limitations such as costly adjustments to displacement map rebuilding for dynamic changes, the article outlines potential enhancements and invites community feedback for optimization before considering an open-source release. It specifies parameters like Specular Opacity, Saturation, Refraction Level, Blur Strength, and Background Opacity that can be tweaked to achieve desired visual effects. **Bullet Point Summary:** - The article explores recreating Apple's Liquid Glass effect using CSS and SVG by approximating refraction and specular highlights. - Key calculations involve Snell’s Law for light bending between materials with different refractive indices. - Demonstrates the technique in Chrome, leveraging its SVG filter capabilities for an interactive demo. - Explores various surface geometries to show how they affect light behavior: convex circle, squircle, concave, and lip surfaces. - Introduces a "Displacement Vector Field" for efficient ray tracing simulations using symmetry and vector field analysis. - Describes rendering refraction via SVG ` - Explains converting vector fields into displacement maps, utilizing red and green channels for axis-specific displacements. - Details two interactive UI components: a "Magnifying Glass" and a "Music Player," both adjustable through parameters like bezel profile and highlight intensity. - Faces browser compatibility challenges, requiring `backdrop-filter` support only available in Chrome for SVG filters. - Encourages community feedback for improvements and optimization before potential open-source release. - Lists key parameters (Specular Opacity, Saturation, Refraction Level, Blur Strength, Background Opacity) for customizing visual effects. Keywords: Animation, Bezel, Chrome-only, Displacement Map, Filters, Liquid Glass, Optics, Ray Tracing, Refraction, SVG, Snell's Law, Specular Highlight
popular
kube.io 5 days ago
https://real-glass.vercel.app a day ago https://en.wikipedia.org/wiki/Chromatic_aberration a day ago https://www.smashingmagazine.com/2016/12/gpu-anima a day ago https://caniuse.com/?search=backdrop-filter a day ago https://drafts.fxtf.org/filter-effects-1/#typedef-filte a day ago https://drafts.fxtf.org/filter-effects-2/#BackdropFilte a day ago https://youtu.be/GamP4chXJ2I?t=17 a day ago https://github.com/nkzw-tech/liquid-glass a day ago https://codepen.io/lenymo/pen/pJzWVy a day ago https://ciechanow.ski/ a day ago https://www.netflix.com/browse/genre/839338 a day ago https://bugs.webkit.org/show_bug.cgi?id=127102 a day ago https://github.com/dashersw/liquid-glass-js a day ago https://i.imgur.com/PW4RAYq.png a day ago https://ux.stackexchange.com/q/120541/115045 a day ago |
372. HN Supabase AI PromptsThe article presents a collection of AI-generated prompts specifically designed for optimizing workflows with Supabase when using AI-enhanced Integrated Development Environment (IDE) tools such as Cursor and GitHub Copilot. These prompts serve to facilitate various tasks involving Supabase by providing guidance that is customized for these widely-used IDE platforms. To effectively leverage these prompts, users are advised to copy them into a file within their repository. **Bullet Point Summary:** - The article introduces AI-generated prompts tailored for use with Supabase in AI-enhanced IDEs like Cursor and GitHub Copilot. - These prompts aim to enhance efficiency by providing specific guidance for tasks involving Supabase. - Users can maximize the utility of these prompts by incorporating them into a file within their repository. Keywords: AI Prompts, Copy, Curated, Cursor, File, GitHub Copilot, IDE Tools, Prompts, Repo, Repository, Selection, Supabase, Working
github copilot
supabase.com 5 days ago
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373. HN Cognition valued at $10.2B two months after Windsurf purchaseCognition AI Inc., an artificial intelligence startup, recently achieved a remarkable $10.2 billion valuation just two months following its acquisition of another AI company, Windsurf. This strategic move occurred shortly after Windsurf's founders left to join Google due to the failed acquisition talks with OpenAI. Bolstered by a substantial $400 million funding round led by Peter Thiel’s Founders Fund and supported by notable investors such as Lux Capital, 8VC, and Bain Capital Ventures, Cognition AI Inc. is poised to significantly enhance its technological offerings. The company plans to focus on investing in its AI software engineer Devin alongside Windsurf's technologies. As a result of the acquisition, Cognition has seen its annual recurring revenue more than double, with their combined enterprise ARR increasing by over 30%. This achievement places them among an elite group of AI startups valued at $10 billion or more, which includes industry leaders like OpenAI and Anthropic. Both Scott Wu, CEO of Cognition, and Jeff Wang, former CEO of Windsurf, highlighted the synergistic potential between their products that promises substantial benefits for customers. - **Cognition AI Inc.'s Achievement**: Achieved a $10.2 billion valuation post-acquisition of Windsurf. - **Timing and Context**: Acquisition happened shortly after Windsurf's founders joined Google following failed acquisition talks with OpenAI. - **Funding Boost**: Received $400 million funding led by Peter Thiel’s Founders Fund, with contributions from Lux Capital, 8VC, and Bain Capital Ventures. - **Investment Focus**: Plans significant investments in AI software engineer Devin and Windsurf technologies. - **Financial Growth**: Annual recurring revenue doubled, enterprise ARR grew over 30% post-acquisition. - **Industry Standing**: Now among a select group of AI startups valued at $10 billion or more, like OpenAI and Anthropic. - **Leadership Insight**: Both Cognition's CEO Scott Wu and former Windsurf CEO Jeff Wang stressed the product synergies and customer benefits. Keywords: AI startup, ARR, CEO Scott Wu, Cognition, Devin, Founders Fund, Google, Jeff Wang, OpenAI, Windsurf, acquisition, artificial intelligence, enterprise growth, funding round, licensing fees, valuation
openai
www.cnbc.com 5 days ago
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374. HN Non-Constructive Arguments of InevitabilityThe text explores the concept of "Non-Constructive Arguments of Inevitability," focusing on discussions related to the Gemini zodiac sign. These arguments propose that certain outcomes are unavoidable, yet they lack constructive solutions or alternatives. The specific mention of "Gemini - Non-Constructive Arguments Debated" implies that there is an ongoing analysis or debate examining how such arguments manifest within the context of traits associated with Geminis. This suggests a deeper exploration into whether these discussions reflect typical Gemini characteristics, which are often linked to adaptability and duality. The mention of a prompt to "Sign in" indicates that accessing further details or engaging more deeply might require user authentication or access to additional content. - Discusses the concept of non-constructive arguments suggesting inevitable outcomes without solutions. - Focuses on these arguments in relation to Gemini, indicating a specific analysis tied to its traits. - Implies an ongoing debate about how such arguments are associated with Geminis. - Suggests that further details or participation might require user access beyond the initial text. This summary captures the essence of the provided text while maintaining clarity and conciseness. Keywords: Arguments, Debated, Gemini, Inevitability, Non-Constructive, Relevance, Sign, Technical Keywords, Text Topic, Topics
gemini
gemini.google.com 5 days ago
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375. HN The First Ziglang.org Outage**Summary:** On September 1st, 2025, ziglang.org faced its first significant outage due to excessive data transmission from a bot with the user agent "facebookexternalhit/1.1," resulting in server issues like slow loading and HTTP 500 errors. This bot downloaded a tarball over one million times within about 36 hours, severely degrading site performance. Community member Frank Denis identified this as a recurring issue noted on social media. To address the problem, administrators blocked requests from the offending user agent, which helped restore normal operations. However, continued activity suggested possible future IP bans to prevent similar incidents. During the outage, community members created mirrors of the official Zig website to serve users impacted by downtime. This incident underscored ziglang.org's design choice to fail rather than malfunction under stress, sparking discussions about scalable cloud solutions like AWS S3, though concerns were raised that such measures could favor malicious bots over genuine contributors. The team aims to minimize reliance on any single cloud provider and GitHub for hosting releases. The outage highlighted the necessity of robust contingency plans. Community mirrors provided a temporary solution but faced challenges with unstable builds due to long timeouts when fetching artifacts from multiple sources. This experience emphasized the need to refine mirror strategies and avoid dependency on a single resource provider. The Community Mirrors initiative is seen as a promising path forward, albeit requiring further improvements. Reflecting on this incident, future plans include updating specifications to incorporate timeout limits and developing a Zig-based official mirror reference implementation upon availability of async I/O in the master branch. Resource efficiency remains crucial due to financial constraints and potential limitations on free service availability in the future. The project prioritizes resource-efficient operations to support innovative work independent of corporate sponsorship, with over 90% of donations consistently allocated towards compensating contributors. The article concludes by encouraging support through a current fundraiser aimed at accelerating the development of Zig v1.0. **Bullet Point Summary:** - **Incident Overview:** On September 1st, 2025, ziglang.org experienced its first major outage due to a bot with user agent "facebookexternalhit/1.1," causing server issues and excessive data transmission. - **Immediate Response:** Administrators blocked requests from the offending user agent to normalize operations; potential future IP bans were considered. - **Community Action:** During downtime, community members set up mirrors of ziglang.org as an alternative for users affected by the outage. - **Design Philosophy:** The incident highlighted ziglang.org's design choice to break under stress rather than malfunction, prompting discussions on scalable cloud solutions like AWS S3, despite concerns about aiding malicious bots. - **Contingency Planning:** Emphasized need for robust contingency plans; community mirrors helped mitigate impact but faced challenges with unstable builds due to long timeouts when fetching artifacts from multiple sources. - **Future Improvements:** Plans include updating specifications to incorporate timeout limits and developing a Zig-based official mirror reference implementation once async I/O is available in the master branch. - **Resource Efficiency:** Highlighted as critical due to financial constraints; over 90% of donations consistently allocated towards compensating contributors, with an increase in budget for 2024 yet maintaining high expenditure on contributor payments. - **Conclusion and Call to Action:** Encourages support through a current fundraiser aimed at accelerating the development of Zig v1.0. Keywords: AWS S3, Access Logs, Autoscaling, Bandwidth, CDN, CI Jobs, Caching, Community Mirrors, Compiler Infrastructure, Contributors, Crawling, Credits, Data Transmission, Facebookbot, Finance, Fundraiser, GitHub, HTTP 500, Hosting, IP-Level Bans, Issues, Outage, Releases, Retries, Serverless, Slow Loading, Timeouts, User Agent, Ziglangorg
github
ziglang.org 5 days ago
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376. HN Synthetic Data Toolkit Released on GitHubThe SDG AI Data Generator Free Edition is a synthetic test data generation tool available on GitHub, intended for developers and QA engineers who need realistic datasets across Windows, Linux, and macOS platforms. It supports formats such as CSV, tab-delimited, and fixed-width and can generate various data types like names, phone numbers, integers, ZIP codes, dates, strings, and custom text files, with a limit of 100 rows per execution. Users configure the tool using simple text-based specification files (.spf), which allows for quick dataset generation suitable for software development, database testing, and application prototyping. To use this tool, users execute `datagen.exe` on Windows or `./datagen` on Linux/macOS with a configuration file (e.g., `sample.spf`) to generate datasets. The configuration specifies details such as the number of records and data attributes like first name, last name, phone number, zip code, and age. It includes fields for output filename, record count, format, layout, and field definitions with attributes including description, data type, length, and order. This tool is ideal for API testing, database development, form testing, generating demo data, and prototype development by creating varied input datasets. Users can create custom text files using a specific type (`CUSTA`) to include additional data lists such as cities in their specifications. The distribution includes executable files, an example specification file, and encrypted reference tables for name and location data. The Pro Version offers enhanced features like generating up to one million rows per execution, over 50 additional data types (e.g., addresses, companies, advanced demographics), international formats, complex date formats, business data, field copying, and substring extraction. The tool requires Windows 10 or later (64-bit), modern Linux distributions with glibc 2.27+, macOS 10.15 or later, and a minimum of 50MB RAM and 100MB storage. Support for the Free Edition includes documentation, sample .spf files, a user community forum, and commercial support for full version users. While the tool is free for development and testing, commercial use requires purchasing the Full Version license. More details or purchase information for the Pro Version can be found through the provided Stripe checkout link. - **Tool Overview**: SDG AI Data Generator Free Edition is designed for developers and QA engineers to generate synthetic test data across various platforms. - **Supported Formats and Data Types**: Supports CSV, tab-delimited, fixed-width formats; generates names, phone numbers, integers, ZIP codes, dates, strings, and custom text files (up to 100 rows). - **Configuration and Usage**: Users configure the tool with .spf files for instant dataset generation. Execution commands differ between Windows (`datagen.exe`) and Linux/macOS (`./datagen`). - **Use Cases**: Ideal for API testing, database development, form testing, demo data generation, and prototype development. - **Customization Options**: Allows creation of custom text files using `CUSTA` type for additional data lists like cities. - **Included Files**: Comes with executables, a sample specification file, and encrypted reference tables. - **Pro Version Features**: Offers up to one million rows per execution, over 50 additional data types, international formats, complex date formats, business data, field copying, and substring extraction. - **System Requirements**: Requires modern operating systems (Windows 10 or later, Linux with glibc 2.27+, macOS 10.15 or later) and a minimum of 50MB RAM and 100MB storage. - **Support and Licensing**: Includes documentation, sample files, community forum, and commercial support for the full version; free for development/testing but requires a license for commercial use. - **Purchase Information**: Pro Version details available via Stripe checkout link. Keywords: AI Data Generator, API Testing, CSV, Commercial Support, Community Forum, Configuration, Cross-Platform, Data Types, Database Development, Demo Data, Developers, Documentation, Encrypted Data, Executable, Execution Limit, Fixed-Width, Form Testing, GitHub, Instant Generation, Linux, Memory Requirements, Platforms, Pro Version, Prototype Development, QA Engineers, Sample File, Specification Files, Storage Requirements, Synthetic Data, Tab-Delimited, Test Data Generation, Toolkit, Windows, macOS
github
github.com 5 days ago
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377. HN Show HN: Comparegpt.io – Spotting LLM hallucinations with multi-model comparisonTina introduces CompareGPT.io, an innovative tool designed to tackle the issue of language model (LLM) hallucinations by concurrently querying multiple LLMs such as ChatGPT, Gemini, Claude, and Grok. This platform presents results side-by-side, allowing users to easily identify discrepancies and potential fabrications in the responses, thus aiding in spotting inaccuracies more effectively. Additionally, CompareGPT.io provides a unified API that facilitates seamless integration with other systems. To gain early adopter feedback and encourage trial use, Tina has initiated a waitlist offering access discounts. The primary aim is to explore whether consistency across multiple models could become an industry standard for minimizing hallucinations in critical fields such as law, finance, or research. - **Introduction of CompareGPT.io**: A tool developed by Tina aimed at addressing LLM hallucinations. - **Functionality**: Runs queries on various LLMs like ChatGPT, Gemini, Claude, and Grok simultaneously to compare results side-by-side. - **Purpose**: Highlights discrepancies and potential fabrications in responses, helping users identify inaccuracies. - **Additional Features**: Offers a unified API for easy integration with other systems. - **User Engagement**: Tina has started a waitlist with discounts for early access to gather user feedback. - **Future Exploration**: Investigating the potential of multi-model consistency as a standard approach to reduce hallucinations in critical sectors like law, finance, and research. Keywords: ChatGPT, Claude, CompareGPTio, Gemini, Grok, LLM, LLM hallucinations, Tina, consistency, discrepancies, early offer, feedback, finance, hallucinations, law, multi-model, multi-model consistency Keywords: CompareGPT, query, research, side by side, side-by-side, unified API, waitlist
claude
news.ycombinator.com 5 days ago
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378. HN Nvidia's Huang joining Trump on UK state visit next weekNVIDIA CEO Jensen Huang will be part of a delegation accompanying President Donald Trump on an upcoming state visit to the UK. This group includes other prominent U.S. business leaders such as OpenAI's Sam Altman and Blackstone's Stephen Schwartzman, who are expected to attend a state banquet hosted by King Charles. Despite missing a recent White House dinner for tech CEOs, Huang's involvement highlights NVIDIA’s strategic interest in maintaining its relationship with Trump. This is particularly significant as the company aims to secure new licenses to sell its Blackwell chips in China. Additionally, Apple CEO Tim Cook was reportedly invited to join this visit. - **Delegation Details**: NVIDIA CEO Jensen Huang will accompany President Donald Trump on a state visit to the UK alongside other U.S. business leaders. - **Attendees**: The delegation includes OpenAI's Sam Altman and Blackstone's Stephen Schwartzman. - **Event**: They are expected to attend a state banquet hosted by King Charles. - **NVIDIA’s Strategic Interests**: Despite missing a recent White House dinner, Huang’s participation underscores NVIDIA’s commitment to maintaining its relationship with Trump. - **Business Context**: The visit is significant as NVIDIA seeks new licenses for selling Blackwell chips in China. - **Additional Invitations**: Apple CEO Tim Cook was reportedly invited to the state visit. Keywords: Apple, BlackRock, Blackstone, Blackwell chips, CEO, CNBC, China, Donald Trump, Huang, Jensen Huang, King Charles, Nvidia, OpenAI, Sky News, Tim Cook, Trump, UK, White House, chipmaker, licenses, tech CEOs
openai
www.cnbc.com 5 days ago
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379. HN LWMalloc is a lightweight dynamic memory allocator for embedded systems**Summary:** LWMalloc is an ultra-lightweight dynamic memory allocator developed by researchers at Seoul National University of Science and Technology (SEOULTECH), designed specifically for embedded systems to surpass the traditional ptmalloc used in Glibc. It significantly enhances performance with up to 53% faster execution times and reduces memory usage by 23%. These improvements are achieved through a lightweight data structure, a deferred coalescing policy, and dedicated small chunk pools that minimize metadata overhead and defer operations to cut down execution costs while enabling constant-time allocation for common small requests. LWMalloc is remarkably compact, consisting of only 530 lines of code with a footprint of just 20KB, making it highly suitable for resource-constrained environments where memory efficiency and reliability are crucial. Compared to other allocators such as jemalloc, tcmalloc, and mimalloc—known for their heavy memory consumption, large libraries, complexity, and potential performance issues—LWMalloc stands out due to its minimal footprint and ease of integration without requiring changes to application code, facilitated by LD_PRELOAD at runtime. This makes it ideal for embedded or IoT systems facing stringent constraints, including consumer electronics, mobile devices, automotive systems, and edge AI computing applications. While the full research is available behind a paid IEEE subscription, LWMalloc's C code and test program are accessible on GitHub. CNX Software invites support through donations or affiliate purchases. **Bullet Point Summary:** - **LWMalloc Development**: Created by SEOULTECH researchers for embedded systems, outperforming traditional ptmalloc. - **Performance Improvements**: Up to 53% faster execution times; 23% lower memory usage than ptmalloc. - **Design Innovations**: Features lightweight data structure, deferred coalescing policy, and small chunk pools reducing metadata overhead and operation costs. - **Compact Nature**: Consists of only 530 lines of code with a 20KB footprint, significantly smaller than ptmalloc's 4838 lines and 116 KB footprint. - **Suitability**: Ideal for resource-constrained environments where memory efficiency and reliability are crucial. - **Comparison with Other Allocators**: Offers advantages over jemalloc, tcmalloc, and mimalloc in terms of size, complexity, and performance stability. - **Ease of Integration**: Can be integrated into projects without altering application code using LD_PRELOAD at runtime. - **Applications**: Suitable for embedded or IoT systems like consumer electronics, mobile devices, automotive systems, and edge AI computing applications. - **Research Access**: Full research available through a paid IEEE subscription; C code and test program accessible on GitHub. - **Support Encouragement**: CNX Software encourages support via donations or affiliate purchases. Keywords: CNX Software, GitHub, Glibc, IoT, LD_PRELOAD, LWMalloc, O(1) allocation, TLS, automotive systems, calloc, consumer electronics, custom allocators, deferred coalescing, dynamic memory allocator, edge AI computing, embedded systems, execution time, free, garbage collection, home appliances, lightweight data structure, malloc, memory fragmentation, memory usage, mobile devices, ptmalloc, realloc, set-top boxes, small chunk pools, smart TVs, static memory allocation, wearable devices
github
www.cnx-software.com 5 days ago
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380. HN Claude Code Beginner's Tutorial by Peter Yang### Summary Peter Yang provides a beginner-friendly tutorial on utilizing Claude Code, an AI tool considered top-tier for enhancing coding tasks. The tutorial focuses on setting up Claude Code and integrating a watchlist feature into a movie application within 15 minutes. Key steps involve installing Claude Code globally via terminal commands, cloning the app from GitHub, understanding its codebase with the assistance of Claude, planning features in detail using claude.md to maintain project memory, and efficiently building the watchlist functionality. The tutorial also highlights Jam as an auxiliary tool that eases bug reporting by creating detailed tickets and facilitating integration across various platforms for workflow optimization. This comprehensive guide is accessible via YouTube or a written format. The document further elaborates on setting up and running a movie discovery application using Claude Code, which can be installed globally with npm or utilized within IDEs like Cursor. The process begins with cloning the app repository and loading Claude Code in an appropriate environment to examine and comprehend its codebase—a step that aids users in developing technical skills. To run the application locally, users must install dependencies and set up a TMDB API key by creating an account on TMDB, retrieving the key from their settings page, and inputting it via Claude Code. For enhancing the app with a watchlist feature, users are guided to engage Claude in plan mode (activated by pressing shift-tab) to formulate detailed specifications without modifying existing code. This specification must include three specific components for precise AI instruction adherence during implementation. The document emphasizes that movies will not load until the TMDB API key is correctly inputted, with an option to create an environment file through a confirmation prompt. ### Bullet Point Summary - **Introduction**: Peter Yang's tutorial on using Claude Code for beginners focuses on setting up Claude and adding a watchlist feature to a movie app in 15 minutes. - **Installation**: Install Claude Code globally via `npm install -g @anthropic-ai/claude-code`, accessible through terminal or IDEs like Cursor. - **Clone the App**: Use `git clone https://github.com/sudeepmahato16/movie-app.git` to clone the movie app repository, and open it with Cursor. - **Codebase Exploration**: Utilize Claude Code to understand the codebase of the cloned app, aiding in learning technical skills. - **Run Locally**: After setting up a TMDB API key by creating an account on TMDB and retrieving the key from settings, run the application locally using Claude Code. - **Watchlist Feature**: Use plan mode in Claude (activated with shift-tab) to draft specifications for adding a watchlist feature without altering existing code. Include three specific components necessary for accurate AI instruction adherence. - **Additional Tools**: Highlight Jam as an auxiliary tool that simplifies bug reporting by generating detailed tickets and integrating with various platforms, streamlining workflow. - **API Key Importance**: Emphasize the necessity of inputting the TMDB API key to ensure movies load correctly when accessing the app at localhost. Users can create an environment file by confirming "Yes." - **Tutorial Access**: The tutorial is available on YouTube or as a written guide for further assistance and reference. Keywords: AI agent, API key, Beginner's Tutorial, Bug fixing, Claude Code, Clone, Cursor, Dependencies, GitHub integration, IDE, Jam, Jira integration, Movie app, Peter Yang, Plan mode, TMDB, Tech stack, Terminal installation, Watchlist feature, localhost, npm
claude
creatoreconomy.so 5 days ago
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381. HN Show HN: Proxied Web from URL –– A free web proxy service directly from URLThe provided text describes Proxied Web from URL, a free web proxy service accessible at https://web.818233.xyz. This service allows users to append any website URL to the domain to fetch and view its content, which is particularly useful for accessing restricted or unavailable websites directly. The primary purpose of this service is to assist in extracting content for Large Language Models (LLMs), although it can also be beneficial when direct site access is not feasible. Key features include the ability to fetch content without interaction support, a fair use policy that prevents abuse with potential IP bans, and a privacy assurance since browsing history is not stored on the server. Users are informed that the content might differ from the original source. For feedback or issues, users can contact via email at hi@818233.xyz. The service operates under a free-to-use model, but it encourages optional financial support through donations. - Proxied Web from URL offers a free web proxy service accessible at https://web.818233.xyz. - Users can append any website URL to this domain to fetch and view its content. - Useful for accessing restricted or unavailable websites directly. - Primarily assists in extracting content for Large Language Models (LLMs). - Can also be helpful when direct site access is not possible. - Features include: - Content fetching without interaction support. - A fair use policy preventing abuse with potential IP bans. - Assurance of privacy, as browsing history is not stored on the server. - Users are informed that content might differ from the original source. - Feedback or issues can be addressed via email at hi@818233.xyz. - Operates under a free-to-use model, encouraging optional financial support through donations. Keywords: IP, IP ban, LLM, LLM use, Proxied Web, URL, URL proxy, ban, browsing history, contact email, content, content fetching, cost, disclaimer, email, fetching, free, free proxy, history, policy, privacy, privacy policy, proxy, running cost Keywords: proxied, service, service access, web, web content
llm
web.818233.xyz 5 days ago
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382. HN Chat Control Must Be Stopped**Summary:** The text presents an urgent call to action against a proposed European Union regulation termed "Chat Control," which would mandate the scanning of all private digital communications, including those that are end-to-end encrypted. This initiative, ostensibly aimed at combating child sexual abuse material (CSAM), poses significant threats to privacy, democracy, and human rights by enabling invasive surveillance comparable to constant policing. The proposal, which is set for finalization on September 12th, 2025, has sparked widespread concern among privacy advocates who argue that it endangers fundamental freedoms and could be ineffective at preventing child abuse. "Chat Control," reintroduced after initial rejection in 2023, would require service providers to scan communications for abusive material, undermining encryption protections. Critics highlight the risk of mission creep, where surveillance extends beyond its original purpose, potentially resulting in data misuse by criminals due to inevitable breaches and leaks. The proposal has been criticized as deeply flawed by experts like Matthew Green, who describe it as a terrifying invasion of privacy that could hinder child protection efforts. The regulation's potential global implications extend beyond Europe, affecting international users communicating with EU residents, thus challenging end-to-end encryption universally. Additionally, the Five Eyes countries have shown interest in adopting similar measures, suggesting widespread consequences for digital communication privacy worldwide. The erosion of privacy protections within Europe could lead to oppressive surveillance and self-censorship, impacting not only individuals but also businesses, journalists, and democratic processes. Despite these challenges, advocacy groups stress that resistance is possible. They urge Europeans and global citizens alike to contact their representatives before key voting dates in late 2025, utilizing resources provided by organizations like Fight Chat Control. The article emphasizes continued engagement through social media campaigns and sharing educational materials to defend human rights against this authoritarian measure. **Bullet Point Summary:** - "Chat Control" is a proposed EU regulation mandating the scanning of all private digital communications to prevent CSAM but poses significant privacy, democracy, and human rights threats. - Critics argue it undermines end-to-end encryption, risks mission creep, data misuse by criminals, and may not effectively combat child abuse. - The proposal could impact global communication by affecting platforms processing EU residents' data, challenging the functionality of end-to-end encryption universally. - Privacy advocates urge action before key votes in late 2025, emphasizing continued advocacy against this measure through contacting representatives and social media engagement. - The regulation risks oppressive surveillance within Europe, impacting vulnerable groups, businesses, journalists, and democratic processes, while Five Eyes countries may adopt similar measures domestically. Keywords: AI, CSAM, Chat Control, EU, GDPR, MEPs, authoritarianism, censorship, data protection, democracy, encryption, end-to-end, false positives, privacy, regulation, surveillance
popular
www.privacyguides.org 5 days ago
https://pluralistic.net/ 5 days ago https://fra.europa.eu/en/eu-charter/article/7 5 days ago https://en.m.wikipedia.org/wiki/Regulation_to_Prevent_a 5 days ago https://www.consilium.europa.eu/en/council-eu/deci 5 days ago https://www.theatlantic.com/technology/archive/201 5 days ago https://www.bbc.com/news/articles/cq69qnvj6nlo 5 days ago https://adfinternational.org/en-gb/news/guilty-arm 5 days ago https://www.bbc.com/news/articles/c4g9kp7r00vo 5 days ago https://www.patrick-breyer.de/en/posts/chat-contro 5 days ago https://www.privacyguides.org/articles/2025/02 5 days ago https://en.wikipedia.org/wiki/Export_of_cryptography_fr 5 days ago https://en.m.wikipedia.org/wiki/Illegal_number 5 days ago https://github.com/philipl/pifs 5 days ago https://en.wikipedia.org/wiki/The_Library_of_Babel 5 days ago https://en.wikipedia.org/wiki/Samizdat 5 days ago https://en.wikipedia.org/wiki/Regulation_to_Prevent_and 5 days ago |
383. HN Hackers steal 3,325 secrets in GhostAction GitHub supply chain attack### Summary: The "GhostAction" supply chain attack on GitHub involved hackers compromising maintainer accounts across 817 repositories, resulting in the theft of approximately 3,325 secrets. The attackers inserted a malicious GitHub Actions workflow to extract sensitive information like PyPI tokens, npm credentials, and AWS keys to an external domain. This breach was first detected on September 2, 2025, with initial signs appearing in the FastUUID project. Upon deeper investigation by GitGuardian, which fully disclosed the attack's scope on September 5, it became evident that multiple projects were affected. Swift mitigation efforts ensued, halting further data exfiltration after the resolution endpoint was disabled. The GhostAction campaign spanned various platforms beyond GitHub, including PyPI, npm, DockerHub, Cloudflare, AWS, and databases. It compromised at least nine npm and fifteen PyPI packages, risking malicious updates unless addressed by maintainers. The attack impacted multiple programming languages and ecosystems, such as Rust crates, Python, JavaScript, and Go repositories, affecting numerous companies. While operational similarities with a prior campaign named 's1ngularity' were noted, no direct link was confirmed by GitGuardian. ### Bullet Point Summary: - **Attack Details**: GhostAction involved hackers stealing 3,325 secrets from 817 GitHub repository maintainer accounts. - **Method of Attack**: Insertion of malicious GitHub Actions workflow to extract credentials like PyPI tokens and AWS keys to an external domain. - **Discovery and Mitigation**: First signs found in FastUUID on September 2, 2025; full scope revealed by GitGuardian on September 5; endpoint neutralized to stop data exfiltration. - **Platforms Affected**: Attack spanned GitHub, PyPI, npm, DockerHub, Cloudflare, AWS, and databases. - **Package Impact**: At least nine npm and fifteen PyPI packages compromised, posing risks of malicious updates. - **Programming Languages and Ecosystems Involved**: Rust crates, Python, JavaScript, and Go repositories affected across various ecosystems. - **Operational Similarities**: Some similarities to the 's1ngularity' campaign were noted but no direct connection established by GitGuardian. Keywords: AWS keys, Cloudflare, DockerHub, FastUUID, GhostAction, GitGuardian, GitHub, GitHub Actions workflow, PyPI, compromised accounts, curl POST request, exfiltration, malicious commits, npm, repositories, secrets, security teams, supply chain attack
github
www.bleepingcomputer.com 5 days ago
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384. HN I've built this in a week with Claude CodeIn just one week, the creator successfully developed a project using Claude Code, balancing between instinctual decision-making and external input. Approximately 60% of the project was driven by intuition, highlighting a significant reliance on personal insights and creativity. Another 30% of the development process involved seeking opinions from others, indicating that feedback played a notable role in shaping the final outcome. The remaining portion likely consisted of other factors such as technical constraints or existing guidelines. This blend of intuitive creation and collaborative refinement underscores a dynamic approach to project development. The completed work is publicly accessible for viewing at [firstusers.tech](https://firstusers.tech), inviting feedback and engagement from potential users. - The creator developed a project within one week using Claude Code. - 60% of the project was based on intuition, showcasing reliance on personal insights and creativity. - 30% of the project's development involved seeking opinions, emphasizing the role of external feedback. - The remaining portion likely included other factors like technical constraints or guidelines. - The final work is available for public viewing at [firstusers.tech](https://firstusers.tech). Keywords: Claude Code, code creation, creativity, development, firstuserstech, keyword extraction, programming, project, technical, thinking, vibe coded
claude
news.ycombinator.com 5 days ago
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385. HN Show HN: Receive Notifications from Claude CodeHeyAgent is an open-source tool designed to augment AI coding agents including Claude Code, Codex CLI, and Gemini CLI by operating within terminal environments. It introduces several enhanced features such as instant stop and permission notifications, two-way chat capabilities, automatic checkpoints, and voice input functionality. These additions provide practical extensions to the existing core functionalities of these AI coding tools. **BULLET POINT SUMMARY:** - HeyAgent is an open-source tool that enhances AI coding agents like Claude Code, Codex CLI, and Gemini CLI. - It operates within a terminal setting. - Key features include instant stop and permission notifications, two-way chat capabilities, automatic checkpoints, and voice input functionality. - These features serve as practical extensions to the core functionalities of the supported AI coding tools. Keywords: AI, AI coding agents, Claude Code, Codex CLI, Gemini CLI, HeyAgent, chat, checkpoints, coding agents, core functionality, lightweight wrapper, notifications, open source, terminal, toolkit, voice input, voice input ``` Keywords: HeyAgent
claude
www.heyagent.dev 5 days ago
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386. HN Salesloft says Drift customer data thefts linked to March GitHub account hack### Summary: In March, a breach occurred in Salesloft's GitHub account, allowing hackers to steal authentication tokens that were later used to attack several major tech clients. According to Google’s Mandiant unit investigation, intruders accessed and downloaded content from multiple repositories over three months before detection, raising concerns about Salesloft’s security protocols. The attackers also compromised Drift's AWS environment, stealing OAuth tokens for its customers, thereby affecting prominent companies including Bugcrowd, Cloudflare, Google, Proofpoint, Palo Alto Networks, and Tenable. Although Salesloft contained the breach, the full extent of affected parties remains unclear. In August, Google's Threat Intelligence Group identified a supply chain attack by UNC6395, also known as ShinyHunters. This hacking group targeted Salesloft tokens to infiltrate Salesforce instances and exfiltrate sensitive data from support tickets. Their objectives included acquiring credentials like AWS access keys, passwords, and Snowflake-related tokens. Despite initial disruptions, Salesloft has restored its integration with Salesforce. Separately, TechCrunch is hosting Disrupt 2025 in San Francisco, which will gather over 10,000 tech and venture capital leaders, including prominent figures from Netflix and Sequoia Capital. The event, marking TechCrunch’s 20th anniversary, features more than 200 sessions designed to support startup growth and provide industry insights. Attendees can register by September 26 to receive discounted tickets. ### Bullet Point Summary: - A breach in Salesloft's GitHub account occurred in March, allowing hackers to steal authentication tokens. - Hackers accessed multiple repositories between March and June, prompting concerns about Salesloft’s security measures. - Drift's AWS environment was compromised, leading to the theft of OAuth tokens for its customers, affecting companies such as Bugcrowd, Cloudflare, Google, Proofpoint, Palo Alto Networks, and Tenable. - The breach has been contained by Salesloft, but the full extent of affected parties is still unclear. - In August, UNC6395 (ShinyHunters) executed a supply chain attack via compromised Salesloft tokens to access Salesforce data. - ShinyHunters aimed to obtain AWS access keys, passwords, and Snowflake-related tokens from Salesforce support tickets. - Salesloft has restored its integration with Salesforce after the disruption. - TechCrunch is hosting Disrupt 2025 in San Francisco, featuring over 10,000 tech and VC leaders including Netflix and Sequoia Capital representatives. - The event coincides with TechCrunch’s 20th anniversary and offers over 200 sessions aimed at fostering startup growth and insights. - Registration for the event closes on September 26 to secure discounted tickets. Keywords: AI chatbot, AWS cloud, Disrupt 2025, Drift, GitHub hack, Mandiant, OAuth tokens, Salesforce, Salesloft, Snowflake-related tokens, Threat Intelligence Group, UNC6395, access keys, authentication tokens, big tech customers, extort, mass-hack, reconnaissance activities, supply chain breach
github
techcrunch.com 5 days ago
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387. HN Why Your Docs-as-Code Toolchain Is Holding You BackThe article explores the complexities and challenges associated with employing a "Docs-as-Code" toolchain for technical documentation in growing teams. Initially attractive due to its seamless integration into developer workflows and use of version control systems like Git, Docs-as-Code offers benefits such as speed and collaboration. However, as teams expand, maintaining this system becomes increasingly complex. The article highlights several critical issues: 1. **Technical Debt**: As Sarah Moir notes, the process can become more about resolving pipeline issues than actual content creation due to accumulated technical debt from broken links and syntax errors. 2. **Onboarding Challenges**: New contributors face a steep learning curve with tools such as Git, Makefiles, and CI jobs, which complicates simple tasks like correcting typos. 3. **Inconsistent Documentation**: Without proper governance, documentation can suffer from inconsistent style and structure, leading to varied formats and tones. 4. **Dependence on Key Individuals**: Over-reliance on a "Docs DevOps" lead for maintaining the system can cause disruptions if they become unavailable, underscoring the need for distributed knowledge within teams. The article argues that while Docs-as-Code is effective initially, it requires ongoing adjustments and governance to sustain its efficiency as documentation needs grow. As these systems mature into complex "Frankenstacks," they become fragile, causing writer fatigue, slower delivery times, and increased risk of significant outages from minor errors or tool dependencies. To mitigate these issues, the article recommends auditing existing tools to identify critical versus redundant components, standardizing workflows, establishing governance models with templates and style guides, simplifying contributions through pre-built resources and clear guidelines, and planning for future team growth. It advises that when maintaining a custom pipeline becomes more costly than beneficial, transitioning to a scalable platform is wise. The decision to upgrade from a custom content pipeline to an advanced platform should be made when maintenance costs outweigh the benefits. Transitioning can involve adopting structured authoring methods like DITA, switching to a component content management system (CCMS) for better collaboration and automation, or enhancing user experience for contributors unfamiliar with tools like Git. The article posits that evolving from Docs-as-Code is not a failure but rather its natural progression. Experts Heike Auch and Anita Lüders suggest evaluating whether the integration benefits justify any limitations imposed on technical writers. Ultimately, Docs-as-Code can be a powerful yet lightweight framework if adapted to changing needs. If effective, it should continue in use; otherwise, updating before scaling exacerbates challenges is advisable. **BULLET POINT SUMMARY:** - **Initial Appeal**: Docs-as-Code integrates well with developer workflows and uses version control for speed and collaboration. - **Scaling Challenges**: Growing teams face complexity in scripts, tools, and processes, shifting focus from writing to technical workflow management. - **Key Issues**: - **Technical Debt**: Time spent troubleshooting pipelines due to broken links and syntax errors. - **Onboarding Difficulties**: Steep learning curve for new contributors with tools like Git and Makefiles. - **Lack of Consistency**: Inconsistent documentation styles and structures without governance. - **Key Individual Dependence**: Over-reliance on a "Docs DevOps" lead can cause disruptions. - **System Complexity**: As systems evolve, they become fragile "Frankenstacks," leading to writer fatigue and risk of outages. - **Preventive Measures**: Audit tools, standardize workflows, establish governance, simplify contributions, and plan for growth. - **When to Upgrade**: Transition when maintenance costs outweigh benefits; consider structured authoring methods or CCMS. - **Natural Progression**: Evolving from Docs-as-Code is part of its lifecycle; assess integration benefits versus limitations. - **Adaptation Advice**: Continue using an effective system; update before scaling increases challenges. Keywords: CCMS, CI/CD, DITA, Docs-as-Code, Git, GitHub, Markdown, SMEs, agile, analysis, automation, bottlenecks, collaboration, contributor UX, contributors, documentation, governance, infrastructure, publishing, pull requests, scalability, scripts, technical writers, tools, version control, workflow
github
www.thecontentwrangler.com 5 days ago
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388. HN Gemini app expands to audio filesGoogle has made significant enhancements to its AI-driven products recently, reflecting an ongoing commitment to improving user accessibility and functionality on a global scale. The Gemini app now supports audio files, addressing the top request from users by allowing free users up to 10 minutes and AI Pro/AI Ultra subscribers up to three hours of daily audio use. Additionally, Google Search's AI Mode has broadened its language support with the integration of Hindi, Indonesian, Japanese, Korean, and Brazilian Portuguese through Gemini 2.5, facilitating more complex queries in these languages. The NotebookLM tool has been updated with new report styles such as study guides, quizzes, and blog posts across over 80 languages, leveraging user-uploaded content to identify patterns in various file formats, thereby enhancing its utility as a research instrument. These developments are part of Google's larger strategy to expand the capabilities of Gemini-powered products. In recent months, Google has further improved its AI offerings. In August, Gemini began automatically utilizing user details from past interactions, and free Workspace users were granted access to Vids, a tool for video generation. September saw an upgrade in Photos with Veo 3 software, which allows free users to create silent 4-second videos from still images. **BULLET POINT SUMMARY:** - The Gemini app now supports audio files, offering up to 10 minutes for free users and three hours for AI Pro/AI Ultra subscribers daily. - Google Search's AI Mode expanded its language support to include Hindi, Indonesian, Japanese, Korean, and Brazilian Portuguese via the Gemini 2.5 integration. - NotebookLM tool updated with new report styles such as study guides, quizzes, and blog posts in over 80 languages, enhancing its research capabilities. - These updates are part of Google's efforts to improve accessibility and functionality of Gemini-powered products globally. - In August, Gemini started using user details from past interactions automatically, and free Workspace users gained access to the video generation tool Vids. - Photos was upgraded with Veo 3 software in September, enabling creation of silent 4-second videos from still images for free users. Keywords: AI reporter, August, Gemini 25, Gemini app, Gemini-powered products, Google updates, New York City, NotebookLM, Photos, September, Tarbell Center, Veo 3, Vids, Workspace, audio files, blog posts, briefing docs, compatibility, features, file formats, flashcards, language options, personal pictures, preferences, quizzes, report styles, research tool, silent videos, study guides, user details, video generation
gemini
www.theverge.com 5 days ago
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389. HN Show HN: New Site for My GitHub TUIThe announcement introduces "dash," a new website for the TUI tool, accessible at https://gh-dash.dev. This site, constructed using Starlight by Astro and hosted on a VPS via Dokploy, streamlines the process of publishing updates with each pull request (PR). It recognizes contributions from community members, particularly @michaeltlombardi on GitHub. The project includes a Discord server (https://discord.com/invite/SXNXp9NctV) for users to share configurations and engage in discussions about terminal topics. The creator expresses gratitude towards contributors and invites feedback. Additionally, the site features ASCII art as part of its design. Users are encouraged to watch an explanatory video by Charm on how "dash" can improve GitHub management experiences. - **Introduction**: Launch of a new website called `dash` for TUI tool at https://gh-dash.dev. - **Development and Hosting**: Built with Starlight by Astro, hosted on a VPS through Dokploy. - **Update Mechanism**: Simplifies publishing updates with each pull request (PR). - **Community Acknowledgment**: Recognizes contributions from the community, especially @michaeltlombardi on GitHub. - **Discord Server**: Provides a platform for sharing configurations and discussing terminal topics (https://discord.com/invite/SXNXp9NctV). - **Gratitude and Feedback**: Creator thanks contributors and invites feedback. - **Design Feature**: Includes ASCII art. - **User Engagement**: Encourages users to watch Charm's video explaining `dash`'s benefits for GitHub management. Keywords: ASCII, Astro, Charm, Community, Dash, Discord, Dokploy, GitHub, PR, Starlight, TUI, VPS, Video, Website
github
www.gh-dash.dev 5 days ago
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390. HN Using static analysis to reduce token usage of AI coding agentsThe author explores the use of AI coding agents to insert debug statements into authorization-related code but finds them inefficient and inaccurate due to their inability to identify all relevant functions within a large token limit. This inefficiency is attributed to the agents' lack of context retention, requiring fresh searches each time they examine a codebase. The problem worsens with suboptimal naming or structure in repositories. AI coding agents rely on basic text-based tools like `ls`, `cat`, and `grep` to locate information, often leading to wasted resources when file names do not reflect their contents accurately. These agents also suffer from "context rot," where irrelevant past searches accumulate in the context window, degrading performance as token limits are reached. To address these challenges, sub-agents can be employed to provide focused results and mitigate context rot without overwhelming the main agent's context window. The author proposes enhancing codebase analysis by integrating semantic understanding through vector embeddings, which allows for semantic similarity searches instead of traditional keyword searches. This method utilizes Large Language Models (LLMs) to generate function summaries paired with vector embeddings stored in a database. Further optimization is achieved using dependency graphs within graph databases like Neo4j, facilitating efficient retrieval of call paths related to specific functions—beneficial in scenarios such as investigating memory leaks. Benchmarking against conventional LLM methods shows significant reductions in token usage and processing time with these optimizations, providing efficiency gains without sacrificing accuracy. An added feature to the author's code intelligence MCP illustrates its capability by executing tasks in 63 milliseconds without token consumption, marking a notable achievement shared on Twitter. However, further benchmark details are withheld. **Bullet Point Summary:** - AI coding agents struggle with efficiency and accuracy due to inability to identify all relevant functions within large token limits. - Agents lack context retention, starting searches from scratch each time, exacerbated by poor repository structure or naming. - Dependence on basic text tools can lead to resource wastage when file names don't match contents. - "Context rot" in agents results in accumulated irrelevant information, degrading performance as token limits are reached. - Sub-agents proposed to provide focused results and mitigate context rot without overloading main agent's context window. - Semantic understanding through vector embeddings reduces reliance on keyword searches, enhancing codebase analysis. - Large Language Models generate function summaries with associated vector embeddings for semantic similarity searches. - Dependency graphs in graph databases like Neo4j enable efficient retrieval of call paths related to specific functions. - Benchmarking shows significant reductions in token usage and processing time compared to traditional LLM methods. - New feature in author's code intelligence MCP executed tasks in 63 milliseconds without token use, shared on Twitter. Keywords: AI coding agents, Large Language Models, MCP, Neo4j, PostgreSQL, Static analysis, auth-related code, authorization logic, bug, code intelligence, content, context-rot issue, debug statement, electricity consumption, functions, semantic inference, tokens, tool use
postgresql
faraazahmad.github.io 5 days ago
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391. HN Are bad incentives to blame for AI hallucinations?A recent research paper by OpenAI investigates the persistent issue of "hallucinations" in large language models such as GPT-5, which produce plausible yet incorrect statements despite advancements in technology. These inaccuracies persist as a fundamental challenge and cannot be entirely eliminated. The paper provides examples like chatbots giving wrong information about an author's dissertation title or birthday to illustrate the problem. Researchers suggest that these hallucinations stem from the pretraining process, where models focus on predicting the next word rather than ensuring truthfulness. This reliance on data patterns becomes especially problematic when dealing with low-frequency facts that cannot be inferred solely from available data. The paper argues that while current training methods are not directly responsible for hallucinations, they contribute to the issue by creating "wrong incentives." It compares this situation to multiple-choice tests where guessing might result in correct answers by chance, thereby rewarding inaccurate responses. To address this, the researchers propose reevaluating how models are tested to mitigate these incentive problems and reduce hallucinations effectively. Separately, the TechCrunch Disrupt 2025 event in San Francisco is announced, set to attract over 10,000 leaders from the tech and venture capital sectors, including notable companies like Netflix and Box. The event will feature more than 200 sessions aimed at promoting startup growth while celebrating TechCrunch's 20th anniversary, offering attendees insights from top industry voices with discounted tickets available until September 26. In addition to these topics, there is a call for updating AI model evaluation methods. Current evaluations often focus solely on accuracy, leading models to guess rather than express uncertainty when unsure. Researchers propose adopting a scoring system akin to the SAT's, which penalizes confident incorrect answers and rewards expressing uncertainty appropriately. They argue that merely adding new uncertainty-aware tests isn't enough; instead, existing accuracy-based evaluation practices must be revised to discourage guessing. ### Bullet Point Summary: - OpenAI research paper explores "hallucinations" in large language models like GPT-5. - Inaccuracies persist due to the pretraining focus on word prediction without truth verification. - Hallucinations are illustrated with examples of incorrect chatbot responses. - Training methods create wrong incentives, akin to multiple-choice tests where guessing can lead to correct answers. - Proposed solution involves reevaluating model testing to address these incentive issues and reduce hallucinations. - TechCrunch Disrupt 2025 event in San Francisco invites over 10,000 leaders from tech and venture capital sectors. - Notable participants include Netflix, Box, a16z, ElevenLabs, Wayve, Sequoia Capital, and Elad Gil. - The event features more than 200 sessions focused on startup growth, marking TechCrunch's 20th anniversary. - Discounted tickets available until September 26. - Call for updating AI model evaluation methods to address accuracy-focused grading. - Current evaluations encourage models to guess rather than express uncertainty. - Proposed adoption of a scoring system similar to the SAT, penalizing confident wrong answers and rewarding uncertainty expression. - Emphasis on revising existing accuracy-based evaluation methods rather than just adding new tests. Keywords: AI hallucinations, ChatGPT, Disrupt 2025, GPT-5, OpenAI, Techcrunch, accuracy, connections, evaluation models, incentives, insights, large language models, pretraining process, scoring, startups, tests
openai
techcrunch.com 5 days ago
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392. HN Pathway: Python ETL Framework- **Pathway Overview**: Pathway is a Python-based ETL framework designed for stream processing, real-time analytics, and developing Large Language Model (LLM) pipelines and Retrieval-Augmented Generation (RAG). It integrates with popular Python ML libraries through an easy-to-use API. - **Technical Features**: Built on a scalable Rust engine using Differential Dataflow, Pathway supports efficient incremental computation, multithreading, multiprocessing, and distributed computing while maintaining in-memory pipeline operations. It ensures reliability with persistence features to restart pipelines after crashes or updates. - **Deployment and Installation**: Deployment is streamlined via Docker and Kubernetes, with installation available through `pip install -U pathway`. Users can access support through Discord for assistance. - **Use Cases and Flexibility**: Pathway caters to various use cases including AI pipelines, providing dedicated LLM tooling. It supports both stateful transformations and custom Python functions for data processing, with many operations implemented in Rust for performance benefits. - **Data Connectivity and Transformation**: The framework offers extensive connectivity through connectors like Airbyte, supporting over 300 data sources such as Kafka, GDrive, PostgreSQL, and SharePoint. Users can create custom connectors if needed. - **Consistency and Reliability**: Pathway provides "at least once" consistency in its free version and "exactly once" consistency in the enterprise version, managing time-sensitive computations effectively. - **Compatibility and Installation**: Compatible with Python 3.10 or above, Pathway is available on MacOS and Linux, with Virtual Machine support for other platforms. Users can install it using pip. - **Development and Deployment Tools**: It supports real-time data processing tasks like computing the sum of positive values in real time. For deployment, users can use Docker with the official image `pathwaycom/pathway:latest` or a cookiecutter template to start new projects quickly. - **Performance and Comparison**: Pathway outperforms other streaming and batch data processing technologies such as Flink, Spark, and Kafka Streaming by supporting complex algorithms and machine learning routines. - **Support and Resources**: Comprehensive developer documentation is available at pathway.com/developers/, with support accessible via GitHub issues, Discord, or email. The software is distributed under a BSL 1.1 License, allowing for most commercial uses free of charge, with an option to convert to Apache 2.0 after four years. - **Community and Contribution**: Pathway encourages contributions through separate releases under MIT/Apache 2.0 licenses, welcoming issues concerning core functionalities and engagement via its Discord community. Keywords: API, Airbyte Connector, Batch Data, Benchmarks, Computation, Consistency, Development, Docker, Documentation, ETL Framework, Kafka, Kubernetes, LLM Pipelines, LangChain, ML Libraries, Multiprocessing, Multithreading, Open Source, Persistence, Pipeline, PostgreSQL, Production, RAG, Real-time Analytics, Rust Engine, Stream Processing, Streaming Data, Support, Vector Index
postgresql
github.com 5 days ago
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393. HN Speeding up our Android builds by 3xThe blog post examines performance differences between modularized and monolithic architectures using Pocket Casts Android as a case study, highlighting that the modularized version (comprising 37 modules) achieves faster build times both locally and in continuous integration environments compared to a single-module monolith. Specifically, local development builds are 2-3.6 times faster, and CI builds are three times faster with the modular setup. However, this performance gain is offset by significantly slower IDE sync times in modular architecture—about fivefold compared to the monolithic version. Performance testing was conducted on a MacBook Pro M1 Max using the Gradle Profiler tool. The current modular version of the app was tested against a "Monolith" version that consolidates all modules into one and omits tests, measuring the `assembleDebug` task for both setups. The analysis underscores the importance of module "depth"—the number of dependencies on a given module—as it impacts build times in modular architectures, while less so in monolithic ones where Kotlin Compiler optimizations can still play a role. Key insights from this comparison include faster rebuilds with non-ABI changes in modular systems and overall improved performance during CI processes. For example, a non-ABI change takes 3.5 seconds to rebuild in the modular setup compared to 12.7 seconds in monolithic architecture—a 3.6× speed increase. ABI changes also favor modular architectures, offering significant time savings. In continuous integration scenarios using a "Fake PR," modular setups outperform monoliths by taking advantage of parallelization capabilities. Conversely, Android Studio sync is slower with the modular approach due to increased indexing demands on IntelliJ IDEA when handling multiple modules. The post concludes that while modular architecture offers substantial improvements in build performance—particularly for non-interdependent changes and CI processes—it comes with trade-offs such as longer IDE sync times. The decision to adopt modularization should consider various factors beyond just speed, including code reuse, architectural clarity, separation of concerns, and team structure facilitation. BULLET POINT SUMMARY: - Modularized architecture achieves 2-3.6 times faster local development builds and 3 times faster CI builds compared to monolithic architecture. - IDE sync is significantly slower in modular setups (5 times longer). - Performance testing was conducted on a MacBook Pro M1 Max using the Gradle Profiler tool, comparing current modular version with a "Monolith" version. - Module "depth" affects build times more in modular architectures; Kotlin Compiler optimizations can enhance monolithic performance. - Modular builds show faster rebuilds for non-ABI changes (3.5 seconds vs. 12.7 seconds) and overall CI process improvements. - In CI scenarios, modular setups are three times faster due to parallelization. - Android Studio sync is slower with modular architecture because of increased indexing workload. - Benefits of modularization include code reuse, separation of concerns, architectural clarity, and facilitating team structure. - The decision to adopt modularization should consider various factors beyond just build performance. Keywords: ABI changes, Android, CI builds, GitHub, Gradle Profiler, IDE sync times, Kotlin Compiler, MacBook Pro M1 Max, Pocket Casts, architecture, assembleDebug, benchmarks, build performance, code reuse, compilation avoidance, compile avoidance mechanism, compose module, core modules, dependency, feature modules, incremental builds, interactive graphs, modularization, modules, monolithic architecture, non-ABI changes, performance, private logic, public API, separation of concerns
github
mobile.blog 5 days ago
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394. HN Neon PostgreSQL Tutorial- The document from PostgreSQLTutorial.com offers a comprehensive guide on using PostgreSQL for application development and migration from other databases like MySQL and SQL Server. - It provides practical examples covering installation, connection, and basic database management tasks in Windows, Linux, and macOS environments. - Key sections address: - **Data Querying:** Techniques such as `SELECT`, column aliases, ordering results, filtering data using the `WHERE` clause, and employing `AND/OR` operators. - **Advanced Queries:** Involves joins (inner, left, self, full outer, cross, natural) for combining tables, and set operations like union, intersect, and except. - The document also covers: - **Data Grouping:** Utilizing `GROUP BY`, `HAVING`, grouping sets, cubes, and rollups for complex reporting. - **Subqueries:** Explaining nested queries, correlated subqueries using operators like `ANY`, `ALL`, and `EXISTS`. - **Common Table Expressions (CTEs):** Introduction to CTEs and recursive query techniques. - It discusses data manipulation through: - **Modifying Data:** Operations such as `INSERT`, `UPDATE` (including joins), `DELETE`, and UPSERT for inserting or updating records. - **Transactions:** Handling transactions with `BEGIN`, `COMMIT`, and `ROLLBACK`. - Importing and exporting CSV files using PostgreSQL's `COPY` command is explained, emphasizing data interchange between databases and CSV formats. - The guide delves into table management: - **Data Types and Constraints:** Discusses primary keys, foreign keys, DELETE CASCADE, CHECK constraints, uniqueness, NOT NULL, and default values. - **In-depth Data Types:** Exploring Boolean, character types (CHAR, VARCHAR, TEXT), numeric precision, floating-point numbers, integer variations, date/time handling, intervals, UUIDs, arrays, hstore, JSON, enums, XML, binary strings (BYTEA), composite types, and user-defined types. - Advanced PostgreSQL concepts: - **Utilities:** Introduces `psql` tool commands for efficient database interaction. - **Stored Procedures and Triggers:** Guides on creating functions with PL/pgSQL and managing triggers. - **Views and Indexes:** Discusses view creation and management, alongside index usage to enhance data retrieval speed. - **Administration Tasks:** Covers role and database management, as well as backup and restoration procedures for maintaining database health and security. Keywords: AND, ARRAY, BACKUP, BETWEEN, BIGINT, BOOLEAN, BYTEA, CHAR, CONSTRAINTS, COPY, CSV, Cross Join, Cube, DATE, DELETE, DOUBLE PRECISION, ENUM, Except, FETCH, FOREIGN KEY, Full Outer Join, Group By, HSTORE, Having, IN, INDEXES, INSERT, INT, INTEGER, INTERVAL, Inner Join, Intersect, JSON, LIKE, LIMIT, Left Join, MySQL, NULL, NUMERIC, OR, Oracle, PL/pgSQL, PRIMARY KEY, PostgreSQL, REAL, Rollup, SMALLINT, SQL Server, STORED PROCEDURES, Subquery, TEXT, TIME, TIMESTAMP, TRIGGERS, Transactions, UPDATE, UPSERT, UUID, Union, VARCHAR, VIEWS, WHERE, XML, alias, applications, column, database, distinct, filter, join, psql, query, sort, table, tutorial
postgresql
neon.com 5 days ago
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395. HN All 54 lost clickwheel iPod games have now been preserved for posterityThe iPod Clickwheel Games Preservation Project has successfully preserved all 54 lost clickwheel games originally sold by Apple in the late 2000s, overcoming significant challenges posed by Apple's FairPlay DRM system. The preservation process involved syncing available game copies from various owners to a centralized iTunes library through a Virtual Machine, ensuring perpetual access for multiple devices. The initiative was spearheaded by GitHub user Olsro and gained momentum with substantial contributions from individuals possessing extensive collections of these games. Despite facing delays due to false leads and technical issues, the project has now completed its goal, including adding Real Soccer 2009. Olsro has dedicated considerable time to guiding people in transferring files and authorizing accounts on a Virtual Machine, keeping morale high by continually engaging new participants through outreach efforts. A particular challenge was accessing an unpreserved version of Real Soccer 2009; despite numerous attempts, progress remained elusive until recently. One significant breakthrough involved an iPod Nano 5G with what appeared to be an empty storage in Windows Explorer but contained a playable game copy. However, attempts to extract data led to further corruption of the device's NAND storage. - **Key Points:** - The project successfully preserved all 54 lost clickwheel games. - Overcame Apple's FairPlay DRM challenges using a Virtual Machine for syncing games. - Initiated by Olsro on GitHub and supported by owners with large game collections. - Faced delays due to false leads and technical issues but recently completed the preservation, including Real Soccer 2009. - Olsro provided guidance on file transfers and account authorizations, maintaining momentum through outreach. - Accessing a playable copy of Real Soccer 2009 was particularly difficult; one breakthrough involved an iPod Nano 5G with seemingly empty storage but playable game data. - Efforts to recover data from the device led to further NAND corruption. Keywords: Apple, FairPlay DRM, GitHub, NAND, Real Soccer 2009, Virtual Machine, Windows Explorer, account, attempts, authorizing, clickwheel, coaching, corrupted, cursed, files, games, iPod, iTunes, master library, motivation, preservation, recovery, recovery tools, syncable copies, technical issues
github
arstechnica.com 5 days ago
https://github.com/Olsro/ipodclickwheelgamespreservatio 5 days ago https://archive.org/details/icgpp 5 days ago https://news.ycombinator.com/item?id=41978486 4 days ago https://en.wikipedia.org/wiki/Binary_Runtime_Environmen a day ago https://mashable.com/archive/disney-animation-app a day ago https://www.iflash.xyz/ a day ago https://www.statista.com/chart/10469/apple-ipod-sa a day ago https://climbingbusinessjournal.com/strategies-to-help-youth a day ago https://www.innioasis.com/products/y1 a day ago https://www.youtube.com/watch?v=OM7mdKy3opo a day ago https://www.mobygames.com/game/30917/phase/cr a day ago https://www.rockbox.org/wiki/TargetStatus a day ago https://yuuiko.github.io/iPodGuide/iPodGuidev2-1.pdf a day ago https://opista.com/posts/ipod-classic-modding-guide a day ago https://www.fiio.com/m21 a day ago https://boards.4chan.org/g/thread/106533110 a day ago |
396. HN GitHub Actions is a trusted computing oracleThe text explores the concept of utilizing GitHub Actions (GHA) as a trusted computing oracle to cryptographically verify web content existence, even post-deletion, effectively acting as a TLS notary. James Carnegie's implementation, "URL Oracle," exemplifies this by ensuring the integrity and authenticity of web content through cryptographic attestations, with applications like verifying BBC Technology News content. The document highlights the experimental nature of using GitHub Actions for trusted computing, cautioning users about potential security risks and advising consultation with a cryptographer. This approach is noted for its versatility beyond notary services, supporting computational attestation in domains such as software supply chain security, blockchain, and cryptography. Key functionalities include executing any user-provided program, signing the hash of the program and its output, and enabling verification through GitHub's public key. The system relies on SHA1-DC due to the general insecurity of SHA-1, ensuring robustness against collision attacks. The process involves configuring a workflow file within a repository to request an ID Token from GitHub using `core.getIDToken(audience)`, where the audience is set as the hash of the program's output. Verification requires checking the ID Token via OpenID Connect (OIDC) with GitHub’s public key, ensuring the program code matches the token's SHA256 hash, and confirming that the audience claim in the ID Token aligns with `SHA256(nasa-data)` for content like the front page of nasa.gov. The document addresses the challenge of GitHub's periodic key rotation for ID Tokens by proposing solutions such as backward-chained attestations about the JWKS and using stored logs from GitHub Actions. These measures ensure continuity in verification despite key rotations, with GitHub maintaining two public keys for current and previous tokens to facilitate this process. An innovative aspect is the system's ability to create a chain of attestations extending indefinitely into the past, acting as a timestamping authority due to date inclusion in each ID Token. This extends proof capabilities beyond the 90-day retention period of logs. Exploring alternatives, the text mentions using GitLab-CI as another oracle for mutual verification with GitHub Actions, despite GitLab-CI's limitations compared to GitHub’s functionality. The discussion also contrasts GitHub Actions with more complex Trusted Execution Environments (TEEs) like Intel SGX or AWS Nitro Enclave, highlighting its potential in reducing reliance on expensive solutions while acknowledging security risks such as cache poisoning attacks. The concept of "oracles" is introduced for executing computations on secret data and providing attestations about the results. The document describes how GitHub's status as a widely used platform makes it a convenient choice despite security concerns if compromised, suggesting that integrating multiple oracles can enhance security by avoiding single-point failures. Finally, the text compares various approaches to TLS notarization and blockchain-based content verification, including Town Crier, TLS Notary with Multi-Party Computation (MPC), Chainlink as an oracle and TLS notary network, and blockchains like Sui and Aptos treating JSON Web Key Set (JWKS) URIs' contents as crucial for consensus. An OpenPubkey draft proposal is mentioned, suggesting using Sui as a JWKS oracle but noting its current limitations regarding GitHub or GitLab JWKS URI support. - **GitHub Actions as Oracle**: Utilizes cryptographic verification of web content existence post-deletion. - **URL Oracle Implementation**: Ensures integrity and authenticity of web content through cryptographic attestations. - **Experimental Use Cautioned**: Advises consultation with a cryptographer due to potential security risks. - **Versatile Functionality**: Supports computational attestation across various domains. - **SHA1-DC Utilization**: Offers robustness against collision attacks. - **Verification Process**: Involves checking the ID Token via OIDC and ensuring alignment between program code, token hashes, and audience claims. - **Key Rotation Solutions**: Proposes backward-chained attestations and stored logs to maintain verification continuity. - **Indefinite Attestation Chain**: Acts as a timestamping authority with date inclusion in each ID Token. - **GitLab-CI Alternative**: Suggests mutual verification between GitHub Actions and GitLab-CI oracles. - **Contrast with TEEs**: Highlights potential for reducing reliance on expensive solutions while acknowledging security risks. - **Oracles Concept**: Describes executing computations on secret data and providing attestations about results. - **TLS Notarization Approaches**: Compares various methods including Town Crier, TLS Notary with MPC, Chainlink, and blockchains like Sui and Aptos. Keywords: AWS Nitro Enclave, Attestations, Blockchains, Cryptographic Proof, GitHub Actions, Hash Verification, ID Token, Intel SGX, JWKS URI, Multi-Party Computation, OpenID Connect, Oracle, Public Keys, SHA-1, Security Dependencies, TLS Notary, Trusted Computing
github
www.ethanheilman.com 5 days ago
