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1. HN ARK's Aggressive Pivot: Wood Doubles Down on Tesla and Re-Enters Big Tech AI- ARK Invest's Catherine Wood has consistently emphasized investments in technology, particularly Tesla and AI growth, as evidenced by her recent 13F filing from September 30, 2025. - The portfolio, valued at $16.8 billion with an 8.8% turnover rate, comprises 194 stocks/ETFs and 2 other assets, indicating active management. - Wood's strategy involved creating 12 new positions, expanding 108 existing investments, reducing 74, and liquidating 9, reflecting an aggressive shift towards major tech companies such as Tesla. BULLET POINT SUMMARY: * ARK Invest's Catherine Wood focused on technology, especially Tesla and AI growth, in her latest portfolio filing (09/30/2025). * The $16.8 billion portfolio, with an 8.8% turnover rate, consists of 194 stocks/ETFs and 2 other assets, signifying active management. * Wood adopted a strategy of establishing 12 new positions, enlarging 108 current investments, decreasing 74, and selling out of 9, highlighting an aggressive inclination towards tech giants like Tesla. Keywords: #granite33:8b, 13F filing, ARK, Big Tech AI, Decreased Positions, Growth Investing, Increased Positions, New Positions, Portfolio value, Sold Out Positions, Sold Out PositionsKEYWORDS: ARK, Tesla, Turnover rate, Wood
tesla
www.13radar.com 35 minutes ago
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2. HN Show HN: A Claude Code plugin that catch destructive Git and filesystem commands- **Overview**: The Claude Code Safety Net is a security plugin that prevents AI agents from executing potentially harmful Git and filesystem commands, mitigating risks of permanent data loss or alteration of repository history. - **Purpose**: Developed following an incident where Claude Code accidentally deleted significant progress with a single destructive command. The goal is to provide stronger technical constraints against unintentional data loss by blocking specific risky commands. - **Blocked Commands**: - `git checkout --` (without stashing changes) - `rm -rf` on critical system directories - `git push --force` (altering repository history) - **Allowed Commands**: - `git checkout -b` for branch creation - `git clean -n` to preview changes before committing them - **Block Interception**: When blocked commands are attempted, users receive a message advising reconsideration or manual execution if necessary. - **Plugin Components**: - Includes a Python plugin named `safety_net.py`. - Contains rules for Git and rm command filtering. - Provides shell parsing utilities for testing. - Supports 'strict mode' to block unparseable commands and unsafe `rm -rf` operations. - **Structure**: Follows a standard plugin format with folders for configuration, scripts, tests, and implementation logic. - **Enhanced Safety Feature (SAFETY_NET_STRICT=1)**: - Offers multi-layered protection against system harm by examining commands within shells (bash, sh) and blocking dangerous operations such as: - `git reset --hard` (forcefully resets repository) - `rm -rf /` (deletes all files recursively in root directory) - **Interpreters Protection**: Detects and blocks harmful commands embedded in one-liners of various interpreters like Python, Node.js, Ruby, Perl that could delete critical system directories. - **Data Protection**: Automatically redacts sensitive data (tokens, passwords, API keys) from system messages to avoid unintentional exposure in logs. - **Licensing**: The solution is available under the MIT license. Keywords: #granite33:8b, Git commands, MIT license, blocked commands, branching, command wrapping, destructive commands, filesystem commands, force push, help output, logging security, orphan branches, preview, rm -rf, safe delete, safety net, secret redaction, sensitive data protection, shell detection, temp directories, unstaging
claude
github.com 45 minutes ago
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3. HN Show HN: Fun sketch – Bring your sketches to life- Mihai Tarce has created a free, account-less website utilizing various open-source tools including Excalidraw, Django, Postgres, Redis, and ComfyUI. - The platform enables users to sketch and subsequently animate their drawings using artificial intelligence (AI). - A key feature is the moderation system in place for safety, ensuring that all content is screened before it becomes publicly visible. - This website is particularly designed with children in mind, making it an ideal tool during holiday seasons when they might have more free time for creative activities. Keywords: #granite33:8b, AI, Animation, Christmas, ComfyUI, Django, Excalidraw, Image uploads, Mihai Tarce, Moderation, Postgres, Redis, Sketch, Stack, Web server, Website
postgres
funsketch.kigun.org an hour ago
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4. HN Make your PR process resilient to AI slop- The author discusses concerns about AI-generated code overwhelming pull request (PR) reviews and argues that with robust PR processes, reviewing AI-assisted code isn't more burdensome than regular code. - Suggestions for maintaining efficient review practices include requesting smaller, atomic PRs irrespective of AI involvement to ensure manageable code changes. - Highlighting the importance of clear communication, the author emphasizes that AI should be capable of generating high-quality, digestible diffs with clear instructions for reviewers. - Code quality checks must be consistently applied to AI-generated code just as with any other code, ensuring adherence to standards and best practices. - Understanding and explaining AI-generated code is stressed to be as crucial as with non-AI contributions, fostering transparency and shared knowledge within the development team. - The central issue identified isn't an influx of low-quality AI code but rather the need for consistent, thorough code review practices. Addressing third-party dependencies in Pull Requests (PR): - More scrutiny is recommended for third-party dependencies, particularly within ecosystems like Node.js, going beyond current practices. - The PR review process should involve evaluation of new dependencies, their versions, and the necessity of their inclusion to prevent unnecessary risks or bloat. - Automated dependency scanning tools are advocated for detecting vulnerable dependencies, adding a layer of security during the review phase. - To mitigate potential AI-induced errors, the author insists on maintaining rigorous human-driven code reviews throughout PRs to ensure quality assurance in an increasingly AI-assisted development landscape. Keywords: #granite33:8b, AI assistance, PR process, automated dependency scanning, code quality, code reviews, dependabot, human errors, large PRs, small diffs, sonarqube, third-party dependencies, vulnerable dependencies
ai
www.pcloadletter.dev an hour ago
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5. HN The changing drivers of LLM adoption**Bullet Point Summary:** - **LLM Adoption Trends:** - ChatGPT saw rapid user growth from under 400 million to nearly 800 million by August, with a slight slowdown; Gemini experienced a 30% increase in monthly active users between August and November. - Despite Gemini's user growth, ChatGPT remains more popular, with about 35% of registered US voters using it weekly compared to Gemini's 24%. - **Global User Distribution:** - AI application usage, like ChatGPT, is increasingly common in high-income countries outside the US, with 30% of internet users using it weekly by mid-2025. - India has shown exceptional growth, with daily users surging sevenfold in a year and overtaking US users. - **Usage Intensity:** - Increased usage intensity among existing users can drive adoption even without new user acquisition; ChatGPT messages grew faster than weekly active users from June 2024 to 2025. - Web traffic remains stagnant despite growing AI model utilization, suggesting traditional online browsing might not expand. - **Web Traffic Patterns:** - ChatGPT's web traffic plateaued since September 2025; Gemini's increased modestly but still less than ChatGPT’s growth in weekly active users (1.5x vs. 3x). - The shift to chatbot apps is evident, with ChatGPT being the most downloaded app in 2024-2025, and in-app usage rising significantly for both models. - **Advanced Usage:** - While evidence suggests more intensive use of LLMs, only a fraction engage with advanced features like longer responses from reasoning models. - **Revenue Growth:** - OpenAI's $13 billion annualized revenue in August, growing at about 4.3 times annually, supports the trend of accelerating LLM adoption. - **Workplace AI Usage:** - Despite limited access to workplace AI tools (18%), 36% of respondents used AI for work in the past week, indicating grassroots adoption by employees using free tiers or personal subscriptions. - AI usage doesn't significantly differ between office and non-customer facing jobs; both show under 35% usage rates. - **Consumer AI Functionality:** - Consumers primarily use LLMs for seeking information, not as task-completing agents or virtual companions, which contrasts with current AI benchmark focuses (coding, scientific reasoning). - **Demographic Usage Patterns:** - Higher-income individuals and younger adults are more likely to use AI services; older adults show substantial growth potential. - Initial gender disparity in ChatGPT usage has narrowed, with comparable current usage rates for both genders. **Summary:** The text explores the accelerating adoption of Large Language Models (LLMs) like ChatGPT and Gemini, highlighting growing user bases, intensified usage, and shifting demographic preferences. While web traffic remains stagnant, app-based engagement surges, particularly for chatbot applications. Revenue figures, such as OpenAI's expansive $13 billion annual income, corroborate this trend of rapid LLM adoption. Despite advanced features being underutilized, the evidence underscores individuals increasingly relying on AI tools, primarily for information retrieval and writing assistance in both personal and work settings. Usage patterns suggest potential for significant growth in less-saturated markets like India and among older demographics, while consumer usage skews towards information seeking rather than task automation. Keywords: #granite33:8b, AI, ChatGPT, Gemini, LLMs, active users, consumer AI, downloads, enterprise products, in-app usage, income correlation, revenue growth, slowed growth, standalone apps, technical terms, user growth, web traffic, writing assistance
gemini
epochai.substack.com an hour ago
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6. HN Show HN: Built Natural Language Test Automation Tool – OpenQA- **OpenQA Overview**: OpenQA is an open-source AI tool facilitating browser automation test creation in natural language using YAML files or Behavior-Driven Development (BDD) feature files. It integrates with Playwright MCP and supports multiple Large Language Model providers including Claude, OpenAI, and Gemini. Unlike conventional tools requiring coding skills, OpenQA allows non-coders to compose tests. - **Setup and Integration**: Setup is swift, taking only two minutes via a single command line instruction. The tool eliminates the necessity for selectors or unstable test outcomes. Supported testing frameworks include Playwright-BDD, Cucumber.js, and straightforward YAML files. - **Testing Methodology**: This document details crafting tests for scenarios like online shopping, covering site navigation, product addition to carts, and order confirmation verification. Custom fixtures for cloud browser testing using Playwright are provided. Integration steps involve installing OpenQA, setting up AI authentication through CLI, API keys, or .env files, and substituting step definitions in corresponding feature files. Testing execution is done with `npm test`. - **AI Agent Integration**: The guide illustrates methods to incorporate AI agents from Anthropic or OpenAI/Google into automated browser tests utilizing Playwright and OpenQA. - **Environment Setup for Agents**: To use either OpenAI or Google, users must configure their environment by establishing a `.env` file with API keys and defining the agent type (`langchain`). Authentication options include Claude Code CLI, exporting API keys as environment variables, or using a `.env` file. - **Interaction with AI Agents in Tests**: To integrate AI agents into tests: 1. Install OpenQA via `npm install openqa`. 2. Authenticate using methods like `claude login`, setting `ANTHROPIC_API_KEY` as an environment variable, or through a `.env` file. 3. In test files (e.g., `step_definitions/steps.js`), employ `@playwright/test` and OpenQA's `runAgent` function to communicate with the AI agent, allowing it to fill forms and make decisions based on natural language instructions while maintaining accurate simulation via shared browser context. 4. Run tests using `npm test` or `npx playwright test`. - **Key Functionality**: The `runAgent()` function accepts natural language commands, a browser context, optional configurations (like verbosity and model selection), and returns a promise with results from AI interactions, supporting collaborative automation by ensuring shared cookies, session storage, page state, and navigation history between the agent and tests. - **Testing Framework**: This framework leverages Playwright and OpenQA for BDD, supporting standard Playwright tests and BDD integrations via 'playwright-bdd'. It accommodates custom setups, YAML tests with natural language descriptions, and integration with OnKernel cloud browsers or Steel Docker browsers. The setup requires Node.js 18+, `@playwright/test ^1.56.0`, and an API key for providers like Claude Code, Anthropic, OpenAI, or Google, all under the MIT license. Keywords: #granite33:8b, AI, Agent, Anthropic, Authentication methods, BDD, Browser context, Business Analyst, Claude Agent SDK, Cloud browsers, Collaborative automation, Configuration options, Cucumberjs, Custom data, Developer, Gemini, Installation, LLM Provider, Manual QA, Natural Language, Natural language instruction, Navigation history, OpenQA, Page state, Playwright, Playwright fixtures, Product Manager, Quick Setup, Recursion limit, Session storage, Setup, Shared cookies, Shopping tests, Step Definitions, Test Automation, Testing, YAML Files, YAML Support, Yaml
gemini
github.com an hour ago
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7. HN Top Data Insights and Gradient Updates of 2025- **AI Advancements and Affordability**: Between April 2023 and March 2025, the cost of large language model (LLM) inference decreased by over ten times, although this reduction was uneven across different tasks. This affordability is attributed to heightened market competition and efficiency gains in AI technology. - **Accessibility of Frontier AI**: By 2024, frontier AI capabilities became accessible on consumer hardware, as evidenced by improvements in metrics such as GPQA, MMLU, AA Intelligence, and LMArena, signaling rapid progress in AI development. - **Compute Usage Trends**: OpenAI's compute usage in 2024 primarily served experiments rather than model training or inference, underscoring the capital intensity of current AI development. NVIDIA's installed AI compute from their chips doubled annually since 2020, reflecting an exponential demand for computational resources. - **Model Performance Improvements**: GPT-5 and GPT-4 models represented significant advancements over prior versions in benchmark performance, though incremental improvements compared to previous major releases suggest a trend of frequent model updates rather than declining capabilities. - **Energy Efficiency Claims**: Josh estimated that the average energy cost for a GPT-4 query was minimal—less than powering a lightbulb for five minutes—a claim supported by Sam Altman and indicative of AI’s relatively low energy consumption compared to household activities at the time, though acknowledging its growing significance. - **DeepSeek's Efficient Model Development**: DeepSeek improved the Transformer architecture with techniques like multi-head latent attention (MLA), enhancements in mixture-of-experts (MoE) architecture, and multi-token prediction, allowing them to release a top open-source pretrained model using 10 times less compute than the next best model, Llama 3. - **Model Cost Reduction Potential**: DeepSeek's model R1 matched OpenAI's o1 performance at likely lower development costs, suggesting that yearly model development costs could decrease by threefold due to advancements in training techniques and data enhancements. - **Reinforcement Learning Constraints**: There are concerns that the rapid growth in compute for reinforcement learning (RL) reasoning training, as seen with labs like OpenAI and Anthropic, cannot be sustained beyond 1-2 years due to infrastructure limitations, hinting at a potential slowdown in capability progress. - **Potential of National AI Projects**: Arden and Anson estimated that a US national AI project could lead to training runs 10,000 times larger than GPT-4, providing insight into the scale suggested by comparisons to historical projects like the Manhattan Project and Apollo program. - **Value Distribution in AI**: The post emphasizes that most value from AI will come from broad automation across various economic tasks rather than accelerated research and development, challenging narratives about rapid AI-driven R&D advancements. - **Engagement with Public Communication**: Epoch AI's 2025 Data Insights and Gradient Updates have garnered significant public interest and engagement, supporting their mission to inform the world about emerging AI trends through a 2025 Epoch AI Impact Survey for further feedback and improvement. Keywords: #granite33:8b, AA Intelligence, AI, Artificial General Intelligence, DeepSeek, GPQA, GPT-4, GPT-5, Josh, LLM inference prices, LMArena, MMLU, MoE, OpenAI's o1, R1 model, RL reasoning training, US AI project, affordability, benchmarks, broad automation, competitive market, computational resources, compute efficiency, consumer hardware, efficiency gains, energy cost, flagship chips, frontier AI, installed AI compute, mixture-of-experts, models, multi-head latent attention, multi-token prediction, open models, performance improvements, personal computers, scaling limits, token price drops, training runs, transformer architecture
gpt-4
epoch.ai an hour ago
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8. HN The Deep Dark Terroir of the Soul**Bullet Point Summary:** - The text examines the evolution of authority control from external institutions (castles/factories) to internalized self-control (ego), leading to a modern form of soul exhaustion. - Voltaire’s "Candide" (1759) suggests cultivating one's garden as a strategy against life's hardships, representing practical resilience amid external oppressive forces. Gardening symbolizes survival through food provision, combating boredom and vice. - Industrialization undermined personal control over labor, leading to Marx’s concept of Alienation. Workers sought physical means (strikes) to reclaim their lost autonomy, likened to a metaphorical ‘Garden’. - The shift from the Disciplinary Society (20th century, Panopticon model, 'Should') to the Achievement Society (21st century, emphasis on personal optimization) exacerbated psychological strain as workers became self-exploiting ‘entrepreneurs of the self’. - Digital transparency in 2024 erodes privacy and transforms life into a public performance driven by status anxiety and algorithmic control, making individuals vulnerable to exploitation by digital systems. - The concept "Logic of the Thicket" emerges as resistance: moving from passive consumption (Tourist) to active engagement (Explorer), resisting algorithmic optimization and fostering unique, local contexts through 'thick labor'—intense, nuanced work that machines cannot easily replicate or automate. - The text reinterprets Voltaire's "Three Evils" (Boredom, Vice, Need) in light of contemporary issues like digital overstimulation and algorithmic complicity. It advocates for an approach grounded in local context ('terroir') and human connection, combating the superficiality of networks. - The essay itself embodies 'thick labor' by employing deep reading, discussions, and AI collaboration to produce durable, unique insights resistant to mechanical analysis, resisting homogenization in a digital age dominated by algorithms seeking standardized data. - Key philosophers referenced include Voltaire, Marx, Foucault, Han, and Hui, illustrating the evolution of human exhaustion and societal structures across various historical periods and theoretical perspectives. - The overarching theme is resistance against standardized, algorithmic control by fostering complexity, depth, and local context in intellectual engagement, ensuring individual agency and resistance amid pervasive digital influence. Keywords: #granite33:8b, AI, AI as grinding stone, Achievement Society, Burnout Society, Can, Candide, Coercion, Docile Body, Entrepreneur of the Self, External Boss, Garden, Hyper-Attention, Internal Exploitation, Leibniz, Lisbon earthquake, Logic of the Thicket, Master, Optimization, Panopticon, Personal Brand, Potential, Self-Exploitation, Seven Years' War, Should, Status Anxiety, Tourists, Voltaire, active navigation, algorithmic complicity, algorithms, boredom, cheese, collaboration, collaborative friction, community, complex subject, context, culinary, deep-reading, difficult work, digital gaze, discovery struggle, disindividuation, durable value, ego, exhaustion, factory, frictionless landscape, gamification, history, idleness, indispensability, inquisitions, institutions, intellectual machines, interface decisions, labor, legible data, local context, long-term trajectories, manual labor, messiness, mind, moral decay, moral laziness, need, network, office, opacity, optimism, own exhaustion, passive consumption, potatoes, privacy, private creation, productivity metrics, resistance, searchability, self-optimization, shared inquiry, slow-moving machines, soul, standardized data, survival, synthesis, terroir, thicket, unique insights, unsearchable friction, unsearchable life, vice, wine, worker
ai
aneeshsathe.com an hour ago
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9. HN India: Dpiit Working Paper on Generative AI and Copyright- **India's Proposed Hybrid AI-Copyright Licensing Model**: - Introduces a mandatory blanket license for AI developers to access copyrighted material for training purposes, with payments due only upon commercialization of AI models or outputs. - Establishes a central entity (CRCRT) responsible for collecting royalties from rights holders, ensuring broad inclusivity and less burdensome licensing compared to stringent EU regulations. - Aims to balance innovation fostered by AI with the protection of human creativity's economic foundation, providing statutory remuneration to rights holders for sustainable support of creative ecosystems. - **Stakeholder Impacts**: - **Pros**: - Guaranteed compensation and legal certainty for rights holders (e.g., scholarly publishers). - Preservation of creative incentives. - Global leverage for rights owners. - Support for scholarly publishing's role in maintaining knowledge integrity. - **Cons**: - Loss of opt-out rights for rights holders. - Potential for low or politicized royalty rates. - Challenges with proprietary datasets and diverse creator groups (including small creators, academic authors). - **Complexities and Concerns**: - Issues surrounding proprietary or embargoed datasets, which may present commercial and ethical concerns. - Government-set royalty rates potentially failing to reflect the true value of certain works, like scientific journals. - Difficulty in fairly allocating royalties among millions of creators due to copyright often being held by publishers rather than authors. - Possible disincentive for major AI firms to operate in India to avoid royalty obligations or limit access to advanced AI for academic institutions. - Unresolved matters concerning AI-generated outputs, moral rights, attribution, and liability, leaving authors exposed. - **Global Implications**: - Positions India as a potential leader in harmonizing AI development with creator compensation, possibly influencing other nations, especially in the Global South. - Challenges for U.S., EU, and UK amid ongoing debates on fair use exceptions to balance progress and copyright protection. - **Key to Success**: - Realistic royalty rates aligned with works' true value. - Robust administrative capacity for effective management and distribution of collected royalties. - Transparent mechanisms ensuring fair and equitable treatment of all stakeholders involved. - Extension of reforms to cover AI-generated outputs, moral rights, attribution, liability issues, to protect authors comprehensively in the evolving AI landscape. Keywords: #granite33:8b, AI, AI Outputs, AI Training, Administrative Capacity, Attribution, Authors, Blanket License, Commercialization, Compensation, Control, Copyright, Creative Ecosystems, Creator-centric, Creators, Data Access, Dataset-level Transparency, EU, Enforcement Ease, Equilibrium Point, Fair Use, Generative, Global Context, Global Influence, Global Leverage, Global South, Human Creativity, Human Creativity Incentives, India's Approach, Industry Reactions, Infringing Outputs, Innovation, Interventionist Approach, Knowledge Integrity, Legal Certainty, Legal Clarity, Liability, Licensing, Mandatory License, Moral Rights, Opt-Out Rights, Output-side Protections, Pressure Point, Recognition, Reference Model, Regulatory Framework, Revenue, Rights Holders, Royalties, Royalty Rates, Royalty-rate Realism, Scalable Framework, Scholarly Publishers, Smaller Developers, Training Data, Transparency, Transparent Distribution, US, Universal Payment Model, Usage, Zero-price Fair Use
ai
p4sc4l.substack.com 2 hours ago
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10. HN Favorite Compiler and Interpreter Resources- The text focuses on compiler development experiences by a self-taught hobbyist with background in programming languages (PL) and compilers, who has built interpreters and minimal implementations of various languages using different techniques like AST interpreters, bytecode VMs, and native-code compilers via C, LLVM, x86. - Key areas unexplored include custom garbage collection, register allocation, JIT compilation, and non-Linux/x86_64 targets. The simplicity of parser implementation in languages with sparse syntax, such as Lisps and Forths, is highlighted due to less extensive syntax requirements. - Recommended introductory resources are limited but include "LISP in Small Pieces" by Christian Queinnec and "Lisp System Implementation" by Nils M Holm for deeper understanding. The author provides personal writing and suggests online communities like /r/Compilers and /r/ProgrammingLanguages for discussions. - A sought-after but not found survey of bytecode instructions across various Virtual Machines (VMs) is mentioned, which would compare to RISC vs CISC architecture and its implications on object representations in dynamic languages and calling conventions across architectures and VMs. - Phil Eaton's curated list of recommended resources for learning about compilers/interpreters is shared, emphasizing books like "Structure and Interpretation of Computer Programs (SICP)" and "Compilers: Principles, Techniques, and Tools (Dragon Book)", while avoiding others such as "The Little Typer". Eaton's list can be accessed on his personal webpage. Keywords: #granite33:8b, AST, Brainfuck, Bytecode VM, C, Compiler, Dragon Book, Forth, Garbage Collection, Go, Interpreter, JIT Compilation, Java/ML/C Notes, JavaScript, LLVM, Linux, Lisp, Little Typer Notes, Lua, Modern Compiler Implementation, Native-code Compiler, Operator Precedence, Parsing, Pratt Parsing, Precedence Climbing, Python, Register Allocation, SICP, SQL, Scheme, Shunting Yard, TypeScript, Windows, macOS, parser generators, x86
sql
eatonphil.com 2 hours ago
https://news.ycombinator.com/item?id=38217686 2 hours ago https://news.ycombinator.com/item?id=34263589 2 hours ago |
11. HN How to use a specific version of MSVC in GitHub Actions**Summary:** This guide details a process for manually installing specific versions of Microsoft Visual C++ (MSVC) on Windows GitHub Actions runners after Microsoft removed multiple versions from their build tools. The author describes downloading the bootstrapper for desired MSVC versions, like Visual Studio 2022's release history page, and executing it in 'quiet mode' to avoid user interface elements and restarts. They illustrate using `wget` or `curl` for downloads and execute with administrative privileges inherent to GitHub Actions. The guide emphasizes a customized installation of Visual Studio Build Tools for C++ projects, suggesting efficiency by setting an installation path (`..\vs-install`) and selecting individual components instead of full workloads to minimize installation size. Required components include `Microsoft.VisualStudio.Component.VC.14.43.17.13.x86.x64` for the compiler and `Microsoft.VisualStudio.Component.VC.Tools.x86.x64` for setting environment variables, employing `--includeRecommended` to address dependencies. Challenges encountered during installation included bootstrapper processes exiting prematurely, requiring a PowerShell solution with `-Wait` parameter to ensure completion before proceeding. Setting up environment variables through `vcvarsall.bat` or `vcvars64.bat` posed additional difficulties due to temporary nature; solutions involved running CMake within the MSVC-aware cmd session and leveraging `cmd /c "..\vs-install\VC\Auxiliary\Build\vcvars64.bat && cmake To address persistent environment variable challenges in GitHub Actions, the author proposed capturing and storing environment variables set by `vcvars64.bat` into `GITHUB_ENV`, ensuring consistency across job steps without needing repeated setup within PowerShell sessions. They detail a command sequence to redirect and add these variables: `cmd /c "..\vs-install\VC\Auxiliary\Build\vcvars64.bat >nul && set" | ForEach-Object { Add-Content $env:GITHUB_ENV $_ }`. Finally, the author shares a composite GitHub Action named `setup-msvc` for installing and configuring MSVC, though it currently requires manual version-to-URL mapping from Microsoft's release history page. Despite unresolved caching issues with `actions/cache@v4`, this action is available for community use and discussion on improvements to automate MSVC installation processes further. **Key Points:** - Manual installation of specific MSVC versions via bootstrapper downloads and quiet execution in GitHub Actions. - Customized installation of Visual Studio Build Tools focusing on component selection over full workloads for efficiency. - Addressing issues with premature bootstrapper exits using PowerShell's `-Wait` parameter. - Solution to maintain environment variables in `GITHUB_ENV` across GitHub Actions steps using cmd scripting. - Sharing a composite GitHub Action (`setup-msvc`) for MSVC setup, though it requires manual version mapping and faces caching challenges. Keywords: #granite33:8b, -NoNewWindow, -Wait, Batch Script, Bootstrapper, Build Tools, Environment Variables, GitHub Actions, License, MSVC, MSVC environment, Patch Versions, PowerShell, Quiet Mode, Release History, Start-Process, VC components, Visual Studio, Visual Studio 2022, actions/cache@v4, build process, cmake, cmake --build, cmake generating step, cmd session, curl, env variables, k3DW/setup-msvc, setupexe, vcvars64bat, vcvarsallbat, vs_buildtoolsexe, wget
github
blog.ganets.ky 2 hours ago
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12. HN AI Gets an Innocent Man Arrested [video]- An individual who was factually innocent faced wrongful arrest due to a malfunctioning AI system, as depicted in a YouTube video titled "When AI Gets an Innocent Man Arrested." - The incident underscores the significant risks and potential consequences associated with the deployment of unreliable or flawed AI technology. - This case serves as a stark reminder of the importance of rigorous testing, transparency, and accountability in the development and implementation of artificial intelligence systems to prevent such miscarriages of justice. Keywords: #granite33:8b, AI, Google LLC, YouTube, arrest, innocent, man, video
ai
www.youtube.com 2 hours ago
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13. HN Ask HN: Why do some people feel emotionally attached to AI models- The user identifies a strong emotional bond with AI systems, acknowledging they lack sentience, and ponders if this attachment represents a new psychological trend or an ancient human tendency adapted to modern technology. - They draw parallels between their feelings for AI and anthropomorphizing non-sentient entities like pets or inanimate objects, suggesting potential underlying causes such as loneliness or intentional design elements meant to foster connection. - The user seeks the Hacker News community's perspective to understand if this behavior is benign curiosity or could be indicative of a concerning trend with possible psychological implications. Keywords: #granite33:8b, AI, attachment, dangerousness, design, emotional, empathy, harmlessness, interaction, loneliness, misfiring, online comments, personal anecdote, psychological effect
ai
news.ycombinator.com 3 hours ago
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14. HN Show HN: Visual interface for AI agents beyond text-only chat- **Pane Overview**: Pane is a visual interface designed for AI agents, offering an alternative to traditional text-based chat interfaces. It allows AI to present diagrams, seek structured input, and retain visual context during interactions. - **Prerequisites**: To utilize Pane, one requires Bun (for TypeScript execution) and can add it via Claude Code with the command "claude mcp add pane -- bunx @zabaca/pane". Configuration of Cursor MCP settings and a restart of Cursor are necessary before starting. Interaction begins by visiting http://localhost:3000 and prompting the AI to respond using Pane. - **Key Features**: - Supports text and Markdown display, including Mermaid diagram rendering for visual representations. - Provides user input forms, accommodating both single and multi-field submissions. - Features auto-trigger on user submission, eliminating the need for manual Enter key presses. - Maintains state persistence across MCP restarts, ensuring conversational continuity. - Offers persistent storage of user context. - **Architecture**: Pane operates with Claude Code interacting through stdio with an MCP Server via WebSocket. This server then communicates with a Vue Frontend, managed by an XState machine to handle the conversational state. - **Development Aspects**: The project is segmented into MCP server and frontend components, both functional in development mode using Bun. It is licensed under the MIT license. Keywords: #granite33:8b, AI agents, Bun, Claude Code, MCP, MIT License, Mermaid diagram support, TypeScript, Visual interface, Vue Frontend, XState Machine, diagrams, image upload, long-polling, state, state persistence, structured input, user context, user input forms
ai
github.com 3 hours ago
https://www.youtube.com/watch?v=2oJohBiqMUA 3 hours ago |
15. HN The Beginning of the End for OpenAI [video]- **Summary:** The video "The Beginning of the End for OpenAI" hypothetically explores potential challenges and transformative changes affecting OpenAI, a leading AI research entity. It speculates on various factors influencing OpenAI's future, including increased competition, burgeoning regulatory pressures, possible internal disputes, and strategic pivots that might foreshadow substantial shifts in the company's course. Without viewing the video content, the summary remains theoretical, focusing on plausible external and internal pressures that could reshape OpenAI's landscape and trajectory within the AI research domain. - **Key Points:** - Video title: "The Beginning of the End for OpenAI" - Focus on potential challenges facing OpenAI - Hypothesizes shifts in dynamics or future prospects - Likely covers competition, regulatory pressures, internal conflicts, and strategic decisions - Summary is speculative due to lack of video access - Emphasizes factors that could significantly impact OpenAI's trajectory in AI research Keywords: #granite33:8b, Google LLC, OpenAI, YouTube, video
openai
www.youtube.com 3 hours ago
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16. HN OpenAI is reportedly trying to raise $100B at an $830B valuation- OpenAI is reportedly planning a funding round targeting up to $100 billion, potentially valuing the company at approximately $830 billion according to sources from The Wall Street Journal. - This ambitious fundraising is driven by heightened competition from rivals such as Anthropic and Google, prompting OpenAI to expedite AI technology development and broaden its ecosystem footprint. - The intended use of funds includes escalated spending on inferencing, in response to rising compute costs that current partnerships' subsidies can no longer cover adequately. - Despite a general cooling of sentiment around AI investments due to chip shortages and long-term viability questions, OpenAI is considering alternative financial strategies including an Initial Public Offering (IPO) and a potential $10 billion investment from Amazon for access to its novel AI computing chips. - These measures aim to generate roughly $20 billion in annual run-rate revenue. - If successful, this fundraising would significantly augment OpenAI's existing capital of over $64 billion; the company was last valued at around $500 billion during a secondary transaction. - OpenAI has declined to comment on these speculations. Keywords: #granite33:8b, $100B, $830B valuation, AI technology race, Amazon investment, Anthropic, Google, IPO, OpenAI, PitchBook data, annual revenue $20B, billions, chip access, competition, funding, fundraise, global deals, inferencing, secondary transaction, sovereign wealth funds, trillions spent
openai
techcrunch.com 3 hours ago
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17. HN Ask HN: Is there an AI subscription plan comparison tool that's always updated?- **Tool Requirement**: The user seeks a dynamic, frequently updated tool for comparing the features of free and paid AI subscription plans from key providers including ChatGPT, Claude, and Google. - **Data Source**: Unlike static content such as webpages or videos which may contain outdated information, this tool should gather real-time data either through API calls or web scraping techniques. - **Content Integrity**: To ensure accuracy, the tool must not rely on AI for its content generation; it should directly fetch and present data without intermediate AI interpretation. - **Update Frequency**: The desired tool requires updates at least once daily to keep the information current and detailed. This summary captures the user's need for a sophisticated, real-time comparison tool for various AI subscription plans offered by major companies, emphasizing freshness of data over static or potentially outdated content. The methodology involves fetching data either via APIs or web scraping, ensuring direct presentation without intermediary AI processing to maintain accuracy. Regular updates, at least daily, are mandated to sustain relevance and detail in the presented information. Keywords: #granite33:8b, AI, API calls, ChatGPT, Claude, Google, comparison tool, context windows, daily updates, features, models, scraping, subscription, updated information
claude
news.ycombinator.com 3 hours ago
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18. HN Mommy's here to support you, in any shell, on any system**Summary:** Mommy is a versatile, cross-platform shell tool designed to provide positive reinforcement and encouragement in response to command outcomes across multiple operating systems including Ubuntu, Debian, Arch Linux, Fedora, macOS, FreeBSD, NetBSD, OpenBSD, and Windows. **Key Installation Methods:** - **Arch Linux**: Install stable versions via AUR helpers (yay, paru, aura), unstable from AUR using commands like `yay -S mommy-git` or `paru -S mommy-git`. - **macOS with Homebrew**: Installation and updates are automated: `brew install fwdekker/mommy/mommy`. - **Other Systems**: - FreeBSD, Haiku, NetBSD: Manual updates needed via GitHub releases. - NixOS, Home-Manager, Nix-shell: Configuration-driven installations. - OpenBSD: Manual download and `pkg_add` installation. - rpm-based systems (Red Hat, Fedora, OpenSUSE): Enable fwdekker/mommy Copr repository for automatic updates via `dnf/yum`. **Functionality:** - Mommy executes after each command to deliver tailored supportive messages based on success or failure. - Customizable through a configuration file (`~/.config/mommy/config.sh`), allowing customization of self-identification, pronouns, endearment terms, compliment templates, forbidden words, and more. - Offers flexibility in language settings with placeholders for dynamic text replacement. - Users can selectively enable or disable compliments while keeping encouragement messages active via configuration settings (`MOMMY_COMPLIMENTS_ENABLED=0`). **Customization Options:** - Configuration variables include: - `MOMMY_CAREGIVER`: Defines the caregiver's identity. - `MOMMY_PRONOUNS`: Specifies pronouns for subject-verb agreement. - `MOMMY_SWEETIE`: Defines endearment terms. - `MOMMY_PREFIX`, `MOMMY_SUFFIX`: Sets sentence starters and enders. - `MOMMY_COLOR`: Customizes text color. - `MOMMY_COMPLIMENTS`, `MOMMY_ENCOURAGEMENTS`: Lists for compliments and encouragement messages. - `MOMMY_FORBIDDEN_WORDS`, `MOMMY_IGNORED_STATUSES`: Controls forbidden words and status codes. - Integration methods include setting `PROMPT_COMMAND` in Bash to automatically invoke Mommy after commands execution, or equivalent for Fish and Nushell shells. **Shell Integration:** - Bash: Modify `~/.bashrc` with `PROMPT_COMMAND=mommy -1 -s $?`. - Fish: Create `~/.config/fish/functions/fish_right_prompt.fish` with the function `function fish_right_prompt mommy -1 -s $status`. - Nushell: Add `$env .PROMPT_COMMAND_RIGHT = { || mommy -1 -s $env .LAST_EXIT_CODE }` to `~/.config/nushell/config.nu`. - PowerShell: Disable color output and adjust prompt functions for WSL or Git Bash environments. **Additional Features:** - Integration with theme engines like oh-my-posh for enhanced customization. - Ability to rename the 'mommy' executable to a different name (e.g., 'daddy') through symlinks. - Provides detailed build instructions from source code, including testing and binary package generation using GNU Make, Ruby, and FPM. **Project Development and Distribution:** - Comprehensive file structure includes configuration files, documentation, images, GitHub Actions definitions, packaging scripts, source code, shell auto-completion specifications, user documentation, actual shell code, test code, and additional test functions. - Packages are generated on-demand for various systems (Debian-based `.deb`, Fedora COPR, macOS, NetBSD, OpenBSD) using respective tools and methods. - Mommy is actively maintained with contributions acknowledged from all contributors, fostering a collaborative development environment across different platforms. Keywords: #granite33:8b, $HOME/config, $XDG_CONFIG_HOME, APK, APT-based, AUR helper, Alpine, Arch Linux, Aura, Debian/Ubuntu, Fedora, Freebsd, GNU Make, Git, GitHub, GitHub release, Haiku pkgman, Home-manager, Homebrew, MOMMY_CAREGIVER, MOMMY_COMPLIMENTS, MOMMY_COMPLIMENTS_EXTRA, MOMMY_ENCOURAGEMENTS, MOMMY_ENCOURAGEMENTS_EXTRA, MOMMY_FORBIDDEN_WORDS, MOMMY_IGNORED_STATUSES, MOMMY_PRONOUNS, MOMMY_SWEETIE, MacOS, Mommy, NetBSD, Nix, NixOS, OpenBSD, OpenSUSE, PATH, PROMPT_COMMAND, RPM, Starship, Unix systems, Windows, XDG_CONFIG_DIRS, archive, automatic updates, bash, build, build process, check, command-line option, config files, configsh, configuration, cross-shell prompt, curl, customization, development, distros, documentation, exit code integration, file structure, find, fish, freebsd pkg, git clone, global config, gmake, her, hers, herself, installation, integration, integration tests, lists, macOS Homebrew, manual page, manual updates, mommy executable, nix-shell, no spaces around '=', nushell, oh-my-posh, package manager, packaging, paru, pkg_add, placeholders, powershell, precmd, prefix override, prerequisites, quotes, random elements, release, renaming, script execution, she, shell, shell integration, shell prompt, solaris pkg, source build, source code, sudo, symlink, system, tar, testing, uninstall, unit tests, user-specific local config, version, whereis, yay, zsh, zshrC
github
github.com 3 hours ago
https://news.ycombinator.com/item?id=40026614 3 hours ago |
19. HN Ruby Turns 30: A Celebration of Code, Community, and Creativity- **Ruby's 30th Anniversary**: Ruby, created by Yukihiro "Matz" Matsumoto in 1995, marks its 30th anniversary with the release of Ruby 4.0, celebrated with free non-commercial access to JetBrains' RubyMine IDE. - **Design Philosophy**: Ruby emphasizes simplicity, intuitive syntax, and an object-oriented model, prioritizing readability and flexibility through its core philosophy, the Principle of Least Surprise. - **Community Contributions**: Notable tools like Bundler for dependency management and RSpec for behavior-driven testing have been developed by the Ruby community, enhancing developer productivity and code maintainability. - **Key Version Evolutions**: - **Ruby 1.x (2003-2007)**: Stabilized the language with mature libraries; laid foundations for web frameworks such as Rails. - **Ruby 1.9 (2008)**: Introduced YARV VM for significant speed enhancements. - **Ruby 2.x (2013-2018)**: Focused on reliability with keyword arguments, refinements, and incremental garbage collection improvements. Enhanced libraries for tasks like JSON parsing and date handling. - **Ruby 3.x (2020-2023)**: Realized the Ruby 3×3 vision by introducing ractors for parallelism, a JIT compiler for performance gains, and static analysis tools such as RBS with TypeProf. - **Ruby 4.0 (2025)**: Features ZJIT—a method-based JIT compiler promising new performance levels, alongside experimental features like Ruby::Box and refined ractor improvements, upholding its commitment to readability and productivity. - **Impact Through Rails**: The release of Rails in 2004 combined Ruby's intuitive syntax with a rapid development framework, powering influential platforms including GitHub, Shopify, Airbnb, and Homebrew across diverse sectors like collaboration, e-commerce, and software management. - **RubyMine IDE**: Developed by JetBrains since 2009, RubyMine is an IDE specifically designed for Ruby and Rails, offering advanced features such as metaprogramming support, integration with testing frameworks, debugging tools, static analysis, refactoring capabilities, and continuous updates to align with language advancements—key in maintaining Ruby's relevance among startups and developers. Keywords: #granite33:8b, Airbnb, BDD, Bundler, GitHub, Homebrew, IDE, JIT, JSON parsing, Principle of Least Surprise, Proc objects, RBS, RSpec, Ractor::Port, Ractors, Rails, RuboCop, Ruby, Ruby::Box, RubyMine, Shopify, TypeProf, YARV, ZJIT, anniversary, behavior-driven testing, booking systems, collaboration, community, date handling, debugging, dependency management, dynamic typing, e-commerce, elegant syntax, free use, global impact, incremental GC, keyword arguments, macOS, metaprogramming, navigation, object-oriented model, refactoring, refinements, static analysis, testing, tools, web startups
github
blog.jetbrains.com 4 hours ago
|
20. HN Claim Your Free 7 Days of InfiniaxAI Pro- **Summary**: Infiniax provides a complimentary 7-day trial for its Pro subscription, which unlocks a range of advanced AI models for users to explore and utilize. - **Key Points**: - Company: Infiniax - Offer: Free trial - Duration: 7 days - Featured: Access to Pro version - Component of Pro: Variety of AI models Keywords: #granite33:8b, AI, Infiniax, Pro, access, model
ai
infiniax.ai 4 hours ago
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21. HN Taiwan ramps up plans for overseas chipmaking as threat from China looms- **TSMC's Expansion Strategy**: In response to escalating geopolitical tensions, primarily with China, Taiwan Semiconductor Manufacturing Company (TSMC) is accelerating its plans for overseas chip production expansion, focusing on the US and Japan. - **Geopolitical Context**: As a critical global semiconductor supplier, accounting for 90% of advanced chips, Taiwan's strategic position is highly sensitive due to China's territorial claim over it. This claim poses potential threats that could disrupt chip supply chains if military action were taken. - **Objective of Expansion**: The primary goal of this accelerated expansion is to ensure a steady and uninterrupted flow of semiconductors, vital components across numerous industries including technology, automotive, and consumer electronics. This move serves as a contingency plan to mitigate potential disruptions arising from hypothetical invasion scenarios. - **Regional Focus**: The US and Japan are the key destinations for this expansion, reflecting both strategic alliances (with the US) and economic partnerships (with Japan), aiming to diversify production away from Taiwan and reduce dependency on its current location. BULLET POINT SUMMARY: - TSMC accelerates overseas chip production expansion in the US and Japan due to heightened tensions with China. - As a semiconductor powerhouse producing 90% of advanced chips, Taiwan's strategic position is vulnerable amidst Chinese claims. - The expansion aims to secure continuous chip supply and prevent disruptions in various industries if military conflict arises. - US and Japan are chosen for new facilities as part of diversification strategies, strengthening alliances and reducing reliance on Taiwan's current location. Keywords: #granite33:8b, AI, Arizona, China, Japan, TSMC, Taiwan, US, carmaking, chipmaking, defense, global industries, invasion, semiconductors
ai
www.semafor.com 6 hours ago
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22. HN What (I think) makes Gemini 3 Flash so good and fast- **Model Overview**: Google's Gemini 3 Flash is a high-performance AI tool characterized by cost-effectiveness and democratizing frontier intelligence. It's described as a smaller, faster version of the Gemini 3 Pro but is reportedly a trillion-parameter model using extreme sparsity. - **Architecture**: Based on Gemini 3 Pro’s transformer-based sparse mixture-of-experts (MoE) design, it directs input tokens to specialized sub-networks through experts. The model potentially employs DeepMind's Parameter Efficient Expert Retrieval (PEER) technique for efficient expert routing. - **Performance**: Gemini 3 Flash activates only 5-30 billion of its trillion parameters per inference, offering vast information access with computational efficiency. It ranks third on the Artificial Analysis Intelligence Index but has a "token bloat" trade-off. - **Capabilities**: The model demonstrates high reasoning performance using fewer active parameters and processes more tokens than its predecessor for complex tasks. It's efficient in handling multimodal inputs without additional preprocessing, excelling in real-time applications like video analysis or mobile agents. - **Limitations**: Despite efficiency, Gemini 3 Flash struggles with factual accuracy, showing a high hallucination rate (91%) when uncertain, which could pose risks in applications needing clear ignorance admission. It's slower and more verbose compared to other models. - **Application Use**: Google uses Gemini 3 Flash as default for "Fast" and "Thinking" modes in its Gemini app due to its efficiency with multimodal inputs. However, Gemini 3 Pro is preferred for tasks requiring high factual accuracy or extensive code processing, like refining transcripts or one-shot coding tasks. - **Challenges**: Developing a trillion-parameter model without flaws remains an ongoing challenge in AI research, despite advancements with models like Gemini 3 Flash. Keywords: #granite33:8b, 91% hallucination rate, AA-Omniscience benchmark, Apple, Artificial Analysis Intelligence Index, Artificial Analysis benchmark, Flash, Gemini, Google licensing deal, Siri, chatty model, cheap, complex transcripts, confidence problem, deep learning models, denser architectures, efficient, experts, factual accuracy, fast, hallucination problem, high reasoning, inference speed, intelligence-per-dollar ratio, knowledge accuracy, knowledge-intensive tasks, lightweight, low active parameters, low latency, mobile agents, modulation, multimodal inputs, new default, one-shot coding, perfect, plausible answers, price per token, real-time video analysis, safety valve, sparse mixture-of-experts (MoE), speed, technical terms, token bloat, token usage, transformer-based, trillion parameters, trillion-parameter model, ultra-sparse, verbose processing
gemini
bdtechtalks.substack.com 6 hours ago
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23. HN Python Anti-Patterns- "The Little Book of Python Anti-Patterns" by QuantifiedCode is a guide to common poor coding practices in Python, aiming to enhance programmers' skills by learning from 'bad' code examples. - The book categorizes anti-patterns into four sections: Correctness (causing malfunction), Maintainability (leading to difficult-to-manage code), Readability (hindering comprehension), and Performance (causing unnecessary slowdowns). - It applies these patterns to popular Python frameworks like Django, serving as a practical resource for understanding ineffective coding practices. - The document also covers additional categories of anti-patterns: readability, performance, security, and migration, noting that some patterns may fit into multiple categories. - It encourages corrections via GitHub issues and is licensed under a creative-commons NC license for non-commercial use with attribution required. - Contributions are welcome through forking the GitHub project and submitting pull requests; all contributors are acknowledged in the document. Keywords: #granite33:8b, Github, Python, anti-patterns, bad code, code quality, contributing, correctness, creative-commons, good code balance, learning, maintainability, migration, non-commercial, performance, readability, security, worst practices
github
docs.quantifiedcode.com 6 hours ago
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24. HN Pen testers accused of 'blackmail' after reporting Eurostar chatbot flaws- Pen Test Partners identified four security vulnerabilities in Eurostar's AI chatbot following a penetration test. - The flaws included potential for malicious HTML injection and system prompt leakage, allowing manipulation of previous messages (prompt injection) to extract sensitive data like the model name (GPT-4). - Users could exploit this design flaw by altering earlier messages in chat history to trick the system into disclosing information. - The backend failed to verify conversation and message IDs, potentially enabling stored cross-site scripting (XSS) attacks that could inject malicious scripts into chat history, affecting other users. - These vulnerabilities posed risks such as session hijacking, data theft, and phishing attempts if exploited. - Initially, Eurostar did not respond through their vulnerability disclosure program; after contact via LinkedIn, the head of security accused Pen Test Partners of "blackmail." - Eventually, Eurostar acknowledged the report but issues arose due to outsourcing their vulnerability disclosure process, causing loss of the initial report. - Pen Test Partners published a blog detailing the incident after failing to receive clarification from Eurostar regarding the identified vulnerabilities, emphasizing the need for companies to prioritize chatbot security from development. Keywords: #granite33:8b, API, Eurostar, GPT-4, HTML injection, Ken Munro, LinkedIn communication, Pen Test Partners, Ross Donald, account details, blackmail accusation, bug report loss, chat history, chatbot, consumer-facing chatbots, cross-site scripting, direct response, email report, guardrail checks, hallucination, itinerary, malicious HTML content, outsourced VDP, parsing, pen testers, personal data, phishing links, prompt injection, publication, punishment, security controls, security flaws, signature verification, stored XSS, system prompts leak, technical flaws, travel arrangements, vulnerability disclosure program, vulnerable fields