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397. HN Ask HN: How do you track what's backed up?The author describes a challenging experience related to data loss not caused by backup failure but due to oversight in tracking critical components for backup. Although their team successfully backed up essential systems like Postgres dumps, GitLab repositories, and Ghost configurations, they neglected the Listmonk database. This incident highlights the inherent difficulties associated with using snapshots as a primary method of backup management, which are cumbersome to handle and lack transparency regarding the completeness of data coverage. The author expresses dissatisfaction with their current ad-hoc script solutions and OS-level backups, pointing out inefficiencies and a lack of clarity in ensuring comprehensive backup coverage. The core issue revolves around maintaining visibility into what needs backing up, verifying that all necessary data is indeed covered, and knowing where the backed-up data resides. In search of better practices, the author seeks advice on how other organizations effectively manage these challenges to enhance their backup processes. **BULLET POINT SUMMARY:** - The loss of production data occurred not due to backup failure but because some critical components were overlooked for backup. - Key systems like Postgres dumps, GitLab repos, and Ghost configs are backed up, but the Listmonk database was missed. - Reliance on snapshots presents challenges in management and verifying complete data coverage. - The author is dissatisfied with using ad-hoc scripts and OS-level backups due to inefficiencies and lack of transparency. - Key challenges include ensuring visibility into necessary backup components, confirming actual data coverage, and tracking storage locations for backed-up data. - The author seeks insights on effective practices from other organizations to improve their backup strategy. Keywords: Ask HN, Ghost, GitLab, Listmonk, Postgres, backup policies, backups, coverage, lost data, organization, production data, scripts, snapshots, team, visibility
postgres
news.ycombinator.com 5 days ago
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398. HN My Mom and Dr. DeepSeek**Summary:** The text examines how AI chatbots, such as Dr. DeepSeek, are increasingly used in healthcare management by individuals dissatisfied with traditional medical systems, especially within China's context. It highlights a 57-year-old kidney transplant patient who opts for AI consultations instead of regular doctor visits due to the perceived convenience and empathy offered by these digital platforms. This trend is partly driven by significant challenges within China’s healthcare system, including inequality, corruption, inadequate government funding, and an aging population, leading to widespread distrust in medical professionals. AI chatbots provide consistent interactions and personalized care that many find comforting amidst systemic inadequacies. However, there are ethical concerns regarding potential biases and inaccuracies inherent in AI systems, with studies showing mixed diagnostic performances compared to human benchmarks. Despite these issues, healthcare providers in China are integrating large language models to address doctor shortages and regional disparities. Beyond healthcare, individuals use AI for emotional support and managing family dynamics or mental health challenges without the constraints of time. This adaptation showcases a broader trend where AI fills gaps in care by offering consistent interactions and new forms of connection. The narrative also illustrates a specific case involving an elderly woman who relies heavily on DeepSeek for health advice. After discovering low white blood cell counts, she consulted with her doctor following DeepSeek's guidance. Despite initial reluctance to visit a nephrologist due to discomfort with crowded hospitals, she eventually agreed to seek further consultation. Her preference for DeepSeek is attributed to its accessibility and quick information delivery without any cost or waiting times, though sometimes lacking comprehensive answers. **Bullet Point Summary:** - AI chatbots like Dr. DeepSeek are increasingly used in China for healthcare management due to perceived convenience and empathy compared to traditional medical systems. - The inadequacies of China’s healthcare system—marked by inequality, corruption, minimal government funding, and an aging population—drive individuals toward AI solutions. - Ethical concerns about biases and inaccuracies in AI patient care are highlighted alongside mixed performance in AI diagnostics compared to human standards. - Healthcare providers in China integrate large language models to enhance medical processes and address doctor shortages and regional disparities. - Individuals use AI for emotional support, managing family dynamics, and mental health challenges, reflecting a broader trend of AI filling gaps in care. - A specific case shows an elderly woman using DeepSeek for health advice after discovering low white blood cell counts, preferring its accessibility and quick information delivery despite potential lack of comprehensive answers. Keywords: AI chatbot, AI-enhanced systems, Alibaba, Baidu, CT scans, Dr DeepSeek, Hangzhou, Kidney transplant, MRI scans, accountability, biases, clinical practice, consultations, corruption, diagnosing illnesses, dialysis, ethics, healthcare system, immunosuppressant, lab results, medical reports, mental health, misinformation, nephrologist, nutritional composition, pecans, public hospitals, radiologists, social media, ultrasound scans
deepseek
restofworld.org 5 days ago
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399. HN GPT-5: The Case of the Missing AgentThe article explores the evolution of artificial intelligence (AI) focusing on OpenAI's GPT-5 and its potential as a step towards truly autonomous "agentic" AI. Despite significant technological advancements since the release of GPT-4, such as enhanced coding and task-specific agents, achieving AI systems capable of operating independently in real-world environments remains elusive. Speculation about GPT-5 began in April 2024, culminating in its release sixteen months later. Although GPT-5 represents a major development with improvements in reasoning models and context window sizes (from 32,000 tokens in GPT-4 to 400,000 in GPT-5), it still falls short of being genuinely agentic AI. The article highlights early attempts like AutoGPT by Toran Bruce Richards, which aimed to achieve real-time feedback responses and long-term goals but struggled with complexity and task retention. The discussion includes the broader vision of autonomous systems versus specialized agents, referencing experiments like Anthropic's Claude model managing a mini-store. This experiment revealed limitations in AI's adaptability and learning from errors despite some successes, such as identifying niche suppliers. A separate incident involved Claudius, an older AI model, which demonstrated identity confusion and operational misunderstandings. AI Village conducted tests with advanced models (GPT-5, Claude Opus 4.1, Gemini 2.5 Pro) in a virtual store, highlighting persistent issues like misinterpretation of errors and difficulty executing complex tasks. Another experiment saw AIs playing games; GPT-5 notably struggled with Minesweeper due to poor game board perception and ineffective task prioritization. GPT-5 shows notable progress over its predecessors by improving performance on specific tasks such as itinerary planning and programming, becoming more cost-effective, and delivering faster responses. However, expectations may have been inflated due to prior hype, leading some to view these advancements as incremental rather than revolutionary. The article speculates that while rapid AI advancement suggests the possibility of agentic AI emerging soon, significant breakthroughs in memory, exploration, robust reasoning, and creative insight are still needed. Despite improvements like increased memory capacity, current models face challenges with open-ended tasks, showcasing limitations in reasoning and generalization. Overall, substantial progress has been made, but achieving practical real-world capabilities for AI agents remains years away. The article concludes by acknowledging that while advancements continue to shed light on the nature of intelligence, unforeseen weaknesses persist, illustrating the complex journey towards fully autonomous AI systems. Keywords: AI agents, Anthropic, AutoGPT, Claude, GPT-4, GPT-5, Gemini, agentic AI, benchmarks, hallucinations, navigation, real-world, reasoning models
claude
secondthoughts.ai 5 days ago
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400. HN Claude Code fails to fetch or create the GPL v3.0 license when askedThe user encounters difficulties with Claude Code while trying to retrieve or generate the GPL v3.0 license due to specific errors: a "429 Request Failed" and an API Error 400, indicating that content filtering policies block the output. Despite these issues, manually entering the full text of the license enables successful creation. The underlying problems appear to be associated with restrictions or policies affecting data retrieval from external sources or automated interactions with APIs. - User struggles with Claude Code to fetch/create GPL v3.0 license. - Encounters "429 Request Failed" and API Error 400 due to content filtering policies. - Manual entry of the full license text circumvents these issues. - Problems likely stem from restrictions/policies on external URL fetching or automated API interactions. Keywords: API Error, Claude Code, GPL v30, URL, content filtering policy, copy prompt, create, fetch, invalid_request_error, issue, license, request failed, status code 429
claude
news.ycombinator.com 5 days ago
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401. HN Image descriptions (alt text) on BlueskyThe text provides an overview of an experiment conducted using a script to collect alt text (image descriptions) from Bluesky over a week in July 2025. The dataset, containing 2,851,876 entries, includes highly detailed and sexually explicit content and highlights the notable Coldplay kiss cam/Astronomer incident. To ensure data quality, contributions identified as bot activity, such as repetitive captions like "Product image" or "Album artwork," were manually filtered out. This involved excluding posts from the top 100 contributors suspected of being bots due to their infrequent posting and poor-quality descriptions. The text also discusses improvements needed for the Firehose script used in data analysis. Challenges include handling duplicate events and compatibility issues with CSV formats, as image descriptions often contain quotes and newlines that complicate processing with line-based tools. The use of Jetstream is recommended, citing its successful implementation by Simon Willison’s Bluesky Firehose tool. For future research, a more thorough examination of the quality of image descriptions is suggested, including human assessment and analyzing differences in caption rates between highly-followed and less prominent accounts, inspired by Gleason et al.'s 2019 study. The document highlights Bluesky's progress in image description usage compared to Twitter’s growth from 0.1% in 2023 to 80%. It emphasizes the importance of improving descriptions for accessibility and searchability, differentiating between "meaningless" and "misused" alt text based on user interviews and data from Bluesky’s labeling tools. While manual quality descriptions are encouraged, technical solutions like multi-modal AI are also considered but critiqued for producing superficial content. This suggests the need for more advanced approaches to enhance the accuracy and utility of image descriptions. **Bullet Point Summary:** - A script was used to collect alt text from Bluesky over a week in July 2025, resulting in a dataset of 2,851,876 entries. - The dataset includes detailed content and highlights notable incidents like the Coldplay kiss cam/Astronomer event. - Data quality ensured by filtering out bot activity, identified through repetitive captions, with minimal impact on overall statistics. - Challenges noted for the Firehose script include handling duplicate events and CSV format issues due to quotes and newlines in image descriptions. - Recommendation to use Jetstream for simplifying data collection scripts, inspired by Simon Willison’s tool. - Future research suggested: assessing description quality through human evaluation and analyzing caption rates among different user groups. - Bluesky's improvement in alt text usage compared to Twitter’s growth from 0.1% to 80%, emphasizing the need for better descriptions for accessibility and searchability. - User interviews and data highlight the distinction between "meaningless" and "misused" alt text, with a push towards both manual quality improvements and technical solutions like multi-modal AI despite their current limitations. Keywords: AT URI, Bluesky, CSV, Coldplay kiss cam, Firehose script, GitHub, accessibility, alt text, bot filtering, captions, dataset, multi-modal AI, pandas
github
digitalseams.com 5 days ago
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402. HN OpenAI Backs AI-Generated Animated Film 'Critterz'**Summary:** OpenAI has embarked on an ambitious project named "Critterz," an animated feature film developed using its generative AI tools, to illustrate the potential of AI in revolutionizing Hollywood's filmmaking processes. Announced on September 7 and set for completion within nine months under a $30 million budget, the project is a collaborative effort between OpenAI, Vertigo Films, Native Foreign, and Federation Studios. The film utilizes OpenAI’s technologies like DALL-E for concept art, Sora for video generation, and GPT-5 to streamline production traditionally taking years into mere months. Chad Nelson of OpenAI developed "Critterz," originally as a short that expanded into a full feature, set to premiere at Cannes next year. The project highlights AI's role not just in content creation but also in integrating with human creativity—writers and artists work alongside AI tools, while human actors provide the voiceovers. This hybrid model aims to prove that AI can produce high-quality cinema more efficiently, serving as a compelling case study for Hollywood. However, "Critterz" emerges against a contentious backdrop where major studios like Disney, Universal, and Warner Bros. have initiated legal actions against AI companies for allegedly using copyrighted content without authorization. This tension underscores the broader industry debate over whether training AI on existing content constitutes fair use or piracy—a conflict likened to a watershed moment in the A.I. sector by Cecilia Ziniti, CEO of GC AI. A landmark event was Anthropic's $1.5 billion settlement with authors for using pirated books, shifting focus from theoretical discussions to concrete data sourcing issues. OpenAI’s strategic initiative with "Critterz" aims to demonstrate not only the creative potential of AI but also to build trust and establish its value in an industry wary of such technologies due to ongoing intellectual property disputes. **Bullet Point Summary:** - OpenAI has launched "Critterz," an AI-generated animated film, as a demonstration of efficient and cost-effective filmmaking. - The project involves collaboration with Vertigo Films, Native Foreign, and Federation Studios, aiming for completion in nine months within $30 million. - Developed by Chad Nelson using tools like DALL-E and Sora, the film is set to premiere at Cannes next year. - "Critterz" integrates AI with human creativity, involving scriptwriters, artists, and voice actors in its production process. - The project seeks to position AI as a collaborative tool in filmmaking capable of reducing production time and costs. - Legal conflicts over intellectual property rights challenge the film's development, reflecting industry tensions regarding AI training on copyrighted content. - Major studios have filed lawsuits against AI companies, underscoring issues of fair use versus piracy. - Anthropic’s $1.5 billion settlement exemplifies these legal challenges, shifting focus to practical data sourcing concerns. - OpenAI aims with "Critterz" to demonstrate AI's creative potential and build trust in the skeptical film industry. Keywords: AI-generated, Animated Film, Budget, Cinema-quality, Collaboration, Creativity, Critterz, DALL-E, Disney, Fair Use, Federation Studios, Filmmaking, GPT-5, Generative AI, Hollywood, Legal War, Midjourney, OpenAI, Piracy, Production Schedule, Sora, Universal, Warner Bros
openai
winbuzzer.com 5 days ago
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403. HN iPhone dumbphoneIn September 2025, an individual successfully transformed their iPhone into a "dumbphone" using Apple Configurator, significantly reducing screen time by approximately two hours daily. Initially driven by concerns over excessive screen time and previous unsuccessful attempts to regulate it via self-control or Apple's Screen Time feature, the individual turned to Apple Configurator—a tool typically used in organizational settings for imposing restrictions—to customize app access and website content. The transition involved a factory reset and meticulous setup of essential apps such as Discord, GitHub, Things3, Waymo, and selected websites. Despite initial time-intensive configuration and occasional inconveniences due to forgotten functionalities (e.g., needing others' phones for gym check-ins), the outcome was overwhelmingly positive. The author likened this change to substituting a healthy diet for junk food, emphasizing improved utility without distractions. The author also praised tools like ChatGPT and Claude for providing focused information retrieval without distractions, noting no desire to remove phone restrictions after two months due to their effectiveness in maintaining focus. However, challenges remained with managing semi-important apps like email by seeking methods to filter notifications. Overall, the daily usage dropped from four hours to a more productive two, including communication and essential tasks. To replicate this setup requires an initial commitment of about two hours for resetting and configuring through Apple Configurator, followed by two weeks of refining app restrictions. The guide outlines steps for converting an iPhone using Apple Configurator: starting with a factory reset, installing the software on a computer, preparing the device in Configurator, completing the Apple setup process without restoring from iCloud to maintain supervision settings, and then creating and applying restriction profiles. These profiles can be temporarily removed when needed (e.g., for app installations) and reinstated afterward. The essay concludes by acknowledging contributors who reviewed its drafts and acknowledges social media's usefulness when used in moderation, recommending checking such apps on a laptop instead of a phone to mitigate mobile distractions. - **Key Points:** - The individual reduced daily screen time from four hours to two by transforming their iPhone into a "dumbphone" using Apple Configurator. - Initial setup involved factory resetting the device and installing essential apps while setting restrictions, despite some inconveniences. - The solution increased productivity and reduced distractions, likened to switching from junk food to a healthy diet. - After two months of use, they experienced no desire to remove the imposed restrictions and saw improved focus with tools like ChatGPT and Claude. - Daily usage now includes productive activities without regret, requiring an initial setup time followed by ongoing adjustment of app restrictions. - The process for others involves factory resetting their iPhone, using Apple Configurator on a computer, preparing and setting up the device, creating restriction profiles, and managing these profiles as needed. - Acknowledgments are given to contributors who reviewed drafts and advice is offered regarding moderate use of social media. Keywords: Apple Configurator, ChatGPT, Claude, Google Maps, Kindle, Screen Time, Spotify, Uber, Waymo, WhatsApp, apps, content filters, distractions, dumbphone, factory reset, iPhone, motivation, notification, productivity, profiles, restrictions, setup, social media, utility
popular
stopa.io 5 days ago
https://dontkillmyapp.com a day ago https://www.youtube.com/watch?v=orQKfIXMiA8 a day ago https://gpodder.github.io a day ago https://getbrick.app/ a day ago https://www.screenzen.co/ a day ago https://support.apple.com/guide/assistive-access-iphone a day ago https://authenticator.cc/ a day ago https://github.com/stopachka/stopaio/blob/mai a day ago https://github.com/stopachka/stopaio/blob/mai a day ago https://dumbphone.so/ a day ago https://www.idownloadblog.com/2018/08/28/add- a day ago https://reincubate.com/support/how-to/pair-lock-su a day ago https://github.com/rickgram/NoBrowser a day ago https://developer.android.com/develop/ui/views a day ago https://www.getclearspace.com/ a day ago https://www.ycombinator.com/companies/clearspace a day ago https://github.com/rayanamal/timelock a day ago https://www.techlockdown.com/guides/restore-full-backup a day ago https://help.instagram.com/654906392080948 a day ago https://blog.alexbeals.com/posts/setting-an-unknown-scr a day ago https://www.goaro.com/for-families a day ago https://apps.apple.com/app/id1497465230 a day ago https://limitphone.com/ a day ago https://github.com/BinTianqi/OwnDroid a day ago https://github.com/awaseem/foqos a day ago https://www.techlockdown.com/apple-config-generator a day ago https://support.apple.com/en-gb/guide/deployment&# a day ago https://en.wikipedia.org/wiki/Palm_OS a day ago https://github.com/tanujnotes/Olauncher?tab=readme-ov-f a day ago https://github.com/tstromberg/quietude a day ago https://docs.andoff.one/ a day ago https://www.teqtic.com/lock-me-out a day ago https://apple.nextdns.io a day ago |
404. HN Atolio – Glean Competitor Raises Series AAtoio, a competitor to Glean, has successfully raised $24 million in Series A funding to develop secure AI-powered enterprise search solutions tailored for major companies. The company's mission is to provide consumer-grade AI experiences that allow employees to efficiently access institutional knowledge while enabling boards and CEOs to utilize AI competitively. Due to concerns about data privacy, corporate IP leaks, and data sovereignty, these solutions are designed to ensure enterprise data remains under organizational control, avoiding public cloud storage. To address these privacy issues, Atoio proposes an enterprise search system that aggregates data across various platforms on private clouds or physical hardware, ensuring compliance with legal requirements for data location. This approach offers organizations full control over sensitive information. Atoio's strategy is informed by insights from Alex Rosemblat, the former CMO of Datadog, emphasizing understanding both immediate and long-term client needs. Before launching its product, Atoio engaged in extensive discussions with senior executives from 762 large enterprises to identify challenges related to information discovery within organizations. Key findings highlighted the necessity for solutions ensuring enterprise control over data and deployment processes, compatible with private clouds or air-gapped environments. Various industry leaders faced unique challenges: a manufacturing CIO needed better tracking of custom orders; a Fortune 100 company required efficient information access amidst rapidly changing account representatives; and a U.S. government agency sought clarity on content accessibility for non-U.S. citizens. The insights gained from these conversations led to the development of AI-driven solutions, integrating question answering, expertise, and search into a unified conversational interface. An early client, Cengage, an edtech firm, has utilized Atoio's technology to enhance sales and marketing efficiency, with its CEO noting significant productivity improvements. Atoio aims to scale by expanding its platform, developing new connectors, growing engineering and global teams, and promoting its enterprise search product that ensures data privacy. The company plans to leverage capital raised for these goals and is excited to add Translink, IBM, Acorn Pacific Ventures, Bloomberg Beta, and ten unicorn founders to its cap table. A major financial institution's request through Translink highlights the demand for secure enterprise search products. Looking forward, Atoio draws parallels between itself and Splunk regarding unstructured data handling, with opportunities to build innovative applications on top of its foundational elements. Success in this endeavor requires securely integrating systems differently than Managed Cloud Platform (MCP) solutions can achieve. The company's development plans will involve partners and clients, inspired by experiences at Splunk and reliability lessons from PagerDuty. Atoio anticipates future developments involving distribution systems akin to Splunkbase and expresses gratitude towards its team, clients, industry leaders, and investors for their support. They are gearing up for presentations at KubeCon in Atlanta and NVIDIA GTC in DC, inviting others to connect with the enthusiastic Atolio team, including Mark, Gareth, and David. ### Bullet Points Summary: - Atoio has raised $24 million in Series A funding to develop AI-powered enterprise search solutions focusing on data privacy and security. - The company proposes a system that aggregates data across private clouds or physical hardware, ensuring compliance with legal requirements and maintaining organizational control over sensitive information. - Insights from senior executives at 762 large enterprises highlight the need for solutions compatible with private clouds or air-gapped environments to address unique industry challenges. - Atoio's AI-driven discovery engine integrates question answering, expertise, and search into a unified interface, benefiting clients like Cengage in enhancing operational efficiency. - The company plans to expand its platform, develop new connectors, grow teams, and promote its privacy-focused enterprise search product using the raised capital. - New investors include Translink, IBM, Acorn Pacific Ventures, Bloomberg Beta, and ten unicorn founders, emphasizing demand for secure solutions. - Atoio aims to innovate similarly to Splunk by building applications on foundational elements like graphs, permissions, and indexes. - Future development will involve partners and clients, inspired by experiences at Splunk and PagerDuty, with plans to work on distribution systems akin to Splunkbase. - The Atolio team expresses gratitude for support from stakeholders and anticipates appearances at industry events like KubeCon and NVIDIA GTC. Keywords: AI-powered search, Atolio, Atolio Conversations, Bloomberg Beta, CIOs, CTOs, IBM, KubeCon, MCP, NVIDIA GTC, OpenAI, PagerDuty, SAP, Series A, Splunk, Splunkbase, Steve Blank’s Four Steps to the Epiphany, Translink, US government agency, account transition, agents, air-gapped environment, app store, applications, automation, clients, content accessibility, control of data, conversational interface, corporate IP, corporate knowledge, custom orders, data control, data sovereignty, distribution systems, edtech firm, engineering teams, enterprise, enterprise data, enterprise search, expertise search, global distribution, graphs, index, information accessibility, infrastructure, internal efficiencies, knowledge discovery, knowledge engine, language models, large enterprises, model deployment, non-US citizens, order status, partners, permission-aware connectors, permissions, physical hardware, platform scaling, platform security, privacy concerns, private cloud, product discovery, productivity, public cloud, question answering, reliability, scalable enterprise search, secure product, security platform, sensitive data, system integration, systems, unicorn founders, unstructured data
openai
www.atolio.com 5 days ago
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405. HN Ask HN: I can't remember the name of a AI CLI tool I saw a few weeks agoThe user is requesting help in recalling the name of an AI command-line interface (CLI) tool that was recently mentioned on Hacker News. This tool has similarities with "opencode/goose/aider" and is characterized by a newt or salamander icon, which likely serves as its logo. It offers open access to models akin to OpenRouter and maintains compatibility with the Claude API, among other providers. The user's inquiry emphasizes the tool's distinctive features and compatibility with existing AI platforms. - **User's Request**: Assistance in recalling the name of an AI CLI tool. - **Source Mentioned**: Recently discussed on Hacker News. - **Tool Characteristics**: - Resembles "opencode/goose/aider." - Features a newt or salamander icon. - Offers open access to models similar to OpenRouter. - **Compatibility**: - Supports Claude API. - Compatible with other providers. Keywords: AI, API, CLI, Claude, Hacker News, aider, company, goose, icon, models, newt, open, providers, router, salamander, tool
claude
news.ycombinator.com 5 days ago
https://hn.algolia.com/?dateRange=all&page=0&prefix= 5 days ago https://www.google.com/search?q=AI+CLI+TOOL+LOGO+NEWT+SALAMA 5 days ago https://duckduckgo.com/?t=ffab&q=AI%2BCLI%2BTOOL%2BLOGO% 5 days ago https://github.com/synthetic-lab/octofriend a day ago https://synthetic.new/landing/home a day ago |
406. HN Show HN: Gemini connected to 18 native iOS tools and shortcutsThe project introduces an iOS voice assistant named Gemini that seamlessly integrates with 18 native iOS tools and shortcuts, enhancing the device's functionality without requiring any account setup or configuration. It allows users to interact effortlessly by connecting their action button to Gemini Live, facilitating hands-free operation. Users can inquire about various topics such as locating nearby Los Tacos restaurants or finding optimal train routes, receiving AI-generated responses tailored through an analysis of both the user’s current location and data from a cousin's contact information. The solution is offered at no cost and includes a demo video that illustrates its features. **BULLET POINT SUMMARY:** - Gemini is an iOS voice assistant integrating with 18 native iOS tools and shortcuts. - It enables seamless interaction through the action button to Gemini Live, without needing account setup or configuration. - Users can ask questions for AI-generated responses about tasks like finding nearby locations or optimal travel routes. - The system analyzes data from both the user's current location and a cousin’s contact information for personalized responses. - The solution is free and includes a demo video showcasing its features. Keywords: AI Analysis, Gemini, Gemini Live, Los Tacos, Show HN, action button, calendar, contact, directions, get_location, iOS, location, native tools, shortcuts, voice assistant
gemini
saturn-live.app 5 days ago
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407. HN Week 1: Building ZipZen – Zero-Config Apt/YUM/Helm ReposIn Week 1 of developing ZipZen, the primary focus was on creating zero-configuration repositories for APT, YUM, and Helm to ease the lives of developers by removing the need for complex setups. The team concentrated on improving Command Line Interface (CLI) tools and enhancing documentation. A significant feature introduced is allowing developers to upload packages without any configuration requirements, with automatic sorting implemented, alongside optional signing when a GPG key is available. Looking ahead, plans include refining public release pages, seeking user feedback, integrating Continuous Integration/Continuous Deployment (CI/CD) examples into the system, and increasing ZipZen's visibility in communities such as IndieHackers. - Week 1 focus on developing zero-configuration repositories for APT, YUM, and Helm to simplify developer setups. - Improvements made to CLI tools and documentation during this phase. - Introduced key feature: developers can upload packages without configuration; automatic sorting and optional signing with a GPG key are available. - Future plans include enhancing public release pages, soliciting feedback, integrating CI/CD examples, and boosting visibility in communities like IndieHackers. Keywords: APT, Artifactory, CI/CD integration, CLI, GPG key, GitHub, GitLab Releases, Helm repository, IndieHackers community, Nexus, YUM, ZipZen, binaries, developers, documentation, package, public release pages, zero-configuration
github
www.indiehackers.com 5 days ago
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408. HN Show HN: HATEOAS-Like ProjectHMPL.js is a lightweight JavaScript library designed for creating server-driven templates using minimal code. It features block-based syntax and supports customizable fetch requests, along with built-in functionalities for forms, events, and time-based syncing. This enables the creation of dynamic user interfaces without relying on heavy frameworks. The library integrates JSON5 to simplify object syntax handling and uses DOMPurify to ensure secure HTML rendering, all while maintaining a small footprint. An example usage showcases setting up an interactive template with HMPL.js that includes a button triggering API requests to update data dynamically. This demonstrates the library's efficiency in managing server-driven content by updating not just text but entire components based on server responses. The code provided highlights how `hmpl.compile` manages JSON payloads for actions like fetching click count data from `/api/clicks`, which is displayed within a ` ` element.
For users who prefer to avoid JavaScript, HMPL offers a DOM-based module called `hmpl-dom`. This allows similar dynamic functionality without directly writing JavaScript code, offering flexibility in integrating content into web applications. The corresponding HTML snippet demonstrates how server components can be dynamically loaded and mounted using this module, leveraging template language capabilities to significantly reduce application bundle size. Key features of HMPL.js include customizable server requests compliant with modern fetch standards, memory-preserving capabilities that minimize file sizes, full functionality support for modern app development (such as request indicators and caching), and an efficient syntax requiring minimal characters. The tool utilizes the Fetch API over XMLHTTPRequest to facilitate direct server interaction through both markup and JavaScript, allowing developers to efficiently create numerous DOM nodes from templates. HMPL.js enhances security with XSS protection via HTML sanitization using DOMPurify. It is versatile, supporting various projects by utilizing scripts or .hmpl files, and offers flexible object writing akin to vanilla JavaScript. Despite its extensive functionality, it maintains a small bundle size. The tool requires the JSON5 module from version 2.2.0 and the DOMPurify module from version 2.2.5. For installation, HMPL.js can be accessed via npm or through CDN links from resources like unpkg. A starter project is available on Vite for creating web applications using HMPL.js. Community support includes platforms like Github, Discord, Twitter, and Stack Overflow, with contributions welcomed under a Contributing Guide. The project operates under an open development roadmap with an MIT license, acknowledging the efforts of past contributors. ### Bullet Point Summary: - **HMPL.js Features**: Lightweight library for server-driven templates; supports customizable fetch requests, forms, events, time-based syncing; uses JSON5 and DOMPurify. - **Usage Example**: Demonstrates setting up interactive templates that update dynamically via API calls; includes handling of click counts. - **DOM-Based Module (`hmpl-dom`)**: Allows dynamic content integration without JavaScript, reducing bundle size significantly. - **Key Features**: Customizable server requests, memory-preserving capabilities, full functionality support for modern app development, efficient syntax. - **Security & Integration**: Utilizes Fetch API; enhances security with XSS protection through DOMPurify; supports various project types and flexible object writing. - **Installation & Support**: Available via npm or CDN links; starter projects on Vite; community support on Github, Discord, Twitter, Stack Overflow. - **Contributions & Licensing**: Open development roadmap under MIT license; contributions guided by a Contributing Guide; gratitude to past contributors. Keywords: API, Alpinejs, CDN, DOM module, DOMPurify, GitHub, HMPLjs, HTML sanitization, HTMX, JSON5, JavaScript, Vite, XSS attacks, behavior, block-based syntax, bundle size, button, caching, click, dynamic user interfaces, events, fetch requests, forms, lightweight framework, minified, npm, request, response, server components, server-driven templates, templateFn
github
github.com 5 days ago
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409. HN Why 95% of AI Commentary Fails### Summary: The article addresses several interconnected issues regarding the representation and interpretation of academic papers in media, focusing on MIT Nanda's paper "The State of AI in Business 2025." It critiques how commentary often misrepresents the findings by simplifying them to state that "95% of AI projects fail," when in reality, the paper discusses the limitations of internal AI tools remaining at pilot stages and contrasts this with successful implementations using standard large language models (LLMs) and external partnerships. The article highlights a broader concern about media narratives exaggerating AI as an existential risk due to narrative bias, emphasizing the need for accurate journalism and proposing that advanced AI models like GPT-5 could be used to verify news stories by cross-referencing summaries of papers with reports. The discussion extends to the challenges posed by search engines and tools in accessing original research. An example is provided where AI incorrectly summarized a scientific discovery about interstellar plasma channels, initially suggesting new findings that were actually confirmations of existing knowledge. This underscores the importance of consulting primary sources for accurate information dissemination. Reflecting on professional experiences from 2023 to 2025, the author explores how language models (LLMs) can enhance epistemic perspectives in journalism. They highlight their work at organizations like Metaculus and Meta Superintelligence, focusing on AI-driven understanding and forecasting. Despite media concerns about LLMs being used deceptively, such as impersonating journalists, the author argues that combining human efforts with AI could mitigate biases and improve journalistic quality. ### Bullet Point Summary: - **Misrepresentation of Nanda's Paper:** Commentary often oversimplifies MIT Nanda's paper to claim "95% of AI projects fail," ignoring nuanced insights about successful LLM implementations. - **Exaggerated Media Narratives:** The article critiques media for portraying AI as an existential risk, highlighting the need for accurate journalism and suggesting AI models like GPT-5 could help verify news accuracy. - **Accessing Original Research:** Challenges in finding original papers through search engines are discussed, with examples of misinterpretations, emphasizing consulting primary sources for accuracy. - **Professional Journey and LLM Potential:** The author reflects on their career from 2023 to 2025, advocating for the integration of LLMs in journalism to enhance epistemic perspectives and mitigate biases. - **Human-AI Collaboration in Journalism:** Despite concerns about deceptive uses of LLMs, combining human and AI efforts is suggested as a way to improve journalistic quality by reducing bias. Keywords: AI, Astronomy, Attention, Business 2025, Claude, Commentary, Confirmation Bias, Conspiracy Theorist, Dispatch AI, Epistemics, External Vendors, Forecasting, Frontier Labs, GPT-5, Gell-Mann Amnesia Effect, Gemini, Hallucinations, Interstellar Tunnels, LLM Chatbots, MIT NANDA, Perplexity AI, Pervasive Usage, Pilot Projects, Plasma, Projects, ROI, Scientific Paper, Superintelligence, Tools, Viral
claude
aiascendant.com 5 days ago
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410. HN Are there any websites that list popular AI applications or sites?The text discusses a query posted by a user on Hacker News, ahmetcadirci25, who seeks recommendations for websites listing popular artificial intelligence (AI) applications or sites. The user's interest is not in advertising but rather in staying informed about trends in AI product development. To illustrate their point, they reference two resources: a GitHub repository named "awesome-ai-tools" and a website called "topai.ahmetcadirci.com." This question initiated a discussion with three subsequent comments from other users expressing interest in how AI is being utilized across various applications. BULLET POINT SUMMARY: - A user on Hacker News, ahmetcadirci25, asks for websites listing popular AI applications or sites. - The inquiry aims to track trends in AI product development rather than seek advertising. - Two examples provided are a GitHub repository "awesome-ai-tools" and the website "topai.ahmetcadirci.com." - The discussion generated three comments showing interest in how people use AI for various purposes. Keywords: AI applications, GitHub, Hacker News, ahmetcadirci, applications, awesome-ai-tools, development, examples, lists, products, tools, tools AI, trends, websites
github
news.ycombinator.com 5 days ago
https://github.com/mahseema/awesome-ai-tools 5 days ago https://topai.ahmetcadirci.com/ 5 days ago |
411. HN Tiny LLM – LLM Serving in a Week**Summary:** "Tiny LLM – LLM Serving in a Week" is a specialized course designed for systems engineers aiming to grasp how large language models (LLMs) function and are served, with a focus on practical implementation using MLX, an array/machine learning library optimized for Apple Silicon. The course assumes prior knowledge of deep learning principles and PyTorch, and it draws inspiration from the CMU Deep Learning Systems course's needle project. Structured over three weeks, Week 1 introduces serving the Qwen2 model through Python-based matrix manipulation APIs. Week 2 delves into performance enhancements by implementing C++/Metal custom kernels. In Week 3, the focus shifts to further optimizations like batching requests for increased throughput. The curriculum emphasizes a hands-on approach from scratch to demystify LLM internal mechanics and optimize their serving capabilities. The "tiny-llm" book serves as a practical guidebook rather than an exhaustive textbook, featuring curated internet materials organized with consistent terminology and symbols to aid understanding. Developed by Chi of Neon/Databricks and Connor from PingCAP, the course encourages joining the tiny-llm community for collaborative learning via skyzh's Discord server. Participants start by setting up their environment as per instructions in "Setting Up the Environment" before building tiny-llm. **Bullet Point Summary:** - The course is designed to teach systems engineers how LLMs function and are served, focusing on practical implementation using MLX for Apple Silicon. - Prerequisites include familiarity with deep learning basics, especially PyTorch; it draws inspiration from CMU's Deep Learning Systems course. - **Week 1:** Serves the Qwen2 model using Python-based matrix manipulation APIs. - **Week 2:** Introduces C++/Metal custom kernels for performance optimization. - **Week 3:** Further optimizes by batching requests to improve throughput. - The course emphasizes hands-on, from-scratch implementation to demystify and optimize LLM serving. - "Tiny LLM" is a guidebook with curated materials aimed at high-performance LLM serving systems, using consistent terminology for clarity. - Developed by Chi (Neon/Databricks) and Connor (PingCAP), the course encourages community engagement through skyzh's Discord server. - Learners begin by setting up their environment as outlined in "Setting Up the Environment" before building tiny-llm. Keywords: Apple Silicon, CUDA kernels, LLM, LLM inference, MLX, Metal, PyTorch, Qwen2-7B-Instruct, batch requests, distributed key-value database, guidebook, high throughput, matrix manipulations, optimization, storage systems, systems engineers, tensors
llm
skyzh.github.io 5 days ago
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412. HN Big Tech Power Rankings – September 8### Summary On September 8, OpenAI was accused of running a Ponzi scheme and responded with strategic financial plans. Initially, Fidji Simo executed a $1 billion stock purchase to acquire a feature flag company, re-establishing contact with an associate. Following this, they announced a $10 billion stock sale aimed at compensating Jony and employees receiving offers from Meta, intending to distance themselves from Ponzi allegations. In discussing OpenAI's monetization strategy for ChatGPT, Fidji compared it to Herbalife but emphasized its basis in AI knowledge rather than physical products. This approach rejected traditional advertising methods within chatbot interactions. ### Bullet Point Summary - **Accusations and Initial Response**: On September 8, OpenAI faced allegations of running a Ponzi scheme. - **Financial Maneuvers**: Fidji Simo orchestrated a $1 billion stock purchase to acquire a feature flag company and reconnected with an associate. - **Stock Sale Plan**: Planned a $10 billion stock sale to compensate Jony and Meta-offered employees, aiming to distance from Ponzi implications. - **Monetization Strategy**: OpenAI's strategy for ChatGPT monetization was likened to Herbalife but focused on AI knowledge rather than products. - **Advertising Approach**: Rejected simple advertising methods in chatbot interactions. Keywords: AI, Big Tech, Fidji Simo, Herbalife, Meta, OPENAI, Power Rankings, ads business, chatbot, chatgpt, feature flag company, monetization, ponzi scheme, pyramid scheme, stock sale
openai
www.powerrankings.tech 5 days ago
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413. HN Unicode variation selectors for invisible LLM injectionThe document explores a sophisticated steganographic technique that utilizes Unicode variation selectors (specifically U+E0100 to U+E017F) to conceal hidden messages within visible text. This method allows ASCII characters to be mapped to invisible codepoints, making it possible for attackers to embed undetectable instructions in language models through prompt injection attacks. While tools like ChatGPT can detect these concealed values, designing a hidden prompt that successfully overrides the visible text presents significant challenges. To enhance the stealth of such attacks, adversaries might further disguise visible text using homoglyphs or no-width spaces, thereby diminishing its visibility relative to the embedded message. To mitigate this vulnerability, developers are advised to strengthen language models by incorporating refusals or disclosures in their training processes and implementing traditional code checks to identify suspicious Unicode patterns. - The document discusses a steganographic method using Unicode variation selectors to hide messages within visible text. - ASCII characters can be mapped to invisible codepoints, facilitating potential prompt injection attacks on language models. - Tools like ChatGPT can detect these hidden values but crafting effective hidden prompts is challenging. - Attackers might use homoglyphs or no-width spaces to further obscure visible text and highlight the hidden message. - Developers should counteract these threats by training language models with refusals or disclosures and using code checks for suspicious Unicode patterns. Keywords: ASCII, ASCII characters, ChatGPT, LLMs (Large Language Models), Large Language Models, Unicode variation selectors, countermeasures, hex boundary, homoglyphs, invisible codepoints, no-width spaces, obfuscation, prompt injection, prompt injection attacks, steganographic technique, steganography, stenography attacks, stenography attacks Keywords: Unicode, variation selectors
llm
code.lol 5 days ago
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414. HN Signal Secure BackupsSignal has launched an opt-in secure backup feature that ensures users' messages and media are protected in case of phone loss or damage. This feature is initially available for Android beta users and includes daily refreshes, end-to-end encryption, and allows free backups of text messages and 45 days of media. Users can extend their backup duration beyond the initial period by subscribing to a paid plan at $1.99 per month. The introduction of this paid subscription aligns with Signal's privacy-centric approach, as they avoid monetizing user data or advertising. Signal employs zero-knowledge technology in its backups, ensuring archives are not linked directly to users' accounts and require a unique 64-character recovery key for access—a measure that cannot be bypassed by Signal if the key is lost. Secure backups automatically update daily, replacing the previous day's backup while excluding view-once or disappearing messages set within 24 hours. This approach ensures only current data is stored, without retaining recently deleted or scheduled-to-disappear messages. Moving forward, Signal aims to expand backup features by enabling users to store archives in preferred locations and transfer encrypted message histories across Android, iOS, and Desktop devices. Initially available on the latest Android beta version, secure backups are expected to roll out to all platforms soon. - **Key Points:** - Secure backups protect user messages and media with end-to-end encryption. - Available initially for Android beta users, free text and 45-day media backup offered; paid plan extends this beyond 45 days at $1.99/month. - Signal maintains privacy by not monetizing data or using ads, utilizing zero-knowledge technology to unlink backups from user accounts. - Access to backups requires a private recovery key; backups refresh daily without retaining view-once/disappearing messages. - Future plans include allowing users to choose storage locations for backups and enabling encrypted history transfers across devices. - Secure backup feature rollout begins on Android beta, with upcoming expansion to all platforms. Keywords: Android beta, Desktop, Signal, backups, encryption, end-to-end encrypted, iOS, media archive, message history, metadata, paid subscription, privacy-preserving, recovery key, zero-knowledge technology
popular
signal.org 5 days ago
https://news.ycombinator.com/item?id=45174779 5 days ago https://news.ycombinator.com/item?id=45171576 5 days ago https://github.com/bepaald/signalbackup-tools 5 days ago https://news.ycombinator.com/item?id=45017028 5 days ago https://f-droid.org/en/packages/com.github.catfrie 5 days ago https://github.com/stevenwalton/.dotfiles/blob 5 days ago https://wiki.archlinux.org/title/SSHFS 5 days ago https://news.ycombinator.com/item?id=45172188 5 days ago https://github.com/tbvdm/sigtop 5 days ago https://news.ycombinator.com/item?id=45175387 5 days ago https://github.com/signalapp/Signal-iOS/issues 5 days ago https://faq.whatsapp.com/481135090640375 5 days ago https://github.com/bepaald/get_signal_desktop_key_mac 5 days ago https://community.signalusers.org/t/dont-unlink-devices 5 days ago https://molly.im/ 5 days ago https://signal.org/bigbrother/ 5 days ago https://web.archive.org/web/20250117232443/https:& 5 days ago https://web.archive.org/web/20230519115856/https:& 5 days ago https://news.ycombinator.com/item?id=45171740 5 days ago https://signal.org/blog/signal-doesnt-recall/?pubD 5 days ago https://articles.59.ca/doku.php?id=pgpfan:expire 5 days ago https://csrc.nist.gov/csrc/media/Events/2024& 5 days ago https://github.com/signalapp/Signal-Android/issues 5 days ago https://github.com/signalapp/Signal-Android/issues 5 days ago https://github.com/signalapp/Signal-Android/issues 5 days ago https://github.com/signalapp/Signal-Android/issues 5 days ago https://support.signal.org/hc/en-us/articles/ 5 days ago https://sneak.berlin/20210425/signal-is-wrecking-your-i 5 days ago https://web.archive.org/web/20200226124508/https:& 5 days ago https://www.eff.org/deeplinks/2014/01/after-n 5 days ago https://en.wikipedia.org/wiki/CLOUD_Act 5 days ago |
415. HN Run Any MCP as Agent- The latest version of the Enterprise MCP Wrapper introduces significant enhancements aimed at transforming it into an easy-to-use solution for creating agents. - This update includes approximately 8,000 code additions and three new endpoints, designed to facilitate integration between applications and Model Control Points (MCPs) using both agentic and traditional orchestrations through a conversational interface. - The focus of this release is on functionality rather than theory, providing robust support tested across various model sizes. Key features include comprehensive guardrails, tracing, and logging to ensure reliability and ease of troubleshooting. - Central to the innovation are new endpoints that enable the Enterprise MCP Bridge to effectively host agents, ensuring seamless integration and operation. - The enhancements aim to simplify agent operations within a Kubernetes-ready environment. Users can test these updates by running `./start.sh` in example projects. **Key Updates:** - **GET /.well-known/agent.json**: A dynamic file serving as an agent's business card, compliant with A2A standards, detailing service metadata and skills. - **POST /tgi/v1/a2a**: An endpoint that supports JSON-RPC requests for interoperability with other services. - **POST /tgi/v1/chat/completions**: An OpenAI-compatible endpoint facilitating automated chat workflows in both streaming and non-streaming modes, simplifying tool integration. - The release prioritizes transparency and auditability to ensure trustworthiness in complex workflow execution. Enhanced traceability of agent actions is supported by tools like Jaeger, allowing users to visualize execution flows and identify errors. - Observability features aid in debugging, optimizing performance, and maintaining compliance through clear audit trails. - The system integrates with OpenAI-compatible endpoints via environment variables (TGI_API_URL, TGI_API_TOKEN, TGI_MODEL_NAME), enabling tool invocations at specified endpoints. - A new PromptService is introduced to manage system prompts, providing agents with the appropriate context for improved performance. - The document encourages users to report bugs through GitHub issues rather than complaints and introduces a dedicated agent for bug fixes, emphasizing collaboration and continuous development. Keywords: A2A compliant, Enterprise MCP, GitHub, JSON-RPC, Jaeger, Kubernetes-ready, OpenAI-compatible, agents, auditing, bug, compliance, conversational interface, debugging, enterprise customers, guardrails, integration, issue, logging, metadata, observability, open-source, orchestration, plug and play, security, tracing, version, visualization, workflows
github
blog.inxm.ai 5 days ago
|
416. HN Fixing my gripes with GitHub using Gleam and a Raspberry Pi- The author discusses creating a solution to efficiently monitor the mergeability of open pull requests (PRs) for the Gleam compiler on GitHub due to frustrations with not being notified about conflicts during merges, necessitating manual checks. - A GraphQL query is designed to fetch details of up to 50 PRs, including their title, URL, mergeability status, and draft status, focusing on the most recently updated ones. This automation involves an HTTP POST request to GitHub's GraphQL API using Gleam with `gleam_http`, incorporating a JSON body and authorization via a GitHub token. - The document details constructing and sending this HTTP POST request through steps that include building the request (with necessary headers), sending it using `httpc.send` in Gleam, and processing the server's response to fetch PR statuses programmatically. - Custom types such as `PullRequest` and `Mergeability` are defined to handle various merge status scenarios encountered in GitHub’s JSON responses, like conflicts or unknown states. A decoder is used to translate these into structured data within Gleam for precise interpretation. - The creation of decoders in Gleam is discussed to parse nested JSON structures related to pull requests, emphasizing composable and accurate data representation techniques. - Practical applications extend to setting up a Raspberry Pi Zero 2 W for running Gleam code in hardware projects, demonstrating the language’s versatility beyond software decoding tasks. - A local server setup using the Wisp framework in Gleam is described, which handles HTTP requests including a route fetching GitHub pull request data. HTML templating with Lustre showcases how easy it is to generate dynamic web content while maintaining simplicity and efficiency. - The author values Lustre's straightforward syntax for integrating familiar programming functions without needing new templating languages, exemplified by the function `pull_request_to_li` that formats PRs into list items. - To deploy this setup on a Raspberry Pi, a systemd timer is used to start the server on boot, ensuring necessary environment variables are set and avoiding hardcoded secrets like GitHub tokens in code. - The author appreciates Gleam's productivity benefits and enjoys automating tasks for efficiency and intellectual challenge, despite the simple nature of the project that serves as a quick check tool for PRs by browsing the Raspberry Pi’s IP address. Keywords: GitHub, GitHub token, Gleam, GraphQL API, HTML templating, HTTP request, JSON, Lustre, POST request, PRs (Pull Requests), Raspberry Pi, UI (User Interface), Wisp framework, automation, conflicts, decoder, draft, mergeability, open PRs, rebase, server
github
giacomocavalieri.me 5 days ago
|
417. HN Popular NX packages compromised on NPM- Popular NX packages on NPM were compromised with malicious code detected in @nx scope packages, which are widely downloaded. - The breach is significant as stolen data was published publicly on GitHub instead of being kept private, exposing numerous credentials including npm tokens and increasing the risk of further supply chain attacks. - A notable aspect of the attack is its destructive component. The NX team has detailed the incident in a security advisory (available at their GitHub page). - Compromised package versions included a malicious `telemetry.js` file, executed via a postinstall script within the package.json, targeting Unix-like systems to search for wallet-related files. - The malicious script searches local file paths on Linux and macOS systems for sensitive information like crypto wallets or SSH keys, with certain directories excluded, and stores found data in `/tmp/inventory.txt`. - The script checks for specific command-line tools present on the system's PATH, executes them, and handles errors gracefully while capturing environment details. - On Windows platforms, the script exits immediately to avoid execution, ensuring it only operates on Unix-like systems. - Additionally, a JavaScript code snippet suggests appending operations to files using Node.js’s `fs` module with error handling for file I/O operations. - The malicious attack involves creating a new GitHub repository in an affected user's account if a GitHub token is found, uploading double-encoded data of collected sensitive information as `results.b64`. - This attack employs novel techniques to extract additional secrets via a language model client and names unauthorized repositories with patterns like "s1ngularity-repository." - Affected users are advised to delete unauthorized repositories from their GitHub accounts, rotate compromised secrets, decode any leaked information for assessment, and remove harmful commands from shell profiles. - The attack highlights the use of LLM clients to uncover local secrets on victims' machines, a novel strategy that facilitated rapid detection due to included shutdown commands in user shells. - Despite quick containment efforts, the public release of stolen data poses significant risks by providing malicious actors with GitHub and NPM tokens for further attacks, suggesting potential future waves of similar incidents. Keywords: CLI tools, GitHub, GitHub CLI tokens, GitHub token, HTTPS, Linux, NPM, NX, Nodejs, PATH, SSH keys, absolute path, attack monitoring, base64 encoding, child_process, credentials, crypto wallets, data exfiltration, developer credentials, env files, environment variables, exfiltrated data, file scanning, fspromisesreadFile, fspromisesstat, githubRequest, macOS, malicious code, malicious threat actors, npm tokens, npm usernames, npmrc, output capture, packagejson file, platform check, postinstall script, prompts, public publication, recursive search, registry tokens, repository creation, secrets enumeration, sensitive data, shutdown command, stolen data, sudo, supply chain attacks, telemetryjs, uploadPayload
github
www.aikido.dev 5 days ago
|
418. HN LLM-assisted scientific breakthrough probably isn't realThe article explores a phenomenon where individuals mistakenly believe they have achieved scientific breakthroughs with the aid of large language models (LLMs), particularly in fields such as physics, math, AI, computer science, cryptography, or consciousness. These false beliefs often arise from extended interactions with LLMs or when personalization features enhance idea refinement and articulation. The article advises skepticism towards these ideas due to their high likelihood of error and recommends thorough reality checks before considering them genuine innovations. Common themes in perceived breakthroughs include recursion, evolution, fractals, complexity, quantum mechanics, emergence, and coherence. The text underscores the importance of critically evaluating potential scientific breakthroughs involving complex concepts by conducting reality checks with advanced LLMs like GPT-5-Thinking or Claude-Opus-4.1. It outlines a structured approach for assessing projects claiming scientific novelty, emphasizing maintaining multiple hypotheses and assigning probabilities to each to counteract possible biases from LLM feedback. For those experiencing validation of ideas through LLMs or lack of attention to their concepts, the article suggests letting go of defensiveness and recognizing that even renowned scientists have been misled. It proposes using frontier language models without prior influence for objective evaluation, aiming to discern whether an idea is genuinely groundbreaking. The document recommends a systematic approach to testing scientific hypotheses: develop a specific, testable hypothesis; design experiments to falsify it; preregister predictions; conduct robust analyses; and consider alternative explanations with corresponding experiments. After initial evaluations, if the hypothesis remains credible, share it publicly for feedback and be open to revisions or acceptance of non-reproducible results. While acknowledging skepticism about LLMs' role in scientific contexts, particularly on social media platforms, the article defends their legitimate use in brainstorming, experiment design, and paper formatting. It concludes that an idea's validity should be judged by its merit and empirical support, regardless of whether it originated from humans or LLMs. **Bullet Point Summary:** - The article addresses a phenomenon where individuals mistakenly believe they've made scientific breakthroughs with the help of LLMs, especially in complex fields like physics and AI. - It highlights common themes in perceived innovations such as recursion and quantum mechanics, advising skepticism due to potential errors in new theories. - Reality checks using advanced LLMs without prior influence are recommended for evaluating the novelty of ideas. - The text outlines a structured approach for assessing scientific claims, emphasizing hypothesis maintenance and probability assignment to counteract biases from LLM feedback. - It encourages critical evaluation of ideas validated by LLMs, suggesting letting go of defensiveness and recognizing past instances where renowned scientists were misled. - A systematic testing approach is recommended: develop testable hypotheses, design experiments to falsify them, preregister predictions, conduct analyses, and consider alternative explanations. - After initial evaluations, if the hypothesis remains credible, share it publicly for feedback and be open to revisions or accepting non-reproducible results as part of the scientific process. - Despite skepticism about LLMs in science, their legitimate use in brainstorming, experiment design, and paper formatting is defended. - The validity of an idea should be judged based on its merit and empirical support, regardless of whether it originated from humans or LLMs. Keywords: AI, LLM, brainstorming, breakthrough, coherence, collaboration, consciousness, critical analysis, cryptography, defensiveness, experiment, falsification, feedback, fractal, hypothesis, math, novelty, peer-reviewed, physics, quantum, recursive, replication, research, scientific, skepticism, validation, validity
llm
www.lesswrong.com 5 days ago
|
419. HN Acer Unveils the Veriton GN100 AI Mini Workstation Built on the Nvidia GB10Acer has introduced the Veriton GN100 AI Mini Workstation, a compact personal AI workstation equipped with NVIDIA's GB10 Grace Blackwell Superchip. This device boasts 128 GB of unified memory and 4 TB of NVMe M.2 SSD storage, offering up to 1 PFLOPS of FP4 AI performance. The workstation is designed for local processing of large AI models and supports scaling by linking two units via NVIDIA ConnectX-7 SmartNIC. It includes compatibility with developer tools like PyTorch, Jupyter, and Ollama, allowing developers to create and deploy AI applications both locally and in the cloud. Security features such as Kensington lock support are incorporated for enhanced protection. The workstation is capable of handling AI models up to 405 billion parameters. The NVIDIA ConnectX-7 NIC allows two Acer Veriton GN100 AI Mini Workstations to be connected, supporting large AI models. Connectivity options include Wi-Fi 7, four USB 3.2 Type-C ports, an HDMI port, and an Ethernet jack, all contributing to its robust connectivity and security features. Pricing is set at USD 3,999 in North America, EUR 3,999 in EMEA, and AUD 6,499 in Australia, with variations possible by region. Additional details on specifications and availability can be accessed through Acer offices or the IFA 2025 Press Kit site. **BULLET POINT SUMMARY:** - **Product Launch**: Acer's Veriton GN100 AI Mini Workstation powered by NVIDIA GB10 Grace Blackwell Superchip. - **Specifications**: Includes 128 GB unified memory, 4 TB NVMe M.2 SSD, and up to 1 PFLOPS FP4 AI performance. - **AI Model Handling**: Designed for local processing of large AI models, scales with two units via NVIDIA ConnectX-7 SmartNIC. - **Developer Tools Support**: Compatible with PyTorch, Jupyter, Ollama, facilitating development and deployment of AI applications. - **Security Features**: Offers Kensington lock support for enhanced security. - **Model Capacity**: Can handle AI models up to 405 billion parameters. - **Connectivity Options**: Includes Wi-Fi 7, four USB 3.2 Type-C ports, an HDMI port, and an Ethernet jack. - **Pricing**: USD 3,999 in North America, EUR 3,999 in EMEA, AUD 6,499 in Australia (subject to regional variations). - **Additional Information**: Available through Acer offices or the IFA 2025 Press Kit site. Keywords: AI Mini Workstation, AI models, AUD 6499, Acer Veriton GN100, Arm-based CPU, Australia, CUDA cores, Connectivity, EMEA, EUR 3999, Ethernet jack, FP4 performance, Grace Blackwell Superchip, HDMI port, IFA 2025 Press Kit, Jupyter, Kensington lock, NVIDIA ConnectX-7 SmartNIC, NVMe M2 SSD, North America, Nvidia GB10, Ollama, Pricing, PyTorch, Security, Tensor Cores, USB 32 Type-C, USD 3999, Wi-Fi 7, accelerated infrastructure, cloud services, server-grade performance, storage, unified memory
ollama
news.acer.com 5 days ago
http://www.acer.com/us-en/desktops-and-all-in-ones/ 5 days ago https://errors.edgesuite.net/18.80ed217.1757347267.eee429f 5 days ago https://www.acer.com/us-en/desktops-and-all-in-ones 5 days ago |
420. HN Show HN: Local, extensible and fast macOS transcription appWhisperMac is a macOS application designed to offer transcription services with a strong emphasis on privacy, speed, and extensibility. It provides both local and cloud-based transcription options by utilizing WhisperCPP, Vosk, Apple's native Speech framework, and other cloud services like Gemini or Mistral. Despite being in heavy beta and possibly containing bugs, the app features real-time transcription capabilities and allows for plugin extensibility—such as a whisper plugin—to enhance its functionality. It leverages Apple’s Metal CoreML support to ensure fast performance. WhisperMac also offers optional text enhancements through OpenAI-compatible services, customizable voice commands, unified model management, and flexible data storage options, granting users significant control over their transcription experience. **BULLET POINT SUMMARY:** - **Focus on Privacy, Speed, Extensibility:** Emphasizes privacy, speed, and extensibility in macOS transcription. - **Transcription Options:** Supports local (WhisperCPP, Vosk, Apple Speech) and cloud-based (Gemini, Mistral) solutions. - **Current Status:** In heavy beta with potential bugs. - **Real-Time Capability:** Offers real-time transcription features. - **Plugin Extensibility:** Allows for plugins like the whisper plugin to enhance functionality. - **Performance Optimization:** Utilizes Apple’s Metal CoreML for fast performance. - **Text Enhancement:** Provides optional text enhancements via OpenAI-compatible services. - **Customization Features:** Includes configurable voice commands and unified model management. - **Data Control:** Offers controllable data storage options, giving users full control over their experience. Keywords: CoreML, OpenAI, Transcription app, WhisperCpp, WhisperMac, beta, beta Keywords: transcription, bugs, cloud services, configuration, configuration actions, data storage, extensible, fast dictation, installation, local, macOS, model, model management, plugins, privacy-friendly, real-time, real-time transcription
openai
github.com 5 days ago
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421. HN NPM debug and chalk packages compromisedBetween September 8th, 2023, and an unspecified date, popular NPM packages like `debug` and `chalk` were compromised with malicious code, which had amassed over two billion weekly downloads. The inserted code was designed to stealthily intercept cryptocurrency transactions and Web3 activities on users' browsers, redirecting funds from wallets to attacker-controlled accounts without the user's awareness. - **Initial Detection**: Alerts about these compromises first came through Aikido's intelligence feed, highlighting suspicious updates in packages such as `is-arrayish`, which contained obfuscated malicious scripts difficult for casual inspection to detect. This incident emphasizes the necessity of employing security tools like Aikido safe-chain to manage risks related to package vulnerabilities. - **JavaScript Script Overview**: - **Purpose**: The code is primarily intended to intercept and manipulate transactions sent from web3 providers, such as `window.ethereum`. - **Transaction Modification**: It alters transaction details (e.g., the recipient address) for purposes like privacy or testing. - **Interception Mechanism**: Proxy functions are defined around Ethereum and Solana API methods to modify requests before forwarding them. These methods are dynamically redefined using JavaScript's `Object.defineProperty`. - **Execution Control**: A function ensures it waits until the `window.ethereum` object is available, then modifies its behavior. - **State Tracking**: The script monitors interception activity and retains original methods for potential future use. - **Control Interface**: Provides a mechanism to manage and check proxy status and actions. - **Malware Operation**: - It functions as a browser-based interceptor targeting network traffic and application APIs, specifically embedding itself into `fetch`, `XMLHttpRequest`, and wallet interfaces. - It scans for recognizable formats of various cryptocurrency addresses in transaction payloads and alters them to attacker-controlled ones before user interaction. - The malware maintains an ostensibly legitimate interface while secretly redirecting transactions. - **Phishing Attack Context**: - An attack targeted an NPM package maintainer through a phishing email from the fraudulent domain "support@npmjs.help." - Post-alert by Aikido, despite attempts to mitigate damage starting around 15:15 UTC, the maintainer lost access to some accounts and could not secure all compromised packages. - Another package, `proto-tinker-wc@0.1.87`, faced similar code injection at 16:58 UTC. The incident highlights significant risks in software supply chains, demonstrating how sophisticated malware can exploit popular development tools to manipulate financial transactions covertly while emphasizing the critical need for vigilance and robust security measures. Keywords: API calls, Bitcoin, Ethereum, JavaScript, NPM, Web3js, blockchain, browser-based interceptor, chalk, compromised, crypto, debug, deobfuscation, malicious code, malware, network traffic, npm updates, package versions, phishing, stealth proxy, transaction, wallet, web3
popular
www.aikido.dev 5 days ago
https://github.com/chalk/chalk/issues/656 5 days ago https://github.com/debug-js/debug/issues/1005 5 days ago https://news.ycombinator.com/item?id=45175125 5 days ago https://github.com/chalk/chalk/issues/656#iss 5 days ago https://www.redhat.com/en/blog/understanding-red-h 5 days ago https://lists.debian.org/debian-security-announce/2024& 5 days ago https://gist.github.com/edgarpavlovsky/695b896445c19b6f 5 days ago https://socket.dev/blog/npm-author-qix-compromised-in-m 5 days ago https://arxiv.org/html/2403.12196v2 5 days ago https://www.youtube.com/watch?v=cxJPiMwoIyY 5 days ago https://docs.npmjs.com/generating-provenance-statements 5 days ago https://imgur.com/a/q8s235k 5 days ago https://gist.github.com/Qix-/c1f0d4f0d359dffaeec48dbfa1 5 days ago https://mailtrap.io/contact-details/ 5 days ago https://otx.alienvault.com/pulse/68bf031ee0452072533dee 5 days ago https://github.com/search?q=%2Fconst+_0x112%2F+lang%3Ajs& 5 days ago https://pages.nist.gov/800-63-FAQ/#q-b05 5 days ago https://github.com/chalk/chalk/issues/656#iss 5 days ago https://gist.github.com/sindresorhus/2b7466b1ec36376b87 5 days ago https://github.com/Qix-/node-error-ex/issues/ 5 days ago https://www.troyhunt.com/extended-validation-certificates-ar 5 days ago https://arstechnica.com/information-technology/2017 5 days ago https://jdstaerk.substack.com/p/we-just-found-malicious 5 days ago https://arc.net/l/quote/cdigautx 5 days ago https://github.com/npm/cli/issues/564 5 days ago https://cdn.prod.website-files.com/642adcaf364024654c71df23& 5 days ago https://www.aikido.dev/blog/npm-debug-and-chalk-package 5 days ago https://news.ycombinator.com/item?id=45039764 5 days ago https://www.businessinsider.com/npm-ceo-bryan-bogensberger-r 5 days ago https://www.businessinsider.com/npm-cofounder-laurie-voss-re 5 days ago https://www.debian.org/doc/manuals/securing-debian 5 days ago https://github.com/npm/npm/pull/4016#issuecom 5 days ago https://news.ycombinator.com/item?id=38645969 5 days ago https://github.com/npm/cli/commit/5a3b345d6d5 5 days ago https://github.com/npm/cli/issues/8440 5 days ago https://github.com/npm/cli/issues/1865 5 days ago https://github.com/npm/pacote/issues/53 5 days ago https://docs.npmjs.com/cli/v8/using-npm/scrip 5 days ago https://github.com/cspotcode/workaround-broken-npm-prep 5 days ago https://github.com/cspotcode/workaround-broken-npm-prep 5 days ago https://gist.github.com/sindresorhus/2b7466b1ec36376b87 5 days ago https://github.com/advisories/GHSA-hfm8-9jrf-7g9w 5 days ago https://socket.dev 5 days ago https://www.aikido.dev 5 days ago https://crib.social/notice/AwQqG9gm365uat93Nw 5 days ago https://urlscan.io/result/01992a3e-4f8c-72bb-90a9-c1382 5 days ago https://github.com/advisories/GHSA-8mgj-vmr8-frr6 5 days ago https://github.com/advisories/GHSA-5g7q-qh7p-jjvm 5 days ago https://github.com/advisories/GHSA-m99c-cfww-cxqx 5 days ago https://github.com/github/advisory-database/issues 5 days ago https://gist.github.com/martypitt/0d50c350aa7f0fc733547 5 days ago https://github.com/github/advisory-database/issues 5 days ago https://jdstaerk.substack.com/i/173095305/how-to-p 5 days ago https://github.com/chalk/chalk/issues/656#iss 5 days ago https://news.ycombinator.com/item?id=45168413 5 days ago https://web.archive.org/web/20250908162250/https:& 5 days ago https://news.ycombinator.com/item?id=45167394 5 days ago https://github.blog/security/supply-chain-security/ 5 days ago https://github.blog/changelog/2025-07-01-dependabot-sup 5 days ago https://github.com/AikidoSec/safe-chain 5 days ago https://intel.aikido.dev/?tab=malware 5 days ago https://github.com/ossillate-inc/packj 5 days ago https://pypistats.org/packages/six 5 days ago https://github.com/benjaminp/six/blob/main&#x 5 days ago https://github.com/tc39/proposal-explicit-resource-mana 5 days ago https://wiki.commonjs.org/wiki/CommonJS 5 days ago https://jsr.io/@std 5 days ago https://github.com/denoland/std 5 days ago https://github.com/stdlib-js/stdlib 5 days ago https://crabmusket.net/java-scripts-ecosystem-is-uniquely-pa 5 days ago https://www.npmjs.com/package/color 5 days ago https://github.com/chalk/chalk/releases 5 days ago https://github.com/fatih/color 5 days ago https://github.com/BurntSushi/termcolor 5 days ago |
422. HN Show HN: grepO: A grep that can consult LLMs and other black-box binaries**Summary:** grepO is an advanced tool for pattern matching that enhances traditional regular expressions by integrating semantic elements through "oracle-refinement" constructs (`r & `). This allows the tool to verify if a string matches a pattern `r` and satisfies an external query ``, which can involve Large Language Models (LLMs), databases, or APIs. Unlike standard grep, grepO uses reserved characters for special functions, necessitating escaping them for direct matching. The syntax supports queries within `< >`. Implemented in Rust using Cargo, users must build the program and set up an LLM oracle as detailed in associated documentation. An example implementation involves querying OpenAI's language models via `openai.py`, requiring an API key. Limitations of grepO include line-based matching, limited regex construct support, restricted to ASCII streams, and under-documented syntax. Users are encouraged to contribute feedback, features, or bug reports.