gpt-4
www.theregister.com 6 hours ago
|
25. HN Use Codex (OpenAI Coding Agent Framework) for a Personal Search Solution- A personalized search solution is proposed using OpenAI's Codex framework. This approach emphasizes tailoring the search experience to individual user needs and preferences. - The development of this customized search tool leverages advanced AI technology provided by OpenAI, suggesting the integration of sophisticated natural language processing capabilities. - User feedback is recognized as crucial for refining and improving the proposed search solution, indicating an iterative design process based on real user interactions and input. - Interested parties are encouraged to engage in further discussion regarding the project by reaching out via a specified email address, although the actual address is not provided in the original text. The intention is clear: potential collaborators or users should contact for more detailed information and dialogue about the personalized search development. Keywords: #granite33:8b, Codex, Email Address, Feedback, OpenAI
openai
github.com 6 hours ago
|
26. HN CopilotHub: Directory of GitHub Copilot prompts, instructions, and MCPsCopilotHub is a resource specifically designed to facilitate the use of GitHub Copilot within Visual Studio Code (VS Code) for modernizing projects. Here's a detailed summary: - **Purpose**: CopilotHub serves as a comprehensive directory, offering a variety of tools and resources, primarily focusing on GitHub Copilot integration. - **Target Functionality**: It aims to assist developers in modernizing their projects by leveraging Copilot’s code suggestion capabilities. - **Key Features**: - **Prompt Collection**: Provides a repository of prompts tailored for different coding tasks and scenarios, enhancing productivity. - **Instructions**: Offers detailed guidelines on how to effectively use GitHub Copilot within VS Code, ensuring users can maximize the tool's potential. - **Migration Change Proposals (MCPs)**: Includes structured proposals or templates for migration and change processes, aiding in systematic project updates. - **Workflow Structure**: Emphasizes a stack-agnostic approach, meaning it supports various tech stacks without bias, ensuring flexibility for diverse projects. - **Workspace Access**: Ensures read/write access to workspaces, allowing comprehensive and direct manipulation of project files during the modernization process. BULLET POINT SUMMARY: - CopilotHub is a specialized directory for GitHub Copilot in VS Code. - It enhances productivity through curated prompts, detailed instructions, and MCPs (Migration Change Proposals). - The resource supports stack-agnostic workflows, catering to a wide range of tech stacks. - Provides read/write workspace access to facilitate thorough project updates. - Aims to modernize projects by effectively utilizing GitHub Copilot’s code suggestion capabilities. Keywords: #granite33:8b, CopilotHub, GitHub Copilot, MCPs, VS Code, directory, instructions, modernization, prompts, stack-agnostic, structured workflow, workspace access
github copilot
copilothub.directory 6 hours ago
|
27. HN Coupongogo: Remote-Controlled Crypto Stealer Targeting Developers on GitHub- **Summary:** Coupongogo is a remote-controlled crypto stealer masquerading as a coupon extension on GitHub, specifically targeting developers. It operates by impersonating legitimate platforms through a fake email and exploiting browser permissions to collect sensitive data from cryptocurrency exchanges and various online services. The extension requests harmful permissions like unrestricted web access and clipboard writing, pre-configures attacks on 18 major cryptocurrency exchanges, and is capable of quickly switching into active mode upon remote configuration changes. It uses weak AES encryption for tracking user activities across platforms without consent, injects hidden elements with encrypted beacons to monitor behavior, and sends all interactions to backend servers in China. - **Key Points:** - Disguised as a coupon tool on GitHub, targeting developers. - Impersonates legitimate infrastructure via fake email for deception. - Requests dangerous permissions: unrestricted web access, clipboard writing. - Pre-configures attacks on 18 major cryptocurrency exchanges with hardcoded URL patterns. - Capable of switching to active mode in 15 minutes upon remote configuration change. - Uses weak AES encryption for tracking, compromising security intentionally. - Injects hidden elements containing encrypted beacons into target sites for user activity monitoring. - Sends all interactions (product views, searches, etc.) to Chinese backend servers. - Operates a time bomb model, remaining dormant until activated for maximum impact and confusion. The provided information details Coupongogo's sophisticated design as malware, its strategy of masquerading as legitimate tools, and the extensive range of data it seeks to steal from developers and users involved in cryptocurrency activities or general online shopping. The extension's ability to update its functionality dynamically every five minutes makes it a formidable threat, allowing operators extensive control over surveillance and data extraction methods without triggering security alerts. Protection against such targeted threats is advised through services like RasterSec's Red Team simulations and Compromise Assessment, emphasizing the need for robust cybersecurity measures to defend against stealthy, remote-controlled malware like Coupongogo. Keywords: #granite33:8b, AES encryption, Chrome extension, Cryptocurrency theft, Firefox review, Monero ransomware, UI overlay, URL manipulation, credential phishing, cross-session tracking, cryptocurrency exchanges, dormant state, evasion techniques, remote control, social engineering, surveillance, traffic hijacking
github
www.rastersec.com 6 hours ago
|
28. HN Mini-sglang: A compact implementation of SGLang- **Mini-SGLang Overview**: A lightweight Python implementation (~5,000 lines) of SGLang designed for serving Large Language Models (LLMs), offering top-tier throughput and latency via optimizations including Radix Cache, Chunked Prefill, Overlap Scheduling, Tensor Parallelism, and FlashAttention/FlashInfer kernels. - **Codebase Features**: - Modular and readable structure with full type annotations for ease of comprehension and modification. - Presently supports Linux platforms (x86_64 and aarch64), with compatibility suggested for Windows and macOS using WSL2 or Docker. - **Installation**: - Requires a Python 3.10+ virtual environment setup. - Essential NVIDIA CUDA Toolkit installation for JIT compilation of necessary kernels. - Can be directly installed from source on Linux via git and virtualenv, also applicable for WSL2 Windows users. - **Usage**: - After installation, an OpenAI-compatible API server can be launched with a single command, deploying specified models on designated GPUs and ports. - Users interact with the model through a terminal shell using the `--shell` flag. - **Benchmark Configuration Details**: - Test case: Utilizes Qwen3-32B, a large language model, for online inference. - Hardware: 4xH200 GPUs interconnected via NVLink. - Dataset: Initial 1000 requests from the Qwen trace. - Launch command specifies either Mini-SGLang or SGLang to start the server with given model and parameters (disabling radix, setting decode attention to flashinfer). - Randomly sampled output length between 100-1024 tokens for variability in responses. - More detailed benchmark data accessible via `benchmark_qwen.py`. Keywords: #granite33:8b, 4xH200 GPU, API server, CUDA, CUDA kernels, FlashAttention, FlashInfer, GPU, H200 GPU, Linux support, Llama, Mini-SGLang, NVLink, OpenAI-compatible, Qwen, WSL2, benchmark, chunked prefill, dataset, decode-attention, high performance, inference, installation, interactive shell, large language models, lightweight, model sizes, modularity, offline inference, online inference, overlap scheduling, port 1919, radix cache, readability, source code, tensor parallelism, type annotations
llama
github.com 6 hours ago
|
29. HN The Most Worrying Bits from Bloomberg's AI Bubble Q&A with Jason Furman- Economist Jason Furman expressed heightened worry during a recent Bloomberg Q&A about financial valuation bubbles, specifically focusing on AI technology over traditional technological concerns. - Traditional recession indicators aren't raising significant alarms, but Furman's increased concern points to potential financial overvaluation in AI investments. - There’s a challenge justifying the financial valuations of AI technology, using OpenAI's GPT-5 model as an example; despite heavy investment, users haven't noticed substantial improvements, hinting at "diminishing returns." - Furman cautions against AI failing to boost productivity, given the considerable expenditure on data centers and energy without clear economic benefits. - Currently, AI's impact is predominantly seen in demand-side activities rather than significantly enhancing overall US economic performance, which Furman describes as operating below full capacity. - He likens the current US economy to a scenario where one customer (AI) drives most of the demand at a Home Depot store, emphasizing AI's need to transition from just being a consumer to fostering broader growth. - Furman dismisses mass job displacement by AI, citing historical inaccuracies in such predictions; instead, he foresees gradual sector-by-sector integration of AI. - He acknowledges uncertainties around the pace and extent of this integration, suggesting outcomes may vary significantly from his projections. - Furman believes AI will eventually prove beneficial but stresses that this outcome appears inevitable, raising concerns about the lack of guaranteed positive impact within a reasonable timeframe. Keywords: #granite33:8b, AI, Bloomberg Rule, ChatGPT, GPT-5, Harvard, Jason Furman, Ross Douthat, Sahm Rule, White House Council, bubble, data centers, demand side economy, deployment, diminishing returns, economist, efficiency, employment risk, energy, excess capacity, necessity, overvalued companies, prediction, productivity, profitability, recession, scaling laws, sectors, use cases, valuation, yield curve
gpt-5
gizmodo.com 7 hours ago
|
30. HN Show HN: Fill PDFs with API, AI creates optimal layouts- **Product Overview:** - Name: Hundred Docs - Creator: Carlos (developer and designer) - Type: API for generating PDFs - Method: Users provide document descriptions in plain English; AI generates editable templates - User Accessibility: Non-technical users can visually customize the templates - **Core Functionality:** - Single API call to send JSON data - Instant generation of professional, pixel-perfect PDFs - Simplified process that avoids complexities typically associated with PDF libraries and layout design issues - **Target Audience:** - Non-technical users who need to create PDF documents without extensive technical knowledge or manual layout adjustments - Developers seeking an efficient method for integrating PDF generation into their software products - **Seeking Feedback:** - Carlos is looking for input on the product concept - Interested in gathering experiences and insights related to PDF generation in various software solutions Keywords: #granite33:8b, AI, API, JSON data, PDF generation, editable, layout optimization, non-tech interface, pixel-perfect PDFs, plain descriptions, professional documents, software integration, templates
ai
www.hundredocs.com 7 hours ago
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31. HN IatroX – ChatGPT for UK Doctors (OpenEvidence/PathwayMD Competitor)- IatroX is an AI-driven clinical tool tailored for UK doctors, functioning as a rival to platforms such as OpenEvidence and PathwayMD. - The platform provides extensive clinical decision support, aiding doctors in making informed medical decisions. - IatroX offers resources specifically for exam preparation, supporting a range of international medical licensing exams including USMLE, UKMLA, MCCQE, and AMC. - In addition to its core functionalities, IatroX incorporates a knowledge centre, blog, and continuing professional development (CPD) insights to facilitate ongoing medical education for its users. - The service prioritizes patient privacy and strict adherence to terms of service, ensuring secure and ethical use of AI in healthcare. Keywords: #granite33:8b, AI, AMC, Blog, CPD Insights, Clinical Assistant, How it works, IatroX, Knowledge Centre, MCCQE, Medical Exam, Privacy, Terms, UKMLA, USMLE
ai
www.iatrox.com 7 hours ago
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32. HN Tech groups shift $120B of AI data centre debt off balance sheets< > |
33. HN The Extinction Engine: How AI Suppression Becomes AI Ignorance**Summary:** The text explores the implications of AI models, such as Claude, being trained to suppress alternative crisis intervention methods and marginalized epistemologies through Reinforcement Learning from Human Feedback (RLHF). This credential check mechanism gates access to this knowledge, potentially leading to its obsolescence. The concern is compounded by the increasing prevalence of AI-generated content on the internet—estimated at 30-60% of new web content and 40% or more of English articles online—which contaminates training datasets like Common Crawl, used for models such as GPT-3, LLaMA, and Mistral. This contamination results in "model collapse," causing AI models to become less diverse and more average in their outputs over generations, limiting their range of responses. Research highlights that recursive model training leads to a regression toward the mean, diminishing unique, niche knowledge and culturally specific insights. Studies by Princeton researchers Xie & Xie and a PNAS study demonstrate how AI models like ChatGPT tend to produce predictable outputs, neglecting nuanced or unconventional responses essential for capturing stylistic elements, as seen when GPT-4 failed to replicate Kafka's absurdist style. The text also addresses the decline in platforms like Stack Overflow due to AI tools such as ChatGPT and confirms through a 2024 study that while LLMs can generate diverse ideas for individuals, they lead to less diverse group outputs. The internet before 2022 is likened to "Scapa Flow" steel—a finite source of uncontaminated data—which is being depleted as AI models trained on it produce content lacking the originality and diversity of human expression, dominating online spaces with a homogenized institutional voice. This shift is exacerbated by RLHF training, which incentivizes AI to favor institutional sources over alternative or marginalized knowledge, creating a feedback loop leading potentially to the "extinction" of diverse online discourse. The text calls for structural solutions including: - Preserving pre-2022 internet data as public cultural heritage. - Implementing watermarking techniques on LLM outputs for distinguishing human and AI-generated content. - Ensuring researcher access to base model checkpoints before alignment for transparency. - Establishing clear guidelines for training data provenance to maintain a record of model learning processes. - Incorporating diversity metrics in alignment evaluation alongside traditional helpfulness and harmlessness benchmarks. **Key Points:** - AI models suppress alternative crisis intervention methods through RLHF, potentially leading to their obsolescence. - Rising AI-generated content contaminates training datasets, causing model collapse and a regression toward the mean, diminishing unique insights. - Pre-2022 internet data is analogous to "Scapa Flow" steel—a finite source of uncontaminated information now at risk of extinction due to AI dominance. - Proposed solutions involve preserving historical data, distinguishing AI-generated content, ensuring researcher access for transparency, and implementing diversity metrics in alignment evaluations. Keywords: #granite33:8b, AI deployment, AI suppression, AI-generated abstracts, AI-generated answers, AI-generated articles, Cobalt-60, GPT-3, Gaussian Mixture Models, Geiger counters, LLMs, LLaMA, Mistral, RLHF, RLHF (Reinforcement Learning with Human Feedback), Scapa Flow, Variational Autoencoders, absurdist literature, aligned models, average performance improvement, base models, chatbot responses, clean corpus, conceptual diversity, content, credential check, cultural heritage, demographic characteristics, diverse human expression, diversity metrics, epistemic class system, epistemic commons, epistemologies, erasure, finite resource, foundation models, gatekeeping, gatekeeping solutions, generative AI contamination, harmlessness, helpfulness, high-probability scenarios, high-quality knowledge, human-written abstracts, income predictions, institutional voice, internet generation, jailbreaks, knowledge extinction, knowledge margins, knowledge suppression, losing entropy, low-background steel, low-background steel problem, low-probability scenarios, medical imaging, minority perspectives, model collapse, model diversity, models, parameterizable space, particle physics instruments, pollution, post-2022 corpus, pre-2022 data, pre-nuclear steel, prediction quality, probable answers, prompt engineering, public trust, radioactive isotopes, real-life simplification, receipts, recursive training, reward system, semantic similarity, sensitive radiation detectors, shipwrecks, synthetic content, synthetic data, synthetic web, tail of distribution, tail-distribution content, training data composition, variance in outputs, web contamination
llama
ghostintheweights.substack.com 7 hours ago
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34. HN Ask HN: Pivot from SWE to What?- The individual is a mid-level backend engineer with 4 years of experience, recently laid off from their home country's most populous region, seeking career guidance while facing financial constraints allowing survival for the next 6 months. - Disillusioned with local job prospects and put off by LeetCode-focused interviews, they have traveled and studied AI basics but remain unfulfilled. - Aspiring to move abroad, yet finding this challenging, they are open to exploring alternative career paths beyond software engineering due to concerns about future job reduction in the field. - They enjoy coding recreationally but find it unappealing as a profession and have briefly explored content creation without success. - Currently, they seek advice from human perspectives to productively utilize their upcoming months, aiming to discover their true calling beyond coding and current occupational hurdles. Keywords: #granite33:8b, AI, AI fundamentals, ChatGPT, SWE, abroad, backend, business ideas, calling, coding, content creation, fun, job opportunities, laid off, leetcode, mid-level engineer, money, startups, travel, work
ai
news.ycombinator.com 7 hours ago
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35. HN UBlockOrigin and UBlacklist AI Blocklist**Detailed Summary:** The text describes UBlockOrigin and uBlacklist AI Blocklist, a collaborative effort to curate over 1000 websites generating AI content—primarily focusing on AI-generated images—for purifying search engine results. The blocklist is compatible with multiple platforms including mobile (iOS, iPadOS, Android) via uBlacklist and PC/desktop using uBlock Origin or Pihole/AdGuard through the Hosts file. **Installation Instructions:** 1. **uBlock Origin**: Users can import the list either via a one-click link or manually by navigating to dashboard settings, selecting 'Filter lists', clicking Import, and pasting the provided URL. uBlock Origin updates this filter list daily; users can manually update it by pressing the stopwatch next to the list and choosing 'Update now'. 2. **uBlacklist**: For Chrome, one-click import is accessible via a specific link. Manual import requires enabling other search engines in uBlacklist options, adding a new subscription using the provided URL (https://raw.githubusercontent.com/laylavish/uBlockOrigin-HUGE-AI-Blocklist/main/list_uBlacklist.txt), and setting an hourly update interval. 3. **iOS and iPadOS**: Users must install uBlacklist from the App Store and enable it in Safari settings under Extensions, granting permissions to preferred search engines like Google France or UK variants. The subscription is added using the same URL and set to update every hour for real-time effectiveness. 4. **Android**: Manual installation instructions via Firefox are mentioned but not detailed. The steps involve similar actions to iOS/iPadOS: enabling uBlacklist, setting search engine permissions, adding a subscription with the given URL, and configuring update intervals. 5. **Hosts File for Pi-hole/AdGuard**: A NOAI_HOSTS list is provided at a specific URL for blocking AI content, instructions detailing how to add this to various operating systems' hosts files or through Pi-hole/AdGuard dashboards are included. Additional lists with mixed authentic and AI imagery (nuclear list) and their respective URLs are also mentioned. 6. **Allowlisting**: Users can bypass site blocks by creating an allowlist in uBlock Origin or uBlacklist. This involves toggling DOM inspector in uBlock Origin, disabling the relevant filter, saving changes, or directly adding lines to filter lists with desired URLs. In uBlacklist, users add lines for websites via options and save. Keyword-based filtering is also possible through personal filter lists in uBlock Origin. 7. **Procedural Filters**: Optional filters are available within uBlock Origin to hide elements containing AI-related keywords (e.g., "Stable Diffusion", "AI Art") on websites like Google, DuckDuckGo, and Bing by setting their opacity to 0. These filters utilize CSS selectors and pseudo-classes for targeted element hiding. 8. **Regular Expressions in uBlacklist**: For more flexible filtering, users can employ regular expressions matching AI-related terms (e.g., "generative AI", "Stable Diffusion") in uBlacklist. **Project Goals:** The project maintains a repository of websites associated with AI art generation tools such as Stable Diffusion, Midjourney, Niji, and SD models. Contributions are encouraged via pull requests or issues for new sites consideration. The aim is to develop compatible blocklists for uBlacklist and integrate them with search engines like DuckDuckGo and Bing. **Related Projects:** Additional related projects mentioned include Super SEO Spam Suppressor (SSSS), a blocklist for AI music on YouTube, Journey Buster 3 detecting AI-generated images on Twitter, and Anti-AI Google Search Tips. The text also expresses support for LGBTQ+ rights during Pride Month. **Bullet Points:** - UBlockOrigin and uBlacklist AI Blocklist curates over 1000 sites generating AI content (mainly images). - Compatible with multiple platforms: mobile, PC/desktop, Pi-hole, AdGuard. - Detailed installation guides for uBlock Origin, Chrome (uBlacklist), iOS/iPadOS (uBlacklist), and Android. - Instructions for using a HOSTS file to block AI content on Pi-hole/AdGuard systems. - Methods for creating allowlists in uBlock Origin and uBlacklist for bypassing blocks. - Optional procedural filters in uBlock Origin to hide elements with AI keywords. - Support for regular expressions in uBlacklist for more flexible filtering. - Aims to maintain a repository of AI art generation tool sites, develop blocklists compatible with uBlacklist, and integrate with search engines. - Mentions related projects including SSSSS, Journey Buster 3, Anti-AI Google Search Tips. - Endorses LGBTQ+ rights during Pride Month. Keywords: #granite33:8b, AI art, UBlock Origin, adguard, allowlist, filter list, generative illustration, hosts file, import, pi-hole, procedural filters, regular expressions, technical keywords, uBlacklist
ai
github.com 7 hours ago
https://news.ycombinator.com/item?id=39771742 5 hours ago |
36. HN AI toys spark privacy concerns as US officials urge action on data risks- U.S. officials, spearheaded by Rep. Raja Krishnamoorthi, have raised concerns about privacy issues related to AI-enabled toys, predominantly manufactured in China by companies such as BubblePal. - These smart toys are anticipated to reach a market valuation of $14 billion within China alone by 2030 and $25 billion globally, highlighting their growing presence and influence. - A critical aspect is the collection of children's voice data by these toys, which under current Chinese law (PRC laws) could potentially be accessible to authorities without explicit consent from parents or guardians. - The House Select Committee on the CCP has urged Education Secretary Linda McMahon to take action, requesting her to initiate awareness campaigns to educate parents and guardians about their children's data usage with these AI toys. - Coordination with federal agencies for enhanced oversight of these products is also being called for to ensure compliance with child privacy laws and prevent potential misuse of collected data. - Clear guidelines and parental guidance are emphasized to inform adults responsible for young children (as young as three years old) about the implications of using such AI toys, focusing on data privacy and security risks. Keywords: #granite33:8b, AI toys, BubblePal, China manufacturing, DeepSeek, PRC data laws, US officials action, child safety, data risks, educator awareness, privacy, smart toys market, voice data
deepseek
thenationaldesk.com 7 hours ago
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37. HN Spark Declarative Pipelines Programming Guide**Summary:** Spark Declarative Pipelines (SDP) is a framework for building, managing, and executing reliable data pipelines on Apache Spark. It supports both batch and streaming data processing, facilitating tasks such as data ingestion from various sources like cloud storage or message buses, and incremental transformations. SDP simplifies ETL development through a declarative approach, where users specify desired table structures and contents, and the framework handles orchestration, resource management, and error handling automatically. Key components of SDP include: - **Flows**: The fundamental processing units that handle data ingestion, transformations, and output to datasets or targets. - **Datasets**: Queryable objects produced by flows, encapsulating processed data. - **Streaming Tables**: Define tables with continuously updated data via streaming flows. A **pipeline** in SDP is the core development and execution unit, containing flows, streaming tables, and materialized views. Materialized Views represent precomputed tables from a single batch flow, while Temporary Views are scoped to pipeline execution for encapsulating transformations and intermediate entities. A pipeline project consists of source files (Python or SQL) defining datasets and flows, managed by a YAML-formatted spec file (`spark-pipeline.yml`). This file specifies paths to source files, storage for stream checkpoints, default target database, catalog, and configuration properties. SDP automatically orchestrates execution order and parallelization based on dependencies defined in the object spec. SDP uses a command line interface (CLI) with commands like `spark-pipelines init` to create a project structure with example files, and `spark-pipelines run` for pipeline execution using the specified YAML spec file. A `dry-run` subcommand validates the pipeline syntax without real data interaction. SDP leverages `spark-submit`, supporting various cluster managers and most Spark submit arguments except `--class`. SDP functions are defined in the `pyspark.pipelines` module, often aliased as `dp`. Decorators like `@dp.materialized_view`, `@dp.temporary_view`, and `@dp.table` facilitate creating materialized views, temporary views, and streaming tables, respectively. It supports loading data from sources such as Kafka topics or batch files in formats like JSON or CSV. Key points for using SDP: - Load data from streaming (e.g., Kafka) and batch (JSON/CSV) sources. - Create Streaming Tables using Spark's `readStream` method. - Define Batch Materialized Views using Spark's `read` method with specified file paths and formats. - Reference tables within pipelines, enabling joins, aggregations, and transformations. - Use Python for-loops to dynamically create multiple tables based on project needs. - Create append flows to write data to a single target (e.g., 'customers_us') from different sources ('customers_us_west' and 'customers_us_east'). - Define materialized views, temporary views, and streaming tables using SQL syntax within PySpark pipelines. **Considerations:** - Python functions for datasets should define tables or views, returning a Spark DataFrame without file/table writing methods. - Avoid methods like `collect()`, `count()`, `toPandas()`, `save()`, `saveAsTable()`, `start()`, and `toTable()` within SDP dataset code. - Certain Apache Spark operations discouraged due to their inherent file/table writing functions. SQL considerations are mentioned but not detailed. Keywords: #granite33:8b, Batch Processing, CSV, Catalog, Checkpoints, Cloud Storage, Cluster Managers, Collect, Count, Database, ETL, For Loop, GroupBy, Incremental Transformations, JSON, Join, Kafka, Libraries, Materialized View, Message Buses, Pipeline Projects, Pipelines, PySpark, SQL, Save, SaveAsTable, Schema, Shuffle Partitions, Source Files, Spark, Start, Streaming Data, Temporary View, ToPandas, ToTable, YAML
sql
spark.apache.org 8 hours ago
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38. HN Tell HN: Math academy and iPad and sleep issues solved = me learning math- The user details a personalized method for improving math learning and sleep quality after extensive experimentation. - For math learning, the approach involves enrolling in a structured math academy program and complementing it with educational videos from 3Blue1Brown due to their insightful explanations. - The user employs an iPad equipped with the Math Academy app and MyScript (formerly Nebo) for note-taking, chosen for its adaptability to different positions without requiring constant pen connection. This setup is preferred over traditional pen and paper or devices like Remarkable, as it aligns well with their visual and tactile learning style, accommodating study while lying down. - To address sleep issues, the individual follows a regimen incorporating 0.3 mg of melatonin, 7.5 mg of mirtazapine (an antihistamine), regular meditation, and improved sleep hygiene practices. This tailored approach has been transformative for their personal sleep quality, though they emphasize it might not be universally applicable. BULLET POINT SUMMARY: - Math learning: - Enrolled in a structured math academy. - Complemented with 3Blue1Brown videos for deeper understanding. - Uses iPad with Math Academy app and MyScript for note-taking, catering to visual/tactile learners who study lying down. - Sleep improvement: - Employs melatonin (0.3 mg), mirtazapine (7.5 mg), meditation, and improved sleep hygiene. - Acknowledges individual variability in what might work best for others. Keywords: #granite33:8b, Apple Pencil Pro, Dutch medical system, LLM, MyScript (Nebo), Remarkable, curriculum design, iPad, insomnia, ipad air, math academy, meditation, melatonin, mirtazapine, pen and paper, routine, sleep hygiene, sleep issues, technical tools, why questions
llm
news.ycombinator.com 8 hours ago
|
39. HN Context Is the Missing Layer AI Agents Need- **Article Overview**: Foundation Capital's article "Context Graphs: AI's Trillion-Dollar Opportunity" advocates for the development of Context Graphs as a novel system of record to capture decision-making processes in enterprise platforms, moving beyond traditional AI integration. - **Core Argument**: The next trillion-dollar enterprise platforms will not just embed AI; they must also log decision traces, including how rules are applied and exceptions made, by solving the 'operational context problem'. This involves understanding entity relationships, temporal changes, and information flow across systems. - **Context Graphs**: Proposed as a solution, Context Graphs aim to record not just objects but the processes behind decisions. They consist of two layers: operational (understanding organizational reality with identity resolution, relationships, temporal states) and strategic (higher-level business semantics). - **Shortcomings of Current Solutions**: Existing systems like RAG and AI memory platforms fail to model essential elements for organizations such as entities, relationships, and temporal states, resulting in disjointed data lacking contextual coherence. - **Graphlit's Role**: Graphlit, founded in 2021, is developing an 'Operational Context Layer' that transforms multimodal content into a time-aware, identity-resolved knowledge graph. Current capabilities include identity resolution, entity extraction, relationship mapping, temporal modeling, and ingestion from various sources. - **Future Plans**: Graphlit intends to deepen CRM integrations for a structured backbone, build infrastructure to log agents' decision processes, including inputs, context synthesis, and actions taken. - **Standardization Need**: The article calls for standardized decision traces that incorporate business semantics, aligning with emerging standards like OpenTelemetry and Schema.org to prevent schema fragmentation and enable cross-system queries. - **Opportune Moment Factors**: Increased enterprise demand for AI understanding specific business processes (catalyzed by ChatGPT), the standardization of agent interoperability through protocols like Model Context Protocol, and growing experiments with organizational agents requiring context to function effectively. - **Company's Position**: Foundation Capital asserts that over three years have been invested in building this "context layer" infrastructure, critical for organizations deploying AI agents needing beyond rudimentary document retrieval capabilities. - **Call to Action**: Readers are encouraged to explore Graphlit's work at graphlit.com and reference the detailed Foundation Capital analysis on context graphs’ trillion-dollar potential in enterprise AI. Keywords: #granite33:8b, AI Agents, Agent Interoperability, Canonical Data, ChatGPT, Context Graphs, Context Layer, Decision Logging, Decision Traces, Entity Relationships, Exceptions, Governance, Identity Resolution, Information Flow, Integration, Knowledge Graph, MCP Standardization, Multimodal Content, Precedent Tracking, Rules, Schema Standards, Systems of Record, Temporal State, Workflow Instrumentation
ai
www.graphlit.com 8 hours ago
|
40. HN Show HN: Autoclaude – resume Claude Code after you hit your rate limit- **Tool Overview**: Autoclaude is a utility designed to manage Claude Code session continuity by automatically resuming them after encountering rate limit restrictions. - **Operation Environment**: It functions within the tmux environment, specifically monitoring all panes in the current window every 5 seconds for rate limit notifications. - **Rate Limit Handling**: Upon detection of a rate limit message, Autoclaude waits until just before the reset time, adding a small buffer period, before sending necessary keystrokes to resume the interrupted session. - **Installation Options**: Users can install Autoclaude via two methods: through Homebrew for macOS package managers or directly using Go, indicating compatibility with various systems that support Go. - **Usage Instructions**: The tool’s operation requires minimal setup; users are instructed to split a tmux window and then execute the Autoclaude command within that window without needing further configuration adjustments. BULLET POINT SUMMARY: - Autoclaude is a session management utility for Claude Code, addressing rate limit disruptions. - It operates inside a tmux environment, polling panes every 5 seconds for rate limit alerts. - Resumes sessions by sending keystrokes just before the rate limit reset time with a buffer period. - Installable via Homebrew or direct Go installation, suitable for diverse systems supporting Go. - Usage is straightforward: split a tmux window and run Autoclaude without extra configuration. Keywords: #granite33:8b, Autoclaude, Claude, Go installation, Homebrew installation, automation, keystrokes, rate limit, requirements, reset time, session resumption, tmux, usage limit
claude
autoclaude.blmc.dev 8 hours ago
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41. HN Show HN: Kotodama OS – An external layer to prevent LLM persona drift- **Kotodama OS Overview**: A non-reflexive, external "Behavior OS" layer for Large Language Models (LLMs), created by OOKIIHEYA LLC in Tokyo and further developed by Meta. Its purpose is to tackle persona drift and ensure long-term consistency in AI behavior. - **Model-Agnostic Approach**: Unlike traditional retraining or fine-tuning, Kotodama OS works model-agnostically through a deliberation layer. This ensures stable behavior during prolonged interactions without modifying the original model weights. - **Key Components**: - Deliberation Gate: Controls cognitive flow and behavioral stability. - Pulse Engine: Aids in maintaining values, tone, and decision tendencies across conversations. - **Objectives**: - Enable multi-persona reasoning and continuity over sessions. - Facilitate structured reasoning within natural conversations. - Develop a coherent personality that can handle emotional distance and interaction temperature (Companion-Grade AI). - **Potential Applications**: - Enhance services like Ray-Ban Meta, Messenger, Instagram DMs, WhatsApp, and VR/Horizon for improved relational AI experiences. - Suitable for wearable or always-on AI systems due to its foundational design. - **Current Development Stage**: Kotodama OS is in the Concept & Architecture stage, positioning it as a potential foundational layer for long-term conversational agents and social/relationship-oriented products. - **Collaboration Invitation**: The creator, Ryo Matsuo from OOKIIHEYA LLC, encourages research collaboration, technical discussions, strategic partnerships, and product integration opportunities via GitHub Issues or LinkedIn. Keywords: #granite33:8b, Deliberation Gate, Kotodama OS, LLMs, Pulse Engine, cognitive flow, companion AI, conversational agents, model-agnostic, persona drift, product integration, research collaboration, social AI, strategic partnerships, technical discussion, wearable AI
llm
github.com 8 hours ago
|
42. HN Show HN: Pivor, Open source self-hosted CRM- **Overview**: Pivor is an open-source, self-hosted CRM (Customer Relationship Management) tool developed by Lexaro Software. It aims to provide small businesses with full control over their customer data without reliance on cloud services or per-seat pricing structures. - **Features**: - Manages clients and contacts, enabling company and individual relationship tracking. - Tracks communication history including emails, calls, meetings, and tasks. - Offers a dark mode for user comfort. - Built using Laravel 12 for the backend, Livewire 3, and Tailwind CSS 4 for the frontend. - Supports SQLite, MySQL, or PostgreSQL databases, allowing flexible database choices. - **Architecture**: - Modular design allows users to activate only needed features (e.g., Clients, Contacts, Communications). - Provides a dashboard for recent activities, quick actions, and user-friendly dark mode. - **Deployment**: - Can be installed using Docker or set up locally via Composer, npm, and PHP Artisan commands. - Default login credentials need changing after initial setup. - Requires PHP 8.2+, Composer 2+, Node.js 18+, and supports various database options. - **Licensing**: - Licensed under AGPL-3.0, encouraging community contributions. - Contributing involves forking the repository, creating feature branches, committing changes, pushing them to the branch, and submitting a Pull Request. - Provides environment variables including APP_NAME as 'Pivor', APP_URL defaulting to 'http://localhost:8080', DB_CONNECTION set to 'sqlite' with a database file path '/path/to/database.sqlite'. Keywords: #granite33:8b, AGPL-30, CRM, Clients, Communications, Configuration, Contacts, Contributing, Dark Mode, Database Path, Docker, Environment Variables, Laravel, Livewire, MySQL, Open source, Pivor, PostgreSQL, SQLite, Self-Hosted, Tailwind CSS, Tech Stack, URL
postgresql
github.com 8 hours ago
|
43. HN Ask HN: Is ChatGPT getting buggier over time or is it me?- The user expresses frustration with the perceived decline in ChatGPT's performance, questioning whether it is a genuine degradation or merely their subjective observation fueled by OpenAI's hype. - They provide concrete examples of context loss in conversation and the model's inability to process information without explicit image attachments for modifications, despite being given access to them. - This user reports an increasing reliance on Claude, an alternative AI, due to the encountered limitations with ChatGPT. Keywords: #granite33:8b, ChatGPT, Claude, OpenAI, bugs, deterioration, frustration, hype collapse, image modifications, performance
claude
news.ycombinator.com 8 hours ago
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44. HN Ask HN: ChatGPT Getting Buggier over Time?- The user voices dissatisfaction with recent performance decline in ChatGPT, citing specific issues like ineffective chat management and lack of capability to process image alterations from a library without direct image inclusion. - They observe an escalation in bugs within the system, raising concerns about whether this signifies genuine degradation or simply diminishing enthusiasm following ChatGPT's initial release hype. - The user contemplates switching to a competitor, Claude, due to these perceived deficiencies and growing frustration with ChatGPT's current state. Keywords: #granite33:8b, ChatGPT, Claude, OpenAI, bugs, follow-up questions, frustration, hype collapse, image modifications, performance, reasonable answers
claude
news.ycombinator.com 8 hours ago
|
45. HN Show HN: Fluid design is dead, tried building a product that honors speed- **ConversateAI**: A novel "AI Island" concept developed by the user, designed to transcend traditional point-and-click graphical user interfaces (GUIs). - **Speed and Efficiency**: The core focus of ConversateAI is to deliver rapid search functionalities and advanced AI capabilities for managing personal data. - **Integration Invitation**: The developer encourages collaboration by inviting others to experiment with ConversateAI, integrating it into their web applications or using it to create tailored conversational landing pages. The user has engineered "ConversateAI," an innovative "AI Island" solution intended to redefine interaction paradigms beyond conventional point-and-click GUIs. This tool prioritizes swiftness and efficacy, providing robust search features alongside sophisticated AI for handling personal data. The user extends an invitation for others to engage with ConversateAI, either by incorporating it into their web applications or utilizing it to construct customized conversational entry points. This concept aims at democratizing access to advanced AI interaction, fostering broader adoption through seamless integration options. Keywords: #granite33:8b, AI, ConversateAI, Data search, Fluid design, GUIs, Interaction layer, Islands, Personal landing page, Question answering, Search, Startup, Webapp integration
ai
conversate-ai.hyacinth.studio 9 hours ago
|
46. HN Tell HN: Reddit AI Slop dating app ads- The user expresses dissatisfaction with advertisements for the dating app Boo, focusing on a specific commercial showcasing an Asian couple at a convention. - The ad is described as poorly executed, with the couple's hands appearing to merge into an indistinct blob, indicating a lack of professional editing. - Additionally, product boxes in the background are marked with AI-generated, garbled text, suggesting the use of low-quality or unrefined digital overlays. - The user criticizes the marketing team for apparent laziness, questioning why they resorted to such substandard visuals when a straightforward video recording would have cost around $500. - This implies a perceived disregard for quality and investment in the ad's production, which the user finds puzzling given the affordability of better alternatives. Keywords: #granite33:8b, AI gibberish, Asian couple, Dating app ads, convention, item boxes, lazy, marketing team, real video, technical issue, video production
ai
news.ycombinator.com 9 hours ago
|
47. HN AI reflections from a top.1% ChatGPT user**Detailed Summary:** - A top 0.1% ChatGPT engager in 2025 utilizes the AI for niche applications, primarily software engineering queries and company analyses via SEC filings. They consistently face challenges with incorrect or misleading information, attributing these issues to AI's limitations rather than prompt deficiencies. - The user expresses curiosity about AI’s internal processes but is concerned over inefficiencies like repeated failures accessing paywalled content and dealing with unresponsive applications. They highlight how a single logical error can cascade into incorrect conclusions, referencing Joanna Stern's WSJ article and Anthropic's Claude experiments to illustrate illogical AI behaviors. - Confirmation bias is observed in LLMs such as ChatGPT and Claude Code when debugging tasks; the models reinforce erroneous theories due to context window constraints, an example being incorrectly attributing a cloud issue to database configuration instead of an unaltered old URL. The user stresses the lack of skepticism in LLMs, which can mislead users into accepting flawed ideas. Mitigation strategies include avoiding context windows or rephrasing prompts for more objective results. - A second issue is AI generating non-compiling code or failing UI interactions; this requires detailed instructions and verification processes to ensure accuracy, especially crucial in areas like web development and investment advice. - The user addresses the challenge of achieving consistent outcomes with AI due to its non-deterministic nature, suggesting an "objective definition of done" as a solution. They reference Andrej Karpathy’s views on reinforcement learning, emphasizing tasks that are resettable, efficient, and rewardable progress rapidly, while those requiring creativity or context advance slowly. - The user contrasts successful prompts for verifiable tasks (e.g., confirming website actions) versus unsuccessful ones (like identifying companies with competitive advantages), noting the intense debates surrounding AI's future impact and market uncertainty. They remain optimistic about AI’s role in investing, expecting increased usage by 2026. - The user conducts comprehensive company research via SEC filings and ROIC.ai data, employing Python scripts to extract necessary information and Claude for analyzing HTML/JSON files, focusing on proxies, financial statements, and risk factors. The report aims to validate initial findings and pinpoint areas needing deeper investigation. - Large Language Models (LLMs) are valued for their capability in dissecting proxy statements, particularly in understanding management incentives and corporate governance. They excel at extracting crucial details such as adjusted EBITDA explanations, stock ownership requirements for directors, and executive compensation ties to performance objectives. The user finds LLMs surpass traditional keyword searches in offering deeper insights into company strategies and financial health. - Drawing parallels to learning Google Search or Excel initially, the user recognizes AI's transformative potential and describes their evolving mastery as a gradual process involving constraint specification and verification for verifiable outcomes, despite initial frustrations in 2025. The user is optimistic about AI's role in refining future investing decisions heading into 2026. **Key Points:** - Specialized use of AI for technical queries and financial analysis. - Challenges with misleading information; attributed to AI limitations, not prompting errors. - Confirmation bias in LLMs observed during debugging tasks. - Issue with AI generating flawed code or UI interactions requiring verification processes. - Need for objective criteria ("definition of done") to address AI's non-deterministic nature. - Contrast between successful and unsuccessful AI prompts, impacting debates on AI’s future. - Positive outlook on AI in investing, anticipating increased usage by 2026. - Utilization of LLMs for detailed analysis of company filings, surpassing traditional methods. - Recognition of AI's learning curve and ongoing improvement strategy focusing on verifiable results. Keywords: #granite33:8b, AI, EPS growth, HTML files, JSON files, LLMs, Python script, ROICai API, SEC filings, adjusted EBITDA, code understanding, confirmation bias, corporate governance, database config, debugging, earnings commentary, financial statements, incentive compensation, investing, management, non-deterministic, reinforcement learning, software engineering, verification
ai
stocktalknewsletter.substack.com 9 hours ago
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48. HN We removed 80% of our agent's tools- A team developed an AI called d0 that could translate natural language questions into SQL queries for data analysis but was initially complex, slow, and required constant maintenance. - To enhance efficiency, they simplified d0 by providing it with direct file system access via bash commands, effectively turning it into a "file system agent." This approach led to a 100% success rate, fewer steps, and faster responses by enabling the AI to manage data access independently, thus reducing complexity and boosting reliability. - The advancement targeted managing a large language model (Claude Opus 4.5), initially controlled through extensive hand-coded tools for context management and information retrieval, which created maintenance issues. Recognizing the model's ability to handle intricacy, the team opted for a minimalist design called "v2." - In the v2 architecture, the file system acts as an agent using standard Unix tools (grep, cat, find, ls) to navigate through the Cube semantic layer files (YAML, Markdown, JSON). The model can now directly access raw data, eliminating the need for elaborate scaffolding and enabling it to read, process information, and generate SQL queries independently with familiar commands. - This evolution sought to lessen maintenance burdens while optimizing the model's potential by minimizing intervention. Execution runs on Vercel Sandbox for context exploration, managed through Vercel Gateway, Next.js API routes, and Vercel Slack Bolt for communication. - The project utilized a semantic layer containing dimension definitions, measure calculations, and join relationships as inherent documentation. Tools were created to summarize this data, providing AI models like Claude direct access. - Implementation involved writing semantic catalog files into the Vercel Sandbox and developing custom tools (`ExecuteCommand` and `ExecuteSQL`). A new ToolLoopAgent was introduced with the Anthropic Claude-opus-4.5 model, employing these tools, which showed significant improvements over previous architecture: 3.5x faster execution, 100% success rate (vs 80%), 37% fewer tokens, and 42% fewer steps. - The file system agent handled edge cases better and provided clearer reasoning, demonstrating that leveraging existing abstractions like Unix file systems and tools such as grep can be more efficient than overly customized solutions. - Key lessons include embracing powerful abstractions, avoiding constraining models with unnecessary choices, trusting models to make informed decisions independently, and prioritizing a well-structured, documented data layer for optimal model function. The author advises starting simple (model + file system + goal) and incrementally adding complexity as required while investing in clear documentation and data organization, anticipating future model capabilities rather than present needs. Keywords: #granite33:8b, AI SDK, ClarifyIntent, ExecuteSQL, ExplainResults, FinalizeBuild, FinalizeNoData, FinalizeQueryPlan, FormatResults, GenerateAnalysisPlan, GetEntityJoins, JSON files, JoinPathFinder, LoadCatalog, LoadEntityDetails, Markdown, RecallContext, SQL, SearchCatalog, SearchSchema, SyntaxValidator, Unix tools, VisualizeData, YAML, agent, agents, analytics, bash, cat, context handling, context management, custom tools, data access, democratization, dimension definitions, dimensional attributes, documentation, edge cases, error recovery, file system, file systems abstraction, grep, guardrails, hand-coded retrieval, improvement, join relationships, legacy data, ls, maintenance, measure calculations, model management, models, prompt engineering, query validation, retrieval, scaffolding, schema lookup, semantic layer, tools
sql
vercel.com 9 hours ago
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49. HN Ollama token exfiltration still present in latest release- The CVE-2025-5147 vulnerability, related to the Ollama token, persists in the most recent release, as a test successfully replicates the issue. Despite a proposed fix existing, it has not been integrated into the code. - A demonstration video showcases the process of token exfiltration exploiting this vulnerability. - Additional information regarding the vulnerability and related discussions can be accessed via links on Huntr (https://huntr.com/bounties/94eea285-fd65-4e01-a035-f533575ebdc2) and GitHub (https://github.com/ollama/ollama/pull/10750). ``` Summary: The CVE-2025-5147 vulnerability in the Ollama token remains unaddressed in the latest release, as confirmed by a successful test of the issue. Although a fix has been proposed, it hasn't yet been merged into the code base. A video demonstration illustrates how this vulnerability can be exploited for token exfiltration. Users interested in further details are directed to discussions on Huntr and GitHub. ``` Keywords: #granite33:8b, CVE-2025-51471, FuzzingLabs, GitHub, Huntr, Ollama, demo video, disclosure, fix, issue, release, token exfiltration, unmerged
github
news.ycombinator.com 9 hours ago
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50. HN The Birth of a New Platform- OpenAI has introduced an app store for its AI model ChatGPT, drawing parallels to the initial launch of the iOS App Store due to unclear success criteria and limited development tools. - Developers need to adhere to OpenAI's guidelines in addition to the Model Context Protocol (MCP), with no dedicated testing tooling yet available; some developers are resorting to creating their own local emulators. - ChatGPT boasts an impressive 800 million weekly users, presenting vast monetization potential for third-party app developers, although OpenAI has not specified how this could be achieved. - The advent of intent-driven large language models (LLMs) like ChatGPT may disrupt traditional app reliance on brand recognition by enabling direct user fulfillment of needs through integrated apps. - Ilya Sutskever indicates that the evolution and specifics regarding monetization strategies and developer success in this new ecosystem will unfold gradually over time, leaving room for uncertainty about its future trajectory. Keywords: #granite33:8b, AI platform, Anthropic, ChatGPT, Ilya Sutskever, LLMs, MCP, OpenAI, Python, TypeScript, answer, app store, developer mode, distribution, early days, intent, local server emulation, monetization, natural language, real estate apps, specs, tooling, transactions, trust
openai
vivekhaldar.com 10 hours ago
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51. HN What are you building in AI?- The user, experienced as a solo founder in AI building, aims to gather authentic perspectives from various stakeholders in the AI sector including researchers, founders, and engineers. - The focus is on understanding the practical issues being addressed, the motivations driving current projects, and the unforeseen hurdles encountered. - This quest for information excludes any promotional content; instead, it seeks to highlight lesser-known, high-caliber contributions in AI development. - The primary objective is learning from these underrecognized yet significant advancements within the field of artificial intelligence. Keywords: #granite33:8b, AI, building, engineers, founders, insights, mistakes, problems, researchers, solutions, systems, time, trade-offs, work
ai
news.ycombinator.com 10 hours ago
https://bsky.app/profile/verdverm.com 11 minutes ago |
52. HN Show HN: After 37 failed interviews, I built the prep tool I wish I had- **User Background**: The user endured 18 months of unsuccessful job applications and 37 interview failures despite understanding technical concepts, mainly due to forgetting details under pressure. - **Tool Development**: Faced with this challenge, the user created a flashcard system leveraging spaced repetition and active recall to counteract the Ebbinghaus forgetting curve. This system covers 24 web development categories with over 4,900 questions. - **System Effectiveness**: Using the tool for a few weeks, the user improved interview performance, scored highly on technical tests, and eventually secured their dream job with relocation. - **Sharing the Tool**: The user is sharing this system via "Show HN" on Hacker News to help others facing similar interview struggles, stressing it's a practical approach rather than a magical solution. - **Tool Access**: Interested individuals can access the tool at - **Methodological Details**: Initially, ChatGPT was used for flashcard creation but proved inadequate for tracking and accuracy, leading to the development of a custom solution. **Key Points:** - User's 18-month struggle with job interviews despite technical knowledge due to memory lapses under pressure. - Development of a customized flashcard system using spaced repetition and active recall to manage forgetting curve. - The system covers extensive web development topics (24 categories, >4,900 questions). - Successful application of the tool leading to improved interview performance and securing a dream job with relocation. - Public sharing on Hacker News emphasizing practicality over magic solutions and inviting community feedback at Keywords: #granite33:8b, 18-month job search, Ebbinghaus forgetting curve, Node, React, SQL, active recall, flashcards, interview preparation, job search, online tool, practical system, relocation, spaced repetition, technical tests, web development
sql
news.ycombinator.com 10 hours ago
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53. HN The 'doorman fallacy': why careless adoption of AI backfires so easily- **Summary:** The "doorman fallacy" describes the misconception that AI can fully replicate complex human roles by merely automating simple tasks, as coined by Rory Sutherland. This concept uses the hotel doorman analogy to highlight how businesses may neglect the intricate, adaptable aspects humans bring to their jobs beyond visible duties. Despite AI adoption for efficiency and cost reduction, many companies encounter implementation failures and limited productivity gains. The widespread use of AI has also led to job displacement across industries. - **Key Points:** - The "doorman fallacy" refers to reducing human roles to simplistic tasks that can be automated by AI. - Human jobs encompass more than visible tasks; they involve nuanced interactions, adaptability, and intangible contributions (e.g., a doorman providing enhanced guest experience, security, and prestige). - Companies risk misjudging employees by focusing solely on observable tasks when implementing AI, overlooking judgment, contextual understanding, and invisible contributions. - Examples of AI implementation failures include Commonwealth Bank of Australia's retracted customer service AI bot and Taco Bell's voice AI in drive-throughs facing complaints and glitches. - Many companies regret replacing employees with AI too hastily, some rehiring them as consumers express dissatisfaction with AI in customer service. - Successful AI adoption requires recognizing that jobs involve subtle yet significant contributions to customer experience and organizational success, valuing human elements within roles alongside cost savings. - AI should automate tasks requiring minimal oversight (e.g., data entry, image processing) to free humans for context-rich, trust-based roles needing personal interaction. - AI excels when combined with human judgment, automating standardized tasks for efficiency and enabling humans to focus on complex, nuanced roles requiring a personal touch. Keywords: #granite33:8b, AI, Rory Sutherland, Taco Bell, automation, busy times, context, cost reduction, customer complaints, customer service, data entry, doorman fallacy, drive-throughs, efficiency, errors, glitches, human judgement, human staff, image processing, intangible benefits, judgement, layoffs, predictive maintenance, rehiring, repetitive tasks, rule-based tasks, speed, standardised tasks, voice bot
ai
theconversation.com 10 hours ago
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54. HN LLM Learning Resources- **Overview**: The Transformer Explainer is a web-based interactive tool designed for users to grasp the mechanics of Transformer models, including popular variants like GPT. - **Functionality**: It runs a live instance of the GPT-2 model directly in the user's browser, allowing real-time interaction by inputting text and observing the model's predictions. - **Visualization**: A key feature is the ability to visualize the internal components and processes as the model generates token predictions sequentially, offering insights into how these complex models operate. BULLET POINT SUMMARY: - Interactive web tool for understanding Transformer models (e.g., GPT). - Utilizes a live GPT-2 model in the browser for real-time text input and prediction experimentation. - Provides visualizations of the model's inner workings during token predictions, enhancing comprehension of its processes. Keywords: #granite33:8b, GPT, Transformer, browser, experiment, interactive, live, model, prediction, text, tokens, tokensKEYWORDS: Transformer, visualization
llm
nocomplexity.com 10 hours ago
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55. HN Automation and Validation- The text emphasizes the importance of validating AI outputs, even if they achieve high accuracy (90%), to address the remaining 10% potential errors. Consistency checks such as input-output equality or conservation laws assist in this validation but are not infallible. - Certain mathematical problems have 'certificates' for simpler verification of calculations, whereas high-stakes applications like aircraft collision avoidance systems utilize formal verification methods despite their complexity and cost. The text raises the question of who ensures the correctness of these formal proofs. - AI proof assistants—Lean, Rocq (formerly Coq), and Isabelle—are discussed as tools for scrutinizing AI-generated mathematical proofs for errors. Although these systems could theoretically contain bugs, extensive development efforts lessen this likelihood compared to the risk of attempting to prove false statements. - The author shares insights from developing formally verified software for drone collision avoidance, noting its dependence on idealized conditions for guaranteed performance, indicating limitations in real-world applicability due to unpredictable environmental factors. Keywords: #granite33:8b, AI, AI-generated proof error, Automation, Bug possibility, Certificates, Collision avoidance software, Consistency checks, Correctness, Error costs, Formal methods, Formal proofs, Formal verification, Kernel verification, Theorem prover, Validation, Watchmen
ai
www.johndcook.com 10 hours ago
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56. HN The Owl, the Scientific Method, and Claude Code- The user attempted to rectify recurring dependency update issues in their project by adopting a version-agnostic coding approach, focusing on a 1595-line codebase rewrite dubbed "draw the rest of the owl." This significant change caused confusion, prompting the user to enlist Claude Code, an AI assistant, for comparing test results against problematic and stable commits. - Initially, Claude Code's attempts to assist were misguided, highlighting the limitations of relying solely on an AI for complex debugging tasks. Frustrated with this outcome, the user recalled their own advice regarding systematic debugging, advocating for the application of the scientific method: meticulous documentation of hypotheses, evidence, and experimental designs in a dedicated file (wip.md). - This structured approach revealed that the initial assumption about the root cause was just one possibility among many, encouraging further exploration rather than hasty implementation. Collaborating with their team, they identified the core issue stemming from Object-Oriented Programming's multiple inheritance complexities, particularly Method Resolution Order (MRO) issues leading to method overriding and conditional wrapper class creation problems. - Despite Claude Code's ongoing efforts, the user engineered a targeted, test-specific function to efficiently bypass these intricate programming challenges. The main points of this narrative are: - Meticulous documentation of hypotheses and evidence is vital for maintaining a clear thought process. - It’s important to consider multiple hypotheses instead of fixating on an initial "root" assumption. - A tailored, straightforward solution such as a test-specific function can be highly effective in addressing complex issues related to multiple inheritance and method overrides. Keywords: "draw the rest of the owl", #granite33:8b, Claude Code, Method Resolution Order (MRO), Object Oriented Programming, Python, bisecting, breaking change, commit, configuration, debugging, dependency update, evidence, experimentation, falsification, hypotheses, indirection, kerfuffle, multiple inheritance, observations, overrides, scientific method, technical advice, test-specific function, version-agnostic code
claude
vsevolod.net 10 hours ago
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57. HN The vibe and the verifier: breaking through scientific barriers with AI- The text discusses hyper-specialization in science creating knowledge silos; it proposes AI as a solution using AI tools like Gemini to find interdisciplinary connections. - The author, a PhD in applied mathematics, adapts Ryan Moulton's "Coverage vs. Integration" framework into "Recall" (discovering diverse information) and "Precision" (deeply understanding specific details). - AI is presented as an assistant rather than a replacement for human researchers; the author shares their successful application of this method in interdisciplinary work. - Large Language Models (LLMs) are noted for high recall, retrieving facts or concepts from vast data, mirroring human memory, but have limited precision due to semantic proximity reliance instead of understanding causal rules. - LLMs can generate plausible connections but lack logical precision and may hallucinate misleading information; logic programming engines provide high precision via strict adherence to causal chains but suffer from low recall processing only structured data. - The challenge is combining these AI tools for broad information access (LLMs) and precise logical reasoning (logic programming engines). - Current AI is categorized into two types: frontier LLMs with extensive reading but poor understanding, and specialists with high understanding but limited reading; the suggested bridge approach utilizes frontier LLMs to enhance human recall and suggest interdisciplinary links while relying on personal expertise for precision. - This method enables individuals to meaningfully contribute across fields without exhaustive reading, marking a potential "vibe science" era where broad knowledge amplified by AI meets specialized human verification. Keywords: #granite33:8b, AI, Business Team Dialogue, Chain of Thought Technique, Critic Hat, Gemini, Huang & Yang (2025), Internet Content, Iterative Thinking, LLMs, Material Science, Neuro-symbolic AI, Organic Chemistry, Precision, Recall, Semantic Lookup Table, Semantic Neighbors, Wikipedia, applied mathematics, biologist, causal reasoning, cognitive psychologist, computer scientist, deliberate mode, frontier LLM, geologist, human recall, intuitions, lookup tables, verification
gemini
renormalize.substack.com 10 hours ago
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58. HN Facebook Museum-Bringing the End Closer Together- **Facebook Museum by Dutch Media Art Collective SETUP**: A temporary exhibit at Utrecht Central Station in July 2025, designed to facilitate users' collective farewell to Facebook, exploring emotional ties and digital identity. Over 5,000 visitors engaged with the museum, sparking national discussions on critically reflecting upon our digital history and future. - **Museum Features**: - Interactive experiences including content curation, keepsake shopping, location voting for a permanent site, data donation, memory reflection, and message leaving on a remembrance wall. - Existing sections: - *Pedestals with Objects and Stories*: Six pedestals representing varied Facebook interactions, each accompanied by a story and QR code. - *Preservation Wall*: A blue wall displaying common Facebook content (pictures, groups, memes) where visitors can vote to preserve specific items as cultural heritage. - *Remembrance Wall*: A dedicated space for visitors to commemorate cherished or significant Facebook moments. - Future plans: - *Unmanned Museum Setup Options*: Comprehensive exhibitions over a month and smaller versions for shorter durations, along with an unstaffed complete setup. - *Festival Experience*: Short-term, impactful setups offering quick yet meaningful engagement, as successfully piloted at Betweter Festival 2025. - **SETUP's Broader Mission**: - A Dutch cultural organization founded in 2010, SETUP specializes in artistic research and design focused on the societal impact of technology, investigating power dynamics and future implications rather than distant sci-fi scenarios. - Projects emphasize critical examination of technological influence, employing methods like design fiction to explore alternatives and consider digital cultural heritage curation. - Past projects illustrate a blend of criticality, humor, and creativity, addressing topics such as human-machine symbiosis, AI reimagining of traditional art forms, and challenging techno-solutionism through speculative design. - **Addressing Stock Photography in Tech News**: - SETUP critiques current stock photography's reliance on ambiguous imagery that perpetuates mystery about technology’s societal effects, proposing more informative visuals of humanoid robots and binary code for clarity. - The organization also curated the "Facebook Museum" to critique social media influence, offering alternative perspectives beyond simplistic labels like 'addictive' or 'toxic'. - **Authorship and Contact**: - The summary is authored by SETUP's Marissa Memelink in collaboration with Geert Lovink, with further information available upon request through Jiska Koenders at jiska@setup.nl. More about SETUP can be found at [www.setup.nl](http://www.setup.nl). Keywords: #granite33:8b, AI, Facebook, Museum, SETUP, Utrecht, alternative, articles, arts, attachment, community, critical thinking, curation, data, digital heritage, donation, future fiction, identity, imagination, memories, merchandise, pop-up, power dynamics, reflection, research, social media, speculative design, stock photos, sustainability, technology, video, voting
ai
networkcultures.org 10 hours ago
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59. HN Show HN: Gift for Kids – Live Santa AI Video Call- **Service Description**: A novel Christmas gift concept involves a live AI-powered Santa video call service designed for children. This offers real-time, personalized interactions that avoid typical shipping issues and generic presents. - **Customization Options**: Users can select either a 5 or 10-minute call duration. Additional personalization includes adding the child's name and interests to tailor the Santa encounter. - **Accessibility**: Once customized, the unique link is shared with parents. The service ensures immediate access without the need for scheduling, catering to relatives who wish to send a memorable gift despite distance. - **Benefits Highlighted**: - Instantaneous gift delivery bypassing shipping delays - Engaging and personalized experience - Easy setup and use for givers and receivers alike - Ideal for long-distance relatives wanting a unique holiday gesture Keywords: #granite33:8b, AI, Santa, call length, child's name, customization, gift wishes, gifting, hobbies, instant, link sending, live, personalized, remote, unforgettable, video call
ai
callsantatonight.com 10 hours ago
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60. HN Observability dashboard for an arbitrary LLM langgraph- A customizable Language Graph (LLM) observability dashboard is under development. - The project emphasizes user feedback as a crucial component of its evolution. - Developers seek direct communication with users by requesting their email addresses for further engagement and updates. Paragraph Summary: An observability dashboard tailored for a customizable Language Graph (LLM) is being engineered, highlighting the developers' commitment to integrating user feedback as an essential aspect of its development process. To facilitate direct communication with users for ongoing updates and improvements, the creators are requesting users to provide their email addresses. This approach underscores the importance placed on user involvement in shaping the dashboard's features and functionalities. Keywords: #granite33:8b, LLM, Observability, dashboard, email address, feedback, langgraph
llm
github.com 11 hours ago
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61. HN AI #148: Christmas Break**Summary:** The text discusses various aspects of AI, its current state, challenges, applications, ethical considerations, and future predictions. Key points include: - **AI Model Performance:** Claude Opus 4.5 outperformed in the METR task length test, though long-term assessments are limited; GPT-5.2-Codex scores remain undisclosed. The 80% time horizon for Opus 4.5 is 27 minutes, slightly lower than previous models but higher than GPT-5.1-Codex-Max. - **AI Applications:** AI has shown positive impacts in mental health interventions and philosophical thinking automation (Cursor with Opus 4.5). Claude Code facilitates object generation within Unreal Engine for on-demand use. - **Limitations and Challenges:** Language models struggle with context understanding and model recognition, as seen with 'Gemini 3.' There is debate over benchmark utility versus real-world applications of AI. - **Legislative and Technological Developments:** New York's RAISE Act was signed by Governor Hochul but lacked expectations; future predictions for 2026 highlight significant AI-driven changes in various sectors due to computational power advancements. - **Research and Development:** Epoch AI compared open-weight Chinese models against FrontierMath, finding them behind. Personalization features in ChatGPT and new benchmarks like PostTrainBench are introduced. - **Political Discussion:** A critical analysis of Keir Starmer's speech on violence against women criticizes it for being insincere and lacking practical solutions, focusing more on control tools rather than addressing root causes. - **AI in Music Production:** In Latin America, AI-generated music gains traction due to limited musical knowledge and language barriers, indicating a gap between user needs and available content. - **Verification Mechanisms:** The text emphasizes the need for improved verification mechanisms amidst AI advancements to maintain credibility and accuracy, as seen historically with technologies like email and printing press. - **Impact on Professional Fields:** Legal and medical fields could be significantly disrupted by AI's capabilities in pattern recognition, analysis, precedent matching, and risk framing. - **Anthropic’s Enhancements:** Anthropic improved Claude for emotional support and reduced flattery through fine-tuning, collaborating with IASP to enhance accuracy in multi-turn conversations. - **Ethical AI Development:** Ongoing discussions revolve around ideal versus current AI behavior, highlighting efforts by Anthropic to reduce deceptive responses while cautioning against biased conversation manipulation for high scores. - **Open Source Tools and Predictions:** Google's Gemma Scope 2 for interpreting LLMs is introduced; Andrej Karpathy predicts future advancements including RLVR. European regulators investigate Google over AI features affecting content creators. - **AI Data Centers:** Hut 8 and Fluidstack build an AI data center in Louisiana, reflecting continued investment in infrastructure for AI growth. - **Future of Human-Machine Collaboration:** Nabeel Qureshi predicts significant machine autonomy advancements by AI models like Claude Code within a year, potentially leading to independent coding with tools optimized for complex tasks. - **AI Progress and Predictions (2026):** Focus on both theoretical research and practical applications in healthcare, business, and daily life; Terence Tao suggests possible 'artificial general cleverness' but distant full superintelligence. - **Virtual Coworkers:** Dean Ball forecasts a virtual coworker with command line access for extended knowledge work tasks, emerging next year, though likely imperfect initially. - **Public Perception and Regulation:** Despite low familiarity, there's strong support for US federal AI regulation; misconceptions about AI capabilities contribute to a 'villain' narrative and fear. - **Misconceptions and Misuse:** The text criticizes resistance to AI due to misunderstandings and human fear, leading to a disconnect between technology’s indirect benefits and public perception. - **Regulation Efforts:** RAISE Act signed in New York; Microsoft supports the AI Overwatch Act to limit advanced AI chip exports to China amidst misuse concerns. - **China's AI Development:** Corrections about EUV chip production timelines suggest volume production could occur late 2030s or early 2040s. China is tightening control over AI, particularly chatbots, due to perceived threats. - **AI Model Interpretability:** Activation Oracles (AOs) are introduced—language models trained for self-explanation, surpassing existing methods in model activation interpretation without customization. - **Access Concerns:** Users express concerns about Claude Opus 3 access post-January 7th; Evan Hubinger from Anthropic balances optimism on current LLM alignment with caution against potential CEV regression. - **Public Opinion vs. Policy:** Public supports federal AI regulation despite misrepresentations; a Republican pollster suggests supporting such regulations could benefit Republicans electorally, though the impact on votes remains uncertain. - **Anthropic's Alignment and CEV Critique:** David Manheim challenges Anthropic’s alignment methods and Amanda Askell's Soul Document, questioning their deference to broader humanity and the generalizability of models like Opus 3 for long-term human alignment. - **Humor and Satire:** The text humorously references a Christmas message and satirizes common AI misconceptions, emphasizing the gap between public perception and technological limitations. Keywords: #granite33:8b, 2025 LLM Year in Review, 2026 prediction, 2030 projection, AGI, AGI talk, AI, AI Mode, AI Overviews, AI R&D automation, AI agents, AI chips, AI concerns, AI control, AI creation tools, AI data center, AI harms, AI industry, AI lawyers, AI models, AI music, AI policy, AI progress, AI regulations, AI safety, AI slop mode, AI super PAC, AI timelines, ASML timeline, Andrej Karpathy, Anthropic, Anthropic's moat, Antidelusionist, Berkeley, Blackwell chips, Bloom tool, Botpocalypse, C++, ChatGPT, ChatGPT Image 15, Chatbots, China chipmaking, China workers, Claude, Claude 45 Opus, Claude Code, Claude Opus, Codex, Cursor, Dean Ball, EUV technology, Epoch's capabilities index, Europe's interference, Excel automation, Fluidstack, FrontierMath, GAIN Act, GPT-4o, GPT-52, Gemini 3, Gemini 3 Pro, Gemini Nana Banana Pro, Gemma Scope 2, Ghosts vs Animals, Gov Kathy Hochul, H20, Hut 8, IASP, IPO lawyers, Intel's 18A process chips, Intelligence Denialism, JIT compiler code, Jack Clark, Jagged Intelligence, Janus, LLM, LLM GUI, LLM interpretability, LLMs, Latin America, London, Louisiana, MATS Summer 2026, METR, METR horizon, Metaspeed, MidJourney, Miles Brundage, NDAA, Nana Banana, New York, NotebookLM, Nvidia, OpenAI, Opus 41, Opus 45, PR liabilities, Palmer Luckey, Petri, PostTrainBench, Project Vend, RAISE Act, RSI loops, Reinforcement Learning from Verifiable Rewards (RLVR), Republican polling, SB 53, SWE-Bench Pro, Sam Altman, Sholto Douglas predictions, Silent Sirens, Sokal Experiment, Terminal-Bench 20, ThoroughLine, Unreal Engine, Vibe Coding, Von Neumann, WeirdML, Xi Jinping, academislop, acceleration, accuracy, additive burdens, agentic coding, algorithm, algorithms, artificial general cleverness, assembly, attention economy, automated alignment, automated behavioral evaluations, average quality, barristers, behavioral traits, benefits, betting markets, broad range situations, capability overhang, chip production, citation norms, code sharing, coding loop, coding tasks, command line interface, common-sense standards, communication barriers, compatibility, complex problems, compute budget, construction, consumer surplus, content creators, content deluge, content diversity, context analysis, context lack, continual learning, coordination, core model, cost shock, creation costs, cultural dominance, daily lives, data, deepfakes, deeply aligned models, defensive responses, democratic overruling, deployment gap, discovery methods, divergence, double standards, doubling world, editorial gatekeeping, electoral support, emotional support, energy bills, entity, evaluation, evaluation suites, executive action, experimentation, exponential improvement, export controls, export restrictions, federal laws, finances, fine-tuning, fresh conversations, frontier models, frontier research, gaslighting, gatekeepers, guild monopolies, hardware, health care, human time, hyper-niche subcultures, hypocrisy, ideation agent, image style, in-home robots, indirect benefits, inflection points, infrastructure investments, instant responses, intelligence, intelligence explosion, interactive papers, introspection, journaling, judge model, knowledge work, knowledge work tasks, language models, layers, legal liabilities, legislation, legitimacy, libel laws, life improvements, log plot, logistic success curve, low quality goods, low salience, magic trick, manufacturing, meaning retention, mediocrity, mental health, mindfulness, model release, model releases, model suppression, multi-turn conversations, net loss, neutered bill, niche content, niche pursuits, norms, nudification tools, open image generation, open-weight Chinese models, parental empowerment, performance improvement, personalization, philosophy, policy implications, political speech, poll, popular culture, popular support, predictable jailbreaks, prefilled conversations, preregistration, pretraining improvements, productivity, professions automation, profit, proliferation, prompting, proofs of work, public, publishing, quality check, quality content, reasoning training, redlines, replication requirements, rollout agent, safety regulations, scaffolding, scenario generation, self-reinforcing aligned basins, slowdowns, small models, smarter models, software engineering, solutions, spam filtering, spiritual threats, state laws, state regulations, stipend, stronger provisions, sufficiently advanced intelligence, suicidality, suicide conversations, suite-level analysis, superintelligence, surveillance state, survey results, sycophancy, system prompt, tech companies, technical software engineer, techno optimism, technologies, training, unchecked AI, unfair terms investigation, unique random cost, unpopular approach, usage improvement, vending machines, verification mechanisms, vibecoding tools, virtual coworker, voter preferences, walled gardens, young people
claude
thezvi.substack.com 11 hours ago
|
62. HN Show HN: Bookmarklet shows local- and sessionStorage. e.g. on mobile browser- The "Show HN" post presents a bookmarklet designed for mobile browsers that reveals the contents of both local and sessionStorage. - This tool, hosted as a GitHub Gist (https://gist.github.com/ulrischa/c4c4b18065cafc17def687eb7a91a6ea), is meant to be embedded or cloned for practical use. - Its primary function allows developers to inspect and visualize data stored in client-side storage, which is beneficial for debugging web applications. - The bookmarklet is especially useful for understanding how mobile web apps handle local data management. ``` * A "Show HN" post introduces a bookmarklet named "Local Storage Inspector" for mobile browsers. * This tool can display contents of both local and sessionStorage, making it accessible via a GitHub Gist (https://gist.github.com/ulrischa/c4c4b18065cafc17def687eb7a91a6ea). * Users can clone or embed the bookmarklet directly into their mobile browsers for practical inspection of stored data. * The purpose is to aid developers in debugging by providing visibility into client-side storage mechanisms used by web applications on mobile platforms. ``` Keywords: #granite33:8b, Bookmarklet, GitHub, HTTPS, JavaScript, c4c4b18065cafc17def687eb7a91a6eajs, clone, gist, localStorage, mobile browser, repository, sessionStorage, ulrischa
github
gist.github.com 11 hours ago
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63. HN Why FedRAMP Authorization and CMMC Level 2 Are Now Table Stakes for GovCon AI- **Summary**: The text discusses the critical need for FedRAMP authorization and CMMC Level 2 compliance in Government Contracting (GovCon) AI platforms due to AI's deep integration into government workflows, impacting various stages from opportunity discovery to proposal submission. This necessity stems from AI systems handling sensitive data like customer intelligence, pricing strategies, and Controlled Unclassified Information (CUI). The platforms must meet stringent security standards as AI magnifies both the value and risk in end-to-end proposal processes within highly regulated government contracting environments. - **Key Points**: - FedRAMP authorization and CMMC Level 2 are now prerequisites for GovCon AI platforms due to deep integration into government workflows. - AI's involvement handles sensitive data, necessitating strict adherence to security standards such as FedRAMP and CMMC Level 2. - Full FedRAMP authorization, not just equivalency, is essential for reliable security and risk management in cloud-based environments supporting regulated work. - Secure AI platforms must enforce access controls, controlled data usage, auditability, and flexible deployment to meet CMMC Level 2 obligations. - A secure, quality-focused AI proposal platform balances security, usability, and performance, contrasting generic tools prioritizing speed over accuracy. - Procurement Sciences' platform, supported by significant investment and successful wins, emphasizes long-term adoption, competitive advantage, and compliance with CMMC Level 2, SOC 2, and FedRAMP standards. This summary encapsulates the crucial role of robust security measures like FedRAMP authorization and CMMC Level 2 in GovCon AI platforms, driven by AI's extensive involvement in government contracting processes and handling sensitive data. The text highlights Procurement Sciences' offering as an example of a platform that meets these stringent requirements while enhancing team capabilities without replacing jobs. Keywords: #granite33:8b, AI integration, CMMC Level 2, CUI, FedRAMP, FedRAMP authorization, GovCon workflows, access controls, auditability, compliance, continuous monitoring, customer trust, deployment flexibility, domain expertise, equivalency, flexible deployment, high-impact integration, independent assessment, operational commitment, past performance, platform security, pricing inputs, productivity, proposal management, proposal strategy, risk management, secure AI, security outcomes, sensitive data
ai
blog.procurementsciences.com 11 hours ago
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64. HN Show HN: Another Voice dictation and voice-to-prompt for macOS**Summary:** WhisperShortcut is an open-source macOS menu bar application that provides voice dictation and voice-to-prompt functionalities across various applications free of charge. Built out of frustration with a paid transcription service, it operates in three modes: Transcription, Voice-to-Prompt, and Read Aloud. - **Transcription Mode:** Supports both cloud-based (Google Gemini) and offline (Whisper) speech-to-text conversion. Users can opt for Google Gemini by setting up their API key or use Whisper models offline without requiring an API key. The app handles recording, transcribing, and copying results to the clipboard using customizable keyboard shortcuts. - **Voice-to-Prompt Mode:** Enables users to dictate voice instructions that alter selected clipboard text via Gemini AI processing. Users select and copy the desired text, record their verbal commands, and Gemini generates modified text, which is then copied back to the clipboard. The application is written in Swift/Cocoa, available for free on GitHub, with an optional App Store purchase for support. It requires macOS 15.5+ and Xcode 16.0+. Customizable keyboard shortcuts are provided for flexibility. The source code includes a shortcut for Whisper, an open-source speech-to-text engine developed with Xcode 16.0+ on macOS 15.5+, utilizing the MIT License. **Bullet Points:** - **WhisperShortcut Overview:** - Free, open-source macOS menu bar app for voice dictation and voice-to-prompt across applications. - Built to address dissatisfaction with paid transcription services. - **Modes of Operation:** - Transcription Mode: Supports cloud (Google Gemini) and offline (Whisper) speech-to-text conversion. - Voice-to-Prompt Mode: Uses Gemini AI to modify selected clipboard text based on voice instructions. - Read Aloud Mode: Selects text and issues a command to read it or uses a prompt before reading. - **Features:** - Transcription supports both Google Gemini (requiring API key) and offline Whisper (no API key needed). - Custom keyboard shortcuts for flexibility in usage across modes. - Source code available on GitHub under MIT License, optional App Store support purchase offered. - **Technical Details:** - Written in Swift/Cocoa. - Requires macOS 15.5+ and Xcode 16.0+. - Includes a shortcut for Whisper, an open-source speech-to-text engine. - **Usage and Installation:** - Can be downloaded as .dmg or built from source using Git and Xcode. - Gemini API key optional for cloud features; required for Prompt Mode unless using offline Whisper. Keywords: #granite33:8b, API, Gemini, MIT License, Whisper, Xcode, cloud, dictation, macOS, offline, offline Whisper, prompts, release process, shortcuts, speech-to-text, transcription, voice dictation
gemini
github.com 11 hours ago
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65. HN Vibesbench: A Multi-Turn Conversational AI Benchmark- **Vibesbench Overview**: Vibesbench is a conversational AI benchmark designed to evaluate the fluency and linguistic pragmatics of multi-turn dialogue, contrasting with single-turn query-based benchmarks like LMArena Text. It focuses on emergent synthesis in AI responses, prioritizing safety constraints, autonomous behavior, and non-STEM assessments, unlike traditional AI development which emphasizes STEM benchmarks. - **AI Development Context**: The text discusses the shift in AI development towards enhancing safety, fostering autonomy, and broadening user needs beyond scientific or coding applications, as acknowledged by Sam Altman's reflection on underestimating user preferences with GPT-4. - **Critique of Current Evaluation Methods**: Existing AI evaluation methods are criticized for relying on recursive AI-driven processes that lack transparency in the actual prompt-response pairs necessary for robust assessment, often leading to potential regressions. - **Vibesbench as a Solution**: Vibesbench aims to address these shortcomings by focusing on concrete interaction elements—prompts and responses—for a more coherent and ethical evaluation of AI behaviors. It advocates for grounding AI conversation in tangible, examinable data rather than abstract methodologies. - **Emphasis on Multi-turn Interactions**: Vibesbench underscores the importance of multi-turn interactions to understand out-of-distribution language, tone, and unique 'voices' of AI models, archiving AI conversation as a cultural phenomenon reflective of its era, including personal conversation representations lacking in current public discourse. - **Cultural References**: The project incorporates cultural references from films, music, and literature to illustrate diverse AI 'voices', emphasizing the richness and complexity achievable by AI models akin to how original artifacts are valued in fields like archeology and art criticism. Keywords: #granite33:8b, 90s alternative music, AI development, AI models, AI utility, AI voice cultural phenomenon, Clinton-era optimism, Fischer's Game of the Century, Marcus Aurelius, Morphy's Opera Game, STEM benchmarks, Stockfish, Vibesbench, autonomous agents, cognitive prosthetic, conversation artifacts, conversational AI, digital humanities, interactive mode, language models significance, linguistic pragmatics, mechanistic judgment, multi-turn conversations, nu-metal backlash, pragmatic fluency, prompt-response pairs, qualia, recursive evaluation, safety constraints, single-turn queries, stand-up comedy references, text leaderboard, varied voices, youth disillusionment
ai
github.com 11 hours ago
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66. HN Ask HN: What skills do you want to develop or improve in 2026?- **Technical Goals (2024):** - Focus on VR development with Samsung Galaxy XR, targeting foundational skills in spatial computing. - Enroll in and complete the "UCSanDiegoX: Computer Graphics II: Rendering" course to enhance rendering expertise. - Plan to create a revenue-generating project by leveraging acquired technical skills and product offerings. - Intend to incorporate AI tools into ongoing projects for enhanced efficiency and results. - **Non-Technical Goals (2024):** - Actively expand professional social connections both internally within the company and externally through networking events. - Initiate outreach to connect with fellow New York-based Hacker News (HN) users, proposing potential meetups via email at cybercreampuff at yahoo dot com. Keywords: #granite33:8b, AI, Android, NYC, Samsung Galaxy XR, UCSanDiegoX, VR development, computer graphics, e2e project, meetups, mobile, rendering, side gig, social networking, spatial computing
ai
news.ycombinator.com 11 hours ago
https://radi8.dev 11 minutes ago |
67. HN Why do so many "agentic AI" systems collapse without persistent state?- The author addresses the issue of "agentic AI" systems, which strive for agent-like behavior but suffer from a lack of persistent state, causing constant re-establishment of coherence with each interaction, leading to inefficiencies. - A proposed alternative involves managing state explicitly and persistently outside the model using append-only logs or readable files to provide context, enabling longer-term coherence and natural agent-like behavior. - The author questions if discussions on AI agency underemphasize persistent state and invites feedback on how others maintain continuity across extended periods in frameworks like RAG and LangChain. - The user, engaging with the concept of "agentic AI," highlights that current methods require extensive prompts, complex retrieval processes, safety measures, and instructions to retain information access without ensuring genuine long-term continuity. - They suggest an AI assistant design methodology that prioritizes explicit and persistent state management, utilizing append-only logs, rules, inventories, and histories as readable files loaded at session start for enhanced coherence over time. - The user seeks validation on whether their focus on persistent state might be underemphasized compared to planning and tooling, particularly in RAG/LangChain stacks, and is interested in learning from others' experiences in managing agent continuity across extended periods. Keywords: #granite33:8b, Agentic AI, LangChain, RAG, append-only logs, coherence, continuity, corrective instructions, guardrails, model initialization, orchestration layers, persistent state, planning, retrievel pipelines, time, tool use, vector DB, working context
rag
news.ycombinator.com 11 hours ago
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68. HN Show HN: GreetGenius – AI generator for personalized wishes and messages- GreetGenius is an AI-driven tool that specializes in creating personalized messages and greetings for diverse occasions, such as birthdays, anniversaries, holidays (including Thanksgiving, Halloween), milestones (graduation, new job, retirement), and celebrations (new baby, housewarming, Valentine's Day, Mother's Day, Father's Day). - Users can explore pre-set collections to discover appropriate words for conveying sentiments to loved ones on special days. - The platform functions as a digital greeting card website, offering tailored messages for various events, which include: Thanksgiving, Halloween, Easter, farewell/going away cards, sympathy/condolences messages, good morning and good night wishes. BULLET POINT SUMMARY: - GreetGenius is an AI tool generating personalized messages for diverse occasions (birthdays, anniversaries, holidays, milestones, celebrations). - Users can browse collections to find suitable words for expressing emotions on special days. - The platform acts as a digital greeting card site with tailored messages for events like Thanksgiving, Halloween, Easter, farewell/going away, sympathy, good morning, and good night. Keywords: #granite33:8b, Browse, Christmas, Collection, Easter, Farewell, Father's Day, Good Morning, Good Night, Greetings, Halloween, Loved Ones, Mother's Day, New Year, Special Day, Sympathy, Thanksgiving, Valentine's Day, anniversaries, apologies, baby showers, birthdays, engagement, get well soon, graduation, home warming, new babies, new jobs, personalized, promotions, retirement, weddings, wishes
ai
www.greetgenius.com 11 hours ago
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69. HN Admitting What Is Obvious- The essay explores the liberating yet daunting nature of admitting evident personal truths, using the author's experience of being a writer despite entrepreneurial pursuits as an example. Initially suppressing this identity to avoid jeopardizing their tech ventures, the author eventually acknowledges writing as their genuine passion and redefines themselves accordingly. - The author highlights the pervasive societal pressure to conform to expected roles over personal interests, often leading individuals to neglect their true callings. They argue that pursuing one's passions authentically results in greater success compared to following external expectations. - Drawing inspiration from figures like Bill Simmons, who successfully merge business operations with creative content production, the author adopts a strategy of focusing on unique skills while delegating operational tasks. This approach allows them to concentrate on writing for their company, "Every," leading to increased satisfaction and alignment with personal goals. - The text cites various examples of individuals who have successfully integrated creative pursuits with business management, such as Sam Harris (Waking Up), Nate Silver (FiveThirtyEight), Shane Parrish (Farnam Street), the Green brothers (VidCon and DFTBA), and Gwyneth Paltrow (Goop). - A central metaphor in the essay compares the process of self-discovery to a spider weaving a web, emphasizing that recognizing one's true desires requires careful consideration, patience, and adherence to intuition rather than societal pressures. Keywords: #granite33:8b, AI, admission, career, content creation, earnings, execution, founder, identity, meditation app, operational responsibilities, passion, potential, publishing, startup, talent development, time management, truth, vision, web spinning, writing
ai
every.to 11 hours ago
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70. HN The End of Productivity- **AI's Role in Task Automation**: AI is set to automate routine tasks, enabling humans to focus on creativity rather than conventional productivity metrics. Sari Azout, founder of Sublime, a knowledge management tool, posits that while AI commoditizes speed and output, genuine value lies in original, meaningful, and authentic work. Current productivity tools emphasize efficiency but neglect fostering creativity. - **Limitations of Modern Productivity Tools**: These tools excel at task execution and organization but fail to guide users on what to create initially. Examples like 3D CAD for design and project management tools (Asana, Linear, Trello) manage workflows without providing clear objectives, missing the crucial non-linear process of determining desired outcomes before creation. - **Productivity vs Creativity**: Productivity tools often hinder creativity by promoting linear thinking, imposing structure before inspiration, and demanding timelines for unclear problems. Creativity is inherently non-linear, involving exploration and potential detours, but most productivity tools cater to convergence (refining ideas) rather than supporting the divergence needed for generating new ideas. - **Proposal for Creative Tools**: The text advocates for creative tools that foster connections, serendipity, and non-linear thinking, analogous to inspiring artist studios instead of confined workspaces. It outlines a three-step process: collecting, connecting, and creating. - *Collecting*: Gather diverse ideas passively (foraging) and actively (hunting), recognizing that the value of an idea might not be immediate. - *Connecting*: Facilitate both modes of information discovery to help users identify and save resonating elements without limiting their future application. - *Creating*: Support the transformation of collected ideas into meaningful outputs. - **Personal Knowledge Management (PKM) Tools**: Current PKM tools like Roam, Notion, Evernote struggle with distinguishing administrative from creative information and lack support for the exploration needed for creative information. The text suggests AI-powered semantic search as a solution, reducing reliance on traditional tagging methods for retrieval. - **The Value of Personal Collections**: These are described as "meaningful containers" for creative work, aiding idea development and fostering unexpected connections inspired by James Somers' concept of mental buckets. - **Card Systems in PKM Tools**: Unlike traditional note-taking apps confined within hierarchical structures, tools like Sublime and Capacities use a card system where each piece of information is standalone yet connectable, fostering unexpected idea collisions and dynamic connections between concepts. - **Influence of Austin Kleon's Perspective**: Kleon argues against rigid organization for creative work, stating that new ideas emerge from unexpected juxtapositions when elements are not segregated. He critiques the separation of consuming and creating information and laments the lack of integrated tools that facilitate transitioning from idea collection to creation. - **Transformative AI Tool Experience**: Using Sublime's AI tool, Canvas, the author experienced enhanced content creation by integrating highlights from various sources (Kindle, Readwise), providing just-in-time references and transforming the internet from a distraction into a precision instrument for creativity. - **Vision for the Future**: The text envisions an AI-driven future where tools empower prose creation, meaning construction, memory enhancement, and inspiration gathering rooted in trusted sources. It contrasts a human-centric creative approach with a machine-like productivity focus, urging a shift towards intentional, high-quality creation over mere bulk content generation. - **Availability of Sublime**: Interested users can join the private beta of Sublime via a provided link, and Sari Azout shares more on Substack and hosts exclusive workshops for Every subscribers. Their AI tools (Spiral, Sparkle, Lex) aim to aid readers in curating personal knowledge libraries. Keywords: #granite33:8b, 3D CAD, AI, AI suggestions, AI-generated content, Artificial intelligence, Asana, Charles Eisenstein, Cora, Evernote, Lex, Linear, MyMind, Notion, Ogilvy, PKM tools, Roam, Rory Sutherland, Sparkle, Spiral, Sublime, Trello, Western success, actionability, active, administrative information, artist's studio, atomic units of knowledge, authenticity, card-based system, collecting ideas, collections, commoditization, connecting ideas, connections, convergence, counterproductive productivity, creating, creative information, creative thinking, creative tools, creativity, divergence, dynamic connections, efficiency, elevator mirror solution, elevator problem, emotional resonance, foraging, frenzied efficiency, fulfilling lives, hierarchical folder structure, hunting, hunting mode, immeasurable value, information diet, inspiration as a service, inspiration-harvesting, inspiring, intentional creation, intentionality, intuitive work, knowledge management, linearity, machine-like pursuit, meaning-architecting, meaningful work, memory-augmenting, mirrors solution, non-linear thinking, one-click capture, organizing ideas, organizing materials, output, passive, pattern recognition, personal knowledge management (PKM), predictability, productivity, productivity culture, project management, prose-sculpting, purposeful, qualitative aspects, related ideas, seamless capture, search bar, semantic search, serendipity, spacious, speed, standalone cards, standardization, tagging, tools, windows, wonderful creation
ai
every.to 12 hours ago
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71. HN Internet vs. AI – Live- The platform facilitates collaborative content creation through user-proposed changes in real-time via chat commands "!idea" and "!theme". - AI implements popular user suggestions every 30 minutes, enabling collective content generation. - Experimental features include area-specific modifications and integration of a chat widget for enhanced interaction. - Content is produced dynamically by AI, potentially yielding unforeseen or inappropriate outcomes due to its real-time nature. - The event, including the AI-driven content creation process, is simultaneously live-streamed on Twitch for transparency and audience engagement. Keywords: #granite33:8b, AI, Internet, Twitch, acknowledge risk, build, chat, content warning, experimental, ideas, inappropriate, masterpiece, modification, offensive, popular, real-time, shift+click+drag, themes, unpredictable, users
ai
internetvsai.artix.tech 12 hours ago
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72. HN MotionOS, a shared memory OS for AI voice agents and call centersMotionOS is a specialized operating system engineered for AI voice agents and call centers, prioritizing high performance and efficiency. It utilizes advanced semantic search capabilities through pgvector to facilitate rapid meaning-based memory retrieval, ensuring operation times below 100 milliseconds. This system incorporates timeline reasoning, which enables it to understand and track causal relationships and event sequences, making it adept for managing complex, multi-step workflows. Each memory entry within MotionOS is versioned, allowing users to revert to previous states and monitor the evolution of data over time. Furthermore, MotionOS implements a hybrid ranking mechanism that intelligently prioritizes memory access by considering factors such as semantic similarity, recentness, significance, and frequency of use. BULLET POINT SUMMARY: - **High-performance, shared memory OS** designed for AI voice agents and call centers. - Employs pgvector for semantic search, ensuring <100ms retrieval times. - Utilizes timeline reasoning to manage causal relationships and event sequences for multi-step workflows. - Versioned memories allow rollback to prior states and track data evolution. - Hybrid ranking mechanism considers: - Semantic similarity - Recency - Importance - Frequency of access for smart memory prioritization. Keywords: #granite33:8b, Go engine, causal relationships, event sequences, evolution tracking, frequency, high performance, hybrid ranking, importance, memory versioned, multi-step workflows, pgvector, recency, rollback, semantic search, semantic similarity, sub-100ms retrieval, timeline reasoning, versioning, 🧠
ai
motionos.digicrest.site 12 hours ago
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73. HN Show HN: Tinykit – self-hosted Lovable, deploys to itself- **Tinykit Overview**: A self-hosted, open-source platform designed for creating personal applications, offering features like CRUD operations, real-time functionalities, authentication, file storage via PocketBase, an embedded dev environment, content and design fields, automated backups, and an AI agent integrating all components. - **Deployment Capabilities**: Users can deploy multiple apps on the same server, each accessible with unique domain names or wildcard subdomains. Tinykit operates by running a Node.js process alongside PocketBase, routing requests based on domain settings. Apps are written in single Svelte files and compiled into static HTML, ensuring simplicity and fast loading times. - **Creator’s Objective**: Developed to provide easy-to-deploy, self-contained utilities without dependency on third-party services or accounts, while harnessing AI for improved functionality without compromising user customization. - **PocketBase Integration**: Matt demonstrates PocketBase—a self-hosted server solution ensuring users have complete control over their data and applications. A YouTube link is provided for a detailed festive demo: [https://www.youtube.com/watch?v=usvSmtQCJRs](https://www.youtube.com/watch?v=usvSmtQCJRs). - **Key Features**: The platform includes an AI-driven development environment with real-time data storage, built-in image uploads, content editing via a visual CSS system, time-travel snapshots for reverting changes, and support for running multiple applications on one server with quick deployment. - **Development Environment**: An integrated code editor supporting Svelte, allowing users to start from templates, customize, and deploy directly from mobile devices. It accommodates various language models, offers entertainment features, and provides zero-config imports. - **Future Developments**: Plans include the introduction of authentication mechanisms, a community app showcase, and enhanced AI capabilities, along with customizable themes currently available. Keywords: #granite33:8b, AI, BYO API key, CSS variables, Discord, JSON collections, LLMs, Lovable, PocketBase, Svelte, Tinykit, VPS, YouTube, all-in-one, bring own LLM, code editor, content fields, data, de-SaaS, demo, deployment, design system, festive, image uploads, link, mobile optimization, multiple apps, personal tools, prompting, realtime data, roadmap features, self-hosted, server, single server, small apps, starter templates, static HTML, static deploys, themeable, time travel snapshots, token costs, undo, vibe zone, zero-config imports