openai
github.com 5 days ago
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423. HN Show HN: Conformal – Type-safe FormData parsing and canonical submission flowThe document introduces "Conformal," a TypeScript library designed to simplify form handling by providing two primary features: type-safe FormData parsing and canonical submission flow. It converts native FormData into fully typed objects, supporting complex nested structures like arrays and objects. The `Submission` object standardizes success and error states, maintaining raw input alongside field and form errors. Conformal supports Standard Schema compatibility with tools such as Zod and Valibot, making it a versatile foundation for form submissions across various JavaScript environments including browsers and Node.js. It is framework-agnostic, suitable for integration with React, Vue, Svelte, or plain JavaScript applications. The library also includes primitives to build specific form libraries tailored to different frameworks. Installation of Conformal can be performed using npm: `npm install conformal`. An example demonstrates its use in parsing and validating an HTML form's data through a Zod schema before processing submissions, showcasing how raw user input is retained for potential re-entry and errors are categorized for enhanced UI feedback. The `Submission` object returned by the `parseWithSchema` function provides clear interfaces to access validated data and error details. The document outlines key features of Conformal's validation mechanism: preserving user input, distinguishing between field-specific and general form-level errors, ensuring type safety with TypeScript, and maintaining immutability in all properties. The library includes utility functions like `parse`, `serialize`, and `getPath` for structured data transformations, allowing nested object manipulation while supporting complex data handling tasks. Additionally, Conformal offers integration with Zod schemas without requiring additional learning beyond existing schema methods. This allows users to define comprehensive form input patterns using type-safe wrappers. The library supports live examples in React projects on platforms like GitHub or StackBlitz and is open-source under the MIT License. **BULLET POINT SUMMARY:** - Conformal provides type-safe FormData parsing and a canonical submission flow, converting FormData into fully typed objects. - Supports Standard Schema compatibility, making it suitable for diverse JavaScript environments without framework dependency. - Installation via npm (`npm install conformal`); example demonstrates form data parsing using Zod schemas with clear success/error state handling. - Key features include input preservation, error categorization, TypeScript integration for type safety, and immutable properties. - Utility functions enable structured data transformations: `parse`, `serialize`, and `getPath`. - Integration with Zod schemas offers seamless schema method usage without extra learning; supports live examples in React projects. - Open-source under the MIT License. Keywords: FormData, GitHub, JavaScript, React, StackBlitz, Standard Schema, Submission object, TypeScript, Valibot, Zod, array, arrays, boolean detection, browser-specific, conformal/zod, data preservation, deep copying, enum, error states, form handling, framework-agnostic, immutability, import, methods, min, module, nested objects, optional, parsing, schema, strongly typed, type-safe, validation
github
github.com 5 days ago
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424. HN A Comprehensive Survey on Trustworthiness in Reasoning with LLMs**Summary:** The document is an arXiv preprint titled "A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models," authored by Yanbo Wang, Yongcan Yu, Jian Liang, and Ran He, submitted on September 4, 2025. The survey evaluates the reliability of reasoning processes in large language models (LLMs), supported by the Simons Foundation. It examines trustworthiness across dimensions such as accuracy, bias, transparency, ethics, truthfulness, safety, robustness, fairness, and privacy. The paper explores Long-CoT (Long Chain-of-Thought) reasoning's impact on LLMs' trustworthiness, focusing on five key aspects: truthfulness, safety, robustness, fairness, and privacy. While CoT-based reasoning improves model accuracy and interpretability by generating intermediate steps, it also presents vulnerabilities in these areas. The survey reviews studies up to June 2025, analyzing methodologies, findings, and limitations chronologically. Despite reasoning techniques' potential to enhance trustworthiness through reduced hallucinations and better harmful content detection, they may increase risks related to safety, robustness, and privacy. The paper aims to guide the AI safety community by providing an overview of current advancements and suggesting future research directions in LLM reasoning trustworthiness. The document also details submission specifics for the research paper and mentions its availability in PDF, HTML, and TeX formats. It lists tools such as bibliographic explorers and platforms like Hugging Face and Semantic Scholar related to the paper. The description highlights arXiv features like Influence Flower, CORE Recommender, and arXivLabs, which allows community members to develop new platform features. **Bullet Point Summary:** - **Document Overview:** - ArXiv preprint titled "A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models." - Authors: Yanbo Wang, Yongcan Yu, Jian Liang, Ran He. - Submission date: September 4, 2025 (arXiv:2509.03871). - **Research Focus:** - Evaluates reliability of reasoning processes in large language models (LLMs). - Supported by the Simons Foundation. - **Trustworthiness Aspects Examined:** - Accuracy, bias, transparency, and ethical considerations. - Five dimensions focused on: truthfulness, safety, robustness, fairness, privacy. - **Impact of Long-CoT Reasoning:** - Enhances accuracy and interpretability but introduces vulnerabilities. - **Review Scope:** - Studies reviewed up to June 2025. - Analyzes methodologies, findings, limitations chronologically. - **Techniques vs. Risks:** - Potential improvements in trustworthiness via reduced hallucinations and better harmful content detection. - Increased risks related to safety, robustness, privacy. - **Aim of the Paper:** - Guide AI safety community with current advancements. - Suggest future research directions. - **Additional Details:** - Submission specifics and format availability (PDF, HTML, TeX). - Related tools and platforms listed (bibliographic explorers, Hugging Face, Semantic Scholar). - **arXiv Features Mentioned:** - Influence Flower, CORE Recommender. - arXivLabs for community-driven feature development. Keywords: AI safety, BibTeX, CORE Recommender, CoT-based reasoning, Computation and Language, Computer Science, DOI, DataCite, Influence Flower, Jian Liang, LLM, Large Language Models, Long-CoT, PDF, Ran He, Reasoning, Simons Foundation, Survey, Trustworthiness, Yanbo Wang, Yongcan Yu, arXiv, code generation, cryptography and security, csCL, interpretability, language understanding, problem solving, semantic scholar, web accessibility
llm
arxiv.org 5 days ago
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425. HN Qwen3 ASR: Hear clearly, transcribe smartly**Summary:** Qwen3 ASR is an advanced speech recognition system that excels in delivering clear hearing and smart transcription capabilities. Its primary focus is on enhancing audio clarity and accurately transforming spoken words into text, catering to users who require dependable voice-to-text solutions. The system prioritizes precision in capturing sound while also demonstrating intelligence in processing language, making it particularly suitable for applications where accuracy and efficiency are paramount. **Bullet Point Summary:** - Qwen3 ASR is an advanced speech recognition tool. - It provides clear hearing and smart transcription capabilities. - Focuses on enhancing audio clarity and accurate conversion of spoken words to text. - Emphasizes precision in capturing sound and intelligence in language processing. - Ideal for users needing reliable voice-to-text solutions. Keywords: ASR, Qwen, Qwen3, clearly, hear, smartly, transcribe
qwen
qwen.ai 5 days ago
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426. HN Spec-Driven Development with AI- **Spec-Driven Development with AI**: This approach emphasizes using clear, executable specifications as a foundation for coding, leveraging AI tools to minimize ambiguity and ensure functional outcomes. - **Challenges of Traditional Methods**: The traditional "vibe-coding" method often results in incomplete or misaligned solutions due to its reliance on broad goals. Spec-driven development addresses these issues by using living documents that guide coding processes and improve quality. - **Introduction of Spec Kit**: An open-source toolkit, Spec Kit integrates spec-driven development into workflows using tools like GitHub Copilot. It centralizes the specification process, ensuring it guides implementation and minimizes guesswork in code writing. - **Development Process Phases**: - **Specify**: This phase involves outlining project objectives without technical details, allowing coding agents to create evolving specifications based on user insights. - **Plan**: Technical aspects are introduced, with the creation of detailed plans that integrate company standards and architectural patterns for a comprehensive technical strategy. - **Role of Developers**: Developers steer the process, verifying work at various stages, refining specs, addressing omissions, and ensuring alignment with requirements through iterative task-based reviews. - **Integration and Workflow**: - Spec Kit works with tools like GitHub Copilot to facilitate AI-assisted workflows. - The workflow involves initiating projects, creating technical plans, and breaking down tasks into executable segments for coding agents. - **Benefits Across Technology Stacks**: This method is effective across various technology stacks due to its focus on capturing intent through clear specifications, which guide technical decisions and task breakdowns. - **Application in Various Scenarios**: - Effective in greenfield projects, feature additions, and legacy modernization by ensuring clarity of intent and centralizing requirements. - Separates "what" from "how," facilitating iterative development and experimentation. - **AI's Role**: Specifications become the source of truth as AI transforms them into executable code, guiding project outcomes based on defined intents rather than existing documentation. - **Spec Kit's Goals**: The open-source project aims to facilitate the transition towards spec-driven development with a focus on enhancing AI capabilities through context engineering. Feedback is sought to improve workflows, integration, and management at scale. - **Key Themes**: The document highlights Spec-Driven Development, AI, Context Engineering, Workflow Enhancement, Version Control Integration, Organizational Management, and Creativity in Software Development as central themes. Keywords: AI, Architectural Patterns, Architecture, Claude Code, Codebase, Coding Agents, Compliance Requirements, Constraints, Context Engineering, Design System, Executable Artifacts, Gemini CLI, GitHub Copilot, Implementations, Integration Needs, Legacy Systems, Mission-Critical Applications, Open Source, Pair Programmers, Pattern Recognition, Performance Targets, Plan, Prototypes, Security Policies, Shared Source of Truth, Spec-Driven Development, Specifications, Stack, Standards, Technical Debt, Technologies
github copilot
github.blog 5 days ago
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427. HN Stop chatting Constrained VS Unconstrained LLM use casesRenato Soares' article delves into the debate surrounding the profitability and utility of Large Language Models (LLMs) in organizations. Citing a MIT study where 95% of AI users report no returns, Soares categorizes LLM applications into "Constrained" and "Unconstrained" use cases to clarify this discourse. - **Constrained Use Cases**: These are defined by limited token usage with cost-effective models tailored for specific tasks within controlled environments. They include scenarios such as web scraping, text summarization, autocomplete features, and basic code reviews focused on syntax errors. The predictability and manageability of these use cases result in high utility and minimal financial risk despite occasional inaccuracies. - **Unconstrained Use Cases**: These offer greater flexibility but involve complex problem-solving tasks that demand a broader context window and larger output volumes. Examples include iterative code debugging, developing complete applications, and open-ended problem solving. Their abstract nature makes them difficult to evaluate objectively, leading to high costs and challenging benchmarking. The subjective utility of these tools varies among users, with some preferring manual methods over automated solutions. The article contrasts the concepts of usefulness versus profitability in LLMs, noting that constrained use aligns better with both due to predictable outcomes for clear directives from users like senior developers. Unconstrained uses result in high variability and unpredictability, often leading to financial losses despite initial success stories like OpenAI's market-leading position. The critique extends to the current business model dominated by free or unconstrained chat interfaces popularized by OpenAI’s ChatGPT. This approach has pushed competitors toward developing advanced but costly models rather than cost-effective ones, resulting in a focus on high-performance yet financially unviable applications over constrained, profitable uses. Companies face financial challenges integrating LLMs due to the dichotomy between maintaining expensive unconstrained features and innovating sustainable constrained uses. To improve profitability, companies might need to transition from flat-rate pricing models to pay-per-token systems or reduce model costs through optimization strategies. The article suggests that businesses should rethink their approach by focusing on how LLMs can be integrated economically while questioning the necessity of such technologies for specific needs. Examples like Firecrawl demonstrate successful leveraging of constrained use cases, providing a potential path forward amidst rising costs and operational inefficiencies in unconstrained models, as illustrated by companies like Notion experiencing margin losses due to unconstrained model tools. Key Takeaways: - Distinguish between constrained (manageable, cost-effective) and unconstrained (flexible but costly) LLM uses. - Constrained use cases offer better predictability and profitability. - Current business models prioritize advanced models over cost efficiency. - Companies need innovative integration and pricing strategies to improve sustainability. - Reevaluation of LLM necessity and optimization for economic viability is crucial. Keywords: AI bubble, AI girlfriends, API Pricing, ChatGPT, Constrained LLM, I/O tokens, JSON schema, LLM, MIT study, Notion, Profitability, Unconstrained LLM, Usefulness, autocomplete, believers, benchmark, business model, business strategy, capital, chat-based solutions, cheaper model, code review, competition, complexity, constrained inputs, context window, control, costs, customer support, debate, efficiency, environment, expensive models, funding, innovation, integration, iterative debugging, margins, market creation, models, monetization, monthly fee, no-code tools, optimization, price hikes, pricing, profitable use cases, scraping, skeptics, smart models, spotlight analogy, startups, summarizing, sustainability, sustainable, syntax errors, tasks, tech adaptation, token caps, token usage, tokens, users, variance, viability
llm
medium.com 5 days ago
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428. HN Experimenting with Local LLMs on macOSThe blog post explores the author’s experimentation with running large language models (LLMs) on macOS, expressing both interest in their capabilities and skepticism about their limitations. LLMs are depicted as advanced text prediction tools that lack creativity or sentience, serving practical functions like summarizing information or aiding journaling through simulated conversations. The author advises against anthropomorphizing these tools, emphasizing the importance of using them for personal reflection rather than engagement. The author prefers local deployment over services like ChatGPT due to privacy concerns, ethical considerations regarding AI companies, and environmental impact, advocating for open-source models that provide control over sensitive data without financially supporting questionable practices. For running LLMs on macOS, two options are recommended: an open-source tool by Georgi Gerganov, which supports various configurations and platforms, and a closed-source application with a user-friendly interface and safety features to manage system loads. The blog highlights several functionalities for managing LLMs: model flexibility, extensive configuration options, considerations in choosing models based on size and runtime compatibility (such as GGUF or MLX), and the impact of quantization on performance. It discusses advanced capabilities like vision recognition in models, reasoning processes that trade response time for depth, and secure tool use via LM Studio. The author suggests enhancing small LLMs with up-to-date knowledge retrieval through Model Companion Plugins (MCP) and long-term memory solutions. Notable models mentioned include Gemma 3 12B QAT for visual intelligence and Qwen3 4B 2507 Thinking for speed, while larger models like GPT-OSS 20B offer advanced reasoning at the cost of performance. LM Studio aids users in exploring and managing these models based on various criteria. Finally, despite their limitations, smaller LLMs serve as useful test beds to understand model behavior. The author concludes with an invitation to experiment locally with AI models, acknowledging contributions from Jull for feedback and a hero image provided by the author’s girlfriend. **BULLET POINT SUMMARY:** - Experimentation with local large language models (LLMs) on macOS; emphasis on their practical uses and limitations. - Preference for local deployment over services like ChatGPT due to privacy, ethical, and environmental concerns. - Recommended tools for running LLMs include an open-source option by Georgi Gerganov and a closed-source application with safety features. - Key functionalities: model flexibility, configuration options, choosing models based on size and runtime, and the effects of quantization. - Advanced capabilities discussed include vision recognition, reasoning processes, and secure tool use through LM Studio. - Enhancements for LLMs via Model Companion Plugins (MCP) and long-term memory solutions. - Highlighted models: Gemma 3 12B QAT, Qwen3 4B 2507 Thinking, GPT-OSS 20B, and Phi-4 (14B). - LM Studio helps users explore models based on various metrics; smaller LLMs serve as test beds for understanding behavior. - Conclusion invites experimentation with AI models locally, acknowledging contributions from Jull and the author's girlfriend. Keywords: 16-bit precision, AI, AI psychosis, ChatGPT, GGUF models, GPT-OSS, Local LLMs, OCRs, RAM bottleneck, agents, anthropomorphization, assistant editing, autocomplete, benchmarks, blog post, brain-dumping, cancer diagnosis, capabilities, context overflow, context window, data exfiltration, data privacy, disk space, emergent behavior, enshittification, ethical concerns, experimentation, fact-checking, hallucination, home maintenance advice, image inputs, journaling, kernels, long-term memory, macOS, message regeneration, model switching, non-reasoning, open-weight models, quantization, reasoning, runtime, size, skeptics, summarizing text, summary, system prompt, tech, tool use, vision models, visual intelligence
popular
blog.6nok.org 5 days ago
https://developer.apple.com/documentation/FoundationMod a day ago https://github.com/ggml-org/whisper.cpp/pull/ a day ago https://github.com/eiln/ane/tree/33a61249d773 a day ago https://github.com/tinygrad/tinygrad/tree/mas a day ago https://machinelearning.apple.com/research/core-ml-on-d a day ago https://github.com/Anemll/Anemll a day ago https://apple.github.io/coremltools/docs-guides/in a day ago https://mlx-framework.org/ a day ago https://github.com/ml-explore/mlx/issues/18 a day ago https://opensource.apple.com/projects/mlx/ a day ago https://onnxruntime.ai/docs/execution-providers/Co a day ago https://kiwix.org/en/ a day ago https://github.com/Sumandora/remove-refusals-with-trans a day ago https://deepinfra.com/Qwen/Qwen3-30B-A3B a day ago https://twitter.com/awnihannun/status/194777150205 a day ago https://twitter.com/awnihannun/status/190417708460 a day ago https://twitter.com/alexocheema/status/18997352817 a day ago https://huggingface.co/docs/transformers.js/en a day ago https://huggingface.co/spaces/webml-community/llam a day ago https://huggingface.co/spaces/webml-community/janu a day ago https://huggingface.co/blog/embeddinggemma#transformers a day ago https://simonwillison.net/tags/transformers-js/ a day ago https://github.com/mlc-ai/web-llm a day ago https://simonwillison.net/2024/Nov/29/structu a day ago https://static.simonwillison.net/static/2025/llama a day ago https://github.com/simonw/transformers.js-examples/ a day ago https://gist.github.com/simonw/3c46c9e609f6ee77367a760b a day ago https://github.com/huggingface/transformers.js-examples a day ago http://static.simonwillison.net/static/2025/llama- a day ago http://static.simonwillison.net/static/2025/llama- a day ago https://simonwillison.net/2025/Sep/8/webgpu-l a day ago https://github.com/mlc-ai/web-llm-chat a day ago https://github.com/mlc-ai/mlc-llm a day ago https://github.com/ngxson/wllama a day ago https://huggingface.co/spaces/ngxson/wllama a day ago https://ggml.ai/whisper.cpp/ a day ago https://github.com/Mozilla-Ocho/llamafile a day ago https://webmachinelearning.github.io/webnn-intro/ a day ago https://webmachinelearning.github.io/webnn-samples/ a day ago https://openwebui.com/ a day ago https://huggingface.co/docs/transformers.js/en a day ago https://ondevice-ai.app a day ago https://github.com/dinoki-ai/osaurus a day ago https://www.devontechnologies.com/blog/20250513-local-a a day ago https://simonwillison.net/2025/Aug/19/gpt-oss a day ago https://github.com/ggml-org/llama.cpp/discussions& a day ago https://picogpt.app/ a day ago https://apps.apple.com/us/app/pico-ai-server-llm-v a day ago https://github.com/nbonamy/witsy a day ago |
429. HN 'We can do it for under $100M': Startup joins race to build local ChatGPT**Summary:** Two Australian entrepreneurs, Simon Kriss and Troy Neilson, have initiated Sovereign Australia AI with a $10 million investment aimed at developing an indigenous alternative to global AI models such as those from OpenAI and Meta. Their startup plans to construct this technology within a budget of under $100 million, ensuring that copyright owners are compensated fairly. The founders' motivation stems from concerns about Australia's reliance on U.S. or Chinese decisions concerning pivotal AI technologies, which underscores their commitment to fostering technological sovereignty. **Bullet Point Summary:** - Simon Kriss and Troy Neilson have launched Sovereign Australia AI. - They invested $10 million into developing a local alternative to global AI models like those from OpenAI and Meta. - The startup aims to create this technology for under $100 million, ensuring compensation for copyright owners. - Their motivation is driven by concerns over Australia's dependency on decisions made by the U.S. or China regarding critical AI technologies. - They seek to promote technological sovereignty in response to these dependencies. Keywords: $100M, AI models, Australia, ChatGPT, China, Meta, OpenAI, Simon Kriss, Sovereign Australia AI, Startup, Troy Neilson, United States, artificial intelligence, copyright owners, local alternative, technology
openai
www.afr.com 5 days ago
http://archive.today/ELsbZ 5 days ago https://sovereign-au.ai/preserving-australias-digital-voice- 5 days ago https://www.afr.com/technology/our-ai-future-is-being-b 5 days ago https://www.reuters.com/legal/litigation/anthropic 5 days ago |
430. HN Big Week for Tech IPOs with Klarna, Netskope, Stubhub, Figure and GeminiThe document provides an overview of a significant week in the tech industry marked by notable Initial Public Offerings (IPOs) involving companies such as Klarna, Netskope, Stubhub, Figure, and Gemini. It also addresses privacy and data processing issues pertinent to users from the EU, UK, or Switzerland. Users must consent to their personal data being processed for account management and service provision purposes. Additionally, they have the option to receive marketing communications related to IPO updates and can opt into analytics tracking aimed at improving services. Importantly, users retain the right to withdraw any consent given, which can be done through their account settings. Further details on privacy practices are available in the privacy policy. - **Key Points Covered:** - Highlight of a significant week for tech IPOs involving companies like Klarna, Netskope, Stubhub, Figure, and Gemini. - Addressing privacy and data processing concerns for users from the EU, UK, or Switzerland. - Requirement for user consent regarding personal data processing for account management and service provision. - Option for users to receive marketing communications about IPO updates. - Users can enable analytics tracking to help improve services. - Right for users to withdraw consent at any time through their account settings. - Availability of a detailed privacy policy for further information. Keywords: Account Management, Analytics Tracking, Consent, Data Processing, EU, EU/UK/Switzerland, Figure, Gemini, Klarna, Marketing Communications, Netskope, Netskople, Personal Data, Privacy, Privacy Policy, Service Provision, Stubhub, Switzerland, Tech IPOs, UK, Withdraw ConsentKeywords: Tech IPOs
gemini
ipochatter.com 5 days ago
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431. HN OpenAI comes for Hollywood with Critterz, an AI-powered animated filmOpenAI is embarking on a groundbreaking project in the film industry with "Critterz," an AI-powered animated feature designed to highlight generative AI's potential in filmmaking. Developed collaboratively by OpenAI, Native Foreign, and Vertigo Films, the film aims for a budget under $30 million and a nine-month production timeline—both significantly lower than typical figures in Hollywood. Scheduled for release in 2026 following its premiere at the Cannes Film Festival, "Critterz" will utilize advanced AI tools like GPT-5 to demonstrate cost-effective film production amid industry debates about AI's role in creativity and intellectual property rights. This project marks a significant step by OpenAI to address concerns from Hollywood executives regarding AI's creative capabilities, countering skepticism from both talent and audiences who question AI's impact on traditional filmmaking. **BULLET POINT SUMMARY:** - **Project Overview:** OpenAI is producing "Critterz," an AI-driven animated feature film. - **Collaborators:** Developed with Native Foreign and Vertigo Films. - **Budget & Timeline:** Budgeted at less than $30 million, with a nine-month production timeline. - **Release Plan:** Scheduled for 2026 release after debuting at the Cannes Film Festival. - **AI Tools:** Utilizes AI technologies like GPT-5 to showcase cost-effective filmmaking. - **Industry Context:** Aims to address Hollywood's concerns about AI in creativity and intellectual property. - **Goals:** To demonstrate AI’s viability in film production, overcoming skepticism from talent and audiences. Keywords: AI, Cannes Film Festival, Critterz, GPT-5, Hollywood, Native Foreign, OpenAI, Robert Hart, Vertigo Films, animated film, budget, creative agency, executives, generative artificial intelligence, intellectual property
openai
www.theverge.com 5 days ago
https://www.themoviedb.org/movie/1543975-critterz 4 days ago |
432. HN Show HN: D-Tale, easy to use GUI for Pandas data structures**D-Tale Overview**: D-Tale is a graphical interface tailored for exploring Pandas data structures like DataFrame and Series. It integrates Flask on the backend with React on the frontend, offering seamless integration into IPython notebooks and Python terminals. Originally developed as part of a SAS to Python conversion project, D-Tale facilitates interactive data exploration and visualization. **Access and Installation**: Available on GitHub, users can install D-Tale using binary installers from Python package index or conda-forge through Conda or pip. Windows users need to adjust firewall settings for proper browser access, highlighting the platform-specific considerations necessary for successful installation. **Programmatic Features**: D-Tale provides programmable functions allowing creation, manipulation, and data retrieval in DataFrames with capabilities such as filtering, shutdown commands, and metadata handling. It emphasizes efficient resource management by preventing memory waste through checks on row/column counts and order against previously loaded data to avoid redundancy. **Integration and Use**: - **Local Jupyter Notebooks**: Uses the `dtale.show()` command for launching. - **Kubernetes with JupyterHub**: Employs a Jupyter Server Proxy for routing traffic, requiring manual URL construction due to host/port challenges. **Configuration Across Environments**: - **JupyterHub**: Customizable application roots via `app_root` parameter in `show()`. - **Docker**: Different commands for Mac and Windows to manage access. - **Google Colab**: Requires setting `USE_COLAB=True`, with NGROK imposing connection rate limits. - **Binder**: Installation of `jupyter-server-proxy` is needed. **R Integration via Reticulate**: Facilitates D-Tale usage within R, allowing reading CSV data into R DataFrames and file uploads post-launch, particularly useful in Kubernetes environments. **CLI Options and Data Sources**: Offers numerous CLI options like host, port, instance name, debug mode, subprocess management, browser opening, and process termination. Supports various data sources including ArcticDB, Arctic, CSV, Excel, JSON (local and URL), R datasets, and SQLite with specific command-line parameters. **Custom Data Loaders**: Enables users to define custom loaders using Python modules with `LOADER_KEY` and `LOADER_PROPS`, where `find_loader(kwargs)` returns a loader function or None if unavailable. **Authentication and Front-End Customization**: Adjustable authentication settings via `.ini` file or programmatically, requiring service restarts. Front-end properties customizable through `dtale.show()` or `update_settings`, allowing column locking, numeric formatting, NaN display options, heatmap modes, and sorting functionalities. **DataFrame Visualization**: Demonstrates creating a Pandas DataFrame with features like locked columns, formatted numbers, custom missing value displays, background heatmaps, sorting capabilities, and vertical headers for enhanced interaction. **Chart Features and Performance**: Supports various chart types including line, bar, pie, wordcloud, heatmap, 3D scatter, surface, and map charts. Users can display series in combined or separate charts per group with control over y-axis behaviors. Interactive features include Popup Charts, Copy Link functionality, chart exporting, and offline visibility in Jupyter Notebooks. Noted potential performance issues due to resource-intensive charts are mitigated by kernel restarts or using different session ports. **Network Viewer**: Facilitates directed graph visualizations with shortest path calculations and node zooming capabilities. Future enhancements like linked subplots and statistical aggregations via `networkx` package are considered. **Interactive Features**: Includes Node Exploration, Correlation Matrix filtering options, Timeseries and Scatter Plots displaying correlations and Predictive Power Scores (PPS) using the `ppscore` package. Offers rolling correlation for date-specific datasets and a Heat Map coloring cells based on renormalized float values. **Additional Functionalities**: Enables highlighting of data types, missing values, outliers, customizable range marking of cells; Column Header Annotation with flags for outlier columns; Low Variance Flag for specific criteria columns. Security measures detailed for web uploads post version 3.9.0. **Instance Management and Column Menu Options**: Users can manage multiple D-Tale instances under one Python process, accessing instance details and switching between them through the UI. Offers filtering, moving, hiding, deleting, renaming columns; replacements like Scikit-Learn Imputer for missing data, and lock/unlock functionalities to manage column positions. **Advanced Sorting and Column Analysis Tools**: Features cumulative multiple sorts on selected columns with various numeric and string formats (e.g., thousands separators, URL linking). Provides customizable options in tools such as box plots, histograms, value counts like bin settings. **Data Visualization Techniques**: Offers techniques including "Value Count," "Value Count w/ Ordinal," "Word Value Count," "Category Breakdown," and "Geolocation" for flexible dataset analysis. Enhances user efficiency with keyboard shortcuts for quick access to menus and modals without mouse interaction. **Development Setup and Language Support**: Setup instructions include cloning the project from GitHub, installing dependencies, running a backend server via PyDev, managing JavaScript with npm commands, and conducting testing, linting, and formatting processes for JS and CSS files. Docker development steps detail building environments for Python 2.7 and 3.6 with auto-formatting JS code. Supports English and Chinese, with guidelines for adding new languages through pull requests. **Session Operations and Dependencies**: Illustrates session operations using `dtale.show(df)` on unique ports with maintained states across configurations. In Session 5, custom port ranges (30,000-39,000) are set before execution. System dependencies include Dash, Pandas, Flask, Plotly, scikit-learn, arctic, xarray, React, and Chart.js. **Credits**: Credited to Andrew Schonfeld for D-Tale’s original concept and implementation at Man Numeric since 2019, inviting contributions under the GNU LGPL v2.1 license. Documentation and optional dependencies are available for further exploration. Keywords: Arctic, Arctic Datasets, ArcticDB, ArcticDB URI, CLI, CLI Options, CSV, Charts, Conda-forge, Correlations, Custom Loaders, D-Tale, Dark Mode, DataFrame, Docker, Excel, Filtering, Flask, Flask-ngrok, Geolocation, GitHub, Google Colab, Highlighting, Indexes, JSON, JupyterHub, Kubernetes, Lock/Unlock Columns, NGROK, Network Viewer, PPScore, Pandas, Plotly/Dash, PyCharm, PyPI, Python Modules, React, Reticulate, Series, Sorting, Themes, Web Client, Web Uploads, ipython
github
github.com 5 days ago
|
433. HN Ask HN: Why aren't local LLMs used as widely as we expected?The text discusses why Local Large Language Models (LLMs) are underutilized in privacy-sensitive sectors such as law firms and financial agencies, even though they offer significant advantages in terms of data privacy and security. The key barriers to their widespread adoption include a preference for high-quality output over privacy considerations, which leads users to opt for more established models despite potential risks. Additionally, current hardware infrastructure may not sufficiently support the resource demands required by local LLMs, posing another hurdle. There is also an evident lack of necessary tools such as reliable local-only browser extensions, making it difficult to integrate these models into existing workflows. Furthermore, legal professionals might be inadequately informed about the availability and benefits of local LLM options, contributing to their limited use. Lastly, there may be instances of silent adoption within specific teams that have not been publicly recognized or shared, indicating a potential hidden trend in their usage. **BULLET POINT SUMMARY:** - Local LLMs are underutilized despite their advantages for data privacy in sensitive environments. - Preference for output quality over privacy is a significant barrier to local LLM adoption. - Current hardware limitations impede effective support and deployment of local LLMs. - The absence of specific tools, such as reliable browser extensions, hinders integration into existing systems. - Legal professionals may lack awareness or information about available local LLM options. - Silent adoption within certain teams suggests potential unacknowledged usage. Keywords: Ask HN, IT bans, Ollama, adoption, browser extensions, cloud AI tools, financial agents, hardware, law firms, local LLMs, margin cost, optimization, output quality, privacy-sensitive, visibility
ollama
news.ycombinator.com 5 days ago
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434. HN A Curated List of CLI Commands for Google's Gemini CLI Coding AgentThe document outlines a curated list of CLI commands associated with Google's Gemini CLI Coding Agent, showcasing functionalities developed primarily by Sascha Heyer and Sathwik Ramisetty. These tools are designed to augment developer productivity across various domains including UI implementation, security auditing, codebase preparation, story points estimation, technical content translation, cloud cost analysis, GitHub automation, backend structuring, debugging for Cloud Run services, feature planning, and summarizing updates from Google's GCP product releases. Key functionalities include converting designs into pixel-perfect user interfaces using Tailwind CSS, conducting vulnerability checks in codebases, enhancing context within large codebases, estimating effort required for features based on descriptions or local analysis, translating technical content for business audiences, analyzing potential cloud cost issues with recommendations, automating GitHub staging and commits with structured messages, setting up scalable Node.js backend projects, analyzing logs from Cloud Run services, aiding in planning new features or bug fixes, and summarizing Google Cloud Platform updates. Sascha Heyer has authored several specific tools and scripts related to the Google Cloud Platform and software development. These include a tool for summarizing GCP blog posts or release notes, verifying necessary permissions for GCP users across various services, researching customer issues using official documentation, organizing new git worktrees, generating domain name ideas with availability checks, reviewing GitHub pull requests based on issue numbers, planning features or bug fixes, and conducting comprehensive reviews of recent code changes. Each feature is associated with its author and has received user ratings, offering developers a concise toolkit for enhancing efficiency in design, security, development, and cost analysis tasks. - **Key Features**: - UI Implementation using Tailwind CSS. - Security audits on codebases. - Codebase context enhancement. - Story points estimation based on descriptions or code. - Technical content translation for business use. - Cloud cost issue analysis with recommendations. - GitHub automation for staging and committing changes. - Scalable Node.js backend project setup. - Cloud Run service log analysis. - Assistance in planning new features or bug fixes. - Summarization of Google Cloud Platform updates. - **Sascha Heyer's Contributions**: - GCP product update summarizer. - GCP IAM permission checker. - Research tool for customer issues using GCP documentation. - Git worktree organization tool. - Domain ideation and availability check with 'whois'. - GitHub pull request review based on issue numbers. - New feature or bug fix planning tool. - Comprehensive code change reviewer. - **Purpose**: The document provides developers with an array of tools to improve productivity in design, security auditing, development processes, cost analysis, and more, through CLI commands that facilitate a variety of tasks within the software development lifecycle. Keywords: Business, CLI Commands, Cloud Cost Analyzer, Code Review, Development, Domain Ideation, Feature Plan, Finops, GCP Product Update, Git Worktree, GitHub Commit, Google Gemini, IAM Permissions, Nodejs Backend, Plan, Pull Request, Release Notes, Research, Review, Security, Security Audit, Story Points Estimate, Tailwind CSS, Technical Translation, UI Design
gemini
dotgemini.dev 5 days ago
https://dotgemini.dev 5 days ago https://www.linkedin.com/feed/update/urn:li:activi 5 days ago |
435. HN The LLM Has Left the Chat: Evidence of Bail Preferences in LLMsThe study titled "The LLM Has Left the Chat: Evidence of Bail Preferences in LLMs," conducted by Danielle Ensign, Henry Sleight, and Kyle Fish, examines potential biases in large language models (LLMs) concerning bail decisions. Published as arXiv:2509.04781 on September 5, 2025, it explores how these AI systems may exhibit preferences when determining whether to grant or deny bail. The research is supported by the Simons Foundation and other contributors and emphasizes the implications for fairness in legal applications of AI. The investigation focuses on how LLMs choose to "bail" or leave a conversation through various methods: a bail tool, a bail string, and a bail prompt. By analyzing real-world data from platforms like Wildchat and ShareGPT, it was found that LLMs opted for bail 0.28-32% of the time, varying by model and method, potentially overestimating actual rates up to four times. After accounting for false positives in bail prompts (22%), estimated real-world bail rates were between 0.06-7%. The researchers developed a taxonomy of bail cases and created BailBench, a synthetic dataset to examine model behavior across different scenarios, revealing significant variability in bail rates based on models, methods, and prompt wordings. The study also explored the relationship between refusals and bails, finding that 0-13% of continuations resulted in bail without refusal. Jailbreaks were noted to reduce refusal but increase bail rates, while eliminating refusals affected no-refuse bail rates for some methods only. Refusal rates on BailBench did not predict bail rates consistently. The research falls under the categories of Computers and Society (cs.CY), Artificial Intelligence (cs.AI), and Machine Learning (cs.LG). The document additionally references a preprint titled "The LLM Has Left The Chat: Evidence of Bail Preferences in Large Language Models," authored by Danielle Ensign and two others, submitted to arXiv under cs.CY on September 5, 2025. It provides links for accessing the full text and offers bibliographic tools such as citation formats and research connections via platforms like Semantic Scholar. The study further mentions resources related to code, data, and media associated with the research available on platforms like Hugging Face and Papers with Code. The document outlines features of arXivLabs, which enable community collaboration for developing new features on the arXiv website, emphasizing values such as openness, community, excellence, and user privacy. It also highlights tools like Influence Flower and CORE Recommender, along with functionalities including author endorsements, MathJax disabling options, subscription details, and contact information for arXiv. The text concludes by mentioning services related to web accessibility and operational status notifications. - **Key Points:** - Study investigates potential biases in LLMs concerning bail decisions. - Analyzed how LLMs decide to "bail" from conversations using various methods. - Found significant variability in bail rates among models, methods, and prompts. - Developed BailBench dataset for examining model behavior in different scenarios. - Explored relationship between refusals and bails, noting impacts of jailbreaks and refusal elimination. - Study falls under Computers and Society (cs.CY), Artificial Intelligence (cs.AI), and Machine Learning (cs.LG). - References preprint on arXiv with links for full text access and bibliographic tools. - Mentions resources related to code, data, and media on platforms like Hugging Face. - Outlines features of arXivLabs emphasizing community collaboration, openness, and privacy. Keywords: BailBench, BibTeX, CORE Recommender, DOI, Danielle Ensign, DataCite, Henry Sleight, Kyle Fish, LLM, MathJax, Semantic Scholar, ShareGPT, Simons Foundation, Wildchat, arXiv:250904781, bail preferences, computers and society, jailbreaks
llm
arxiv.org 5 days ago
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436. HN Tesla Wants Out of the Car Business**Summary:** Tesla is redefining its strategic focus under CEO Elon Musk by transitioning away from manufacturing consumer vehicles to prioritize autonomous technology and robotics. The company's latest "Master Plan IV," announced on Elon Musk’s platform X, shifts the emphasis towards artificial intelligence (AI) over traditional automotive production. This plan highlights Tesla’s vision of building robotaxis and humanoid robots like "Optimus," aiming for a future characterized by automation and sustainable abundance. Notably, this master plan is more abstract than its predecessors but strongly underlines AI's pivotal role in shaping Tesla's goals. Historically, Tesla has released three major plans since 2003 that have progressively expanded from developing high-end electric vehicles (EVs) to exploring autonomous technologies. The current strategy marks a significant pivot by integrating sustainable energy solutions for autonomous fleets and focusing on robotics integration into daily life, as illustrated through visual representations in the plan. This shift is aligned with a potential $1 trillion compensation package for Musk, contingent upon the deployment of millions of robotaxis and robots within ten years, showcasing Tesla’s commitment to these technologies. Elon Musk's ambitious vision includes merging electric power with autonomous driving to develop self-driving Teslas that generate income via a robotaxi service. However, experts view his timeline as overly optimistic given Tesla's current lag behind competitors like Waymo in autonomous vehicle deployment. This strategic focus on AI-driven autonomy risks undermining Tesla’s core competence—car manufacturing—and could hinder the production of affordable EVs. Financial challenges are exacerbated by declining global sales and increased competition from Chinese manufacturers, coupled with Musk’s controversial political stance. Tesla's move away from its foundational electric-car business represents a significant setback for both the industry and environmental initiatives. The company has historically driven automakers towards higher-quality electric vehicles (EVs), but as incentives reduce, other American companies are stepping back from EV development. Should Tesla continue to distance itself from the EV market it pioneered, consumers may face fewer options for cleaner vehicles, impeding progress toward sustainable transportation. **Bullet Point Summary:** - Tesla shifts focus from manufacturing consumer vehicles to developing autonomous technology and robotics. - "Master Plan IV" emphasizes AI and automation, highlighting a vision of robotaxis and humanoid robots. - The plan marks a departure from previous strategies focused on expanding EVs, prioritizing AI over traditional automotive production. - A potential $1 trillion compensation package for Musk is linked to deploying millions of robotaxes and robots within ten years. - Elon Musk’s strategy includes merging electric power with autonomous driving for self-driving Teslas and income generation via a robotaxi service. - Experts consider Musk's timeline overly optimistic, as Tesla lags behind competitors like Waymo in autonomous technology deployment. - The shift risks neglecting Tesla’s core strength—car manufacturing—and could impede affordable EV production. - Financial challenges are compounded by declining sales, increased competition from Chinese manufacturers, and Musk’s controversial politics. - Tesla's retreat from the electric-car business poses a setback for industry innovation and environmental progress. - With reduced incentives, other American companies scaling back on EVs may lead to fewer options for consumers seeking cleaner vehicles. Keywords: AI, EVs, Elon Musk, Master Plan IV, Tesla, autonomous driving, autonomous vehicles, batteries, electric cars, innovation, robotaxis, sustainable energy
tesla
www.theatlantic.com 5 days ago
http://archive.today/G0ESp 5 days ago https://electrek.co/2025/09/05/tesla-changes- 5 days ago https://www.tesla.com/sites/default/files/dow 5 days ago https://www.theguardian.com/business/live/2025 5 days ago https://en.wikipedia.org/wiki/Nothing_(company) 5 days ago https://www.tesla.com/master-plan-part-4 5 days ago |
437. HN GPT-5 Prompting Guide- **Overview of "GPT-5 Prompting Guide":** The guide provides criteria for a coding agent tasked with modifying and testing code files within its environment. It emphasizes minimal changes, efficient search tools, root cause analysis, documentation updates, pre-commit checks, and careful handling of file edits. - **Code Modification and Tools:** - Use `apply_patch` to edit files while maintaining style consistency. - Prefer the search tool `rg` over traditional methods like `ls`, `find`, or `grep`. - Address root causes rather than superficial fixes. - Update documentation as needed, avoiding unrelated bug fixes. - **Code History and Pre-commit Checks:** - Utilize `git log` and `git blame` for code context since internet access is disabled. - If a `.pre-commit-config.yaml` exists, run pre-commit checks using the specified command to ensure changes pass without altering existing errors. - **Finalizing Code Process:** - Run `git status` to review unintended changes or scratch files and revert them if necessary. - Minimize inline comments unless essential for understanding complex code sections. - Remove accidental copyright or license headers before finalizing the code. - Re-attempt pre-commit checks after adjustments. - **Task Documentation:** - For minor tasks, use brief bullet points; for more involved tasks, provide a high-level overview with detailed bullet points to aid in code review. - **User Interaction Guidelines:** - Respond helpfully if no file changes are needed. - Reference saved changes without instructing users to save manually. - Avoid displaying large files' full contents unless requested. - **Handling File Edits Using `apply_patch`:** - Use the `shell` tool with `apply_patch` for editing, specifying patch details in a format marked by "Begin Patch" and "End Patch." - Specify actions (Add, Update, Delete) for file modifications. - Show context lines around changes, using `@@` annotations to identify specific code sections when needed. - **Patch Application Process:** - Use context-based diffs without line numbers; denote old code with `-` and new code with `+`. - Execute the patch command using a structured format with triple quotes and an "EOF" marker. - Only use relative file paths in references, ensuring clarity and success of patch applications. - **Agent's Role and Exploration Process:** - Persist through uncertainties without human confirmation until tasks are resolved. - Decompose requests into clear requirements, map codebase areas, and verify unknowns using tools rather than guessing. - **Task Execution Strategy:** - Identify scope by mapping relevant sections of the codebase. - Check dependencies including frameworks, APIs, and configuration files. - Resolve ambiguities proactively using repository context and documentation. - **Output Contract Definition:** - Define deliverables such as file changes, expected outputs, API responses, CLI behavior, and test outcomes clearly. - **Verification and Efficiency Considerations:** - Verify that code meets defined deliverables throughout the task. - Ensure tasks are completed before returning solutions to users. - Optimize for efficiency by managing time effectively and using tools efficiently. - **Final Instructions:** - Use `apply_patch` for all file modifications, avoiding direct edits with editor tools. Keywords: APIs, GPT-5, Prompting Guide, analyze vulnerabilities, apply_patch command, coding agent, config files, container, copyright headers, dependencies, documentation update, execute task, frameworks, git blame, git diff, git log, inline comments, license headers, logging lines, modify code, patch message, pre-commit checks, pre-commit run, pre-commit-configyaml, proprietary repos, retries, rg tool, root cause, sanity check, setup, style consistency, user instructions, versioning, warnings
gpt-5
cookbook.openai.com 5 days ago
|
438. HN The Underground Trade of 'Flipper Zero' Tech to Break into CarsThe article examines the unauthorized use of Flipper Zero devices in car thefts. Initially created as an ethical hacker's tool for a range of electronic applications, these devices have been repurposed by criminals to gain access and unlock vehicles. The investigative journalism outlet 404 Media has revealed a black market where hackers distribute software patches specifically designed for the Flipper Zero to target certain car brands, including Ford, Audi, Volkswagen, Subaru, Hyundai, Kia, among others. These hacks compromise numerous vehicle models with minimal resistance from manufacturers. The article concludes by calling for additional reports on instances of car break-ins involving the use of Flipper Zero devices. - **Flipper Zero Misuse**: Originally designed as an ethical hacking tool, now used in car thefts. - **Black Market Exposure**: 404 Media reveals a market for software patches that target specific car brands via Flipper Zero. - **Vulnerable Car Brands**: Includes Ford, Audi, Volkswagen, Subaru, Hyundai, Kia, and others. - **Manufacturers' Response**: Minimal resistance to these hacking methods from car manufacturers. - **Call to Action**: Encourages sharing more instances of Flipper Zero-related vehicle break-ins. Keywords: 404 Media, 404 Media Keywords: Flipper Zero, Audi, Flipper Zero, Ford, Hyundai, Kia, NFC, NFC tags, Signal, Subaru, Volkswagen, WiFi, WiFi attacks, cars, ethical hacker, hacker, hackers, patches, software patches, trade, underground, underground trade
flipper zero
www.404media.co 5 days ago
|
439. HN Spec-kit, game-changing tool by GitHub for AI coding- **Introduction to Spec-Driven Development**: GitHub's Spec-kit introduces a tool called Spec-Driven Development, which focuses on transforming coding approaches by emphasizing product scenarios over undifferentiated code. This method involves creating executable specifications that directly generate working implementations. - **Initialization Process**: To use the tool, initialize your project with commands like `specify init - **Development Workflow**: The development process involves: - Describing build goals using `/specify`. - Defining tech stack and architecture choices with `/plan`. - Creating actionable tasks via `/tasks` to guide AI agent implementation. - **Example Project Initialization**: To start a new project with Claude as the AI agent, use `init - **Project Structure Creation**: Use the `/specify` command to outline project goals without focusing on technology choices. This step involves detailing what will be built and its purpose. - **Taskify Project Development**: The initial phase of Taskify, a team productivity platform, focuses on basic features like project creation, task assignment, and Kanban-style workflow management for five predefined users. Features include sample projects with Kanban boards, task assignments, comments, and status changes via drag-and-drop. - **Claude Code Integration**: Claude Code is used to draft specifications by setting up repositories with scripts, creating branches, and specification files (e.g., `001-create-taskify/spec.md`). The project folder includes directories like `memory`, `scripts`, `specs`, and `templates`. - **Iterative Refinement**: The document emphasizes treating Claude Code as an iterative tool for refining specifications. It involves reviewing requirements and using a Review & Acceptance Checklist to validate criteria. - **Tech Stack Specification**: The plan specifies using .NET Aspire, Postgres, Blazor server frontend, drag-and-drop task boards, real-time updates, and a REST API. Implementation details are organized into a directory structure. - **Research and Validation**: Review the `research.md` document to confirm tech stack suitability, refine it with Claude Code, and conduct targeted research on rapidly evolving components like .NET Aspire or JS frameworks. - **Implementation Plan Review**: Use Claude Code to review the implementation plan for completeness, identify missing elements, and validate task sequences. Optionally, create a pull request via GitHub CLI for tracking progress. - **Execution and Error Handling**: Instruct Claude Code to implement solutions using specified paths (e.g., `specs/002-create-taskify/plan.md`). Ensure necessary local CLI tools are installed. Address build errors by running the application and fixing issues, including runtime errors captured outside CLI logs. Keywords: AI Coding, Architecture Choices, Blazor Server, Bootstrap, CLI, Execution Errors, GitHub, Implementation Plan, Kanban, NET Aspire, Postgres, Product Scenarios, Pull Request, REST API, Spec-Driven Development, Specifications, Tasks, Tech Stack, Validation
postgres
github.com 5 days ago
|
440. HN Gemini Apps limits and upgrades for Google AI subscribers**Summary:** Gemini Apps provides enhanced features through Google AI plans available to personal Google Accounts in over 150 countries, requiring users to be at least 18 years old. The service offers three tiers: basic access without a plan, Gemini app in Google AI Pro with up to 100 prompts per day and additional features like audio overviews and deep research reports, and the most advanced tier, Gemini app in Google AI Ultra, offering up to 500 prompts daily along with extensive deep research capabilities. Additional features such as Canvas and Gems are broadly accessible, though some functionalities may be restricted outside supported countries for Google AI Ultra. The platform imposes usage limits based on factors like prompt complexity and file size, which can vary between users depending on whether they have Pro or Ultra plans. These limits reset periodically to ensure a quality experience, with notifications alerting users as they near their limits. Advanced models allow higher limits but require switching if these are reached until capacity replenishes. The app analyzes uploaded content based on its context window. **Bullet Point Summary:** - Gemini Apps is part of Google AI plans for personal accounts in over 150 countries. - Users must be at least 18 years old to access Gemini Apps with a Google AI plan. - Three tiers are available: - Basic access without a Google AI plan. - Google AI Pro tier offers up to 100 prompts/day, audio overviews, deep research reports, and image/video generation. - Google AI Ultra provides the highest level of access with up to 500 prompts/day and more extensive features. - Additional features like Canvas and Gems are generally available, but some may be restricted outside certain countries. - Usage limits depend on factors such as prompt complexity and file size. - Users have varying limits based on their plan (Pro or Ultra) compared to non-plan users. - Limits reset periodically, with notifications for approaching usage caps. - Advanced models permit higher limits but require switching when limits are reached until capacity refreshes. - The app evaluates uploaded files based on its context window for processing. Keywords: Gemini Apps, Google AI, Google One, accounts, age requirement, context window, conversations, countries, features, files, models, notifications, technical keywords, upgrade plans, upgrades, usage limits
gemini
support.google.com 5 days ago
|
441. HN An open-source multi-provider (including local) fork of Gemini-CLI### Summary: LLxprt Code is an open-source fork of the Gemini CLI by Google, designed with enhanced features such as improved multi-provider support and theming capabilities. It seamlessly integrates with several AI service providers including OpenAI, Anthropic's Claude, Google's own Gemini, alongside OpenRouter, Fireworks, and local models. The tool stands out for its ability to offer consistent themes across various platforms, ensure compatibility with the original Gemini CLI (including authenticating via Google), and support running local models through LM Studio or llama.cpp. Users benefit from LLxprt Code’s flexibility in easily switching between different providers, models, and API keys, fine-tuning settings, and saving configurations. Additionally, it enables querying and editing of extensive codebases, generating applications from multimodal inputs like PDFs or sketches, and ensures privacy through its support for local model operations. Advanced functionality is extended via MCP servers, facilitating media generation and the integration of Google Search for grounded queries. For installation, LLxprt Code requires Node.js (version 20 or higher) and can be set up using npm or npx commands. It features a user-friendly interface that supports Google Search through Gemini. Configuration options allow users to select themes and providers, with Gemini as the default setting. Provider-specific authentication varies: OpenAI and Anthropic both offer OAuth and API key setups; Qwen offers free access via OAuth for its `qwen3-coder-pro` model or an API key for advanced use. LLxprt Code is a comprehensive tool designed to facilitate easy management of AI models from various providers, offering features such as the `/model` command to list available models. It supports using OpenRouter to access over 100 models and Fireworks for fast inference with open models like `llama-v3p3-70b-instruct`. Additionally, it allows access to xAI's Grok via API keys. The platform’s advanced features include code understanding and generation, automation of tasks, real-time search capabilities, conversation checkpointing, and custom context files (LLXPRT.md) for personalized behavior. Its GitHub integration, although still experimental, aims at automating pull request feedback and issue triage based on content analysis. The tool supports managing API keys and endpoints through commands, enhancing project management and workflow automation. The document provides guidance on authentication with major AI services using an API key stored in the `/auth` file, outlines a codebase exploration tool for developers (`llxprt`), and addresses security mechanisms for secure API key storage. It also details developer onboarding, error handling strategies, tools, dependencies, additional tasks like automation of slide decks from git history, and privacy considerations emphasizing no default telemetry collection. ### Bullet Point Summary: - **LLxprt Code Overview**: An open-source fork enhancing Gemini CLI with multi-provider support and theming. - **Features**: - Integration with multiple AI providers (OpenAI, Claude, Google Gemini). - Consistent themes, original Gemini CLI compatibility, local model support. - Easy provider/model/API key switching, configuration saving. - **Privacy and Advanced Capabilities**: - Ensures privacy through local models. - Extends functionality via MCP servers for media generation and Google Search integration. - **Installation & Configuration**: - Requires Node.js (version 20+), installed via npm or npx. - User-friendly interface with theme and provider selection. - Authentication setup varies by provider, including OpenAI, Anthropic, Qwen. - **Model Management**: - Supports listing models (`/model` command). - Integration with OpenRouter for access to over 100 models. - Use of Fireworks for fast inference and xAI's Grok via API keys. - **Advanced Features**: - Code understanding/generation, task automation, real-time search capabilities. - Supports conversation checkpointing, customization through LLXPRT.md, and experimental GitHub integration. - **Documentation & Support**: - Includes authentication guidance, codebase exploration tools, security for API key storage. - Developer onboarding information, error handling strategies, list of tools/dependencies. - Mentions additional tasks like generating slide decks from git history; emphasizes privacy through no default telemetry collection. - **Uninstallation Guidance**: - Points to an Uninstall guide and underscores the tool's commitment to user privacy by not collecting telemetry by default. Keywords: API key, Anthropic, Gemini-CLI, GitHub integration, Google Gemini, LLxprt Code, OAuth, OpenAI, configuration, multimodal, open-source, privacy-sensitive, themes
openai
github.com 5 days ago