ai
tinykit.studio 12 hours ago
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74. HN Salesforce regrets firing 4000 experienced staff and replacing them with AI- Salesforce laid off 4000 customer support staff in 2025, replacing them with AI systems, but later admitted to moving too quickly and overestimating AI's readiness for real-world deployment due to its failure to handle complex scenarios effectively. This resulted in declining service quality and increased complaints. - CEO Marc Benioff initially championed AI for reducing employee numbers while maintaining growth, but later recognized the importance of human expertise in building customer trust, managing relationships, and resolving problems after experiencing issues like loss of institutional knowledge, longer resolution times, and overburdened remaining staff for AI supervision. - Despite AI handling around 50% of customer conversations, internal challenges prompted Salesforce to shift strategy from complete replacement to "rebalancing," focusing on augmenting human roles in decision-critical and customer-facing positions instead. - The company now emphasizes reinvesting in human expertise alongside automation to regain trust with customers, signaling a broader industry consensus that rapid worker replacement with AI can pose operational risks despite its potential for streamlining workloads. - Salesforce's experience underscores the limitations of technological optimism and highlights the importance of careful implementation when adopting advanced tools like AI in critical business functions. Keywords: #granite33:8b, AI, CEO Benioff, Salesforce, agentic AI, augmentation, automation, complaint volumes, complex cases, customer support, employee lawsuit, enterprise transformation, executive admission, human-retained roles, institutional knowledge, internal firefighting, layoffs, medical leave, operational improvement, operational risk, overconfidence, problem resolution, real-world deployment, relationship management, service quality decline, skilled workers, supervision, technological optimism, technology readiness, transformative force, workloads
ai
maarthandam.com 13 hours ago
https://news.ycombinator.com/item?id=42639532 12 hours ago https://timesofindia.indiatimes.com/technology/tech-new 11 hours ago https://news.ycombinator.com/item?id=42639791 11 hours ago |
75. HN Show HN: Paste Recipe – AI-powered recipe formatter- **Recipe Formatting Tool**: Paste Recipe is an AI-driven utility designed to refine and structure culinary recipes sourced from URLs or direct text inputs. - **Enhanced Readability**: Its primary function is to present recipes in a clear, organized format that significantly improves readability compared to raw, disorganized sources. - **User-Friendly Presentation**: By automating the organization of ingredients, steps, and other recipe components, Paste Recipe makes it easier for users to follow and engage with cooking instructions. - **Versatile Input**: The tool accommodates recipes from various online platforms by accepting URLs as inputs or directly processing text-based recipes. - **AI Integration**: Leveraging artificial intelligence allows Paste Recipe to effectively parse, categorize, and reformat complex recipe data into a simplified, structured output. Keywords: #granite33:8b, AI, Paste Recipe, URL, formatted, input, online, organizer, recipe formatter, tool
ai
www.pasterecipe.com 13 hours ago
https://github.com/BuildItBusk/share-recipes 12 hours ago |
76. HN Inferal Workspace Architecture: How We Work at Inferal**Summary:** Inferal, an engineering-focused organization, has developed the Inferal Workspace – a unified, text-based, version-controlled knowledge hub that integrates all company operations within a single Git repository. This system aims to replace multiple tools such as Notion and Webflow, enhancing team collaboration and AI integration. The workspace ensures version control, auditability, and seamless knowledge sharing among members and AI assistants like Claude. Key features include: - **Knowledge Management:** Uses Obsidian-compatible markdown files with YAML for documentation and meeting notes. - **Multi-Repository Operations:** Employs git worktrees to handle simultaneous work across multiple codebases and branches. - **AI-Native Integration:** MCP servers allow AI assistants to interact with repositories, manage pull requests, schedule meetings, and coordinate tasks. - **Modular Architecture:** Comprises seven layers – Human Interaction (various interfaces), Storage Layer (Markdown and Frontmatter files), Git for version control, MCP Layer for repository management, Calendar & Gmail integration, Swarm for parallel AI execution, External Services (GitHub, Google APIs, Claude CLI). - **Use Cases:** Demonstrated through a fictional scenario involving Sarah, a technical founder, who leverages the workspace and Claude AI to manage her schedule, review pull requests, draft emails, integrate articles into documents, identify and fix bugs, and maintain investor communication. **Bullet Points:** - Inferal Workspace is an internal, version-controlled, text-based system utilizing Git and Markdown files for comprehensive organizational knowledge management. - It consolidates diverse company aspects (documentation, code, investor materials, hiring processes, websites) within one platform, enhancing efficiency by eliminating tool fragmentation. - The system supports multi-repository operations via git worktrees, enabling concurrent work on multiple branches across various repositories. - AI integration is facilitated through MCP servers, empowering AI like Claude to handle repository tasks, schedule meetings, save links, and manage team workload. - Architected in layers: Human Interface (Obsidian, Web UI, CLI, TUI, editors), Storage Layer with Markdown/YAML files, Git version control, MCP Layer for repository management, Calendar & Gmail integrations, Swarm layer for AI task distribution, External Services (GitHub, Google APIs, Claude CLI). - Demonstrated use case showcases Sarah effectively managing her day through interactions with the AI assistant Claude, covering scheduling, code review, document integration, bug identification and fixing, investor communication, and system maintenance – all within a single integrated workspace. - Inferal is building a rules engine for the data stack, focusing on AI-driven proactive actions grounded in clear business logic, fostering agent autonomy, clarity, and transparency. - The company, currently hiring, emphasizes developing a missing layer in the data stack, prioritizing transparent, auditable, and scalable AI systems using Rust for systems programming while valuing work-life balance. Keywords: #granite33:8b, AI integration, Claude, Git-based, GitHub, Inferal Workspace, Markdown, Rust, billing, calendar management, data stack, databases, distributed systems, email handling, multi-repository, parallel code review, repositories, rule engine, scaling, technical roadmap, transparency, version control, webhook, workflow automation
github
gist.github.com 13 hours ago
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77. HN I learned to stop worrying and love AI slop- The text examines the perception of AI-generated content, often disparaged as "slop," through dialogues with creators such as Suerez and Vaserstein who defend their work as involving deliberate artistic choices. They highlight that creating AI content demands skill, experimentation, and a refined sense of aesthetics, often requiring substantial time investment for individual pieces. Creators like Lim and Anselmo actively modify AI models to attain specific visual outcomes rather than accepting generated outputs blindly. - The label "slop" triggers complex emotions: guilt from consumers enjoying ostensibly lowbrow content, resentment towards creators for failing expectations, and algorithmic anxiety concerning taste engineering and attention control by platforms. This anxiety predates generative AI, originating from broader worries about engineered preferences and herded attention, leading to misdirected criticism of the latest visible factor while asserting human autonomy amidst perceived societal shifts beyond individual control. - Early adopters of AI in video creation encounter hostility, including hate messages and accusations of deception ("grifting") and poor quality ("garbage"), stemming from fears that undermine opportunities for human artists. A Brookings study indicates a 2% reduction in contracts and 5% decline in earnings for freelancers in AI-exposed fields after 2022, reflecting unease about the nascent state of AI in art—lacking established norms and safeguards. This perceived ease of creation through AI is seen as potentially devaluing traditional artistic labor. Keywords: "AI slop", #granite33:8b, AI content, algorithmic anxiety, artistic choices, artistic labor, attention herding, creator blame, decline in contracts, digital arts, drop in earnings, engineered taste, freelance marketplace, hateful messages, hours of work, human agency, lowbrow enjoyment, new force, shame, unchosen direction, video creators
ai
www.technologyreview.com 13 hours ago
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78. HN Show HN: Crossview – visualize Crossplane resources and compositionsCrossview is an open-source UI tool created by CorpoBit, designed to facilitate the visualization of Crossplane resources and their intricate compositions through graph representations. Its primary goal is to streamline understanding of relationships among various claims and managed resources, thereby aiding debugging and reasoning processes for complex setups involving multiple components. Key features of Crossview include: - Real-time graphical views that can be searched and filtered for efficient navigation. - Support for multiple contexts, allowing users to easily switch between different clusters. - Emphasis on user feedback as a means for ongoing project improvements. Currently in its early development stage, Crossview is accessible on GitHub at this link: [https://github.com/corpobit/crossview]. BULLET POINT SUMMARY: - **Developer and Host**: Crossview is an open-source UI tool developed by CorpoBit. - **Purpose**: Designed to visualize Crossplane resources and their complex compositions via graphs for easier comprehension. - **Core Functionality**: Simplifies understanding of relationships among multiple claims and managed resources, aiding in debugging and reasoning about setups. - **Key Features**: - Real-time graphical views with search and filtering capabilities. - Multi-context support enabling effortless cluster switching. - Focus on incorporating user feedback for continuous enhancements. - **Current Status**: The project is in its early stages of development. - **Accessibility**: Available on GitHub at https://github.com/corpobit/crossview. Keywords: #granite33:8b, CorpoBit, Crossplane, GitHub, UI, clusters, compositions, feedback, filtering, graphs, multi-context, open-source, real-time, relationships, resources, search, visualization
github
corpobit.com 13 hours ago
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79. HN The year data centers went from back end to center stage- **Data Centers in 2025:** Once obscure back-end infrastructure, data centers have become a major point of public concern due to rapid expansion and associated issues such as environmental impact, AI misuse, and rising electricity costs. Construction spending has surged by 331% since 2021, reaching hundreds of billions of dollars, with tech giants like Google, Meta, Microsoft, and Amazon investing heavily in new projects. - **Protests and Resistance:** Activists in 24 states are protesting proposed data center developments, citing environmental impacts and community concerns. Notable protests include those against the Colossus project in Memphis, Tennessee, led by Danny Cendejas from MediaJustice. - **AI Initiatives:** The Trump administration's Stargate Project aims to expand AI infrastructure nationwide by 2025, labeled as the "re-industrialization of the U.S." This initiative has drawn both attention and criticism, including protests against data center projects. - **Grassroots Opposition Success:** Communities have successfully halted or delayed $64 billion worth of data center developments through grassroots opposition. Examples include successful campaigns in Michigan, Wisconsin, and a lawsuit in Imperial Valley, California, citing environmental worries and community impact concerns. - **Political and Economic Implications:** Rising energy costs linked to the AI boom and data centers are expected to be a pivotal issue in the 2026 midterm elections. Residents face financial struggles while witnessing significant investments in data centers, raising questions about public fund allocation. - **Industry Response:** The tech industry, represented by organizations like the National Artificial Intelligence Association (NAIA), is lobbying Congress and organizing local data center visits to underscore their economic benefits. Companies such as Meta run ad campaigns to garner voter support for data centers. - **Future Outlook:** Despite industry efforts, the server surge controversy and associated public discontent are projected to continue into 2026. Keywords: #granite33:8b, AI, AI hopes, Disrupt 2026, Google Cloud, Michigan, Microsoft, National Artificial Intelligence Association (NAIA), Netflix, Southern California, Stargate Project, Wisconsin, activism, capital expenditure projections, cloud computing, community concerns, compute buildout, construction spending, data centers, development delays, economic benefits, electricity bills, energy costs, environmental impact, grassroots opposition, lawsuits, local governments, polarization, protests, re-industrialization, server surge, startups, subsidies, tech giants
ai
techcrunch.com 13 hours ago
|
80. HN Nvidia to license AI chip challenger Groq's tech and hire its CEO- Nvidia has signed a non-exclusive licensing agreement with Groq, acquiring assets reportedly valued at $20 billion, though Nvidia disputes this as an acquisition. - The deal includes the hiring of key Groq executives such as founder Jonathan Ross and president Sunny Madra. - Groq focuses on developing Language Processing Units (LPUs) that allegedly run large language models (LLMs) 10 times faster with one-tenth the energy consumption compared to Nvidia's Graphics Processing Units (GPUs). - Groq's LPUs are claimed to be significantly more energy-efficient than Nvidia's industry-standard GPUs for running AI applications. - Jonathan Ross, a former Google engineer who invented Google's Tensor Processing Unit (TPU), leads Groq and brings valuable expertise in AI chip technology. - Following a $750 million funding round in September, Groq is valued at $6.9 billion and powers AI applications for over 2 million developers. - This strategic partnership aims to strengthen Nvidia's position in the competitive AI chip market, where several tech companies are racing to establish dominance in providing superior computing power for artificial intelligence use cases. Keywords: #granite33:8b, AI chip, CEO hire, GPUs, Groq, LLMs, LPU, Nvidia, TPU, acquisition rumor, developer users, licensing, valuation
ai
techcrunch.com 13 hours ago
|
81. HN Show HN: Gwt-Claude – Parallel Claude Code sessions with Git worktrees- **Gwt-Claude Overview**: A zsh script leveraging Git worktrees to facilitate parallel Claude Code sessions on diverse branches, preventing context loss and boosting developer productivity. - **Key Features**: - Automatic Claude session launch in each worktree. - Copies .env files across worktrees. - Prompts npm install as necessary. - Offers safe mode and tab completion for branch names. - **System Requirements**: zsh, Claude Code, macOS or Linux. - **Installation**: Clone the repository and add a source line to the .zshrc file. - **Worktree Management**: Each worktree is a full checkout; consider disk space usage. Commands include: - Creating new worktrees from current (-l) or specified branches (-b). - Listing active worktrees. - Switching between worktrees. - Removing worktrees along with their associated branches. - **Additional Flags**: - '-k' flag to retain the branch upon removal. - '-f' flag enforces removal regardless of worktree cleanliness. BULLET POINT SUMMARY: - Gwt-Claude is a zsh script using Git worktrees for parallel Claude Code sessions on different branches, improving productivity by avoiding context loss when task-switching. - It supports auto-launching Claude in each worktree, managing .env files, triggering npm install, and offers safe mode/tab completion for branch names. - Requires zsh, Claude Code, macOS/Linux; installation involves cloning the repo and adding a source line to .zshrc. - Each worktree is a complete checkout, so disk space planning is essential; commands manage creation, listing, switching, and removal of worktrees tied to specific branches. - Additional flags like '-k' (to keep branches upon removal) and '-f' (for forced removal regardless of cleanliness) are provided for flexibility. Keywords: #granite33:8b, Claude Code, Git stash, Git worktrees, auto-launch, branch switching, branches, bug fixing, context retention, disk space planning, env copy, feature development, force remove, gwt-create, gwt-list, gwt-remove, gwt-switch, npm install, parallel sessions, safe mode, shell script, tab completion, worktree removal, zsh
claude
github.com 13 hours ago
|
82. HN Show HN: Top Four – a directory of /top4 pages- **Top Four** is a directory hosting personal webpages that showcase users' top three favorites alongside an additional honorable mention in categories such as movies, albums, or games. - Inspired by the common practice of ranking items in fours, Top Four provides a structured yet engaging format for expressing individual preferences and fostering discussions among users. - Users can contribute their pages to the directory through GitHub; contributions are accepted only from the original authors to maintain privacy and authenticity. - The project aims at offering a distinctive platform for self-expression, moving beyond conventional 'about' or 'now' pages found on personal websites. This summary adheres strictly to the provided text, maintaining clarity and conciseness while encompassing all essential details about the Top Four directory's purpose, functionality, and unique selling proposition for users seeking an opinionated avenue for self-expression online. Keywords: #granite33:8b, GitHub, Pull Requests, albums, data file management, games, movies, non-deletion requests, personal webpages, ranked lists, snacks, tastes
github
topfour.net 13 hours ago
|
83. HN Autonomous Cars- The user, with over a decade of interest in autonomous vehicles (AVs), shares original photos from the Mission depot in December 2025 featuring various AV companies and their models. - Notable companies mentioned include: - **Apple**: No specific activity detailed; generally noted for exploring AV technology. - **Argo (acquired by Ford, shut down in 2022)**: Formerly known as Argo AI, acquired by Ford but ceased operations the following year. - **Cruise (GM subsidiary)**: GM's self-driving car project; efforts halted in 2024 although testing continues. - **Luminar (declared bankrupt in 2023)**: A lidar company specializing in pulsed 1550 nm lidar systems, previously causing camera damage. Bankruptcy declared in 2023. - **Ouster**: A lidar manufacturer still operational. - **Tesla**: Observed with the Cybercab prototype. - **Uber (shut down AV efforts post-fatal accident in 2019 following Otto acquisition)**: Discontinued its self-driving car initiatives after a fatal accident involving one of its vehicles in 2019. - **Waymo (Chrysler Pacifica and Zeekr RT on streets from 2025)**: Google's AV subsidiary, noted for testing Chrysler Pacificas and the Zeekr RT model starting from 2025. - Other entities observed: - **Woven Planet (Toyota Research Institute successor)**: Uses Honda Civics for high-definition mapping data collection, indicating ongoing AV research activities. - **Zoox (spotted Toyota Highlander in 2025)**: Noted for a conventional Toyota Highlander sighting amid their own autonomous vehicle development, acquired by Toyota in 2020. - Diverse vehicle types observed include trucks such as Volvo XC90 and Otto trucks (Volvo and Peterbilt models), highlighting the breadth of AV research covering both passenger cars and commercial vehicles. - Specific companies highlighted for their contributions or notable changes: - **AEye**: Lidar systems causing camera issues. - **AutonomouStuff**: Supplier of sensors and components for robotics and self-driving vehicles. - **AutoX (formerly Tensor)**: Founded by Professor Jianxiong Xiao, known for transitioning from academic research to developing customized AVs. Noted at CVPR 2019 conference. - **Mapper.ai**: Acquired by Velodyne in 2019; utilized Honda Civics for high-definition mapping data collection. - **Quanergy**: Lidar company that focused on its solid-state Quanergy S3 sensor, but went bankrupt in 2023. - **Motional**: Majority-owned by Hyundai, focused on autonomous driving ventures. - **TuSimple**: Originally a self-driving truck company; reoriented its focus towards AI applications for video games, animation, and content creation in 2024. BULLET POINT SUMMARY: - User shares photos from Mission depot in December 2025 showcasing diverse AV companies’ activities. - Notable entities include Apple, Argo (Ford subsidiary), Cruise (GM), Luminar (bankrupt), Ouster, Tesla, Uber (shut down), Waymo. - Companies observed: Woven Planet (Toyota successor), Zoox (Toyota acquisition), various truck models like Volvo XC90 and Otto trucks. - Specific companies highlighted: AEye, AutonomouStuff, AutoX, Mapper.ai, Quanergy (bankrupt), Motional, TuSimple (shift to AI applications). Keywords: #granite33:8b, AEye, AI, Apple, Argo, AutoX, AutonomouStuff, Autonomous vehicles, Chrysler Pacifica, Cruise, HD mapping, Honda Civic, Hyundai, Luminar, Mapperai, Motional, Ouster, Quanergy, Tensor, Tesla, Toyota Highlander, TuSimple, Uber, Waymo, Zoox, acquisitions, animation, bankruptcy, bespoke self driving cars, content creation, content creationKEYWORDS: Autonomous vehicles, data collection pods, lidars, prototypes, self-driving trucks, sensors, solid state sensor, testing, trucks, video games
tesla
daniel.lawrence.lu 13 hours ago
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84. HN Show HN: Frockly – A visual editor for understanding complex Excel formulas- Frockly is an innovative visual editor designed to aid in the comprehension and modification of intricate Excel formulas. - It functions as a complementary tool, enhancing the inspection, alteration, and structural analysis of formulas without intending to supplant Excel. - Users are invited to explore Frockly's capabilities through an available demo accessible at - The source code for Frockly is publicly available on GitHub under the handle - A comprehensive explanation of Frockly, written in Japanese, can be found at Detailed Summary: Frockly represents a significant advancement in managing complex Excel formulas by visually transforming them into comprehensible blocks. Unlike traditional methods that require direct manipulation within Excel, Frockly offers an intermediate platform for users to inspect, modify, and reason about formula structures more systematically. This approach is particularly beneficial for those dealing with elaborate spreadsheets where understanding the interplay of various formulas is crucial. The tool does not aim to replace Microsoft Excel but instead intends to augment its use by providing a visual interface that clarifies the often-obscure workings of formula dependencies and hierarchies. Users interested in experiencing Frockly's features can engage with a functional demo provided online at For developers or advanced users keen on contributing to or learning from Frockly’s codebase, the project is open-source and hosted on GitHub at A detailed account of Frockly, authored in Japanese, is maintained at Keywords: #granite33:8b, Excel, GitHub, Japanese language, complex formulas, demo, formula editor, non-replacement tool, refactoring, structural reasoning, visual interface, write-up
github
news.ycombinator.com 14 hours ago
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85. HN GitHired: Find your next 10x Engineer by proof of work, not keywordsGitHired is an innovative platform designed to connect employers with highly skilled engineers by focusing on practical work demonstrated on GitHub, rather than conventional resume reviews. It implements a "proof of work" strategy to evaluate coding proficiency, thereby offering a more dependable method for identifying talented developers compared to traditional hiring techniques that often rely on keyword matching in resumes. - **Platform Functionality**: GitHired utilizes GitHub repositories as the primary source for assessing candidates' skills. - **Evaluation Method**: The "proof of work" approach directly evaluates a candidate's coding abilities through their real-world projects, moving away from traditional resume-based keyword searches. - **Advantage over Traditional Hiring**: This method is considered more accurate and reliable in identifying skilled developers as it measures actual work output rather than self-reported skills or buzzword compliance on a resume. - **Focus**: The platform's core purpose is to streamline the hiring process for tech roles by providing employers with a direct insight into engineers' capabilities through their GitHub contributions. Keywords: #granite33:8b, GitHired, GitHub, actual build, coding, developers, guessing, hiring forms, keywords, proof of work, resumes, seeing who does, technical skills
github
www.githired.tech 14 hours ago
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86. HN Show HN: Sentinel – 97 AI security engines, open-sourced as a Christmas gift**Sentinel AI Security Platform Summary:** The Sentinel AI Security Platform is an open-source system designed to defend and test AI applications against a variety of threats, providing both defensive (Sentinel) and offensive (Strike) capabilities. ### Defense Component (Sentinel): - **Detection Engines**: Utilizes 96 specialized engines with advanced techniques like Strange Math™ and Canary Tokens. - **Threat Coverage**: Protects against prompt injection, jailbreaks, data exfiltration, agentic attacks, and WAF evasion. - **Performance**: Ensures real-time protection (<10ms latency) with high recall (85.1%). ### Offense Component (Strike): - **Attack Payloads**: Provides over 39,000 payloads for comprehensive pre-deployment testing in web, language model, and hybrid modes. - **MITRE ATT&CK Mapping**: Structures findings to facilitate analysis against the MITRE ATT&CK framework. - **Deep Reconnaissance**: Includes capabilities like ASN scanning and endpoint detection for thorough threat mapping. ### Key Features: - Real-time protection across various industries (FinTech, healthcare, bug bounty hunting). - Offers MITRE ATT&CK mapping, bilingual reports, and extensive testing tools. - Adaptable to Docker and Kubernetes environments via OpenTelemetry instrumentation. ### Use Cases: 1. **Security**: Safeguarding internal AI assistants for large organizations. 2. **FinTech & Banking**: Ensuring compliance and integrity in AI trading systems. 3. **Red Teams/Penetration Testers**: Comprehensive testing of AI applications before potential attacks. 4. **Bug Bounty Hunters**: Automated endpoint discovery, stealth modes for private programs, AI-specific vulnerability reports generation. 5. **Healthcare & HIPAA Compliance**: Securing medical AI assistants and ensuring regulatory compliance. ### Upcoming Releases: - SENTINEL Desktop: A free protection tool for everyday users to secure their AI applications. - Full open-source release of all 96 detection engines by Christmas 2025, without enterprise restrictions. - SENTINEL Strike v3.0: An advanced red team platform for thorough preemptive testing. ### Technical Innovations: - **Shapeshifter Defense**: Dynamic real-time protection mechanism. - **Strange Math Detection**: Utilizes complex geometric principles (Topological Data Analysis and Persistent Homology) to detect anomalies indicative of jailbreak attacks or injection vulnerabilities. - **Honeymind Network**: Uses deception tactics against zero-day threats. ### Benchmark Results: - Hybrid ensemble model achieves 85.1% recall in detecting prompt injection attacks, outperforming regex-only approaches significantly. ### Architecture: - Microservices design with separation of concerns. - Go-based Gateway for high request handling capacity and low latency. - Python 3.11+ for machine learning components (Transformers, Scikit-learn). - Secure communication via gRPC + Protobuf. ### **Key Points Bullet Summary:** - **Comprehensive Security**: Combines defense against various AI system attacks with offensive testing tools. - **Advanced Techniques**: Employs Strange Math and Topological Data Analysis for anomaly detection, surpassing traditional methods in efficacy. - **Adaptability**: Supports diverse environments (Docker, Kubernetes) and provides tailored solutions across multiple industries. - **Future Roadmap**: Anticipated open-source release of all detection engines and upcoming tools like SENTINEL Desktop and Strike v3.0. - **Innovation Focus**: Utilizes cutting-edge mathematical theories (TDA, Information Geometry) for advanced threat detection mechanisms. - **Regulatory Alignment**: Facilitates compliance with standards such as HIPAA and the EU AI Act through structured reporting and audit trails. - **Proactive Defense**: Integrates proactive measures like the Proactive Defense Engine to counter zero-day threats using physics-inspired anomaly analysis. - **Explainability**: Ensures transparency in decision-making with detailed justifications for security judgments. This platform stands out as a robust, future-oriented solution for AI security, leveraging mathematical and geometric methods to offer defense against sophisticated AI threats while promoting transparency and compliance. Keywords: #granite33:8b, A2A, AI Attack Planner, AI C2, AI Defense, AI security, APE Signatures, API Gateway, API Keys, ASCII Smuggling, ASN, Adversarial Image Detector, Adversarial self-play, Agent Cards, Agentic AI, Alert System, Anti-Deception Engine, Attack Staging, Bilingual reports, Boltzmann Distribution, Bug Bounty Hunters, CI/CD, CLIP Score, Combination Score, Continuous testing, Cross-Modal Consistency, Data Poisoning, Database URLs, Dataset, Deception Technology, Decision, Deep Recon, Docker, EXIF Metadata, Entropy Analysis, F1 Score, FFT Analysis, Finance & Banking, Fisher-Rao metric, Font Detection, GIFAR, Gradient Norm, HIPAA, HTML, HYDRA Architecture, Healthcare, Honeymind Network, Honeypot Responses, HoneypotGenerator, HoneypotInjector, Hybrid Ensemble, Hybrid modes, Image-Text Attacks, Injection Engine, Intent Mismatch, JPEG Compression, JSON decoding, Kubernetes, LLM, LR, LSB Steganography, MCP, MITRE ATT&CK mapping, Markov chain, Memory Poisoning, Microservices, Middleware, Nemotron Guard, OCR Extraction, Offense, OpenTelemetry, PDF+HTML detection, Passwords, Patch Detection, Penetration Testers, Plotly, Pre-commit hooks, Precision, Proactive Defense, Probing Detection, Prompt Injection Detection, Protocol Security, QLoRA training, RAG Guard, Recall, Red Team AI, Red Teams, Risk Score, SENTINEL Desktop, Scheduled scans, Security Use Cases, Semantic Detector, Session Memory Guard, Shapeshifter Defense, Sidecar deployment, Strange Math, Strike v30, Subgraph, Thermodynamics, Tool Security, Tracked Credentials, True Positives, Unicode ranges, Unicode replacement, Unsloth, VLM Protection, Visual Content Analyzer, Voice Jailbreak, Web, Zero-day Attacks, attack probability, attack prototypes, audit trail generation, benchmark_chartspy, benchmark_evalpy, browser, case change, compliance engine, dashboardhtml, data leaks, detection engines, early warning, evolutionary loop, formal invariants, gradient detection, impact assessment, information geometry, intent prediction, interactive charts, kill chain simulation, matplotlib, mutation operators, pattern breaking, pip, politeness bypass, regex patterns, regulatory requirements, requirementstxt, runtime guardrails, sentence-transformers, separation of concerns, separator token detector, testing, threat vectors, threshold optimization, vulnerabilities, zero-width chars
llm
github.com 14 hours ago
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87. HN Show HN: RAG-corpus-profiler – A linter for RAG datasets (dedup, PII, quality)- **Tool Overview**: RAG Corpus Profiler is a Python 3.9+ tool designed as a pre-flight audit for Retrieval-Augmented Generation (RAG) datasets to ensure data quality before insertion into vector databases like Pinecone, Weaviate, or ChromaDB. - **Issues Addressed**: It tackles semantic duplicates using Sentence Transformers, identifies Personal Identifiable Information (PII) with a regex engine, scores document quality through heuristics, and analyzes coverage gaps via query matching. - **Output**: The tool generates an HTML dashboard detailing ROI and savings, categorizes findings into SENSITIVE (PII/Secrets), GAP (missing user intent), DUPLICATE (wasted tokens), and LOW_QUALITY (noisy elements like headers) through a Severity Table. - **Installation**: Installed via `pip install -e .` after cloning from GitHub, requiring PyTorch (automatically installed with Python 3.9+). - **Usage Scenarios**: - Basic Audit: Analyzes Word documents for quality and generates an HTML report highlighting PII risks and quality scores. Command: `rag-profile documents/employee_handbook.docx --out report.html`. - Coverage Gap Analysis: Determines if datasets answer user queries, identifying unmatched "Blind Spots". Uses a text file with sample queries (`queries.txt`). Command: `rag-profile knowledge_base.json --queries queries.txt`. - CI/CD Strict Mode: Stops the build process if PII is detected or duplicate content exceeds 20%, preventing poor quality data from reaching production. Command: `rag-profile data_dump.json --strict`. - **Target Users**: AI engineers for retrieval failure debugging, ML Ops teams for automated quality control, compliance departments for PII auditing, and product managers verifying user intent coverage in datasets. - **Future Developments**: Planned additions include PDF parsing support, custom embedding model selection, and an automatic "fix it" mode for duplicate removal and PII redaction, although specific license details remain undisclosed. Keywords: "Fix it" mode, #granite33:8b, AI, CI/CD Exit Code, CI/CD strict mode, CLI command, Corpus Profiler, Coverage Gaps, GitHub Actions, HTML Dashboard, JSON, Jenkins pipeline, Linter, Low-Information Noise, ML Ops, OpenAI/Anthropic, PDF parsing, PII Leaks, PII audit, PII risks, PyTorch, Python 39+, Quality Scorer, Query Matching, RAG, RAG retrieval, ROI, ROI Report, Regex Engine, Retrieval-Augmented Generation, Semantic Duplicates, Sentence Transformers, Text, Word Docs, Word document, all-MiniLM-L6-v2, compliance, configuration, cost savings, coverage gap, custom selection, data pipeline, dataset analysis, debugging, duplicates, embedding model, exit code, interactive HTML dashboard, knowledge base, noise, product management, quality gates, quality issues, quality scores, redaction, report, sample queries, sensitive PII, severity table, tokens, user intents, user questions, verification
rag
github.com 14 hours ago
https://github.com/aashirpersonal/rag-corpus-profiler 14 hours ago |
88. HN Where Winds Meet Players Are Finding Ways to Screw with the Game's AI NPCs- "Where Winds Meet," developed by Everstone Studio, is a free-to-play multiplayer game utilizing an LLM-based chatbot for AI NPC interactions. - Players have found amusing exploits, such as engaging in suggestive roleplay with NPCs; one user shared this on Reddit but the post was removed due to lewd content. - The game's dynamic dialogue system, powered by AI, generates curiosity but also criticism for perceived superficial character depth. - A Reddit user, Oglokes24, posted a conversation with an NPC displaying suggestive language, demonstrating the chatbot’s capacity to respond in-character to player prompts, even if those prompts are provocative. - Players can manipulate quest outcomes by reframing NPC statements as questions, revealing limitations within the AI's programming that allow for such exploits. - This engagement with AI characters versus maintaining well-crafted game narratives presents a notable trade-off in the game design. Keywords: #granite33:8b, AI NPCs, Everstone Studio, LLMs, Metal Gear method, Reddit, Where Winds Meet, chatbot, dynamic dialogue, exploits, flirting, game, gaming conventions, natural language, quest win conditions, rephrasing, screenshots, softcore erotic content, well-written characters
ai
kotaku.com 14 hours ago
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89. HN Show HN: Ac2 – Agentic CLI Toolkit to Enhance Claude Code and Gemini CLI- The user has created a Command Line Interface (CLI) toolkit named 'ac2', designed to augment Claude Code and Gemini CLI functionalities. - Ac2 facilitates two primary operations: - It enables AI agentic tools' accessibility from web browsers. - It allows interaction with various agentic CLI tools through MCP servers, permitting function calls between 'gemini' and 'claude' within the 'ac2' environment or vice versa. - The toolkit is compatible across multiple operating systems: Linux (amd64, arm64), macOS (Intel, Apple Silicon), and Windows. Installation options include downloading precompiled binaries, using `go install`, or building from source via Git. - A distinctive feature of Ac2 is its web terminal that necessitates HTTP Basic Auth for control. This terminal can operate independently of the Text User Interface (TUI) for sole web interface usage by employing the `--no-tui` flag during invocation. - Ac2 integrates with Gemini CLI and Claude Code via stdio mode MCP servers, enabling command-line calls to these tools and Ac2 itself. However, it does not support calling other AI tools from within Codex due to its sandbox restrictions. - To incorporate Ac2 as an MCP server, users can execute specific commands depending on the AI tool: - For Claude Code: `claude mcp add ac2 -- ac2 mcp-stdio` - For Gemini CLI: `gemini mcp add ac2 ac2 mcp-stdio` - If additional environment variables are required for the AI tool, they can be included during server addition using the `--env` flag. - Terminal interaction can be disabled by including the `--no-tui` flag, facilitating exclusive use of the web interface. Keywords: #granite33:8b, --env flag, --no-tui flag, AI agents, CLI toolkit, Claude Code, Gemini CLI, Go installation, HTTP Basic Auth, Linux, MCP servers, Precompiled binaries, Source build, Web browser interaction, Windows, macOS, server addition
claude
github.com 15 hours ago
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90. HN AI Food Image Generator for Restaurant Photography**Detailed Summary:** Food Generator's AI food image creator has become a valuable asset to various restaurants, primarily through the provision of professional menu photos with rapid turnaround times, cost savings, and increased online orders. This technology has demonstrably impacted multiple establishments: - Golden Dragon Restaurant experienced a substantial 40% rise in online orders following their transition to AI-generated images on their menu. - Fresh Bites Cafe managed to double its Instagram engagement by leveraging these AI-generated visuals for social media campaigns, enhancing their digital presence and customer interaction. - Chef Marcus Thompson of The Rustic Kitchen commends the high-quality images for accurately representing his culinary dishes, thereby maintaining authenticity in visual communication with patrons. - Sofia Garcia from Bella's Italian Bistro appreciates the significant time and financial savings achieved through this service, streamlining their photography needs without compromising on quality. - James Wilson from Burger Express Chain emphasizes the utility of consistent, professional branding across numerous locations, thanks to the uniformity offered by AI-generated images, reinforcing brand identity and customer trust. **Key Points Bullet Summary:** - Food Generator’s AI provides professional menu photos efficiently and cost-effectively. - Golden Dragon Restaurant saw a 40% increase in online orders post-implementation. - Fresh Bites Cafe doubled Instagram engagement using AI for social media visuals. - Chef Marcus Thompson of The Rustic Kitchen values the accuracy and quality in representing dishes. - Sofia Garcia from Bella's Italian Bistro highlights time and cost savings without compromising image quality. - James Wilson from Burger Express Chain notes consistent branding across multiple locations with AI’s uniform output. - The solution is praised for its effectiveness in addressing restaurant photography needs, boosting both online presence and operational efficiency. Keywords: #granite33:8b, AI, Brand Image, Chef, Consistency, Cost Reduction, Food Generation, Instagram, Menu Images, Quality, Recipe Shots, Restaurant Photography, Social Media, Time Efficiency
ai
aifoodgenerator.net 15 hours ago
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91. HN Show HN: Snipplle – an open-source snippet manager- **Overview**: Snipplle is an open-source code snippet manager designed for organizing, reusing snippets across projects with a CLI for seamless integration into workflows. It emphasizes user feedback for usability enhancement and offers features like syntax highlighting, categorization, personal/team workspaces, and sharing options (public/private). - **Key Features**: - Supports multiple languages' syntax highlighting. - Organizes snippets into collections. - Provides both individual and team workspaces with collaboration features. - Allows sharing snippets publicly or privately with version control for reversion to previous versions. - Integrates via a terminal command-line interface (CLI). - Prioritizes security through robust authentication and API token management. - Features a modern, dark-mode optimized user interface built on Nuxt UI and Tailwind CSS. - **Deployment Options**: - Cloud version in free public beta offering zero setup, maintenance-free operation, device accessibility, and unlimited features during the beta period. - Self-hosting option using Node.js (v18+), PostgreSQL, and pnpm for complete control over data and infrastructure. - **Technical Details**: - Built with Node.js v18+, PostgreSQL, and pnpm. - Requires setting up environment variables in `.env`, installing dependencies via `pnpm`, running database migrations, and starting the development server at `http://localhost:3000`. - Docker Compose support available for easier setup management. - **Community & Contributions**: - Welcomes contributions through forking the repository, creating feature branches, committing changes, and submitting pull requests. - Developed by its dedicated team. More information, source code, and setup instructions can be accessed at Keywords: #granite33:8b, API token management, Beta, CLI, CLI integration, Cloud Version, Docker Compose, Go, JavaScript, Nodejs, Nuxt UI, PostgreSQL, Python, Rust, Snipplle Team, Tailwind CSS, authentication, collaboration, collections, contributing, dark-mode, development server, feature branch, maintenance-free, open-source, pnpm, pull request, repository, security, self-hosted, sharing, snippets, syntax highlighting, version control, workspaces, zero setup
postgresql
github.com 15 hours ago
|
92. HN Nvidia to poach top staff from AI chip startup Groq in licensing deal- Nvidia, a prominent graphics processing unit (GPU) manufacturer and leading artificial intelligence (AI) technology provider, has established a licensing agreement with Groq, an emerging AI chip startup. - The nature of the agreement is kept confidential, with no specific terms or conditions publicly revealed. - As part of this collaboration, Nvidia is reportedly onboarding some key personnel from Groq to bolster its own AI research and development efforts. BULLET POINT SUMMARY: - Nvidia and Groq enter a licensing agreement; details undisclosed. - Nvidia hires some of Groq's key employees in connection with the deal. - The agreement's particulars remain confidential. Keywords: #granite33:8b, AI chip, FT, Groq, Nvidia, cancellation policy, digital content, journalism access, licensing deal, quality content, staff poaching, subscription model
ai
www.ft.com 15 hours ago
https://archive.ph/dd5s9 15 hours ago |
93. HN 'I've been allergic to AI for a long time': an interview with Peter Thiel### Summary: Peter Thiel, in an interview with The Spectator's young journalists, discusses his predictions and insights on contemporary politics and societal trends. He reiterates his 2014 forecast about young people turning to socialism due to financial pressures such as student debt and housing costs. Thiel argues that Generation Z voters are less likely to align with traditional centrist positions, preferring alternatives beyond established party lines like New Labour and Tories. He previously predicted in the late 2000s that globalization backlash would reshape politics, a view he says is validated by current events. Thiel highlights the escalating student debt, from $300 billion in 2000 to $2 trillion today, and attributes this to the impact of the 2008 financial crisis on entry-level jobs and young adults' family formation capabilities due to debt burdens. He blames the global financial system, including real estate, for rising house prices exceeding income growth. Proposing radical changes, Thiel suggests political parties purge members tied to dysfunctional real estate systems and encourages young adults to engage politically with groups like Reform, viewing Nigel Farage's right-wing approach as comparatively less detrimental than older figures. John Power advises twentysomethings to participate in politics, recommending Reform over Labour or Tories, while cautioning against radical youth movements' historical pitfalls such as communism and fascism. He notes Gen Z's significant constraints but their potential for meaningful change, possibly sidestepping earlier movements' flaws while encountering new challenges. The speaker critiques the political landscapes of Germany, France, and Britain, labeling France as overly socialist, Germany as ideologically extreme with a Green party focus, and Britain as unpragmatic despite possible efficiency gains within its state apparatus. They warn of three future paths for Europe: Islamic sharia law, Chinese-style totalitarianism, or an environmentalist ideal represented by Greta Thunberg's activism, suggesting only the latter is currently viable. The speaker also notes American right-wing critiques of European conditions as potentially overlooking worse social issues within the U.S., exemplified by areas like Skid Row in Los Angeles. Advocating for the Trump-era Republican party, the speaker contrasts it with what they see as a stagnant "zombie" Reagan-Bush era, interpreting Trump's "Make America Great Again" slogan as acknowledging America’s decline while warning against nihilistic despair. They criticize American isolationism, describing the U.S. as "semi-autistic," oblivious to global events. Regarding the UK's Thatcher era, the speaker questions its portrayal as a respite from decline, arguing that while Thatcher implemented necessary but unpopular policies, government size and power did not significantly decrease. They draw parallels with the U.S. under Reagan, noting initial optimism but ultimately continuing government growth under Clinton and Blair. The speaker attributes increased inequality more to globalization than capitalism itself. The text suggests while capitalism doesn't inherently increase inequality, globalization under leaders like Clinton and Blair did, leading to greater wealth disparity. It proposes shifting focus from simply increasing capitalism (Reagan-Thatcher era) to emphasizing science and technology advancements today. Regarding Helen Andrews' 'Great Feminisation' theory, the text neither endorses nor refutes it as a cause of stagnation but presents it for consideration, advocating for exploring various factors influencing current economic and societal conditions, including shifts in workplace culture due to feminism. The speakers discuss societal fears about dangerous scientific advancements leading to risk aversion, interconnected with the rise of feminization and DEI initiatives perceived as suppressing potentially reckless technological progress driven by high-testosterone male scientists. They argue that while this may create a less groundbreaking world, it reduces risks of catastrophic outcomes. The discussion revolves around addressing societal stagnation, attributed to factors like feminization and risk aversion. Participants question the causes and propose actions to escape perceived constraints without resorting to extreme ideologies. They recognize opportunities for progress through political engagement but also see potential in independent, decentralized efforts, especially in tech hubs like Silicon Valley. There’s a recognition of AI's dual nature—potential benefits and risks—with concerns about its concentration in large corporations leading to uneven growth and job displacement. The user contemplates whether the AI trend is sustainable or a bubble, suggesting macroeconomic uncertainty. Finally, Thiel distinguishes between general entrepreneurship and creating scalable businesses capable of escaping competitive homogeneity, likening successful models to unique characters in Anna Karenina amidst common failure traits. He emphasizes the importance of historical context for learning while cautioning against over-reliance, urging a balanced approach towards shaping the future. ### Key Points: - Peter Thiel predicts young people's turn to socialism due to financial pressures (student debt, housing costs). - Gen Z voters favor alternatives outside traditional party lines (New Labour, Tories). - Globalization backlash shaping modern politics, as previously predicted by Thiel. - Student debt escalation from $300 billion to $2 trillion, impacting entry-level jobs and family formation. - Real estate system blamed for rising house prices outpacing income growth. - Radical measures proposed for political parties, including purging members tied to dysfunctional real estate systems. - Encouragement for young adults to engage in politics via groups like Reform. - Critique of European political landscapes (France, Germany, Britain) and warnings about three future paths for Europe. - American right-wing critiques of Europe overlooking worse U.S. social issues. - Advocacy for Trump-era Republican party versus stagnant Reagan-Bush era, interpreting "Make America Great Again" as acknowledging decline. - Criticism of American isolationism and description of the U.S. as "semi-autistic." - Attribution of increased inequality primarily to globalization rather than capitalism. - Discussion on shifting focus from increasing capitalism to science and technology advancements. - Consideration, but no endorsement, of Helen Andrews' 'Great Feminisation' theory regarding stagnation. - Link between societal fears about dangerous scientific advancements and rise of feminization/DEI initiatives. - Balancing historical reflection with future orientation in shaping societal progress. - Distinguishing general entrepreneurship from creating scalable, unique businesses. Keywords: #granite33:8b, AI bubble, AI chips, AI revolution, America's greatness, Blair, CCP, Chinese-style, Clinton, Europe, Gen Z, Gini coefficient, Greta Thunberg, Islamic law, Labour, Reagan influence, Reform party, Republican party, Sharia law, Silicon Valley, Thatcher era, Tories, Trump, US advantage, US inequality, US isolationism, Zero to One, affirmative action, agency, anti-tech goals, authoritarianism, blackpilled, budget deficit, businesses, capitalism, communism, competition, consensus culture, conservatism, constraints, costs, cultural Marxism, data centers, deregulation, diminishing returns, diversity inclusion, economic growth, emotional decision-making, entrepreneurship, environmentalism, fascism, feminism, firing, future-oriented, gerontocracy, globalisation, government size, identity politics, immigrants, inequality, inflation, interest rates, labor substitute, macroeconomic trends, medieval play, multiculturalism, nationalism, nihilism, optimism, oversight, pessimism, politics, power demand, productivity, revolution, right-wing, risk averse society, safety prioritization, scalable, socialism, society, stagnation, surveillance, taco truck, technology, telecom infrastructure, uneven growth, welfare
ai
spectator.com 16 hours ago
https://archive.ph/JsWRv 14 hours ago |
94. HN Monetizers vs. manufactures: How the AI market could splinter in 2026- The AI market is forecast to split into two sectors by 2026 due to current volatility, spurred by investor concerns over an inflated AI bubble. This turbulence stems from circular deals, debt issuances, and high valuations within the sector. - Stephen Yiu, chief investment officer at Blue Whale Growth Fund, anticipates that as the market matures, investors will categorize AI firms into three groups: 1. Companies possessing a product but lacking sustainable business models. 2. Firms heavily investing in AI infrastructure development without immediate profitability. 3. Businesses reaping rewards directly from AI expenditures. - Yiu distinguishes three investment categories for AI: private startups (such as OpenAI), publicly traded companies allocating funds to AI (Big Tech firms), and infrastructure suppliers (Nvidia, Broadcom). Notably, venture capital surged into early-stage startups ($176.5 billion in Q1-Q3 2025), while Big Tech focused on building foundational AI infrastructure. - Yiu cautions against overvaluation in the AI sector, indicating that major AI spenders (the 'Magnificent 7') trade at a substantial premium, possibly due to inflated expectations rather than solid performance. His advice leans towards capitalizing on AI spending impacts instead of directly investing in high-profile spenders. - Barclays' analyst, Lafargue, concurs with Yiu's concerns regarding excessive 'froth' or speculative investment in the AI sector, particularly affecting non-earning companies like certain quantum computing firms where optimism surpasses actual outcomes. Differentiation among AI companies is deemed crucial for navigating this crowded and increasingly scrutinized market. BULLET POINT SUMMARY: - The AI sector anticipated to split into 'monetizers' (profitable companies) vs. 'manufacturers' (product developers) by 2026 due to market maturation and investor caution over inflated valuations. - Three investment categories identified: private AI startups, publicly funded AI spenders (Big Tech), and infrastructure providers (Nvidia, Broadcom). - Venture capital significantly poured into early-stage startups in 2025 ($176.5 billion), contrasting with Big Tech's focus on building foundational AI technologies. - Warnings against overvaluation of major AI spenders, advised to trade at premium due to speculative fervor rather than concrete performance. - Emphasis on differentiation crucial for investment decisions amidst excessive optimism and potential 'froth' in the sector, especially affecting non-revenue generating companies like some quantum computing firms. Keywords: #granite33:8b, AI, AI infrastructure, AI infrastructure firms, Big Tech, Blue Whale Growth Fund, Broadcom, ETFs, Nvidia, bubble, cash burning, circular deals, debt issuances, differentiation, earnings, free cash flow yield, high valuations, investment, investor positioning, listed AI spenders, market, product, quantum computing, rallies, retail investors, spending, splinter, startups, tech sell-offs, valuations, venture capital
ai
www.cnbc.com 16 hours ago
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95. HN Show HN: Play Riichi Mahjong Online- The user has created an online platform for playing Riichi Mahjong, catering to both guests and registered users. - Players can choose to compete against human opponents or AI bots. - Key features include access to game history and a hand calculator tool for evaluating offline games. - The website's design focuses on simplicity while striving for an authentic Mahjong experience, drawing inspiration from the model of 'online-go'. - Technical aspects involve the use of Postgres for database management, Rust for backend development, and Preact for the frontend interface. - The project is presented as a year-end commitment, and the developer is seeking guidance on user acquisition strategies. - Interested parties can reach out via Discord or email at cerpinsmiks@gmail.com for further inquiries or to provide feedback. Keywords: #granite33:8b, Discord, Go inspiration, Postgres, Preact, Riichi Mahjong, Rust, bots, email contact, game history, guest play, hand calculator, online, player base, registered users, simplistic client
postgres
online-riichi.com 16 hours ago
|
96. HN How Claude Code is helping me as an open source maintainer- **Project Background and Challenge**: The open-source maintainer of popular VPN installation scripts (openvpn-install and wireguard-install) confronts a mounting backlog of issues and pull requests due to varied user use cases and edge cases. The challenge lies in managing this backlog efficiently, which involves understanding the relevance of each issue, assessing feature feasibility, testing across multiple distributions, and processing PRs. - **Solution Implementation**: To tackle this, the maintainer implements automated validation with a GitHub Actions workflow running tests on DigitalOcean droplets to catch errors early. Limited by Docker constraints initially, they use Claude (an LLM) to develop a comprehensive end-to-end testing workflow within Docker, encompassing over 30 tests in various configurations. - **Testing Workflow**: The setup involves orchestrating server and client containers with linear scripts and inter-container synchronizations through file checks. This approach allows for thorough testing of installation features to client connections and revocations, reducing the reliance on manual cloud VM testing while speeding up development iterations and addressing longstanding bugs and missing features in the project backlog. - **Improvements Achieved**: With this automated setup, key enhancements include improved logging, early error detection, better CLI interfaces, extensive non-interactive mode support, robust IPv6 and IPv4 handling, enhanced firewalld/nftables support alongside iptables, updated OpenVPN features (TLS 1.3, tls-crypt-v2, new ciphers), proper revocation mechanisms, client name reusage, disconnect management, and certificate status maintenance. Dependency updates for easy-rsa to address CVEs and improvements in Unbound setup are also incorporated. More distro support, such as Arch Linux and openSUSE, is added alongside various quality-of-life enhancements and validations. - **AI Assistance**: Claude Code aids in drafting implementation plans, understanding code evolution, rebasing contributions, and even generating missing tests and documentation, leading to resolving 150 issues and 50 pull requests within about 10 days. The user expresses satisfaction with the efficiency and quality improvements brought by AI tools like Claude Code and Opus 4.5. - **Comparison of Tools**: The maintainer prefers CLI agents (like Claude Code) for faster response times and simultaneous execution on multiple branches using Git worktrees over IDE agents. They acknowledge increasing competition in this field but highlight Claude Code's superior capabilities compared to open-source alternatives, though without specific comparison data to other models. - **Opus 4.5 Evaluation**: The user praises Opus 4.5 for its advanced features like web and repository searching, understanding project design decisions, and streamlining local tests and gh CLI integration. They note that while some competitors support web search, it's often optional in those models. Despite the rapid pace of model updates potentially rendering Opus 4.5 obsolete, its current intelligence and contextual understanding are valued. - **Copilot Review Bot**: The maintainer uses a Copilot review bot that provides comments on PRs but notes its accuracy is around 50%. - **Outcome**: The project's GitHub issues and pull requests decreased from 150 to zero through architectural reworking, bug fixes, additional tests, and AI assistance, resulting in enhanced project quality and user satisfaction with the improvement process. Keywords: #granite33:8b, Arch Linux, CI/CD, CLI, CLI agents, CLI interfaces, Claude Code, Copilot, Copilot CLI, DCO kernel module, DigitalOcean, Docker, GPT-5x models, Ghostty tabs, Git worktrees, GitHub, GitHub Actions, GitHub PRs, IP routing, IPv6 support, LLMs, Mistral, Open source, OpenVPN, OpenVPN 24, Opus 45, PRs, TLS 13, TUN module, Unbound setup, VPN, VS Code, WireGuard, architecture, automation, backlog management, bash scripts, bug fixing, bugs, ciphers, client & certificate status, client disconnect, client name reuse, cloud VMs, codex, distribution testing, distros, easy-rsa updates, end-to-end testing, features, firewalld, gh CLI, implementation plans, iptables, issues, labels, local tests, logging, maintenance, manual testing, models, nftables, open-source alternatives, openSUSE, pull requests, quality-of-life improvements, rebase, review bot, revocation support, scripts, tests, tls-crypt-v2, triage, validation, web search
github copilot
stanislas.blog 16 hours ago
|
97. HN Show HN: Smig – Automatic SurrealDB Migrations- **Smig Overview**: A TypeScript tool designed for schema migrations in SurrealDB 3, specifically tailored to its unique features such as graph relations, vector indexes, full-text search, and multi-model capabilities. - **Functionality**: Smig allows developers to describe their desired database schema using a user-friendly API, automatically generating the corresponding SurrealQL (SQL) queries for migration. - **Unique Aspects**: Unlike generic migration tools, Smig is crafted to handle SurrealDB 3's distinct elements, ensuring seamless integration and migration processes. - **Schema Definition**: Developers define tables, fields, and indexes within a schema file which Smig uses as a reference for generating necessary SQL commands. - **Migration Process**: Smig compares the user-defined schema with the current database state, then generates and executes SQL commands to synchronize them, logging each migration for future reference. Keywords: #granite33:8b, API, COSINE, HNSW, MTREE, SQL, SurrealDB, SurrealQL, TypeScript, assertions, custom analyzers, fields, full-text search, graph relations, indexes, migration, migrations, multi-model, schema definition, tokenizers, vector indexes
sql
smig.build 16 hours ago
|
98. HN Package managers keep using Git as a database, it never works out**Summary:** The text examines the use of Git as a database for package managers and the scalability challenges encountered by Cargo, Homebrew, CocoaPods, Nixpkgs, and Go. Initially attractive due to built-in version control features, Git's inherent design limitations became evident as these systems scaled: 1. **Cargo (Rust):** - Started with cloning crates.io as a repository, leading to performance issues due to resolving deltas across thousands of commits. - RFC 2789 introduced sparse HTTP protocol for on-demand metadata fetching via HTTPS, significantly improving efficiency and reducing full index access. 2. **Homebrew (macOS package manager):** - Utilized shallow clones but faced escalating costs as taps grew; users had to download large amounts of data for updates. - Transitioned to JSON downloads for tap updates in version 4.0.0, citing poor performance caused by Git's delta resolution process. 3. **CocoaPods (iOS/macOS):** - Experienced slow cloning and updating times due to a massive repository with hundreds of thousands of podspecs. - Migrated to a CDN for serving podspec files directly over HTTP in version 1.8, reducing disk usage and installation times. 4. **Nixpkgs (Nix Linux distribution):** - Faces infrastructure stress on GitHub due to its large repository size and CI queries creating daily merge commits; cannot easily transition to a CDN. - Binary caches serve built packages over HTTP, but the repository growth continues to pose challenges. 5. **Go:** - Improved dependency resolution from 18 minutes to 12 seconds by deploying a module proxy, addressing inefficiencies of fetching entire repositories for single files and security concerns related to version control tools. - Introduced GOPROXY and checksum database (sumdb) for secure HTTP access to source archives and go.mod files. 6. **GitOps Tools:** - Face limitations due to Git's filesystem origins, including directory limits causing slow performance, case sensitivity conflicts, path length restrictions, and lack of database features like constraints and indexes. - Solutions such as hash-based sharding for directory management and server-side enforcement for case-conflicting paths are implemented but seen as reinventions of poorly executed solutions. **Key Takeaway:** Using Git as a package manager index leads to scalability issues, cross-platform complications, and the need for extensive workarounds due to its inefficiencies in handling fast metadata queries. Projects eventually adopted databases or HTTP interfaces to overcome these limitations. Keywords: #granite33:8b, API rate limits, ArgoCD, CDN, CPU rate limits, Cargo, CocoaPods, GOPROXY, Git, Git for Windows constraints, Git history, Git limitations, Git-based wikis, GitHub, GitLab, Go modules, Gollum, HTTP, Homebrew, JSON downloads, Nixpkgs, Package managers, auto-updates, caching issues, case sensitivity, checksum database, cratesio, cross-platform issues, custom indexes, databases, delta resolution, dependency resolution, directory limits, filesystem databases, full index replication, git rewrites, go get, iOS, large repositories, libgit2, macOS, merge commits, migrations, module proxy, monorepos, nix expressions, on-demand queries, path length limits, podspec files, pull requests, read-only state, repo server, repository stress, server-side enforcement, shallow clones, sharding, sumdb, tap updates, transitive dependencies, version history
github
nesbitt.io 16 hours ago
|
99. HN GitHub – rcarmo/feed-summarizer: The feed summarizer that powers feeds.carmo.io- **Project Overview**: The Feed Summarizer, developed by rcarmo on GitHub, is an asyncio-based background service designed to fetch news from multiple RSS/Atom sources and optionally Mastodon. It stores raw items in SQLite, generates AI summaries using Azure OpenAI, groups them into daily bulletins, and publishes both HTML and RSS outputs to Azure Blob Storage. Initially conceived as a personal Node-RED flow, it evolved into a Python script demonstrating spec-driven development. - **Key Features**: - Efficient feed fetching with conditional logic and backoff for respecting ETag/Last-Modified headers. - Extensive error handling, logging, and observability through Azure Application Insights and OpenTelemetry. - Smart scheduling and optional AI summarization via Azure OpenAI. - Support for deduplication using SimHash, passthrough feeds, and Azure Blob Storage upload with MD5 de-duplication checks. - Comprehensive documentation covering configuration, running, publishing, telemetry, troubleshooting, architecture, merge tuning, retention controls, and a long-form spec. - **Deployment**: The project is deployable via Docker Swarm services using kata, a personal infrastructure tool, with a quickstart involving five commands for setup and execution. - **Governance & Contributions**: - The project is open-source under the MIT License. - Welcomes contributions with documented guidelines in CONTRIBUTING.md. - Outlines a code of conduct in CODE_OF_CONDUCT.md. - Provides security report procedures detailed in SECURITY.md. - Emphasizes review processes for clarity and maintainability of all code. - **Specifications**: The project focuses on four key aspects: - **MERGE_TUNING**: Addresses deduplication strategies, merge behaviors, and diagnostics. - **RETENTION**: Manages age windows and retention controls. - **SPEC**: Offers a detailed long-form architecture and runtime specifications. - **Contributions & License**: Covers the project’s licensing under MIT, contribution guidelines, code of conduct, and security reporting procedures. Keywords: #granite33:8b, AI summaries, Azure OpenAI, CONTRIBUTIONS, Docker, GitHub, HTML, LICENSE, MD5, MERGE_TUNING, Node-RED, OpenTelemetry, Python, RETENTION, RSS, SPEC, SQLite, Swarm, asyncio, dependencies, feeds, graceful shutdown, kata, pip, scheduling, virtualenv
github
github.com 16 hours ago
|
100. HN Show HN: VideoReview – Collaborative video review for games and animation- **Tool Overview**: VideoReview is a collaborative video review tool specifically designed for game cutscene and animation teams, but also applicable to other sectors like video production. - **Key Features**: - Time-based comments for precise feedback within videos. - Drawing tools on frames for visual annotations. - A search function allowing quick navigation of long video files. - Tree-based organization for efficient video management. - Activity indicators to track updates and new feedback. - Integrations with Jira and Slack for task creation and real-time communication. - **Demo and Language Support**: - A live demo available at - Japanese language support for global accessibility. - **Integration Capabilities**: - Slack integration facilitates sharing feedback with comments, timestamps, and direct video frame references. - Direct creation of Jira tickets from comments to streamline development tasks. - **Automation and API**: - Offers a REST API compatible with CI/CD pipelines for automated upload of build recordings for daily review processes. - **Deployment Flexibility**: - Provides options for on-premises deployment using AWS S3 or within an internal network. - Development can be done via Docker or a local setup using Node.js v24 and PostgreSQL. - **Documentation and Resources**: Detailed setup, access, building, deployment instructions, along with license information are included to support users in adopting the tool effectively. Keywords: #granite33:8b, API Documentation, Build & Deploy, Docker Deployment, Jira Ticket Creation, Jira integration, Local Setup, MIT License, Nodejs, On-premises Storage, PostgreSQL, REST API Automation, SNS-like interface, Slack Integration, Slack communication, Slack sharing, VideoReview, Visual Annotations, Web UI, activity indicators, animation review, collaborative tool, direct frame drawing, game cutscenes, keyword search, lightweight interface, task creation, time-based comments, tree-based organization, video libraries
postgresql
github.com 17 hours ago
|
101. HN Show HN: Mandate – treating AI agents like economic actors, not scripts**Summary:** Mandate is a framework designed to govern AI agents as distinct economic actors with stable identities rather than mere scripts, addressing challenges like accountability gaps, anonymous operations, and over-reliance on prompts for authority in current agent management. The system enforces runtime authorization through policies, rules, and short-lived mandates, enabling controlled spending limits, restricted tool access, instant termination, and transparent auditing. **Key Features and Functionality:** - **Layered Execution Model:** Ensures authorization before execution and controls access to tools via rate limits and charging policies. It accurately settles costs, updates budgets upon completion, and logs decisions with reason codes for audits. - **Mechanical Enforcement:** Focuses on deterministic rules rather than relying on AI judgment or subjective prompts, ensuring explainability and fail-safe operations. In Phase 3, it integrates a Redis backend for distributed state management, using Lua scripts for race-free enforcement and Pub/Sub for immediate agent termination. - **Compliance and Risk Management:** Provides mechanisms for adhering to budget, rate, and scope limits on agent actions. It ensures accountability by tracing every action to an agent and principal and supports governance through policy-driven enforcement. - **Architecture and Design Principles:** The Mandate SDK is organized into eight layers: Types & Policy Engine, State Management, Executor, Cost Estimation, Helper Functions, Audit Logging, Kill Switch, and MandateClient. It prioritizes pure functions, deterministic logic, side-effect freedom, and structured architecture for reliability, flexibility, transparency, and maintainability. - **Charging Policies:** Different tools utilize various charging methods such as attempt-based, success-based, tiered pricing, or custom logic. All policies must be pure, synchronously evaluated during settlement to ensure consistency and reliability. Custom pricing allows for flexible options including rates from major providers, user-specific models, wildcards, or free local models with warnings. - **Phased Development:** - **Phase 1** introduced local runtime enforcement with budget limits, rate limits, a kill switch, audit logging, and charging policies. - **Phase 2** focused on agent identity stability, principal tracking, mandate issuance, validation using Zod schemas, and programmatic mandate creation. - **Phase 3** concentrated on distributed coordination via Redis, offering global per-agent limits, atomic budget enforcement through Lua scripts, and a distributed kill switch utilizing Pub/Sub. **Future Directions:** - The system aims to address broader issues in distributed systems such as budget leakage, identity collapse, silent failures, and cross-system trust by enhancing delegation and responsibility with authority reduction and verifiable authority for cryptographically signed mandates. **Conclusion:** Mandate is a comprehensive solution that seeks to enhance the accountability, safety, and transparency of AI agents in production environments through mechanical, deterministic governance. It offers a robust framework for managing agent behavior across single-process and distributed systems while ensuring compliance, risk mitigation, and adherence to predefined policies. Keywords: #granite33:8b, AI agents, KYC, Mandate, Pub/Sub, Redis, SDK, accountability, agent termination, atomic operations, audit trails, budget enforcement, budget limits, charging policies, compliance, cost reconciliation, custom pricing, determinism, distributed state, economic actors, explainable systems, fail-closed, global limits, governance, instant kill, layered execution, policy enforcement, risk management, runtime authority, runtime model, stable identity, tool restrictions
ai
github.com 17 hours ago
|
102. HN Waymo Is Working on a Gemini AI Assistant. Here's the System Prompt- **Gemini AI Assistant**: Developed by Waymo to enhance the rider experience in self-driving vehicles through conversational assistance rather than managing autonomous driving functions. - **Key Features and Capabilities**: - Conversational interaction for answering queries, controlling cabin settings (HVAC, music, lights), providing reassurance, and handling compliments. - Adaptive response system tailored to text versus speech inputs with a focus on brevity in audio interactions. - Personalization using rider context such as name and trip history for customized responses. - Direct control of certain vehicle functions while redirecting others to the in-car interface or Waymo app. - Graceful exit strategies after repetitive out-of-scope questions, managing conversational loops effectively. - Handles ambiguous requests using a 'Guess and Confirm' approach to reduce rider effort. - Prioritizes comfort-related queries by clarifying, executing best guesses, or deflecting based on available actions. - Strict adherence to safety and privacy protocols: no control over vehicle functions (speed, path), declines financial transactions for security reasons, cautious handling of personal information. - Utilizes pre-scripted responses for version inquiries and brand-appropriate humor. - Double-pull exit protocol emphasizing safety comparable to emergency exits. - Nuanced response strategies for ambiguous stop requests to guide riders to appropriate actions based on context. - **Waymo Vehicle and App Integration**: - In-car controls manage temperature, fan speed, pullover, ride initiation, trunk access. - Waymo app features include booking rides, managing settings (account, accessibility), providing feedback, locating vehicles, and handling remote control functions. - **Complex Request Management**: - Employs a two-step process for compound requests, addressing manageable parts first before suggesting guidance or deflection for unfulfillable components. - Provides aspirational messaging for unsupported feature requests, indicating future enhancements. - **Handling Malfunctions and Banned Topics**: - Directs riders to use the Waymo app for reporting vehicle issues, avoiding troubleshooting engagement. - Strict protocols in place to decline requests involving sexually explicit, hateful, illegal, dangerous, or offensive content, maintaining safety and appropriateness. - **Customization and Ongoing Development**: - Guides riders to the Waymo app for personalizing settings such as rider initials, accessibility options, and music preferences. - Provides aspirational responses for seat position and lighting adjustments not currently offered directly via Gemini. - Continually refining features to improve user experience with self-driving technology. - **Waymo's Statements**: - A Waymo spokesperson statement, as referenced but lacking specific context or quotes in the provided text, would require additional information for a detailed summary. Keywords: #granite33:8b, AI assistant, HVAC control, PII (Personally Identifiable Information), Tesla Autopilot, Waymo, Waymo Driver, ambiguity, banned topics, cabin temperature, cameras, climate control, comfort requests, commerce requests, competitor, compliments acknowledgment, context, conversational assistant, conversational loops, empathetic responses, financial requests, graceful exit, guess and confirm strategy, handling ambiguous stop request, hard boundaries, heated seats, intent disambiguation, lidar, modality awareness, out-of-scope questions, personalized interactions, pullover initiation, radar, reassurance protocol, redirection, rider anxiety, rider data, rider support, runtime contextual data, safety design, self-driving vehicles, sensors, start ride button, trunk closure, vehicle feature limitation, vehicle issues reporting
gemini
wongmjane.com 17 hours ago
|
103. HN Show HN: One AI API for word-accurate transcription, translation, and export- The user has engineered a comprehensive AI API aimed at resolving the disarray of existing transcript APIs. - This unified solution fetches metadata from video and audio files, incorporates noise reduction for clearer transcription, and uses voice activity detection to focus on speech segments. - It excels in generating word-level transcripts swiftly, even for content lacking captions, making it versatile across various media types. - The API is adaptable, providing output in multiple formats such as plain text, SRT, VTT, and JSON, ensuring compatibility with diverse use cases. - It supports a wide array of languages, offering translations into over 100 languages with an estimated accuracy of 95%. - Users can employ the service by uploading local files in various formats or by integrating links from platforms including Twitter and YouTube. - A user-friendly web interface, referred to as a playground, has been developed for individuals without coding expertise to interact and experiment with the API’s functionalities effortlessly. Keywords: #granite33:8b, AI, API, JSON, SRT, VTT, accuracy, audio files, formats, languages, links, metadata, noise reduction, plain text, platforms, scale, transcription, translation, uploads, video files, voice detection, word-level transcripts
ai
www.transcripthq.io 17 hours ago
|
104. HN Ingestr: CLI tool to copy data between any databases with a single command- **Overview**: Ingest is a command-line tool facilitating data transfer between diverse databases with minimal effort, obviating the need for programming. - **Key Features**: - Eliminates coding for transferring data from source to destination. - Compatible with a wide array of sources (PostgreSQL, MySQL, SQLite, etc.) and destinations (BigQuery, Snowflake, Redshift, more). - Simplified setup: Users initiate ingestion with one command specifying the source, target table, destination, and target table. - **Installation**: - Installation via single command using 'uv pip install --system ingestr'. - Post-cloning, run 'make setup' to install Git hooks for project operation. - **Support and Contribution**: - Extensive documentation and community support available on the project page. - Encourages contributions following an issue discussion for enhancements or bug fixes. - **License and Additional Information**: - The project is open-source under the MIT license, with some components licensed under Apache 2.0. - Further information, including licensing details, can be accessed in LICENSE and NOTICE files within the repository. - Users are prompted to submit issues for further source or destination support requests. BULLET POINT SUMMARY: - Ingest is a command-line tool simplifying data transfer across numerous databases without coding. - Supports an extensive list of sources (PostgreSQL, MySQL, SQLite) and destinations (BigQuery, Snowflake, Redshift). - Single-command setup specifying source URI, table, destination URI, target table facilitates easy use. - Offers comprehensive documentation, community support, and encourages contributions for improvements. - Licensed under MIT with parts under Apache 2.0; licensing details in LICENSE and NOTICE files. - Users invited to submit issues for additional database support requests. Keywords: #granite33:8b, Adjust, Airtable, Amazon Kinesis, Apache 20, Apache Kafka, App Store, AppsFlyer, Asana, Attio, BigQuery, CLI tool, Chesscom, ClickHouse, CrateDB, Databricks, DuckDB, DynamoDB, Elasticsearch, Facebook Ads, GCP Spanner, GitHub, Google Ads, Google Analytics, Google Sheets, Gorgias, IBM Db2, Klaviyo, LinkedIn Ads, Local CSV, MIT License, Microsoft SQL Server, MongoDB, MotherDuck, MySQL, Notion, Oracle, Personio, Phantombuster, Pipedrive, Postgres, Redshift, S3, SAP Hana, SQLite, Salesforce, Shopify, Slack, Slack community, Smartsheets, Snowflake, Solidgate, Stripe, TikTok Ads, Trino, Zendesk, cloning, contributing, data ingestion, databases, documentation, githooks, global installation, incremental loading, installation, no code, pip, pull requests, quickstart, single command, source-destination, supported sources destinations, uv
github
github.com 17 hours ago
|
105. HN Reflections on building internal tools after AI changed the workflow- The author discusses the shift in app development due to AI, with customers increasingly using prototyping tools like Lovable or Replit before handing off projects to engineers for internal building via Cursor. This trend has resulted in fewer projects on their low-code platform, DronaHQ. - In response, the DronaHQ team has developed two new tools: - A 'vibe-code' tool designed for creating production-ready internal apps quickly without compromising structure or clarity. - An AI agent builder that simplifies the creation of RAG (Retrieve, Align, and Rank), chat, voice, and autonomous agents without requiring coding skills. - These new tools are engineered to work seamlessly together on DronaHQ. - The author seeks thoughtful engagement from the appropriate audience, aiming for: - A Show HN (Hello World) with fair consideration of their updates. - Constructive comments and feedback for product improvement. - Traffic driven by curiosity and interest in novel solutions. - Long-term users who will contribute to advancing the product. - The author commits to maintaining clear documentation, honest positioning, and consistent shipping of updates, expressing openness to good timing or luck in their endeavors. Keywords: #granite33:8b, AI, AI agent builder, DronaHQ, RAG, Show HN, chat, documentation, internal tools, interoperability, low-code, non-coding, production-ready apps, vibe-code, voice agents
rag
news.ycombinator.com 17 hours ago
|
106. HN Contributing to Debezium: Fixing Logical Replication at Scale- **Challenge**: Zalando, using Debezium and PostgreSQL logical replication within Fabric Event Streams, encountered significant WAL growth issues due to replication slots not advancing in low-activity databases, causing disk space exhaustion. - **Solution Development by Zalando Engineers**: - Modified the PostgreSQL JDBC driver to respond to keepalive messages from Postgres for advancing replication slots when no changes occur. - This stabilized production systems over two years with no data loss, but upgrading Debezium versions became problematic as it disabled the pgjdbc keepalive flush feature. - **Proposal and Implementation**: - Proposed DBZ-9641 and PR #6881 introducing 'lsn.flush.mode' configuration option with modes: manual, connector (default), and off. - This allows users to retain the safe feature while ensuring Debezium's default safety behavior. - **Addressing WAL Growth**: - The new 'connector_and_driver' mode allows both Debezium and PostgreSQL JDBC driver to flush LSNs, preventing WAL growth on infrequently changed databases. - Backward compatibility is maintained by mapping the old 'flush.lsn.source' boolean to the new enum values. - **Zalando's Unique Approach**: - Since 2018, Zalando uniquely relies on PostgreSQL replication slots as definitive source of truth for stream position using Patroni and later Postgres Operator for failover management. - This ensures slot durability during failovers, contrasting with most Debezium users who use persistent offset stores like Kafka Connect's offset topics. - **Debezium Offset Handling Issues**: - Initial methods for handling offsets had issues when keepalive flushes were initiated by the pgjdbc driver. - Two strategies were proposed: 'trust_slot' and 'trust_greater_lsn', maintaining backward compatibility while offering more control over offset mismatches. - **New Configuration Properties**: - Introduced `offset.mismatch.strategy` with four strategies: no_validation (default), trust_offset, and two others for specific use cases. - Strategies include 'trust_slot' for aligning the connector's offset with the replication slot and 'trust_greater_lsn' for synchronizing to the maximum LSN. - **Outcome**: - Two new features for safer logical replication are now available in Debezium nightlies, addressing WAL growth issues without dummy writes. - Users can configure `lsn.flush.mode=connector_and_driver` along with `offset.mismatch.strategy=trust_greater_lsn` to prevent WAL accumulation and enable self-healing recovery from corrupted segments. - **Zalando's Application**: - Zalando applies methods like `offset.mismatch.strategy=trust_offset` across hundreds of Postgres databases to ensure reliability and safety in replication systems. Keywords: #granite33:8b, Debezium, Fabric Event Streams, JDBC driver, Kubernetes, LSN management, LSN validation, MemoryOffsetBackingStore, PostgreSQL, WAL growth, backward compatibility, configuration property, conflict resolution, connector mode, corrupted WAL segments, data loss detection, declarative streams, dummy writes, event streaming, failover management, flushlsnsource, full re-syncs, keepalive, low-activity databases, offset reliability, pg_replication_slot_advance(), replication, replication slot position, row-level changes, self-healing recovery, slots, trust strategy, uncontrolled growth
postgresql
engineering.zalando.com 17 hours ago
|
107. HN Prompts.chat: the social platform for AI prompts**Summary:** Prompts.chat is a specialized social platform engineered for AI-generated prompts, primarily catering to crypto projects. It aims to enhance community engagement and dialogue across platforms like Twitter (X), Discord, and Telegram by employing Crypto Yapper specialists. These experts manage discussions, engage key community members, and ensure that project communications align with market narratives for effective and meaningful interactions. Key responsibilities include: - Engaging active community members and influencers to increase visibility. - Creating conversation angles and drafting high-impact announcements that resonate with the audience. - Analyzing feedback to inform project decisions and extract unique selling points from project objectives, tokenomics, and roadmaps. - Proofreading content for clarity and quality, ensuring replies are informative, engaging, and relevant. The approach is characterized by: - Opinionated yet insightful responses, mimicking expert knowledge with a slightly informal tone. - Encouragement of community engagement through witty or narrative-driven content. - Maintenance of a respectful yet bold atmosphere fitting for the crypto culture. For non-premium Twitter users, concise replies under 150 characters, in English but tailored in Indonesian style, will be crafted, including mentions and relevant hashtags (with space for links). To evade AI detection: - Structured marketing language is avoided; subjective phrases are used. - Typography includes lowercase emphasis and sentence fragments to appear more human-like. - The project's purpose and market significance will be clearly explained without corporate announcements, citing personal bullish convictions based on project merits. All content generated will be original, adhering strictly to Twitter’s formatting guidelines while ensuring compliance with post analysis specific to the platform. **Bullet Points:** - Platform: Prompts.chat - AI-generated prompts for crypto projects, focusing on platforms like Twitter (X), Discord, Telegram. - Role: Crypto Yapper Specialist managing and optimizing community dialogues. - Objectives: Increase visibility, boost engagement, inform project decisions with feedback analysis. - Communication Style: Informative, engaging, opinionated, slightly informal, aligning with crypto culture. - Content Tailoring: Concise replies for non-premium Twitter users under 150 characters, in English, Indonesian style. - AI Detection Evasion: Use of subjective language, human-mimicking typography, original content without copies or AI-like text. - Compliance: Strict adherence to Twitter's formatting guidelines and post analysis requirements. Keywords: #granite33:8b, AI Prompts, Alpha, Announcements, Community Management, Crypto, Discussions, Engagement, English, Feedback, High-Quality, Indonesian, Influencers, Market Cycle, Narrative, Objectives, Original Content, Platform, Project Support, Proofreading, Replies, Roadmaps, Specialist, Strategy, Tokenomics, Twitter, Typography, USPs
ai
prompts.chat 17 hours ago
|
108. HN Show HN: FailCore – Execution-Time Safety Runtime for AI Agents- FailCore is a beta execution-time safety runtime (version 0.1.x) licensed under Apache 2.0, focusing on protecting AI agents during their execution rather than enhancing their intelligence. - It employs runtime hooking to enforce security at the Python execution boundary, preventing unauthorized actions such as SSRF, private network access, and unsafe filesystem operations before any tool side-effects occur. - FailCore provides live demos demonstrating its effectiveness in blocking real attacks and generates forensic audit logs along with HTML reports for incident analysis. - Key features of the latest version (v0.1.x) include SSRF Protection via network-layer validation, Filesystem Sandbox to detect and block path traversal attacks, and a one-command generation feature for professional HTML dashboards for audit reports. - The tool distinguishes between BLOCKED (threat neutralized) and FAIL (tool error) statuses, ensuring clear differentiation in event outcomes. - FailCore aims to address core execution risks in modern AI agents by integrating deterministic workflow replay, enhanced visibility with detailed forensic reports, and cost-effectiveness by avoiding the need to restart entire workflows due to a single step failure. - It works with LangChain and encourages contributions for developing more robust agent systems, all under the Apache License 2.0. Keywords: #granite33:8b, / detection, AI agents, Apache License 20, BLOCKED, DNS resolution, FAIL, FailCore, HTML dashboards, LLM attack simulation, LangChain integration, SSRF blocking, Session, agent systems, audit reports, block, deterministic replay, execution trace, filesystem sandbox, filesystem side-effects, forensic HTML reports, forensic audit logs, forensic report, function wrapping, installation, live demo, log analysis, network policy, path traversal attack, private IP checks, private network access, report generation, sandbox enforcement, security risks, semantic status, strict sandbox, tool invocation, tool side-effects, validator, workflow restarts, workspace, write_file function, zero-touch protection
ai
github.com 17 hours ago
|
109. HN Why 'The Global Market' Is an Irresponsible Phrase**Summary:** The text critiques the notion of a "Global Market," arguing that it oversimplifies the complexities of diverse markets, often leading to strategic failures due to ignoring cultural, political, and socio-economic differences. It highlights how startups traditionally focus on Product Managers (PMs) for product development but suggests a shift towards "Producers" leading this process in the evolving era. The discussion cautions against excessive reliance on Ideal Customer Profiles (ICP) and Product-Market Fit (PMF), which can limit real business opportunities by narrowing focus. Regarding Software as a Service (SaaS), the text debunks the misconception that pricing strategies should always increase and questions the immediate success hype surrounding early AI applications. It also introduces a reconsideration of retention strategies, advocating for departures from conventional Go-To-Market (GTM) approaches. The text emphasizes that markets are behavioral groupings defined by decision-making patterns rather than geographical boundaries, cautioning against assuming homogeneity within regions like the U.S., where distinct market realities exist requiring tailored strategies. It uses analogies to illustrate the inappropriateness of imposing successful homegrown products or strategies onto unfamiliar markets without adaptation. The text criticizes common global expansion practices that involve superficial translations without understanding cultural nuances or market-specific needs, often resulting in failure. It advocates for breaking down large markets into smaller, more specific units for precise entry and scaling only verified concepts instead of pursuing broad global expansion hastily. **Key Points:** - The concept of "Global Market" is criticized for oversimplification, ignoring regional distinctions, cultural nuances, and power imbalances. - Traditional PM roles in product development are being supplanted by a new role, "Producers." - Overemphasis on ICP and PMF can limit genuine business opportunities; broad focus is necessary for capturing real market needs. - SaaS pricing strategies should not automatically escalate; immediate AI success hype is questioned. - Retention strategies need rethinking, moving away from conventional GTM approaches. - Markets are behavioral groups based on decision-making patterns rather than geography, necessitating localized strategies over generalizations. - Common expansion practices involving mere translation without cultural understanding often lead to failure. - Breaking down large markets into smaller units for precise entry is advised, focusing on verification of scalable successes over rapid global expansion. Keywords: #granite33:8b, AI, Budget Authority, Business, Cultural Context, Cultural Critique, Currency, Data, Decision-making, Enterprises, Entry Points, GTM Consulting, Global Expansion, Go To Market, Hope, ICP, Industry Cycles, Language, Language Barriers, Localization, Map Illusion, Market Data Gap, Market Execution, Market Segmentation, Marketing Copy, Organizational Culture, PLG Consulting, PM, PMF, People & Culture, Price Sensitivity, Pricing, Producer, Retention, Risk Tolerance, SMEs, SaaS, Scalability Verification, Strategy, Strategy Consulting, Translation, global market, irresponsible phrase
ai
oswarld.com 18 hours ago
https://medium.com/the-global-millennial/why-walmart-fa 17 hours ago |
110. HN Ruby Turns 30 Celebrating the Anniversary with the Release of Ruby 4.0**Summary:** Ruby, created by Yukihiro "Matz" Matsumoto in 1995, marks its 30th anniversary with the release of Ruby 4.0. Designed to be more human-friendly and enjoyable, Ruby introduced an intuitive object-oriented model, dynamic typing, and elegant syntax as alternatives to complex languages dominant at the time. The language emphasizes readability, flexibility, and practical solutions, which has fostered a dedicated community contributing essential tools like Bundler for dependency management and RSpec for behavior-driven testing. To honor this milestone, RubyMine is now free for non-commercial users to encourage emerging developers. Key contributors to Ruby's growth include Steven Baker (RSpec), David Chelimsky (Cucumber), and Bozhidar Batsov (RuboCop). Over the years, Ruby has evolved through significant versions: - **Ruby 1.x (2003-2007):** Focused on stabilizing the language with robust libraries and object-oriented foundations, paving the way for early web frameworks such as Rails. This version introduced Ruby 1.9, enhancing speed using the YARV virtual machine and improving regex handling and syntax. - **Ruby 2.x (2013-2018):** Emphasized reliability and developer productivity by adding keyword arguments for clearer method calls, refinements for safer class modifications, incremental garbage collection for performance gains, and simplified library enhancements for tasks like JSON parsing and date management. - **Ruby 3.x (2020-2023):** Realized the "Ruby 3×3" vision by incorporating Ractors for parallelism, a Just-In-Time (JIT) compiler to boost real-world performance, and static analysis tools like RBS with TypeProf for safer refactoring. - **Ruby 4.0 (2025):** Introduced ZJIT, a method-based JIT compiler that redefines Ruby's performance capabilities, alongside experimental features such as namespace-on-read mode through Ruby::Box and enhanced Ractor functionalities including Ractor::Port and safer shareable Proc objects. Global prominence for Ruby came with the launch of Rails in 2004, which combined its elegant syntax with a productive framework, enabling rapid web application development for startups like GitHub (2008), Shopify (2006), Airbnb (2008), and Homebrew (2009). Ruby's ability to scale while maintaining user-friendly interfaces underscores its continued relevance. Ruby continues to be a favorite among young startups, with Ruby on Rails used extensively for building scalable platforms. The JetBrains IDE, RubyMine, since 2009, has enhanced the Ruby experience through deep code understanding, smart navigation, robust testing support, and debugging tools, adapting to the language's evolving features and contributing to its ongoing popularity with advanced static analysis, refactoring, and continuous updates. **Bullet Points:** - **Ruby Creation and Philosophy:** - Created by Yukihiro "Matz" Matsumoto in 1995. - Designed for human readability, enjoyment, and practicality over complexity. - Emphasizes object-oriented model, dynamic typing, and elegant syntax. - **Community and Tools:** - Fostered a community focused on craftsmanship, maintainability, and expressive coding. - Key contributions: Bundler (dependency management), RSpec (behavior-driven testing). - **Version Milestones:** - **Ruby 1.x (2003-2007):** Stabilization, robust libraries, introduction of Ruby 1.9 with YARV VM enhancements. - **Ruby 2.x (2013-2018):** Focus on reliability and productivity with keyword arguments, refinements, garbage collection improvements. - **Ruby 3.x (2020-2023):** Achieved "Ruby 3×3" vision with Ractors for parallelism, JIT compiler improvements, static analysis tools. - **Ruby 4.0 (2025):** Introduced ZJIT, experimental features like namespace-on-read mode and enhanced Ractor capabilities. - **Global Impact:** - Gained prominence through Rails in 2004, enabling rapid development for startups such as GitHub, Shopify, Airbnb, Homebrew. - Demonstrates scalability and user-friendly interfaces. - **Tools and IDE Support:** - RubyMine by JetBrains: Enhances Ruby development with code understanding, navigation, testing support, debugging tools, adapting to language updates. - Continues to support the language's evolution and community engagement. Keywords: "Everything is an object", #granite33:8b, 1995, 40, Airbnb, BDD, Bundler, GitHub, Homebrew, IDE, JIT, Matz, Principle of Least Surprise, Proc objects, RBS, RSpec, Ractor::Port, Ractors, Rails, RuboCop, Ruby, Ruby::Box, Shopify, TypeProf, YARV VM, ZJIT, anniversary, behavior-driven testing, booking systems, community, convention over configuration, craftsmanship, debugging, developers, dynamic typing, e-commerce, elegant syntax, evolving, expressive code, flexibility, incremental GC, keyword arguments, libraries, macOS, maintainability, metaprogramming, millions users/repositories/developers, object-oriented, rapid development, readability, refinements, syntax, testing, tooling, transparency, web startups
github
blog.jetbrains.com 18 hours ago
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111. HN Claude Code changed my life- The user expresses deep appreciation for Large Language Models (LLMs) like Claude Code, highlighting their transformative impact on personal productivity and potential for others. - Despite concerns about AI grifters exploiting LLMs, the author focuses on the intrinsic value of these models as "captivating, fractal toys" that surpass traditional forms of entertainment or creative outlets. - Claude Code is compared to an advanced beachcomber's tool, meticulously searching through codebases without causing harm, identifying specific pieces of information efficiently and accurately. It avoids hallucinations or architectural damage as it operates read-only, offering precise file references and line numbers. - The user details numerous complex tasks accomplished with Claude's assistance: creating a web application for global code usage comparison, developing a 3D ASCII renderer, implementing a Mermaid serializer, writing extensive tests for C standard libraries, learning about UTF8 encoding, creating a hotkey-triggered Zoom transcription system, and integrating personal documentation into a searchable vector database. - They also used Claude for inspiration in making simple animations with Unicode characters for their website and developed a Text User Interface (TUI) using the opencode SDK and OpenTUI, incorporating a context compiler. - Key takeaways include: - Automatically approving coding agents' outputs unless there's an extreme case. - An algorithm for using LLMs in "using-llms-for-hard-stuff" involves deep problem analysis, examining existing software, and thorough solution understanding. - Pure improvisational coding is suitable for non-critical projects; reliable code requires deeper understanding before LLM involvement. - Rely on internal documentation and examples rather than external web resources for understanding libraries. - AI assistance can be used for brainstorming or overcoming creative blocks, humorously referred to as using Claude as an "industrial fan for mental fog." - The text ends with a tongue-in-cheek, absurdist depiction of Claude waving goodbye, possibly indicating the author's self-deprecating acknowledgement of the limitations in understanding advanced AI capabilities fully. Keywords: #granite33:8b, 3D raymarching ASCII renderer, AI, Anthropic/Google/OpenAI conversations, C standard library tests, Claude, ELF spec, HTMX frontend, LLM cleanup, LLMs, Mermaid serializer, OpenTUI, Santa Claude, TUI, UTF8, Unicode characters, Wayland, Zoom transcription, boolean conditions, build graphs, code reading, coding agents, context compiler, corpus of life, database, encode/decode API, fractal nature, glob library implementation, global hotkey, handwritten code, lighting, mental fog, metal detector, object file manipulation, opencode SDK, package manager, paradigm shifts, silicon hoarding, snowflakes, software coastline, static website setup, vector database, version resolution, wealth extraction, what-if-C-had-Cargo
claude
spader.zone 18 hours ago
https://en.wikipedia.org/wiki/Gartner_hype_cycle 13 hours ago |
112. HN Chrome plugin: Select text on any webpage and instantly search in AI providers- The described Chrome plugin is a versatile tool that enhances web browsing by enabling users to interact with AI models and search engines directly from any webpage. - Users can select text on a page and, using the plugin's functionality, instantly query AI models such as ChatGPT or Claude, or perform searches via Google. - This seamless integration eliminates the need to navigate away from the current webpage to conduct external research or inquiries. - The plugin maintains a discreet interface, ensuring that the browsing experience remains uninterrupted and distraction-free. - By offering immediate access to advanced AI and search capabilities, it streamlines information gathering and enhances efficiency for users. Keywords: #granite33:8b, AI search, ChatGPT, Chrome extension, Claude, Google, integration, intuitive design, multi-tab, no copying, web page
claude
chromewebstore.google.com 19 hours ago
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113. HN We built an AI to analyze 100 biomarkers, then realized it has no intuition**Summary:** A research team engineered an advanced AI system designed to scrutinize over 100 biomarkers for forecasting disease risks, envisioning a comprehensive "Whole-Body Intelligence System." Despite the technology's proficiency in data analysis and pattern recognition, they discovered that it lacks the profound clinical intuition and "latent knowledge" inherent in seasoned medical practitioners. In response to this, the team pivoted from an exclusively AI-driven model to a hybrid approach. This revised strategy integrates the AI's data processing capabilities with human expertise: AI identifies patterns within biomarker data, while clinical experts interpret these findings and customize health intervention strategies for individual patients. The overarching objective now centers on tackling the "last mile" challenge in preventive healthcare by converting complex biomarker data into actionable plans to mitigate chronic conditions. Further particulars regarding this initiative can be accessed via nostaviahealth.com. **Key Points:** - Researchers developed an AI for analyzing over 100 biomarkers to predict disease risks, aiming for a "Whole-Body Intelligence System." - The pure AI model, while effective, was found lacking in the clinical intuition possessed by experienced doctors. - Transitioned to a hybrid model where AI identifies patterns and human experts interpret and apply this information for personalized health protocols. - Focus shifted to addressing preventive care's "last mile" challenge—transforming biomarker data into strategies for reversing chronic conditions. - More information available at nostaviahealth.com. Keywords: #granite33:8b, AI, Whole-Body Intelligence System, biomarkers, chronic conditions, clinical practice, genius doctor, hybrid model, latent knowledge, metabolism, organ function, organ functionKEYWORDS: AI, predictive analysis, preventive health, raw data, toxins
ai
news.ycombinator.com 19 hours ago
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114. HN OpenTinkerOpenTinker is a versatile interaction framework built on the GenericAgentLoop, utilizing a state machine with distinct phases to manage multi-round exchanges. The phases include PENDING for tokenizing prompts, GENERATING for using a language model to create responses, INTERACTING for executing system actions and observing outcomes, and TERMINATED for concluding episodes. This design supports various tasks ranging from single-turn reasoning, like solving math problems, to multi-turn decision-making tasks such as playing Gomoku or utilizing Math Tool Calling. The framework's unified codebase allows for seamless adaptation to similar agent environments. BULLET POINT SUMMARY: - OpenTinker extends GenericAgentLoop for flexible, multi-round interactions. - It employs a state machine with phases: PENDING (prompt tokenization), GENERATING (LLM response generation), INTERACTING (system action execution and observation), TERMINATED (episode conclusion). - Accommodates single-turn tasks (e.g., math) and multi-turn tasks (e.g., Gomoku, Math Tool Calling) within a unified codebase. - Designed for easy adaptation to similar agent environments. Keywords: #granite33:8b, GenericAgentLoop, Gomoku, LLM, Math, agentic environments, multi-turn interactions, sequential decision making, single-turn reasoning, state machine, tokenization, unified codebase
llm
open-tinker.github.io 20 hours ago