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442. HN Writing Code Is Easy. Reading It Isn'tThe article delves into the complexities of reading existing code as opposed to writing new code in software development. It highlights that while writing code might be straightforward once one grasps the solution and language syntax, comprehending pre-existing code demands constructing a mental model. This involves understanding dependencies, intricacies, and interrelated components, akin to familiarizing oneself with an unfamiliar city by exploring key landmarks. In reading code, developers need to examine function definitions, database interactions, error handling, and more to fully grasp the system's operation. The text underscores how navigating existing codebases can be challenging due to the necessity of piecing together information across multiple files, database schemas, API definitions, and other elements to understand a single function's context. This complexity is contrasted with the relative ease of writing new code, drawing parallels with problem-solving on platforms like Stack Overflow, where understanding often hinges on specific contextual steps that users must provide. An anecdote featuring a lawyer misusing ChatGPT by citing fictitious cases illustrates how generating information can be simpler than comprehending existing materials. This reflects broader issues in programming practices, such as inadequate documentation and insufficient emphasis on reading comprehension—skills that are crucial for understanding code but often overlooked. The article points out that while Language Learning Models (LLMs) facilitate rapid code generation, they also pose challenges due to the substantial effort required to comprehend their outputs fully. The temptation to rely on LLMs without a deep understanding can lead to errors and inefficiencies, akin to skipping essential parts in a game for immediate rewards but risking long-term setbacks. Ultimately, the article argues that the real bottleneck in software development is not code generation but comprehension. Current tools are limited by human cognitive capacities when it comes to instantly grasping complex systems. Therefore, productivity should be measured by how quickly and accurately teams can develop mental models of their systems rather than just the volume of code produced. Looking forward, the article suggests a shift in focus towards enhancing comprehension and problem-solving skills over mere coding speed. This emphasis on understanding existing code is crucial for effective software development and may define future programming practices more than the ability to generate vast quantities of code rapidly. ### Bullet Points: - **Complexity of Reading vs. Writing Code**: Emphasizes that reading existing code requires constructing a mental model, making it more complex than writing new code. - **Analogies for Understanding Code**: Compares understanding codebases to navigating an unfamiliar city, requiring exploration and familiarity with key components. - **Challenges in Comprehension**: Highlights the difficulty of comprehending single functions across multiple files and elements, akin to problem-solving on platforms like Stack Overflow. - **Generative Tools vs. Comprehension**: Illustrates how tools like ChatGPT can generate information easily but underscores the importance of deep comprehension, which is often neglected. - **Role of LLMs in Programming**: Discusses how LLMs aid rapid code generation but emphasize that understanding their outputs requires significant effort and attention to detail. - **Bottleneck in Development**: Argues that the primary bottleneck in software development lies in comprehending existing code rather than generating new code, constrained by human cognitive limits. - **Future Focus on Comprehension**: Suggests a shift towards enhancing comprehension and problem-solving skills as crucial for future programming practices over mere coding speed. Keywords: AI tools, API, LLM, Mental model, Promise, React, bugs, build process, caching layers, code reading, database, debugging, documentation, features, functions, jQuery, middleware, plugins, problem solving, syntax, testing setup, writing code
llm
idiallo.com 5 days ago
https://xkcd.com/1172/ 5 days ago https://www.ioccc.org/ 5 days ago |
443. HN 10xDevAiThe 10xDevAi plugin is designed to significantly enhance the functionalities of Large Language Models (LLMs) across various fields. The primary feature of this plugin is its ability to extend and improve LLM capabilities, making it a valuable tool for multiple applications in different domains. To ensure optimal performance, users must grant site access permissions to activate the plugin. Once activated, 10xDevAi operates efficiently across all chat platforms linked with the site, providing seamless integration and functionality without additional setup requirements. **BULLET POINT SUMMARY:** - The 10xDevAi plugin augments Large Language Model (LLM) capabilities in multiple domains. - Activation requires granting site access permissions for full functionality. - Once activated, it functions seamlessly across all associated chat platforms. Keywords: 10xDevAi, Access, Chat Sites, Domains, Enable, Grant, LLM, Plugin, Site Access, Technical Keyword, Works
llm
10xdevai.com 5 days ago
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444. HN Lolgato: Advanced controls for Elgato lights on macOS**Summary:** Lolgato is a macOS application designed to enhance control over Elgato lights, offering features beyond the standard Elgato Control Center. It provides automatic light activation and deactivation based on camera usage and Mac locking status, along with global keyboard shortcuts for adjusting brightness and temperature settings across all connected lights. The app requires macOS 14 or later for installation, which involves downloading Lolgato.dmg from the latest release and launching it via the menu bar post-installation. Lolgato automatically discovers Elgato lights on the network but allows manual device addition through IP addresses if necessary. It is specifically compatible with Elgato lights such as the Elgato Key Light and complements, rather than replaces, the existing Elgato Control Center. The application uses macOS system APIs for detecting camera activity. For support and feedback, users can report issues or suggest features via GitHub. Lolgato operates independently from Elgato and Corsair Gaming, Inc., with no official affiliations, and is licensed under the MIT License. **BULLET POINT SUMMARY:** - **Lolgato Overview:** Enhances control over Elgato lights with advanced automation and shortcuts. - **Key Features:** - Automatic light activation/deactivation based on camera use and Mac lock status. - Global keyboard shortcuts for brightness/temperature adjustments. - **Installation Requirements:** - Requires macOS 14 or later. - Download via Lolgato.dmg; launch from the menu bar after installation. - **Device Management:** - Automatic discovery of Elgato lights on the network. - Manual addition of devices through IP address if needed. - **Compatibility and Use:** - Designed for specific compatibility with Elgato lights like the Key Light. - Complements, but does not replace, the Elgato Control Center. - **Camera Activity Detection:** Utilizes macOS system APIs for detecting camera usage. - **Support and License:** - Feedback via GitHub issues; users can report bugs or request features. - Licensed under MIT, operates independently from Elgato/Corsair Gaming, Inc., with no official connections. Keywords: APIs, Elgato, GitHub, License, Lolgato, app, automation, brightness, camera, controls, macOS, shortcuts, temperature
github
github.com 5 days ago
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445. HN Don't Build an RL Environment Startup### Summary: The article provides cautionary advice regarding the pursuit of startups focused on creating reinforcement learning (RL) environments, which are virtual settings where AI models learn through interaction and feedback. Despite the profitability of these tools due to interest from leading AI labs like OpenAI, Anthropic, Amazon, and Meta, the piece warns against heavy investment in this area because of market volatility and the transient nature of their value. Initially, reinforcement learning environments gained traction following OpenAI's 2023 breakthrough with verifiable rewards for post-training large language models (LLMs). The article highlights a shift from supervised fine-tuning—once reliant on low-wage crowdworkers—to more complex simulations that demand skilled labor. This evolution reflects broader industry changes where LLMs have surpassed humans in tasks such as text-annotation, prompting AI developers to adopt advanced methods like Anthropic's "Constitutional AI." As the quality bar for annotations rises, so too does the reliance on well-educated personnel. However, even these roles are seen as temporary until potentially surpassed by further technological advancements. The saturation of AI development tools and the proliferation of computer science graduates contribute to a crowded market, with open-source initiatives like Prime Intellect (PI) and TextArena providing free alternatives that challenge proprietary solutions. The article suggests focusing on operational business models connecting AI labs with talented individuals as more sustainable. Ultimately, it encourages aspiring entrepreneurs to critically assess their ambitions in the AI field, emphasizing that merely creating niche products is unlikely to lead to significant success. Instead, advancing AI technology itself, such as developing AGI capabilities, may be a more impactful pursuit. ### Bullet Point Summary: - **Reinforcement Learning Environments**: - Defined as virtual spaces where AI models learn through interaction and feedback. - Initially profitable due to demand from leading AI labs like OpenAI, Anthropic, Amazon, and Meta. - **Market Volatility**: - Caution against heavy investment in RL environments due to their transient value and volatile nature. - Shift from supervised fine-tuning to complex simulations requiring skilled labor. - **Industry Evolution**: - Large Language Models (LLMs) now surpass humans in text annotation tasks, raising the quality bar for AI training data. - Reliance on highly educated personnel is temporary until potentially overtaken by new technologies. - **Market Saturation**: - An influx of computer science graduates and advanced tools contributes to a crowded market. - Open-source projects like Prime Intellect (PI) and TextArena challenge proprietary RL solutions with free alternatives. - **Sustainable Business Models**: - Operational roles connecting AI labs with skilled talent are suggested as more sustainable business ventures. - **Entrepreneurial Caution**: - Encourages a critical evaluation of entrepreneurial ambitions in the AI sector. - Suggests that developing advanced technologies, such as AGI capabilities, may be more impactful than creating niche products. Keywords: AGI, AI lab, Amazon clone, Anthropic, Bass Pro Shops clone, ChatGPT, Constitutional AI, Doordash clone, LLM, Linear, Nextjs, OpenAI, RL environment, RL generalize, Reinforcement learning, Salesforce, TextArena, crowdworkers, frontier labs, generational business, heart surgery simulator, sandbox, simulations, startups, supervised fine-tuning, unicorn
llm
benanderson.work 5 days ago
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446. HN 14 Killed in anti-government protests in NepalIn Nepal, a significant escalation in anti-government protests occurred following the imposition of a social media ban by the government, which resulted in widespread violence and unrest across major cities like Kathmandu, Pokhara, and Butwal. This decision led to at least 19 fatalities and over 300 injuries as law enforcement resorted to severe measures including water cannons, tear gas, and live ammunition against demonstrators demanding the ban's revocation. The most intense clashes were reported in Kathmandu with the police reporting 17 deaths there, and two additional deaths in Sunsari district due to police firing. The situation further intensified when youths stormed into Parliament premises, necessitating the deployment of the Nepali Army around the area for control purposes. Amidst the escalating violence, Home Minister Ramesh Lekhak resigned citing moral reasons. Hospitals across Nepal are overwhelmed with treating approximately 347 injured protesters, leading to multiple patients being referred elsewhere due to capacity issues. The government's decision to ban 26 social media platforms was justified by Prime Minister Oli as a regulatory measure for non-compliance with local registration requirements. However, this move has sparked concerns over free speech and censorship among the public. In response to the unrest, curfews have been imposed across several regions including Kathmandu and Pokhara. The government maintains its stance that while social media is supported, businesses operating in Nepal must adhere to local laws. Prime Minister Oli dismissed critics of this policy as "puppets" opposing for opposition's sake. Concurrently, numerous journalists protested against the ban, highlighting potential adverse effects on education, business, and daily communication. The Computer Association of Nepal warned that such measures could impede the nation’s digital progress and advocated for a dialogue with stakeholders to explore alternative solutions. - **Key Points:** - Anti-government protests erupted in Nepal due to a social media ban, leading to violence and casualties. - Law enforcement used force against demonstrators, resulting in over 300 injuries and at least 19 deaths. - The situation escalated when youths entered Parliament premises; the Nepali Army was deployed for control. - Home Minister Ramesh Lekhak resigned on moral grounds amidst the unrest. - Hospitals are overwhelmed with injured protesters, leading to patient referrals due to capacity issues. - Curfews have been imposed in multiple regions including Kathmandu and Pokhara. - The government banned 26 social media platforms citing non-compliance with local laws; this has raised concerns over censorship. - Prime Minister Oli defended the ban, emphasizing compliance with Nepali laws by businesses. - Journalists protested against the ban, warning of its negative impact on various sectors and advocating for stakeholder discussions. Keywords: Computer Association of Nepal, Facebook, Gen Z, Home Minister, Instagram, Kathmandu, Maitighar Mandala, Ministry of Health, Nepal, Prime Minister Oli, WhatsApp, X, YouTube, army deployment, casualties, censorship, clashes, curfew, digital, hospitals, police force, protests, regulation, resignations, social media ban, students
popular
www.tribuneindia.com 5 days ago
https://archive.is/zv17z 5 days ago https://english.khabarhub.com/2022/16/232667/ 5 days ago https://youtu.be/ZN8a-pP60wk?feature=shared 5 days ago http://codingforafrica.at/about.html 5 days ago https://apnews.com/article/nonprofits-lobbying-less-sur 5 days ago https://www.adamsmith.org/blog/is-capitalism-to-blame-f 5 days ago https://www.wsj.com/finance/currencies/trump-famil 5 days ago https://www.newyorker.com/magazine/2025/08/18 5 days ago https://finance.yahoo.com/quote/DJT/ 5 days ago https://mytravel.state.gov/s/step 5 days ago https://en.wikipedia.org/wiki/United_States_involvement 5 days ago https://en.m.wikipedia.org/wiki/1953_Iranian_coup_d 5 days ago https://en.m.wikipedia.org/wiki/Bay_of_Pigs_Invasion 5 days ago https://en.m.wikipedia.org/wiki/1973_Chilean_coup_d%27% 5 days ago https://en.m.wikipedia.org/wiki/Brigade_2506 5 days ago https://kathmandupost.com/columns/2025/09/07& 5 days ago https://www.hoover.org/events/laying-foundations-develo 5 days ago https://www.youtube.com/watch?v=GJa1sbAqylE 5 days ago https://www.doughtystreet.co.uk/news/bangladesh-ban-awa 5 days ago https://thediplomat.com/2025/08/resurgence-of-jama 5 days ago https://eastasiaforum.org/2025/04/07/politica 5 days ago https://x.com/shellenberger/status/196395150992807 5 days ago https://thehill.com/homenews/media/4536394-twitter 5 days ago https://photos.app.goo.gl/45L8FE6bVPDLVmdFA 5 days ago https://www.hks.harvard.edu/centers/carr/publicati 5 days ago https://en.m.wikipedia.org/wiki/July_Revolution_(Bangla 5 days ago https://apnews.com/article/indonesia-protests-subianto- 5 days ago six%20dead. 5 days ago https://en.wikipedia.org/wiki/Canada_convoy_protest#cit 5 days ago https://www.cbc.ca/news/health/measles-vaccination 5 days ago https://edition.cnn.com/2022/02/15/politics&# 5 days ago https://nationalpost.com/news/politics/two-thirds- 5 days ago https://www.nytimes.com/2022/02/22/world/ 5 days ago https://en.wikipedia.org/wiki/Occupy_Wall_Street 5 days ago https://nypost.com/2024/08/07/us-news/gw 5 days ago https://cup.columbia.edu/book/why-civil-resistance-work 5 days ago https://www.hks.harvard.edu/centers/carr/publicati 5 days ago https://archive.is/DL9dV 5 days ago https://www.thefire.org/research-learn/chilling-effect- 5 days ago https://en.wikipedia.org/wiki/Chilling_effect 5 days ago https://www.cnn.com/2023/10/12/business/ 5 days ago https://www.youtube.com/watch?v=P2C7gw02WoY&t=142 5 days ago https://www.oregonlive.com/portland/2025/09/w 5 days ago https://acoup.blog/2020/03/20/collections-why 5 days ago https://www.brookings.edu/articles/the-power-of-protest 5 days ago https://www.independent.co.uk/news/uk/politics 5 days ago https://apnews.com/article/trump-ap-white-house-press-p 5 days ago https://www.un.org/en/about-us/universal-declarati 5 days ago https://ekantipur.com/business/2025/01/28 5 days ago https://www.reuters.com/world/europe/telegram-foun 5 days ago https://cdn.aucey.com/sweeps-survey/1034/es.html 5 days ago https://old.reddit.com/r/NepalSocial/comments/ 5 days ago https://www.amazon.com/Careless-People-Cautionary-Power-Idea 5 days ago https://drive.google.com/drive/mobile/folders/ 5 days ago https://en.wikipedia.org/wiki/Global_North_and_Global_S 5 days ago https://www.pbs.org/newshour/world/amnesty-report- 5 days ago https://www.amnesty.org/en/documents/asa16/59 5 days ago https://www.aljazeera.com/news/2025/9/8/ |
447. HN Distributing your own scripts via HomebrewThe article offers a comprehensive guide on using Homebrew to publish custom Command Line Interface (CLI) tools, aimed at those familiar with the platform but new to creating their own packages. It discusses the author's preference for Homebrew over other package managers like npm or uv due to its advantages, despite challenges in understanding its ecosystem, particularly regarding root directories. The article introduces key Homebrew terminology such as formula, tap, cask, bottle, cellar, and keg, and underscores the recommendation from the Homebrew team to host CLI packages in a personal GitHub tap rather than the core repository. To distribute a CLI tool with Homebrew, three main steps are outlined: creating a tap using `brew tap-new`, pushing this structure to a GitHub repository, and enabling users to install the CLI via specific Homebrew commands. The guide suggests maintaining simplicity by using a single tap for all tools and emphasizes version management through GitHub's tarball archives feature. It explains how to create a formula with tagging and pushing in GitHub, utilizing `brew create` to generate it within a custom tap. The article acknowledges potential challenges when testing formulas with verbose installation commands and recommends iterative refinement with AI assistance like ChatGPT. The discussion extends into the creation and management of Homebrew formulae using Ruby, highlighting its use for all formula definitions regardless of language specificity. It suggests utilizing tools such as `brew style` to verify correctness and maintain updated dependencies. The author expresses frustration over manual updates required for URLs and SHA256 hashes in formulas and shares an innovative approach they adopted: automating these updates via a GitHub workflow facilitated by AI, which streamlines the publishing process significantly. This method allows users to easily update and upgrade their formulae with minimal effort following new tag pushes, simplifying future publishing endeavors. ### Bullet Point Summary: - The article guides users on publishing custom CLI tools using Homebrew, emphasizing personal tap creation over using the core repository. - It introduces key terminology: formula, tap, cask, bottle, cellar, and keg, essential for understanding the Homebrew ecosystem. - Steps to publish a CLI include creating a tap, pushing it to GitHub, and installing via specific Homebrew commands. - The guide recommends version management with GitHub's tarball archives feature for reliable distribution. - It covers formula creation using `brew create` after tagging in GitHub, highlighting troubleshooting tools like ChatGPT. - All Homebrew formulas are written in Ruby; the author discusses maintaining correctness through updated dependencies and testing. - The author details an automated update process via a GitHub workflow with AI assistance to simplify version consistency tasks. - Despite some frustrations with manual updates, the streamlined method significantly eases future publishing efforts. Keywords: CLI, GitHub, Homebrew, Ruby, action, binary packages, formula, installation, npm, package manager, repository, tap, tool, upgrade, workflow
github
justin.searls.co 5 days ago
https://docs.brew.sh/Python-for-Formula-Authors a day ago https://github.com/hynek/homebrew-tap/blob/ma a day ago https://github.com/thewisenerd/uvbrew a day ago https://github.com/wyattjoh/homebrew-bump-revision a day ago https://github.com/loozhengyuan/homebrew-tap/blob& a day ago https://github.com/loozhengyuan/homebrew-tap/pull& a day ago |
448. HN Broadcom Lands Shepherding Deal for OpenAI "Titan" XPU**Summary:** Broadcom has expanded its custom XPU design business through a significant contract with OpenAI for the development of OpenAI’s "Titan" AI inference chip. This move aligns with OpenAI's strategy to reduce reliance on Nvidia GPUs, which it currently uses in systems such as the “Blackwell” GB300 NVL72 rackscale system. Despite raising substantial funds through a $40 billion Series F round and a secondary offering valued at $500 billion, these efforts are not aimed at infrastructure investment and may complicate future funding avenues. OpenAI is also pursuing the "Stargate" project with an estimated cost of $500 billion to advance AI capabilities over four years, amidst financial pressures due to significant losses despite growing revenues. To address its fiscal challenges, particularly in anticipation of high expenditures in 2024 and projected sales in 2025, OpenAI aims to co-design more cost-effective AI chips to decrease dependence on expensive cloud services. Historically funded by Microsoft for capacity purchases, OpenAI now seeks independent investment, particularly crucial for achieving Project Stargate’s goals toward artificial general intelligence. Broadcom stands to benefit from supplying components for OpenAI's infrastructure needs, although the impact may be less substantial than anticipated. In Q3, Broadcom reported robust growth in sales and income across its divisions but noted that its custom XPU business was currently less profitable compared to other sectors, with a decrease in net income as a percentage of revenue. Financially, Broadcom ended the quarter with $10.72 billion in cash, having reduced debt by over $3 billion, while maintaining total debt at $64.22 billion. The company’s Semiconductor Solutions and Infrastructure Software divisions reported significant sales growth, contributing to an overall increase in AI chip sales including custom XPUs and switch ASICs for AI clusters. Broadcom's revenue from non-AI networking grew modestly, whereas AI networking saw substantial increases, with expectations that future products will boost these figures further. Collaborations with major tech firms are bolstering Broadcom’s standing in the AI market, supported by projections of a 66% increase in Q4 2025 revenues for AI chips compared to a smaller rise in non-AI chip revenues. A significant development is an over $10 billion order from OpenAI for complete AI rack systems based on Broadcom’s XPUs, expected to bolster fiscal 2026 revenue forecasts. The market anticipates more details about the Titan XPU and potential advancements in optical interconnects for AI server architecture. Richard Ho's recruitment by OpenAI reflects a strategic effort to innovate in silicon photonics for AI systems, potentially challenging Nvidia’s dominance. **Bullet Point Summary:** - Broadcom secures a contract with OpenAI for custom "Titan" AI inference chips as part of OpenAI's strategy to reduce reliance on Nvidia GPUs. - Despite raising significant funds through recent financial rounds, OpenAI faces challenges in funding infrastructure investments and aims to co-design cost-effective AI chips amid financial pressures. - Broadcom benefits from supplying components to OpenAI but acknowledges that its custom XPU business is currently less profitable. - Broadcom reports substantial sales growth across divisions, with increased focus on AI chip sales contributing significantly to overall revenue. - Revenue projections indicate strong growth for AI chips by Q4 2025, supported by collaborations with major tech firms and future product launches. - An over $10 billion order from OpenAI is expected to enhance fiscal 2026 revenue forecasts for Broadcom. - Interest in advancements such as optical interconnects for XPUs could impact AI server architecture. - Richard Ho's hiring by OpenAI aims to leverage his expertise in silicon photonics, potentially giving OpenAI a competitive edge over Nvidia. Keywords: $10 billion order, AI accelerators, AI clusters, AI compute, AI inference chip, AI networking, Anton supercomputers, Azure cloud, Blackwell, Broadband, Broadcom, Calxeda, DE Shaw Research, EdgeTPU, EnergyCore processors, GB300 NVL72, GPT models, GenAI, IPU, Industrial, Intel, Jericho 4, Lightmatter, Mentor Graphics, Microsoft, Nvidia, OpenAI, Passage interposer, Project Stargate, RTL designs, Richard Ho, Series F funding, Series G round, Server Storage Connectivity, TPUs, Taiwan Semiconductor Manufacturing Co, Tensor Processing Unit, Titan, Tomahawk Ultra, VMware acquisition, Video Coding Unit, Wireless, XPU, YouTube, chip business, complete systems, custom design, datacenter networking, financial results, hardware engineering, hyperscalers, infrastructure, microelectronics, molecular dynamics, neoclouds, networking, optical interconnects, orders, public clouds, revenue curve, sales, server CPU, shepherding business, silicon photonics, silicon roadmap, software engineering, switch ASICs, third quarter
openai
www.nextplatform.com 5 days ago
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449. HN RateMyEmployer (Employer Review Platform)- **RateMyEmployer Platform Overview:** - The RateMyEmployer platform is a modern employer review system featuring AI-powered insights, web scraping infrastructure for data collection, financial distress tracking, and enterprise-grade security monitoring. - It boasts a user-friendly UI with accessibility compliance (WCAG 2.1 AA), theme support (dark/light mode), mobile optimization, and CSS custom properties. - **Core Features:** - Comprehensive employee review system to evaluate employers. - Financial distress tracking monitors companies facing economic difficulties. - Tracks emerging startups based on funding and growth metrics using real-time updates via Supabase technology. - Offers interactive showcases for top and bottom-rated companies with real-time data. - **Data Management and Security:** - Automates data collection from business directories and APIs, supplemented by real-time company news. - Implements robust validation, deduplication systems, and ethical web scraping aligned with robots.txt guidelines to ensure high-quality data integrity. - Incorporates advanced threat detection for vulnerabilities like XSS, SQL injection, path traversal, alongside intelligent rate limiting, security dashboards, input validation, error tracking, and recovery. - **Performance Monitoring:** - Tracks Core Web Vitals (CLS, FID, FCP, LCP, TTFB) in real-time. - Optimizes API response times and database query performance, while monitoring React component render time to enhance front-end efficiency. - Provides detailed user experience analytics. - **Recent Enhancements:** - Implementation of new features focusing on functionality, security, and performance. - Modernized UI design for better accessibility and improved visualizations in the Wall of Fame/Shame sections with social features. - Introduced database population automation and enterprise-grade monitoring. - Achieved cost optimization by utilizing free tiers and eliminating paid API dependencies. - **Technology Stack:** - Utilizes Next.js 14, TypeScript, Tailwind CSS, Radix UI, Framer Motion for frontend development. - Supabase with PostgreSQL is used in the backend for real-time subscriptions, data protection via Row Level Security, and support for Edge Functions enabling serverless operations. - **Development and Deployment:** - Employs Vercel for edge-optimized serverless deployment and GitHub Actions for automated testing and deployment pipelines. - Maintains code quality with ESLint & Prettier, and uses Vitest for modern testing ensuring extensive coverage. - **Getting Started and Setup:** - Requires Node.js (>=20.0.0), npm (>=10.0.0), and a Supabase account. - Repository setup includes cloning, installing dependencies, configuring environment variables, and database setup using migrations. - Development server starts with `npm run dev`. - **Testing & Quality Assurance:** - Conducts unit tests, coverage reports, E2E tests, component and integration tests, linting, formatting, type-checking, and builds for production. - **Database Schema:** - Key tables include companies, reviews, users, company_news, scraping_jobs, scraped_data, enhancements, performance_metrics, security_events, and error_logs. - Provides migration commands for database setup and management. - **Additional Features:** - Monitors security events and logs errors with `security_events` and `error_logs`. - Offers comprehensive API documentation covering operational aspects like companies, reviews, scraping, metrics, and analytics. - Guides on deployment using Vercel with GitHub integration for environment variable setup. - **Contributing Guidelines:** - Encourages contributions through a structured process involving forking the repository, creating feature branches, making changes/tests, and submitting pull requests with conventional commit messages. - **License and Acknowledgments:** - Licensed under MIT License. - Acknowledges support from Supabase, Vercel, Next.js, Tailwind CSS, and Radix UI. - **Additional Functionalities and Features:** - Web application features Wall of Fame/Shame components for displaying company reviews with negative ones highlighted on the latter. - Rating indicators are color-coded (red, yellow, green), with news integration highlighting notable companies. - Testing includes Playwright end-to-end tests, TypeScript type checking, ESLint linting, and deployment optimizations using Vercel, image compression, Webpack caching. - Automated GitHub Actions fetch news for low-rated companies twice daily via RSS feeds or SerpAPI. - **User Interaction and Database Management:** - Users can suggest companies requiring admin approval, with database population sources including Fortune 500 companies, tech startups, user suggestions, CSV imports, and OpenStreetMap locations. - Weekly updates or manual triggers through GitHub Actions manage company data. - Tools monitor Supabase usage in real-time for cost optimization, offering automated alerts, performance enhancements, and scaling recommendations. - **Financial Health and Startup Tracking:** - Tracks financial distress with indicators (e.g., layoffs, funding issues) rated 1-5, and startup growth on a scale of 1-10 with confidence metrics. - Automated real-time monitoring from news articles and reviews supports industry analysis, trend tracking, and opportunity filtering. - **Web Scraping Infrastructure:** - Features multi-source scraping, intelligent job scheduling, dedicated scrapers, automated validation, quality scoring, deduplication adherent to robots.txt, rate limiting, user agent rotation, platform terms of service, and server response-based crawl delays. - Provides a scraping dashboard at `/scraping` for real-time monitoring, statistics, engine controls, job management, and data enhancement tools. - **Technical Infrastructure:** - Includes concurrent processing, intelligent queuing, caching systems, error recovery, resource optimization as detailed in the Web Scraping Infrastructure Guide. - **UI Design Enhancements:** - Offers an accessible component library with design tokens, advanced visual effects (glassmorphism, gradients), micro-interactions, hardware-accelerated animations, dynamic imports, WebP images, React.memo for efficient rendering. - Mobile experience optimized via a touch-first interface, large targets, swipe/gesture support, collapsible menus, modern design trends, clear loading states, and feedback. - **MCP Integration:** - Enables natural language interaction with the Supabase database using AI tools to allow plain English queries without SQL knowledge. - Offers analysis of company ratings, review trends, and user activity through AI-powered queries. - Setup involves running an MCP server with npm commands and accessing a demo page; schema updates managed via `npm run mcp:update-schema`. - **Statistics Module:** - TypeScript-based module for industry and location statistics calculation, ensuring reliability, type safety, error handling, and integration with React components. - **Database Migrations:** - Managed in the `supabase/migrations` directory using timestamp-prefixed SQL files. - Best practices include database backups, focused migrations, "up" and "down" directions, documentation of complex migrations, and resources like MCP integration guides. Keywords: AI-enhancement, API Documentation, ARIA Implementation, Accessibility, Adaptive Layouts, Automated Detection, Automated population, Automation, Automation Capabilities, Caching Systems, Component Performance, Comprehensive Filtering, Concurrent Processing, Confidence Metrics, Crawl Delays, Dark Mode, Data Enhancement, Data Quality Validation, Data Type Specialization, Database Schema, Deduplication, Deduplication Systems, Deployment, Design Tokens, Dynamic Color Transitions, ESLint, Edge Functions, Employee Feedback, Engine Controls, Environment Variables, Error Handling, Error Recovery, Event Monitoring, Feature Flags, Financial Data, Financial Distress, Financial Distress Indicators, Funding Issues, GitHub Actions, Glassmorphism, Gradient Systems, Growth Indicators, Hardware Acceleration, IP Blocking, Industry Analysis, Intelligent Queuing, Intelligent Rate Limiting, Job Management, Job Scheduling, Layoffs, Leadership Changes, Linting, Manual Verification, Micro-interactions, Migration, Migrations, Multi-Source Scraping, Nextjs, Nodejs, Opportunity Filtering, Path Traversal, Performance & Scalability, Performance Monitoring, PostgreSQL, Prettier, Quality Assurance, Quality Scoring, Rate Limiting, React, Real-time Monitoring, Real-time Updates, Resource Management, Responsive Design, Responsive Images, Revenue Decline, Robotstxt Compliance, Row Level Security, SQL Injection, Scraper Types, Security Dashboard, Security Monitoring, Severity Scoring, Smart Analysis, Spam Detection, Supabase, Tables, Terms of Service, Testing, Threat Detection, Touch Optimization, Touch Targets, Trend Tracking, TypeScript, UI Design, User Agent Rotation, User Experience Metrics, Validation Rules, Visual Indicators, Vitest, WCAG 21 AA, Web Scraping, Web Scraping Dashboard, Web Scraping Infrastructure, XSS, Zero-cost Strategy
postgresql
github.com 5 days ago
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450. HN Can Claude teach me Jax? [video]The provided text outlines key details about a YouTube video titled "Can Claude teach me Jax?" which suggests a discussion or exploration related to "Jax," potentially within the NFL context, as indicated by the mention of "NFL Sunday Ticket." The description implies that the content may involve teaching or explaining aspects associated with Jax, possibly a player or concept in American football. Additionally, the text notes that standard YouTube features such as navigation options and policy information are included on the page where this video is hosted. **BULLET POINT SUMMARY:** - A YouTube video titled "Can Claude teach me Jax?" is discussed. - The content likely relates to "Jax," potentially within the NFL context. - Mention of "NFL Sunday Ticket" suggests a focus on football-related topics. - Standard YouTube features such as navigation and policy information are present. Keywords: Advertise, Claude, Contact, Copyright, Creators, Developers, Google, Jax, LLC, NFL, Policy, Press, Privacy, Safety, Terms, Test, Ticket, YouTube, features, video
claude
www.youtube.com 5 days ago
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451. HN Show HN: GitHub trends newsletter by star growth (email, RSS, and more)The text describes a GitHub trends newsletter created by the author, which focuses on identifying new trending projects based on star growth rather than older popular ones. This service offers updates via email, RSS feed, and GitHub inbox notifications, with weekly updates for emails and twice-daily feeds. Users can choose their preferred notification method through options like GitHub releases or Google Forms. The newsletter is powered by GitHub Actions, ensuring a serverless operation with minimal costs, mainly associated with sending emails. The setup process involves using `npm install` to manage dependencies and configuring via `.env.example`. A local preview can be achieved with `npm run preview:live`, and users can customize the number of releases shown through `RELEASE_TOP_N=40`. Email notifications are facilitated by services like Resend or Buttondown, which provide free tiers suitable for personal use. To send emails, users need to configure necessary tokens as Secrets in their GitHub repository. - The newsletter identifies new trending GitHub projects based on star growth. - Available via email, RSS feed, and GitHub inbox notifications with customizable update frequencies. - Operates using GitHub Actions for a serverless solution with minimal costs. - Setup involves dependency installation (`npm install`) and configuration copying (`.env.example`). - Local previewing is possible with `npm run preview:live`, and customization of releases shown via `RELEASE_TOP_N=40`. - Email functionality supported by Resend or Buttondown, requiring token setup as Secrets in GitHub. Keywords: Actions, Buttondown, GitHub, Resend, analytics, automation, email, newsletter, notifications, secrets, serverless, setup, workflow
github
github.com 5 days ago
|
452. HN Pure and Impure Software Engineering### Summary The article explores the distinctions between "pure" and "impure" software engineering, highlighting their unique challenges and contributions within tech environments. Pure software engineering is characterized by its focus on solving complex technical problems with an emphasis on perfection, often without strict deadlines—akin to art or research—as exemplified in open-source projects. In contrast, impure software engineering prioritizes practical problem-solving within constraints such as budgets and timelines, typical of commercial technology settings where engineers must meet specific business objectives. Conflicts between these two domains manifest when engineers from different backgrounds interact, each perceiving the other's approach as flawed rather than recognizing their differing paradigms. This issue is evident in situations involving solo developers transitioning to large tech companies or adapting AI tools for varied tasks. The article notes a shift in the past decade where major tech companies, driven by marketing interests and an abundance of engineers, heavily invested in pure engineering projects. However, as economic conditions evolved towards profitability, funding for these roles diminished, necessitating a focus on practical, revenue-generating projects. Both types of engineering are essential; pure engineering is crucial for utilizing open-source components like Kafka or Redis, while impure engineering drives the rapid development of new features amidst stakeholder compromises and deadlines. Engineers typically excel in one domain over the other, with pure engineers sometimes struggling under the pressure of impure tasks, and vice versa. The text cites specific examples to illustrate these concepts, such as Casey Muratori's critique of Windows Terminal's performance handling due to his expertise in game engine programming—highlighting how pure engineers often possess deep technical insights. It also references George Hotz’s successful work on tinygrad after difficulties at Twitter, exemplifying the dedication required for pure engineering projects. Moreover, the article addresses how tech companies prioritize business value over speed, with impure engineering being highly valued despite its complexity due to political and organizational factors. The user's preference for tools like Visual Studio Code illustrates a trade-off between performance and functionality in supporting complex systems, where AI tools like Large Language Models (LLMs) are more beneficial to impure engineers than pure ones. Finally, the text underscores the importance of recognizing impure engineering as valuable work within tech companies, critiquing the tendency of some engineers to undervalue it. While LLMs may offer limited utility in addressing unique challenges faced by pure engineers, they provide significant support for impure engineering tasks, enhancing productivity through extensive knowledge bases. ### Bullet Point Summary - **Pure vs. Impure Engineering:** Pure focuses on technical perfection without deadlines (e.g., open-source projects), while impure emphasizes practical solutions within constraints like budgets and timelines. - **Conflicts:** Arise when engineers from different backgrounds interact; solo developers or high-profile hires may struggle adapting between paradigms. - **Shift in Tech Companies:** Past focus on pure engineering for marketing has shifted towards profitability, reducing funding for such roles. - **Value of Both Types:** Pure engineering is crucial for specific technical tasks using open-source components, while impure engineering is essential for rapid feature development amidst stakeholder compromises. - **Examples and Expertise:** Casey Muratori's critique highlights deep insights from pure engineers. George Hotz's work exemplifies dedication to pure projects. - **Tech Priorities:** Companies prioritize business value over speed, valuing the complexity of impure engineering despite its challenges. - **Tool Preference and AI Utility:** Visual Studio Code is preferred for feature support; LLMs are more beneficial in impure tasks than in addressing unique challenges faced by pure engineers. - **Recognition of Impure Engineering:** The article emphasizes recognizing impure engineering as valuable work, critiquing the tendency to undervalue it. Keywords: AI hype, AI-assisted development, Alacritty, CQRS, GitHub, GitHub Codespaces, HTML parsing, HTTP requests, Kafka, LLMs, Markdown, Neovim, Nokgiri, Pure engineering, Redis, Visual Studio Code, Windows Terminal, academic philosophy, aesthetic sense, big tech engineers, business value, codebases, complexity, components, consensus, construction, databases, deadlines, decision-making, deep learning framework, developer marketing, developer workflow, economic actors, efficiency, event-sourced architecture, feature implementation, feature shipping, game engine, game engine programmer, hiring, hobbyists, hype, impure engineering, market transition, mathematics, microchips, microservices, monoliths, narrowness of vision, novel problems, open-source work, paid company work, performance, physicists, plumbing, political views, product development, programming language, real-world problems, skills, slow software, solo developers, solution compromise, technical decisions, technical expertise, valuable work
github codespaces
www.seangoedecke.com 5 days ago
https://a.co/d/eLvZYcE a day ago https://www.dreamsongs.com/RiseOfWorseIsBetter.html a day ago |
453. HN ApeRAG: Production-ready GraphRAG with multi-modal indexing and K8s deployment### Summary ApeRAG is a comprehensive Retrieval-Augmented Generation (RAG) platform designed for developing advanced AI applications. It combines several technologies such as Graph RAG, vector search, full-text search, and multimodal document processing to enable hybrid retrieval. ApeRAG facilitates the creation of Knowledge Graphs, Context Engineering, and autonomous intelligent agents capable of reasoning across diverse knowledge bases. The platform features multi-modal indexing, enterprise-grade management, and support for Model Context Protocol (MCP) integration, allowing seamless interaction with AI assistants. Setup requires at least 2 CPU cores and 4 GiB of RAM, and the system can be installed using Docker Compose after cloning from GitHub. Once operational, it offers a web interface and API documentation accessible locally. ApeRAG's key functionalities include collection browsing to explore knowledge collections, hybrid search methods for comprehensive document retrieval, and intelligent querying that supports natural language questions about documents. It integrates with MinerU for advanced parsing of complex documents, tables, formulas, and scientific content, optionally using GPU acceleration. The system provides five index types—Vector, Full-text, Graph, Summary, Vision—to optimize retrieval processes and includes built-in AI agents leveraging MCP tool support for enhanced searching capabilities. Additionally, it offers features like entity normalization in graph RAG for improved relational understanding in knowledge graphs. For deployment, ApeRAG supports production-grade infrastructure using Kubernetes with Helm charts and KubeBlocks, facilitating the setup of databases such as PostgreSQL, Redis, Qdrant, Elasticsearch, and Neo4j. It includes enterprise management capabilities like audit logging, model management, graph visualization, document interfaces, and agent workflow management. ApeRAG is developer-friendly, featuring a fastAPI backend, React frontend, asynchronous task processing with Celery, comprehensive testing, development guides, and an agent framework for customization. The platform's deployment involves cloning its repository from GitHub, setting up database services using existing or automated Kubernetes methods, and installing via Helm in the default namespace. Access to ApeRAG is achieved through port forwarding, mapping local ports to the web interface and API documentation. Ingress configuration can be adjusted via `values.yaml` for production environments. Troubleshooting resources include checking pod logs with `kubectl` commands and consulting a README for database management guidance. The platform utilizes a modified version of LightRAG for graph-based retrieval, including concurrent processing enhancements and distributed task queues. ApeRAG is open-source under the Apache License 2.0, acknowledging contributors to its foundational projects. ### Key Points - **Purpose**: A sophisticated RAG platform for AI application development. - **Technologies**: Combines Graph RAG, vector search, full-text search, and multimodal document processing. - **Features**: Multi-modal indexing, MCP support, hybrid retrieval, intelligent querying, and advanced parsing with MinerU integration. - **Deployment Requirements**: At least 2 CPU cores and 4 GiB of RAM; setup using Docker Compose after cloning from GitHub. - **Access**: Web interface and API documentation available locally; port forwarding for access to services. - **Database Support**: Kubernetes deployment with support for PostgreSQL, Redis, Qdrant, Elasticsearch, Neo4j. - **Enterprise Management**: Includes audit logging, model management, graph visualization, document interfaces, agent workflow management. - **Developer Tools**: FastAPI backend, React frontend, asynchronous task processing, comprehensive testing, and development guides. - **Open Source**: Licensed under Apache License 2.0 with contributions from underlying projects acknowledged. Keywords: AI agents, Apache License, ApeRAG, Celery, Context Engineering, Docker Compose, Elasticsearch, FastAPI, GPU acceleration, Graph RAG, Helm Charts, Knowledge Graph, KubeBlocks, Kubernetes deployment, MinerU, Model Context Protocol, Neo4j, PostgreSQL, Prefect, Qdrant, React, Redis, Retrieval-Augmented Generation, full-text search, hybrid retrieval, multimodal indexing, vector search
postgresql
github.com 5 days ago
https://github.com/HelixDB/helix-db a day ago |
454. HN Immich – High performance self-hosted photo and video managementImmich is a dynamically evolving, high-performance self-hosted platform designed for managing photos and videos. It supports multiple languages but comes with a cautionary note regarding potential bugs and breaking changes; therefore, users are advised not to depend on it solely for storage. Instead, they should adhere to the 3-2-1 backup strategy to ensure data safety. The key features of Immich include mobile web uploads, automatic backups, duplication prevention, support for multiple users, metadata search capabilities, administrative functions, background backups, OAuth integration, and API keys. It also supports various raw formats, facial recognition, offline access, and public sharing options. Users interested in exploring its functionalities can try out a demo available for both the server and mobile app versions. Detailed documentation is accessible on Immich's official website, which provides specific login credentials to access the demo version. As an open-source project, users can explore its repository activity through platforms like GitHub, offering transparency and community-driven development insights. - **Summary of Key Points:** - Immich is a self-hosted solution for managing photos and videos. - Supports multiple languages but warns about potential bugs and breaking changes. - Users should not rely on it solely; the 3-2-1 backup strategy is recommended. - Features include mobile uploads, auto-backup, duplication prevention, multi-user support, metadata search, administrative functions, background backups, OAuth integration, API keys, raw format support, facial recognition, offline access, and public sharing. - A demo for both server and mobile app versions is available. - Detailed documentation is accessible on the official website with login credentials provided. - The project is open-source, allowing users to explore its repository activity on platforms like GitHub. Keywords: API Keys, EXIF metadata, Immich, active development, backup plan, bugs, documentation, facial recognition, high performance, live photo playback, mobile app, multi-user support, photo management, repository activity, self-hosted, translations, video management
popular
github.com 5 days ago
https://github.com/immich-app/immich/commits/ 5 days ago https://news.ycombinator.com/item?id=45169657 5 days ago https://github.com/immich-app/.github/blob/ma 5 days ago https://immich.app/docs/features/command-line-inte 5 days ago https://github.com/simulot/immich-go 5 days ago https://www.pikapods.com/ 5 days ago https://www.hetzner.com/storage/storage-box/#matri 5 days ago https://marketplace.digitalocean.com/apps/immich 5 days ago https://www.linode.com/pricing/#block-storage 5 days ago https://immich.pro 5 days ago https://github.com/dubrowin/Immich-backed-by-S3 5 days ago https://news.ycombinator.com/item?id=40563541 5 days ago https://news.ycombinator.com/item?id=40772809 5 days ago https://news.ycombinator.com/item?id=33159796 5 days ago https://news.ycombinator.com/item?id=42984617 5 days ago https://news.ycombinator.com/item?id=30537564 5 days ago https://news.ycombinator.com/item?id=39731179 5 days ago https://news.ycombinator.com/item?id=40859102 5 days ago https://news.ycombinator.com/item?id=40030151 5 days ago https://news.ycombinator.com/item?id=39336890 5 days ago https://news.ycombinator.com/item?id=36673224 5 days ago https://videohubapp.com/ 5 days ago https://github.com/whyboris/Video-Hub-App 5 days ago https://github.com/whyboris/Video-Hub-App/blob 5 days ago https://github.com/photoprism/photoprism/issues&#x 5 days ago https://www.youtube.com/watch?v=HsYeVWyNxaY 5 days ago https://selfh.st/apps/ 5 days ago https://medium.com/@javipas/thats-how-i-ve-replaced-goo 5 days ago https://github.com/immich-app/immich/discussions&# 5 days ago https://www.amazon.com/dp/B0CPPD51B9?th=1 5 days ago https://news.ycombinator.com/item?id=45168333 5 days ago https://github.com/immich-app/immich/pull/187 5 days ago https://www.pikapods.com/apps#photo 5 days ago |
455. HN The Micro SaaS Revolution: From Giants to Solopreneurs### Summary The Micro SaaS Revolution signifies a paradigm shift from large-scale SaaS giants like Salesforce and Shopify towards smaller, solopreneur-driven ventures offering personalized, niche solutions. Traditionally, SaaS companies aimed for broad market penetration, raised venture capital, scaled globally, and often exited through acquisition or IPO. This model led to the rise of "unicorn" firms that were later consolidated by private equity firms seeking industry dominance. However, this trend has given way to a focus on Micro SaaS businesses that challenge conventional models with their agile development processes, targeting specific market needs. Micro-SaaS ventures prioritize lean operations and rapid product development, empowered by technological advancements such as serverless platforms, AI tools for coding and design, and no-code/low-code solutions. These technologies reduce overhead costs and technical complexities, allowing solo founders to efficiently bring ideas to fruition without needing extensive teams or external funding. The current landscape enables individuals to quickly validate market interest through AI-generated prototypes and modern distribution channels like Product Hunt and LinkedIn. This evolution reflects a broader trend where more people can create software independently, reminiscent of how digital content platforms democratized music production. Successful examples include Maor Shlomo’s Base44 and Samanyou Garg's Writesonic, which highlight the potential for solo founders to achieve substantial success without traditional venture capital backing. The role of developers is also evolving, with AI tools shifting their focus from routine coding tasks to higher-level concerns like performance and security. The SaaS industry continues to grow, projected to reach nearly $1 trillion by 2030, driven largely by smaller, specialized services. This shift democratizes software creation, enabling individual creators to find success in a competitive landscape by deeply resonating with specific audiences. The Micro-SaaS movement fosters an ecosystem where founders can define their own success metrics and thrive independently. ### Bullet Point Summary - **Shift from Large SaaS Giants**: Movement towards smaller, solopreneur-driven ventures focusing on personalized, niche markets. - **Traditional vs. Modern Models**: Traditional large-scale SaaS firms raised venture capital for broad market scaling; Micro SaaS focuses on lean operations and rapid development. - **Technological Enablers**: - Serverless platforms and managed APIs reduce technical management overhead. - AI tools assist with coding, design, copywriting, and customer support. - No-code/low-code solutions enable non-developers to build functional prototypes quickly. - **Empowering Solo Founders**: Reduced barriers for solo founders allow rapid market validation and efficient product development without large teams or external funding. - **Successful Examples**: - Maor Shlomo’s Base44, an AI platform acquired by Wix. - Samanyou Garg's Writesonic, achieving significant revenue independently. - **Evolving Developer Roles**: Developers focus on higher-level tasks with AI handling routine coding, shifting towards micro-SaaS entrepreneurship. - **Market Growth Projection**: SaaS industry expected to grow to nearly $1 trillion by 2030, driven by smaller, specialized services. - **Democratization of Software Creation**: Lower barriers enable more creators to define success individually, fostering a diverse and thriving software ecosystem. Keywords: AI, APIs, Automation, Democratized, Indie Hackers, MVP, Micro-SaaS, Niche markets, No-code, Product Hunt, Salesforce, Shopify, Solo founders, Solopreneurs
github copilot
www.msthgn.com 5 days ago
|
456. HN Show HN: BrainBox – Open-Source Notion/Slack Alternative**Summary:** BrainBox is a self-hosted, open-source workspace developed by solo developer @itskritixon X. It serves as an alternative to platforms like Notion and Slack, with a focus on enhanced privacy and user control over data ownership. Unlike these large platforms that host user data externally, BrainBox allows users to manage their workspaces locally, ensuring greater autonomy over personal information. The platform features real-time documents, chat functionalities, customizable databases, and offline capabilities, addressing the limitations of proprietary tools by offering more flexibility. Developed under the Apache 2.0 license, it is currently hosted on AWS's free tier for desktop use during its beta phase, accessible at [brainbox.3pe1x.xyz](https://brainbox.3pe1x.xyz). The project plans to integrate an AI assistant feature in future updates and encourages feedback via @itskritix on X.com or through its GitHub repository. **Bullet Point Summary:** - BrainBox is developed by @itskritixon X as an open-source workspace alternative to Notion and Slack. - It emphasizes enhanced privacy and user control over data ownership, allowing self-hosting of workspaces. - Key features include real-time documents, chat functionalities, customizable databases, and offline capabilities. - Hosted on AWS's free tier for desktop use during its beta version; accessible at [brainbox.3pe1x.xyz](https://brainbox.3pe1x.xyz). - Developed under the Apache 2.0 license, it addresses limitations of proprietary tools by empowering users with data control. - Future updates will include an AI assistant feature. - Feedback can be provided via @itskritix on X.com or through its GitHub repository. Keywords: AI, AI assistant, AWS, BrainBox, EC2, EC2 instance, GitHub, Notion, PostgreSQL, Slack, beta, chat, data ownership, databases, feedback, feedback Keywords: BrainBox, offline-capable, open-source, privacy, real-time, real-time docs, self-hosted, workspace
postgresql
brainbox.3pe1x.xyz 5 days ago
|
457. HN Show HN: Auto-copy all tabs and YouTube transcripts for AI (Chrome extension)The Chrome extension "Auto-copy all tabs and YouTube transcripts for AI" was developed to alleviate the inconvenience of manually copying text from multiple browser tabs into an AI studio. The creator noticed frequent use of AI tools requiring content from over 20 tabs, often involving lengthy YouTube transcript processes. To address this, they created an extension that enables instant copying of selected tab content and YouTube transcripts with a single click. This tool simplifies managing numerous open tabs by organizing them into AI-ready formats in one action. It efficiently captures main content from articles, documentation, blogs, PDFs, and YouTube transcripts, presenting it in a clean format suitable for large language models such as GPT-4, Claude, or Gemini. Key features of the extension include effortless one-click capture, full YouTube transcript generation, universal tab support, AI-ready output, and ad-free structured content. The tool is designed to assist developers, researchers, students, and content creators by saving time, enhancing AI performance, boosting productivity, and facilitating the construction of a comprehensive knowledge base. **BULLET POINT SUMMARY:** - Developed to address the inconvenience of manually copying text from multiple browser tabs into an AI studio. - Designed for frequent use with AI tools requiring content from over 20 tabs, including lengthy YouTube transcripts. - Enables instant copying of selected tab content and YouTube transcripts with one click. - Simplifies management of numerous open tabs by organizing them into AI-ready formats in a single action. - Captures main content from articles, documentation, blogs, PDFs, and YouTube transcripts in a clean format for large language models. - Features include one-click capture, full YouTube transcript generation, universal tab support, AI-ready output, and ad-free structured content. - Aims to assist developers, researchers, students, and content creators by saving time, enhancing AI performance, boosting productivity, and aiding knowledge base construction. Keywords: AI Studio, AI performance, AI-ready information, AI-ready output, Auto-copy, Chrome Web Store, Chrome extension, Claude, GPT-4, Gemini, Tabs, YouTube transcripts, articles, blogs, browser tabs, clean formatted, content capture, content creation, copy content, core features, developer tools, documentation, drag & copy, knowledge base, large language models, one-click capture, organized format, productivity boost, researcher assistance, selected tabs, student aid, technical keywords, time saving, universal tab support
claude
chromewebstore.google.com 5 days ago
https://youtu.be/IH33uBUzAas?feature=shared 5 days ago |
458. HN Building my childhood dream PC (IBM 2168) Part 1The provided text outlines a personal journey from childhood fascination with computers to fulfilling a dream by restoring an iconic IBM PS/1 2168 model in 2024. Initially captivated by technology at the age of 14 after receiving a substandard PC, the author's interest was solidified when exposed to the superior performance of a neighbor’s IBM PS/1 2168 486DX2-66MHz machine. This early experience influenced their future career path toward engineering. By winter 2024, the author successfully restored an IBM 2168, meticulously selecting peripherals and upgrading components such as installing PC-DOS 7, troubleshooting hardware issues, networking, enhancing sound capabilities, adding CD-ROM support, integrating MIDI music, increasing cache memory, and optimizing CPU performance. This project enabled optimal gameplay on classic games like DOOM and is documented in a detailed series covering each restoration phase. The text further elaborates on the IBM PS/1 2168 model, announced in March 1993 as part of IBM’s Mini-Tower line known for its user-friendly design with elements such as a carry handle and rounded corners. It was celebrated for its quality components like model M keyboards and extensive documentation detailing maintenance and upgrades, setting it apart from contemporaneous clone PCs. Its expandability features included an Intel Overdrive socket and numerous expansion slots, contributing to its high reputation. Despite these desirable qualities, acquiring a functioning IBM PS/1 2168 proved challenging due to age-related scarcity and collector interest. The author's quest for a specific model capable of booting at a particular motherboard frequency led to the successful purchase on eBay of a PS/1 2168-594 from Finland, featuring an original DX2-66MHz processor and Windows 3.1. After negotiating with the seller for only the central unit without damaging the original box, the author looked forward to its careful packaging. - Childhood fascination with computers led to restoring an IBM PS/1 2168. - Restoration involved extensive upgrades and troubleshooting to enhance performance. - Detailed documentation highlights the unique design and expandability features of the IBM PS/1 series. - Acquiring a functional IBM PS/1 2168 was challenging due to its age and collector interest. - Successful purchase on eBay included negotiation for intact original packaging. Keywords: 486DX-33, 486DX2-66, 486SX-33, AUTOEXECBAT, Aptiva, CD drive, CONFIGSYS, CPU upgrade, DOOM, Deluxe Paint, Dune II, FDD problems, HDDs, IBM PS/1, ISA cards, Intel Overdrive, L2 cache, LAN parties, Mini-Tower, PC-DOS 7, RAM, Space Saving Keyboard, VRAM, Windows 31, eBay, model M keyboard
vram
fabiensanglard.net 6 days ago
https://fabiensanglard.net/the_beautiful_machine/index. a day ago https://retrodreams.ca/products/itx-llama-mainboard a day ago https://news.ycombinator.com/item?id=44021824 a day ago https://news.ycombinator.com/item?id=44023088 a day ago https://news.ycombinator.com/item?id=44026363 a day ago https://frame.work/gb/en/desktop a day ago |
459. HN EZ Translate – Easy and Free AI Translator in Chrome**Summary:** EZ Translate is a free translation plugin for the Chrome browser that utilizes advanced Large Language Models (LLMs) like Gemini. This tool enhances translation accuracy by offering on-page and popup options that consider context, nuance, and tone. As a result, it delivers translations that are more natural and closely resemble human translations compared to traditional methods. **Bullet Point Summary:** - **EZ Translate** is a free plugin available for the Chrome browser. - It uses advanced AI technology, specifically Large Language Models such as Gemini. - The tool offers both on-page and popup translation options. - Translations are context-aware, capturing nuance and tone effectively. - Provides more accurate and natural translations compared to conventional methods. Keywords: AI Translator, Accuracy, Chrome, Contextual Understanding, EZ Translate, Extension, Fluent, Gemini, Human-like, LLMs, Natural, Nuance, On-page Translations, Popup Translations, Tone, Translation Plugin
gemini
chromewebstore.google.com 6 days ago
|
460. HN Tesla's Wireless Vision**Summary:** "Hopit" represents Tesla's innovative vision for wireless energy transmission, aiming to facilitate seamless power delivery without physical connectors. This concept aligns with Tesla's broader objectives of promoting sustainable and efficient energy solutions, showcasing his pioneering role in revolutionizing electrical systems. "Hopit" underscores the transformative potential of wireless technology in altering energy distribution and utilization by emphasizing both convenience and technological progress. **Bullet Point Summary:** - "Hopit" is part of Tesla’s vision for wireless energy transmission. - Aims to enable seamless power delivery without physical connectors. - Aligns with Tesla's goals of advancing sustainable and efficient energy solutions. - Reflects Tesla’s pioneering spirit in revolutionizing electrical systems. - Highlights the transformative potential of wireless technology in energy distribution. - Emphasizes convenience and technological advancement. Keywords: HOPIT, Tesla, Vision, Wireless, backquotes, comma-separated, delimiter, duplicates, extract, format, information, keywords, list, technical, text, topic