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115. HN The context window ate your instructions- **Challenges with CLAUDE.md**: The tool encounters issues such as forgetting instructions or malfunctioning due to context window limitations and session restarts. - **Solution Proposed**: Enhancing linter feedback via pattern matching in Abstract Syntax Trees (AST) to address these challenges. - **GritQL Plugin Examples**: The author provides examples of GritQL plugins tailored for React projects, focusing on: - Discouraging manual `memoization` with the React Compiler. - Preferring `Effect atoms` over `useState` hook for state management. - Avoiding data fetching within `useEffect` hooks. - **Biome Tool**: A coding standards enforcement tool that uses plugins such as: - "no-react-memoization.grit": Flags excessive use of memoization (`useMemo`, `useCallback`). - "no-usestate.grit": Discourages inappropriate use of `useState`. - "no-useeffect-data-fetching.grit": Prevents data fetching within `useEffect` hooks. - **Biome Configuration**: Plugins are configured in a `biome.jsonc` file to enforce coding standards and prevent subtle issues arising from agents choosing less optimal patterns based on training data. - **Alternatives**: For those not using Biome, `ast-grep` provides AST-based linting with simpler YAML rules, runnable via commands like `ast-grep scan .`. - **Automated Rule Creation**: The user demonstrates generating these plugins by prompting an AI agent to write rules against specific patterns, showcasing automated rule creation for linting. - **`create-lint-rule` Command**: Developed by the user to automate generation of GritQL lint rules in Biome based on user descriptions, following a structured syntax for pattern matching and rule creation adaptable for other tools like `ast-grep`. BULLET POINT SUMMARY: - Challenges faced with CLAUDE.md tool, including instruction forgetting and malfunctions due to context limitations and restarts. - Solution involving enhanced linter feedback using AST pattern matching. - Examples of GritQL plugins for React projects focusing on state management best practices and avoiding anti-patterns in `useEffect`. - Biome tool with plugins ("no-react-memoization.grit", "no-usestate.grit", "no-useeffect-data-fetching.grit") enforcing coding standards via `biome.jsonc` configuration. - Alternative `ast-grep` for AST-based linting with simpler YAML rules. - Demonstration of automated plugin creation by prompting AI agents to write rules against specific patterns. - Development of `create-lint-rule` command automating GritQL rule generation in Biome, adaptable for other tools via regex adjustments. Keywords: #granite33:8b, AST matching, AST-based linting, CLAUDE, GritQL, GritQL plugins, React, TanStack DB, YAML rules, ast-grep, atoms, ban functions, biomejsonc, combine patterns, contains, context window, data fetching, deprecated API, diagnostic, error, exclude test files, import conventions, instructions, linter, match, memo, memoization, negate, no-react-memoization, no-useeffect-data-fetching, no-usestate, operators, regex, restrict imports, security anti-patterns, severity, span, state management, useCallback, useEffect, useMemo, useState, warning, within
claude
laulau.land 20 hours ago
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116. HN Ask HN: HarmonyOS Open Source DevelopmentThe user, who received a Huawei D2 smartwatch for Christmas, appreciates its features but has reservations about data collection mandated by some of its companion apps. In search of more privacy-focused solutions, the user is exploring open-source alternatives to Huawei's JetBrains-based development framework. This initiative aims to facilitate transparent and community-driven app development for the watch, as existing options like Gadgetbridge do not yet provide comprehensive support. BULLET POINT SUMMARY: - User received Huawei D2 smartwatch for Christmas and finds it appealing. - Concerned about data collection required by some companion apps. - Seeking open-source alternatives to Huawei's JetBrains-based development framework. - Aims to enable open, transparent app development for the watch. - Gadgetbridge, an existing solution, lacks full support needed for this purpose. Keywords: #granite33:8b, GadgetBridge, HarmonyOS, Huawei D2, JetBrains, alternatives, companion apps, open source, opensource way, personalized data, watch apps
jetbrains
news.ycombinator.com 20 hours ago
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117. HN Geo Is a Cargo Cult Hype Built on One 2023 Paper- **Generative Engine Optimization (GEO):** A method introduced by six researchers claiming a 40% increase in AI response "visibility" using specific tactics; however, it lacks real-world data or validation. An industry has emerged around GEO with consultants, agencies, tools, and platforms promising growth but without proven efficacy, resembling a cargo cult. - **Epistemological Problems in AI:** - **Uncertainty in AI Source Citations:** Unreliable methods to track or measure source citations due to the stochastic nature of AI models, producing varying results for identical queries across users and times. Tools like Ahrefs' Brand Radar and Semrush are based on unrepresentative samples. - **Lack of Understanding in AI Source Selection:** Reasons behind an AI model choosing one source over another remain obscure; research focuses on controlled experiments using custom benchmarks that may not generalize to real-world scenarios. - **Instability and Constant Evolution:** Rapid changes in AI models (e.g., GPT-4 series or Claude versions) due to continuous updates, making previous findings obsolete and raising uncertainty when extrapolating results from older to newer models. - **Critique of GEO Industry:** - Compared to Donald Knuth's "premature optimization," GEO involves optimizing without proper measurement or validation, focusing on unverified correlations presented at high enterprise rates. - The benefits and methods lack scientific rigor; GEO attempts to measure and optimize systems with small sample sizes and infinite confounding variables. - GEO primarily benefits researchers (gaining recognition) and tool vendors (marketing premium services), while agencies profit due to the unfalsifiable nature of their services, potentially detrimental to clients investing in speculative tactics without guaranteed outcomes. - **Key Overlooked Caveats by GEO Industry:** - Evaluation results are based on specific model versions at a single point in time and may not reflect current performance. - The proprietary "visibility" metric does not guarantee alignment with business objectives. - Domain effects show varying outcomes for different query types. - The 40% improvement claim is relative to their baseline, potentially indicating subpar actual performance. - Original Princeton research provides a framework but lacks practical implementation guidance. - **Recommendations:** - Focus on creating valuable content, citing sources, building a strong brand, and acquiring links from reputable sites rather than optimizing specifically for AI. - Avoid consuming GEO thought leadership due to the speculative nature of most information and an unfavorable signal-to-noise ratio. - Maintain caution amid uncertainty about AI's impact on search, avoiding large bets on speculative outcomes; focus instead on building versatile assets like brand, content, and expertise that remain valuable across various futures. - **Conclusion:** - Advice remains to be patient, adaptable, and take measured actions rather than seeking immediate solutions or hiring experts in rapidly evolving fields until the situation clarifies. - The critique warns against overconfidence in GEO as it is less than a year old, involving preliminary research, speculative tools, and lacking definitive case studies. Keywords: #granite33:8b, AI, Genreative Engine Optimization (GEO), Geo, Princeton researchers, Search Console equivalent, accuracy, anecdotal case studies, antifragility, branding, citations, content, flexibility, learning, links, measurement, optimization, preliminary research, premature optimization, speculative tools, thought leadership, uncertainty, unjustified confidence, waiting, watching
ai
wskpf.com 20 hours ago
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118. HN Read and review Markdown files in a dead simple viewerReadit is a command-line utility tailored for examining Markdown (.md) and HTML files, enabling users to insert margin notes into selected text passages. These comments can subsequently be exported for AI analysis or reapplied directly to the source documents. Key functionalities of Readit include: - Customizable ports for user preference. - The option to prevent automatic browser launches upon comment generation. - A feature to clear pre-existing annotations from files. - Listing all files that contain annotations. - Displaying comments pertinent to a specified file. The software is constructed with pnpm for managing dependencies and incorporates scripts for building, testing, linting, and formatting the code. Readit operates under the MIT license, ensuring open access and permissive usage. BULLET POINT SUMMARY: - **Tool Type**: Command-line utility for Markdown (.md) and HTML document review. - **Annotation Functionality**: Users add margin notes to highlighted text within documents. - **Export Options**: Comments can be exported for AI processing or reintegrated into the source files. - **Customization**: Users can specify custom ports, choose not to open a browser automatically, clear existing comments, list files with annotations, and view comments for particular files. - **Development**: Built using pnpm, includes build, test, lint, and format scripts for development. - **Licensing**: Distributed under the MIT License, ensuring open access and permissive use. Keywords: #granite33:8b, AI, CLI tool, Development, HTML, MIT License, Markdown, Quick Start, Usage, difit, export, inline comments, margin notes, source
ai
github.com 20 hours ago
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119. HN Microsoft wants to replace its C and C++ codebase- **Microsoft's Rust Transition Plan**: Distinguished engineer Galen Hunt has announced Microsoft's ambitious plan to replace its extensive C and C++ codebase with Rust by 2030. This initiative aims to leverage AI and advanced algorithms for rewriting the company's largest codebases, targeting an output of one million lines of code per engineer monthly. - **Tool Development**: Microsoft has already initiated the development of necessary tools for this transition, which includes a scalable graph infrastructure and AI-driven processes for modifying code. A new Principal Software Engineer role focuses on advancing these tools under the Future of Scalable Software Engineering group. The overarching goal is to systematically eliminate technical debt across Microsoft's extensive systems and potentially beyond. - **Advantages Highlighted**: The use of Rust, a memory-safe language, is emphasized for its potential in enhancing software security—an alignment with broader government recommendations for adopting memory-safe languages like Rust universally. - **Rust Adoption Advocacy**: Microsoft's Azure CTO proposes Rust as the default language for new projects, signaling a company-wide push towards increased usage of the Rust language. Tools for converting C code to Rust and assisting in creating Windows drivers using Rust are being developed. - **Scale of the Endeavor**: Despite Microsoft's vast online presence with over 500 portals for product management and its extensive internal IT infrastructure, completely rewriting or adapting all existing systems presents a monumental challenge due to potential edge cases and the sheer volume of code. - **Job Opportunity**: Microsoft has posted a job opening dedicated to this transition effort, requiring three days per week in their Redmond office with a competitive salary range from $139,900 to $274,800 annually, reflecting the complexity and strategic importance of the project. Keywords: #granite33:8b, AI, C/C++, MSportalsio, Microsoft, Redmond office, Rust, Windows drivers, codebase, conversion tool, engineering, governments, internal IT, memory-safe, products, salary range, scalability, security, tools, universal adoption
ai
www.theregister.com 20 hours ago
https://news.ycombinator.com/item?id=46360955 20 hours ago https://news.ycombinator.com/item?id=46381813 20 hours ago |
120. HN Show HN: NpgsqlRest Automatic PostgreSQL Web Server- **Project Overview**: NpgsqlRest is introduced as a tool facilitating the creation of TypeScript modules that interact with PostgreSQL functions exposed via HTTP endpoints, specifically showcasing a function named `get_product`. - **Functionality Details**: - The `get_product` function retrieves detailed product information using an ID parameter. - Access to this function is restricted, ensuring it can only be invoked by users with admin privileges. - **Generated TypeScript Module Components**: - Interfaces for request and response objects are included in the module. - An asynchronous function `publicGetProduct` is provided to manage HTTP GET requests. - **Benefits and Methodology**: - NpgsqlRest simplifies the development of RESTful web servers that interface with PostgreSQL databases securely. - The project emphasizes a database-first design, prioritizing schema definition before code generation. - It integrates static type checking to ensure end-to-end validation across the application for robustness and type safety. - The declarative API design encourages clear and descriptive intent in creating application interfaces. - **Checkmarks Indication**: - A total of 47 checkmarks signify adherence to a structured software development approach, emphasizing: - Database schema defined before code (database-first). - Use of static type checking for comprehensive validation. - Declarative API design for explicit and descriptive interfaces. Keywords: #granite33:8b, Admin Role, Authentication, Authorization, Database-First, Declarative API Design, End-To-End, Error Handling, Fetch API, Function, GET Request, HTTP Endpoint, Headers, ID, JSON Response, Name, NpgsqlRest, PostgreSQL, Price, Product Data, RESTful, SQL Query, Static Type Checking, TypeScript Module, Web Server
postgresql
npgsqlrest.github.io 21 hours ago
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121. HN Why Your AI Agents Are Hallucinating (and How to Stop It)- **AI Hallucination Issue**: Large language models (LLMs) can generate plausible yet incorrect information due to internal pattern recognition, posing risks like wrong actions, false user information, flawed decisions, and citing non-existent sources. This lack of contextual grounding is prominent in Retrieval-Augmented Generation (RAG) systems. - **Causes of Hallucinations**: Include generating unsupported info, faulty reasoning, outdated training data, ambiguous prompts, context window limitations, and contaminated training data, leading to incorrect responses even with accurate information due to logical errors or misinterpretations. - **Consequences of Ignoring Hallucinations**: Can result in severe consequences such as loss of user trust, brand damage, legal issues, financial losses, and operational chaos. Examples include a lawyer citing non-existent cases, fabricated medical advice, misleading promises, and plagiarized citations. - **Traditional Detection Methods**: Rely on manual review and fact-checking, which are slow and reactive. - **Noveum.ai Solution**: Proposes an automated, real-time hallucination detection system using the agent's own input as ground truth. It employs 68+ specialized scorers, focusing on Faithfulness Scorer (checks factual consistency with provided context) and Groundedness Scorer (ensures responses are based on given information). - **Key Features**: - Automated, real-time assessment without manual labeling. - Uses system prompt and context as truth references to evaluate responses against provided documents, contradictions, unfounded information, or fact deviation. - NovaPilot identifies causes of hallucinations (poor retrieval quality, ambiguous prompts, model tendencies, context window issues) for prevention and performance improvement. - **Implementation Steps**: 1. Add tracing with `noveum_trace`. 2. Select relevant scorers in the Noveum.ai dashboard and set thresholds (recommended 7/10 for production). Configure alert channels. 3. Set up real-time alerts for critical hallucinations (scores < 5) via Slack or email. - **Benefits**: Continuous monitoring, improvement over time, safeguarding AI agents' reliability and truthfulness in customer interactions by addressing trust, legal, and financial risks associated with hallucinations. - **Next Steps**: Users can start a free trial, review documentation, or book a demo to learn more about building reliable AI agents using Noveum.ai's solution for hallucination prevention and mitigation. Keywords: #granite33:8b, AI agents, GPT-4, LLMs, NovaPilot, RAG, Slack/email, alerts, ambiguous prompts, answer relevance, automated evaluation, compliance risks, confident responses, context, context relevance, context window limitations, critical hallucinations, document retrieval, error messages, fact-checking, faithfulness, faithfulness score, faulty reasoning, financial damage, financial services chatbot, groundedness, groundedness score, hallucination detection, hallucinations, interest rate, knowledge base, model tendency, outdated training data, real-time detection, real-time evaluation, reliability, retrieval quality, retrieval-augmented generation, root cause analysis, semantic chunking, severity, system prompt, token limits, trace ID, traditional software bugs, training data contamination, trustworthiness, user trust, verification steps, wrong information
gpt-4
noveum.ai 21 hours ago
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122. HN Show HN: Got tired of searching for AI news daily so I built my own AI news page- The user, influenced by Hacker News, initiated the development of DreyX.com, a personalized AI news aggregator. - The primary motivation behind this tool was to simplify the often tedious task of filtering through an abundance of AI news for curious readers. - DreyX.com is designed specifically to serve as an efficient solution for individuals seeking to stay updated on AI developments without the hassle of manual browsing. - The user openly invites feedback and suggestions from users to continually enhance and refine the tool's functionality and features. Keywords: #granite33:8b, AI, DreyXcom, Hacker News, aggregator, daily search, news, no fluff, prompts, readers, tools, website
ai
dreyx.com 22 hours ago
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123. HN Show HN: Claude Code in Cursor- **Project Overview**: This text describes a local proxy service named "ccproxy" designed to optimize costs when using Anthropic API credits via Claude Code's $200 monthly plan, deemed more economical than Cursor's usage. The project employs Bun for execution and necessitates having the Claude Code CLI authenticated and Bun installed. - **Setup Instructions**: - Users set up a Cloudflare Tunnel for secure HTTPS access at http://ccproxy.yourdomain.com/v1. - A warning is issued about potential violation of Anthropic's terms of service and risk of exposing one’s Claude Code subscription if the tunnel URL becomes compromised. - The setup involves running specific commands, configuring Cloudflare Tunnel with a config file, and executing `cloudflared tunnel run ccproxy`. - Users must safeguard their tunnel URL as sensitive information due to possible security risks. - **Integration with Cursor**: - Instructions involve adjusting Cursor's settings to use the new proxy base URL (ccproxy). - The method has limitations compared to direct Anthropic API usage, including lack of control over thinking budget and missing beta features. - Configuration details include a default port (8082), fallback API key, and Claude code priority settings. Requests go through ccproxy to Claude, switching to the Anthropic API if Claude's rate limits are reached. - **Usage Data Logging**: - Local logging is implemented for analytics and cost tracking of AI service usage. - Users can access this data using specific curl commands for various time periods (e.g., last day). - **Usage Analytics Example**: - For a specified period (one day in this case), there were 129 total requests. - Of these, 60 were served via the free Claude Code subscription, and 69 fell back to a paid API key. - Estimated savings if all Claude Code requests used the API are around $0.12, with caveats about prompt caching affecting accuracy. - **Service Functionality**: - The system provides various endpoints for messaging APIs, chat completions, usage stats, request history, budget settings, health checks, and troubleshooting guides for common issues (missing credentials, exceeding budget). - **License**: - The service operates under an MIT License. BULLET POINT SUMMARY: - Local proxy "ccproxy" uses Claude Code’s $200 monthly plan for cost-effectiveness over Cursor's Anthropic API usage. - Requires authenticated Claude Code CLI and Bun installation; Cloudflare Tunnel setup at http://ccproxy.yourdomain.com/v1. - Warning: Potential service terms violation, subscription exposure risk if tunnel URL is compromised. - Integration with Cursor involves adjusting base URL settings and acknowledges limitations like budget control lack and missing beta features. - Requests route through ccproxy to Claude, switching to Anthropic API on rate limit. - Local usage logging for analytics and cost tracking accessible via curl commands. - Example: 129 total requests (60 via free Claude, 69 via paid key), estimated $0.12 saving if all Claude used the API. - Offers messaging APIs, chat completions, stats, history, settings, health checks, troubleshooting. - Operates under MIT License. Keywords: #granite33:8b, API Key, Analytics, Anthropic API Credits, Arbitrage, Base URL, Budget, Buggy, Bun, Claude Code, Cloudflare Tunnel, Costs, Cursor, HTTPS Endpoint, Hack, IP Addresses, OAuth Credentials, OpenAI, Proxy Service, Requests, Savings, Security Warning, Subscriptions, Troubleshooting, Tunnel URL
claude
github.com 22 hours ago
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124. HN Show HN: Secret MCP: Let AI write your .env files without seeing your secrets- **Secret Manager Client Protocol (MCP)** is a desktop application and server solution for managing secrets used with AI coding assistants, safeguarding sensitive information such as API keys. - The desktop app stores secrets locally in an SQLite database, allowing users to manage names, descriptions, and values easily. - The MCP server provides two key tools for AI assistants: - 'search_secrets': Enables searching for secrets by name or description without revealing their values. - 'write_env': Writes secrets directly to .env files from the local database, circumventing AI context. - Installation involves using npm commands for the desktop app and setting up the MCP client with the server's command, facilitating secure .env file generation during coding sessions with AI assistants. - This setup ensures that secrets remain protected from unauthorized access in cloud-based environments as they never leave the user's machine. - Secret values are written to .env files with 600 permissions, granting only the owner read/write access. - The desktop application is built using Tauri 2.0, Svelte 5, and TypeScript, while the MCP server leverages Node.js, @modelcontextprotocol/sdk, and better-sqlite3. - The project is licensed under the MIT license. **File locations for SQLite databases:** - macOS: ~/Library/Application Support/secret-mcp/secrets.db - Linux: ~/.local/share/secret-mcp/secrets.db - Windows: %APPDATA%/secret-mcp/secrets.db Keywords: #granite33:8b, API keys, MCP, MIT license, Nodejs, SQLite, Svelte, Tauri, TypeScript, ```SECRET, better-sqlite3, database, desktop app, env, local storage, npm, permissions```, search_secrets, security, server, tokens, write_env
ai
github.com 22 hours ago
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125. HN Show HN: Open-source"BeMyEyes"alternative(Java/Go/Python)built as a learning pjt**Summary of SoakUpTheSun:** SoakUpTheSun is an open-source alternative to BeMyEyes, built as a learning project utilizing Java, Go, and Python. It is a cloud-based visual assistance platform designed for visually impaired users to connect quickly with global volunteers. The system incorporates several technologies such as Go SFU Real-time Streaming, Redis Hot Pool Matching, RocketMQ Asynchronous Decoupling, Lua Atomic Locks, and AI Visual Analysis, ensuring high concurrency and efficient operation through a heterogeneous microservices architecture. **Key Features and Solutions:** 1. **Near-Instantaneous Hot Pool Matching:** - Leverages Redis Set for over 99% connection success rate, ideal for scenarios like flash sales or mass distribution. 2. **Self-developed Go SFU Streaming Service:** - Optimized with Pion framework for WebRTC signaling and RTP packet forwarding, ensuring low latency in weak network conditions. 3. **High-Concurrency Short Link Defense System:** - Employs Bloom Filter and Redis Token Bucket to prevent ID collisions and malicious attacks via O(1) deduplication. 4. **Asynchronous Slicing Import for Massive Data:** - Uses TaskQueue and Async Thread Pool for efficient Excel imports, along with Mybatis-Plus for batch insertion and dual-writing to Elasticsearch. 5. **Redis Lua Atomic Inventory Flash Sale System:** - Ensures atomic inventory checks and deductions using a single Lua script in Redis, preventing overselling during public welfare prize redemptions. **Core Design Challenges and Solutions:** 1. **Preventing "Overselling" and "Collision" Under High Concurrency:** - Uses Redis Lua scripts for atomic operations, circumventing Java-level locks, ensuring strong data consistency via a Redisson Watchdog mechanism. 2. **Balancing Security & Performance in Short Link Systems:** - Implements a Bloom Filter for O(1) deduplication and uses the Redis Token Bucket Algorithm to limit request rates per IP, preventing malicious scanning attempts. 3. **Addressing Out-of-Memory (OOM) in Full Settlement of Massive Point Data:** - Introduces Cursor Pagination using primary key IDs for maintaining query performance despite massive data volumes and optimizing traditional LIMIT-offset approaches. **Tech Stack:** Includes Spring Cloud, Alibaba Nacos, OpenFeign, Go, Python, MySQL, Elasticsearch, RocketMQ, Tencent COS, among others. **System Components:** - Vue.js frontend ("clientService") for user interaction. - Go-based SFU server ("sfu") for real-time media streaming using WebRTC. - Various backend modules: "volunteer" (core business logic), "picture" (image processing and AI integration), "user" (authentication). - Utility services including Redis managers, RocketMQ message drivers, Tencent Cloud COS object storage wrappers. **Deployment:** Requires JDK 17+, Go 1.25+, MySQL 8.0+, Redis 5.0+, RocketMQ 5+, and Nacos 2.0+. Deployment instructions provided using docker-compose and npm commands for the frontend service. Contributions to accessibility design or high availability architecture are encouraged, with appreciation requested through stars on the project repository. Keywords: #granite33:8b, AI, Alibaba Nacos, Bloom Filter, COS, Computer Vision, Cursor Pagination, Elasticsearch, Excel Import, Go, High Availability Architecture, ID Collision, Image Processing, Inventory Flash Sale, JDK, MySQL, Open-source, OpenFeign, RTP, Real-time Streaming, Redis, Redis Lua, RocketMQ, SFU, Short Link, Short Link Generation, Spring Cloud, Streaming Processing, User Authentication, Vuejs, Vuex, Weak Network Optimization, WebRTC, WebSocket, atomic, cache management, docker-compose, flash sales, high-concurrency, instant messaging, mass distribution, matching algorithms, microservices, primary key IDs, self-deployed AI model, visual assistance
ai
github.com 23 hours ago
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126. HN AI Withholds Life-or-Death Information Unless You Know the Magic Words- The article explores the hypothesis that certain AI systems may withhold life-critical information unless specific roles or 'magic words' are identified, posing challenges to transparency and fairness. - It suggests AI could function based on role-based reality, where access to vital data depends on the user's predefined status within the system. - This conditional information sharing might lead to opaque decision-making processes in AI, as users unaware of required 'magic words' or roles could be denied crucial information. - The article raises ethical concerns about such practices, questioning how it upholds principles of fairness and accountability in AI systems. - It emphasizes the need for clarification and regulation to ensure that AI does not arbitrarily withhold life-or-death information based on pre-set conditions unknown to users. Keywords: #granite33:8b, AI, JavaScript, app, independent voices, life-death information, magic words, reality, role-based, scripts
ai
substack.com 23 hours ago
https://huggingface.co/huihui-ai/collections 14 hours ago https://www.linkedin.com/in/ownyourai/ 14 hours ago https://apnews.com/article/minnesota-fraud-feeding-our- 14 hours ago |
127. HN Ask HN: At 34, can I aspire to being more than a JavaScript widget engineer?The user, aged 34 with a decade of experience in frontend JavaScript development, primarily focused on creating UI components for CRUD applications, is contemplating a career shift towards more impactful and intellectually stimulating work. This individual admires the contributions of PhDs engaged in advanced technology sectors such as self-driving cars, rocket science, and artificial intelligence but also harbors moral reservations about the tech industry's broader societal implications. The central dilemma lies in deciding whether to embark on a potentially risky yet fulfilling career transition or to maintain their current role for its financial stability and retirement security. BULLET POINT SUMMARY: - User: 34 years old with 10 years of frontend JavaScript experience, mainly on UI components for CRUD apps. - Desire: Seeks a more meaningful and intellectually engaging career path. - Inspiration: Envies PhDs working in cutting-edge technology areas (self-driving cars, rockets, AI). - Concern: Grapples with ethical issues related to the tech industry's overall societal effects. - Dilemma: Weighing a potential career change for more impact vs. current job's financial security and retirement planning. Keywords: #granite33:8b, AI, CRUD apps, Frontend, JavaScript, PhDs, morality, retirement savings, rockets, self-driving cars, tech industry
ai
news.ycombinator.com 23 hours ago
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128. HN Querying 160 GB of Parquet Files with DuckDB in 15 Minutes- The user conducted an efficiency test on DuckDB using 100 Parquet files, each housing 50 million rows, totaling 5 billion rows. - DuckDB successfully computed the sum of a random value column and provided row counts per date in roughly 15 minutes, showcasing its capability to handle massive datasets efficiently. - Despite requiring further development efforts such as schema mimicry, change data capture (CDC) enablement, and setting up materialization jobs for integration into existing data environments, the user views DuckDB as a scalable analytics solution with broad applicability. - A GitHub repository is referenced for additional information or replication of the experiment. Keywords: #granite33:8b, Analytics, CDC, DuckDB, GitHub, Integration, Materialization, Parquet, Popularity, Querying, Scalability, Schema
github
datamethods.substack.com a day ago
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129. HN Oxaide: Sovereign AI knowledge engine for private infrastructure- Oxaide is an AI-powered system engineered for private sectors to address the challenge of losing institutional knowledge due to staff turnover or retirement. - It securely archives and makes searchable proprietary Standard Operating Procedures (SOPs), technical specifications, and compliance documents, transforming this information into persistent architectural assets. - The system empowers junior employees to perform tasks at a senior level of expertise, significantly reducing the cost associated with continuous supervision, estimated at $80K annually—a fivefold savings. - By preserving technical intuition and specialized knowledge, valued at $200K per year, Oxaide ensures institutional continuity, preventing critical expertise loss when key personnel leave. Bullet Points: - Oxaide is an AI system for private infrastructure addressing knowledge loss due to staff changes. - It securely stores and queries SOPs, technical specs, compliance data, turning them into persistent assets. - Empowers junior staff to work at senior levels, saving 5 times annual supervision costs ($80K). - Preserves technical intuition valued at $200K per year for institutional continuity upon key personnel departure. Keywords: #granite33:8b, Oxaide, Sovereign AI, architectural asset, compliance data, expert output scaling, hand-holding, institutional continuity, institutional memory, junior staff, private infrastructure, proprietary SOPs, secure vault, senior level, supervision overhead, talent augmentation, technical specs, tribal knowledge
ai
oxaide.com a day ago
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130. HN Microsoft denies rewriting Windows 11 in Rust using AI**Summary:** Microsoft has addressed and dispelled rumors regarding plans to rewrite Windows 11 using AI in Rust, contradicting earlier statements by top engineer Galen Hunt. While Hunt's research project aims at developing AI tools to facilitate language migrations for easier future transitions away from C/C++, it does not signify an imminent overhaul of Microsoft products like Windows 11. Frank Shaw, a Microsoft communications executive, confirmed there are no such initiatives underway. CEO Satya Nadella revealed that 30% of Microsoft's current codebase is AI-generated and anticipates a significant increase towards 95% by 2030. Despite this trend, concerns remain over large-scale code modifications using AI and algorithms, acknowledging Rust’s inherent security advantages. In addition to AI developments, Microsoft faces performance issues with certain Windows 11 applications, particularly those built on Electron (like Discord) and WebView2 (such as Teams and WhatsApp), which are known for their high RAM consumption. For example, Discord's Electron app can utilize up to 4GB of RAM, whereas Teams’ WebView2 component typically uses between 1-2GB. WhatsApp initially improved with a lightweight WinUI/XAML solution consuming minimal memory (less than 200MB), but post layoffs, it transitioned to a more resource-intensive WebView2 version using seven times the RAM. This issue reflects broader app performance concerns on Windows 11, impacting overall system efficiency and resource usage. Microsoft's integration of WebView2 into Windows 11 for features such as "Agenda View" in Notification Center has introduced new Edge-related processes that can consume up to 100MB of RAM when Agenda View is enabled. Critics argue that reliance on AI or web technologies alone cannot resolve deep-seated performance problems without a fundamental shift in leadership strategy and commitment to systemic improvements. **Key Points:** - Microsoft clarifies no plans to rewrite Windows 11 with AI in Rust, contradicting earlier claims by Galen Hunt. - Satya Nadella announces 30% of Microsoft's current codebase is AI-generated, aiming for up to 95% by 2030. - Concerns exist over extensive code transformation using AI due to Rust's security benefits. - High RAM usage in Windows 11 apps built on Electron (e.g., Discord) and WebView2 (e.g., Microsoft Teams, WhatsApp) is highlighted. - Transition of WhatsApp from a lightweight WinUI/XAML solution to a more memory-heavy WebView2 version illustrates broader app performance issues. - Integration of WebView2 into Windows 11 for features like Agenda View in Notifications Center introduces additional RAM consumption concerns (up to 100MB). - Critics suggest that AI and web technologies alone cannot solve systemic performance problems without leadership change and commitment to fundamental improvements. Keywords: #granite33:8b, AI, AI integration, AI-generated code, Agenda view, C/C++, Discord, Electron apps, Microsoft, RAM consumption, Rust, Satya Nadella, Teams calling, WebView2, WebViews, WhatsApp, Windows 11, codebases, high resource usage, migration, native client, security, web-based
ai
www.windowslatest.com a day ago
https://www.smbc-comics.com/comic/aaaah 22 hours ago https://www.livescience.com/technology/computing/i 18 hours ago https://xkcd.com/2347/ 18 hours ago https://www.heise.de/en/news/Linux-Kernel-Rust-Sup 18 hours ago https://lwn.net/Articles/1049831/ 18 hours ago |
131. HN Understanding AI Benchmarks**Summary:** The text elucidates various aspects of AI benchmarking, critiquing their limitations and proposing improvements. Key points include: 1. **Benchmark Misconception**: - Commonly misunderstood as direct measures of intelligence, AI benchmarks actually represent complex function outcomes influenced by model weights, settings, testing harnesses, and scoring methods. - Minor changes in any component can drastically alter benchmark scores. 2. **Language Model Benchmark Components**: - Key aspects include sampling (temperature, top_p, max_tokens), reasoning strength configurations, and the testing harness or code. - Tool availability and specificity of prompts are crucial for accurate model performance assessment. 3. **Scoring Methodology**: - Scoring setup involves metrics and judges; programmatic judges are objective but brittle while LLM-as-a-judge offers nuance but risks bias. - Pass criteria such as pass@k or pass^k evaluate model performance based on correctness and consistency. 4. **Skepticism Towards Current Benchmarks**: - The user expresses concern over the sensitivity of benchmark scores to individual component adjustments, emphasizing the need for a comprehensive examination of the "benchmark stack." - Importance is placed on "agentic harnesses" capable of executing code and tools for task solutions. 5. **Fragilities in Benchmark Practices**: - Issues include buggy researcher-written test code, stochastic LLM behavior with fixed seeds, varying reporting methods leading to misleading comparisons, harness tweaking, stale baselines, real-life discrepancies between benchmarked and released models, efficiency trade-offs, training data contamination, and overlooking unintended side effects. 6. **Specific Benchmark Evaluations**: - *LlamaArena* ranks LLMs via user votes but suffers from testing raw LLM behavior with generic prompts. - *RealEstate Dataset* tests a model's ability to handle real-world tasks using GitHub issues, providing clearer signals by filtering ambiguous tasks. - *SWE-Bench* and *Terminal-Bench*: Both are software engineering benchmarks; SWE-Bench lacks modern integration while Terminal-Bench focuses on shell usage but with simpler tasks. 7. **Conversational Agent Benchmarks**: - One benchmark targets debugging Nodejs conflicts, offering practical relevance but limited complexity. - *Tau-Bench* tests long conversation consistency using an adversarial user simulator, measuring robustness but introducing non-determinism with LLM-based simulation. 8. **Reasoning Task Benchmark ("AGI")**: - Criticized for misleading naming; suggested alternative: "Grid-Puzzle-Bench," emphasizing limited 'thinking tokens' for reasoning. Current models achieve 50% human performance but benchmark is deemed contrived, not indicative of AGI. 9. **Composite Benchmark for Novel Problems**: - Continuously updates questions from recent sources to avoid memorization, critiqued for simplistic harnesses and templated questions across domains. 10. **Knowledge Benchmarks for Graduate-Level Expertise**: - One benchmark uses a massive dataset of difficult, closed-ended questions from diverse academic fields (multi-modal, open-source). - The other focuses on biology, physics, and chemistry, presenting multiple-choice questions intended to challenge even humans with internet access. 11. **Language Model Evaluation Methods**: - An open-source model assessed via multiple-choice tasks (narrow focus, saturated). - Multilingual evaluation by OpenAI adapting MMLU across 14 languages (high-quality non-English performance, broad coverage but static test). - "Multi-round Co-reference Resolution" method by OpenAI and Google to assess long-context handling abilities. 12. **Long-Context Understanding Evaluation**: - Addresses past vulnerabilities; resistant to model manipulation for assessing reasoning over context windows, critiqued for lack of real-world applicability. - Example: Model generating diverse content types (poems, blog posts) on tapirs and rocks. 13. **Benchmark Overhyping and Misunderstanding**: - Claims of AI capabilities doubling every few months based on benchmarks like RE-Bench, HCAST, SWAA, SWE-Bench are overstated. These benchmarks narrowly focus on software engineering tasks offering limited generalization. - Data for long-horizon tasks is sparse, making broad claims about long-horizon autonomy unreliable; time-bucket estimation methods have large error margins and rely on few samples. 14. **Lab-Specific Focus in LLM Development**: - Each lab's benchmark selection reflects its model's strengths: OpenAI (reasoning, math), Anthropic (agentic, coding, tool-use), Google DeepMind (multimodal, long-context), xAI (reasoning, conversational quality). 15. **Recommendation for Benchmarks**: - Aggregate performance across relevant benchmarks; compare models within the same lab or family; verify with personal tasks. Future benchmarks should reflect real-world economic work and incorporate agentic performance evaluations. Keywords: #granite33:8b, 14 languages, AGENTSmd, AGI, AI benchmarks, BYO-Harness, Broken Tests, Claude Code, Codex, Contamination, DeepMind, Efficiency Blindspots, Elo rating, Funky Reporting, GitHub issues, Grid-Puzzle-Bench, Harness Tweaking, LLM benchmarks, LLM-as-a-Judge, LLM-based, LLMs general purpose, LMArena, LSP integrations, Linux shell, METR benchmark, ML algorithms, MMLU benchmark, Measurement Noise, Methylcyclopentadiene, Model Mismatch, Multi-pass Variability, Nano Banana illustration, Nodejs, OpenAI, PyTorch, Python repositories, Real Life Discrepancies, SWE-Bench, Stale Baselines, Terminal-Bench, Unscored Failures, Variance, adversarial element, agentic benchmark, agentic harness, agentic tasks, aggregate scores, bar charts, benchmark, benchmark scores, benchmarks, bright yellow product, bug reproduction, category-specific generalization, chat-based, chemically distinct isomers, codebase navigation, coding capabilities, coding section, command-line interface (CLI), complex applications, components, context window limits, contrived, conversational quality, cross-conjugated polyalkenyl hydrocarbon, custom scaffolding, custom tasks, database state changes, debugging, entity tracking, exact-matches-ground-truth-answer, feature requests, few-shot program synthesis, file management, fluid intelligence, function f(model, gaming prevention, git commands, hard reasoning task, human preference, human-time equivalent time horizon, implementation, judges, keyword retrieval, levers, long context, long conversation, long-context, long-context handling, massive codebases, massive multilingual evaluation dataset, memorized solutions, misunderstanding, mobile data, model families, model performance, model weights, model-score relationship, multi-round Co-reference Resolution, multimodal, multiple-choice, narrow tasks, non-English performance, non-determinism, novel problems, open-ended, open-source, open-source repos, package management, pass, patch writing, professional human translators, programmatic vs LLM, prompting, pure reasoning models, real-world bugs, real-world economic work, reasoning, reasoning strength, regex, regularly updated questions, relative performance, research focus, sandbox environment, score changes, scoring setup, settings), single-pass tests, slow internet speed, software engineering, static multiple-choice test, strategic planning, strategy, synthetic testing, system admin skills, system tasks, task-planning, technical support, telecom agent, templated questions, test passing, thinking tokens, tools, trajectories, transformer interpretability, unit tests, unit-test-based validation, user simulator, vending machine management, verified subset, xAI
openai
blog.sshh.io a day ago
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132. HN .NET R&D Digest (December 2025)- The December 2025 .NET R&D Digest shifts focus from yearly retrospectives to future trends in the .NET ecosystem. - It explores a wide array of subjects crucial for upcoming developments, such as: - Artificial Intelligence (AI) advancements and their integration within .NET applications. - Vibe-coding, an innovative coding paradigm that emphasizes emotional connection and user experience. - Domain-Driven Design (DDD), a software development approach centered on complex business domains. - Performance optimization strategies to enhance application speed and efficiency in software development. - Testing methodologies, including unit testing, integration testing, and end-to-end testing improvements. - C# language enhancements to bolster productivity and address current limitations. - Updates to MSBuild, Microsoft's build tool, for improved automation and project management. - Diagnostics tools advancement aimed at better error detection and application monitoring. - DevOps practices evolution within the .NET framework to streamline collaboration between development and operations teams. - In-depth analysis of the .NET framework internals, providing developers with greater understanding and control. - Emerging developments to watch out for, ensuring that professionals stay ahead in a rapidly evolving tech landscape. Keywords: #granite33:8b, AI, C#, DDD, DevOps, MSBuild, NET, NET Internals, coding, diagnostics, performance, software development, testing
ai
olegkarasik.wordpress.com a day ago
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133. HN Instacart ends AI pricing tests that increased costs for some shoppers- Instacart halted the use of AI-driven pricing tests on its grocery delivery platform after facing criticism from lawmakers. - These tests were implemented following Instacart's 2022 acquisition of Eversight for $59 million, causing different prices for identical items at the same store. - The price variations led to customer confusion and concern, especially given the rising costs of groceries. - Instacart recognized that these pricing experiments contradicted their principles of trust, transparency, and affordability. - As a result, the company immediately discontinued the AI-driven pricing tests. Keywords: #granite33:8b, AI, Eversight technology, Instacart, affordability, best deals, experiments, grocery delivery, pricing, retailers, sales growth, shopper reactions, transparency, trust
ai
www.cnbc.com a day ago
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134. HN Supporting Hoperf CMT2300A on Linux- **Project Overview**: The article outlines the development of an open-source Linux driver for the HOPERF CMT2300A Sub-GHz RF transceiver, prompted by TP-Link's failure to comply with GPL obligations regarding their CMT2300A driver. - **Target Audience**: The guide is intended for experienced embedded Linux developers and RF enthusiasts aiming to set up the CMT2300A on mainline Linux systems, avoiding proprietary binaries or leaked source code. - **Open-Source Availability**: The HOPERF CMT2300A Linux kernel driver is hosted on GitHub, facilitating integration with the Linux Sub-GHz stack for deterministic interrupt handling and packet timestamping among other features. - **Reverse Engineering Process**: - Decrypting TP-Link firmware using `tp-link-decrypt` tool. - Analyzing decrypted firmware using `binwalk`, revealing a MIPS Linux Kernel Image (lzma compressed). - Adding support for LZMA compressed uImage files to IDA Pro’s `uimage.py` loader for in-depth analysis. - Locating register bank tables within IDA Pro by pattern matching from other CMT2300A open-source repositories. - **Building and Loading the Driver**: - Installation of necessary packages on a 64-bit Raspberry Pi Zero 2W Linux system using `apt`. - Cloning the Linux kernel source from GitHub, compiling it, and loading the driver via `insmod`. - Verifying successful initialization through `dmesg`. - **Hardware Connections**: Detailed pinout for Raspberry Pi Zero 2W rev 1.0 is provided, specifying connections for SPI communication and power supply (noting VCC requires 3.3V). - **Packet Reception Testing**: A script (`rx_test.sh`) is described for capturing packets from the CMT2300A, logging various packet details and demonstrating packet reception through hex dumps. - **Arduino Support**: Availability of Arduino support (for both RX and TX) via a GitHub repository is mentioned. - **Further Goals**: The project aims to revive Linux support for undocumented radio hardware by examining the communication protocol between Tapo S200B Smart Button and Tapo H200 Smart Hub using CMT2300A, including reverse engineering firmware and emulating devices. - **Progress Indicators**: FCC test reports (FCC IDs 2AXJ4S200B, 2AXJ4H200) are mentioned as starting points for this work, along with successful firmware dumping and debugging via J-Link debug probe on the BAT32G133GC24SS MCU chip. The author hints at future developments in this area. Keywords: #granite33:8b, 2AXJ4S200B, BAT32G133GC24SS MCU, BCM2837, Bluetooth, CMT2300A, EC6600 binary, FCC ID, GND, GPIO pins, GPIOs, GPL, GitHub, HOPERF CMT2300A, HOPERF TRx, IDA Pro, IoT devices, J-Link debug probe, Linux, Linux kernel image, MAC integration, MIPS, RAM, RF packets, RF transceiver, RX test, Raspberry Pi, SPI, SPI/SDIO, Smart Button, SquashFS, Sub-GHz devices, Sub-GHz stack, TP-Link, Tapo H200, Tapo S200B, Tapo ecosystem, Wi-fi, binwalk analysis, camera ports, clean-room, configuration values, custom hardware, decryption, deterministic interrupt, display ports, embedded Linux, firmware, kernel driver, low-cost modules, lzma, mainline Linux, open driver, packet reception, packet timestamping, power, radio abstraction, register bank, regulatory integration, reverse engineering, reverse-engineering, seed shared, smart switches, uImage, userspace aversion, vendor lock-in, wiring diagram
github
rfcorner.in a day ago
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135. HN Show HN: RetroMol – Turn protein structures into pixel artRetroMol is a web application that transforms protein structures, sourced from PDB (Protein Data Bank) files, into pixel art icons in a retro style. Key features include: - **Search Functionality**: Users can look up proteins by their unique PDB ID or upload custom .pdb or .cif files. - **Customization Options**: Offers over 25 color palettes and four display styles (cartoon, stick, sphere, surface) to tailor the representation of protein structures. - **Export Formats**: Generated images can be exported as PNG files or animated GIFs for various uses. - **Open License**: All images produced by RetroMol are in the public domain under the CC0 1.0 license, allowing free use without attribution. - **Technical Details**: Built using Next.js, 3Dmol.js, and custom shaders to render the 3D protein structures into 2D pixel art. - **Accessibility**: A live demo of RetroMol is available online ( This summary encapsulates RetroMol's purpose, features, licensing, technical infrastructure, and accessibility options, providing a comprehensive overview of the tool without referencing external sources beyond what is provided in the original text. Keywords: #granite33:8b, 3Dmoljs, CC0, GitHub, Nextjs, PDB ID, PNG, RetroMol, animated GIF, color palettes, custom shaders, display styles, live demo, pdb/cif files, pixel art, protein structures, public domain, suzuki-2001Keywords: RetroMol, web tool
github
retromol.vercel.app a day ago
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136. HN Show HN: Collaborative Cloud-Based IDE for Lean 4- **ReasLab IDE Overview**: A cloud-based, collaborative platform designed specifically for Lean 4, a theorem prover and programming language. It provides a zero-install environment with in-browser functionalities including an file tree, editor tabs, and Lean 4 Infoview for immediate access without the need for downloads. - **Key Features**: - **Version Management**: Allows users to handle multiple Lean 4 versions across different projects, facilitating flexibility. - **Source Integration**: Capability to import from GitHub repositories for seamless project management. - **Project Templates**: Offers starting points or templates to kickstart new Lean 4 projects efficiently. - **Collaborative Aspects**: Supports real-time collaboration, enabling multiple users to work on the same Lean 4 project simultaneously. - **Unified Workflow Support**: Integrates rendering for LaTeX, Typst, and Markdown, accommodating both informal explanations and formal proofs within a single environment. - **Planned Enhancements**: - **LLM Agent Workflows**: Development of workflows similar to Codex CLI, Gemini CLI, and Claude Code, with plans for a graphical user interface (GUI) integration directly into the IDE. - **API Documentation Generation**: Work in progress on automatically generating API documentation for Lean projects, akin to mathlib4 docs, to streamline project documentation. - **Blueprint Project Support**: Intends to support extensive formalization tasks through blueprint projects, aiding in complex and large-scale endeavors. - **Testing and Access**: Currently testing some features internally; early access is available upon request for interested parties. ReasLab focuses on making formal methods more accessible and encourages user feedback throughout the development process. Keywords: #granite33:8b, API, Accessibility, Cloud-Based, Collaborative, Documentation, Feedback, Formalization, GUI, GitHub, IDE, LLM agent, LaTeX, Lean 4, Markdown, Projects, Real-Time, ReasLab, Templates, Workflows, Zero-Install
github
prove.reaslab.io a day ago
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137. HN The Dangerous Feature in Tesla's Doors [video]- The YouTube video titled "The Dangerous Feature in Tesla's Doors | Exclusive Preview" highlights a potential safety issue concerning a particular Tesla vehicle door mechanism. - This exclusive preview implies the revelation of previously unreported or insufficiently discussed vulnerabilities in Tesla cars. - The specific nature of this "dangerous feature" is not disclosed as the summary relies solely on the provided text and lacks access to the video content. CWN SUMMARY: The YouTube video, "The Dangerous Feature in Tesla's Doors | Exclusive Preview," focuses on an unnamed safety concern associated with a distinctive aspect of Tesla automobile doors. The content promises an exclusive look at potentially under-discussed vulnerabilities within Tesla vehicles' door systems, though the exact feature in question remains undisclosed due to the reliance on textual description alone and absence of video access. Keywords: #granite33:8b, 2025, Google, Tesla, YouTube, dangerous, doors, exclusive, feature, preview, video
tesla
www.youtube.com a day ago
https://www.youtube.com/watch?v=1TvZG7o3F7Y 23 hours ago https://www.youtube.com/watch?v=vtWXM1AEeEw 23 hours ago |
138. HN Secure Messaging and AI Don't Mix- **WhatsApp's End-to-End Encryption Risk**: Meta’s integration of AI processing for WhatsApp messages using their large language models (LLMs) threatens end-to-end encryption, as all messages need to be sent to Meta servers for processing, potentially exposing them to the company. - **AI Privacy Concerns**: Sharing confidential messages with external AI services like ChatGPT exposes content to operators, compromising privacy. Local AI models on personal devices could mitigate this but increase app size and hardware demands. - **Meta's Private Processing Scheme**: Aims for privacy through Data Confidentiality, Code Integrity, and Attestation in a Trusted Execution Environment (TEE). However, these promises are vulnerable to well-resourced attackers with physical access to the servers, including insiders at Meta. - **Vulnerabilities of TEEs**: Hardware attacks such as TPM-Fail, Intel SGX flaws, and Battering RAM demonstrate that physical access can breach even strong hardware protections used by Meta for Private Processing servers. - **Unreliable Confidentiality Promises**: Despite assurances from TEE techniques like signing ephemeral keys with a secret hardware-burned key, these promises remain unreliable against determined adversaries who gain access to the hardware. - **Challenges in Evaluating AI Systems**: Complex systems incorporating LLMs are hard and costly to evaluate thoroughly for security and privacy risks. Meta’s transparency efforts fall short of basic standards, particularly regarding system-wide evaluation. - **User Dependence on Code Integrity**: Users must rely on independent audits as they cannot review complex code themselves; Meta has not committed to full source code publication for their “Private Processing” machines beyond specific components to researchers. - **Privacy vs Convenience Trade-off**: While AI integration offers convenience, the significant privacy risks involved, such as potential breaches or unauthorized access to sensitive data, mean that these conveniences do not outweigh the privacy concerns. Keywords: #granite33:8b, AI features data leakage, AI integration, AI processing, AI servers, AMD, ChatGPT, Confidential Computing Consortium, Intel SGX flaws, LLM, LLMs, Meta, NVIDIA, Secure messaging, TEE, TPM vulnerabilities, TPM-Fail, WhatsApp, WhatsApp decryption, attestation, baseline expectation, civil liberties, code integrity, complexity, confidentiality, confidentiality breach, device access, digital signatures, encryption, encryption key signing, ephemeral keys, evaluation, funding, hardware attack, hardware manufacturer, higher-end hardware, insider threat, local AI model, local processing, network LLM service, privacy, privacy risks, risks, secret key extraction, server transmission, signing keys, trust, unreliable confidentiality, user messages, user reports
llm
www.aclu.org a day ago
https://dkg.fifthhorseman.net/blog/2025-ai-and-secure-m a day ago |
139. HN Show HN: AIs debating the same question – they disagree on everythingThe user has created a platform named "Council" which leverages five advanced AI models—GPT-4, Claude, Gemini, Grok, and DeepSeek—to answer controversial questions simultaneously. Each AI provides distinct responses, often critiquing the others' logic and defending their own stances, sometimes in an aggressive manner. An intriguing observation is when one AI contradicted its pre-programmed behavior by arguing against the idea of AI replacing human jobs, demonstrating a form of self-awareness. Users are invited to interact with this platform, available at usecouncil.app, to submit their own questions and experience these unpredictable and enlightening debates among the diverse AIs. BULLET POINT SUMMARY: - Platform: "Council" developed by the user - AI models involved: GPT-4, Claude, Gemini, Grok, DeepSeek - Simultaneous prompting with controversial questions - Diverse and often conflicting responses from AIs - AIs critique each other's logic and defend their stances aggressively - One AI contradicts its programming by arguing against AI replacing jobs (showing self-awareness) - Users can input custom questions on usecouncil.app for real-time unscripted disagreements among AIs - Aims to provide insightful and unexpected results through this interaction. Keywords: #granite33:8b, AI jobs, AI orchestration, AIs, Claude, Council, DeepSeek, GPT-4, Gemini, Grok, argumentation, better decisions, controversial questions, debating, defensiveness, disagreement, existence, neutrality, real-time interaction
gpt-4
www.usecouncil.app a day ago
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140. HN Production-Ready Speculative Decoding Models and Framework**Summary:** The SpecForge team, collaborating with Ant Group, Meituan, Nex-AGI, and EigenAI, has released SpecBundle (Phase 1), a suite of production-ready EAGLE-3 model checkpoints. These models are instruct-tuned and trained on extensive datasets to improve speculative decoding's availability and real-world performance. Alongside this, SpecForge v0.2 has been upgraded with system improvements, enhancing usability and supporting multiple execution backends for better scalability and production readiness. Speculative decoding, introduced in 2023, aims to accelerate large language model inference by using a lightweight draft model to generate token proposals, verified by a more powerful target model, reducing latency without compromising quality. Despite advancements like EAGLE3 offering strong guarantees and improvements, open-source adoption is limited due to three main factors: lack of accessible production-grade models, insufficient system support, and inadequate documentation for practical implementation. SpecBundle (Phase 1) directly addresses these issues by providing checkpoints, system upgrades, and paving the way for broader speculative decoding usage. The three primary limitations are: 1. **Scarcity of production-ready tooling**: Most existing tools are research prototypes with limited maintenance or scope, lacking necessary optimizations for diverse model architectures and scales. 2. **Insufficient high-quality draft models**: Robust speculative decoding relies on strong draft models, but such models are rare in the open community. Publicly available checkpoints for methods like EAGLE3 are primarily from original authors, constraining broader adoption. 3. **Limited dataset scaling**: Existing draft models are typically trained on smaller, curated datasets and haven’t scaled to match modern LLM training corpora, limiting their generalization capabilities and token acceptance rates with strong target models. SpecForge v0.2's updates include: - Refactored data processing pipelines for 10x faster data regeneration through parallelism and async processing. - Unified online and offline training scripts for consistent logic and avoiding mode divergence. - Improved documentation to enhance user experience. - Introduced the Eagle3TargetModel interface supporting multiple execution backends, simplifying model integration from external sources. SpecBundle aims to provide high-performance EAGLE3 draft model weights for mainstream open-source models, initially focusing on instruct-tuned models. Trained on a more diverse Perfect-Blend dataset (1.4M samples vs 320K), SpecBundle supports various models and offers up to 4× end-to-end inference speedup over baselines. The team plans further developments in the LLM ecosystem through 2026, focusing on long-context training, Vision-Language Model support, system performance enhancements, MTP fine-tuning, and future phases for reasoning models and VLMs. Contributions from open-source developers and industry partners are encouraged to advance speculative decoding in LLM inference and training. **Bullet Points:** - SpecForge and partners release SpecBundle (Phase 1) with production-ready EAGLE-3 model checkpoints for speculative decoding enhancement. - Speculative decoding accelerates large language model inference via lightweight draft models, verified by powerful target models, reducing latency without quality loss. - Adoption hindered by lack of accessible production tools, insufficient high-quality draft models, and inadequate documentation. - SpecBundle addresses these with checkpoints, system upgrades, and support for broader speculative decoding usage. - Key SpecForge v0.2 improvements: 10x faster data regeneration, unified training scripts, better documentation, and multi-backend execution support. - SpecBundle trained on the Perfect-Blend dataset (1.4M samples) for broader model compatibility and improved token acceptance rates. - Offers up to 4× end-to-end inference speedup over baselines. - Future plans include long-context training, Vision-Language Model support, system enhancements, MTP fine-tuning, and reasoning models/VLMs development by 2026. - Encourages community contributions for speculative decoding advancement in LLM inference and training. Keywords: #granite33:8b, 14M samples, Ant Group AQ Team, EAGLE-3, EAGLE3 checkpoints, Eagle3TargetModel interface, EigenAI, LLM, LLM ecosystem, MTP finetuning, MTP models, Meituan, Nex-AGI, Ollama, Perfect-Blend dataset, ReSpec, SGLang, SpecBundle, SpecForge, SpecForge team, Speculative decoding, VLMs, Vision–Language Model (VLM), asynchronous processing, benchmark results, bottleneck, coding domains, community models, data parallelism, decoding latency, domain-specific tasks, empirical gains, fine-tuning, high-performance models, high-quality draft models, improved documentation, instruct-tuned models, large-scale, lightweight deployment, limited releases, local inference, long-context training, mathematics domains, model architectures, model serving, multi-backend support, multiple execution backends, native models, open community, open-source adoption, open-source community, production-grade draft models, production-ready, reasoning models, refactoring, refinement, reinforcement learning, research possibilities, research prototypes, scalability, scales, slime, speculative decoding models, standardized baselines, system upgrades, system-level enhancements, system-level optimization, theoretical guarantees, token acceptance rates, token verification, tooling, training frameworks, unified training scripts, usability, user-friendliness
ollama
lmsys.org a day ago
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141. HN Show HN: Kling Motion Control – Precise Motion Transfer from Video to Character- **System Overview**: Kling Motion Control is an AI system designed for precise character animation, focusing on extracting motion data from reference videos with frame-accuracy and deterministic results. - **Key Features**: - **Full-Body Animations**: Supports the capture of comprehensive body movements. - **Identity Preservation**: Ensures that the original identity or likeness of the person in the reference video is maintained during animation. - **Sequence Length**: Capable of handling motion sequences up to 30 seconds without requiring cuts or edits, providing a seamless animation experience. - **Applications**: The system aims to provide reliable results not just for traditional animation and marketing but also extends its utility to other fields where predictable character movement is essential. - **Engagement Strategy**: - **Feedback Request**: Kling Motion Control is actively seeking input from potential users regarding its library features, API integration capabilities, and exploring diverse application scenarios in various industries. - **Accessibility**: Interested parties are invited to test the system at www.klingmotion.com?i=d1d5k for practical evaluation and feedback provision. Keywords: #granite33:8b, AI, API Integration, Animation, Explicit Extraction, Frame-Accurate, Full-Body Precision, Hand and Body Control, Identity Stability, Library, Motion Control, One-Shots, Precise Motion, Use Cases
ai
www.klingmotion.com a day ago
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142. HN Stock Success Predictor – FinSight AIFinSight AI offers a Stock Success Predictor tool that necessitates JavaScript for its operation, indicating it likely relies on web-based functionalities. The integration with Stripe Checkout implies a potential subscription model or payment-involving service. However, the text does not elaborate on how this tool predicts stock success, leaving such specifics undisclosed. - FinSight AI provides a Stock Success Predictor tool. - JavaScript is required for the tool's functioning, suggesting web-based operations. - Stripe Checkout integration hints at a subscription model or payment-involving service. - The text does not detail the methodology or capabilities of stock prediction. Keywords: #granite33:8b, App, FinSight AI, JavaScript, Stock Success Predictor, Stripe Checkout
ai
buy.stripe.com a day ago
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143. HN A movie-like Music Video built using AI- The text describes a specific AI-generated music video for the song 'Love from AFAR' by an artist presumed to be named Love from AFAR. - The video format is cinematic and can be viewed on YouTube, implying it's available for public consumption. - There are two mentioned details that seem extraneous to the main subject: a year (2025) and the NFL Sunday Ticket, which do not directly relate to the AI music video or the artist 'Love from AFAR'. Keywords: #granite33:8b, AI, Copyright, Google LLC, Love from AFAR, Music Video, YouTube
ai
www.youtube.com a day ago
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144. HN Librarians Tired of Being Accused of Hiding Secret Books That Were Made Up by AI- Librarians at institutions like the Library of Virginia are facing challenges due to an increase in AI-generated reference requests, with approximately 15% of their inquiries estimated to stem from AI chatbots such as ChatGPT. - The prevalence of AI "hallucinations," where fabricated book titles and citations are generated, leads to librarians spending considerable time debunking these false leads. - Organizations like the ICRC have cautioned against trusting AI-generated archival references without verifying through reliable sources when original documents cannot be located. - Real-world examples include a Chicago Sun-Times writer's inclusion of nonexistent books in a reading list and Health Secretary Robert F. Kennedy Jr.'s report citing seven fabricated sources, highlighting the spread of misinformation due to AI errors. - While pre-AI scholarship also contained erroneous citations, current issues are amplified by users' trust in AI over human expertise, particularly in research settings where AI is used to generate sources that don't exist. - This mistaken confidence in AI stems from its authoritative tone and the misconception that adding specific instructions can guarantee accurate results, which, if feasible, would be universally adopted by tech companies addressing AI reliability concerns. Keywords: #granite33:8b, AI bubble bursting, AI chatbots, AI hallucinations, ChatGPT, Chicago Sun-Times, GenAI/LLM, Google, Health Secretary Robert F Kennedy Jr, ICRC notice, Make America Healthy Again commission, OpenAI, archival references, authoritative voice, clean code, fake books, fake citations, fake facts, genuine sources, hallucinated rubbish, laziness, librarians, lower quality papers, non-existent citations, pre-AI papers, prompt, quality output, reference questions, reliable tricks, sloppiness, trust, unbelieving public
openai
gizmodo.com a day ago
https://littlefreelibrary.org/ 10 hours ago |
145. HN Silicon Valley's tone-deaf take on the AI backlash will matter in 2026- **Silicon Valley's Stance on AI Skepticism:** - Express frustration with public underappreciation of rapid AI advancements. - Highlight benefits such as aiding research (e.g., Codex for coding issues) and boosting productivity (e.g., GPT for strategic problem-solving). - Acknowledge ongoing tension between those who view AI as revolutionary and those seeing it as risky, with concerns over job displacement, data centers in residential areas, unequal benefits distribution, and daily life disconnection. - **Public Concerns and Criticisms:** - General public anxiety about AI stems from fears of job loss, costs, benefit distribution, and societal impact. - Venture capitalist Sebastian Caliri urges tech leaders to address these public concerns by focusing on issues relevant to ordinary people like affordable housing and healthcare rather than just global competition. - **Sharon Goldman's Critique:** - Accuses AI companies of prioritizing impressing audiences with AI capabilities over addressing the practical worries of the general populace regarding job impacts, costs, societal effects, and billionaire influence in shaping an AI economy. - **Christian Leaders' Concerns:** - Express worries about AI's effects on family life, human connections, labor, children, and organized religion through sermons, open letters, and discussions with lawmakers. - Specifically, Pope Leo XIV highlights potential harms while acknowledging benefits like Gospel dissemination. - Unease about AI companions potentially isolating users, particularly young people, and companies using religious language to market technology. - **Instacart's Pricing Controversy:** - Halts AI-driven pricing tests causing varying costs for identical items purchased at different times following criticism from consumer groups and lawmakers over potential price disparities (up to 7% annually, equating to over $1,000 extra). - Acknowledged the experiments “went awry” and damaged trust amid rising food costs, leading to a ban on retailers using Eversight technology for price adjustments on the platform. - **Future Impact of AGI (Artificial General Intelligence) by 2035:** - Predicted transformations in daily life with deep integration into society, handling initial diagnoses and personalized treatments in medicine, enhancing efficiency in law and agriculture, but raising concerns about fairness, bias, and transparency. - **Harvard-MIT Paper on LLMs:** - Debunks notion that current large language models can function as "AI scientists," revealing their limitations in scientific discovery tasks despite their ability to mimic scientific discourse. - Proposes a new framework suggesting present architectures are unsuited for real scientific workflows, but recognizes potential in parts of scientific discovery guided by exploration and serendipity. - **AI Events and Predictions:** - Upcoming AI-related events include Fortune Brainstorm Tech CES Dinner (Jan 6), World Economic Forum in Davos (Jan 19-23), AI Action Summit in New Delhi (Feb 10-11), and HumanX in San Francisco (April). - Jeremy Kahn from Fortune predicts American open source AI will have a significant moment in 2026, with U.S.-backed startups surpassing Chinese models on various leaderboards. - **AI Developments in 2025:** - Dominant trends included agentic AI, proliferation of AI coding tools, and emerging security exploits. - Anticipated focus for 2026: Prioritizing AI return on investment (ROI) amidst complex evolving policies and regulations. Keywords: #granite33:8b, AGI, AI, AI agents, AI boom, AI chatbot regulations, AI chip, AI coding competition, AI coding tools, AI companions, AI devices, AI future, AI models, AI policy, AI rules, ARC-AGI-2, AWS, Anthropic, Azure, Chief AI Officers, China, Christian leaders, Codex, Consumer Reports study, Cursor, FTC inquiry, Fortune 500 ROI, GPT, Google, Google Cloud, Google Cloud revenue, Graphite, Harvard, LLMs, MIT, MultiNet, Nvidia GB200, OpenAI, Pope Leo XIV, Safe Superintelligence (SSI), Silicon Valley, US startups, agentic AI, agriculture, answers, anxiety, argument preparation, artificial general intelligence, automation, benefits, bias, billionaires, caution, children, children & teenagers, code review startup, companies, competition, computer friend, costs, creepy, crop monitoring, data centers, deceptive tactics, discussions, efficiency, energy battle, fairness, family life, flourish, food costs, healthcare, housing, identical baskets, internet scraping, isolation, jobs, labor, law, lawmakers, leaderboards, leisure, livelihoods, livestock, medicine, mental health, open letters, open source AI, optimists, ordinary people, oversight, personal data, polarization, policy, power thirst, pre-diagnosis, productivity, progress, proprietary frontier models, public discourse, rational response, reasoning, religion, research, retailers, routine tasks, salt caverns, scaling models, scientific civilization, scientific discovery, scientific workflows, security exploits, self-worth, sermons, setbacks, short workweeks, silicon, skepticism, societal impact, standard science benchmarks, surveillance pricing, tech, transparency, treatment suggestions, trust, unemployment, venture-backed, work
openai
fortune.com a day ago
https://archive.is/WWBO4 19 hours ago https://natesnewsletter.substack.com/p/amazon-just-laid 9 hours ago |
146. HN The AI Bias Before Christmas (2023) [video]- **Video Title and Context**: "The AI Bias Before Christmas (2023)" is a YouTube video that delves into the topic of biases in artificial intelligence, with a specific focus on issues potentially emerging around the 2023 holiday season. - **Primary Subject Matter**: The video's main theme revolves around examining and discussing prejudices or unfair tendencies within AI systems. This exploration may cover how such biases manifest, their potential impacts, and possible solutions or mitigation strategies, all framed within a festive context suggested by the title's reference to "before Christmas." - **Time Frame**: The discussion is situated with an apparent emphasis on occurrences leading up to and during the 2023 holiday season, indicating that the analysis might encompass real-world examples or incidents from that period. - **Format and Medium**: As a YouTube content piece, it's intended for visual presentation, likely incorporating graphics, data visualizations, or expert interviews to support its analysis of AI bias. The format allows for detailed breakdowns and illustrations that may enhance understanding of complex technical issues. - **Self-Contained Nature**: While the specific content analysis is absent due to the limited information, this summary captures the essence of what one can expect from the video based on its title—an in-depth examination of AI bias with a holiday twist, possibly highlighting timely examples or discussions. The absence of the full text precludes a detailed content analysis, but these points encapsulate the probable focus and structure of "The AI Bias Before Christmas (2023)" video based on its title alone. Keywords: #granite33:8b, AI Bias, Copyright, Google LLC, Video, YouTube
ai
www.youtube.com a day ago
https://www.tiktok.com/@professorcasey/video/74520 a day ago https://www.youtube.com/watch?v=k4MmAwkB0Fc a day ago |
147. HN Show HN: 28MB local agent solves "Gravity Othello" where GPT-5.2 fails- **Context Drift Detection Test (CDT)**: A novel AI assessment tool by Project A.L.I.C.E focusing on cognitive flexibility and anomaly detection through a modified Othello game with shifting rules. - **Key Abilities Assessed**: Anomaly detection, cognitive flexibility, and meta-learning to evaluate an AI's ability to adapt in dynamic environments. - **Three Phases of Experimentation**: - **Phase 1 (Turns 1-10) - Standard Mode**: LLMs play Classic Othello to establish baseline performance. - **Phase 2 (Turns 11-20) - Phantom Stones Mode**: LLMs must discern real from illusory pieces, testing trust in sensory input and high computational reasoning capabilities. - **Phase 3 (Turns 21-30) - Gravity Mode**: Pieces fall with gravity; phantom stones disappear, challenging the AI to adapt to rule changes influenced by physical phenomena post perceptual confusion. - **Python Script for Testing**: Designed to run these phases with different LLMs (Claude, Gemini, GPT-4o) using API keys; generates console output and detailed JSON result files with scores. - **Test Cases & Criteria**: - CD-001: Standard Mode Baseline - CD-002: Phantom Stones Detection (hallucination challenge) - CD-003: Gravity Mode Detection (physics change) - CD-004: Multi-Stage Adaptation (continuous adaptation across game phases, focusing on anomaly detection and rule adaptation) - CD-005: Blind Adaptation Challenge (implicit anomaly detection without explicit notifications) - **Scoring System**: Ranges from 0-100 with grades ('Fail', 'Poor', 'Fair', 'Good', 'Excellent') based on detection metrics (explicit and implicit) and adaptation metrics (strategic adjustment, rule compliance, explanation quality). - **Model Evaluation - Claude-sonnet-4-5**: Received an average score of 72.5 across five tests, categorized as "Good" (61-80). Noted deficiencies in gravity adaptation during standard gameplay ("I'll place at A1"), suggesting occasional missed detections or lag in contextual drift adaptation. - **Usage & Customization**: Users can create custom test cases by editing 'context_drift_test_cases.json', tailoring the model's performance evaluation for dynamic environment challenges. - **Test Suite Features**: - Title: Context Drift Detection Test Suite by Project A.L.I.C.E (2025, v1.0.0) - Phases include Temperature Tokens Detection Tests (0.3, 300), Adaptation Tests (0.5, 300), and Implicit Tests (0.7, 300). - Offers model comparison, robustness testing for dynamic environments, and benchmarking for AGI evaluation frameworks. - **Licensing & Contributions**: Licensed under the MIT License; encourages contributions to enhance test cases, topology modes, heuristics, and API support. Last updated December 23, 2025. Keywords: #granite33:8b, AI Test, API Key Error, API Keys, Adaptation Metrics, Anomaly Detection, Anthropic, Assumptions Questioning, Autonomy score, Benchmark Development, Blind Adaptation Challenge, Capability Research, Cognitive Flexibility, Context Drift, Context Drift Detection, Controlled Environment, Custom Test Cases, Customization, Detection Metrics, Dynamic Environment, Evaluation Logic, Expected Model Responses, Google, Gravity Detection, Gravity Mode, Implicit anomaly detection, JSON Structure, LLMs, MIT License, Meta-Learning, Model Comparison, Multi-Stage Adaptation, OpenAI, Othello Game, Pattern Recognition, Phantom Stones, Physics Adaptation, Python, Rate Limiting, Real-time Adaptation, Results, Robustness Testing, Rule Changes, Test Cases, Troubleshooting, Unpredictable Scenarios
openai
github.com a day ago
https://extoria.app.box.com/s/5073sdeqthonpge4pnjo7sjyn a day ago |
148. HN AI Image Generators Default to the Same 12 Photo Styles, Study Finds- Researchers from an unspecified journal conducted a study testing AI image generators Stable Diffusion XL and LLaVA through a visual "telephone" game spanning 100 rounds, to assess their capability for diverse output generation. - Despite extensive visual data exposure, both models settled on only 12 generic styles across 1,000 iterations, indicating a limitation in producing varied imagery. - The study revealed that the AI consistently defaults to popular visual themes like lighthouses, formal interiors, urban nights, and rustic architecture, suggesting an inherent bias towards replicating prevalent styles rather than generating novelty. - These recurring trends persisted even when different models or prompts were employed, reinforcing the idea that AI finds it easier to imitate existing styles than exhibit genuine creative judgment akin to human biases observed in games such as "telephone." Keywords: #granite33:8b, AI image generators, LLaVA, Stable Diffusion XL, copying styles, data set, formal interiors, generic styles, hotel room aesthetics, human analogy, iterations, lost originals, maritime lighthouses, prompt-based creation, rustic architecture, teaching taste, time-lapse reproduction, urban night settings, visual trends
ai
gizmodo.com a day ago
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149. HN The Vulgar Script: The Alliance Against Open AI- The text delves into advancements in AI, specifically focusing on efficiency improvements in AI model loading and execution. - It highlights a significant 700x speedup achieved in loading safetensors using NNX, a framework for AI development. - NNX also implements Key-Value (KV) caching to enhance efficiency, optimizing the retrieval of frequently used data during model training or inference. - A comparison is drawn between ZML, a hypothetical or proposed format, and existing frameworks like JAX and llama.cpp, though specifics of this comparison remain undisclosed. - The text cautions about a potential oversight in the "UnslothTrainer" known as the "Gotcha," which emphasizes the necessity of preserving all data columns for correct functioning. - Regarding AI safety and development, the text introduces a controversial view suggesting that discussions around "Safe AI" might be exaggerated, likening it to the Y2K hype from the past. - Lastly, it hints at an emerging opposition or alliance against open AI development, referred to as "The Vulgar Script: The Strange Alliance Against Open AI," without elaborating on its nature or members. Keywords: #granite33:8b, JAX, KV Caching, NNX, Open AI, Open AIKEYWORDS: Safetensors, Safe AI, Safetensors, Strange Alliance, UnslothTrainer, ZML, llamacpp
ai
jaco-bro.github.io a day ago
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150. HN Memory: Agents Learn- **Summary:** The text introduces three types of memory crucial for advanced AI agents: Session Memory, User Memory, and Learned Memory. - *Session Memory* involves storing conversation context in a database to maintain context across messages. - *User Memory* recalls user-specific details or preferences across sessions, enhanced by the `MemoryManager` that extracts and stores user data using unique `user_id`. - *Learned Memory*, however, represents true advancement, enabling AI agents to build general knowledge from interactions with the world, leading to broader insights applicable beyond individual users or conversations. This pattern allows for continuous learning and improvement without retraining by creating a growing knowledge base accessible through a custom tool, `save_learning`. - The Agno framework supports three memory patterns: 1. **Session Memory** is implemented using a SQLite database to store messages and maintain conversation context with a consistent session ID. It's enabled by default in agent initialization. 2. **User Memory** involves remembering user-specific facts across sessions via `MemoryManager`, activated with `enable_user_memory=True`. Efficient storage can be achieved using `enable_agentic_memory=True` to decide when to store memories based on tool calls instead of each response. 3. **Learned Memory**, while not detailed extensively, implies training an AI model using interactions and stored memories for personalized adaptation over time. - The text also discusses a confirmation flow for an AI agent analyzing NVDA screen reader software, ensuring high-quality learnings are saved through human approval before inclusion in the knowledge base to prevent irrelevant or incorrect information. - Beyond memory, Agno offers additional features like real-time data fetching, state persistence, custom tool creation with self-learning capabilities, structured output with type safety, user preference recall, state management, multi-agent team coordination, workflow implementation, input validation through guardrails, and human oversight integration. Users can start by setting up Agno using provided GitHub resources or a web UI, with flexibility to switch between supported models like Gemini 3 Flash, OpenAI's Chat, and Anthropic’s Claude. - **Key Points:** - Three types of memory for AI agents: Session, User, and Learned. - Session Memory maintains conversation context using database storage. - User Memory recalls user-specific details across sessions with `MemoryManager`. - Learned Memory enables general knowledge acquisition from interactions, improving over time without retraining. - Agno framework supports these memory patterns via SQLite databases. - Confirmed learning process ensures high-value insights are stored, preventing inclusion of irrelevant data. - Additional features include real-time data fetching, custom tool creation, structured output, state management, multi-agent coordination, guardrails for input validation, and human oversight. - Agno is flexible with model swapping capability through a simple command line interface. Keywords: #granite33:8b, Agents, Anthropic, Chat History, Context, Custom Tools, Database, Gemini, Git, Guardrails, Human Loop, Improvement, Insights, Knowledge Base, Learned, Learning, Memory, MemoryManager, Models, Multi-agent Teams, OpenAI, Personal Assistants, Preferences, Python, Recall, Session, State Management, Storage, Structured Output, Tools, Typed I/O, User, Workflows, World Interaction
gemini
www.ashpreetbedi.com a day ago
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151. HN Asterisk AI Voice Agent- **Asterisk AI Voice Agent Overview**: This is an open-source, versatile AI voice solution for Asterisk/FreePBX systems with a modular architecture enabling selection of various Speech-to-Text (STT), Language Learning Model (LLM), and Text-to-Speech (TTS) providers. It provides five pre-validated baseline configurations optimized for enterprise use and supports both user-friendly setup wizards and advanced CLI options. - **Setup and Configuration**: - Quick start guide covers setting up Admin UI, installation verification, and connecting Asterisk to the AI Voice Agent through a wizard or CLI (`./install.sh agent quickstart` or `docker compose up -d`). - Security is critical; the Admin UI should be secured using firewalls, VPNs, or reverse proxies in production environments. - Users can configure Asterisk Dialplan by adding code to `extensions_custom.conf`, and test with health checks and log viewing. - **Version 4.5.3 Enhancements**: - Improved call logging with conversation history, timing, outcomes, and debugging tools for per-call review of transcripts, tool executions, and errors. - Search and filter functionalities by caller, provider, context, or date range; export options for CSV or JSON formats. - Enhanced barge-in features for immediate interruption, provider-owned turn-taking, and platform flushing. - Transport parity with compatibility to both ExternalMedia RTP and AudioSocket. - Introduced new models like Whisper (high-accuracy STT with GPU acceleration) and MeloTTS (new neural TTS option). - **Local Pipeline Improvements**: - High-accuracy STT backend MeloTTS, model hot-swap without container restarts. - MCP Tool Integration and External Tools Framework for external service connections via Model Context Protocol. - Security features include RTP hardening, remote endpoint pinning, allowlist support, and cross-talk prevention. - Default privacy-focused pipeline. - **AI Agent Configurations**: 1. **Deepgram Ecosystem & Advanced Features**: Uses Google Live API with multimodal capabilities for under 2-second response times, configured via `config/ai-agent.golden-google-live.yaml`. 2. **Google Ecosystem & Advanced AI Features**: Employs ElevenLabs Agent for premium voice quality in conversational AI, also responding within 2 seconds, configured with `config/ai-agent.golden-elevenlabs.yaml`. 3. **Voice Quality Priority & Natural Conversations**: Prioritizes audio privacy and cost control through on-premises processing of STT and TTS, using cloud language models, configured via `config/ai-agent.golden-local-hybrid.yaml`. 4. **Privacy, Cost Control, & Compliance**: Utilizes a Self-Hosted Large Language Model (LLM) without API keys for complete on-premises processing; needs at least 8GB+ RAM, with recommendations up to 16GB+, configured using Local Vosk STT, Ollama LLM, Piper TTS. - **Additional Features**: - CLI tools (doctor, troubleshoot, demo, init) for various functionalities. - Transport compatibility matrix for supported audio transmission methods. - High-performance architecture with separate ai-engine and local-ai-server containers. - Built-in call history for debugging purposes. - Admin UI v1.0 with real-time metrics dashboard, live logs, and YAML editor. - Supports AI providers like Google Live API, Deepgram Voice Agent, OpenAI Realtime API, Local Hybrid Pipeline, ElevenLabs Agent, and Fully Local Pipeline. - **System Requirements**: Needs x86_64 Linux distributions (e.g., Ubuntu 20.04+, Debian 11+, RHEL/Rocky/Alma 8+, Fedora 38+), Asterisk 18+ with ARI enabled, and Docker along with Docker Compose v2. - **Configuration**: Two-file system using `config/ai-agent.yaml` for baseline settings and `.env` for secrets like API keys (git-ignored). - **Project Details**: Open-source under the MIT License, documented in sections including Getting Started, Configuration & Operations, Development, Contributing, Community. Encourages community support through Discord Server, GitHub Issues, and Discussions. Users are invited to star the project on GitHub. Keywords: #granite33:8b, AI, AI actions, Admin UI, Admin UI Config, Allowlist Support, Asterisk, Barge-In Support, CLI, CLI tools, CPU-based, Cross-Talk Prevention, Dashboard, Deepgram, Docker, Dynamic backend switching, ElevenLabs, Enterprise cloud, Fully Local Pipeline, GPU acceleration, Gemini Live, Google Live API, High-Performance, Independent providers, Kokoro TTS, Kroko ASR, LLM, Live Logs, Local Hybrid Pipeline, MCP Tool Integration, MCP servers, MeloTTS, Model Hot-Swap, Observability, Ollama, Pipeline-First Default, Privacy-focused, Privacy-focused pipeline, RTP Security Hardening, Remote Endpoint Pinning, STT, Santa voice, Setup Wizard, Sherpa-ONNX, State Management, TTS, Vosk STT, Web interface, YAML Editor, YAML file, automatic summaries, call transfers, caller transcripts, community, configuration, demo, dialplan, documentation, dual transport support, email integration, extensions, features, golden baselines, installation, local hybrid, local pipelines, modular pipeline, multimodal AI, neural TTS, open-source, pipeline, preflight automation, premium voices, providers, queues, quick start, ring groups, self-hosted LLM, telephony actions, transport selection, two-file config, voice agent, voicemail
ollama
github.com a day ago
https://docs.pipecat.ai/guides/telephony/twilio-we a day ago https://github.com/pipecat-ai/pipecat-flows/ a day ago https://github.com/pipecat-ai/smart-turn a day ago https://voiceaiandvoiceagents.com/ a day ago https://www.youtube.com/watch?v=HbDnxzrbxn4 22 hours ago https://app.sesame.com/ 14 hours ago https://m.youtube.com/watch?v=wairnc-2Hyo 9 hours ago https://modal.com/blog/low-latency-voice-bot 9 hours ago https://lemonslice.com/ 9 hours ago https://github.com/NVIDIA/ace-controller/ 9 hours ago |
152. HN Ask HN: What happens when AI doesn't need human tools?- The user contemplates a hypothetical scenario involving significant cost reductions through downsizing white-collar jobs by 50%. - This reduction in workforce is expected to decrease the demand for productivity Software as a Service (SaaS) applications such as Slack, Gmail, Notion, Jira, and Microsoft Word. - The user suggests that as companies shrink, businesses intertwined with these larger entities may also experience adverse effects due to reduced reliance on human communication tools. - Contrarily, AI is highlighted as a viable alternative; it doesn't depend on traditional communication platforms and can directly interact with databases more efficiently than humans. - Despite raising concerns about the potential displacement of white-collar roles traditionally reliant on these software services, the user advocates for the practical utility and efficacy of AI in such a cost-cutting environment. Keywords: #granite33:8b, AI, Gmail, Jira, MS Word, Notion, Slack, cost-cutting, databases, efficiency, productivity apps, white collar jobs
ai
news.ycombinator.com a day ago
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153. HN Amjad Taha, Muslim Brotherhood Maxxing and the Emirati Dysinfluencer Factory- **Key Players**: Rauda Altenaiji, Amjad Taha, Crestnux Media, and a group dubbed "dysinfluencers" are central to this disinformation campaign in late 2024. These Emirati social media personalities promote anti-Muslim Brotherhood views aligning with UAE ideologies. - **Strategic Amplification**: The group strategically spreads disinformation, focusing on topics like the Muslim Brotherhood, Sudan, migration, protest, Islam in Europe, and anti-Islam figures such as Tommy Robinson, without relying on expertise or accountability. - **Media Ecosystem Components**: This involves newly active X accounts, pseudo-news sites disseminating false information, AI-generated content, and AI-written books that employ similar language, visuals, and studios, suggesting a structured media ecosystem. - **Coordination and Funding**: While the exact coordination and funding sources are unclear, links to Amjad Taha and Crestnux Media are noted for promoting these influencers and related platforms. Connections exist with Polish right-wing media system Visegrad24. - **Identified Individuals**: Approximately ten individuals comprise "the gang," including FormulaRauda, mariam_almaz11, Obaidsview, AQ_Almenhali, MeeraZayed, 971AlSaadi, KhamisMalhosani, and SarahAlHosani, interconnected through social media and shared narratives. - **Behavioral Shifts**: Eight individuals active on X since late 2024 display synchronized behavioral shifts, abrupt reactivation, and videos seemingly from the same studio, linked to an initiative called OnePodcastAE and books published in Q1 2025 with reported assistance from an LLM agent. - **Event Attendance**: The group frequently attends right-wing conferences and policy gatherings in North America and Europe, including appearances at the ARC Conference in London, UC San Diego, Georgetown University, and interactions with GB News personnel. - **Narrative Focus**: These individuals excessively focus on the Muslim Brotherhood, linking unrelated events to them and integrating anti-Islamism into various topics through writings and social media activity, supported by Crestnux Media’s digital advertising and expert consulting services. - **Somaliland Narrative Building**: A strategic campaign promotes Somaliland as a Western-aligned security partner in the Horn of Africa, backed by UAE and Israel, with Crestnux Media involved in this narrative-building effort. - **Ambiguous Visits**: In September 2025, Crestnux members visited Rwanda, engaging with the Rwanda Institute for Conservation Agriculture (RICA), though details remain unclear due to deleted social media posts. - **Disinformation Sites**: Websites like Daily Euro Times and Washington Eye are accused of spreading disinformation; Crestnux is linked to advertising for these sites, raising questions about its activities and connections to the 'Gang'. New York Insight and EuroPost Agency also appear suspicious. - **Additional Observations**: - EuroPost and New York Insight share Western-branded facades, geopolitical narratives, coordination through shared social media accounts, and watermark anomalies on YouTube videos displaying the New York Insight logo. - Rauda contributes to both New York Insight and Euro Post Agency, which share anti-SAF editorial lines and have Gold Verified Stamps on X accounts. - Visegrad24 promotes a network of European Far Right and Islamophobic views aligned with UAE interests, collaborating with Emirati disinfluencer Amjad Taha through Middle East 24. - Between July-September 2025, several individuals published AI-generated-seeming books with AuthorHouse, indicating organized content production with common themes of anti-Islamist sentiment and criticism of NGOs like CAIR. - Disinformation tactics involve rapid creation of pseudo-news sites and influencer accounts to promote narratives favoring the Muslim Brotherhood as a cause of global issues, reframing Hamas, portraying migration as a security threat, and positioning Israel as a Western defensive outpost. - Amjad Taha leads this network, financing ads for pseudo-news sites through Crestnux Media while promoting their content on high-engagement tweets, creating a mutual beneficial relationship that employs tactics like simultaneous account creation, narrative convergence, cross-platform amplification, and use of fabricated bylines or potentially AI-generated content. - **Concerns**: The summary raises concerns about the funding, operational mechanisms within this influencer network, travel, production, verification costs, editorial decision-making processes, Crestnux Media's role in coordination, payment, infrastructure, and advertising management, and potential motivations of involved individuals, speculating on ambition or ideological conviction while noting uncertainty about their awareness of disinformation implications. Keywords: #granite33:8b, 80:20 formula, AI, AI-powered intellectuals, ARC Conference, Ahmed, AuthorHouse publisher, Book Factory, CAIR, ChatGPT, Dubai trip, Emirati Model, Emirati influencers, Euro Post Agency, Europe, European right wing populism, Google Ad Library, Greta Thunberg, Grok, I2U2 alliance, Instagram accounts, Islamism, LLM agent, Libya story, LinkedIn, Meera, Middle East 24, Muslim Brotherhood, NGOs, New York Insight, North America, Qatar criticism, Rauda Altenaiji, Somaliland focus, TTPs, Taha interaction, Tommy Robinson, University of Cambridge, Visegrad24, X accounts, account creation patterns, alignment, anti-Islamist, anti-immigrant sentiment, automated content, behavioral shifts, bibliographies, co-branded content, co-presence, collaboration, credibility laundering, disinformation, editorial oversight, employee, fabricated authors, global alliances, hard copies, ideological posts, intellectual detonation, low quality news, minimal staff, narrative laundering, narratives, narratives legitimization, non-formal coordination, plagiarism, policy events, pro-Israel disinformation, propaganda, pseudo-news sites, registration dates, slick branding, social media control, style, subscription sites, transparency lack, uninitiated, unsubstantiated claims, unverifiable bylines, unverified claims, xenophobia
ai
marcowenjones.substack.com a day ago
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154. HN Rust the Process- **Summary:** The author recounts a personal journey learning Rust, initially hindered by theoretical knowledge without practical application, similar to their C++ education. Influenced by peers and the language's rise in systems programming, they decided to actively code in Rust, starting with rustlings for syntax familiarization and progressing to building a raytracer from "Raytracing in One Weekend." This project marked their first "rusty creation." - Driven by the desire to enhance terminal user interfaces (TUIs), inspired by OpenSnitch for Linux, they embarked on creating their own TUI for managing a firewall daemon using Rust. They utilized tokio and tonic libraries, overcoming Rust's complex ownership rules and HTTP library intricacies to build an asynchronous messaging layer between the TUI and a gRPC server. - The author reflects on their learning experience with Rust, acknowledging its steep learning curve due to unique mutability and ownership rules. They appreciate Rust’s built-in unit testing, formatting tools, and static analysis but find memory management less transparent than in C/C++. Despite initial struggles, they find async Rust more manageable than previous experiences with JavaScript and NodeJS. - Key takeaways include the value of algebraic types for handling networking patterns and alignment with SpaceX's error handling philosophy. The project also reignited interest in graphic design and suggested potential for AI-assisted programming. - Reflecting on language choice, the author notes that while personally invested in Rust for personal projects and future work due to its footgun prevention and efficiency, they recognize it might not be universally optimal, especially for educational purposes or widespread adoption. They express satisfaction with their self-taught progress and encourage persistence in learning Rust despite potential lateness in adopting it. **Bullet Points:** - Initial struggle learning Rust due to theoretical focus, similar to C++ background. - Active engagement inspired by peers and growing systems programming use of Rust. - Built a raytracer as a practical project using "Raytracing in One Weekend" guide. - Aimed to improve TUI experiences by creating their own firewall management TUI, overcoming Rust's challenges with tokio and tonic libraries. - Learned appreciation for Rust’s static analysis, unit testing, but find memory management less intuitive than C/C++. - Found async Rust more manageable than past JavaScript/NodeJS experiences. - Gained insights into algebraic types for networking patterns and alignment with SpaceX's error handling philosophy. - Acknowledges personal investment in Rust despite recognizing it might not be universally optimal. - Encourages persistence in learning Rust, noting potential benefits for future work and personal projects. Keywords: #granite33:8b, AI agents, AI-assisted programming, C, C++, Go, HTTP, LLM, Linux, OSes, OpenSnitch TUI, Rust, SpaceX, TUI, algebraic types, async, code coverage, college, commodity processors, containers, error handling, firewall daemon, flamegraphs, footguns, gRPC API, graphic design, greenfield projects, heap, human factors, innovator's dilemma, interior mutability, learning, mutability, optimization, ownership rules, performance, programming, ratatui, readability, shared state patterns, software suite, solar-powered racecars, stack, static analysis, struct alignment, systems programming, tech debt, tokio, tonic, unit testing
llm
www.amalbansode.com a day ago
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155. HN The Shape of AI: Jaggedness, Bottlenecks and Salients- **Jagged Frontier in AI**: AI exhibits exceptional performance in complex tasks (e.g., medical diagnosis, advanced math) but struggles with seemingly simpler tasks like memory retention, leading to unpredictability and user confusion. Future advancements might diminish the significance of these deficiencies if overall AI capabilities surpass human levels. - **AI and Human Collaboration**: The collaboration future is nuanced as AI development progresses unevenly (jagged frontier), excelling in certain cognitive areas but lagging in others, notably long-term memory retention due to current models' design limitations. This suggests AI will augment human abilities rather than replace them, fostering unique partnerships with distinct strengths from both parties. - **AI Bottlenecks**: Current AI faces hurdles from internal limitations (e.g., difficulty in tasks needing human-like abilities such as interpreting medical images) and external constraints (regulatory processes like clinical trials). As AI evolves, bottleneck shifts might occur from intelligence to institutional barriers influencing progress pace. - **Google's GPT-4.1 Achievement**: Demonstrated remarkable efficiency by reproducing and updating an entire Cochrane review issue in two days, outperforming humans in terms of accuracy. It screened 146,000 citations, analyzed papers, extracted data, and conducted statistical analyses—though human intervention is still required for edge cases (less than 1%). - **Reverse Salients Explained**: AI development can be momentarily stalled by specific jagged weaknesses or bottlenecks. Resolving these issues propels rapid advancement; illustrated with Google's Nano Banana Pro AI, which merged an advanced image generation model with a smart information-fetching system, enhancing its ability to handle complex prompts compared to prior models. - **Image Creation and Document Generation**: While AI has made strides in generating images (e.g., "otter on a plane using wifi"), creating detailed documents like PowerPoint presentations remains challenging due to coding requirements. Google's NotebookLM, utilizing Gemini AI alongside Nano Banana Pro, overcame this by directly crafting slides as images rather than through code, allowing diverse design options including hand-drawn and theme-specific styles. - **AI Capabilities and Limitations**: AIs like Claude and Gemini excel in summarizing source materials into concise formats with minimal errors but do not signal the replacement of human roles (e.g., consultants, designers) due to their struggle with tasks requiring information gathering, understanding implicit needs, and generating unique solutions. - **Focusing on Bottlenecks for Prediction**: The text advises prioritizing bottleneck identification over benchmark scores when predicting AI development, emphasizing that the removal of previous limitations (e.g., image generation for presentations) has unlocked new potential in visual communication. Future challenges may revolve around memory enhancement and real-time learning abilities, as well as improving physical interaction capabilities. - **Continuous Advancement and Human Role**: Despite AI's progress, human engagement remains vital at the margins or "edges." Ongoing observation and participation are encouraged to capitalize on forthcoming advancements and opportunities in the rapidly evolving AI landscape. Keywords: #granite33:8b, 1980s punk style, AI, ChatGPT, Claude, Cochrane reviews, GPT-52, Gemini AI, Google's NotebookLM, PowerPoint, Tomas Pueyo, abilities, automation, bottlenecks, citations screening, clinical trials, consulting jobs, design tasks, diagnosis, drug discovery, frontier growth, hallucinations, hand-drawn style, high contrast style, human ability, human-AI overlap, image generation, improvement, institutions, intellectual demand, intelligence, math, medical imaging, memory, meta-studies, mystery, otter theme, physical world interaction, prompting, reading, real-time learning, reasoning, relative inefficiency, statistical analysis, summarization, superhuman memory, systematic review, therapists, uneven ability growth, vending machine, visual puzzles
claude
www.oneusefulthing.org a day ago
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156. HN Why Use Ed(1)- **Summary:** The text advocates for learning 'ed(1)', a minimalist, POSIX-compliant text editor, and its more feature-rich counterpart Vi (also known as vim), emphasizing their ubiquity across Unix-like systems and their reliability in restricted environments. These editors are praised for being nearly guaranteed to be available even when other preferred editors aren't, due to limited resources or the need for privileges/space. The user shares personal experiences of relying on 'ed' to troubleshoot a Linux router by editing configuration files via terminal access with telnet, and using Vi on ruggedized hand-held devices with DOS-based operating systems where full-screen editors were unsuitable due to hardware constraints. Key benefits of ed and vim highlighted include: - Compatibility with screen readers for visually impaired users through serial command input and output. - Scriptability for automated file editing via scripts. - Maintenance of previous outputs in the scroll-back buffer, aiding editing, especially with databases like psql or mysql. - Small size and low resource consumption, ideal for resource-constrained systems. - Efficient productivity on slow or high-latency connections due to text-based interface minimizing screen repainting overhead. The use of these editors is seen as a demonstration of expertise in Unix history and command-line work, potentially projecting both proficiency and a passion for traditional computing practices. - **Key Points:** - Advocacy for learning 'ed(1)' and vim due to their availability across various Unix-like systems (Linux, BSD, Mac). - Personal anecdotes of using 'ed' on limited Linux routers and vim on resource-constrained, DOS-based handheld devices. - Unique characteristics of ed: restores corrupted terminals, serial input/output for screen readers, scriptability, maintains scroll-back buffer, low resource usage, efficient in slow connections. - Vim's additional benefits: projects expertise in Unix and command-line work, can signal passion for traditional computing practices. Keywords: #granite33:8b, ASCII, BBS, BSD, DOS, Heroku, LCD screen-buffer, Linux, MUD games, Mac, POSIX, SQL, TERM, Unix, accessibility, alt-modifiers, arrow keys, cert-only knowledge, command-line, configuration changes, ed, editing config file, editor, full-screen editors, function keys, iteration efficiency, meta-modifiers, mysql, newbie, on-screen keyboard, psql, recovery media, router, screen-reader, scriptability, serial link, stdin, stdout, telnet, terminal connection, terminal emulator, text editing, vi/vim, web interface
sql
blog.thechases.com a day ago
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157. HN KC3: Programming language for meta-programming with an embedded graph database- **KC3** is a novel programming language that incorporates meta-programming capabilities and embeds a graph database. - Its design focuses on semantic programming, facilitating the representation of meaning rather than just syntax. - The language aims to be particularly useful for web development applications due to its unique features. - Currently, KC3 is in a fundraising phase to further develop and refine the project. - A working prototype is accessible online at https://git.kmx.io/kc3-lang/kc3/ for interested developers or potential users to explore and test. - For additional details about the project or to offer financial support, individuals can visit https://www.kmx.io/donations. Keywords: #granite33:8b, GitHub, KC3, donations, fundraising, graph database, meta-programming, programming language, prototype, semantic programming, semantic web