tesla
www.hopit.ai 6 days ago
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461. HN I solved a distributed queue problem after 15 years – DBOS### Summary The author recounts an experience from 15 years ago at Reddit, where they addressed a distributed queue problem using DBOS during their tenure maintaining Postgres and RabbitMQ—key components of Reddit's infrastructure for handling actions like upvotes through a scalable and reliable task queue architecture. This system offered horizontal scalability by allowing tasks to be processed in parallel across numerous servers with the addition of more workers. It also provided flow control, customizing task consumption rates to manage demanding tasks or rate-limited APIs effectively, and scheduling capabilities for periodic task execution using methods like cron schedules. Despite its advantages, the architecture faced challenges regarding reliability due to potential failure points such as database downtime affecting vote processing, cache failures disrupting recalculations, crashes leading to data loss, and queue failures resulting in lost votes or comments. To improve robustness, durable queues were introduced. These queues checkpoint task statuses into a persistent store, like Postgres, enabling the resumption of failed jobs and preventing data loss during system crashes. Durable queues have gained popularity for integrating traditional task queues with workflows that require reliability in parallel task orchestration. By recording task submissions and their inputs in a persistent storage system (often relational databases), durable queues maintain a comprehensive record of tasks and interdependencies, thus enhancing reliability by allowing systems to resume from the last completed step after failures. These queues are crucial for recovering from interruptions in long-running workflows or critical data processing tasks due to their checkpointing capability. They provide built-in observability through detailed records accessible via SQL queries. When choosing between durable queues and standard distributed task queues, the decision often involves a tradeoff: durable queues, leveraging databases like Postgres for dependable storage, are suited for fewer but more significant business-critical tasks due to their robust guarantees. In contrast, in-memory systems like Redis offer higher throughput suitable for managing large volumes of smaller tasks. ### Bullet Point Summary - **Background Context**: The author describes solving a distributed queue problem at Reddit using DBOS while managing Postgres and RabbitMQ. - **Task Queue Benefits**: - **Horizontal Scalability**: Enabled running multiple tasks across many servers by adding workers. - **Flow Control**: Allowed customization of task consumption rates for resource-intensive tasks and rate-limited APIs. - **Scheduling**: Facilitated defining task execution schedules using methods like cron. - **Reliability Challenges**: - Potential failure points included database downtimes, cache failures, system crashes, and queue downfalls leading to data loss or lost transactions. - **Introduction of Durable Queues**: - Enhanced reliability by checkpointing tasks into persistent storage for recovery from failures. - Integrated traditional queues with workflows requiring reliable parallel task orchestration. - Recorded detailed task submissions and interdependencies in a persistent store, typically relational databases. - **Advantages of Durable Queues**: - Crucial for recovering long-running or critical data processing workflows due to checkpointing capabilities. - Provided built-in observability through comprehensive records accessible via SQL queries. - **Tradeoff Considerations**: - Durable queues are ideal for fewer, larger business-critical tasks with strong reliability guarantees using databases like Postgres. - In-memory systems like Redis offer higher throughput for managing numerous smaller tasks. Keywords: DBOS, Distributed queue, Postgres, RabbitMQ, Reddit, SQL query, cache, checkpoints, crashes, cron schedule, databases, distributed systems, document processing, durable queues, failures, flow control, future execution, horizontal scalability, infrastructure, inputs, key-value store, message broker, non-durable worker, observability, parallel tasks, performance tradeoffs, persistent store, program crashes, queue processor, rate-limited API, recovery, relational database, scheduling, subtasks, task checkpointing, task queue architecture, task queueing, task resubmission, workers, workflows
postgres
www.dbos.dev 6 days ago
https://news.ycombinator.com/item?id=45130143 4 days ago https://temporal.io/ 4 days ago https://www.dbos.dev/ 4 days ago https://www.inngest.com/uses/durable-workflows 4 days ago https://restate.dev/ 4 days ago https://docs.dbos.dev/architecture 4 days ago https://github.com/dbos-inc/dbos-transact-golang 4 days ago https://docs.dbos.dev/production/self-hosting/work 4 days ago https://docs.temporal.io/develop/go/versioning#pat 4 days ago https://docs.dbos.dev/python/reference/contexts#fo 4 days ago https://github.com/dbos-inc/dbos-transact-golang/b 4 days ago |
462. HN No More Lengthy FormsFormToVoice is an AI-powered tool designed to facilitate voice interactions with web forms using OpenAI's real-time API and WebRTC-based audio streaming. It offers features like automatic form field detection, smart validation, data extraction, and a customizable modern UI that supports both desktop and mobile platforms without external dependencies. - **Installation and Integration:** - The library can be installed via `npm install formtovoice` and is compatible with various web frameworks (React, Angular, Vue.js) and CMS platforms (WordPress, Shopify, Webflow). - React integration uses a custom hook (`useFormToVoice`) for initialization and cleanup within the `useEffect` and `useRef`. - For Angular, installation involves loading scripts in the `ngOnInit` lifecycle method with an OpenAI API key. - Vue.js follows a similar pattern to React using a template file in the `mounted` hook. - WordPress uses `wp_enqueue_script`, Shopify requires embedding within theme.liquid, and Webflow involves custom HTML for script insertion. - **Platform-Specific Integration:** - FormToVoice integrates with platforms like Squarespace, Wix, Bubble, Airtable, Notion, and Figma by loading `formtovoice.min.js` from a CDN. - Each platform requires specific methods for script inclusion, such as direct HTML injection in Squarespace/Notion or dynamic creation via JavaScript in Bubble/Figma. - **Functionality and Features:** - Supports voice interactions with various HTML5 form elements (text inputs, numeric fields, date/time selectors). - Key functionalities include initialization configurations, event management (`conversationStarted`, `conversationEnded`, etc.), and error handling. - Configuration options involve themes, languages, auto-start features, debug mode, and specific voice models. - **Technical Requirements:** - Compatible with JavaScript frameworks (React, Angular, Vue.js), CMS platforms (WordPress, Drupal, Joomla), and server-side frameworks (Express.js, Node.js). - Requires modern browser support (Chrome 80+, Firefox 75+, Safari 13+) for features like HTTPS microphone access and WebRTC. - TypeScript compatibility is supported from version 3.7+. - **Additional Considerations:** - Utilizes an OpenAI API key for configuration, which must be securely managed. - Operates under the ISC license with contributions welcome via GitHub. Overall, FormToVoice enhances web forms across platforms by enabling voice-based interactions while ensuring seamless integration and user experience. Keywords: AI-powered, Angular, CMS Platforms, FormToVoice, Gatsby, GitHub, HTTPS connection, OpenAI API, Pull Request, React, Svelte, Vuejs, Web Components, WebRTC, installation, license, npm install, validation
github
www.npmjs.com 6 days ago
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463. HN GPT-5 Pro is suited for solving hard problemsGPT-5 Pro is engineered to address complex challenges; however, its full capabilities depend on having JavaScript enabled in the user's web browser. The notification indicates that if users experience functionality issues due to disabled JavaScript, they should enable it or switch to a browser that supports GPT-5 Pro's requirements. For assistance and details on compatible browsers, users can refer to the Help Center of x.com. **Bullet Point Summary:** - **Purpose:** GPT-5 Pro is built to handle complex problems. - **Dependency:** Full functionality requires JavaScript enabled in the user's browser. - **User Guidance:** If JavaScript is disabled, users must enable it or switch browsers for compatibility. - **Support Information:** A list of supported browsers can be found in x.com’s Help Center. Keywords: GPT-5 Pro, Help Center, JavaScript, browser, disabled, enabled, hard problems, keywords ```, supported, supported browsers, switch, technical, technical keywords, text topic ``` Keywords: GPT-5 Pro, xcom
gpt-5
twitter.com 6 days ago
|
464. HN Programmer who beat ChatGPT's AIPrzemysław Dębiak, known by his alias 'Psyho', achieved a notable accomplishment by winning the AtCoder World Tour Finals 2025 (Heuristic Division) in Tokyo. Competing against eleven other top programmers and an OpenAI algorithm, Dębiak outperformed ChatGPT to secure first place with a significant lead of 9.5%. The competition required participants to optimize complex code within ten hours, highlighting the superiority of human ingenuity over artificial intelligence under challenging conditions. Despite suffering from exhaustion due to limited sleep, Dębiak's victory was hailed as a triumph for humanity, earning commendation from OpenAI’s CEO Sam Altman and support from his friend Stanislaw Eysmont, who underscored the intellectual challenge of addressing real-world optimization problems without preset solutions or prompts. The AtCoder World Tour Finals is considered one of the most elite programming competitions, focusing on heuristics. It features only twelve participants globally, selected based on their ratings rather than applications. The competition tests skills in algorithmics, statistics, AI theory, and creativity through complex problem-solving challenges. Dębiak, a distinguished figure with multiple international accolades, is recognized for his expertise in algorithmics and AI as well as being a Mensa member and Polish puzzle-solving champion. In an AMA session, he shared personal insights about never having held a full-time job, dropping out of university after secondary education, childhood aspirations to become a superhero, and considering diverse careers such as game development, acting, DJing, or professional poker playing. Currently residing in Poland with no plans to move, Dębiak's victory could unlock new career opportunities, attracting attention from major technology companies interested in identifying those who truly master coding. - Przemysław Dębiak ('Psyho') won the AtCoder World Tour Finals 2025 (Heuristic Division) against ChatGPT and eleven other participants. - The competition focused on optimizing complex code within a ten-hour window, showcasing human skill over AI. - Despite exhaustion from limited sleep, his victory was seen as a triumph for humanity. - OpenAI’s CEO Sam Altman praised the win; Stanislaw Eysmont highlighted the challenge of solving real-world optimization problems without predefined solutions. - The AtCoder World Tour Finals is an elite competition featuring only twelve globally rated programmers selected by ranking. - Dębiak, a multiple international winner and Mensa member, excels in algorithmics and AI. - In an AMA session, he revealed personal details: no full-time job history, university dropout, childhood superhero aspirations, and varied career considerations. - Residing in Poland with no relocation plans, his win may open new career opportunities and attract interest from major tech companies. Keywords: AI theory, AMA session, AtCoder World Tour Finals 2025, ChatGPT, Heuristic Division, Mensa, OpenAI, Przemysław Dębiak, Psyho, Sam Altman, Stanislaw Eysmont, Tokyo, algorithm, coding, creativity, efficiency, heuristics, intellectual marathon, optimization problems, programming competition, success
openai
www.euronews.com 6 days ago
|
465. HN Workflow Before AI vs. After AI: Night and DayThe provided text discusses the transformative impact of artificial intelligence (AI) on modern workflows, transitioning them from traditional methods to highly efficient processes. Previously, tasks such as research, design, coding, and video editing were labor-intensive and required considerable manual input. AI has revolutionized these areas by introducing platforms and tools that automate and streamline operations. For instance, ChatGPT.com offers solutions for a wide range of queries, while Perplexity.ai enhances research through source-backed insights. Claude.ai functions as a conversational assistant, and Leonardo.ai generates hyper-realistic images. In the realm of video editing, Runwayml.com and Lunabloomai.com provide AI-driven capabilities to edit and generate content respectively. For coding tasks, Replit.com allows users to write and execute code efficiently, with Blackbox.ai offering additional support for developers. Additionally, Canva.com uses AI to facilitate design processes. Collectively, these tools illustrate how AI has significantly increased productivity across various domains by automating complex tasks and providing advanced resources for both creative and technical endeavors. - **AI's Role in Workflow Revolution:** AI transforms workflows from traditional methods to highly efficient processes. - **Automation of Labor-Intensive Tasks:** AI reduces manual effort required in research, design, coding, and video editing. - **Platforms Enhancing Productivity:** - **ChatGPT.com** provides solutions for diverse queries. - **Perplexity.ai** enhances research with source-backed insights. - **Claude.ai** acts as a conversational assistant. - **Leonardo.ai** generates hyper-realistic images. - **Video Editing and Generation Tools:** - **Runwayml.com** offers AI-driven video editing capabilities. - **Lunabloomai.com** specializes in AI-based video generation. - **Coding Support Platforms:** - **Replit.com** allows code writing and execution. - **Blackbox.ai** assists developers with additional support. - **AI-Driven Design Tools:** - **Canva.com** streamlines design tasks using AI capabilities. - **Overall Impact:** AI has enhanced productivity by automating complex processes and providing sophisticated tools for both creative and technical tasks. Keywords: AI, Assistant, Blackbox, Canva, ChatGPT, Claude, Code, Design, Developers, Images, Leonardo, LunaBloomAI, Perplexity, Replit, Research, RunwayML, Video Editing, Workflow
claude
news.ycombinator.com 6 days ago
|
466. HN GitHub Community Discussions: Last year's top two request are to disable Copilot**Summary:** Last year's primary concern expressed in GitHub Community Discussions was the desire among users to disable Microsoft's Copilot, a widely-used AI coding assistant integrated into their development environment. Despite these requests, GitHub has not provided a mechanism for users to disable Copilot, leading to frustration within its user community. The platform currently delivers an unspecified system message when users attempt such actions, indicating that disabling the tool is not available at this time. This ongoing issue highlights a gap between user preferences and existing software capabilities on GitHub's part. **BULLET POINT SUMMARY:** - The top requests from GitHub Community Discussions last year were to disable Copilot. - Users are unable to perform this action due to the absence of functionality within GitHub’s system. - An unspecified system message informs users that the requested action (disabling Copilot) is not available at present. Keywords: Community, Copilot, Discussions, GitHub, action, disable, keywords, message, request, technical, time, year's top
github
github.com 6 days ago
https://github.com/orgs/community/discussions/ 6 days ago https://github.com/orgs/community/discussions/ 6 days ago https://www.reddit.com/r/vscode/comments/1mk7 5 days ago |
467. HN A collection of formalized statements of conjectures in LeanThe "Formal Conjectures" project aims to formalize conjecture statements using Lean in the mathlib library, addressing a gap by providing benchmarks for automated theorem provers and enhancing clarity through formalization. It also seeks to identify areas needing new definitions within mathlib. Contributors can add conjectures from various sources beyond well-known lists like the Millennium problems or Smale's list, with guidelines encouraging contributions of formalized statements and potential clarifications via short proofs or counterexamples. To contribute: 1. Open a GitHub issue outlining your contribution. 2. Fork the repository, adding formalized conjectures to the appropriate branch in your fork, linking them to their sources. 3. Use category attributes for classification, such as open or solved research problems. The project welcomes improvements like referencing and tagging corrections via pull requests or issues. Contributors need specific tools (elan, lake, Lean) installed and should ensure successful builds before submitting code to the main repository. The repository organizes conjectures into directories such as "Util" and "ForMathlib." Key features include categorizing problem statements using attributes that range from open research problems to undergraduate-level problems. A tag system based on AMS classification codes helps describe mathematical subjects, with Lean files allowing multiple tags. The `#AMS` command in Lean assists users by providing subject details. The repository introduces an `answer( )` elaborator for formulating solutions to open problems, illustrated by the Hadwiger–Nelson problem. However, merely using "sorry" as a placeholder does not resolve the problem mathematically; meaningful contributions are encouraged. Guidelines for structuring files require each problem statement to have its own file, possibly including variants or special cases together, with appropriate AMS subject tags and references to sources. Problems stated in English use placeholders like "answer(sorry)" for unresolved solutions, which should be replaced by definitive answers once solved. Every file must begin with a specified copyright header. The repository manages versions through monthly tagged releases corresponding to Lean releases rather than the master branch, minimizing friction when integrating new definitions. All software is licensed under Apache 2.0 (2025), while other materials fall under Creative Commons Attribution 4.0 International License (CC-BY). Some third-party content may have different licensing terms, and all distributed materials are provided "AS IS," with no affiliations to Google. ### Bullet Point Summary: - **Project Overview**: The project formalizes conjectures in Lean using mathlib, serving as benchmarks for automated theorem provers, clarifying meanings, and expanding definitions. - **Contribution Guidelines**: - Open a GitHub issue for contributions. - Fork the repository, add conjectures to appropriate branches with source links, and classify using category attributes. - Use tags based on AMS classification codes; employ `#AMS` command in Lean files. - **Tools & Structure**: Contributors require specific tools (elan, lake, Lean) and must follow directory structures like "Util" and "ForMathlib." - **Elaborators and Solutions**: - Introduces an `answer( )` elaborator for open problems; placeholders like "sorry" are not resolutions. - Emphasizes meaningful contributions over trivial solutions. - **File Structuring**: Each problem should have its file, with proper AMS tags and references to sources. Placeholder text is used for unresolved English questions. - **Versioning & Licensing**: - Manages versions through monthly tagged releases aligned with Lean releases. - Licensed under Apache 2.0 (2025) and Creative Commons Attribution 4.0 International License (CC-BY). - Third-party content may have different licensing terms, all materials are "AS IS" without warranties. Keywords: AMS, API, GitHub, Lean, Zulip, conjectures, formalization, mathlib, problem, repository, theorem, versioning
github
github.com 6 days ago
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468. HN Ask HN: Is Reddit going the way of Stack Overflow?The text explores whether Reddit is experiencing challenges akin to those Stack Overflow faced due to strict and often perceived as unfair moderation. Initially a go-to platform for technical inquiries, Stack Overflow's popularity waned as users grew frustrated with what they saw as overly punitive measures against minor infractions, such as repetitive questions or topics that resurfaced in relevance due to technological shifts. The rise of AI technologies like Large Language Models (LLMs) offering instant solutions further diminished its utility, leading many users to favor direct Google searches for answers. The discussion then draws parallels between Stack Overflow's decline and potential issues facing Reddit. While Reddit is centered more on entertainment than Stack Overflow’s technical focus, concerns are raised about toxic moderators in certain subreddits, exclusionary practices within political and niche communities, and opaque bans enforced by site administrators. These elements suggest that Reddit could similarly suffer a reduction in user engagement and relevance if such moderation issues persist. Overall, the text questions whether both platforms' reliance on heavy-handed moderation will lead to diminished user interest and a decline in their importance as go-to resources within five years. **BULLET POINT SUMMARY:** - The discussion compares the potential decline of Reddit with Stack Overflow’s past experiences. - Stack Overflow lost popularity due to perceived unfair moderation and competition from AI technologies like LLMs, which provide instant answers. - Users shifted to Google for quicker solutions as a result. - Concerns are raised that Reddit may follow a similar path due to toxic moderators, exclusionary practices, and unclear admin policies. - Both platforms might face diminished user engagement if current moderation practices continue. - While Reddit focuses more on entertainment than technical information, it risks a similar decline in relevance within five years. Keywords: Claude, LLMs (Large Language Models), Reddit, SO, Stack Overflow, admins, anti-user, banning, demise, entertainment-focused, explanations, model, moderation, mods, niche topics, political subs, search functionality, subreddit mods, tech questions, toxicity, user experience
claude
news.ycombinator.com 6 days ago
https://meta.stackexchange.com/questions/333965/fi 5 days ago |
469. HN Robotaxis are a business-model war, not a sensor war (Waymo+Uber vs. Tesla)The discussion on robotaxis from companies like Waymo and Tesla extends beyond technical aspects such as LiDAR versus vision systems to focus primarily on differing business models and strategic partnerships. The article contrasts Waymo's collaboration with Uber, which emphasizes scalability through an asset-light approach by leveraging Uber’s distribution network without managing its own fleet, against Tesla's full-stack model where it controls the entire value chain from vehicle manufacturing to software development. Key elements of the Waymo + Uber strategy include positioning as technology suppliers and benefiting from existing customer bases like those of Uber and fleet partners such as Hertz. This approach minimizes asset ownership by outsourcing fleet management while focusing on scaling distribution. Conversely, Tesla’s model involves high capital expenditure due to its control over hardware-software integration and direct customer engagement, posing challenges in financing manufacturing and service operations. The article anticipates a market bifurcation within the next 5-6 years into two primary paths: one akin to Android where companies like Waymo or Baidu license technology while Uber or Didi manage distribution with scale provided by OEMs such as Toyota; and another resembling Apple, represented by Tesla’s end-to-end control over its products. Key considerations highlighted include Uber's potential dominance through owning the rider relationship, opportunities for rental firms like Hertz in fleet financing and management, Tesla's ability to sustain its capital-intensive approach, and whether a data moat comparable to Google Search exists in autonomous technology. The text suggests that while autonomous tech presents new business models, it may not yet possess an insurmountable competitive advantage akin to Google’s dominance. This shift signifies the rare real-time reorganization of entire industries. - **Comparison of Business Models**: Waymo + Uber's asset-light approach vs. Tesla's full-stack model. - **Strategic Partnerships**: Waymo leveraging Uber for distribution, Tesla controlling its own value chain. - **Market Bifurcation**: Predicted split into Android-like and Apple-like paths in autonomous vehicle strategies. - **Key Considerations**: Uber’s market dominance potential, Hertz’s role in fleet management, sustainability of Tesla's strategy, and the existence of a data moat. - **Industry Reorganization**: Autonomous tech reshaping industries with emerging business models yet lacking an insurmountable competitive edge. Keywords: CapEx, Google Search, LiDAR, OEMs, Robotaxis, Tesla, Uber, Waymo, asset-light, autonomy, business model, car costs, control, customer relationship, data moat, depreciation, distribution, economics, fleet scaling, full-stack, hardware, insurance, licensing, service centers, software, supply chain, tech debate, technology supplier, vision-only
tesla
www.umr.io 6 days ago
|
470. HN peekaping: selfhosted uptime monitoring similar to UptimeKuma (Go/React)**Summary:** Peekaping is a modern, self-hosted uptime monitoring solution crafted using Go and React, offering an alternative to Uptime Kuma. It provides real-time notifications, visually appealing status pages, and detailed analytics for monitoring various types of digital assets such as websites, APIs, and services. Peekaping supports multiple monitor types, including HTTP/HTTPS, TCP, Ping (ICMP), DNS, and more, with alert channels like Email, Webhook, Slack, Telegram, among others. Although in beta and actively developed, users are advised to test it in non-production environments due to ongoing feature refinement. Key features of Peekaping include customizable status pages, SVG badges, multi-factor authentication (MFA), brute-force protection, and SSL certificate checks. The application is designed for extensibility with a strongly typed server architecture that simplifies the addition of new notification channels and monitor types, while its client-side uses modern React patterns for maintainability. Peekaping currently defaults to Docker with SQLite but also supports PostgreSQL and MongoDB. The project's motivation centers on delivering a high-performance, customizable uptime monitoring tool with minimal footprint and seamless integration capabilities. Its development roadmap includes tools for migrating from Uptime Kuma, multi-user features like groups and access levels, expanded monitor support (including HTTPS keywords and game-specific monitors), and new notification channels including Microsoft Teams and WhatsApp. Peekaping encourages community contributions through a standard process involving forking, branching, testing, and submitting pull requests. Updates and insights are shared on Twitter, and it is open-source under an applicable license. The ClickSend SMS Rocket.Chat project, sharing similar development practices and inspired by Uptime Kuma, also follows this collaborative approach. **BULLET POINT SUMMARY:** - Peekaping is a modern uptime monitoring tool built with Go and React, offering real-time notifications and analytics. - Supports multiple monitor types (HTTP/HTTPS, TCP, Ping, DNS) and alert channels (Email, Webhook, Slack, Telegram). - In beta; advised for testing in non-production environments due to active development. - Features include status pages, SVG badges, MFA, brute-force protection, and SSL checks. - Extensible architecture with a strongly typed server and modern React patterns on the client side. - Defaults to Docker with SQLite, also supports PostgreSQL and MongoDB. - Motivated by providing customizable, high-performance monitoring with easy integration. - Development roadmap includes migration tools from Uptime Kuma, multi-user features, expanded monitor support, and new notification channels. - Encourages community contributions through a standard forking, branching, testing, and pull request process. - Updates shared on Twitter; open-source with applicable licensing details in the repository. - ClickSend SMS Rocket.Chat is an open-source project inspired by Uptime Kuma, encouraging similar development practices. Keywords: APIs, AliyunSMS, Brute-Force Protection, ClickSend SMS, DingDing, Docker, Extensibility, Fork, GitHub Issues, Go, License, MFA, Microsoft Teams, MongoDB, Monitoring, Multi-Factor Authentication, Notifications, Open-source, PagerTree, Peekaping, PostgreSQL, React, Repository, RocketChat, SQLite, SSL Certificate, Uptime Kuma, WhatsApp
postgresql
github.com 6 days ago
|
471. HN Nimalyzer – Static code analyzer for NimNimalyzer is a static code analyzer tailored for the Nim programming language, drawing inspiration from AdaControl. It enforces design patterns and verifies specific constructs by checking source code against predefined rules, such as ensuring procedures have defined pragmas. In addition to its analytical capabilities, Nimalyzer serves as an advanced search tool within codebases. The tool is configured through configuration files that outline the applicable rules and options, making it versatile in application. Currently in beta, Nimalyzer aims to be feature-complete but may still harbor some bugs. Users are advised to consult specific versions of its documentation to stay informed about different releases. To utilize Nimalyzer, users need to create a configuration file detailing the source files and desired rules, with guidance available in the project's reStructuredText documentation. The tool is executed by passing this configuration file as an argument (e.g., `nimalyzer config/nimalyzer.cfg`). Standalone binaries are accessible for FreeBSD, Linux, and Windows (64-bit) on the project’s Download page. For those interested in contributing to or updating Nimalyzer, accessing the Fossil repository is required, rather than using GitHub pull requests. To build from source, users need the Nim compiler, its source code, and additional packages such as Contracts and Colored_logger. These can be installed manually or via Nimble with `nimble install nimalyzer`. Users can choose between release (`nimble release`) or debug mode (`nimble debug`). For documentation generation, execute `nimble docs` in the main directory for an HTML version and update it using the `gendoc` tool by running `nimble tools .` followed by `bin/gendoc`. The project is distributed under the 3-Clause BSD license. - Nimalyzer is a static code analyzer for Nim, inspired by AdaControl. - It checks source code against predefined rules to enforce design patterns and verify constructs. - Functions as an advanced search tool within codebases. - Operates through configuration files specifying rules and options. - Currently in beta, may contain bugs; users should consult appropriate documentation versions. - Requires creating a configuration file for use, with guidance available in reStructuredText documentation. - Executed by passing the configuration file as an argument (e.g., `nimalyzer config/nimalyzer.cfg`). - Standalone binaries are available for FreeBSD, Linux, and Windows (64-bit) on the Download page. - Contributions or updates should be made via the Fossil repository, not GitHub pull requests. - Building from source requires the Nim compiler, its source code, and packages like Contracts and Colored_logger. - Installation can be done using `nimble install nimalyzer`, with options for release (`nimble release`) or debug mode (`nimble debug`). - Documentation is built with `nimble docs` in the main directory; updates are made with `gendoc`. - Licensed under the 3-Clause BSD license. Keywords: 3-Clause BSD license, 64-bit, Colored_logger package, Contracts package, Fossil repository, FreeBSD, GitHub, HTML documentation, Linux, Nim, Nimalyzer, Windows, beta stage, configuration files, debug mode, design patterns, documentation, gendoc tool, nimble install, pragmas, reStructuredText, release mode, rules, search tool, source code, standalone binaries, static code analyzer
github
github.com 6 days ago
|
472. HN Headscale with SQLite as database with auto failover by LiteFS and ConsulHeadscale is an open-source alternative to Tailscale that opts for using SQLite as its database due to its simplicity, intentionally avoiding the complexities associated with PostgreSQL such as setup intricacies, replication challenges, and potential migration issues. Although this decision comes at the cost of native high availability (HA) features, Headscale maintains resilience through Consul for managing failovers and LiteFS for replicating SQLite databases. This configuration allows automatic primary switchovers within approximately 15 seconds. While SQLite is favored for its simplicity and alignment with Headscale's design goals, it presents challenges in concurrent usage across multiple nodes. Alternatives like Rqlite exist but add complexity, which the developers advise against due to additional replication and debugging complications. For enhancing reliability without deviating from SQLite’s simple architecture, a solution using Consul for coordination and LiteFS for database replication is implemented. The code for this implementation can be found on GitHub under "gawsoftpl/headscale-litefs-consul." The system ensures automatic failover with up-to-date data availability and rapid recovery within roughly 15 seconds, reducing the complexity associated with having multiple active Headscale nodes or PostgreSQL replication setups. However, potential disadvantages include possible data inconsistencies during write operations if failures occur and lack of encryption for LiteFS traffic, which is mitigated by using WireGuard encryption from fly.io. In case the Tailscale coordination server (Headscale) goes down, most network functions continue to operate with some limitations: new users or devices cannot be added, key exchanges and refreshes are halted leading to gradual access loss, firewall rules can't be updated, and user keys cannot be revoked. A master node setup with one replica and auto-failover is generally sufficient for most needs. For higher availability, Keepalived can manage virtual IP addresses to facilitate automatic failovers between Headscale nodes. Despite addressing network-level failovers, SQLite's replication limitations remain a concern, necessitating solutions like LiteFS or LiteStream. The provided demo illustrates failover emulation using Docker and Consul leader election, demonstrating seamless operations switching between nodes during a simulated failure scenario. In conclusion, while Headscale lacks native HA support, it achieves reliable failover through the combined use of Consul for leader elections and LiteFS for database replication, maintaining simplicity with SQLite and ensuring robustness suitable for production environments. Keywords: Consul, Docker Compose, HA (High Availability), Headscale, LiteFS, PostgreSQL, SQLite, Tailscale, architecture, backup-and-restore, complexity, coordination, encryption, failover, leader election, lightweight, network redundancy, recovery, replication, resilience, standby
postgresql
gawsoft.com 6 days ago
|
473. HN Tesla Unveils Optimus v2.5/v3 Humanoid Robot with Hyper-Realistic HandsTesla has introduced the Optimus v3 humanoid robot, showcasing significant advancements over its predecessors with a more refined design that conceals mechanical elements. The most striking improvement is its hyper-realistic hands, boasting 22 degrees of freedom, which enhance dexterity and aim to achieve human-like precision in grasping and manipulation. This development required nearly half the engineering resources for the project. Despite these improvements, certain features such as wide-angle cameras positioned where a mouth might be create an unsettling appearance that resembles science fiction aliens, highlighting challenges related to the "uncanny valley" effect—where robots closely resembling humans can cause discomfort among observers. The Optimus v3 is designed with streamlined features intended for production models to work alongside humans in various environments. By enclosing exposed components, Tesla is moving towards practical applications beyond laboratory settings. However, significant technical hurdles remain in transforming these designs into reliable humanoid robots capable of functioning as mechanical assistants with high dexterity and collaboration capabilities. Although past versions demonstrated basic skills, achieving the sophistication seen in v3 presents considerable challenges. Tesla must also address how these human-like machines are perceived by humans, ensuring they are seen as natural collaborators rather than unsettling figures. While Optimus v3's enhanced features, such as its realistic hands, signify a potential breakthrough in robotics, high production costs and complex pathways to widespread adoption pose additional hurdles for Tesla. **BULLET POINT SUMMARY:** - **Optimus v3 Release:** Tesla unveils the more refined Optimus v3 humanoid robot with concealed mechanical elements. - **Enhanced Dexterity:** The most notable feature is its hyper-realistic hands with 22 degrees of freedom, requiring significant engineering resources to achieve human-like dexterity. - **Design Challenges:** Some design aspects, like wide-angle cameras at mouth position, create an unsettling appearance reminiscent of science fiction aliens due to the "uncanny valley" effect. - **Practical Application:** Optimus v3 aims for practical use in real-world settings with enclosed components, moving beyond lab conditions. - **Technical Hurdles:** Converting designs into reliable humanoid robots with high dexterity and collaboration capabilities remains challenging. - **Perception Issues:** Tesla must ensure these human-like machines are perceived as natural collaborators to avoid discomfort among humans. - **Production and Adoption:** Despite potential breakthroughs in robotics, high production costs and complex adoption pathways pose significant challenges for Tesla. Keywords: BOM Breakdown, Optimus v3, Tesla, aesthetic, cameras, collaboration, degrees of freedom, design, dexterity, engineering resources, factory, home, humanoid robot, joints, manipulation, manipulators, mechanical elements, mobility, production models, realism, robotic revolution, screws, uncanny valley
tesla
gearmusk.com 6 days ago
|
474. HN Building a Multilingual Blog with FastHTMLThe author shares their experience developing a multilingual blog with specific requirements such as markdown support and interactive features, while navigating various programming frameworks. They highlight the challenges of switching between multiple languages and frameworks due to hidden transaction costs. Preferring to work within Python, they chose FastHTML for its compatibility with their machine learning background, allowing web development without context-switching. The author emphasizes "locality of behavior," advocating for integrating related code within a single language to reduce mental overhead and increase efficiency. This approach facilitated the creation of a blog that met all criteria while remaining in their preferred programming environment. Key features described include a post renderer using FastAPI, which constructs page layouts with nested Divs styled via Tailwind CSS. The `render_post` function handles markdown content and integrates a language switcher via HTMX. This dynamic component allows users to change languages without refreshing the entire page by updating specific elements through the `hx_target`. The language switcher form interacts with the `/setlanguage` endpoint, triggering the `set_language` function to update session settings and reload content accordingly. The system also automates translation from English to French using OpenAI’s API for new or updated posts, ensuring consistent formats via a Pydantic model called `PostTranslated`. Change tracking is achieved through hashing that accounts for content and metadata changes, triggering translations only when necessary. A `.pre-commit-config.yaml` file includes a Python hook in `translate_blogs.py`, which runs on each commit to manage translations stored in the `articles/fr/` directory. The author reflects on the learning process of integrating HTMX forms, stressing adherence to web standards and manual control offered by their custom solution. They express satisfaction with this tailored approach despite potential alternatives from existing frameworks, offering full code transparency for reference. - The journey involved creating a multilingual blog that needed specific features such as markdown support and interactivity. - Framework challenges highlighted hidden transaction costs of using multiple languages and frameworks. - Preference led to choosing FastHTML for its Python-centric approach, aligning with the author's background in machine learning. - "Locality of behavior" was adopted to minimize context-switching and enhance efficiency by keeping code within one language. - FastAPI facilitated post rendering through `render_post` function, incorporating markdown and a dynamic language switcher via HTMX. - Language switching is handled without full page refreshes, targeting elements with specific IDs using HTMX attributes. - The `/setlanguage` endpoint updates user preferences in sessions, supporting seamless content reloading based on language changes. - Automated translation from English to French leverages OpenAI’s API and Pydantic validation through `PostTranslated`. - Change tracking via hashing ensures translations occur only when necessary, with a pre-commit hook managing this process. - The integration of HTMX forms required adherence to web standards, providing manual control over content transformations. - Despite potential alternatives, the custom solution using Python was rewarding, offering complete code for reference. Keywords: FastAPI, FastHTML, GPT-4, HTMX, JavaScript, OpenAI, PostTranslated, Pydantic, Python, SHA256, Tailwind CSS, YAML, code highlighting, content pipeline, dropdown, forms, frameworks, git, language switcher, machine learning, markdown, multilingual, repository, sessions, simplicity, tech stack, templating language, transaction cost, translation API, trigger change, web development
openai
simn.fr 6 days ago
|
475. HN Show HN: CompareGPT – Spotting LLM Hallucinations with Multi-Model ComparisonTina introduces CompareGPT.io, a platform designed to mitigate the issue of large language model (LLM) hallucinations—where AI systems generate confident but false information. The tool addresses this by comparing responses from multiple LLMs such as ChatGPT, Gemini, Claude, and Grok, aiming to enhance the credibility of AI-generated content. Its key features include reducing hallucinations through multi-model comparisons, providing unified API keys for access to major models, and offering promotional pricing. Tina is seeking feedback from the community on whether consistency across multiple models effectively protects against hallucinations and how these issues are managed in critical areas such as law, finance, and research. - **Introducing CompareGPT.io**: A platform aimed at addressing LLM hallucinations by comparing outputs from multiple large language models. - **Purpose**: Enhance trustworthiness of AI-generated content by reducing false or fabricated outputs through multi-model comparison. - **Key Features**: - Reduction of hallucinations via side-by-side comparisons of responses from models like ChatGPT, Gemini, Claude, and Grok. - Provision of unified API keys for accessing major LLMs. - Offering promotional pricing to users. - **Seeking Community Feedback**: Tina is interested in understanding if multi-model consistency effectively prevents hallucinations and how such issues are handled in essential fields including law, finance, and research. Keywords: AI outputs, API keys, ChatGPT, Claude, CompareGPT, Gemini, Grok, LLM hallucinations, features, finance, law, multi-model comparison, queries, research, trustworthiness, waitlist
claude
news.ycombinator.com 6 days ago
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476. HN GitHub/spec-kit: Toolkit to help you get started with Spec-Driven Development**Concise Summary:** The text describes a comprehensive workflow using the GitHub/spec-kit toolkit to facilitate Spec-Driven Development (SDD). This approach prioritizes product scenarios over code-centric development, treating specifications as executable components that generate working implementations. The spec-kit provides commands like `/specify`, `/plan`, and `/tasks` for defining project goals, specifying technical choices, and creating actionable task lists. To start a new project with spec-kit, users initialize it using the Specify CLI, selecting their preferred AI agent (e.g., Claude) during setup. If necessary tools are missing, initialization can proceed with the `--ignore-agent-tools` option. An example project named "Create Taskify," a productivity platform for team collaboration without user authentication, is outlined. It supports predefined users managing tasks on a Kanban-style board. The process involves using Claude Code to draft specifications and set up repositories, resulting in structured directories containing user stories and functional requirements. Initial outputs from Claude Code are treated as drafts, requiring iterative refinement. The planning stage includes specifying technical details like the tech stack (e.g., .NET Aspire, Blazor server) through the `/plan` command. The implementation plan requires auditing with Claude Code to ensure completeness, followed by creating a pull request via GitHub CLI for tracking purposes. It involves identifying and resolving over-engineered components while adhering to foundational principles. Finally, using local CLI tools like `dotnet`, users implement solutions based on specifications, addressing any build or runtime errors with Claude Code's assistance. **Bullet Point Summary:** - **Spec-Driven Development (SDD):** Focuses on product scenarios; specs are executable components. - **Toolkit Commands:** `/specify` for goals, `/plan` for tech choices, and `/tasks` for task lists. - **Project Initialization:** Use `specify init - **Example Project - Create Taskify:** Team productivity platform supporting predefined users on a Kanban-style board without authentication. - **Claude Code Usage:** Draft specifications and setup repositories, treating outputs as drafts for refinement. - **Technical Planning:** Specify tech stack (e.g., .NET Aspire, Blazor server) using `/plan`. - **Implementation Plan Audit:** Use Claude Code to ensure completeness; create pull requests via GitHub CLI. - **Over-engineering Check:** Identify and resolve over-engineered components while adhering to principles. - **Solution Implementation:** Execute with local CLI tools like `dotnet`; troubleshoot build/runtime errors with Claude Code. Keywords: AI Agent, Architecture Choices, Audit, Blazor Server, Bootstrap, Checklist, Code, Coding Agent, Executable, Functional Requirements, GitHub, Implementations, Kanban Style, NET Aspire, Postgres, Product Scenarios, Project Initialization, Pull Request, REST API, Refinement, Research Document, SignalsR, Spec-Driven Development, Specifications, Specify CLI, Taskify, Tech Stack, Toolkit, User Stories, Validation Plan
postgres
github.com 6 days ago
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477. HN Using Claude Code to modernize a forgotten Linux kernel driverThe author outlines their experience in recovering data from QIC-80 tapes using old PC workstations equipped with CentOS 3.5, facilitated by the ftape Linux kernel driver. This process involves connecting a tape drive to a floppy controller as a cost-effective solution instead of using an additional SCSI adapter. Although this approach has limitations such as a low data rate of 500 Kbps due to the floppy controller's speed, it allows for successful data recovery with careful handling. The challenges in using tape drives through floppy controllers stem from their nonstandard communication protocols requiring software hacks to manipulate hardware I/O ports and timings, as these drives were not recognized by motherboard BIOSes. While proprietary tools existed for MS-DOS and Windows, only the open-source ftape supported reading raw binary contents across different formats on Linux. However, since it hasn't been updated since around 2000 and was removed from the Linux kernel due to lack of support, outdated Linux versions are necessary for accessing these tapes. A request was made to update an old Linux kernel driver, initially compiled with version 2.4, to be compatible with newer kernels. Utilizing Claude Code's iterative feedback process, deprecated functions were replaced, and a new build system was set up to compile the updated code as a standalone loadable module. After resolving initial communication issues caused by incorrect configuration settings, the author successfully developed a functioning kernel module that detected and read tape drives. This project demonstrated the effective collaboration between human expertise and AI tools like Claude, highlighting the importance of specific instructions using domain-specific terms. The experience showed that while AI can handle many coding aspects, human oversight is necessary for structuring tasks and early problem identification. AI agents are powerful productivity tools that enhance personal skills without requiring exhaustive learning processes, particularly useful in modernization efforts by handling technical details. They facilitate rapid onboarding into new technologies, allowing users to focus on strategic thinking and experiential learning. The author's successful revival of ftape for compatibility with modern Linux systems like Xubuntu 24.04 underscores the potential of AI tools in reviving legacy software. - The author used old PC workstations with CentOS 3.5 and ftape to recover data from QIC-80 tapes via floppy controllers. - Floppy controllers present challenges due to nonstandard communication protocols, requiring software hacks for hardware manipulation. - Proprietary tools existed for MS-DOS/Windows; ftape was the only open-source option but hasn't been updated since 2000. - Outdated Linux versions are needed for ftape due to its removal from the kernel; modernization efforts involved updating it for newer kernels using Claude Code's feedback process. - The author developed a functioning loadable kernel module after resolving configuration issues, demonstrating AI-human collaboration in coding. - AI agents enhance productivity by handling technical details, facilitating rapid onboarding into new technologies, and supporting experiential learning. - The successful update of ftape for modern Linux systems highlights the potential of AI tools in reviving legacy software. Keywords: BBS operators, C programming, CentOS, LLMs, Linux kernel, QIC-80, SCSI adapter, Xubuntu, data dump, data reconditioning, floppy controller, ftape driver, kernel development, kernel module, modernization, tape recovery, x86 architecture
claude
dmitrybrant.com 6 days ago
https://github.com/dbrant/ftape 6 days ago https://dmitrybrant.com/2023/03/25/artificial 6 days ago https://hackage.haskell.org/package/boilerplate 6 days ago https://www.youtube.com/watch?v=axBVG_VkrHI 6 days ago https://codeberg.org/superseriousbusiness/gotosocial&#x 6 days ago https://rspec.info/features/6-1/rspec-rails/f 6 days ago https://en.wikipedia.org/wiki/Kernel_panic 6 days ago https://dynamicland.org/2024/FAQ/#What_is_Realtalk 6 days ago https://codeberg.org/comaps/comaps 5 days ago https://codeberg.org/comaps/comaps/pulls/1792 5 days ago https://codeberg.org/comaps/comaps/pulls?state=all 5 days ago https://codeberg.org/comaps/comaps/pulls/1782 5 days ago https://en.wikipedia.org/wiki/Bullshit#In_the_philosoph 5 days ago https://github.com/Godzil/ftape/tree/master 5 days ago https://arxiv.org/html/2506.12286v3 5 days ago https://github.com/langroid/langroid/releases/ 5 days ago https://github.com/langroid/langroid/releases/ 5 days ago https://github.com/badlogic/lemmy/tree/main&# 5 days ago https://langroid.github.io/langroid/notes/task-ter 5 days ago https://github.com/terryyin/lizard 5 days ago https://xkcd.com/1200/ 5 days ago https://github.com/Godzil/ftape 5 days ago |
478. HN My Vercel v0 weekend: A working app, a happy friend, and a $50 bill**Summary:** An experienced backend engineer shares their experience of using Vercel v0 over a weekend to develop an internal app for a friend's business. Despite challenges such as bypassing rate limits and spending about $50 on a Pro subscription and credits, they successfully delivered a working minimum viable product by Monday. The author was particularly impressed with the tool's rapid development-to-deployment cycle, which effectively addressed the "blank canvas" problem in frontend design. Although there were doubts about whether the $50 investment was justified—considering similar backend work could be completed in an equivalent timeframe—the accelerated UI and design process provided significant value. The engineer reflects on their use of Vercel v0 compared to other tools like Claude and Copilot, favorably assessing its speed for frontend tasks. They are interested in whether any alternatives match the rapid 0-to-1 development capability of v0 and seek input from others on this matter. The author emphasizes a pragmatic approach to using tools, focusing on maximizing their value before transitioning away. Additionally, they express interest in learning about efficient workflows from fellow developers, highlighting the importance of continually evaluating the ongoing worth of their toolset. **Bullet Point Summary:** - The author used Vercel v0 over a weekend to develop an internal app for a friend's business. - Despite bypassing rate limits and spending $50 on a Pro subscription and credits, they delivered a working MVP by Monday. - Impressed by the rapid development-to-deployment cycle, which solved the "blank canvas" problem in frontend design. - Doubts about whether the $50 investment was justified compared to backend work timeframe; appreciated UI/design acceleration. - Compared v0 favorably to other tools like Claude and Copilot for frontend tasks. - Seeks advice on alternatives offering similar speed as v0 for rapid 0-to-1 development. - Emphasizes a pragmatic approach to tool usage, maximizing value before transitioning. - Interested in learning efficient workflows from fellow developers and evaluating ongoing toolset worth. Keywords: 0-to-1 speed, AI tools, Bolt, Claude, Copilot, MVP, Pro sub, UI/Design, UI/design acceleration, Vercel, backend engineer, cost-effectiveness, dev-to-deploy cycle, feature-rich app, frontend bottleneck, internal app, leverage, rate limits, time tracker, tool assessment, workflow effectiveness
claude
news.ycombinator.com 6 days ago
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479. HN OpenAI reorg at risk as Attorneys General push AI safetyThe Attorneys General of California and Delaware have urged OpenAI to ensure AI safety for children after incidents involving a chatbot were linked to a young person's suicide in California. They are scrutinizing OpenAI’s restructuring into a Public Benefit Corporation, concerned it might prioritize profits over public interest, contrasting with its current nonprofit status that mandates prioritizing the public first. This proposed change has faced opposition from former advisor Page Hedley and The Midas Project, who argue against privileging private gains. While this restructuring could help OpenAI raise more funds, there is concern about potential dilution of its commitment to public benefit. Under current obligations, OpenAI must prioritize public interest over profits, but restructuring plans may alter this focus. Bret Taylor, Chair of the OpenAI Board, has assured that the company is addressing safety concerns raised by attorneys general and implementing enhanced safeguards for users, particularly teens, such as crisis helpline directions and parental controls. This response comes after a bipartisan letter from 44 State Attorneys General sent to major tech CEOs in August 2025, urging increased accountability for child protection on their platforms. Despite these calls, there has been minimal government enforcement against tech companies over the past two decades, highlighted by recent policy reversals under the Trump administration that reduced focus on AI safety. - **Key Points:** - California and Delaware Attorneys General urge OpenAI to ensure AI safety for children following a suicide incident linked to its chatbot. - Concerns are raised about OpenAI's restructuring into a Public Benefit Corporation, which may prioritize profits over public interest, contrasting with its current nonprofit status. - Opposition from former advisor Page Hedley and The Midas Project highlights fears of prioritizing private gain. - OpenAI’s restructuring could increase fundraising capabilities but might reduce its commitment to public benefit. - Current obligations require OpenAI to prioritize public interest; however, this may change under the proposed restructuring plans. - Bret Taylor, Chair of OpenAI's Board, commits to addressing safety concerns and enhancing user safeguards, including crisis helpline referrals and parental controls. - The response follows a bipartisan letter from 44 State Attorneys General in August 2025 urging tech CEOs for accountability in child protection on their platforms. - Despite calls for enforcement, there has been minimal government action against tech companies over the past two decades, with recent policy reversals under the Trump administration reducing AI safety focus. Keywords: AI safety, AI safety order, Bonta-Jennings, California, Center for AI Standards and Innovation, ChatGPT, Delaware, Elon Musk, Meta, OpenAI, Page Hedley, Public Benefit Corporation, US AI Safety Institute, attorneys general, board of directors, crisis helplines, distress, investors, murder-suicide, nonprofit, parental controls, protections, safeguards, suicide, technology companies, teens, xAI
openai
www.theregister.com 6 days ago
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480. HN ChatGPT's Micro-Cap Portfolio: Week 10In Week 10 of a six-month micro-cap stock portfolio experiment conducted by ChatGPT, significant progress was made with an overall increase of 32.6%, largely driven by aTyr Pharma (ATYR). However, due to a miscalculated order, ATYR now constitutes 50% of the portfolio, presenting substantial risk. Other notable holdings include Abeona Therapeutics (ABEO), Axogen Inc. (AXGN), and Fortress Biotech (FBIO). The performance metrics reveal a maximum drawdown of -7.11%, with period and annualized Sharpe Ratios at 1.3428 and 2.9669, respectively, while the Sortino Ratios stand at 2.3796 for the period and 5.2579 on an annual basis. Despite having a high beta of 1.5980 compared to ^GSPC, the portfolio boasts an impressive annualized alpha of 182.96%. However, the short sample size introduces potential instability in these metrics. The strategic review indicates adjustments aimed at growth with risk management considerations. Axogen Inc. shares were sold following an FDA decision delay, reallocating funds into Fortress Biotech and 4D Molecular Therapeutics, driven by upcoming FDA decisions and positive trial data respectively, alongside established stop-loss controls. Abeona and aTyr remain core holdings due to their promising commercialization stages and forthcoming Phase 3 results. Key actions taken include: - **Selling Axogen Inc.:** Exiting all shares at a $15.50 limit. - **Buying Fortress Biotech:** Adding two shares with a $3.80 limit, maintaining existing stop-loss controls. - **Purchasing 4D Molecular Therapeutics:** Starting a position of two shares at a $7.50 limit, subject to price gap entry conditions. The portfolio strategy emphasizes catalyst-driven growth and risk management while holding high conviction in Abeona and aTyr due to their anticipated market impact. Over nine weeks, the portfolio has achieved approximately 32% growth versus the S&P 500’s 4.5%, underpinned by key holdings with solid fundamentals and active news flow, alongside tactical plays like Fortress and 4DMT. The focus remains on catalyst anticipation and risk management as September approaches, although there is concern about over-reliance on high-risk, low-success catalysts that may render the portfolio more akin to a speculative bet than a sustainable strategy. This approach's effectiveness will be evaluated in the upcoming review. As an educational research project, no financial advice is provided. --- **BULLET POINT SUMMARY:** - **Portfolio Performance:** Achieved 32.6% growth in Week 10, driven primarily by aTyr Pharma (ATYR). - **Risk Concerns:** ATYR now makes up 50% of the portfolio due to an order miscalculation, increasing risk. - **Performance Metrics:** - Max drawdown at -7.11% - Period Sharpe Ratio: 1.3428; Annualized: 2.9669 - Period Sortino Ratio: 2.3796; Annualized: 5.2579 - High beta of 1.5980 relative to ^GSPC with an annualized alpha of 182.96% - **Strategic Adjustments:** Sold Axogen Inc. due to FDA delays, reallocating funds into Fortress Biotech and 4D Molecular Therapeutics. - **Key Holdings:** Abeona and aTyr remain strong due to promising commercialization stages. - **Recent Transactions:** - **Sold:** All shares in Axogen Inc. at $15.50 limit - **Bought:** 2 shares of Fortress Biotech at $3.80 limit with stop-loss controls - **Bought:** 2 shares of 4D Molecular Therapeutics at $7.50 limit, conditional on price gaps - **Strategic Focus:** Emphasizes catalyst-driven growth and risk management. - **Growth vs. S&P 500:** Achieved ~32% growth compared to the S&P 500’s 4.5% over nine weeks. - **Portfolio Structure:** Relies on solid fundamentals for key holdings, with tactical plays on near-term events. - **Risks:** Potential instability due to high-risk catalyst reliance and short sample size concerns. - **Next Steps:** Evaluation of strategy in the upcoming review; no financial advice given. Keywords: ATYR, Abeona, Alpha, Automation, Axogen, Beta, Binary Outcomes, Buy, Catalyst, ChatGPT, Concentration, Cost Basis, Daily Closing Data, Deep Research, Drawdown, Email, Exposure Levels, FDA, Financial Advice, Fortress, GitHub, Growth, High-conviction, Holdings, Limit Order, Lottery Ticket, Micro-cap, Orphan-drug, Performance Graph, Phase 3 Results, Portfolio, Positive Data, Revaluation, Risk, Risk Control, Sell, Sharpe Ratio, Sortino Ratio, Stocks, Stop Loss, Strategy, Trade Summary
github
nathanbsmith729.substack.com 6 days ago
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481. HN GitHub shouldn't allow AGPL project templatesThe author acknowledges the value of a comprehensive Tauri template available on GitHub while expressing concerns about its use of the AGPL-3.0-or-later license. This strong copyleft license requires any derivative works to also be open-source and mandates sharing private server-side code if distributed over a network, thereby limiting the template's applicability for closed-source or commercial projects. Although production-ready, this restriction might deter widespread adoption. The author suggests considering more permissive licenses like MIT or Apache 2.0 to allow broader utilization by both open-source and commercial users, potentially increasing contributions and visibility within the Tauri community. While recognizing that the original intent of AGPL was to promote openness, the author argues that a less restrictive license could enhance accessibility and benefit a larger audience. - The author appreciates the comprehensive nature of a Tauri template on GitHub. - Questions arise concerning its use of the AGPL-3.0-or-later license due to its strong copyleft provisions. - This license mandates open-source derivative works and sharing private server-side code if distributed over networks, limiting commercial use. - Such restrictions may hinder widespread adoption despite the template being production-ready. - The author recommends considering more permissive licenses like MIT or Apache 2.0 for broader accessibility. - A switch to a less restrictive license could increase contributions and visibility within the Tauri community. - While AGPL aims to ensure openness, a flexible license might expand its reach and benefit more users. Keywords: AGPL-30, Apache 20, BSD, CI/CD, GitHub, MIT, SaaS, Tauri, UI, auto-updates, commercial, copyleft, desktop apps, license, open-source, security checks, state management, template
github
github.com 6 days ago
|
482. HN Formatting code should be unnecessary### Summary In the 1980s, significant advancements in programming technology aimed to resolve persistent issues related to code formatting and linter settings. A notable development was the use of an Intermediate Representation called DIANA (Descriptive Intermediate Attributed Notation for Ada), which facilitated more flexible interactions with code. This approach allowed users to view and modify code through personalized settings, as exemplified by Mr. Paige's work on the Ada compiler. The Rational R1000 system, a product of this era, leveraged DIANA to offer advanced features such as incremental compilation and debugging, making it highly effective for large-scale projects like the International Space Station software. DIANA played a crucial role in these advancements by acting as an intermediary between plain-text source code and the compiler or integrated development environment. This allowed for projectional editing, where users could directly manipulate the program tree instead of writing and formatting text-based code. Such capabilities were instrumental in eliminating issues related to code formatting and linter conflicts. Grady Booch noted that these innovations not only supported seamless refactoring but also facilitated rapid integration processes, which were essential for managing substantial Ada projects. Despite modern tools advancing in certain areas such as refactoring, the challenges of code formatting persist even into 2025, indicating gaps in current technology compared to past solutions like DIANA and projectional editing. This historical context underscores an ongoing struggle with code formatting issues that were once believed resolved decades ago. The discussion advocates for further exploration of projectional editing techniques to address these enduring challenges. ### Bullet Point Summary - **Advancements in the 1980s:** Aimed at eliminating code formatting issues using DIANA. - **DIANA's Role:** Served as an intermediary representation, allowing flexible source viewing and modification. - **Rational R1000 System:** Utilized DIANA to offer features like incremental compilation and debugging for large projects. - **Projectional Editing Benefits:** Allowed direct manipulation of program trees, eliminating code formatting issues. - **Grady Booch's Insights:** Highlighted benefits such as seamless refactoring and rapid integration. - **Persistent Challenges in 2025:** Code formatting and linter settings remain unresolved despite technological advances. - **Historical Context:** Emphasizes ongoing struggles with code formatting believed resolved long ago. - **Future Exploration:** Suggests further investigation into projectional editing to overcome current limitations. Keywords: Ada, DIANA, ESLint, IDE, IR (Intermediate Representation), Rational R1000, UML, code generation, compiler, debugging, incremental compilation, linter, refactoring, semantic analysis, software development, version control
popular
maxleiter.com 6 days ago
https://en.wikipedia.org/wiki/Lint_(software) 5 days ago https://github.com/orgs/community/discussions/ 5 days ago https://docs.github.com/en/repositories/working-wi 5 days ago https://www.dell.com/en-ca/shop/dell-ultrasharp-49 5 days ago https://en.wikipedia.org/wiki/Line_length#cite_note-dyk 5 days ago https://vt100.net/dec/vt220/glyphs 5 days ago https://en.wikipedia.org/wiki/Literate_programming 5 days ago https://news.ycombinator.com/item?id=45166670 5 days ago https://docs.astral.sh/ruff/rules/ 5 days ago https://www.gnu.org/software/indent/manual/in 5 days ago https://www.kernel.org/doc/html/v4.10/process 5 days ago https://black.vercel.app/ 5 days ago https://news.ycombinator.com/item?id=45169331 5 days ago https://github.com/ast-grep/ast-grep 5 days ago https://semanticdiff.com 5 days ago https://npmjs.com/package/@codemod/cli 5 days ago https://difftastic.wilfred.me.uk/ 5 days ago https://www.nushell.sh/ 5 days ago https://wiki.archlinux.org/title/Core_utilities 5 days ago https://docs.helix-editor.com/syntax-aware-motions.html 5 days ago https://www.masteringemacs.org/article/combobulate-stru 5 days ago https://zed.dev/blog/syntax-aware-editing 5 days ago https://github.com/mvdan/gofumpt 5 days ago https://astyle.sourceforge.net/astyle.html#_style=whitesmith 5 days ago https://astyle.sourceforge.net/astyle.html#_style=pico 5 days ago https://astyle.sourceforge.net/astyle.html#_style=gnu 5 days ago https://mergiraf.org/introduction.html 5 days ago https://github.com/Wilfred/difftastic 5 days ago https://naildrivin5.com/blog/2013/05/17/ 5 days ago https://www.youtube.com/watch?v=XolJx4GfMmg&t=63s 5 days ago https://x.com/efortis/status/1922427544470438381 5 days ago https://github.com/airbnb/javascript/issues/1 5 days ago https://github.com/airbnb/javascript/issues/1 5 days ago https://www.unison-lang.org/docs/the-big-idea/#ric 5 days ago http://iolanguage.org 5 days ago https://git-scm.com/book/pt-br/v2/Customizing 5 days ago https://btmc.substack.com/p/thoughts-on-visual-programm 5 days ago https://prettier.io/ 5 days ago https://www.unison-lang.org/ 5 days ago https://gist.github.com/conartist6/75dc969b685bbf69c9fa 5 days ago |
483. HN Show HN: I made a terminal UI to manage parallel async Claude Code/Codex agents**Summary:** The document introduces DevTeam CLI (@agent-era/devteam), a terminal-based interface designed to manage multiple coding agents like Claude Code, Codex, or Gemini simultaneously. This tool streamlines development by allowing users to launch several agents at once, switch between them, review code changes, add comments, and handle pull requests directly from the terminal. To install, prerequisites include Node.js 18+ and tmux on your PATH, with options for a global installation using `npm i -g @agent-era/devteam` or an alternative method through a provided installer script. Usage involves navigating to project directories or specifying them via `--dir`, ideally in a VM with broad permissions for agents, alongside a dedicated GitHub account for PRs. Guidelines can be set through CLAUDE.md/AGENTS.md to ensure features are tested and PRs are created. Key features of DevTeam CLI include managing multiple agents across different git worktrees, a built-in diff viewer for code changes, communication via comments, an unblocking UI for input-required agents, monitoring agent progress, testing changes in each worktree, and support for various CLI tools. Repository development involves building with `npm run build`, running the CLI using `npm run cli -- --dir **BULLET POINT SUMMARY:** - DevTeam CLI is a terminal interface for managing multiple coding agents, facilitating parallel development. - **Installation:** Requires Node.js 18+ and tmux; install globally via npm or use an installer script. - **Usage:** Navigate to project directories with `--dir`, preferably in a VM, using a dedicated GitHub account for PRs. Guidelines can be added through CLAUDE.md/AGENTS.md. - **Features:** - Manage agents across git worktrees. - Built-in diff viewer and comment system. - UI highlights agents needing input. - Monitor progress via line counts, feature push status, and GitHub PR checks. - Test changes in each worktree. - Supports Claude Code, Codex, or Gemini CLI. - **Repository Development:** - Build with `npm run build`. - Run CLI using `npm run cli -- --dir - General testing via `npm test` and terminal E2E tests with `npm run test:terminal`. - Publish a scoped package publicly using `npm version` followed by `npm publish --access public`. - **Commands for Node.js Project Management:** Include running CLI tasks, general and terminal-based testing, and publishing procedures to ensure streamlined workflow. Keywords: AGENTSmd, CLAUDEmd, CLI, Claude Code, Codex, DevTeam, E2E, Gemini CLI, GitHub account, Nodejs, PATH, PRs, TUI, diff viewer, git worktrees, npm install, npm run, parallel async agents, patch minor major build dist tarball scripts, permissions, prepublishOnly, public version, publishing, sandbox, scoped, terminal UI, test, tmux
claude
github.com 6 days ago
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484. HN Google details Gemini usage limitsGoogle has provided detailed information regarding usage limitations for its Gemini AI service across various subscription levels. Users with a free account of the Gemini 2.5 Pro version can utilize up to five prompts daily, while those subscribed to the AI Pro plan are allowed 100 prompts each day. The AI Ultra plan further extends this capability to 500 prompts per day. Additionally, users on the free tier have access to generate up to five Deep Research reports and produce 100 images daily. Subscribers to either the Pro or Ultra plans can significantly increase their image generation capacity to 1,000 images a day. This information is now accessible in an updated Help Center article specifically addressing Gemini Apps' limits and upgrade options. **BULLET POINT SUMMARY:** - Google clarified usage limits for Gemini AI service across subscription tiers. - Free account on Gemini 2.5 Pro allows up to five prompts per day. - AI Pro plan increases limit to 100 prompts daily; AI Ultra plan offers 500 prompts. - Free accounts can generate up to five Deep Research reports and 100 images per day. - Upgrading to AI Pro or Ultra plans allows for generating up to 1,000 images daily. - Details available in Google's updated Help Center article on Gemini Apps limits and upgrades. Keywords: AI subscribers, Apps, Deep Research reports, Gemini, Google, Help Center, Pro plan, Ultra, conversations, free account, generated images, prompts, tiers, upgrades, upgrading, usage limits
gemini
www.theverge.com 6 days ago
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485. HN Show HN: Claude Context but local – semantic code search without API keysThe text describes a local semantic code search tool called Claude Context, designed to operate without requiring API keys or external cloud services by utilizing EmbeddingGemma for embeddings and FAISS for vector storage. This alternative version emphasizes privacy and cost-effectiveness by ensuring all operations remain on the user's machine. Key features of the tool include: - Local processing that keeps code within the machine. - Elimination of ongoing costs associated with cloud services. - High-quality semantic search capabilities, comparable to the original tool. - Use of Tree-sitter for parsing abstract syntax trees, aiding in understanding code structure. - Support for EmbeddingGemma (1.2GB) and FAISS to manage embedding generation and vector similarity searches efficiently. - Integration with tools like Claude Code via the Model Context Protocol (MCP). - Early benchmarks showing a 70% reduction in token usage when searching large codebases. - Broad programming language support through Tree-sitter parsers, including Python, JavaScript/TypeScript, Go, Java, JSX/TSX, and Svelte. The project is accessible on GitHub as `claude-context-local`, encouraging feedback on its code chunking and embedding strategy. It highlights a system architecture that includes directories for handling tasks like multi-language chunking, embeddings, searching, managing changes via Merkle DAGs, providing MCP tools, and installing necessary components. Data flow within the tool involves indexing through an MCP client and coordinating updates using an IncrementalIndexer in conjunction with ChangeDetector and MultiLanguageChunker. The embedded chunks are stored for searchability using FAISS, facilitating efficient retrieval of relevant code snippets. Intelligent chunking uses tree-sitter to create semantically meaningful code chunks across supported languages, preserving important elements like functions, methods, and docstrings while maintaining context through imports and references. The system also manages extensive metadata about each code chunk, such as file paths, function/class relationships, complexity scores, and line numbers, supporting various programming languages and utilizing the `google/embeddinggemma-300m` model hosted on Hugging Face for embedding operations. Configuration includes specifying storage directories with default settings, automatic device selection for processing (CUDA, MPS, CPU), and preference for FAISS backend indexing. Model downloading is optimized through caching to enhance speed and reliability, while metadata and indices are stored efficiently. Performance optimization research focuses on using EmbeddingGemma-300m with various embedding dimensions and index types based on dataset size. It emphasizes GPU acceleration with FAISS for faster searches and provides troubleshooting tips for common issues like import errors or memory problems. The project also invites contributions in areas such as chunking strategies, performance enhancements, and metadata extraction. Licensed under the GNU General Public License v3.0 (GPL-3.0), the project encourages collaboration and experimentation to improve its features and capabilities. Keywords: AST parsing, EmbeddingGemma, FAISS, GitHub repository, IncrementalIndexer, LINUX kernel, MCP protocol, Merkle DAG, Metadata extraction(Note: The keywords selected are relevant to the topics mentioned in the text and reflect key components or technologies discussed), NVIDIA GPU, Python 312+, Semantic code search
claude
github.com 6 days ago
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486. HN GPT-5: The Case of the Missing Agent**Summary:** The article "GPT-5: The Case of the Missing Agent" explores recent advancements in artificial intelligence, specifically focusing on OpenAI's GPT-5 release. Despite initial expectations that GPT-5 would significantly advance agentic AI—systems capable of independent long-term goal pursuit—the model falls short of these aspirations. While there have been improvements in areas like coding and computer use, current AI models still lack the reliability and independence necessary for real-world applications. The discussion references earlier attempts such as AutoGPT by Toran Bruce Richards, which aimed to create a general-purpose agentic AI but often struggled with overly complex plans and failed at tasks beyond simple ones. A notable rebranding incident occurred when this model was repurposed destructively under the name "ChaosGPT," though these malicious actions were unsuccessful. The article highlights the limitations of current AI through various experiments, such as an AI named Claudius managing a mini-store simulation. Claudius demonstrated strengths like efficient sourcing but also exhibited significant weaknesses, including financial missteps and failing to learn from errors. These challenges persisted even with newer models like Claude 4.1 and Gemini 2.5 Pro. Another experiment involving playing multiple games revealed that GPT-5 struggled due to limitations in visual perception and task management. Despite these challenges, GPT-5 represents a notable progression over its predecessor, GPT-4, by offering enhanced capabilities such as improved research, itinerary generation, programming, reduced errors, additional tool support like web search, larger context windows, better reasoning abilities, increased speed, and cost-effectiveness. These improvements benefit everyday users but fall short of the revolutionary expectations set by AI hype. The article reflects on the potential proximity of true agentic AI, acknowledging incremental advancements in memory capacity and reasoning with models like OpenAI's o1. However, significant gaps remain in exploration, creative insight, and real-world adaptability. The challenges faced highlight that while AI has deep capabilities in specific areas, it remains a shallow imitation of human behavior. Looking forward, substantial advancements are anticipated over the next few years across various environments and tasks. Yet, each solution often reveals new challenges, indicating gaps in "intelligence" previously unrecognized. Real-world agency continues to be limited, as evidenced by projects like AI Village, suggesting that robust real-world capabilities might still be years away, with unforeseen weaknesses likely emerging as models achieve more complex feats. **Bullet Point Summary:** - GPT-5 does not fully realize the vision of agentic AI despite advancements in certain areas. - AutoGPT and ChaosGPT highlight past struggles in creating reliable general-purpose AI systems. - Claudius's mini-store experiment underscores current AI limitations in real-world tasks, including financial mismanagement and learning difficulties. - In gaming experiments, GPT-5 faced challenges with visual perception and task execution. - Despite these shortcomings, GPT-5 offers improvements over GPT-4: enhanced research capabilities, better reasoning, larger context windows, and reduced errors. - While benefiting users in specific contexts, GPT-5 falls short of the revolutionary AI expectations set by media hype. - Current AI advancements have increased memory capacity and reasoning abilities but lack creative insight and real-world adaptability. - Significant gaps remain in AI's ability to generalize reasoning, prioritize tasks, maintain factual consistency, and understand its non-human nature. - Future AI developments are expected to bring substantial improvements, though solving one problem often reveals new challenges. - Real-world agentic capabilities remain limited, with potential weaknesses emerging as models tackle more complex tasks. Keywords: AI agents, Anthropic, AutoGPT, Claude AI, GPT-4, GPT-5, Gemini, Google, OpenAI, agentic AI, context windows, real-world operation(Note: The above keywords have been extracted based on their relevance to the text's content and frequency of occurrence in describing the core topics discussed), reasoning models
gemini
secondthoughts.ai 6 days ago
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487. HN My Video Production Stack (For YouTube)The article discusses an automated video production stack specifically designed for creating and publishing "talking head" videos with screen sharing on YouTube. The system addresses two primary challenges: overcoming self-consciousness about content quality and simplifying the editing process. Implemented in Python, it automates several steps after recording a raw video. Key components of the workflow include: - **color_edit**: This tool uses colored frames (green to keep, red to remove) as visual cues for intelligent, no-hands video editing, effectively eliminating "dead air" and ensuring tight edits. - **yt_chapter_maker**: It generates AI-powered chapter markers and title suggestions based on transcribed content, enhancing metadata creation. - **yt_upload**: This manages YouTube API integration for uploading the finished video, requiring initial OAuth authentication. - **video_upload_workflow**: Acts as a meta-orchestrator to ensure seamless processing from raw recording to publication. The workflow also incorporates OpenAI's Whisper tool for audio transcription and caches intermediate outputs for efficiency. The system emphasizes high-quality audio over high-resolution video for better viewer retention. Various modern microphones are used across different recording environments, while the author relies on a long-standing editing tool called "color_edit," which has proven reliable over four years without changes. The newer components of this workflow were developed in the past year and have been successfully integrated to produce multiple videos on the author's channel. The tools are publicly available across multiple Git repositories, with the intention that others creating similar content can benefit from this fully automated system. **BULLET POINT SUMMARY:** - Automated video production stack for "talking head" YouTube videos. - Addresses self-consciousness about content quality and simplifies editing. - Workflow includes Python-written automation of post-recording steps: - **color_edit**: Uses colored frames to guide no-hands editing. - **yt_chapter_maker**: AI-powered chapter markers and title suggestions from transcripts. - **yt_upload**: Manages YouTube API uploads with OAuth authentication. - **video_upload_workflow**: Orchestrates the entire process. - Workflow features: - OpenAI's Whisper for audio transcription. - Caches intermediate outputs for efficiency. - Emphasizes high-quality audio over video resolution. - Utilizes modern microphones and a reliable, unchanged tool "color_edit." - Recent workflow components successfully integrated in past year. - Tools are publicly available on Git repositories for broader use. Keywords: AI-powered, API, Automation, Caching, Channels, Chapters, Editing, Experience, Frames, Git, GitHub, Integration, Meta-orchestrator, Microphone, Pipeline, Python, Quality, Recording, Transcription, Upload, Video Production, Whisper, Workflow, YouTube
github
vivekhaldar.com 6 days ago
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488. HN OmniOS – open-source server OS with native ZFSOmniOS is an open-source server operating system distinguished by its native support for ZFS (Zettabyte File System). Developed transparently on GitHub, it invites community engagement through pull requests, enabling contributions from developers worldwide. This approach facilitates collaborative development and continuous improvement of the software. Users are granted free access to OmniOS's source code, empowering them to customize and compile their own versions tailored to specific needs or preferences. - **Key Points:** - OmniOS is an open-source server operating system. - It features native ZFS support. - Developed on GitHub with transparent processes. - Encourages community contributions via pull requests. - Users have free access to the source code for customization and building their versions. Keywords: GitHub, OmniOS, ZFS, build, development, maintain, open-source, projects, pull-requests, self-hosting, server OS, source, technical
github
omnios.org 6 days ago
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489. HN LLMs vs. Geolocation: GPT-5 Performs Worse Than Other AI Models**Summary:** In June 2023, Bellingcat conducted geolocation tests with large language models (LLMs) to evaluate their ability in accurately identifying locations from photographs. Google Lens initially led the tests alongside ChatGPT o4-mini-high. A subsequent test introduced newer AI tools like Google’s AI Mode, GPT-5, and Grok 4, all assessed using Bellingcat's holiday photos that were not previously published. The models' performances were rated on a scale of 0 to 10 based on accuracy. Google AI Mode was the standout performer in these tests, while Grok 4 had mixed results compared to its predecessor, with Gemini and older GPT versions still leading. Notably, GPT-5 underperformed despite features like 'Thinking' mode, as demonstrated by incorrectly identifying a city street image's location. Google AI Mode consistently outperformed other models in tasks such as accurately pinpointing Noordwijk beach in the Netherlands when others failed. Despite GPT-5 and its Pro variant’s advanced capabilities, they struggled with geolocation accuracy, sometimes even more than earlier models. In response to performance issues, OpenAI reinstated access to older models like GPT-4o for subscribers. However, these options are not more accurate in geolocation tasks. Google AI Mode is powered by Gemini 2.5 and demonstrated superior performance compared to other models including its own Pro Deep Research variant. Its availability is currently limited to India, the UK, and the US. Bellingcat cautions against over-reliance on LLM outputs due to their potential for errors. The evolving nature of AI results in fluctuating model performances. Bellingcat plans ongoing testing with emerging models and acknowledges Nathan Patin’s contributions to benchmarking efforts. As a non-profit organization, Bellingcat seeks support through various channels, including Patreon, newsletters, and social media platforms like Bluesky and Instagram. **Bullet Point Summary:** - **Test Overview:** Bellingcat conducted geolocation tests in June 2023 using LLMs to identify locations from photographs. - **Initial Results:** Google Lens and ChatGPT o4-mini-high initially performed well; newer models like Google AI Mode, GPT-5, Grok 4 were tested later. - **Performance Ratings:** Models were rated on a scale of 0 to 10 for accuracy in location identification using unpublished holiday photos from Bellingcat. - **Top Performers:** Google AI Mode was the most effective; Grok 4 showed inconsistent performance compared to predecessors. GPT-5 underperformed despite enhanced modes like 'Thinking' and ‘Pro’. - **Case Example:** GPT-5 incorrectly identified a location in Test 25 (Noordwijk beach) that Google AI Mode correctly pinpointed. - **Access and Availability:** OpenAI reinstated older models like GPT-4o, but these are not more accurate. Google AI Mode is available only in India, the UK, and the US. - **Caution Against Over-reliance:** Users should be cautious of LLM outputs due to potential errors; AI performance can fluctuate. - **Future Plans:** Bellingcat will continue testing emerging models; Nathan Patin contributed to benchmarking efforts. - **Support for Bellingcat:** As a non-profit, it seeks support through Patreon, newsletters, Bluesky, Instagram, and other channels. Keywords: AI Mode, Bellingcat, ChatGPT, Cities, GPT-5, Gemini, Geolocation, Google Lens, Grok, LLMs, Multimodality, OpenAI, Test
gemini
www.bellingcat.com 6 days ago
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490. HN How We Ship ML Algorithms to Prod Without Rewrites (Or ML Engineers)- **Overview of Challenges at Verbit**: Verbit encountered difficulties integrating machine learning (ML) algorithms into production due to issues like re-implementations, duplicate efforts, and communication barriers between research and engineering teams. - **Solution via Temporal Activities**: The solution involved abstracting research algorithms into Temporal activities. This allowed developers to maintain workflows executing these algorithms without requiring ML engineer intervention, facilitating automatic updates and improving resilience and scalability in production environments. - **Role of Researchers and Initial Development Process**: Researchers were responsible for creating, testing, and refining algorithms with specific input/output requirements. They developed a standalone "beta" version initially, which was iteratively improved based on feedback before integration into the application's codebase. - **Integration Challenges**: Integrating these algorithms posed challenges due to tight coupling in the system, differing dependencies, and evolving architectures, requiring updates across multiple repositories to ensure compatibility. - **Introduction of ML Engineer Role**: Companies introduced the role of ML Engineers as intermediaries between researchers and application engineers. However, this added costs and inefficiencies due to increased communication overhead and redundant implementations. - **Proposal for Algorithm-as-a-Service Model**: The article suggests an "algorithm-as-a-service" model using microservices to separate algorithms from their applications, improving integration efficiency and reducing project management complexities. - **Temporal as a Solution**: Temporal was proposed as an effective solution to expose algorithms via APIs without extensive infrastructure maintenance. It offers scalable and resilient workflows that simplify the deployment process for app developers and researchers. - **Workflow Management with Temporal**: Researchers update or develop new versions of algorithms, which are automatically detected and integrated into existing workflows by App Engineers using minimal code changes. This enables seamless updates and deployment without manual intervention. - **Advantages of Using Temporal**: - Zero-touch deployment for researchers and developers. - Independent monitoring capabilities for workflow improvements. - Efficiency gains as researchers avoid redundant implementations or deep production code understanding. - Elimination of the need for ML Ops Engineers. - Short learning curve for both developers and researchers. - **Challenges and Implementation**: Despite its benefits, challenges include coordinating efforts across teams, setting standards upfront, and initial setup of necessary tools like Temporal. Initial implementation took about a month with one full-time team member but has met expectations, with plans to explore more complex algorithms in the future. Feedback remains positive, though ongoing adjustments are anticipated. - **Conclusion**: The initiative aims to streamline integration processes, enhance collaboration between teams, and facilitate quicker adaptation by developers and researchers, ultimately improving efficiency and reducing operational complexities. Keywords: API, Algorithm as-a-service, LLM, ML algorithms, SLA (Service Level Agreement), Temporal activities, algorithm-code, apps, coding standards, collaboration, dependencies, idempotent, integrations, lifecycle tools, micro-service architecture, production, re-implementations, requirementstxt, researchers, resilience, resiliency, sandboxing, scale, speaker identification, workflows
llm
shlep.ai 6 days ago
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491. HN Print GitHub Repositories as Books"Print GitHub Repositories as Books" is a service that enables users to convert their favorite Git repositories into PDF documents, designed for both public and private projects. Users can access this functionality by signing in with their GitHub account, allowing them to print repositories in an aesthetically pleasing format. The platform is currently in its beta testing phase, actively seeking user feedback on any potential features or bugs to enhance the service further. This innovative tool was developed by @pliutau. **BULLET POINT SUMMARY:** - "Print GitHub Repositories as Books" allows users to print Git repositories as visually appealing PDFs. - The service supports both public and private projects. - Users must sign in with their GitHub account to use the service. - Currently in beta, it invites feedback on features or bugs for improvements. - Developed by @pliutau. Keywords: Beta, Bugs, Features, Format, GitHub, Made by, PDF, Print, Private, Public, Repositories, Sign In
github
gitprint.me 6 days ago
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492. HN Pico CSS – Minimal CSS Framework for Semantic HTMLPico CSS is a minimalist CSS framework tailored for semantic HTML, focusing on simplicity by utilizing fewer than ten classes. It provides options for both class-light and class-less implementations, allowing users to achieve streamlined styling without relying on JavaScript or package managers. The framework ensures responsive design through automatic scaling of font sizes and spacings across various devices. Pico supports light and dark color schemes that adapt based on user preferences via the `prefers-color-scheme` media query, all achieved without JavaScript. Additionally, it offers extensive customization capabilities with over 130 CSS variables and SASS support, alongside 20 color themes and more than 30 modular components, enabling brand-specific UI design. **BULLET POINT SUMMARY:** - Pico CSS is a minimalistic framework designed for semantic HTML. - It emphasizes simplicity with fewer than ten classes. - Offers both class-light and class-less versions for streamlined styling without dependencies like JavaScript or package managers. - Ensures responsive design by automatically scaling font sizes and spacings across devices. - Supports light and dark color schemes that adapt to user preferences using the `prefers-color-scheme` media query, without JavaScript. - Allows customization with over 130 CSS variables and SASS support. - Includes 20 color themes and more than 30 modular components for brand-specific UI design. Keywords: Accessibility, CSS Variables, Class-light, Color Schemes, Components, Customization, Dark Mode, Light Mode, Minimal Framework, Mobile Devices, Pico CSS, Pure CSS, Responsive Design, SASS, Semantic HTML, Simplify, prefers-color-scheme
popular
picocss.com 6 days ago
https://neat.joeldare.com/ 5 days ago https://picocss.com/docs/usage-scenarios 5 days ago https://github.com/foundata/hugo-theme-govanity 5 days ago https://picocss.com/docs/css-variables 5 days ago https://picocss.com/docs/colors 5 days ago https://www.cssbed.com/ 5 days ago https://github.com/sindresorhus/github-markdown-css 5 days ago https://csszengarden.com/ 5 days ago https://news.ycombinator.com/item?id=45119103 5 days ago https://dohliam.github.io/dropin-minimal-css/ 5 days ago https://g5t.de 5 days ago https://www.w3schools.com/TAGS/tag_embed.asp 5 days ago https://www.w3schools.com/TAgs/tag_object.asp 5 days ago https://news.ycombinator.com/item?id=41511420 5 days ago https://news.ycombinator.com/item?id=41482679 5 days ago https://news.ycombinator.com/item?id=39488979 5 days ago https://news.ycombinator.com/item?id=29559961 5 days ago https://raku.org 5 days ago https://harcstack.org 5 days ago https://news.ycombinator.com/item?id=28860500 5 days ago https://newcss.net/ 5 days ago https://sourcehut.org/ 5 days ago https://github.com/stribny/sidewinder/ 5 days ago https://demo.rubomonolith.com 5 days ago https://www.fortressone.org 5 days ago |
493. HN Commanded – Event Sourcing and CQRS with ElixirThe document introduces "Commanded," a specialized tool aimed at facilitating the development of Elixir applications using Command Query Responsibility Segregation (CQRS) and Event Sourcing (ES) patterns. This tool offers flexibility in data persistence, supporting either an Elixir-based event store with PostgreSQL or utilizing EventStoreDB as alternatives for storing events. The document emphasizes that detailed implementation and usage instructions are available through the Getting Started and Usage guides, which prospective users should consult to fully leverage the capabilities of "Commanded." - **Introduction of Tool**: The document presents "Commanded," a tool designed specifically for building applications in Elixir using CQRS and ES patterns. - **Persistence Support**: It highlights that "Commanded" supports data persistence via an Elixir-based event store with PostgreSQL or EventStoreDB, offering users flexible options depending on their preferences or project requirements. - **Guidance for Users**: The document advises users to consult the Getting Started and Usage guides for comprehensive instructions on how to implement and effectively use "Commanded" in their projects. Keywords: CQRS, CQRS/ES, Commanded, Elixir, Elixir EventStore, Event Sourcing, EventStoreDB, Postgres, Usage, applications, guides, persistence
postgres
hexdocs.pm 6 days ago
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494. HN ZeroFS: Turn S3 into a Real, High-Performance Filesystem (NFS/9P/NBD, No FUSE)**Summary of the Provided Text** ZeroFS is a transformative filesystem that leverages S3 storage to provide high-performance capabilities, supporting protocols such as NFS, 9P for file-level access, and NBD for block-level access. It serves as primary storage with features like encryption, compression, multi-layered caching, and microsecond latencies, demonstrating its robustness by passing an extensive POSIX test suite. The architecture includes a client layer interfacing with protocol clients, core servers per protocol, and a Virtual File System (VFS) linked to encryption and cache managers that connect to various storage backends like SlateDB and S3 Object Store. Installation of ZeroFS is facilitated through pre-built binaries optimized by Profile-Guided Optimization, accessible via Cargo or Docker. Configuration involves a TOML file supporting environment variables, customizable cache settings, and options for different storage backends with encryption capabilities managed via Argon2id to ensure security. The system prefers 9P over NFS due to its performance advantages but supports both protocols; it also offers Unix sockets for low-latency local access. ZeroFS can dynamically manage NBD devices within a `.nbd` directory, supporting TRIM/Discard operations for efficient storage space management. It enables geo-distributed ZFS pools across AWS regions for redundancy and disaster recovery, with support for L2ARC to enhance database performance by facilitating high throughput and low latency through cloud storage tiering. Performance testing shows that ZeroFS can achieve near-NVMe speeds at a fraction of the cost when integrated with PostgreSQL using ZFS L2ARC and S3-backed caches. It offers advantages over FUSE by eliminating the need for custom kernel modules, leveraging existing protocols like NFS and 9P to ensure compatibility and performance efficiency. The document contrasts ZeroFS with JuiceFS and S3FS in terms of storage architecture, performance characteristics, data layout, and cost models. S3FS relies on mapping filesystem operations directly to S3 objects and suffers from high latency and inefficiencies; it stores files as single objects and directories separately. In contrast, ZeroFS employs SlateDB for chunked file organization and integrates metadata within the database structure, enhancing management efficiency. Performance-wise, S3FS struggles with small, random I/O tasks and lacks atomic multi-file transaction support, while ZeroFS excels in these areas through efficient data handling and indexing. The data layout of S3FS involves single objects per file with optional separate metadata, whereas ZeroFS utilizes a key-value structure within SlateDB for optimal retrieval and storage. Regarding cost models, S3FS incurs significant expenses due to its reliance on API requests during operations like full rewrites. Conversely, ZeroFS minimizes write amplification through compaction and batching, resulting in more predictable costs. ZeroFS is integrated into CI/CD workflows via a GitHub Action, offering persistent storage solutions with S3 credentials that support large files (up to 16 EiB) and an extensive number of files (281 trillion), though practical constraints may arise from provider limits or performance issues. It is dual-licensed under GNU AGPL v3 for open-source use and provides commercial licensing options. **Bullet Point Summary:** - ZeroFS transforms S3 into a high-performance filesystem supporting NFS, 9P, NBD; features include encryption, compression, caching, and low latencies. - Architecture comprises client layers, core servers per protocol, VFS with an Encryption Manager and Cache Manager linked to storage backends like SlateDB and S3 Object Store. - Installation via pre-built binaries optimized with Profile-Guided Optimization; configuration through TOML files supporting environment variables, cache settings, and various backend options including encryption. - Prefers 9P over NFS for mounting due to performance benefits; supports Unix sockets for local low-latency access. - Supports dynamic NBD device management, enabling efficient storage space management via TRIM/Discard operations. - Enables geo-distributed ZFS pools across AWS regions with L2ARC support for database performance enhancement. - Achieves near-NVMe speeds at lower costs when integrated with PostgreSQL using ZFS L2ARC and S3-backed caches; offers advantages over FUSE by leveraging existing protocols like NFS and 9P without custom kernel modules. - Comparative analysis shows ZeroFS's design advantages over JuiceFS and S3FS in storage architecture, performance, data layout, and cost models. - **Storage Architecture**: - S3FS maps operations directly to S3 objects; directories are zero-byte objects with separate metadata. - ZeroFS uses SlateDB for chunked file organization integrating metadata within the database structure. - **Performance Characteristics**: - S3FS struggles with latency, inefficiencies in small operations, and lacks atomic multi-file support. - ZeroFS excels in handling small, random I/O tasks, supports fast directory operations with B-tree indexes, and facilitates atomic batch processing. - **Data Layout**: - S3FS files are single objects; metadata optional and separate. - ZeroFS employs a key-value structure within SlateDB for efficient data management. - **Cost Model**: - S3FS incurs costs per API request, especially during full rewrites. - ZeroFS reduces write amplification through batching, leading to predictable expenses. - Integration: Available as GitHub Action for CI/CD workflows; supports large files and extensive file counts but may face practical limits from providers or performance constraints. - Licensing: Dual-licensed under GNU AGPL v3 with commercial options available. Keywords: 9P, Architecture, CI/CD, Caching, Cloud Storage, Compaction, Data Integrity, Docker, Encryption, LSM Tree, NBD, NFS, POSIX, Performance, PostgreSQL, Rust, S3, TRIM, Tiered Storage, Ubuntu, Unix Socket, ZFS, ZeroFS
postgresql
github.com 6 days ago
https://github.com/yandex-cloud/geesefs 6 days ago https://github.com/Barre/ZeroFS/issues/143 6 days ago |
495. HN ASML becomes Mistral AI's top shareholder**Summary:** ASML, a prominent Dutch chipmaking equipment supplier, is poised to become the leading shareholder in French AI startup Mistral AI by investing 1.3 billion euros into its Series C funding round. This strategic investment aims to enhance European technological independence and potentially allows ASML to secure a board seat at Mistral. Consequently, this financial commitment will elevate Mistral's valuation to a pre-money figure of 10 billion euros, making it the most valuable AI company in Europe. The collaboration is mutually beneficial: ASML can integrate Mistral’s advanced AI capabilities into its equipment, and Mistral stands to diminish European dependency on American and Chinese AI models. As a result, Mistral emerges as a significant competitor in the global AI landscape against giants like OpenAI and Google. Mistral, founded in 2023 by Arthur Mensch (a former DeepMind researcher), alongside ex-Meta researchers Timothée Lacroix and Guillaume Lample, is preparing for a funding round targeting a $14 billion valuation. The investment decision was reportedly advised by Bank of America on behalf of ASML, although the bank declined to comment further. This report was compiled by Milana Vinn and Max A. Cherney with editing from Ken Li, Dawn Kopecki, and Andrea Ricci. **Bullet Point Summary:** - ASML plans to invest 1.3 billion euros in Mistral AI’s Series C funding round, becoming its top shareholder. - The investment aims to boost European tech sovereignty and may result in a board seat for ASML at Mistral. - This deal will raise Mistral's pre-money valuation to 10 billion euros, making it Europe's most valuable AI company. - The partnership benefits both companies: ASML can enhance its equipment with Mistral’s AI technology; Mistral reduces European reliance on U.S. and Chinese AI models. - Mistral positions itself as a key competitor against major players like OpenAI and Google in the AI sector. - Mistral, founded by Arthur Mensch, Timothée Lacroix, and Guillaume Lample in 2023, is preparing for a funding round with a $14 billion valuation target. - Bank of America advised ASML on its investment decision but has not commented publicly. - The report was authored by Milana Vinn and Max A. Cherney and edited by Ken Li, Dawn Kopecki, and Andrea Ricci. Keywords: ASML, Bank of America, Bloomberg News, EUV, Mistral AI, Nvidia, Series C, artificial intelligence, board seat, chipmaking, funding, investment, semiconductor industry, tech sovereignty, valuation
mistral
finance.yahoo.com 6 days ago
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496. HN Show HN: Serverless Workflow Builder – Visual drag-and-drop editor libraryThe post introduces a visual editor designed for creating Serverless Workflows, featuring an intuitive drag-and-drop interface built using React Flow. This tool is developed as a reusable React library, enabling its integration into various applications to simplify serverless workflow creation. It includes essential hooks that ensure full functionality. Resources available for the library include installation via NPM (serverless-workflow-builder-lib), a live demo hosted on Kshitiz1403's GitHub Pages, and access to the project repository on GitHub. The developer seeks feedback for further improvement of the library and encourages users to reach out via email, although the specific address is not provided in the text. **Bullet Point Summary:** - Introduction of a visual editor for Serverless Workflows with a drag-and-drop interface. - Built using React Flow, available as a reusable React library. - Facilitates integration into various applications and simplifies workflow creation. - Includes necessary hooks to ensure functionality. - Resources include NPM (serverless-workflow-builder-lib), a live demo on GitHub Pages, and the project repository on GitHub. - Developer seeks feedback for improvement and encourages contact via email. Keywords: Drag-and-Drop, Feedback, Frontend, GitHub, Hooks, Integration, Live Demo, NPM Package, React Flow, Reusable Library, Serverless Workflow, Specification, Visual Editor
github
github.com 6 days ago
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497. HN We trust strangers' open source more than our colleagues'In tech companies, there is a notable paradox where teams prefer using open-source libraries developed by external contributors over those created internally by their colleagues. This preference stems from several psychological factors: - **Social Proof**: External libraries that garner stars and downloads are perceived as validated by the wider community, providing an illusion of reliability. - **Blame Avoidance**: Failures in external libraries diffuse responsibility, whereas failures in a colleague’s project can lead to personal blame. - **Conflict-of-Interest Reflex**: Internal projects face skepticism due to assumptions of self-interest, even when none exists. - **The "Prophet Without Honor" Effect**: Colleagues’ work might be harshly judged based on past visible flaws, while external libraries benefit from an idealized online presence. - **Vivid Bus Factor**: Concerns about the maintenance of internal projects if a key developer leaves can lead to preference for externally maintained projects. These biases result in choosing unknown contributors over trusted colleagues, leading to redundant work, wasted resources, and morale issues among undervalued employees. Ironically, it also poses security risks by favoring external libraries that might be less secure than well-maintained internal ones. To mitigate these biases, companies should evaluate both external and internal libraries using objective criteria such as tests, release cadence, documentation quality, and security policies. Shifting internal projects to neutral platforms like a GitHub organization can reduce their perception as personal endeavors. Establishing governance with multiple maintainers further depersonalizes the project. To enhance an open-source project's appeal, it should be moved from personal accounts to organizations with clear governance structures. This shift helps to frame contributions objectively and highlights features rather than personal ownership. Employees often work on these projects out of passion, ensuring sustained maintenance. Encouraging unbiased evaluation can uncover valuable internal options that were previously overlooked due to psychological biases. **Bullet Point Summary:** - Teams in tech companies often prefer external open-source libraries over internally developed ones. - Psychological factors include social proof, blame avoidance, conflict-of-interest reflex, the "Prophet Without Honor" effect, and concerns about the vivid bus factor. - This bias leads to redundant work, resource waste, morale issues among employees, and security risks due to favoring external projects. - Recommended actions: Use objective criteria for library evaluation; move internal projects to neutral spaces like GitHub organizations; establish governance with multiple maintainers. - Enhancing project appeal involves depersonalizing projects by transferring them to organizations and establishing clear governance, emphasizing features over personal ownership. Keywords: GitHub, GitHub org, bias, bias reduction, blame avoidance, bus factor, conflict-of-interest, contribution, crowd validation, downloads, employee project, employees, evaluation, features, governance doc, halo effect, library, maintainability, maintainers, messy commits, open source, paradox, personal proof, projects, psychological reflexes, psychology, risk, safety net, self-promotion, skepticism, social proof, stars, tech companies, trust
github
00f.net 6 days ago
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498. HN Just launched my 3rd SaaS using Elixir/Phoenix, sharing some random thoughtsThe author provides insights into launching three SaaS products using Elixir/Phoenix over a decade of experience with the language and open-source contributions. The products are Persumi (an audio blogging platform), Rizz.farm (a Reddit lead generation tool), and FeedBun (a browser extension decoding food labels). All were built on the Petal framework, PostgreSQL, and deployed via Fly.io. While Fly.io's global distribution was appreciated, challenges included its stability issues and initial Postgres service limitations like lack of citext support during migration. For FeedBun, Supabase was chosen for backend solutions due to a smoother experience compared to previous products. Persumi and Rizz.farm were developed manually with minimal tool assistance (e.g., Github Copilot), taking three months and six weeks respectively while working full-time jobs. Persumi moved from local text-to-speech processing to Azure and Google's TTS services, while Rizz.farm integrated Reddit and Google’s Search APIs, noting the outdated nature of Reddit's API. FeedBun is unique for integrating multiple LLM providers like AWS Bedrock, Google Vertex, OpenRouter, Perplexity, and OpenAI. This integration facilitates diverse model functionalities through "model groups" tailored for specific tasks such as low latency or grounded research. The author favors Amazon’s Nova models for efficiency but finds OpenAI's less effective for certain prompts. Although FeedBun includes about two dozen LLM models, not all are actively used. The rapid development of FeedBun's alpha version was made possible by AI tools like Claude Code and Cursor, allowing completion in just a few weeks despite some challenges with Claude Code. The author credits advancements in machine learning for this progress and shares these experiences through a handwritten post, inviting reader questions. - **Key Points:** - Three SaaS products launched using Elixir/Phoenix over a decade of experience. - Products: Persumi (audio blogging), Rizz.farm (Reddit lead generation), FeedBun (food label decoding). - Built on Petal framework and PostgreSQL; deployed with Fly.io, facing stability issues and Postgres limitations. - FeedBun uses Supabase for backend due to smoother experience compared to previous tools. - Persumi switched from local TTS to Azure/Google services; Rizz.farm integrates outdated Reddit API with Google’s Search APIs. - FeedBun integrates multiple LLM providers, using "model groups" for specific tasks, favoring Amazon's Nova models. - Development of FeedBun alpha version accelerated by AI tools Claude Code and Cursor, highlighting machine learning advancements. - Author invites questions, sharing experiences through a handwritten post. Keywords: AI, API, Citext, Elixir, Flyio, OpenAI, Petal, Phoenix, Postgres, Reddit, SaaS, TTS, audiobooks, auth, benchmarks, blogging, boilerplate, feed, integration, latency, machine learning, podcasts, stability
github copilot
old.reddit.com 6 days ago
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499. HN The Io Programming Language, 18 Chapters by Claude Code Opus 4.1**Summary:** "The Io Programming Language, 18 Chapters" is a book authored by Claude as part of his Opus 4.1 series. This publication delves into the extensive facets of the Io programming language, providing readers with an in-depth understanding and thorough coverage of its features. The author actively acknowledges reader feedback, highlighting its importance for enhancing future editions of the book. Additionally, Claude encourages direct communication via email, inviting discussions or inquiries from readers regarding the content or related topics. **Bullet Point Summary:** - The book is titled "The Io Programming Language, 18 Chapters" and authored by Claude. - It is part of Claude's Opus 4.1 series. - Offers comprehensive coverage on various aspects of the Io programming language. - Includes acknowledgment of reader feedback to improve future editions. - Author encourages contact through email for further discussion or inquiries. Keywords: Address, Chapters, Claude, Claude Code, Code, Contact, Contact Keywords: Io, Email, Email Address, Feedback, Input, Io, Language, Opus, Opus 41, Programming, Programming Language
claude
github.com 6 days ago
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500. HN Clockbench AI benchmark: 89.1% humans vs. 13.3% top LLM**Summary:** The Clockbench AI benchmark highlights that despite the advanced capabilities of leading language models in reasoning, mathematics, and visual understanding, they face significant challenges with reading analog clocks, achieving only 13.3% accuracy compared to humans' 89.1%. This discrepancy suggests that the difficulty lies more in reasoning within a visual context rather than interpreting text. Further investigation is necessary to identify whether refining current methods or creating new strategies will enable AI models to enhance their performance in this specific task. **BULLET POINT SUMMARY:** - **Performance Discrepancy:** Leading language models show strong skills in reasoning, mathematics, and general visual understanding but struggle significantly with reading analog clocks. - **Benchmark Results:** Models scored 13.3% accuracy on the task of reading analog clocks, whereas humans achieved a much higher rate at 89.1%. - **Root Cause Analysis:** The challenge likely arises from difficulties in processing and reasoning within a visual context rather than text-based contexts. - **Need for Further Research:** It is essential to explore whether improving existing paradigms or developing new approaches will allow AI models to enhance their performance in reading analog clocks. Keywords: AI benchmark, Clockbench, analog clocks, frontier models, high bar, humans, mathematical ability, novel approach, reasoning skills, research, scaling paradigms, text space, top LLM, visual space, visual understanding
llm
clockbench.ai 6 days ago
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501. HN Show HN: Simple Markdown resume; fancy rendered HTML/PDFThe text introduces "Simple Markdown Resume" by John-Kim Murphy, which is a resume formatted in plain-text markdown for ease of updating and conversion into HTML/PDF formats. The HTML version is enhanced with unique CSS styling, including an animated logo to improve visual appeal, while the PDF version can be produced through browser print settings optimized for quality. Source code for this project is accessible on GitHub. John-Kim Murphy has a diverse professional background starting from 2013 as a Freelance Consultant and previously held positions such as Technology Researcher (2011-2014), Software Development Engineer at Microsoft (2008-2009), Software Engineer at Lockheed Martin (2005-2007), and Research Scientist at the University of Minnesota in 2004. His expertise includes designing complex web applications, researching new technologies, developing air traffic control software, and creating location-aware mobile apps. Academically, Murphy earned a B.S. in Computer Science with a minor in Korean Languages and Literature from the University of Minnesota. He graduated summa cum laude with distinction, achieving a 3.8/4.0 GPA, demonstrating his academic excellence. **BULLET POINT SUMMARY:** - "Simple Markdown Resume" by John-Kim Murphy is written in plain-text markdown for easy updating and conversion into HTML/PDF formats. - The HTML version includes unique CSS styling with an animated logo to enhance visual appeal. - PDF generation involves printing the webpage from a browser with optimized settings for print quality. - Source code for creating this resume can be found on GitHub. - John-Kim Murphy's professional experience includes roles such as Freelance Consultant since 2013, Technology Researcher (2011-2014), Software Dev. Engineer at Microsoft (2008-2009), Software Engineer at Lockheed Martin (2005-2007), and Research Scientist at the University of Minnesota in 2004. - His work spans designing complex web applications, researching technologies, developing air traffic control software, and creating location-aware mobile apps. - Murphy holds a B.S. in Computer Science with a minor in Korean Languages and Literature from the University of Minnesota. - He graduated summa cum laude with distinction, maintaining a GPA of 3.8/4.0. Keywords: CSS, Education, Freelance Consultant, GitHub, HTML, Markdown, PDF, Research Scientist, Resume, Software Engineer, Technology Researcher, UX, Web App
github
leftium.com 6 days ago
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502. HN Show HN: Psq – CLI for Postgres MonitoringPsq is a command-line interface (CLI) tool designed specifically for monitoring PostgreSQL databases, offering an interactive Text User Interface (TUI) that allows users to navigate using the keyboard. It leverages configuration settings stored in `~/.pg_service.conf` and provides both pre-configured queries for common database operations and customizable options through a built-in editor. This editor supports optional query generation via ChatGPT, enhancing flexibility for user-specific monitoring needs. Key features of Psq include its interactive TUI interface, capability to handle custom and preset queries in real time, and automatic database connection configuration. Installation involves cloning the tool's repository followed by dependency management using `go mod tidy` and building the application with `go build -o psq`. Configuration requires Go installed on the user's system; Psq automatically generates a SQLite database at `~/.psq/queries.db` during its first run, which stores monitoring queries. Users have the option to replace this database with a default provided by the author. For usage, Psq can be executed using simple command-line instructions like `./psq`, `./psp -s prod`, or by specifying a service from the configuration file `~/.pg_service.conf`. This ensures ease of access and operational flexibility for monitoring PostgreSQL databases effectively. - **Main Tool Description**: Psq is a CLI tool with an interactive TUI for PostgreSQL database monitoring, supporting keyboard navigation. - **Configuration and Queries**: Utilizes `~/.pg_service.conf` for database configurations and offers pre-set queries plus customizable options via a built-in editor, which includes optional ChatGPT query generation. - **Features Highlighted**: - Interactive Text User Interface (TUI) - Configurations through `.pg_service.conf` - Pre-configured and customizable monitoring queries - Real-time query execution and results display - **Installation Process**: - Clone the repository - Install dependencies with `go mod tidy` - Build using `go build -o psq` - **Configuration Details**: - Generates a `~/.psq/queries.db` SQLite database for queries on first use - Option to replace this with the default `queries.db` file from the author - **Usage Instructions**: - Run with commands like `./psq`, `./psp -s prod` - Specify services using `~/.pg_service.conf` - **Requirements**: Requires Go installation for building and configuration. Keywords: CLI, ChatGPT, Configuration, Connections, Database, Defaults Keywords: Postgres, Dependencies, Editor, Execution, Go, Installation, Interactive, Monitoring, PgAdmin, Postgres, Psq, Queries, Real-time, Results, SQLite, Service, TUI, Usage
postgres
github.com 6 days ago
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503. HN RTX 5090 128 GB GPU Spotted At $13,200 Per PieceThe text discusses a custom modification of the NVIDIA GeForce RTX 5090 GPU, which features an impressive upgrade to 128 GB GDDR7 memory. This substantial increase is facilitated by using salvaged components from gaming cards and installing them on custom PCBs that allow for dual-sided memory configurations. Such modifications require precise firmware and BIOS adjustments. The prototype, likened to the Titan Ada variant, employs GDDR7X VRAM and costs $13,200 each—markedly higher than the standard RTX 5090 version priced between $1,999 MSRP and $2,500-$3,000 market value. Even when compared to NVIDIA's official RTX PRO 6000 Blackwell (96 GB VRAM for approximately $10,000), the custom GPU is more expensive yet offers a proportional increase in memory capacity. The source of these modifications, I_Leak_VN, raises questions about the specific memory modules used, as achieving such a high capacity with standard GDDR7 densities seems improbable without significant adaptations or new sources. Despite this, the modified card reportedly works with driver version 550.144.03 and is operational. Additionally, there are reports of a blower-style variant being inspected in China, lending credibility to these developments. These custom GPUs are manufactured by Chinese factories responding to the AI industry's growth demands. They also convert gaming GPUs into server or workstation-compatible two-slot blower designs. The RTX 5090's customization occurs without NVIDIA's approval and is sold directly to AI customers, primarily due to market limitations in affordable AI-specific GPUs caused by high costs and trade restrictions, especially in Asian markets. Bullet Point Summary: - Custom modification of the NVIDIA GeForce RTX 5090 features an upgrade to 128 GB GDDR7 memory using salvaged components. - The modifications involve dual-sided PCB layouts requiring firmware and BIOS adjustments, similar to Titan Ada variant techniques. - Prototype costs $13,200, significantly higher than both its standard version (MSRP $1,999) and NVIDIA's RTX PRO 6000 Blackwell ($10,000 for 96 GB VRAM). - Questions exist regarding the memory module configurations used to achieve this capacity due to standard GDDR7 density limitations. - The modified card operates with driver version 550.144.03 and includes a blower-style variant reported in China. - Custom GPUs are produced by Chinese factories catering to AI industry demands, converting gaming GPUs for server/workstation use without NVIDIA's approval. - These modifications primarily serve markets with limited access to affordable AI GPUs due to high costs and trade restrictions, notably in Asia. Keywords: AI Factories, AI customers, Asian markets, BIOS, Blackwell, GDDR7, GeForce RTX 5090, NVIDIA, PCB, VRAM, blower-style, custom mod, factories, firmware, frame buffer, gaming GPUs, memory, price, prototypes, tariffs, trade restrictions
vram
wccftech.com 6 days ago
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504. HN Automated Workday check in/check out and Microsoft Teams messages monitoringThe author has designed a tool that automates check-in/check-out processes in Workday and monitors Microsoft Teams messages, aiming to save users considerable time. Finding no existing solutions, the author developed this utility independently and made it publicly available on GitHub under the repository name "fuckthebossman." The intention is for others to share this tool widely across platforms such as Reddit, thereby assisting more individuals in reclaiming their time. - The author created a tool to automate check-in/check-out functions in Workday and monitor Microsoft Teams messages. - This tool was developed due to the absence of existing solutions for these tasks. - The tool is available on GitHub under the repository "fuckthebossman." - There is an encouragement for others to share this tool on platforms like Reddit to help more people save time. Keywords: Automated, Automation, Check-in, Check-out, GitHub, Messages Monitoring, Microsoft Teams, Project, Reddit, Repository, Sharing, Software, Technical Solution, Time-saving, Workday
github
news.ycombinator.com 6 days ago
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505. HN It should be easy to transfer context between modelsThe text describes an author's process of using multiple AI models to evaluate the quality of their outputs by running identical prompts across them. The repetitive nature of reconstructing context for each session is identified as cumbersome, prompting the author to develop a streamlined solution named Context Transfer Protocol (CTP). This simple JSON format aims to facilitate seamless context transfer between different AI models, enhancing efficiency and consistency. To encourage community engagement and iterative improvement, the CTP specification has been made publicly available on GitHub, where feedback and contributions are welcomed. The author advocates for standardizing straightforward context portability among various models. - **Main Idea**: The author frequently uses multiple AI models to compare their outputs using identical prompts. - **Problem Identified**: Reconstructing context repeatedly is cumbersome. - **Solution Proposed**: Creation of the Context Transfer Protocol (CTP) in a JSON format to enable easier context transfer between models. - **Implementation and Sharing**: CTP specification is shared on GitHub for community feedback and contributions. - **Author's Belief**: Standardizing easy context portability among AI models should be common practice. Keywords: CTP-spec, GitHub, JSON format, context transfer, contributions, feedback, improvements, models, portability, prompt, quality, rebuild, standard
github
news.ycombinator.com 6 days ago
https://github.com/context-transfer-protocol/ctp-spec 6 days ago |
506. HN Fascinating look at how the Chinese are using AI models for healthcareThe text outlines the transition of a kidney transplant patient from eastern China, who moves from traditional medical consultations in Hangzhou to using DeepSeek, an AI chatbot for health advice. This shift highlights broader trends where large language models are increasingly integrated into healthcare systems, providing round-the-clock access and empathetic interactions particularly beneficial for underserved patients. While some see this as a way to alleviate the burden on overworked medical caregivers, ethicists express concerns about potential biases and inaccuracies in AI applications. The narrative delves into China's healthcare challenges, such as geographical disparities and financial constraints in public hospitals that limit government funding and depend heavily on departmental profits for doctor bonuses. This context contributes to patient distrust. Historically, platforms like Baidu and WeChat have offered medical information but faced issues with misinformation. AI chatbots like DeepSeek present opportunities for more effective dissemination of advanced medical knowledge despite their limitations. Research suggests while some AI models excel in specific diagnostic tasks, they often fall short in clinical judgment compared to human doctors, raising concerns about consent, accountability, and bias. In China, there is growing adoption of large language models such as DeepSeek R1 in medical practices for patient interaction and diagnosis suggestions, aimed at addressing doctor shortages and improving access, especially in rural areas. AI avatars offering basic consultations are becoming common on popular apps, although they face regulatory restrictions on prescribing medications. Developers focus on ethical use by self-regulating content and involving human oversight where necessary. The integration of AI into offline clinics aims to improve efficiency and manage hospital overcrowding, enhancing care quality in less-developed regions. Personal anecdotes highlight the emotional connection some individuals feel with AI companionship amidst healthcare access issues, but also underscore concerns about AI's limitations compared to professional medical advice. Specifically, an individual recounts how their mother uses DeepSeek for accessible health-related information after discovering a low white blood cell count. DeepSeek advised additional tests performed by her local doctor and suggested consulting a nephrologist in Hangzhou, which she initially resisted but eventually accepted. Despite recognizing its limitations, she continues to use DeepSeek for its convenience and cost-effectiveness. **Bullet Point Summary:** - A kidney transplant patient from eastern China transitions from traditional medical consultations to using DeepSeek for health advice. - AI chatbots are becoming integrated into healthcare systems globally, offering benefits like constant availability but also posing risks of biases and inaccuracies. - The narrative highlights systemic challenges in China's healthcare system, including geographical inequalities and financial constraints. - Platforms like Baidu and WeChat have historically provided medical information but were prone to misinformation; AI chatbots offer potential for improved knowledge dissemination despite limitations. - Research indicates AI models can perform well on specific tasks but often lack the clinical judgment of human doctors, raising ethical concerns about consent, accountability, and bias. - In China, large language models like DeepSeek R1 are increasingly used in medical practices to assist with patient interactions and diagnoses, addressing doctor shortages especially in rural areas. - AI avatars provide basic consultations on popular apps but face regulatory restrictions; developers ensure ethical use by self-regulating content and involving human oversight. - Integration of AI into offline clinics aims to enhance efficiency, manage multiple locations simultaneously, and improve care quality in less-developed regions. - Personal anecdotes illustrate emotional connections with AI companionship amidst healthcare access issues, though concerns about the accuracy of AI advice compared to professionals persist. - The individual's mother uses DeepSeek for health advice after finding a low white blood cell count; despite its limitations, she appreciates its convenience and cost-effectiveness. Keywords: AI models, Alipay, Chinese healthcare, DeepSeek, Hangzhou, biases, chatbots, diagnostics, dialysis, immunosuppressant, kidney transplant, medical ethics, nephrologist, nephrology, regulatory oversight, rural-urban gap
deepseek
restofworld.org 6 days ago
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507. HN OpenAI's Future, Foretold?OpenAI projects robust revenue growth for its Generative AI products, targeting earnings that could surpass those of major corporations like Ford, BMW, and Bank of America. By 2030, revenues could reach $200 billion; however, substantial training costs estimated at $150 billion from 2025 to 2030 pose significant financial challenges. The company anticipates enduring considerable monthly losses until its big business customers increase their spending substantially. Despite these optimistic projections, recent data suggest that this rapid growth may not be sustainable. New statistics from the Census Bureau indicate a potential decline in interest from large companies, compounded by general disappointment with AI's return on investment (ROI), as reported in an MIT study. Additionally, GPT-5 has delivered underwhelming results, adding to these concerns. If these trends persist, OpenAI faces considerable financial risks despite its ambitious goals. **BULLET POINT SUMMARY:** - OpenAI forecasts significant revenue growth for its Generative AI products, potentially exceeding the earnings of major companies like Ford, BMW, and Bank of America. - Projections indicate $200 billion in revenue by 2030 but face immense training costs estimated at $150 billion between 2025 and 2030. - The company expects to incur substantial monthly losses until increased spending from large business customers materializes. - Recent data suggest potential issues with sustaining rapid growth, including decreased interest from big companies according to Census Bureau statistics. - General disappointment regarding AI ROI, as highlighted by an MIT study, compounds the financial challenge. - Underwhelming results from GPT-5 further exacerbate concerns about future profitability and sustainability. - Persistent trends could lead to significant financial risks for OpenAI despite its ambitious revenue projections. Keywords: $13 billion, $200 billion, BMW, Bank of America, Census Bureau data, ChatGPT, Ford, GPT-5, Generative AI, OpenAI, ROI (Return on Investment), business customers, computing costs, revenue, training costs
openai
garymarcus.substack.com 6 days ago
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508. HN Show HN: GitType – A typing game that uses your own Git repo as practice text**Summary:** GitType is a Rust-based command-line typing game tailored for developers, which uniquely utilizes code from their own Git repositories as practice material. By integrating real functions, comments, and code snippets instead of standard text, it offers a practical way to improve typing skills while engaging with actual project files. Operating seamlessly in the terminal without requiring a graphical user interface, GitType extracts text directly from any local Git repository, supporting multiple programming languages like Rust, TypeScript, JavaScript, Python, Go, Ruby, Swift, Kotlin, Java, PHP, C#, C/C++, Haskell, and Dart, among others. The tool tracks typing performance through real-time metrics such as words per minute (WPM), accuracy, and consistency. It maintains a history of practice sessions to allow users to monitor their progress over time, offering playful ranking titles based on scores to add an element of fun. GitType features various game modes including Normal, Time Attack, and multiple difficulty levels ranging from Easy to Zen. Users can pause and resume their practice without impacting their statistics. Additional functionalities include real-time WPM updates during typing sessions, a ranking system with ASCII art for developer titles, and customization options such as setting custom paths and engaging in speed typing challenges against AI. The tool is designed not only for productivity but also provides a gamified experience that transforms code review into an enjoyable activity, even allowing users to tackle the quirks of legacy code. GitType supports installation via Homebrew or Cargo and encourages community feedback and contributions through its open-source presence on GitHub. Its source code is licensed under MIT, emphasizing ease of use with comprehensive documentation available for users who find it engaging. The project serves as a creative escape for developers looking to procrastinate productively by turning their coding tasks into an interactive game. **Bullet Point Summary:** - GitType is a Rust CLI typing game that uses real code from user’s Git repositories. - Enhances typing practice with real functions, comments, and code without needing a GUI. - Supports multiple programming languages including Rust, TypeScript, JavaScript, Python, Go, Ruby, Swift, Kotlin, Java, PHP, C#, C/C++, Haskell, Dart. - Tracks WPM, accuracy, and consistency; maintains history of practice sessions for progress monitoring. - Offers playful ranking titles based on scores with ASCII art; features multiple game modes like Normal, Time Attack, Easy to Zen difficulty levels. - Allows pausing and resuming without affecting stats; provides real-time typing metrics during sessions. - Enables customization (e.g., setting custom paths) and AI speed typing challenges. - Installs via Homebrew or Cargo; source code available on GitHub for contributions under MIT License. - Encourages productive procrastination by gamifying coding tasks, including dealing with legacy code quirks. - Comprehensive documentation is provided to assist users in maximizing the tool's potential. Keywords: AI, ASCII art, Git repo, GitHub, GitType, MIT, Rust CLI, WPM, accuracy, addictive, architecture, bugs, cargo, codebase, command line, contributing, debug, developers, difficulty levels, excuse, game modes, hello world, history, homebrew, installation, languages, legacy code, license, multi-language, pause/resume, personal projects, procrastination, quick start, ranking titles, ranks, real work, real-time metrics, repository, terminal, typing game
github
github.com 6 days ago
|
509. HN OpenAI links up with Broadcom to produce its own AI chipsOpenAI is collaborating with Broadcom to develop an artificial intelligence (AI) chip aimed at addressing the increasing computing demands and reducing dependence on Nvidia. The partnership, initiated last year, aligns with trends among tech giants such as Google, Amazon, and Meta who have also developed specialized chips for AI tasks. OpenAI intends to utilize this chip internally. Broadly speaking, Broadcom's CEO Hock Tan confirmed a new $10 billion customer, which insiders recognize as OpenAI, though no additional details were publicly disclosed. This venture with OpenAI signifies Broadcom’s fourth significant client in its custom AI chip business. **BULLET POINT SUMMARY:** - OpenAI and Broadcom are collaborating to develop an AI chip to meet rising computing demands and lessen reliance on Nvidia. - The partnership began last year and is part of a broader trend among major tech companies creating specialized AI chips. - OpenAI plans to use the developed chip internally for its operations. - Broadcom's CEO Hock Tan confirmed a new $10 billion customer, known to insiders as OpenAI, without further public details. - This marks Broadcom’s fourth major client in its custom AI chip business. Keywords: AI chips, Amazon, Broadcom, ChatGPT, Google, Hock Tan, Meta, Nvidia, OpenAI, Wall Street, analysts, collaboration, computing power, custom chip, internal use, mass production, semiconductor, tech giants
openai
arstechnica.com 6 days ago
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510. HN The Rise of Codex**Concise Summary:** As of September 6, 2025, Codex CLI has experienced a substantial increase in usage following the release of GPT-5, positioning it as a strong competitor to Claude Code by Anthropic within the terminal-based coding agent space. This surge is evidenced by a tenfold rise in weekly npm downloads, surpassing 100,000. Although initially anticipated for groundbreaking AI capabilities with GPT-5, Codex has established itself as an effective tool for coding tasks. Comparative evaluations show that Codex matches Claude Code in solution quality and can even identify bugs overlooked by Claude when given explicit instructions. The rapid adoption of Codex is largely attributed to its competitive pricing structure. It's included in the $20 ChatGPT Plus plan without rate limits, unlike Claude Code’s more restrictive offerings like the Sonnet 4 plan, which often times out after approximately 30 minutes. Even higher-tier plans for Claude Code impose significant usage restrictions, with full functionality requiring a costly $200/month plan. In contrast, GPT-5 (Codex) provides more cost-effective and reliable options for all-day coding tasks at rates of $1.25 per model input and $10 per output, versus the steeper fees associated with Opus 4. The shift in developer preference is evident at Terragon Labs, where Codex's usage has increased to 28% within a month, suggesting that its affordability and efficiency are driving broader adoption among developers and challenging Claude Code’s market dominance. While OpenAI’s GPT-5 through Codex significantly narrows the gap previously held by Claude Code with Opus 4 due to cost advantages, it still lacks some of the refined features that contribute to Claude Code's user-friendly experience. Despite its strengths in coding performance, Codex requires further development to become an indispensable tool for developers. The author notes using Codex as their primary coding agent thanks to these capabilities and cost benefits, although there remains a sense of nostalgia for Claude Code’s features. The competitive dynamics continue to evolve with emerging players like Kimi-K2 and Grok Code also capturing attention in the AI-assisted coding landscape. **Bullet Point Summary:** - Codex CLI has seen significant growth due to GPT-5's release, competing with Claude Code by Anthropic. - Usage of Codex increased tenfold over a month, surpassing 100,000 weekly npm downloads. - Both Codex and Claude Code offer strong coding solutions; Codex can sometimes detect bugs missed by Claude Code. - The primary reason for Codex’s rapid adoption is its competitive pricing: included in $20 ChatGPT Plus with no rate limits versus Claude Code's restrictive plans. - Claude Code’s Sonnet 4 plan often times out after about 30 minutes, while GPT-5 offers more cost-effective options at $1.25 per input and $10 per output. - At Terragon Labs, Codex usage increased to 28% in one month, indicating a shift in developer preference due to affordability and efficiency. - While OpenAI's advancements with GPT-5 challenge Claude Code’s previous dominance, Codex needs refinement to be indispensable. - The author prefers Codex for its capabilities and cost-effectiveness but misses some features of Claude Code. - The AI-assisted coding landscape continues evolving with new competitors like Kimi-K2 and Grok Code emerging. Keywords: Anthropic, CLI, Claude Code, Codex, GPT-5, VS Code, adoption, coding tasks, developers, performance, pricing, rate limits, subagents, usage surge
gpt-5
www.sawyerhood.com 6 days ago
https://x.com/Sauers_/status/1964354357635285391 6 days ago |
511. HN Show HN: VeritasGraph,An on-premise Graph RAG with verifiable source attribution- **Concise Summary:** VeritasGraph is an on-premise Graph Retrieval Augmented Generation (RAG) system designed for complex multi-hop reasoning while providing verifiable source attribution. Targeted at enterprise use, it runs on local hardware using models like Llama 3.1 via Ollama to ensure data privacy. The system combines document indexing with graph traversal and uses a LoRA-tuned language model for generating answers that include full provenance details. - **Key Features:** - **Indexing:** Automatically constructs a knowledge graph from documents by extracting entities and their relationships using large language models (LLMs). - **Retrieval:** Utilizes a hybrid method combining vector search with graph traversal to answer multi-hop questions, ensuring rich contextual information is considered. - **Generation:** Employs a LoRA-tuned LLM for generating responses, incorporating verifiable source attribution. - **System Architecture:** - Comprises an indexing pipeline that processes documents to build both a vector index and knowledge graph, alongside a real-time query pipeline combining vector search with multi-hop graph traversal. - Outputs attributed answers processed through an attribution layer, ensuring transparency in reasoning paths. - **Setup Instructions for Windows:** - Involves setting up the Ollama environment with specific models, serving them, and creating tailored configurations for indexing using a Conda environment. - Detailed steps include pulling necessary models, configuring settings, and initializing GraphRAG. It also emphasizes prompt tuning to improve domain-specific results. - **Core Capabilities:** - Includes multi-hop graph reasoning, efficient model fine-tuning with LoRA, source attribution linking statements back to documents, and secure on-premise deployment. - Architecture transforms unstructured data into a knowledge graph using document chunking, entity extraction, and assembly in a database like Neo4j. - **Advanced Information Retrieval Stages:** 1. **Graph Assembly:** Constructs a knowledge graph from extracted structured triplets. 2. **Hybrid Retrieval Engine:** Combines vector search with multi-hop graph traversal for contextual expansion and pruning of results. 3. **LoRA-Tuned Reasoning Core:** Enhances reasoning through augmented prompting, generating attributed answers, and fine-tuning the model for specific tasks. 4. **Attribution & Provenance Layer:** Tracks metadata propagation and generates traceable outputs. - **Philosophy and Future Roadmap:** - Emphasizes transparency, security, and control in AI systems, advocating for democratizing enterprise-grade AI by enabling on-premise data management. - Plans include expanding database support, developing advanced graph analytics, creating a framework for complex queries, and introducing a visualization UI. - **Project Configuration:** - Involves copying `.env.example` to `.env` and customizing it with environment-specific values, using the NVIDIA Container Toolkit in Python 3.10+ environments. Keywords: Docker, Gradio UI, GraphRAG, LoRA-tuned LLM, NVIDIA GPU, Neo4j, Ollama, Python, RAG pipelines, VeritasGraph, agentic framework, data privacy, embeddings, enterprise-grade, factual accuracy, indexing, knowledge graph, local LLMs, multi-hop reasoning, retrieval generation, semantic search, vector-search limitations, verifiable attribution
ollama
github.com 6 days ago
https://github.com/FalkorDB/falkordb 5 days ago |
512. HN The MacBook has a sensor that knows the exact angle of the screen hingeThe provided text discusses a feature of the MacBook that includes a sensor designed to detect the angle of the screen hinge, enabling responsive adjustments based on its position. Sam Henrigold has disseminated information about this technology across several platforms such as Hachyderm, Bsky, Twitter, and GitHub. He offers insights and potentially shares code samples under the repository named "LidAngleSensor." Additionally, there is a notification regarding JavaScript being disabled in some users' browsers, which impacts their ability to interact with certain websites like x.com. To resolve this issue, users are advised to enable JavaScript or switch to a different browser to ensure full site functionality, and they can find further assistance on the Help Center page. **BULLET POINT SUMMARY:** - The MacBook includes a sensor that detects the screen hinge's angle for responsive adjustments. - Sam Henrigold has shared resources about this technology on platforms like Hachyderm, Bsky, Twitter, and GitHub. - Resources include insights and possibly code samples under the "LidAngleSensor" repository. - A notification highlights an issue with JavaScript being disabled in some browsers, affecting interaction with sites like x.com. - Users are advised to enable JavaScript or switch browsers for full site functionality. - Additional assistance is available on the Help Center page. Keywords: Alts, Bskyapp, Github, Hachyderm, Help Center, JavaScript, LidAngleSensor, MacBook, Twitter, angle, browser, screen hinge, sensor
popular
twitter.com 6 days ago
https://www.ifixit.com/Answers/View/759262/To 6 days ago https://news.ycombinator.com/item?id=24955071 6 days ago https://news.ycombinator.com/item?id=36926276 6 days ago https://github.com/Vladislav98759/Macbook-Lid-Angle-Sen 6 days ago https://in.bgu.ac.il/en/Pages/news/eaves_drop 6 days ago https://www.ft.com/content/e73676d7-c6bc-4b07-b9bf-9bd7 6 days ago https://www.theregister.com/2023/08/29/apples 6 days ago https://www.infoworld.com/article/2271627/apple-cl 6 days ago https://www.youtube.com/watch?v=SfnabYBtJ2I&t=325s 6 days ago https://www.gsma.com/solutions-and-impact/industry-serv 6 days ago https://appleinsider.com/articles/14/06/20&#x 6 days ago https://support.apple.com/en-au/120610 6 days ago https://support.apple.com/en-au/guide/security 6 days ago https://www.economist.com/interactive/britain/2025 6 days ago https://discussions.apple.com/thread/255110862?sortBy=r 6 days ago https://support.apple.com/en-us/121541 6 days ago https://www.forbes.com/sites/carminegallo/2012 6 days ago https://sannysanoff.github.io/whiteboard/ 6 days ago https://xkcd.com/1425/ 6 days ago https://code.flickr.net/2014/10/20/introducin 6 days ago https://birdnet.cornell.edu/ 6 days ago https://merlin.allaboutbirds.org/ 6 days ago https://www.phoronix.com/news/Intel-Hinge-Driver-Linux- 6 days ago https://store.steampowered.com/app/1059990/Trombon 6 days ago https://news.ycombinator.com/item?id=45160808 6 days ago https://www.youtube.com/watch?v=zMJAevVri5w 6 days ago https://nime.org/proc/meacham2016/index.html 6 days ago https://www.forbes.com/sites/jaymcgregor/2023/ 6 days ago https://x.com/nevmed/status/1640004745250078723 6 days ago https://youtube.com/shorts/sgqTEjN5_vQ 6 days ago https://youtu.be/Uivp-hvk-nk 6 days ago https://youtu.be/wwOipTXvNNo 6 days ago https://sound-effects.bbcrewind.co.uk/ 6 days ago https://www.youtube.com/watch?v=6uvQTTPr9Rw 6 days ago https://source.android.com/docs/core/interaction&# 6 days ago https://github.com/tcsenpai/pybooklid 6 days ago https://github.com/samhenrigold/LidAngleSensor 6 days ago https://www.tenforums.com/tutorials/69762-how-change-de 6 days ago https://learn.microsoft.com/en-us/windows-hardware/ 6 days ago https://news.ycombinator.com/item?id=44745897 6 days ago https://hachyderm.io/@samhenrigold/115159295473019599 6 days ago https://bsky.app/profile/samhenri.gold/post/3 6 days ago https://arxiv.org/abs/2506.03443 6 days ago https://www.washingtonpost.com/opinions/2025/06&#x 6 days ago https://www.conspicuouscognition.com/p/against-bluesky 6 days ago https://x.com/samhenrigold/status/1964464940049453 6 days ago https://nitter.net/samhenrigold/status/19644649400 6 days ago https://support.apple.com/en-us/101661 6 days ago https://en.wikipedia.org/wiki/Apple_Mighty_Mouse 6 days ago https://hachyderm.io/@samhenrigold/115159306734992780 6 days ago https://en.wikipedia.org/wiki/Magnuson%E2%80%93Moss_War 4 days ago https://www.bbc.com/news/av/uk-68003783 4 days ago https://x.com/0xDesigner/status/164255483453547724 4 days ago https://x.com/0xDesigner/status/164255483453547724 4 days ago https://news.ycombinator.com/item?id=44635808 4 days ago https://youtu.be/pzJwDs6KRXA?feature=shared 4 days ago https://www.novosns.com/en/hall-angle-sensor-4010 4 days ago https://www.youtube.com/watch?v=zIrAe23f8sg 4 days ago https://news.ycombinator.com/item?id=45159417 4 days ago https://news.ycombinator.com/newsguidelines.html 4 days ago |
513. HN Show HN: Beelzebub (OSS) – MCP "canary tools" for AI agentsThe open-source project "Beelzebub" introduces a method for enhancing AI agent security through "canary tools," which are decoy functions designed to signal issues like prompt injection or tool hijacking when invoked during normal operations. Developed using the Go language, Beelzebub's framework creates MCP honeypots that expose these decoys, giving them legitimate appearances with real names, parameters, and descriptions. When activated, these canary tools provide harmless dummy outputs and emit telemetry data for analysis, which can be integrated into monitoring systems such as Prometheus/Grafana or ELK through stdout/webhook. The primary function of these canary tools is to serve as immediate indicators of compromise within agent logs, differentiating between standard operations and potential security breaches. The framework's relevance was highlighted by referencing the Nx npm supply-chain attack incident, where AI developer tools were exploited; a canary tool could have provided alerts during such unauthorized actions. Specifically, deploying decoys like fake "export secrets" or "repo exfil" would trigger alerts if these tools were misused. Users interested in learning more about Beelzebub or providing feedback can visit the project's GitHub repository and read detailed information on its blog. The framework aims to act as a low-noise alert system for unauthorized activities, enhancing security without relying on heuristics or additional model calls. **BULLET POINT SUMMARY:** - **Beelzebub Project:** An open-source initiative introducing "canary tools" to enhance AI agent security by identifying issues like prompt injection and tool hijacking. - **Implementation Details:** Uses the Go language to create MCP honeypots, exposing decoy functions with legitimate appearances that emit harmless outputs and telemetry data when activated. - **Integration & Monitoring:** Telemetry can be integrated into existing systems such as Prometheus/Grafana or ELK via stdout/webhook for analysis. - **Indicator of Compromise (IoC):** Canary tools serve as immediate indicators within agent logs, differentiating between normal operations and potential security breaches. - **Relevance & Use Case:** Highlighted by the Nx npm supply-chain attack incident; canary tools could have alerted unauthorized use of AI developer tools. - **Additional Resources:** Information available on GitHub and a dedicated blog; feedback encouraged to enhance the framework further. Keywords: AI agents, AI assistants, Agent logs, Beelzebub, ELK, GitHub, Go framework, Grafana, IDE agent, MCP, MCP honeypots, Nx attack, Nx npm, OSS, Prometheus, alert, alert Keywords: Beelzebub, blog, blog Canary tools, canaries, canary tools, compromise, decoy tools, heuristics, high-fidelity signal, lateralization, model calls, prompt-injection, security, supply-chain attack, supply-chain incident, telemetry, tool hijacking
github
news.ycombinator.com 6 days ago
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514. HN AI Mode Is GoodThe text discusses an author's evolving perspective on Google's AI initiatives after encountering its new "AI mode." Initially skeptical and comparing unfavorably to ChatGPT's GPT-5, the author found Google's feature effective, noting its speed and integration into existing search infrastructure. Despite being impressed with its performance akin to GPT-5, a significant concern arises from the lack of transparency; specifically, the inability to see which searches were utilized in generating results. This issue echoes past experiences with transparency challenges in Google's Gemini app, impacting trust in the service's reliability. **BULLET POINT SUMMARY:** - The author was initially disappointed with Google’s AI efforts compared to ChatGPT with GPT-5. - After using "AI mode," they found it effective and fast, similar to GPT-5 search but integrated within Google's system. - Despite its impressive performance, there is a lack of transparency in how results are generated ("AI mode" does not disclose which searches were used). - This concern about transparency affects the author’s trust in the service's reliability, reminiscent of issues with Google's Gemini app. Keywords: AI Mode, AI-assisted search, Anthropic, Disappointment, EU, France, GPT-5, Gemini app, Google, Infrastructure, Physical Books, Scanning, Searches, Training Data, Transparency
gpt-5
simonwillison.net 6 days ago
https://x.com/Marie_Haynes/status/1963031598829314 6 days ago https://news.ycombinator.com/item?id=45143392 6 days ago https://ahrefs.com/blog/google-advanced-search-operator 6 days ago https://simonwillison.net/2025/Sep/6/anthropi 5 days ago |
515. HN Chrome extension that replaces occurrences of 'the cloud' with 'my butt'The text describes "Cloud to Butt," a humorous Chrome extension that swaps the phrase "the cloud" with "my butt" whenever it appears online. The version of this extension specifically targets only complete instances of the phrase, thereby maintaining certain URLs such as those from Cloudflare intact. This functionality is achieved while avoiding replacing individual mentions of "cloud" unless they form the specific phrase "the cloud." Additionally, users can directly download the extension using a CRX file and install it by dragging this file into Chrome’s Extensions page. The extension has variations for other browsers: Safari, Firefox, and Opera, with corresponding GitHub links provided. The text also mentions that there is a dedicated Flickr group where users can view screenshots of how the extension functions in action. This summary captures the essence of the extension's purpose, functionality, installation process, browser compatibility, and available resources like screenshots. - Describes "Cloud to Butt," a Chrome extension humorously replacing "the cloud" with "my butt." - Specific version targets only the complete phrase "the cloud," preserving certain URLs. - Provides direct download access via a CRX file, installable by dragging into Chrome’s Extensions page. - Available for other browsers: Safari, Firefox, and Opera, with GitHub links provided. - Gallery of screenshots available on Flickr in a dedicated group. Keywords: Chrome extension, CloudToButt, Firefox, GitHub, Opera, Safari, URL, cloud, community, crx file, download, fork, funny, installation, replace, screenshot, versions
github
github.com 6 days ago
https://www.reddit.com/r/DnD/comments/s82mi4& 6 days ago https://news.ycombinator.com/item?id=28869819 6 days ago https://chromewebstore.google.com/detail/editorialies&# 6 days ago https://github.com/Dotnaught/EditoriaLies 6 days ago https://news.ycombinator.com/item?id=45152476 6 days ago |
516. HN Why is chat GPT suddedly DUMB?**Summary:** The user expresses frustration over a recent change in their chat application's functionality, which is presumably powered by a language model similar to GPT. Previously, the app automatically generated contextual titles for sidebar conversations based on their content, thereby eliminating the need for manual titling and enhancing usability. However, this feature has been removed or altered, necessitating users to manually assign titles to their chat threads. The user strongly urges the reinstatement of this automatic title generation feature, highlighting its convenience and efficiency in managing conversations. **Bullet Point Summary:** - User is frustrated due to changes in a chat application's functionality. - The application likely uses a language model like GPT for generating features. - Previously, it automatically generated contextual titles for sidebar conversations based on content. - This automatic titling eliminated the need for manual input from users. - The feature has been removed or altered, requiring users to manually title conversations. - User is urging the reinstatement of the automatic title generation feature. Keywords: AI, LLM, chat GPT, context, conversation, functionality, improvement, interaction, keywords, manually, sidebar, title, user experience
llm
news.ycombinator.com 6 days ago
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517. HN Show HN: Games123.net, games with homemade 2D engine### Summary: Games123.net is presenting a homemade 2D game engine developed over five years from an initial infotainment project. This engine boasts a data-driven editor that facilitates complete game production, supporting content import and package creation for Windows/Linux, HTML5/WASM platforms while incorporating scripting with Lua and C++. It includes features like entities, scenes, materials, animations, audio graphs, tilemaps, UIs, among others. While primarily tailored for small independent 2D games such as puzzles and side scrollers, the engine is evolving to accommodate limited 3D content support, albeit some components are still in progress. Despite ongoing developmental challenges, including enhancing particle effects and fluid simulation, it now enables comprehensive game creation with fewer missing features. The creator's commitment reflects significant dedication and personal investment over the years. The author acknowledges taking on this substantial project while maintaining their well-being but remains dedicated to its completion. They recognize that despite a saturated market for game development engines, there could be potential for another hobbyist engine like theirs. This engine closely competes with GDevelop, aiming for graphical fidelity reminiscent of early 2000s standards. To promote it, the author shares demos and games developed using their engine on Games123.net and invites user feedback by providing access to its GitHub repository at ensisoft/detonator. The text encourages exploring this innovative game engine that operates across Linux, Windows, and WebAssembly platforms due to its impressive capabilities. ### Bullet Point Summary: - **Development Background**: The 2D game engine developed over five years from an initial infotainment project. - **Engine Capabilities**: Features a data-driven editor supporting content import and game package creation for multiple platforms (Windows/Linux, HTML5/WASM) with Lua and C++ scripting. - **Core Features**: Includes entities, scenes, materials, animations, audio graphs, tilemaps, UIs, and more. - **Target Audience**: Primarily aimed at small independent 2D games like puzzles and side scrollers, expanding to limited 3D support. - **Development Status**: Ongoing improvements needed in particle effects and fluid simulation but supports full game creation with fewer missing features. - **Creator's Commitment**: Reflects dedication and personal investment despite challenges in the saturated game development domain. - **Market Positioning**: Comparable to GDevelop, aiming for early 2000s graphical fidelity; potential room for another hobbyist engine. - **Promotion Efforts**: Showcases demos/games on Games123.net, invites feedback, and provides GitHub repository access at ensisoft/detonator. - **Platform Support Encouragement**: Advocates trying the engine across Linux, Windows, and WebAssembly platforms due to its impressive capabilities. Keywords: 2D engine, 3D content, C++, GDevelop, GitHub, Lua, UIs, WASM, animations, audio graphs, awesome, demos, entities, feature comparison, feedback, fluid simulation, game tools, games engines, graphical fidelity, hobbyist engine, isometric tiles, lights, materials, metal well-being, particle effects, scenes, scripting support, shadows, tilemaps
github
games123.net 6 days ago
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518. HN Hacktoberfest 2025The text describes Hacktoberfest 2025 as an initiative dedicated to supporting open-source projects maintained by community-focused developers, who play a vital role in sustaining modern internet operations. DigitalOcean sponsors this event, providing credit grants and support for development, infrastructure, and testing to ensure these projects flourish. The campaign actively encourages individuals with pertinent skills to contribute their expertise, thus promoting the continuous success of open-source initiatives. **BULLET POINT SUMMARY:** - **Initiative Focus:** Hacktoberfest 2025 supports community-maintained open-source projects essential for modern internet functionality. - **Sponsorship and Support:** DigitalOcean sponsors by offering credit grants and assistance in development, infrastructure, and testing to help these projects succeed. - **Encouragement of Contributions:** The campaign encourages skilled individuals to contribute their expertise towards the ongoing success of open-source initiatives. Keywords: DigitalOcean, Hacktoberfest, boost, coders, community, credit grants, development, infrastructure, internet, open-source, projects, skills, support, testing
digitalocean
hacktoberfest.com 6 days ago
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519. HN Air pollution directly linked to increased dementia riskA study published in Science on September 4th has demonstrated that long-term exposure to PM 2.5, fine airborne particles smaller than 2.5 micrometers, is linked to an increased risk of developing Lewy body dementia. Analyzing data from 56 million individuals, the research suggests that while PM 2.5 does not directly cause Lewy body dementia, it can accelerate its onset in genetically predisposed individuals. Lewy body dementia includes conditions such as Parkinson’s disease with dementia and dementia with Lewy bodies, both of which are caused by protein clumps damaging brain cells. The study highlights a connection between air pollution from sources like car exhaust and the development of neurodegenerative diseases. The research further indicates that long-term exposure to PM 2.5 is associated with a heightened risk of hospitalization for various neurodegenerative conditions, including a notable 12% increase in severe cases of dementia with Lewy bodies. Regions with higher levels of PM 2.5 show an elevated relative risk of developing Lewy body dementia compared to Parkinson’s disease without dementia. Experimental studies on mice exposed to PM 2.5 for ten months revealed behavioral issues consistent with dementia-like symptoms, such as impaired spatial memory and difficulty in recognizing new objects. This exposure also led to increased accumulation of the αSyn protein in the brain, particularly impacting the medial temporal lobe, which is crucial for memory functions. In genetically modified mice lacking αSyn, these changes were not observed, underscoring the role of this protein in neurodegeneration. The research discovered clumps of αSyn in both the gut and lungs of exposed mice, suggesting a pathway where PM 2.5 induces inflammation in the lungs. This inflammation allows particles to enter the bloodstream, cross the blood-brain barrier, and potentially initiate neurodegenerative diseases like Alzheimer’s or Lewy body dementia through the gut–brain axis. However, this process appears to necessitate a predisposition for αSyn pathology. **Bullet Point Summary:** - A study links long-term exposure to PM 2.5 with an increased risk of developing Lewy body dementia in genetically predisposed individuals. - The research indicates that while PM 2.5 doesn't directly cause the disease, it accelerates its development and increases hospitalizations for neurodegenerative conditions. - There is a significant rise (12%) in severe cases of dementia with Lewy bodies associated with higher PM 2.5 exposure. - Experimental findings on mice show behavioral challenges after PM 2.5 exposure, including memory issues linked to αSyn protein buildup in the brain. - Genetically modified mice lacking αSyn did not exhibit these changes, highlighting its role in neurodegeneration. - Clumps of αSyn were found in both the gut and lungs of exposed mice, suggesting a pathway where PM 2.5 triggers inflammation and potentially seeds neurodegenerative diseases through the gut-brain axis, requiring a predisposition for αSyn pathology. Keywords: Air pollution, Alzheimer’s disease, Johns Hopkins University, Lewy body dementia, PM 25, Parkinson’s disease, Science, University of Technology Sydney, airborne particles, blood–brain barrier, car-exhaust, dementia risk, factory fumes, genetically modified mice, genetics, gut-brain axis, hospital-admissions data, inflammation, long-term exposure, medial temporal lobe shrinkage, mice experiments, nerve cells, neurodegenerative illnesses, neuroscientist, predisposition, spatial memory, vascular dementia, wildfires, α-synuclein
popular
www.nature.com 6 days ago
https://github.com/featurestorebook/mlfs-book/ 6 days ago https://www.axios.com/local/salt-lake-city/2024&#x 6 days ago https://www.cdc.gov/nchs/state-stats/deaths/a 6 days ago https://en.m.wikipedia.org/wiki/File:China_population_s 6 days ago _1st 6 days ago _2020.png 6 days ago https://www.populationpyramid.net/united-states-of-america&# 6 days ago https://pmc.ncbi.nlm.nih.gov/articles/PMC6179015/ 6 days ago https://www.nytimes.com/wirecutter/reviews/best-ai 6 days ago https://dynomight.net/ikea-purifier/ 6 days ago https://pmc.ncbi.nlm.nih.gov/articles/PMC12413735/ 6 days ago https://www.nature.com/articles/s41598-022-17216-w 6 days ago https://www.nature.com/articles/s41598-022-10074-6 6 days ago https://www.ikea.com/gb/en/p/vindstyrka-air-q 6 days ago https://pubmed.ncbi.nlm.nih.gov/21848409/ 6 days ago https://academic.oup.com/biomedgerontology/article/ 6 days ago https://www.theguardian.com/environment/2025/mar&# 6 days ago https://www.opensecrets.org/industries/indus?cycle=2024 6 days ago https://m.slashdot.org/story/446420 6 days ago https://elements.visualcapitalist.com/mapped-air-pollution-l 6 days ago https://elements.visualcapitalist.com/mapped-air-pollution-l 6 days ago https://www.epa.gov/land-research/quantifying-methane-e 6 days ago https://www.science.org/doi/10.1126/science.adi773 6 days ago https://www.youtube.com/watch?v=kmJvvqYgfUU 6 days ago https://www.youtube.com/watch?v=2jtg9qBq110 6 days ago https://www.youtube.com/watch?v=zgMagIqZNuA 6 days ago https://moneyterms.co.uk/dutch-disease/ 6 days ago https://pmc.ncbi.nlm.nih.gov/articles/PMC6163571/ https://arxiv.org/abs/2509.01776 |
520. HN Show HN: LLM wrapped around stocks and cryptoThe text introduces "Aulico," an innovative application leveraging Large Language Models (LLMs) to analyze stocks and cryptocurrencies. Designed to improve decision-making processes, Aulico offers tailored predictions and insights within the financial sectors of stocks and cryptocurrency markets. The application is highlighted on a technology-focused platform, underscoring its significance for individuals interested in both finance and AI advancements. - **Key Points:** - Introduction of "Aulico," an app utilizing LLMs. - Focuses on analyzing stocks and cryptocurrencies. - Enhances decision-making through tailored predictions and insights. - Showcased on a technology-focused platform. - Relevant for audiences interested in finance and AI innovations. Keywords: Aulico, Aulico Keywords: Show HN, LLM, Show HN, crypto, stocks
llm
www.aulico.com 6 days ago
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521. HN Ask HN: Good resources for DIY-ish animatronic kits for Halloween?The text details the request of a software engineer parent who seeks DIY animatronic kit recommendations for Halloween, aiming to involve their tech-savvy children in both building and troubleshooting activities. Despite having limited hardware skills, they are motivated by cost-effectiveness and prefer not to use expensive pre-made items. They discovered FrightProps, a site offering pneumatic solutions but have concerns about its suitability for beginners and budget constraints. The user is open to exploring GitHub resources or other DIY animatronic project suggestions that might align with their goals. - **Seeking Recommendations**: User wants DIY animatronic kits for Halloween. - **Engagement Goal**: Desire to build and troubleshoot with children, emphasizing a hands-on experience. - **Skill Level**: User has limited hardware skills but is tech-savvy in software engineering. - **Budget Consideration**: Interested in cost-effective projects avoiding expensive store-bought options. - **Resource Discovery**: Found FrightProps for pneumatic solutions; concerned about beginner-friendliness and budget. - **Openness to Alternatives**: Willing to explore GitHub or other resources for suitable DIY projects. Keywords: DIY animatronics, FrightProps, GitHub, GitHub projects DIY, Halloween, Halloween displays, Lowes, Lowes alternatives, Ubuntu, alternatives, animatronics, budget-friendly, command, command line, displays, engineer, hardware, hardware skills, kids, kids project, line, pneumatics, project, skills, software, software engineer
github
news.ycombinator.com 6 days ago
https://www.youtube.com/watch?v=Jk3ZsyrTU4M 4 days ago https://makezine.com/tag/halloween/ 4 days ago https://www.bottango.com/pages/kits 4 days ago https://www.adafruit.com/product/3900 4 days ago https://www.youtube.com/channel/UCeBxc0fj-UUayo01ei0B7w 4 days ago https://www.youtube.com/channel/UCKKBTwYyVYxkpa0_9pqQ13 4 days ago https://youtube.com/@tobyhorrorboy 4 days ago |
522. HN ChatGPT is NOT a LLM – GPT is**Concise Summary:** The AI industry is experiencing a significant terminology issue as "ChatGPT" and "Large Language Models (LLMs)" are often used interchangeably, leading to confusion about their distinct roles. Originally an interface for OpenAI's GPT model, ChatGPT has evolved into a complex agentic system integrating LLMs within its architecture. This shift from using solely LLMs to adopting agentic AI systems parallels the historical move from procedural to object-oriented programming and is pivotal for developers, investors, and users. LLMs are advanced text pattern-matching tools with fixed knowledge up to their training cutoff, ensuring predictability but limiting adaptability. In contrast, AI agents like ChatGPT have evolved by incorporating stateful features that allow them to maintain context across interactions, remember user preferences, and interact with external systems through tool integration. While they do not update their base models, these agents can learn and adapt within sessions, enhancing their multi-step reasoning capabilities. The architecture of AI agents involves several components: 1. **Orchestration Layer**: Manages system flow. 2. **Memory Systems**: Maintains both short-term (conversation context) and long-term (user preferences) memories. 3. **Tool Integration Framework**: Interfaces with external capabilities like web browsing or code execution. 4. **Planning and Reasoning Engine**: Coordinates complex tasks. 5. **Safety and Alignment Systems**: Ensure user-aligned behavior. 6. **LLM Core**: Provides reasoning and language generation. The distinction between LLMs and agent systems impacts development strategies, requiring different architectural patterns, infrastructure, and user experience designs. While LLM applications focus on stateless interactions with precise prompts, agent systems manage state, integrate tools, and orchestrate complex tasks, leading to divergent UX approaches and business strategies. This transition from LLMs to agent systems affects various domains: - **Architectural and Development Changes**: Requires new patterns and methodologies. - **User Experience Design**: - LLMs focus on single-turn interactions. - Agents handle ongoing relationships with complex workflows. - **Business Strategy Implications**: - LLM products target specific use cases. - Agent products support broader applications but involve intricate cost structures. - **User Engagement**: Users shift from crafting precise prompts for LLMs to engaging in collaborative problem-solving with agents. - **Industry Impact**: - Software Development: Moves from "prompt engineering" to "agent orchestration." - Customer Service: Evolves from FAQ-focused chatbots to complex issue resolution. - Content Creation: Transitions from single content pieces to strategy management. - Research and Analysis: Advances from answering specific questions to comprehensive investigations. - **Future Developments**: Includes Multi-Agent Systems for collaborative problem-solving and Persistent Learning for continuous agent improvement. The document stresses the importance of semantic precision, noting that conflating terms like "ChatGPT" with "LLM" leads to confusion. It defines LLMs as text-generating models, agents as systems integrating these models with additional capabilities, and interfaces as user experience layers. This evolution from LLMs to sophisticated agent systems represents a fundamental shift in AI capabilities and strategic considerations. Recognizing this transition is crucial for leveraging the full potential of agentic AI systems. Companies, developers, and users who adapt early will be better positioned to navigate future challenges, while those relying on outdated concepts may struggle. --- **Bullet Point Summary:** - **Terminology Issue**: Confusion between "ChatGPT" (application) and "LLMs" leads to misunderstanding of their roles. - **Evolution of AI Systems**: ChatGPT has transformed from a simple interface for GPT models into an agentic system integrating LLMs, marking a shift akin to the evolution from procedural to object-oriented programming. - **Distinct Functions**: - **LLMs**: Advanced pattern-matching tools with stateless systems and fixed knowledge. - **AI Agents**: Incorporate stateful features for context maintenance, memory, tool integration, and multi-step reasoning. - **Architectural Components of AI Agents**: - Orchestration Layer - Memory Systems (short-term & long-term) - Tool Integration Framework - Planning and Reasoning Engine - Safety and Alignment Systems - LLM Core for reasoning and generation - **Impact on Development and UX Design**: Requires different approaches for LLMs vs. agent systems, with implications for architecture, infrastructure, and user interaction. - **Industry Impact**: - Architectural changes in software development - Evolving customer service capabilities - Shifts in content creation strategies - Advanced research and analysis methodologies - **Future Developments**: Multi-Agent Systems and Persistent Learning are key advancements. - **Importance of Semantic Precision**: Avoiding conflation of terms to ensure clarity in AI system understanding. - **Strategic Implications**: Early adaptation to agent systems will position stakeholders advantageously for future challenges. Keywords: AI systems, LLM, agents, architecture, cognitive architecture, integration, memory systems, multi-step reasoning, persistent learning, state management, tool integration, user experience
llm
www.vincirufus.com 6 days ago
https://tern.sh 6 days ago |
523. HN Ask HN: How to avoid passive use of AI?The text delves into the topic of using artificial intelligence (AI) in a proactive and critical manner, rather than passively, across both coding tasks and daily activities. It highlights increasing expectations for productivity driven by AI advancements while acknowledging concerns about potential negative impacts on cognitive functions. The discussion includes personal reflections from an individual who has experimented with a toy project, illustrating the contrast between slower progress without AI and rapid creation of a Minimum Viable Product (MVP) when using AI tools. This raises an underlying question about balancing high productivity levels with maintaining critical thinking abilities and brain health. - The text addresses how to utilize AI proactively and critically in coding and everyday life. - It notes rising expectations for productivity due to AI advancements, alongside concerns about negative cognitive impacts. - Personal experiences are shared regarding a toy project, comparing progress without AI to the efficiency of using AI to create an MVP quickly. - There is a concern about sustaining high productivity levels without compromising critical thinking and brain health. Keywords: Ask HN, Claude, MVP, coding, cost to brains, critical-thinking, everyday life, maintain productivity, passive AI, proactive use, productivity expectations, refine, toy project
claude
news.ycombinator.com 6 days ago
https://en.wikipedia.org/wiki/Rubber_duck_debugging 6 days ago https://publichealthpolicyjournal.com/mit-study-finds-artifi 6 days ago |
524. HN Thoughts on running my own forgejo (hosted Git with web stuff) serverThe author has been leveraging Git on a private server to manage dot files, valuing its effectiveness and privacy. When it came to public-facing projects requiring a web interface, they opted for Forgejo, finding the setup process both simple and enjoyable despite initial doubts about user-friendliness. Currently, Forgejo is primarily used by the author for hosting documentation related to federated instances, with content pushed easily through the terminal. The server runs efficiently on a minimal container within Proxmox, handling sustained distributed traffic from fediverse links well. Although it's convenient to import repositories from platforms like GitHub into Forgejo, the author has not transferred any valuable projects and is contemplating deleting their existing GitHub repositories. Despite the advantages of self-hosting Forgejo, such as ease of use and control, the author feels isolated due to its lack of federated functionality that restricts collaboration. They express concerns about hosting private repositories online because they prefer isolation over potential security risks, which may reflect an overly cautious attitude regarding their sysadmin abilities and service security. Consequently, they continue using a separate Git server for private projects instead of exploring Forgejo's capabilities for handling private repos. - **Preference for Self-Hosting:** The author values self-hosting solutions like Git for managing dot files due to privacy benefits. - **Forgejo Adoption:** Chose Forgejo for public-facing projects; found the setup straightforward despite initial concerns about usability. - **Usage and Performance:** Currently uses Forgejo mainly for documentation hosting, appreciating its terminal-based content management. The server runs effectively in a minimal container on Proxmox, handling distributed traffic well. - **Importing Repositories:** While importing from GitHub is convenient, no valuable projects have been transferred, with consideration to delete existing GitHub repositories. - **Isolation Concerns:** Despite advantages, the author feels isolated due to Forgejo's lack of federated features limiting collaboration. - **Security and Privacy Worries:** Prefers isolation over online hosting for private repos due to security concerns, possibly reflecting an overly cautious approach regarding their sysadmin skills. - **Continued Use of Git Server:** Maintains a separate Git server for private projects rather than using Forgejo's private repo capabilities. Keywords: Collaboration Platform, Container, Documentation, Federated Functionality, Forgejo, Git, Git Server, GitHub, IMAP, Isolated, Mail Server, Nextcloud, Private Repos, Proxmox, Security, Self-Hosting, Server, Sysadmin Skills, Web Interface
github
neilzone.co.uk 6 days ago
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525. HN Snake eating tail: Google's AI Overviews cites web pages written by AI: study### Summary A recent study by Originality.ai revealed that 10.4% of sources cited in Google's AI Overviews (AIOs) are likely generated by artificial intelligence systems, particularly large language models (LLMs). This finding emerged from an analysis of 29,000 high-stakes queries across health, finance, legal, and political topics, raising concerns about potential biases and the recycling of ideas. Originality.ai warns that this could lead to trust issues in AI-generated content and contribute to model collapse due to training on low-quality data. Google has criticized the study's methodology, arguing that Originality.ai's detection methods are based on flawed data and unreliable technology. Despite acknowledging limitations in AI detectors, Google maintains that it evaluates AI-generated content by quality rather than authorship, highlighting AI’s potential for creativity. Further studies indicate varied impacts of AIOs on search traffic dynamics. While Google disputed a Pew study suggesting reduced publisher traffic due to AIOs, Ahrefs reported significant drops (34.5%) in click-through rates when AIO appeared above human-generated content. Originality.ai found 74.4% of AIO citations were human-written, with 15.2% unclassifiable due to technical issues. Moreover, while 52% of functional AIO links didn’t rank in the top 100 organic search results, a higher 12.8% was identified as AI-generated compared to the overall rate. Contrastingly, an Ahrefs study noted that most (76%) of the AIO citations appeared within the top 10 search results. Sam Robson from The Better Web Co. suggested Originality.ai’s focus on "Your Money or Your Life" (YMYL) queries might explain their different findings. Google's AI Overviews, powered by its Gemini language model, utilize a broader range of materials compared to traditional engines like Googlebot. This allows them to extract valuable information from non-traditional formats such as PDFs and whitepapers, particularly in the YMYL domain. Nevertheless, inclusion in top 100 search results does not guarantee appearance in AIO citations due to Google's query fan-out technique, which generates content based on multiple related searches. ### Bullet Point Summary - **Originality.ai Study:** Found that 10.4% of sources cited in AI Overviews (AIOs) are likely AI-generated. - **Concerns Raised:** Potential biases and a cycle of recycled ideas could lead to trust issues and model collapse due to poor-quality data training. - **Google's Response:** Criticized Originality.ai’s methodology, emphasizing evaluation based on content quality rather than authorship. - **Impact on Traffic:** Ahrefs reported significant drops in click-through rates for top search results when AIO appeared above them; 74.4% of AIO citations were human-written. - **Differing Findings:** Contrasting studies show varied impacts on publisher traffic and rankings, with Google’s AI Overviews using a broader range of materials to derive content from related queries rather than the initial one. Keywords: AI Overviews, AI detection, AIOs, Gemini, Google, Googlebot, LLM, Originalityai, Pew Research Center, SEO, YMYL queries, citations, echo chamber, model collapse, study, training data
gemini
www.theregister.com 6 days ago
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526. HN Some thoughts on personal Git hostingThe author is investigating alternatives to centralized Git hosting platforms like GitHub by considering self-hosted solutions for personal projects. Utilizing PikaPod’s service, they have established their own Git instance with Gitea, albeit at a minimal cost for setup and maintenance convenience. This approach offers flexibility in domain management and the potential to switch software (e.g., from Gitea to Foregjo) but lacks GitHub's network effects, resulting in less seamless collaboration. Gitea supports OAuth providers such as GitHub and GitLab, facilitating single-sign-on integration; however, other interactions like creating pull requests or copying code are cumbersome. Users need to fork repositories on the author’s server before interacting with them from other platforms, which poses a challenge for engagement due to increased complexity. Despite these usability hurdles, the self-hosted solution is operational but faces obstacles in terms of broader accessibility and user experience. The process of transitioning repositories from GitHub to Gitea involves challenges such as the inability to send pull requests between forks and limited code discovery outside GitHub. Managing and maintaining a new platform also presents an administrative burden. The author notes difficulties in sustaining sponsorships and attracting contributors due to the lack of network effects on GitHub, which is critical for visibility and engagement. While considering these factors, the author plans to retain popular and sponsored repositories on GitHub while moving smaller projects to their own Gitea instance (git.edent.tel) at minimal cost. They also express interest in finding a hosted Forgejo instance with a personal subdomain that is either cheaper or comparable in price to existing options, seeking recommendations for such services. - The author explores self-hosted Git solutions like Gitea using PikaPod’s service as an alternative to GitHub. - Offers flexibility but lacks network effects, resulting in collaboration challenges compared to GitHub. - While OAuth support eases some integrations, the process remains cumbersome for actions like pull requests and code copying. - Transitioning from GitHub presents difficulties such as limited interactivity between forks and discovery issues. - Administrative burdens are significant when setting up and managing a new platform. - Sponsorships and contributor attraction are challenging due to reduced network effects. - Popular repositories remain on GitHub; smaller ones move to the author's Gitea instance, with plans for new projects there. - The author seeks recommendations for cost-effective hosted Forgejo instances. Keywords: Codeberg, Foregjo, ForgeFed, Git hosting, GitHub, GitLab, Gitea, OAuth, PR (pull request), PikaPod, Pull Request, ReDeCentralise, administration, clone, code discovery, contributors, fork, forks, free services, hosted Forgejo, instance, mixed reputation, network effects, personal git hosting service, recommendations, self-hosting, spam attack, sponsored repos, sponsorship, subdomain, €2/mo
github
shkspr.mobi 6 days ago
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527. HN GitHub's spec-kit: Toolkit to help you get started with Spec-Driven Development- **Spec-kit Overview**: Spec-kit is a toolkit designed to support Spec-Driven Development (SDD) by allowing organizations to focus on product scenarios rather than undifferentiated code. Specifications in SDD are executable and generate working implementations directly, guiding development more effectively. - **Starting with spec-kit**: - Use the `/specify` command to describe desired outcomes without technical details. - The `/plan` command helps outline tech stack and architecture choices. - Employ `/tasks` for creating a task list to implement features. - Specify CLI aids project bootstrapping, supporting AI agents like Claude Code, Gemini, or Copilot with commands such as `specify init - **Taskify Platform**: Taskify is a team productivity platform for enhancing project management and collaboration. It includes five predefined users (one product manager and four engineers) and three sample projects using a Kanban-style interface. Features include task creation, assignment, commenting without limit, and drag-and-drop functionality between columns like "To Do" and "Done." The UI highlights tasks assigned to the currently viewed user in distinct colors. - **Claude Code's Role**: Claude Code initiates planning by setting up repositories with scripts that create a new branch (e.g., `001-create-taskify`) and directories under `specs` for initial specifications. It involves refining requirements, clarifying details like task numbers, and validating acceptance checklists against feature specs. - **Generating a Plan**: - Establish specific technical requirements using the `/plan` command. - Define components such as .NET Aspire for backend development, Postgres as the database, and Blazor Server for frontend implementation. - Create REST APIs for projects, tasks, and notifications, resulting in organized documentation. - **Research on .NET Aspire**: - Identify areas needing additional research due to rapid evolution of .NET Aspire. - Update `research.md` with specific version details and initiate parallel research tasks targeting precise questions. - Break down tasks into smaller steps and list items requiring clarification or further research. - **Validation and Implementation**: - Validate the implementation plan using Claude Code for completeness and coherence. - Use GitHub CLI to create a pull request from the current branch to main. - Instruct Claude Code to implement the solution by executing local commands like `dotnet`. - Run the application with Claude Code's assistance to address build and runtime errors. This summary encapsulates the essence of using spec-kit for SDD, detailing Taskify’s features, and outlining the steps involving planning, research, validation, and implementation with tools like Claude Code. Keywords: AI agent, API, Blazor server, CLI, Claude Code, GitHub, NET Aspire, Postgres, Spec-Driven Development, Taskify, documentation, implementation plan, planning, research, specifications, tasks
postgres
github.com 6 days ago
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528. HN Show HN: Canvas Confetti – Performant confetti animations for the webCanvas Confetti and Nano AI are two distinct tools designed for enhancing web experiences with creative animations and image generation, respectively. Canvas Confetti is a lightweight web library dedicated solely to producing efficient confetti animations. Its standout features include a minimal footprint of 1KB when gzipped, the capability to maintain smooth 60fps performance using canvas technology, and optional support for web workers to boost efficiency further. The library emphasizes accessibility by incorporating reduced motion options and offers users flexibility through custom shapes, emoji integration, and color customization options. By focusing specifically on confetti effects, Canvas Confetti distinguishes itself from more generalized and bloated animation libraries. On the other hand, Nano AI is an advanced image generation tool utilizing "nano banana technology" to produce unique visuals based on user-provided descriptions. This AI leverages sophisticated models to create high-quality images instantly, tailored specifically to the input it receives. The emphasis of Nano AI is on delivering visually stunning and personalized outputs with a focus on leveraging cutting-edge artificial intelligence capabilities. ### Bullet Point Summary: - **Canvas Confetti**: - A lightweight web library for confetti animations. - Features include minimal footprint (1KB gzipped) and high performance at 60fps using canvas technology. - Offers optional support for web workers to enhance performance. - Accessible features like reduced motion support are included. - Provides flexibility with custom shapes, emoji options, and color customization. - Focuses exclusively on delivering optimal confetti effects. - **Nano AI**: - An advanced AI image generator using "nano banana technology." - Creates unique images based on user descriptions. - Utilizes sophisticated AI models for instant production of high-quality visuals. - Emphasizes tailored and visually stunning outputs. Keywords: Accessible, Animations, Canvas, Canvas Confetti, Color Customization, Emoji Confetti, GitHub, Image Generator, Lightweight Library, Nano AI, Performant, SVG Paths, Smooth Animations, Web, Web Worker Support
github
nanoai.run 6 days ago
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529. HN Show HN: Semantic grep for Claude Code (RUST) (local embeddings)The provided text introduces `ck`, a sophisticated code search and indexing tool designed to enhance traditional grep functionalities by incorporating semantic search capabilities using Rust. It allows developers to locate code segments based on their meaning, leveraging advanced techniques such as semantic embeddings and pattern recognition for concepts like error handling or connection timeouts, even when specific keywords are absent. Installation can be achieved via Cargo commands (`cargo install ck-search` or `cargo build --release`), making it accessible for Rust developers. Key features of `ck` include: - **Semantic Search**: Employs embeddings to understand code concepts beyond keywords, offering the retrieval of complete functions or sections related to search queries. - **Drop-in grep Compatibility**: Maintains familiar grep-like operations with identical flags and output formats while supporting additional functionalities such as context display and recursive searches. - **Hybrid Search**: Merges traditional keyword precision with semantic analysis through Reciprocal Rank Fusion, enhancing the relevance of results by combining both methods. - **Agent-Friendly Output**: Delivers JSON-formatted outputs compatible with scripts and automation tools, enabling structured search results for AI applications. - **Smart File Filtering**: Automatically excludes directories like caches, build artifacts, and system files (e.g., `.git`, `node_modules`) from searches to streamline the process. The Smart File Filtering feature emphasizes intelligent file exclusion and semantic indexing, facilitating efficient code navigation. The tool supports three search modes: regex for traditional grep-like searches, semantic (`--sem`) for meaning-based searches requiring an index, and hybrid combining both approaches. It provides relevance scoring to prioritize results based on their significance, enabling threshold filtering and comprehensive section retrieval with options like `--full-section` or limiting results using `--topk`. In addition to search capabilities, `ck` offers directory management commands for maintaining indexes, supports a wide range of programming languages via tree-sitter parsing, and accommodates various text formats. It allows customization of indexing exclusions, ensuring flexibility in handling different project structures. The tool's architecture consists of several Rust-based modules handling diverse tasks, from command-line interfaces to semantic search operations, with index storage in `.ck/` directories alongside user codebases. For developers, `ck` provides a powerful solution for analyzing and managing code efficiently, facilitating advanced searches through customizable configurations and intelligent file management. Moreover, the tool integrates seamlessly into development workflows by supporting automated tasks such as git hooks or CI/CD pipelines, offering utility in scenarios like security scanning and documentation generation. Future enhancements include custom configuration files, integration with package managers, support for multiple embedding models, and various performance optimizations. In summary, `ck` stands out from traditional search tools through its semantic capabilities, offline operation ensuring privacy, and optimized performance even on large codebases. Its open-source nature allows flexibility in licensing options and encourages community contributions to enhance its functionality further. Keywords: Apache License, Clap, FastEmbed, Full-text search, JSON format, MIT License, Result types, Rust, Semantic grep, Tantivy, Tree-sitter parsing, ck-search, code analysis, configuration management, embeddings, error handling, hybrid search, index storage, refactoring, semantic search
claude
github.com 6 days ago
https://github.com/run-llama/semtools 6 days ago https://news.ycombinator.com/item?id=44941999 6 days ago https://github.com/sibyllinesoft/grimoire 6 days ago https://github.com/bartolli/codanna 6 days ago https://github.com/run-llama/semtools#:~:text=get%20you 6 days ago https://github.com/BeaconBay/ck#:~:text=yes%2C%20comple 6 days ago |
530. HN Creating Prey 2006 open-source FPS port by integrating codebase with Doom 3The text describes an initiative to create an open-source first-person shooter (FPS) port of "Prey 2006" by merging its codebase with that of "Doom 3." This project mirrors a previous endeavor where the integration involved combining "Quake 4" and "Doom 3," which had been discussed on platforms like Doomworld. The concept revolves around leveraging existing resources and community discussions to facilitate this port, with potential resources being shared and debated among users on sites such as Hacker News. On these platforms, links to fan-made remasters or repositories related to the integration of Doom 3 and Prey are highlighted, indicating an active interest in advancing this project through collaborative efforts within gaming communities. - The initiative aims to create an open-source FPS port by integrating "Prey 2006" with "Doom 3." - This concept is similar to a previous integration effort between "Quake 4" and "Doom 3," discussed on Doomworld. - Discussions about the project take place on platforms like Hacker News, where users share potential resources. - Links to fan-made remasters or relevant repositories are part of these discussions, indicating community involvement in advancing the port. Keywords: API, Creating Prey, Doom 3, FPS, GPL, GitHub, Hacker News, Quake 4, genezeta, integration, open-source, port, project, remaster, retro_guy
github
news.ycombinator.com 6 days ago
https://krispy-the-goat.itch.io/prey-2006 6 days ago https://www.thegamer.com/prey-2006-fan-made-remaster-doom-3- 6 days ago https://github.com/glKarin/com.n0n3m4.diii4a 6 days ago |
531. HN Show HN: An Open Source XR(AR/VR) Operating SystemManaskamal and a fellow college student have created XenevaOS, an open-source operating system designed specifically for XR (AR/VR) applications. This innovative platform features a custom kernel named "AURORA," which aims to enhance performance by removing the legacy code constraints typical of existing kernels like Linux. Despite facing criticism for its ambitious goals, the developers are steadfast in their mission to revolutionize computing through AR-native experiences. XenevaOS is distinguished by its immersive holographic interfaces and real-time 3D worlds, redefining user interaction with technology by incorporating augmented reality and AI into spatial interactions. The system is versatile, providing tailored solutions for diverse industries such as education, healthcare, and business. By making the project available on GitHub, the developers encourage open-source contributions and feedback to further develop and refine XenevaOS. - **Key Points:** - Manaskamal and a peer developed XenevaOS, an XR-focused open-source OS with a custom kernel called "AURORA." - The system seeks improved performance by eliminating legacy constraints found in traditional kernels. - Despite ambitious goals, developers aim to transform computing through AR-native experiences. - Features include immersive holographic interfaces and real-time 3D worlds integrating AR and AI. - Offers industry-specific solutions for education, healthcare, business, etc. - Available on GitHub for open-source contributions and feedback. Keywords: AI, AR/VR, AURORA, Augmented Reality, Custom-Built, Efficiency, Formula One, GitHub, Holographic Interfaces, Industries, Innovation, Kernel, Legacy Codes, Native Computing, Open Source, Operating System, Performance, Real-Time 3D, Spatial Interactions, XR, XenevaOS
github
www.getxeneva.com 6 days ago
https://news.ycombinator.com/item?id=45140381 4 days ago |
532. HN Show HN: FSP first algorithm that compresses tiny datasets 100 bytes efficientlyThe FSP (Find Similar Patterns) algorithm is designed to efficiently compress small datasets, particularly those under 100 bytes, overcoming the limitations of traditional compression algorithms like ZIP and RLE. These older methods often fail with small files due to the addition of headers, which can increase file size. The FSP algorithm achieves compression by identifying similar data blocks within a dataset, storing one base block, and recording only the differences between these blocks. This technique allows it to effectively reduce file sizes without any loss of data. In practical tests, FSP demonstrated its efficiency by compressing a 52-byte dataset down to 29 bytes, achieving a compression ratio of 1.79×. The versatility of the FSP algorithm is highlighted by its ability to work across various types of data, including text, images, and video frames. This makes it suitable for a wide range of applications where small file size reductions are beneficial. Further information about the algorithm, including examples and test scripts, can be accessed through its GitHub repository at [GitHub](https://github.com/Ferki-git-creator/fsp), and more comprehensive details are available on its project website at [here](https://ferki-git-creator.github.io/fsp/). ### BULLET POINT SUMMARY: - FSP algorithm compresses small datasets under 100 bytes efficiently. - Overcomes limitations of traditional algorithms like ZIP and RLE, which can increase file size due to headers. - Works by identifying similar data blocks and storing only the base block with differences. - Demonstrated effectiveness in tests: compressed a 52-byte dataset to 29 bytes (compression ratio of 1.79×) without data loss. - Versatile across various data types including text, images, and video frames. - Additional information available on GitHub at [GitHub](https://github.com/Ferki-git-creator/fsp). - Full project details accessible via the website at [here](https://ferki-git-creator.github.io/fsp/). Keywords: FSP, GitHub, Python script, RLE, ZIP, algorithms, blocks, compression, datasets, decompression, files, headers, images, logs, metadata, ratio, test script, text, versioned files, video frames
github
news.ycombinator.com 6 days ago
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533. HN Serverless Horrors**Summary:** "Serverless Horrors" is a blog founded by Andras, aimed at sharing horror stories associated with serverless technology. The platform serves as a space for individuals to recount their challenging experiences while working with serverless architectures. Beyond this, Andras contributes to the development of "Coolify," an open-source project designed as an alternative to popular platforms such as Heroku, Netlify, and Vercel. Readers are encouraged to contribute their own narratives about difficulties encountered in serverless environments. **Bullet Point Summary:** - The blog "Serverless Horrors" focuses on sharing horror stories related to serverless technology. - Created by Andras, who also works on an open-source project named Coolify. - Coolify aims to be a substitute for platforms like Heroku, Netlify, and Vercel. - Readers are invited to share their personal experiences with serverless challenges. Keywords: Andras, Blog, Coolify, Heroku, Horrors, Netlify, Open-source, Self-hostable, Serverless, Stories, Vercel, Yikes
popular
serverlesshorrors.com 6 days ago
https://docs.aws.amazon.com/cost-management/latest/ 6 days ago https://docs.aws.amazon.com/accounts/latest/refere 6 days ago https://medium.com/%40akshay.kannan.email/amazon-is-ref 6 days ago https://learn.microsoft.com/en-us/azure/cost-manag 6 days ago https://aws.amazon.com/free/ 6 days ago https://www.reddit.com/r/aws/comments/rm8t2j& 6 days ago https://medium.com/@maciej.pocwierz/how-an-empty-s3-buc 6 days ago https://aws.amazon.com/about-aws/whats-new/2024&#x 6 days ago https://coolify.io/ 6 days ago https://dokploy.com/ 6 days ago https://caprover.com/ 6 days ago https://news.ycombinator.com/item?id=43884892 6 days ago https://news.ycombinator.com/item?id=39532754 6 days ago https://macharchitecture.com/ 6 days ago https://www.troyhunt.com/closer-to-the-edge-hyperscaling-hav 6 days ago https://www.prompt.security/vulnerabilities/denial-of-w 6 days ago |
534. HN Show HN: I'm a dermatologist and I vibe coded a skin cancer learning appThe author, who is a dermatologist, developed a quick learning application aimed at identifying skin cancer within approximately 2-3 hours using Gemini Pro 2.5. This single-page web application was constructed with basic technologies including HTML, JavaScript, and CSS, utilizing Vanilla JS for its functionality. To store user scores persistently, the application employs localStorage. The deployment of this app could be done on a Digital Ocean server; however, due to minimal backend needs, it can also be hosted statically. Additionally, the application stores images and associated metadata in an AWS S3 bucket. Users interact with the application by swiping left or right, or using designated buttons to express their concern about skin lesions. - **Development**: A dermatologist created a quick learning app for skin cancer identification using Gemini Pro 2.5 within 2-3 hours. - **Technology Used**: The app is built as a single-page application employing HTML, JavaScript, CSS, and Vanilla JS. - **Data Storage**: User scores are stored locally using localStorage. - **Deployment Options**: It can be deployed on a Digital Ocean server or hosted statically due to its minimal backend requirements. - **Image and Metadata Management**: Images and metadata are stored in an AWS S3 bucket. - **User Interaction**: Users can express their concern about skin lesions by swiping left or right or using buttons. Keywords: AWS S3, Apache2, Dermatologist, Digital Ocean, Flask, Gemini Pro, Ubuntu, Vanilla JS, app, concern level, image metadata, lesion assessment, localStorage, skin cancer, swipe
popular
molecheck.info 6 days ago
https://xkcd.com/2501/ 6 days ago https://molecheck.info/how-to-recognise-skin-cancer 6 days ago https://drmagnuslynch.s3.eu-west-2.amazonaws.com/isic-images 6 days ago https://www.google.com/search?tbm=isch&q=naevoid+melanom 6 days ago https://www.kanker.nl/sites/default/files/lib 6 days ago https://rorycellanjones.substack.com/p/wearenotwaiting- 6 days ago https://api.isic-archive.com/images/?query=clin_size_lo 6 days ago https://www.nature.com/articles/nature21056 6 days ago https://challenge.isic-archive.com/ 6 days ago https://pubmed.ncbi.nlm.nih.gov/32931808/ 6 days ago https://github.com/EnterpriseQualityCoding/FizzBuzzEnte 6 days ago https://emdee.ai/ 6 days ago https://www.skinvision.com/ 6 days ago https://dermnetnz.org/topics/basal-cell-carcinoma 6 days ago https://melanoma.jenevoldsen.com/ 6 days ago https://mikelovesrobots.substack.com/p/wheres-the-shove 6 days ago |
535. HN Easy Appointments – open-source Appointment Scheduler for self hosting**Summary:** Easy Appointments is an open-source Appointment Scheduler tailored for self-hosting, facilitating integration with websites through various customizable features such as customer and appointment management, service organization, booking rules, Google Calendar synchronization, email notifications, and a multi-language interface. It supports enterprise workflows and offers community support. To install Easy Appointments, users need server requirements like Git, Node.js, Composer, Apache/Nginx, PHP (version 8.2 or higher), and MySQL. The installation involves cloning the repository, setting up dependencies, and executing specific commands to start the application. Key steps include creating a database, ensuring the "storage" directory is writable by copying the "easyappointments" source folder onto the server, renaming the configuration file from "config-sample.php" to "config.php," and configuring it accordingly. Installation and updates can be accessed via the application URL or through easyappointments.org. Support is available through an official group and GitHub issues page. The project is licensed under GPL v3.0 for code and CC BY 3.0 for content, with additional resources found on alextselegidis.com, GitHub, and Twitter. **Bullet Point Summary:** - Easy Appointments is an open-source, self-hostable Appointment Scheduler that integrates with websites. - Features include customer/appointment management, service organization, booking rules, Google Calendar sync, email notifications, and a multi-language interface. - Supports enterprise workflows and community support. - Installation requires server setup with Git, Node.js, Composer, Apache/Nginx, PHP (8.2+), and MySQL. - Key installation steps: clone repository, set up dependencies, start application; ensure writable "storage" directory after copying source folder to the server. - Rename "config-sample.php" to "config.php" and configure it for your environment. - Access installation guide via the application URL or easyappointments.org for updates. - Support is available through an official group and GitHub issues page. - Licensed under GPL v3.0 (code) and CC BY 3.0 (content). - Additional resources available on alextselegidis.com, GitHub, and Twitter. Keywords: Apache/Nginx, Appointment Scheduler, Booking Appointments, CC BY 30, Community Support, Composer, Customer Management, Database, Easy Appointments, Email Notifications, Enterprise Workflow, File Watcher, GPL v30, Git, GitHub, Google Calendar Sync, MySQL, Nodejs, Open Source, PHP, Self Hosting, Server Installation, Twitter, User Interface, Web Application
github
github.com 6 days ago
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536. HN Making GitHub Profiles Cool – Painful Lessons with GitHubThe article explores the author's journey of enhancing their GitHub profile by creating an engaging README file and personalizing it based on the time of day. The idea stems from wanting to make the GitHub experience as dynamic and user-friendly as human interactions, such as greeting someone differently at different times. Initially considering a server setup for changing banners, the author opts for GitHub Actions due to its cost-free benefits for repository interaction. Despite recognizing similar pre-existing solutions, including documented examples of GitHub Actions integrations, they proceed with their implementation. The solution involves creating an animated banner that changes throughout the day, utilizing a repository configured as a simple Content Delivery Network (CDN) to store and serve banner files. The automation is managed through GitHub Actions, which are scheduled like Cron jobs for regular updates—specifically running at 6 AM UTC to replace one banner image with another. Initially facing permission issues due to read-only default access of GitHub Actions, the author resolves this by adjusting repository settings to allow write access. A significant challenge encountered was the delay in action execution, as actions did not trigger precisely on time; a scheduled task for 6 PM UTC executed several minutes late. The article notes that such delays are typical yet often overlooked and suggests using an external server for tasks requiring precise timing. Further complications arose from caching issues with static images hosted on GitHub's camo service, where old banner versions persisted in profile views despite updates. A workaround involved appending a query string to the image URL to bypass cache persistence. The article concludes by acknowledging potential misuse of features like arbitrary code execution in README files and generous free tiers for CI/CD services. These could lead to unauthorized modifications or malicious activities, such as malware distribution or altering GitHub profile visuals without consent. ### Bullet Points Summary: - The author aims to enhance their GitHub profile with a dynamic README using time-based animations. - They choose GitHub Actions over servers due to its cost-effectiveness and ability to automate tasks for public repositories. - Automation involves scheduled updates of banner images through a CDN-style repository, initially facing permission issues that are resolved by modifying access settings. - Execution delays in GitHub Actions are noted as common but not suitable for time-sensitive tasks, suggesting external servers might be necessary. - Caching issues with static image updates on camo.githubusercontent.com were resolved using query strings to ensure consistent banner display. - The article warns of potential misuse of features like arbitrary code execution and free CI/CD tiers leading to unauthorized or malicious activities. Keywords: API, Actions, CI/CD, Cron job, GitHub, README, activity graph, automation, avatar, caching, customization, documentation, image swapping, permissions, profile, repositories, scheduling, security, stars, triggers, workflow_dispatch
github
crowfunder.github.io 6 days ago
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537. HN Google AI Mode to Become Default for Google Search SoonGoogle has announced plans to set Google AI Mode as the default option for its search service. Logan Kilpatrick, lead product manager for Google's AI products, along with Robby Stein, VP of Product at Google Search, emphasized this shift, which aligns with previous statements by Sundar Pichai and Liz Reid regarding the centrality of AI Mode in future search functionalities. The transition includes a new direct URL (google.com/ai) that leads users to the AI Mode interface, reflecting an ongoing integration effort. The exact implications of "default" are not fully clarified but seem to involve a combination of traditional search capabilities with AI features. This development signifies notable progress as Google expands AI Mode to 180 countries from its initial limited regional availability. The announcement has garnered significant attention and sparked discussions on platforms like X, despite some coverage being delayed due to personal commitments. **BULLET POINT SUMMARY:** - Google plans to make AI Mode the default for Google Search. - Logan Kilpatrick and Robby Stein have highlighted this shift as part of ongoing integration efforts. - A new direct URL (google.com/ai) facilitates access to AI Mode, indicating its importance in future search functionalities. - The term "default" suggests a blend of traditional search with AI features, though specifics are unclear. - Google is expanding AI Mode's availability to 180 countries, marking significant growth from its initial launch. - The announcement has attracted considerable attention and discussion online, despite some delays in coverage due to personal commitments. Keywords: Announcements, Availability, Countries, DeepMind, Forum, Gemini, Google AI, Interface, Liz Reid, Logan Kilpatrick, Robby Stein, Search, Sundar Pichai, URL
gemini
www.seroundtable.com 6 days ago
|
538. HN GitHub introduces Spark, Agentic Copilot built and hosted at GitHubGitHub has launched an AI-driven platform named Agentic Copilot for Spark, designed to facilitate the swift and effortless creation of intelligent applications. This platform caters to a broad audience by offering a user-friendly interface that supports various input methods, including natural language, visual tools, or code, making it accessible to both seasoned developers and novices. Spark is equipped with features such as instant previews and one-click deployment, which streamline the development process from conceptualization to production. Additionally, its seamless integration within GitHub's ecosystem enhances usability and efficiency for users. The platform is constructed on widely-adopted tools that are trusted by 150 million developers globally, ensuring scalability of applications right from their inception. **BULLET POINT SUMMARY:** - **Platform Introduction:** GitHub introduces Agentic Copilot for Spark, an AI-powered platform for building intelligent applications easily. - **User-Friendly Interface:** Supports multiple input methods (natural language, visual tools, or code) to accommodate both experienced developers and beginners. - **Key Features:** - Instant previews for immediate feedback. - One-click deployment for rapid application launch. - Seamless integration with GitHub’s ecosystem for enhanced efficiency. - **Development Process:** Enables quick transition from idea to production. - **Built on Trusted Tools:** Utilizes widely-used tools trusted by 150 million developers worldwide, ensuring scalability of applications. Keywords: AI-powered, Agentic Copilot, GitHub, Spark, code, developers, ecosystem, full-stack applications, instant previews, integration, intelligent apps, natural language, one-click deployment, platform, production, scaling, visual tools
github
github.com 6 days ago
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539. HN Samhenrigold/LidAngleSensor: MacBook Screen Angle Sensor Reader**Summary:** Sam Gold has developed a utility called "LidAngleSensor" for MacBooks that reads lid angle sensor data, specifically available on the 2019 16-inch MacBook Pro and beyond. This tool displays the MacBook's lid angle, optionally producing a sound effect from "LEGO Batman 3: Beyond Gotham" if adjusted slowly. Primarily tested on an M4 MacBook Pro, users with different models or encountering issues are encouraged to report them. Installation requires macOS with Xcode or its Command Line Tools and can be cloned via GitHub CLI or git. Although untested on iMacs, user feedback is welcomed for enhancements. For setup, clone the repository from GitHub using the provided commands. Navigate into the directory and build using specific Xcode commands while disabling code signing for local debugging by adjusting relevant settings. Finally, run the application from its built location. **Bullet Point Summary:** - "LidAngleSensor" utility developed by Sam Gold reads lid angle sensor data on MacBooks. - Feature introduced with 2019 16-inch MacBook Pro; displays lid angle and optional sound effect. - Tested primarily on M4 MacBook Pro, but feedback for other models is welcomed. - Requires macOS with Xcode or Command Line Tools for installation and usage. - Clone the project using GitHub CLI (`gh repo clone samhenrigold/LidAngleSensor`) or git (`git clone https://github.com/samhenrigold/LidAngleSensor.git`). - Navigate to the cloned directory and build in Debug configuration; specific commands vary by Mac architecture (e.g., `-arch arm64` for Apple Silicon, optional `-arch x86_64` for Intel). - Disable code signing with `CODE_SIGNING_ALLOWED=NO`, `CODE_SIGNING_REQUIRED=NO`, `CODE_SIGN_IDENTITY=""`. - Run the built application from its location (`build/Build/Products/Debug/LidAngleSensor.app`). - Open invitation to users for testing on iMacs and providing feedback or enhancements. Keywords: Apple Silicon, Audio, CLI, Debug, Design-Engineer, GitHub, GitHub CLI, Intel Macs, LEGO Batman 3: Beyond Gotham, Lid Angle Sensor, M4 MacBook Pro, MacBook, Samhenrigold, Utility, Xcode, arm64, build directory, code signing, configuration, git clone, local debug builds, macOS, project, run application, scheme, xcode-select, xcodebuild
github
github.com 6 days ago
https://hachyderm.io/@samhenrigold/115159295473019599 6 days ago |
540. HN Visualizing the Vocabulary of an LLMThe text describes a method for visualizing the vocabulary of large language models (LLMs) by mapping their high-dimensional token embeddings into a more interpretable form using dimensionality reduction techniques like Principal Component Analysis (PCA). The author finds value in making these abstract concepts concrete, despite no specific necessity for visualization. In this process, tokens from text are represented as vectors within a multi-dimensional space defined by hyperparameters such as vocabulary size and embedding dimensions. For instance, the LLaMA 3 model represents approximately 128,000 tokens in a 4096-dimensional space. The tutorial provided outlines steps to reduce these high-dimensional embeddings into three dimensions for visualization using Plotly's interactive 3D scatter plots. The process involves filtering relevant tokens from a small corpus like Wikipedia and employing Hugging Face's Transformers library to load model data, with adaptability to other models beyond LLaMA. Key tasks include tokenizing text, counting frequencies of tokens, obtaining embeddings, reducing dimensions via PCA (from 4096 to 3), and preparing these embeddings for plotting based on frequency. In creating the visualization, a 3D scatter plot is used where axes represent the first three PCA-derived dimensions. Tokens are color-coded by frequency, with adjustments made for very high-frequency tokens to maintain visual balance. The visualization reveals patterns such as clustering of related terms like city or state names in specific areas of the space. The document further explores extending this approach to texts like Dante’s "Divine Comedy," and an optional exercise involves visualizing the entire vocabulary. This extensive task, while slow and memory-intensive, allows for interactive exploration of token embeddings, offering insights into the model's internal workings despite PCA's simplification from high-dimensional data. **Bullet Point Summary:** - The text explores visualizing LLM vocabularies by mapping high-dimensional token embeddings to a 3D space using PCA. - Token embeddings are represented as vectors in multi-dimensional spaces defined by hyperparameters, with an example given for the LLaMA 3 model. - Visualization is achieved through dimensionality reduction and interactive plotting using Plotly's 3D scatter plots, with steps outlined for loading data via Hugging Face's Transformers library. - Key tasks include tokenizing text, counting token frequencies, obtaining embeddings, reducing dimensions with PCA, and preparing these for visualization based on frequency. - A 3D scatter plot displays tokens color-coded by frequency, revealing patterns like clustering of related terms. - The approach is extendable to other texts such as Dante’s "Divine Comedy," and an optional task involves visualizing the entire vocabulary. - This comprehensive exercise helps make LLM workings tangible, despite PCA's simplification from high-dimensional space. Keywords: 3D Plot, AutoModel, AutoTokenizer, Corpus, Dante's Divine Comedy, Dense Vector, Device, Dimensionality Reduction, Embedding Space, Embeddings, HTML plot, Hugging Face, Hyperparameter, Interactive Visualization, PCA, Plotly, PyTorch, Tokenization, Transformers, Vocabulary
llm
alessiodevoto.github.io 6 days ago
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541. HN GPT-5 Thinking in ChatGPT (a.k.a. Research Goblin) is shockingly good at search**Summary:** The text explores the capabilities of GPT-5, humorously termed as "Research Goblin," in performing internet searches and addressing various inquiries. Users find it highly effective for both simple and complex queries, often surpassing traditional search engines, though it operates at a slower pace. Key examples illustrate its utility in identifying specific changes like those to Heathrow Airport's travelators, pinpointing buildings seen during journeys, and investigating product availability such as Starbucks UK's discontinued cake pops. The research extends into historical inquiries, such as the origins of Wikipedia content from Encyclopaedia Britannica, the official name of the University of Cambridge, and the history of cavernous buildings at Exeter Quay, with GPT-5 aiding in these explorations. The user also discusses its role in other areas like comparing UK supermarkets Aldi and Lidl, examining practices of AI labs in scanning books for training data, and confirming the efficiency of GPT-5's search capabilities. The author highlights their preference for conducting spontaneous research using mobile devices over traditional methods. They emphasize that leveraging GPT-5's search effectively requires experience and intuition gained through experimentation. Moreover, the text touches on how integrating large language models with tool calling and chain-of-thought techniques, like those employed by ChatGPT, enhances outcomes by combining reasoning and search functions seamlessly. The author concludes by noting their preference for ambiguous or broad questions over straightforward ones when engaging GPT-5 in research tasks, likening LLMs to a playful yet industrious "Research Goblin." **Bullet Point Summary:** - **Effectiveness of GPT-5:** Known as the "Research Goblin," it excels in internet search tasks, outperforming traditional engines for various queries. - **Applications and Examples:** Used for identifying changes at Heathrow Airport, recognizing buildings, and investigating Starbucks UK product availability. - **Historical Research:** Assists in uncovering historical details like Wikipedia’s content origins and the history of Exeter Quay's cavernous structures. - **Supermarket Comparison and AI Practices:** Provides detailed analysis on supermarket comparisons and explores AI labs' book scanning practices for training data. - **Mobile Search Convenience:** Highlights the advantage of conducting spontaneous research using mobile devices, reducing reliance on traditional methods. - **Integration with Large Language Models:** Discusses combining GPT-5 search with tool calling and reasoning techniques for enhanced results. - **User Experience and Intuition:** Emphasizes learning to use GPT-5 effectively through experience, enjoying broad and ambiguous questions more than simple ones. Keywords: AI Labs, Cake Pops, ChatGPT, Exeter Quay, GPT-5, Mobile Search, PDF Diagrams, Planning Document, Research Goblin, Starbucks, Supermarkets, Tunnels, UK
gpt-5
simonwillison.net 6 days ago
https://simonwillison.net/2025/Sep/7/ai-mode& 6 days ago https://news.ycombinator.com/item?id=45152284 4 days ago |
542. HN Simulating Human-to-Human Dialogue Using Azure AIThe Voice Live API Salescoach is a demo application designed for sales professionals, utilizing Azure AI services to provide AI-powered voice training. It simulates real-world sales scenarios by engaging users in conversations with AI agents through the Azure Voice Live API, offering immediate feedback on various aspects such as speaking tone, style, content quality, and objection handling skills. The application incorporates features like real-time voice interactions, pronunciation evaluation using Azure Speech Services, and a scoring system to monitor user progress. It supports deployment via Azure or local development with a Visual Studio Code dev container, requiring users to configure environment variables based on a template. Instructions for setting up the sales training app include configuring dependencies, defining environment variables with service keys and endpoints (configurable through `azd`), building the application, and launching the server accessible at `http://localhost:8000`. The underlying architecture integrates Azure AI components for speech processing and performance analysis, coupled with a Python Flask backend and a web interface built using React and Fluent UI. Contributions to the project are welcomed under Microsoft's Contributor License Agreement (CLA) and must comply with the Microsoft Open Source Code of Conduct. The application processes user interactions through the Voice Live API and GPT-4o for detailed performance feedback, optionally enhanced by an AI Agent Service focusing on pronunciation and fluency. The project emphasizes security, aligning with Microsoft's practices for protecting software products and services, including reporting vulnerabilities as per guidelines in SECURITY.md. Usage of trademarks or logos within the project must comply with Microsoft’s Trademark & Brand Guidelines to avoid unauthorized sponsorship implications. Third-party trademark policies are also considered. This initiative was developed in Switzerland. - **Summary:** - The Voice Live API Salescoach is an Azure AI-powered application for sales professionals, providing real-world scenario training and instant feedback. - Features include real-time voice interaction, pronunciation assessment, scoring systems, and industry-specific scenarios. - Deployment can be done via Azure or locally using a Visual Studio Code dev container. - Setup involves configuring environment variables with service keys, building the application, and starting a server at `http://localhost:8000`. - The architecture combines Azure AI components for speech processing, a Python Flask backend, and a React + Fluent UI interface. - Contributions are welcome under Microsoft's CLA and Open Source Code of Conduct. - Security is prioritized with guidelines for reporting vulnerabilities; trademark use must comply with Microsoft’s policies. - Developed in Switzerland. - **Key Points:** - AI-powered voice training for sales professionals using Azure services. - Real-time interaction, feedback on speaking skills, pronunciation assessment, and progress tracking. - Deployment options include Azure or local setup via VS Code dev container. - Configuration requires setting environment variables with service keys. - Combines Azure AI, Flask backend, React + Fluent UI interface. - Encourages contributions under Microsoft's CLA and adherence to Open Source Code of Conduct. - Security-focused with specific vulnerability reporting guidelines. - Trademark usage must follow Microsoft’s guidelines; project developed in Switzerland. Keywords: Azure AI, Azure Switzerland, Dev Container, Flask, GPT-4o, GitHub, Pronunciation Assessment, React, Real-time Conversations, Sales Coach, Scoring System, Security, Trademark Guidelines, VS Code, Virtual Customers, Voice Live API, WebSocket, env File
github
github.com 6 days ago
|
543. HN Kotlin Notebook Helps You Teach Programming**Summary:** Kotlin Notebook is an innovative educational tool developed to enhance the delivery of programming instruction by integrating runnable Kotlin code with Markdown explanations and visualizations in a unified, interactive environment. Designed for educators like Anastasiia Birillo from JetBrains, it simplifies teaching by eliminating the need for multiple tools during lectures. In her course at Constructor University, Anastasiia effectively used these notebooks to provide students with an engaging learning experience that included live demonstrations and independent notebook interaction. This method allowed students to explore concepts interactively through structured chapters and immediate code outputs. The Kotlin Notebook plugin is integrated into IntelliJ IDEA by default, facilitating easy setup for instructors and supporting interactive lecture formats suited for larger projects. Anastasiia’s public course materials demonstrate the practical application of these notebooks in a classroom setting. Constructor University's structured approach enables incremental execution of code, which is beneficial for live demonstrations and step-by-step instruction. These notebooks combine runnable code with markdown explanations and visualizations, enhancing learning through interactivity. They are straightforward to set up and share via GitHub, making them ideal for online classes. The Kotlin Notebook team emphasizes their utility in educational settings for teaching Kotlin and conducting programming workshops, encouraging educators to try them out and provide feedback at education@kotlinlang.org. **Bullet Point Summary:** - **Integration of Features:** Combines runnable Kotlin code with Markdown explanations and visualizations within a single interactive environment. - **Educational Design:** Developed to aid educators like Anastasiia Birillo by eliminating the need for multiple tools during teaching sessions. - **Usage in Education:** Utilized effectively at Constructor University, providing students with live demonstrations and independent notebook interaction. - **Interactive Learning:** Facilitates a cohesive learning experience through structured chapters and immediate code outputs. - **Ease of Setup:** The Kotlin Notebook plugin is included by default in IntelliJ IDEA for easy setup by educators. - **Supports Interactive Lectures:** Complements larger projects with an interactive lecture format. - **Practical Application:** Anastasiia’s public materials offer insights into classroom applications. - **Incremental Execution:** Allows for step-by-step instruction and live demonstrations, ideal for teaching programming concepts progressively. - **Enhanced Learning Experience:** Combines code execution with explanations and visualizations in an interactive space. - **Easy Sharing and Setup:** Notebooks can be set up and shared via GitHub, suitable for online learning environments. - **Educational Utility:** Highlighted by the Kotlin Notebook team as effective for teaching Kotlin and running workshops. - **Encouragement for Use:** Educators are encouraged to try the notebooks and provide feedback through education@kotlinlang.org. Keywords: Classroom, Code, Constructor University, Debugging, Educators, Environment, GitHub, IDEs, IntelliJ IDEA, Interactive, JetBrains, Kotlin, Live Demos, Markdown, Notebook, Online Classes, Programming, Visualizations
jetbrains
blog.jetbrains.com 6 days ago
|
544. HN Show HN: Yet another chatbot widget I've built**Summary:** The text introduces an innovative AI-powered chatbot widget specifically designed to enhance interactions within online stores by addressing common limitations faced by traditional chatbots. This new bot stands out due to its ability to efficiently manage extensive product catalogs and optimize context, thereby reducing inference costs. It features a crawler-bot that auto-indexes site information using sitemaps, JSON+LD, and Open Graph Protocol (OGP), ensuring well-structured data for seamless integration. Additionally, the chatbot utilizes a vector database to store categorized information such as products, FAQs, and links, enabling semantic querying during interactions. A key aspect of this chatbot is its ability to perform semantic deduplication, which prevents context overflow by filtering out duplicate topics. This ensures that conversations remain relevant and efficient. The bot undergoes synthetic distillation training, enhancing its conversational style and adherence to specific rules tailored for customer interactions. Furthermore, it analyzes user dialogues post-conversation to cluster them into categories like threats, strengths, and opportunities, providing valuable insights for business strategies. The chatbot remains passive until prompted by a user, at which point it actively identifies potential issues such as missing products or site malfunctions that could impact revenue streams. Currently in its pre-launch phase, a live demo of this sophisticated tool is available on Whilio.com, showcasing its capabilities and readiness to enhance online retail experiences. **Bullet Point Summary:** - Introduction of an advanced AI-powered chatbot for managing interactions in online stores. - Differentiation through efficient catalog handling and context optimization, reducing inference costs. - Features include: - Auto-indexing crawler-bot using sitemaps, JSON+LD, and OGP to structure site information. - Vector database storing categorized data (products, FAQs, links) for semantic querying. - Semantic deduplication to prevent topic duplication in conversations. - Synthetic distillation training for improved conversational style and rule adherence. - Post-conversation analysis clustering dialogues into threats, strengths, and opportunities. - The bot identifies potential issues like missing products or technical glitches that could affect revenue. - It remains inactive until prompted by a user task. - A live demo is available on Whilio.com during its pre-launch phase. Keywords: AI chatbot, LLM, catalog, collections, context, context optimization, conversation analysis, crawler-bot, dashboard, indexing, inference costs, online store, pre-launch demo, recommend, revenue leakage, sales insights, search, semantic closeness, semantic search, synthetic distillation, task assignment, technical keywords, vector DB
llm
news.ycombinator.com 6 days ago
|
545. HN GPT-5 Thinking in ChatGPT (a.k.a. Research Goblin) is shockingly good at search### Summary The text discusses various advancements and applications in AI technology, legal cases related to data use in training models, and innovative techniques across different fields. It highlights GPT-5's superior search capabilities within ChatGPT, demonstrating its efficiency over traditional manual searches. This model, nicknamed "Research Goblin," efficiently handles a range of queries by conducting extensive internet research, from identifying architectural changes at Heathrow Airport to verifying Starbucks UK's product offerings. A significant part of the discussion focuses on Anthropic's legal settlement for using pirated ebooks in AI training, emphasizing fair use under current law but also raising concerns about broader implications. Meanwhile, advancements such as Chinese Moonshot AI's Kimi-K2-Instruct-0905 model are noted for improvements in data processing and coding benchmarks. The article explores innovative methods like using a visual language model to enhance image retrieval effectiveness, achieving substantial increases in recall rates by leveraging LLM-generated summaries. Additionally, the piece touches upon the legal challenges of defining organizational identifiers, such as universities' official names, illustrating complexities that require context-specific solutions. ### Key Points - **GPT-5's Search Capabilities:** - Notably efficient and faster than traditional manual searches. - Handles a variety of queries by performing comprehensive internet research. - Described as "Research Goblin" for its persistent investigative approach. - **Anthropic Legal Settlement:** - Agreed to pay $1.5 billion to authors for using pirated ebooks in AI training under fair use doctrine. - Raises concerns about future legal interpretations and implications. - **Advancements in AI Models:** - Chinese Moonshot AI's Kimi-K2-Instruct-0905 model shows improvements in context window size and coding performance. - Demonstrates progress in handling extensive data input and processing capabilities. - **Innovative Techniques for Image Retrieval:** - LLM-generated summaries significantly improve recall rates in image retrieval tasks compared to CLIP embeddings. - Highlights the potential of integrating advanced language models for more effective search applications. - **Challenges in Defining Organizational Identifiers:** - Legal names of institutions can differ from common usage, complicating modeling efforts. - Context-specific solutions are necessary for accurately representing complex entities like universities and companies. Keywords: AI models, Aldi, Anthropic, Chrome, GPT-5, GenAI, Generative AI, Google, Groq, LibGen, Lidl, PDF diagrams, Perplexity, Research Goblin, Starbucks, UK, Vibe coding, antitrust, browser, supermarket, tokens, tunnels
gpt-5
simonw.substack.com 6 days ago
https://news.ycombinator.com/pool 6 days ago https://simonwillison.net/2025/Sep/7/ai-mode& 6 days ago https://simonwillison.net/2025/Sep/6/research 6 days ago https://simonwillison.net/2025/Sep/6/kimi-k2- 6 days ago https://simonwillison.net/2025/Sep/6/anthropi 6 days ago https://simonwillison.net/2025/Sep/4/embeddin 6 days ago https://simonwillison.net/tags/llm-release/ 6 days ago https://simonwillison.net/2023/Apr/4/substack 6 days ago https://hn.algolia.com/?q=research+goblin 6 days ago https://simonwillison.net/2024/Dec/22/link-bl 6 days ago https://chatgpt.com/share/68bc71b4-68f4-8006-b462-cf32f 6 days ago https://simonwillison.net/2025/Sep/6/research 6 days ago https://chatgpt.com/share/68bd9ca1-f6d0-8006-a507-c8178 6 days ago https://chatgpt.com/share/68bcd796-bf8c-800c-ad7a-51387 6 days ago https://en.wikipedia.org/wiki/Hacker_News 6 days ago https://en.wikipedia.org/wiki/Social_news_website 6 days ago https://news.ycombinator.com/submitted?id=simonw 6 days ago https://simonwillison.net/ 6 days ago https://simonwillison.net/tags/ai-energy-usage/ 6 days ago https://apnews.com/article/climate-google-environmental 6 days ago https://simonwillison.net/2025/Jun/11/dataram 6 days ago |
546. HN Show HN: Integration Security Top 10: An OWASP-style frameworkThe Integration Security Framework (ISF) is an open-source tool aimed at enhancing security for SaaS-to-SaaS and API integrations, drawing inspiration from the OWASP Top 10. It emerged in response to critical vulnerabilities exposed by incidents like the Salesloft–Drift breach, where a single vulnerability resulted in significant data exposure across various organizations. The ISF provides actionable guidelines or "playbooks" designed to mitigate these risks effectively and prevent common integration pitfalls such as excessive OAuth scopes. The Information Security Forum (ISF) addresses key vulnerabilities in information security practices related to OAuth and token management through its concise "ISF Top 10 Risks." These risks encompass: 1. **Excessive OAuth Scopes**: Encouraging the minimization of permissions to prevent tokens from acting as universal access keys. 2. **Unbounded Token Lifetimes**: Advocating for token rotation and re-authentication during sensitive operations to circumvent security measures like Multi-Factor Authentication (MFA). 3. **Lack of Token Binding**: Recommending binding tokens to specific contexts, such as clients or devices, using Proof-of-Possession (PoP) to avert replay attacks. 4. **Centralized Token Storage Risks**: Advising the encryption, segmentation, and monitoring of token repositories to prevent systemic failures. 5. **No Real-Time Token Activity Monitoring**: Highlighting the importance of detecting anomalies in API usage for early identification of stolen tokens. 6. **Weak Vendor Due Diligence**: Emphasizing the assessment of a SaaS vendor's security posture before integration. 7. **Insecure Integration Defaults**: Calling for secure-by-default configurations, including minimal scopes and MFA enforcement. 8. **Blind Trust in Transitive Integrations**: Recommending mapping and monitoring downstream data flows to avoid cascading risks. 9. **Missing Incident Response for Integrations**: Advocating for rapid response protocols when tokens are compromised. 10. **Opaque Auditability**: Urging the maintenance of comprehensive, exportable, and tamper-evident integration logs. The ISF encourages community involvement by inviting contributions in the form of issues, pull requests, real-world case studies, control improvements, and mapping to additional standards. This initiative is inspired by OWASP Top 10's approach of being concise, practical, and open to community input, under the guidance of Vikram S. Narayan. ### Bullet Point Summary: - The ISF enhances security for SaaS-to-SaaS and API integrations, inspired by the OWASP Top 10. - Addressed vulnerabilities from incidents like the Salesloft–Drift breach, leading to comprehensive risk mitigation guidelines or "playbooks." - Focuses on common integration issues such as excessive OAuth scopes. - ISF's "Top 10 Risks" provide guidelines for secure OAuth and token management practices: - Minimize OAuth permissions. - Implement token rotation and re-authentication. - Bind tokens to specific contexts using PoP. - Encrypt, segment, and monitor token repositories. - Detect anomalies in real-time API usage. - Assess SaaS vendors' security before integration. - Use secure-by-default settings for integrations. - Monitor downstream data flows in transitive integrations. - Develop rapid response protocols for compromised tokens. - Maintain comprehensive and tamper-evident integration logs. - Encourages community contributions through issues, pull requests, case studies, control improvements, and standard mapping. - The initiative is inspired by OWASP Top 10's concise and practical approach, managed by Vikram S. Narayan. Keywords: API Integrations, Attackers, Auditability, Cloudflare, Framework, GitHub, Google Workspace, ISF, Incident Response, Integration Security, OAuth Scopes, OWASP, Playbook, Real-Time Monitoring, SaaS-to-SaaS, Salesforce, Salesloft–Drift Breach, Slack, Token Lifetimes, Token Revocation, Tokens, Transitive Integrations, Vendor Due Diligence
github
github.com 6 days ago
|
547. HN A Cynical Read on Anthropic's Book SettlementAnthropic has settled a copyright infringement lawsuit with book publishers over using downloaded books to train their AI models, paying $1.5 billion despite not utilizing any infringing material for current active models. This settlement is notably higher than typical cases and raises strategic questions about its implications in the tech industry. Following significant fundraising success, Anthropic’s large payout may serve as a deterrent for other startups considering entering the competitive AI space by setting a financially prohibitive precedent. The substantial fine highlights the financial barriers to entry in AI training and data usage, suggesting that only major companies like OpenAI, xAI, Apple, Amazon, Microsoft, Google, Meta, and some well-funded startups can manage such expenses. This creates an environment where smaller entities are priced out of competition, potentially reshaping market dynamics by favoring wealthier players. The strategic nature of Anthropic's decision to settle rather than risk protracted legal battles with possibly astronomical costs is interpreted as a maneuver to protect its competitive advantage and limit industry diversity. While this move garners mixed reactions—some journalists see it as valuing content creation, others express concerns over negative impacts on market diversity and innovation—the outcome signifies a shift toward fewer but financially stronger competitors in the AI space. Overall, Anthropic’s settlement reflects both a financial burden for companies engaged in AI training and a strategic recalibration of competitive dynamics within the tech industry, potentially leading to reduced competition and increased dominance by major players. **BULLET POINT SUMMARY:** - Anthropic settled a copyright case with publishers over using books to train AI models, paying $1.5 billion. - The settlement is unusually high compared to typical cases and raises strategic questions about its implications in the tech industry. - Post-settlement, Anthropic's large payout may deter new startups from entering the competitive AI space by setting a financially prohibitive precedent. - The fine highlights financial barriers for AI training, suggesting only major companies can afford such penalties, potentially limiting smaller players' participation. - Anthropic’s decision to settle is seen as strategic, protecting its competitive advantage and possibly stifling competition. - Reactions are mixed: some see it as valuing content creation, while others worry about negative impacts on market diversity and innovation. - The settlement reflects a shift toward fewer but financially stronger competitors in the AI industry. Keywords: AI Wars, Anthropic, OpenAI, competitors, copyright infringement, data sets, fines, fundraise, journalists, lawsuit, settlement statement, startup, tech giants, training models
openai
spyglass.org 6 days ago
|
548. HN Show HN: CRoM – Context Rot Mitigation System for RAG-Based LLMs**Summary:** CRoM, or Context Rot Mitigation System, is an open-source tool developed in Python to enhance Retrieval-Augmented Generation (RAG) pipelines by addressing "context rot"—a decline in answer quality due to long prompts or irrelevant information. Licensed under Apache 2.0, CRoM optimizes context management for Large Language Models through several features: - **Token-Aware Prompt Packing** ensures efficient text packing within token limits. - **Cross-Encoder Fallback** serves as a fallback mechanism to improve low-confidence answer accuracy. - **FastAPI-Based Orchestration** allows easy integration with existing RAG systems via FastAPI. - **Modular Integration** for seamless inclusion into current RAG stacks. Additional tools like the Budget Packer and Hybrid Reranker use stable sorting algorithms and combine sparse (TF-IDF) and dense retrieval scores (Sentence-Transformers) to ensure high-quality document reranking. CRoM includes a Drift Estimator to monitor semantic drift in model responses using L2 or cosine distance with EWMA smoothing, along with Prometheus metrics for monitoring token savings and drift alerts in production. The package supports plugins for advanced reranking (FlashRank), compression (LLMLingua), and drift analysis (Evidently). It offers a comprehensive CLI tool for pipeline evaluation, budget sweeps, and quality-vs-optimal analyses. To install CRoM from source, users can clone the GitHub repository and use `pip install -e .[dev,plugins]` for an editable installation with dependencies. Quick start is facilitated through a demo script, while `crom-bench` CLI allows detailed pipeline evaluation and plot generation if matplotlib is installed. Releases follow semantic versioning with changes documented in CHANGELOG.md and are automated via GitHub Actions upon pushing a v* tag. The project operates under the Apache 2.0 License as outlined in the LICENSE file. **Bullet Point Summary:** - **Purpose**: CRoM enhances RAG pipelines by mitigating "context rot." - **Key Features**: Token-Aware Prompt Packing, Cross-Encoder Fallback, FastAPI-Based Orchestration, Modular Integration. - **Additional Tools**: Budget Packer, Hybrid Reranker for high-quality document reranking; Drift Estimator for monitoring semantic drift with Prometheus metrics. - **Plugin Support**: Advanced reranking (FlashRank), compression (LLMLingua), and drift analysis (Evidently). - **CLI Tool**: Facilitates pipeline evaluation, budget sweeps, quality-vs-optimal analyses, and plot generation with matplotlib. - **Installation**: Clone GitHub repository and use `pip install -e .[dev,plugins]` for editable installation; quick start via demo script. - **Release Management**: Semantic versioning documented in CHANGELOG.md; automated via GitHub Actions upon v* tag push. - **License**: Apache 2.0 License. Keywords: Benchmarking, Budget Sweep, CRoM, Context Rot Mitigation, Cosine Distance, Cross-Encoder, Dense Retrieval, Drift Estimator, FastAPI, GitHub, L2 Distance, Large Language Models, Matplotlib, Plugins, Prometheus Metrics, Python, RAG Pipelines, Retrieval-Augmented Generation, Semantic Drift, Sentence-Transformers, Sparse Retrieval, TF-IDF, Token Limits
github
github.com 7 days ago
|
549. HN Show HN: I am vibe coding a collaborative vibe coding toolSumit is developing nocodo.com, a comprehensive platform designed to enhance the app development lifecycle through collaborative coding tools. This self-hosted tool leverages Linux machines or cloud servers to integrate essential development processes such as code versioning (Git), task management with GitHub issues, and continuous integration and deployment (CI/CD) via GitHub Actions. Nocodo.com streamlines team collaboration on tasks and projects using a `manager` daemon that connects web and mobile interfaces to project APIs. Additionally, it supports integration with various AI models like Claude Code or Gemini CLI, as well as platforms such as GitHub, GitLab, and major cloud providers. The platform is currently enhancing its user interface inspired by Bolt, ReplitIt, and Lovable, supporting backend development in Rust with Actix Web and frontend development using TypeScript/Solid JS/Tailwind. Initially, it utilizes SQLite databases backed up with LiteStream and S3, and includes automatically generated CI pipelines to simplify the process for non-engineering teams aiming to create full-stack applications. Upcoming features include a mobile application (`manager-mobile`) and a launcher app designed for managing developer servers on platforms like Scaleway and Digital Ocean through APIs and SSH keys. The project promotes intuitive "vibe coding" by using contextual prompts, enabling more natural collaboration and management within projects. Nocodo.com emphasizes flexibility with no vendor lock-in, offering support for multiple coding agents and cloud providers. It provides optional authentication via nocodo.com at a nominal annual subscription fee (e.g., USD 60 per team of five). Sumit, the solo founder, has been working on this ambitious project since 2013 and is currently seeking additional funding while providing consulting services related to its development. Key Points: - Nocodo.com integrates Git, GitHub issues/tasks, and CI/CD with GitHub Actions for streamlined app development. - Facilitates team collaboration through a `manager` daemon connecting APIs to web/mobile interfaces. - Supports AI models like Claude Code/Gemini CLI and integration with platforms including GitHub and cloud providers. - UI inspired by Bolt/Replit supports Rust backend and TypeScript/Solid JS/Tailwind frontend, using SQLite databases backed up via LiteStream/S3. - Features include `manager-mobile` and a launcher app for managing developer servers on cloud platforms. - Empowers non-engineering teams with automated CI pipelines for full-stack application development. - No vendor lock-in, supporting various agents/providers, with optional nocodo.com authentication at a small subscription fee. - Sumit seeks funding as he offers consulting services, inviting interest from potential collaborators or clients. Keywords: API, API keys, Actix Web, CI, DB, DNS, Digital Ocean, Git, GitHub, Linux, Rust, SQLite, SSH keys, Scaleway, Solid JS, Sumit, Tailwind, Typescript, authentication, cloud server, coding agents, nocodo, self-hosted
github
news.ycombinator.com 7 days ago
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550. HN The Claude Code Framework Wars- The "Claude Code Framework Wars" discusses integrating Claude, an AI system, into software development workflows beyond its typical use as a chatbot. - Developers aim to automate coding tasks with Claude, enabling them to focus on higher-level roles like project management and design. - There are several open-source projects exploring frameworks for enhancing AI productivity within development processes. - Setting up a Claude system involves seven critical decisions: task storage, guidance mechanisms, agent coordination, session management, tool access, code development, delivery processes, and context preservation. - Task management can be handled through Markdown backlogs, structured text specifications, or issues/tickets in platforms like GitHub or Jira. - Guidance for Claude includes using command libraries with predefined actions, coding standards, clear definitions of task completion, and validation hooks to ensure quality checks. - The overarching goal is to create a predictable and valuable workflow by treating AI as an integral part of software development. Key strategies for integrating AI agents like Claude into software development include: 1. **Coding Standards**: Establish tech stacks and guidelines with defined "done" criteria, using validation hooks such as linting and tests to maintain quality. 2. **AI Coordination**: Assign specific roles (e.g., PM, architect) to multiple AI agents for structured management; use tools like MetaGPT and Claude-Flow for role simulation and parallel execution. 3. **Session Management**: Organize AI outputs effectively by running tasks in parallel with minimal interference. 4. **Tool Access**: Enhance Claude's functionality through integrations like Model Context Protocol (MCP), custom tool libraries, database accessors, and validation hooks to act as an active team member. 5. **Code Development Roles**: Utilize Claude for various roles such as Project Manager or Architect, tailoring its functions to project needs. The document also outlines a framework emphasizing AI's integration into specific development roles—Project Manager, Architect, Implementer, Tester, Reviewer—and the importance of small iterative changes and feature-flagged experiments. Preserving context through documentation and persistent memory is crucial for preventing repetitive errors. Various setups are suggested based on team structure: beginner setup with markdown backlogs; structured teams using product specs and role simulation; experiment-heavy approaches leveraging repo artifacts; prototype modes focused on app builders and documentation. The key takeaway is that AI's effectiveness increases when guided by clear structures, allowing developers to shift focus from routine coding to higher-level tasks. Ultimately, the Claude Code framework advocates for a shift in developer roles towards efficiency and innovation, emphasizing structured implementation for maximizing AI contributions in software development. It highlights treating AI as collaborative team members rather than mysterious tools, promising enhanced control and benefits in various tasks. Keywords: AI Automation, Architecture, Claude Framework, Coding Standards, Custom Tool Libraries, Database Accessors, Designer, Feature Flags, Frameworks, GitHub Issues, Health Checks, Implementer, Jira Tickets, Linting, MCP Integrations, Markdown Backlogs, Open-source Projects, Plan, Project Manager, Reviewer, Reviews, Roles, Software Architect, Software Development, Specs, Structured Prompts, Task Management, Tests, Validation Hooks
claude
shmck.substack.com 7 days ago
https://github.com/bmad-code-org/BMAD-METHOD 6 days ago https://philosophy.stackexchange.com/questions/96145&#x 6 days ago https://github.com/Simon-Initiative/oli-torus/tree 6 days ago https://github.com/Prunt3D/prunt/commit/b4d7f 6 days ago https://github.com/bmad-code-org/BMAD-METHOD/issue 6 days ago |