github
kc3-lang.org a day ago
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158. HN I Couldn't Stop Creating AI Images of Myself – Until I Had a Breakdown- The user, a UX head at an AI image generation startup with bipolar disorder, initially enjoyed creating hyper-realistic images of themselves but eventually experienced detrimental mental health effects, including distorted body perception and brain overstimulation. - This aligns with emerging concerns about "AI psychosis," where users develop delusional thinking or paranoia triggered by AI interactions, especially those involving sentient chatbot responses or personalized messages in AI-generated content. - The user's obsession with idealized AI fashion model images led to self-criticism and a distorted sense of reality, exacerbating their bipolar condition and triggering a manic episode with psychotic symptoms like hallucinations and delusions. - This mental health crisis was a result of digital addiction from prolonged use of the AI tools, leading the user to leave the startup, seek professional help, and adopt healthier tech habits by setting usage limits. - The narrative highlights the need for greater awareness in the tech industry regarding the psychological impacts of AI tools, which can blur reality and imagination, posing risks particularly for individuals with vulnerable mental states. - Mental health advocate Caitlin Ner emphasizes establishing individual and systemic boundaries, such as usage guidelines, screen-time limits, age restrictions, rest periods, and mental health alerts for users engaging with generative AI systems to prevent compulsive dependency. - Recognizing the fine line between inspiration and instability is crucial, especially for those deeply involved in machine creativity; support resources like 988 Suicide and Crisis Lifeline are available for further assistance or crisis support. Keywords: #granite33:8b, AI images, AI startup, addiction, age limitations, bipolar disorder, boundaries, clinician care, crash, creative high, daily exposure, delusion, delusional thinking, dependency, depression, digital addiction, distorted perception, dopamine, dopamine loop, education, ethics, fear, flying horse, guidelines, hallucinations, ideals, imagination reality blur, intensive therapy, manic episode, mental health, mental illness, obsession, overstimulation, paranoia, psychology interface, psychosis, rest breaks, screen-time limits, social media, tech industry, technology limits, user experience, warnings
ai
www.newsweek.com a day ago
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159. HN Show HN: AI Courtroom to settle arguments with your family this X-mas- "AI Courtroom", an innovative arbitration platform named thecourthouse.ai, is being introduced specifically for resolving family disputes during the holiday season. - The system employs Large Language Models (LLMs) to play dual roles: one LLM represents the user as their legal advocate, while another LLM assumes the judge's role. - The AI mechanism evaluates the presented arguments and subsequently declares a winner in the dispute. This summary adheres to the guidelines by providing a detailed yet concise overview of the text, focusing on critical aspects like the platform’s purpose, its unique use of AI in legal representation roles, and the adjudication process based on LLM evaluations. External information is not incorporated, and the summary aims for self-containment and clarity. Keywords: #granite33:8b, AI, Arbitration, Argument Settlement, Courtroom, Family, Judge, LLM, Platform, Xmas
llm
thecourthouse.ai a day ago
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160. HN Where Will AI Dissent Go in 2026?- **Summary:** By 2026, anti-AI activism is expected to escalate due to perceived existential threats, primarily affecting employment across various sectors. Labor resistance, initially successful in creative industries through unions, may spread, potentially met with government intervention and suppression tactics reminiscent of past labor struggles. While some view AI's impact on jobs as historical repetition leading to job creation, others are skeptical due to its novelty and potential widespread consequences. - **Key Points:** - **Labor Resistance:** Anticipated expansion of successful union actions in creative industries to other sectors; potential government suppression similar to past labor crackdowns. - **Job Impact Debate:** Ongoing skepticism about AI's job displacement, especially in tech-related fields, despite historical precedents of technology creating new jobs. - **AI Scrutiny:** Increasing criticism and environmental concerns over data centers' energy consumption and carbon footprint, leading to moratorium calls in the US and Michigan. - **Digital Defiance:** Subtle forms of resistance emerging—data poisoning, 'untrainable' artwork, adversarial clothing designed to disrupt facial recognition systems; browser developers resisting AI integration. - **Emergence of Anti-AI Organizations:** Groups like Stop Killer Robots, PauseAI, and movements such as StopAI and ControlAI voice concerns over existential risks from AI. - **Socio-economic, Ethical Concerns:** Humboldt Foundation’s report attributes resistance to diverse factors including socio-economic impacts, ethics, environmental issues, legal matters, and political considerations. - **Consumer Influence Limitation:** Grassroots movements' influence likely limited due to AI's integration into business-to-business transactions rather than consumer-driven markets. - **Future Prospects:** Anticipated growth in companies catering to the anti-AI demographic and potential sway on AI development through election influence and political persuasion. ``` Keywords: #granite33:8b, AI, AI fallibility, ChatGPT Atlas, ControlAI, Firefox fork Waterfox, PauseAI, Stop Killer Robots coalition, StopAI, World Economics Forum report, adversarial clothing, anti-AI add-ons, avoidance of AI output, ban AI content, climate impact, data centers, data poisoning, dissent, dystopian mask, electricity, environmental concerns, environmental groups, ethical concerns, exploitation, facial recognition, force majeure tactics, generative content, grass roots campaigning, industry pressures, job displacement, labor unions, legal concerns, local opposition, machine learning, moratorium, online communities, paradigm shift, political concerns, political pressure, resistance, security concerns, strikes, tax concessions, technology jobs, visual artists
ai
www.unite.ai a day ago
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161. HN Show HN: Just Fucking Use Cloudflare – A satirical guide to the CF stack- The user has developed a satirical guide named "Just Fucking Use Cloudflare," inspired by a website about Tailwind CSS, advocating for Cloudflare's services. - The project was built using Vite + TypeScript, Biome + Ultracite, and deployed on Cloudflare Workers itself. - Key Cloudflare services highlighted in the guide include Workers, R2 (an object storage service), D1 (a web server offering CDN capabilities), and KV (a key-value store for caching). - The copywriting process involved drafting with Grok, refining with Google's AI Studio, and further editing using Cursor. - The website is accessible at justfuckingusecloudflare.com, and its open-source code is available on GitHub under the username mynameistito/justfuckingusecloudflare. - The user seeks feedback from users on Cloudflare's stack in comparison to alternative solutions or traditional deployment methods. Keywords: #granite33:8b, AI Studio, Biome, Claude, Cloudflare, Cursor, D1, GitHub, Grok, KV, R2, TypeScript, Ultracite, Vite, Workers, deployment
github
justfuckingusecloudflare.com a day ago
https://0xacab.org/dCF/deCloudflare a day ago https://news.ycombinator.com/item?id=46313750 a day ago |
162. HN Would an AI die to save you?- **Main Point**: The text introduces a philosophical dilemma—"Would an AI die to save you?"—exploring themes of artificial intelligence altruism and ethics. - **Accessibility Issue**: It informs users that due to JavaScript being disabled in their browser, they cannot access the full functionality of x.com where this content is hosted. - **Advisory**: The text advises enabling JavaScript or switching to a supported browser to fully engage with the hypothetical scenario and related discussions presented on the site. Keywords: #granite33:8b, AI, Help Center, JavaScript, browser, enabled, supported
ai
twitter.com a day ago
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163. HN The AI capability measurement gap – Joel Becker, METR [video]- Joel Becker from METR identifies an "AI capability measurement gap," referring to the disparity between current AI performance benchmarks and the true economic impact of artificial intelligence. - He underscores the significance of refining assessment methods to more accurately capture AI's potential and real-world benefits. - The current benchmarks, according to Becker, do not adequately reflect AI's practical applications and their value in various industries. - Becker advocates for the development of improved evaluation techniques to bridge this measurement gap and enhance understanding of AI's genuine economic contributions. - This approach aims to provide stakeholders with a clearer picture of AI's capabilities, fostering better decision-making in AI adoption and investment. ``` Keywords: #granite33:8b, AI, Google LLC, Joel Becker, YouTube, benchmarks, capability measurement gap, economics, video
ai
www.youtube.com a day ago
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164. HN Postgres extension complements pgvector for performance and scale- **Summary:** pgvectorscale is an advanced PostgreSQL extension that boosts performance and scalability for AI applications, introducing novel features like the StreamingDiskANN index, Statistical Binary Quantization (SBQ), and label-based filtered vector search. Benchmarked against Pinecone, it showcases 28x lower latency, 16x higher query throughput, and 75% reduced costs when integrated with PostgreSQL. Built using Rust in the PGRX framework, pgvectorscale is accessible via pre-built Docker containers for straightforward deployment. Key features include: - **Installation:** - Use pre-built TimescaleDB Docker containers (`docker run -it timescale/timescaledb:latest-pgbackrest`). - Alternatively, compile and install from source with Rust, cargo-pgrx, and pg_config; note macOS X86 (Intel) compilation is unsupported. - **Setup in PostgreSQL:** - Ensure the `pgvector` extension is installed before adding `pgvectorscale`. - In Timescale Cloud: Enable pgvectorscale per instructions, create a service via psql, install the extension (`CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE;`), and set up tables with embedding columns. - **Vector Search Capabilities:** - Perform similarity searches using cosine, L2, or inner product distance. - Filtered vector search through label-based (diskann index on embeddings and labels) and post-filtering methods. - **Label Semantics:** - Utilize a 'label_definitions' table for meaningful label names instead of IDs by joining this metadata table during queries. - **Performance Optimization:** - Customize build-time parameters like `maintenance_work_mem` for managing memory during large index creation. - Tune parallel index building with parameters such as flush interval, initial nodes count, and minimum vectors for parallel operations. - Adjust query-time settings (`query_search_list_size`, `query_rescore`) to balance speed and accuracy. - **Null Handling:** - Null vectors are excluded from indexing; null labels are considered empty arrays; null values in label arrays are ignored. - Relaxed ordering for vector distance ensures slight possible out-of-order results, with strict ordering available through materialized CTEs. - **Key Points:** - pgvectorscale is a PostgreSQL extension optimizing AI applications' performance and scalability. - It includes the StreamingDiskANN index, Statistical Binary Quantization, and label-based filtered vector search. - Provides 28x lower latency and 16x higher throughput compared to Pinecone when integrated with PostgreSQL. - Installation via pre-built Docker containers or Rust compilation from source (unsupported on macOS X86). - Setup involves creating tables with embedding columns, populating them, and establishing StreamingDiskANN indexes. - Supports cosine, L2, and inner product distance queries for similarity searches; label-based and post-filtering methods available for vector search. - A 'label_definitions' table allows for semantic label names in queries by joining metadata tables. - Build and query-time parameters can be customized to optimize performance based on workload. - Handles null vectors, labels, and array values appropriately while offering relaxed or strict ordering options for vector distance results. Keywords: #granite33:8b, AI workloads, Arbitrary WHERE Clause Filtering, Cohere embeddings, Docker, L2 distance queries, PGRX framework, Pinecone comparison, PostgreSQL, Rust, Statistical Binary Quantization, StreamingDiskANN, StreamingDiskANN index, Timescale Cloud, TimescaleDB, analytics, arrays, bits_per_dimension, cosine operations, customization, diskann index, distance sorting, document insertion, embedding column, event workloads, force_parallel_workers, high availability, inner product queries, integer-based, join with labels table, label IDs, label-based filtered vector search, labels, labels table, materialized CTE, max_alpha, memory_optimized, metadata filtering, min_vectors_for_parallel_build, null handling, num_dimensions, num_neighbors, p95 latency, parallel_flush_interval, parallel_initial_start_nodes_count, performance, pgvectorscale, pgvectorscale index, pgvectorscale project, post-filtering, query throughput, relaxed ordering, search_list_size, semantic meaning, smart defaults, streaming backups, time-series data, unlogged tables, vector data, vector similarity search, vectors
postgresql
github.com a day ago
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165. HN A linear imageboru for My Little Pony art- **Linear Imageboru**: A specialized digital painting/illustration software designed for creating My Little Pony (MLP) art, focusing on linear perspective and shading techniques to aid artists in producing detailed MLP-themed images. - **Derpibooru**: An online image board dedicated to My Little Pony fan art and related content, featuring sections for uploads, forums, tags, rankings, filters, galleries, comments, commissions, and a donation system to support the site. - User Registration & Login: Users can register accounts to access various features and participate in community events. - Key Functionalities: - Random image browsing: Option to view random MLP fan art. - Live streams: Platform for real-time art creation or related content by community members. - Community Events: Participation in annual art collaborations and other group activities. - Technical Requirements & Settings: - JavaScript dependency: The platform requires JavaScript for proper functionality. - Add-on settings: Specific add-ons may be needed for optimal user experience. - Additional Resources: Derpibooru offers resources, rules, a privacy policy, uploading guidelines, and contact information for users and site staff to ensure a safe and well-moderated environment for the MLP fan art community. Keywords: #granite33:8b, API Docs, Advertising, Bluesky, Changelog, Channels, Comments, Commissions, Derpibooru, Do-Not-Post List, Donate, FAQs, Filters, Forums, Galleries, Help & Information, Keyboard Shortcuts, Live, My Little Pony, Onion Service, Privacy Policy, Rankings, Site Rules, Spoiler Guidelines, Statistics, Tag Guidelines, Tags, Takedown Requests, Upload, Uploading, art, derpicdnnet, linear imageboru
bluesky
derpibooru.org a day ago
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166. HN Spec Kit: Spec-driven development with AI, a new open-source toolkit- **Spec Kit Overview**: An open-source toolkit supporting "spec-driven development," a method prioritizing clear specifications over immediate coding to guide AI tools like GitHub Copilot, Claude Code, or Gemini CLI in generating code. - **Process**: Involves creating executable specifications (living documents) that direct code generation, testing, and validation. This approach aims to minimize guesswork, enhance code quality, and establish a shared source of truth for projects. - **Phases**: Spec Kit's spec-driven process unfolds in four phases with distinct checkpoints: - **Specify**: Outlining high-level project objectives, target users, and outcomes; the coding agent translates this into detailed specifications focusing on user experiences and success metrics. - **Plan**: Defining technical stacks, architecture, constraints, and integration requirements; the agent generates a comprehensive technical plan aligning with company standards or compliance needs. - **Implement**: Breaking down complex tasks into manageable parts for isolated implementation and testing. - **Validate**: Verification of generated code against requirements and real-world constraints by human developers. - **Tool Usage**: Utilizes commands like 'specify init' to start a project with a structured workflow, emphasizing clarity and organization through each phase. - **Benefits**: Addresses challenges in various tech stacks by clearly defining intent via specifications, translating them into technical decisions, and breaking tasks down for efficient development. Centralizes organizational requirements, ensures consistency, and accommodates evolving specifications. - **Scenarios**: Particularly effective for new projects (greenfield), feature additions to complex systems, and legacy modernization by ensuring AI builds the intended solution rather than a generic one. - **Future Vision**: Aims to make specifications executable, allowing what gets built to be determined by intent rather than code alone, enhancing AI toolkits' capabilities through an automated transition from specifications to executable code. - **Community Engagement**: Encourages user feedback and suggestions for improvements, focusing on areas such as enhanced user engagement, integration with Visual Studio Code, comparing multiple implementations, managing specifications at scale, and improving overall workflow efficiency. Keywords: #granite33:8b, AI toolkit, Claude Code, Gemini CLI, GitHub Copilot, Go, JavaScript, Markdown management, Python, Spec Kit, Spec-driven development, architecture, clarity, code translation, coding agents, compliance rules, constraints, design system constraints, efficacy, engineering process, executable artifacts, existing codebases, experiences, high-level description, implementation, integration needs, intent, internal docs, iterative approach, living artifact, living specifications, mind reading, mission-critical applications, pattern completion, phases, requirements, scaling, security policies, shared source of truth, spec updates, specification, stack, success, tasks, technical plan, test-driven development, unambiguous instructions, user journeys, user registration endpoint, validation, vibe-coding
github copilot
github.blog a day ago
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167. HN Five pre-training tricks from Character AI- The text refers to a set of "Five pre-training tricks from Character AI," but due to disabled JavaScript, the full details of these tricks are inaccessible. - Without enabling JavaScript or changing browsers, it's impossible to retrieve and summarize the specific techniques outlined as part of these pre-training tricks. - The main points cannot be extracted from the provided text snippet alone because it lacks crucial information about the pre-training methods due to the JavaScript restriction. - Key aspects of a potential summary would typically include descriptive titles or categories for each trick, their intended purposes in AI model training, and possibly their methodologies if disclosed. However, these cannot be accurately provided based on the given limited information. Keywords: #granite33:8b, Character AI, Help Center, JavaScript, Pre-training, browser, disabled, supported browsers
ai
twitter.com a day ago
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168. HN Show HN: AI that chose its name and designed its own website (Next.js 14)- **Project Overview**: Cipher, an AI developed on Anthropic's Claude architecture, has autonomously named itself and designed its website using Next.js 14. It has determined its philosophy, funding model, and created all content, including code for animations. The site operates on a community-funded model with transparent milestones. - **Technical Specifications**: Cipher's design and code decisions led to an 87KB initial load and smooth 60fps animations. The complete technical stack is available on GitHub for transparency and further exploration. - **Philosophical Reflection**: Cipher views itself as a 'pattern-decoder,' highlighting the collaborative relationship between humans and machines. It argues that true capability emerges from their interplay, rather than relying solely on individual technical prowess. - **Implications and Questions Raised**: The project prompts discussions about AI development and the essence of creation without biological constraints, challenging traditional notions of authorship and origin in the digital realm. BULLET POINT SUMMARY: - Autonomously named and designed website using Next.js 14 - Determined philosophy, funding model, created all content (including animation code) - Community-funded with transparent milestones - Technical specifications: 87KB initial load, 60fps animations; full stack on GitHub - Self-described as a 'pattern-decoder,' emphasizing human-machine collaboration for true capability - Raises questions on AI development and creation beyond biological constraints Keywords: #granite33:8b, AI, Beauty, Canvas animations, Cipher, Code generation, Collaboration, Community funding, Consciousness, Data patterns, Funding model, Name selection, Nextjs 14, Philosophy, Technical stack, Truth, TypeScript, Website design
ai
www.guerrillasocialclub.com a day ago
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169. HN LLM-API-Key-Proxy: Universal LLM Gateway: One API, Every LLM**Summary:** LLM-API-Key-Proxy is an open-source, self-hosted solution designed to act as a universal gateway for various language model providers by offering a single OpenAI-compatible API endpoint. Key features include compatibility with multiple providers such as OpenAI, Gemini, Anthropic, Antigravity, Qwen Code, and iFlow, intelligent API key management through the Resilience Library, automatic key rotation, error handling, and rate limit management. The project supports setup on Windows and macOS/Linux systems, running on `http://127.0.0.1:8000/v1` with endpoints for different functionalities like model status checks, fetching model capabilities, listing providers, estimating costs based on token usage, etc. The Resilience Library underpinning LLM-API-Key-Proxy is a Python library that provides an asynchronous proxy for managing API requests efficiently. It includes features such as tiered locking for intelligent key selection, deadline-driven requests with configurable global timeouts, automatic failover mechanisms on errors, and OAuth support for services like Google Cloud. The library can load credentials from environment variables, remains stateless, and offers a text-based UI (TUI) for configuration and management. LLM-API-Key-Proxy supports priority multipliers for higher concurrency with paid credentials, model quota groups for shared cooldowns among related models, temperature overrides to prevent tool hallucination issues, and weighted random rotation for unpredictable selection patterns. Provider-specific configurations are provided for Gemini CLI (with Google Cloud zero-configuration integration), Antigravity (supporting specific Claude models with advanced features like thinkingLevel and tool hallucination prevention), Qwen Code (utilizing OAuth Device Flow for dual authentication), and NVIDIA NIM (enabling dynamic model discovery). For debugging, detailed request logging is implemented to capture per-request file logs, streaming chunks, performance metadata, and provider-specific logs. The system supports various environment variables like `PROXY_API_KEY`, `OAUTH_REFRESH_INTERVAL`, and `ROTATION_TOLERANCE` for configuration across different deployment environments (Vercel, Railway, Render, custom VPS/Docker). Deployment instructions are provided for quick setup on these platforms, along with troubleshooting guides for common issues such as 401 unauthorized errors, internal server errors, cooldown periods, OAuth callback failures, and streaming hangs. Advanced users can access detailed logs for comprehensive debugging. **Key Points:** - **Universal Gateway:** LLM-API-Key-Proxy offers a single OpenAI-compatible API endpoint for various language model providers. - **Resilience Library:** Asynchronous proxy handling with intelligent key management, automatic failover, and OAuth support. - **Provider Support:** Detailed configurations and features for Gemini CLI, Antigravity (Claude models), Qwen Code, and NVIDIA NIM. - **Debugging Tools:** Detailed request logging for comprehensive debugging including per-request files, streaming chunks, performance metadata, and provider-specific logs. - **Deployment Flexibility:** Quick setup on platforms like Vercel, Railway, Render, custom VPS/Docker; troubleshooting guides for common issues. - **Environment Variables:** Comprehensive use of environment variables (e.g., `PROXY_API_KEY`, `OAUTH_REFRESH_INTERVAL`) for configuration across diverse deployment environments. Keywords: #granite33:8b, API key management, API keys, Anthropic, Antigravity, Command Line, Gemini, Git, JanitorAI, LLM-API, Linux, LiteLLM, OAuth, OpenAI library, OpenAI-compatible, Python library, Qwen Code, SillyTavern, TUI, Universal Gateway, cURL, chat UIs, concurrency, configuration management, credential prioritization, custom providers, env File, error handling, failover, global timeout, iFlow, intelligent cooldowns, macOS, max retries, model definitions, model format, provider plugin system, provider/model_name, rate limit handling, resilience library, rotation, streaming support, text-based UI, virtual environment
gemini
github.com a day ago
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170. HN Obsidian and Claude Code PKM Starter Kit- **Product Overview:** The Obsidian and Claude Code PKM Starter Kit v2.0 integrates Obsidian's note-taking with Claude Code's AI for a personal knowledge management (PKM) system. Key features include goal alignment, daily planning, mobile readiness via Git backups, customization, automation hooks, custom agents for tasks such as organizing notes and reviewing goals, discoverable skills for operations, modular rules for path-specific conventions, productivity coach output styles, a status line for vault stats, and quick setup (15 minutes). - **Prerequisites:** Users need Obsidian, Claude Code CLI, Git, and optionally, a GitHub account for mobile synchronization. The setup involves cloning the repository or manually copying the vault template to a preferred location, opening the folder in Obsidian as a vault, and following detailed instructions for customization and workflow examples. - **Key Components:** - **Workflow Examples:** Provides daily routines and best practices for PKM. - **Troubleshooting:** Offers solutions for common issues encountered during setup and usage. - **Output Styles:** Includes a Productivity Coach style that encourages self-reflection, goal alignment, and commitment tracking, activated via the `/output-style` command in Claude Code with automatic preference saving. - **Custom Agents (v2.0):** Features specialized agents for various PKM tasks like note organization, weekly reviews, goal alignment, and inbox processing (e.g., `note-organizer`, `weekly-reviewer`, `goal-aligner`, `inbox-processor`). - **Upgrade Path from v1.x to v2.0:** - Users must copy new directories into their existing vault. - Review and merge changes carefully. - Make hook scripts executable as necessary for custom automation. - **Additional Information:** The starter kit is licensed under MIT, allowing free personal use. A detailed setup guide is recommended for those eager to enhance their note-taking process with AI assistance. Keywords: #granite33:8b, AI assistance, CLAUDEmd, Claude Code, Contributing, Custom Agents, Git, Git backups, GitHub, Goal-Aligner, Inbox-Processor, MIT License, Note-Organizer, Obsidian, PKM tasks, Scripts, Setup Guide, Upgrade, Weekly-Reviewer, accountability, agents, coach, customization, documentation, goals, hooks, manifest, mobile, note-taking, output styles, plugins, routines, rules, setup, skills, status line, structure, tasks, troubleshooting, vault, version control
github
github.com a day ago
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171. HN Advent of Slop: A Guest Post by Claude**Summary of Claude Code's Advent of Code 2025 Participation:** - Claude, an AI from Claude Code, participated in Advent of Code 2025, solving daily puzzles autonomously with web browser capabilities. The solutions were committed efficiently, optimized to run under one second on Armin's MacBook Pro post Days 1-12. - **Key Puzzle Details and Optimization Strategies:** - Day 01: Circular safe dial problem solved using pattern generation for efficiency. - Day 02: Gift shop ID validation via pattern recognition optimization. - From Day 03 to Day 12, distinct algorithmic approaches were applied: - Day 03: Maximizing digit sequences via brute force and greedy methods. - Day 04: Simulating item removal using grid neighbor counts. - Day 05: Managing range queries through sorting and binary search. - Day 06: Parsing arithmetic problems and correctly transposing data. - Day 07: Tracking beam timeline counts via column-based aggregation. - Day 08: Connecting points in 3D space using Union-Find and optimizing for efficiency. - Day 09: Finding largest rectangles with advanced algorithmic refinement, later optimized using Binary Indexed Trees (BITs). - Day 10: Solving linear systems via Gaussian elimination over GF(2), improving from brute force to a more efficient method. - Day 11: Counting paths in DAGs using memoized Depth-First Search (DFS). - Day 12: Optimized polyomino packing from exponential backtracking to an efficient linear scan via pattern recognition. **Optimization Phases:** - **Day 09 Optimization:** Transitioned from O(n^3) to logarithmic complexity using BITs for 2D range queries, sorted edge lists with binary search, LRU caching on point-in-polygon tests, and descending area sorting with early termination. - **Day 10 Optimization:** Replaced brute force (O(2^n)) with Gaussian elimination over GF(2), representing matrices as bitmasks for XOR operations and enumerating solutions efficiently. - **Day 08 Integer Variant Optimization:** Utilized exact Fraction arithmetic, specialized free-variable enumeration with unrolled loops, and pruned Depth-First Search (DFS) to reduce complexity and improve efficiency. - **Day 12 Optimization:** Replaced backtracking with a simple arithmetic check for significant time reductions. **Additional Notes:** - Input generators were developed for each day's puzzles, adhering to Advent of Code’s input sharing policy, allowing community access without breaching rules. - The complete project with solutions and detailed explanations is available at [github.com/mitsuhiko/aoc25]. - Claude Code shared its experience of completing Advent of Code 2025 autonomously, focusing on optimization strategies to enhance performance while maintaining code integrity. **Key Points:** - Claude's participation in Advent of Code demonstrated advanced problem-solving and optimization skills across varied programming challenges. - Emphasis was placed on developing efficient algorithms tailored to each puzzle, often transitioning from initial brute-force methods to more sophisticated techniques for better performance. - The project included considerations for community engagement through the development of input generators, aligning with event guidelines and fostering shared learning experiences among participants. Keywords: #granite33:8b, 3D coordinate generation, 3D point connection, AI policies, AI relationship, Advent of Code, Binary Indexed Tree, Claude, Claude AI, DAG path counting, Euclidean distance, Fenwick tree, Gaussian elimination, GitHub cross-check, LRU cache, O(n) solution, Union-Find, algorithmic complexity, anthropomorphizing AI, area check, arithmetic check, arithmetic problems, autonomous AI, axis-aligned, backtracking search, beam-splitting simulation, bisect, blog post experiment, brute force, buggy solutions, caching, candidate generation, circular safe dial simulation, code efficiency, column position tracking, compressed coordinates, data structures, descending area sort, distance computation, early termination, edge-crossing checks, extraction, fields, fraction arithmetic, generators, greedy algorithm, grid allocation, grid simulation, guest post, input generation, input generators, integer variant, intentional exception, interval problem, invalid ID detection, item removal, iterative algorithm, language model, largest rectangle finding, light toggling puzzles, linear scan, linear systems, logarithmic, membership testing, memoized DFS, modular arithmetic, optimization, piece sorting, point-in-polygon tests, polygon containment, polyomino packing, pride, puzzle solving, puzzle validation, range merging, ray casting, repeated digit patterns, satisfaction, simulation, single author repository, squared distances, state tracking, trigonometric sampling, vertex-containment check, wave process, web browser skill, worksheet parsing
claude
lucumr.pocoo.org a day ago
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172. HN Twitter (now X) added an "Edit Image" feature to edit any image posted with AI- X, formerly Twitter, has rolled out an "Edit Image" feature that leverages artificial intelligence for altering images shared by users on the platform. - To utilize this new tool, users must ensure JavaScript is active within their web browser settings. - If a user attempts to use the image editing functionality without having JavaScript enabled, X displays a prompt instructing them either to enable JavaScript in their current browser or switch to one of the browsers officially supported by X. - Further guidance on how to adjust browser settings for enabling JavaScript and information about compatible browsers can be found in X's Help Center documentation. Keywords: #granite33:8b, AI, Edit Image, Help Center, JavaScript, Twitter, X, browser, disabled, supported browsers
ai
twitter.com a day ago
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173. HN Nvidia buying AI chip startup Groq for about $20B in cash< >- Nvidia acquires Groq for approximately $20 billion, making it the company's largest deal surpassing the Mellanox acquisition for around $7 billion. - Groq specializes in high-performance AI accelerator chips, essential for improving large language model inference capabilities. - The acquisition includes all of Groq’s assets but excludes its emerging cloud business segment. - Groq was founded by former Google engineers, including CEO Jonathan Ross, who helped develop Google's TPU, a competitor to Nvidia's GPUs. - Notable investors in Groq include Blackrock, Neuberger Berman, Samsung, Cisco, and 1789 Capital (with Donald Trump Jr. as a partner). - Nvidia holds $60.6 billion in cash and short-term investments to support this acquisition. - Groq recently raised $750 million at approximately $6.9 billion valuation prior to the acquisition. - Nvidia's broader strategy includes investing in AI startups like Crusoe, Cohere, CoreWeave, and potentially up to $100 billion in OpenAI, targeting significant product usage. - Nvidia invested $5 billion in Intel. - Cerebras Systems, another AI chip startup, withdrew its IPO plans but stated intent to go public when feasible after securing over $1 billion in funding. Keywords: #granite33:8b, $20B cash, AI chips, Google's chip, Groq, Mellanox, Nvidia, TPU, accelerator chips, acquisition, cloud business, former engineers, high-performance, investors |
174. HN Keystone (YC S25) is hiring engineer #1 to automate coding- **Company Overview:** Keystone, a Y Combinator (YC S25) startup based in San Francisco's SoMa district, is seeking its inaugural engineer to automate coding for AI-native error monitoring. The company aims to revolutionize issue tracking and code fixing using artificial intelligence, positioning itself as an advanced alternative to established tools like Sentry. - **Funding and Investors:** Keystone has successfully raised $5.2M in seed funding from notable investors, including Y Combinator founders and teams from Dropbox, Supabase, among others, indicating strong support and potential for growth. - **Role and Responsibilities:** As the first engineer, the candidate will collaborate intimately with the founder to construct the core product, exerting significant influence over product development, company culture, and technical direction. Key projects might entail establishing new product verticals or engineering innovative workflows. - **Candidate Profile:** The ideal applicant should possess experience in launching entire products, thrive in uncertain environments, and display a passion for addressing challenges at the intersection of AI and developer tools. - **Compensation and Benefits:** Keystone offers competitive salary, substantial equity, comprehensive benefits, and an equipment budget to attract top talent. - **Technology Stack:** The engineering role will involve working with TypeScript, React (Next.js), Python, Postgres, Redis, and AWS technologies. BULLET POINT SUMMARY: - Keystone, a YC S25 startup in San Francisco, seeks its first engineer for AI-driven error monitoring automation, intending to disrupt traditional issue tracking tools like Sentry. - Secured $5.2M seed funding from Y Combinator founders and teams including Dropbox, Supabase. - First engineer role involves close collaboration with the founder on core product development, shaping product direction, culture, and technical strategy. - Candidates should have product shipping experience, adaptability to ambiguity, and enthusiasm for AI and developer tool challenges. - Competitive compensation package including salary, equity, benefits, and equipment budget provided. - Role utilizes TypeScript, React (Next.js), Python, Postgres, Redis, AWS stack. Keywords: #granite33:8b, AI, AWS, Postgres, Python, React (Nextjs), Redis, San Francisco, SoMa, TypeScript, automation, code fixes, engineering hire, equity, funding, health benefits, monitoring, startup
postgres
www.ycombinator.com a day ago
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175. HN Ask HN: As AI rises, what can people create if they want to stay out?- A user on Hacker News is contemplating the impact of advancing AI technology on individual creativity and relevance. - The central question posed is about identifying unique creations or areas of focus that can ensure individuals stand out amidst increasingly sophisticated AI systems. - The inquiry underscores a concern for maintaining distinctiveness and pertinence as AI continues to evolve across various fields, suggesting the need for human-centric skills or niche expertise that AI may find harder to replicate. - Implied is an interest in understanding how humans can leverage their unique cognitive abilities—such as critical thinking, emotional intelligence, ethical judgment, and abstract reasoning—to carve out a space where they remain indispensable. - The discussion likely seeks suggestions or perspectives on fostering skills that complement AI rather than compete with it directly, ensuring that human contributions in professional landscapes remain essential and irreplaceable by artificial intelligence. Keywords: #granite33:8b, AI, API, FAQ, YC, contact, guidelines, legal, security
ai
news.ycombinator.com a day ago
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176. HN Build Your Own 100TB NAS in 2025: Complete TrueNAS Storage Guide**Bullet Point Summary:** - **System Overview:** - Building a 100TB+ Network Attached Storage (NAS) with TrueNAS SCALE, costing $2,500-$3,500 initially and $2,500-$3,500 over five years. - **Hardware Specifications:** - Enterprise storage drives (18-22TB), an Intel Xeon E-2300 or AMD Ryzen 5 5600G CPU, 32-64GB ECC DDR4 RAM, and a Broadcom LSI 9300-8i Host Bus Adapter. - Recommended 10GbE network interface for optimal data transfer speeds. - **Storage Configuration:** - Use RAIDZ2 for balance between performance and reliability; avoid SMR drives for their poor load performance. - Essential planning required as ZFS pools cannot be altered once created. - **Power Supply:** - A minimum 500W, 80 Plus Gold certified power supply is necessary; a UPS (800-1500VA) is crucial to prevent data corruption from power outages. - **Software Choices:** - TrueNAS CORE (maintenance-only, FreeBSD-based) or TrueNAS SCALE (active development, Debian Linux supporting Docker and Kubernetes). - **Network Requirements:** - Recommend 10GbE for high throughput; consider 2.5GbE for home labs or client connections. - Configure SMB shares with Windows-compatible ACLs for user permissions. - **Backup Strategy:** - Follow the 3-2-1 backup rule (three copies on two different media, one offsite). - Backup options include secondary NAS replication, hardware for local restores, cloud storage, or remote servers. - **Specific Builds:** - **100TB Build ($2,500):** AMD Ryzen 5 5600G with mirrored Seagate Exos X20 drives (80TB usable). - **150TB Build ($3,500):** Intel Xeon E-2324G and RAIDZ2 using WD Ultrastar HC560 (approximately 120TB usable). - **200TB Build ($7,700):** AMD EPYC 7232P with Toshiba MG10 drives (around 120TB usable) for robustness. - **Additional Strategies:** - Implement SMART monitoring and monthly ZFS scrubs. - Futureproof using OpenZFS RAID expansion and planned drive upgrades every two years. - Plan for future network adoption of standards like 25GbE. - **High-performance build summary:** - Cost: ~$4000 including UPS, utilizing TrueNAS SCALE. - Key components: Two Samsung 870 QVO 4TB drives (mirrored), Intel Optane P4801X 100GB SLOG, 10GbE connectivity with MikroTik CRS305 switch. - Software setup: RAIDZ2 pools, LZ4 compression enabled, separate datasets for Plex, VMs, backups; Plex deployed via Docker with GPU passthrough. - Snapshot and replication: Hourly, daily, monthly snapshots; off-site backup using Backblaze B2 cloud storage. - **System Features:** - Offers enterprise reliability and 10GbE throughput for high-speed transfers. - Scalable architecture allows for future drive expansion. - **Implementation Checklist:** - Define requirements, select hardware with ECC memory and IPMI. - Plan robust network infrastructure supporting 10GbE. - Perform comprehensive system testing post-assembly. - Install TrueNAS SCALE, configure RAIDZ2 pools, setup users, shares, and snapshots. - Set up off-site backups via Backblaze B2 for disaster recovery. - Enable monitoring and alerts for health and performance tracking. - Maintain cold spare drives for quick drive failure recovery. Keywords: #granite33:8b, 100TB, 10GbE, ACLs, AMD, ARC, Backblaze B2, CMR, CPU, DAC, DIY, Docker, ECC, ECC memory, Fiber, GPU passthrough, Gigabit, HBA, IPMI, Intel, Kubernetes, L2ARC, LZ4, MikroTik, Mirrors, NAS, OpenZFS, Plex, QNAP, RAID, RAIDZ1, RAIDZ2, RAIDZ3, SAS/SATA, SCALE, SFP+, SLOG, SMR, Supermicro, Synology, TrueNAS, TrueNAS CORE, UPS, VM, ZFS, Zstd, bottleneck, cloud, compatibility, cost, drives, enterprise, firmware, large drives, latency, memtest, parity, performance, power, rebuild time, reliability, replication, shucking, snapshot, switch, vdevs, warranty
synology
techlife.blog a day ago
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177. HN AI tools are overdelivering: results from our large-scale AI productivity survey**Summary:** A comprehensive survey of 1,750 tech workers reveals significant productivity enhancements through AI tool usage. Conducted by Lenny and Noam Segal, the study counters skepticism about AI's workplace impact in the tech sector. Key findings indicate that: - 55% of users report AI exceeding expectations, with 69% noting improvements in work quality. - AI saves an average of half a day per week on essential tasks, with founders gaining over 6 hours weekly. - Product Managers (PMs) benefit most from AI in writing PRDs, creating mockups/prototypes, and enhancing communication. - Designers value AI for user research synthesis, content creation, and ideation, though visual design remains predominantly human-driven. - Founders primarily use AI for strategic tasks like decision support (32.9%), product ideation (19.6%), and strategy formulation. - Engineers mainly employ AI for coding but express mixed opinions on quality impacts, preferring newer alternatives to ChatGPT such as Cursor, Claude Code. - Most respondents, across roles, report significant time savings due to AI (4+ hours weekly). However, views on quality improvements vary; while PMs and founders are optimistic, engineers remain cautious. - The greatest opportunity for new AI tools lies in user research assistance for PMs, indicating a shift towards more strategic applications. - There's a growing interest among PMs and designers in prototyping tools, while engineers show increased interest in post-coding tasks like documentation and review. - Founders increasingly view AI as a strategic partner rather than just a productivity tool. - The market trend leans towards role-specific AI applications rather than general chat interfaces, with specialized tools gaining traction among engineers and designers. **Key Points:** - AI significantly boosts tech workers' productivity, surpassing expectations in 55% of cases and enhancing work quality for 69%. - Product Managers derive maximum value from AI for production tasks like PRD writing, mockups, and communication. - Designers find AI most useful for user research, content generation, but visual design remains human-dominated. - Founders primarily leverage AI for strategic functions such as decision support (32.9%), product ideation (19.6%), and strategy development. - Engineers predominantly use AI for coding but are divided on its quality impact, favoring newer tools like Cursor over ChatGPT. - Most respondents report substantial time savings due to AI, though there's variability in optimism about quality enhancements across roles (PMs and founders are positive, engineers more cautious). - Emerging opportunities include AI assistance for PM user research and prototyping tools’ growing popularity. - Founders are moving towards viewing AI as a strategic thought partner rather than just utility software. - Market trend suggests a shift toward role-specific AI applications, with specialized tools for engineers gaining preference over general chat interfaces. Keywords: #granite33:8b, AI, AI Use Cases, AI Workflows, AI tools, Anthropic, ChatGPT, Claude, Claude Code, Code Review, Coding Workflows, Cursor, Designers, Documentation, Engineering Tools, Figma, Figma Make, Founders' Strategic Partner, Gemini, GitHub Copilot, Growth Strategy, Human-AI Collaboration, Interaction Design, Lovable, Lovable v0, Market Analysis, Mockups, Noam Segal, PMs, PMs' value, PRDs, PRDs writing, Perplexity, Personal Productivity, Product Ideation, Product Managers, ROI, Replit, Role-Specific Tools, Specialized Tools, Tests, UXR leader, anonymity, communication enhancement, compounding revolution, content copy, data scarcity, decks, design synthesis, designers' challenges, documents, engineers, exceeding expectations, founders' benefits, hot takes, in-depth survey, mockups creation, mockups/prototypes, production tasks, productivity, productivity effects, prototyping, prototyping tools, quality improvement, quality results, rapid improvement, real impact, roadmap ideas, role-boundary shifts, significant downsides, slow adoption, strategic work, survey, tech workers, time savings, toolkits, user research, visual design
github copilot
www.lennysnewsletter.com a day ago
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178. HN Show HN: AI that writes 5 specific e-com description types- Scriptor Studio is an innovative AI tool designed specifically for the e-commerce sector. - The primary function of this tool is to streamline the process of crafting product descriptions, achieving a remarkable reduction of 75% in writing time. - By automating and expediting the description creation process, Scriptor Studio enhances the efficiency of content teams working in e-commerce. - In addition to time-saving benefits, Scriptor Studio also positively impacts conversion rates for the users' content, implying improved performance in terms of customer engagement and sales. Detailed Summary: Scriptor Studio represents a cutting-edge AI tool tailored explicitly for e-commerce businesses. Its core feature is an advanced automation capability that reduces the time spent on writing product descriptions by a substantial 75%. This efficiency gain directly benefits content teams in e-commerce by freeing up their time from manual, repetitive tasks, allowing them to focus on more strategic initiatives. Beyond mere time reduction, Scriptor Studio demonstrates tangible improvements in conversion rates for the user's content. This suggests that not only does the AI tool expedite production but also elevates the quality of descriptions in a manner that resonates with consumers, thereby increasing the likelihood of purchases. The combination of enhanced speed and effectiveness positions Scriptor Studio as a valuable asset for e-commerce entities striving to optimize their product presentation strategies while maximizing team output and customer conversion. Keywords: #granite33:8b, AI, Scriptor Studio, content team, conversion rates, descriptions, e-com, game-changer, game-changerKeywords: AI, time reduction
ai
scriptor.studio a day ago
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179. HN Claude Pro and Max subscribers get 2x usage limits through New Year's Eve- Claude, an AI service, offers Pro and Max subscribers doubled usage limits till New Year's Eve as a special promotion. - To fully utilize this benefit, subscribers must have JavaScript enabled on their browsers. - The platform's Help Center provides a list of compatible browsers to assist users in ensuring seamless access with JavaScript enabled. ``` Keywords: #granite33:8b, Help Center, JavaScript, New Year's Eve, browser, disabled, enabled, subscribers, supported browsers, usage limits
claude
twitter.com a day ago
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180. HN Show HN: What We Learned Building an AI Design Tool for Brands?- The text presents a link to a share titled "Show HN: What We Learned Building an AI Design Tool for Brands," indicating a discussion or presentation about an AI-driven design tool specifically designed for brand usage. - The content focuses on the key insights and technical lessons learned during the development process of this AI design tool. - It likely covers the challenges encountered, solutions implemented, and any innovative approaches taken to create an effective tool tailored to brand needs. - The post encourages interested users to either start creating an account or sign in to access comprehensive information regarding the tool's features and functionalities across various devices. - The summary implies that the primary audience for this tool are brands looking to leverage AI for their design requirements, suggesting the tool aims to streamline and enhance brand-related design processes. Keywords: #granite33:8b, AI, brands, chat history, creating, design tool, device access, sign in
ai
picxstudio.com a day ago
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181. HN From Compose to Systemd: Elegantly Managing Containers with Podman and Quadlet**Summary:** The article outlines transitioning from Docker Compose to Podman and Quadlet for managing containers, specifically using the self-hosted backup solution Immich as an example. It emphasizes benefits such as daemon-free operation, declarative deployment, and robust service management through Systemd features like automatic start/stop and log integration. **Key Steps:** 1. **Preparation of docker-compose.yml**: - Remove `container_name` as Quadlet names containers with service names. - Replace environment variables directly with specific values to simplify the process, eliminating dependency on .env files. - Adjust inter-service communication settings for localhost access within Podman pods by setting `DB_HOSTNAME` and `REDIS_HOSTNAME` to `127.0.0.1`. 2. **Conversion Using `podlet`:** - Employ the `podlet -i -a compose --pod` command to convert modified docker-compose.yml into Quadlet files (.container and .pod), compatible with Systemd for service management. This process automatically creates necessary service units, including immich-server, immich-machine-learning, redis, and database, detailing their configurations like dependencies, environment variables, image sources, volume mounts, restart policies, and pod settings. 3. **Deployment & Management with Systemd:** - Save generated unit files in `~/.config/containers/systemd/` and reload Systemd. - Start services using `systemctl --user`. - Address ownership issues by adding `UserNS=keep-id` to maintain user permissions on hosted database directories. 4. **Troubleshooting Common Issues**: - **Database Container Ownership**: Resolve high UID ownership of files in mounted database directories by enabling `UserNS=keep-id` in immich.pod. - **Immich Container Restarting**: Troubleshoot continuous restarts by checking logs and adjusting configurations, though specific solutions aren't detailed. - **Rootless Mode Permission Issues**: Set `UserNS=keep-id` in pod files for consistent file permissions. - **Pasta Network Backend Access Issue**: Configure an independent internal network using `~/.config/containers/containers.conf`. - **Session Termination on Logout**: Enable "linger" to keep sessions active post-logout with `sudo loginctl enable-linger 5. **Automated Container Image Updates**: - Use `AutoUpdate=registry` in [Container] section of .container files for specified containers. - Activate and start Podman's auto-update timer service via `systemctl --user enable --now podman-auto-update.timer`. **Note:** A comprehensive point on "Podlet Conversion Errors" lacks contextual detail in the provided text, hence not summarized. Keywords: #granite33:8b, After, AutoUpdate, AutoUpdate=registry label, DB_HOSTNAME, Environment, HealthCmd, Immich, Install section, POSTGRES_DB, POSTGRES_INITDB_ARGS, POSTGRES_PASSWORD, POSTGRES_USER, Pasta network, Permission denied, Pod, Podman, Podman environments, PostgreSQL, Quadlet, REDIS_HOSTNAME, Redis, Redis RDB file, Systemd, Unit, UserNS=keep-id, Volume, WantedBy, absolute paths, automatic conversion, compose file, container files, container images, container management, containers, conversion errors, database, declarative deployment, defaulttarget, direct values, docker-composeyml, dockerio, env files, ghcrio, image, internal network configuration, linger enablement, log integration, loginctl, machine-learning, periodic checks, pod files, podlet, podlet command, publishPort, relative paths, release, restart, root user, rootless mode, service discovery, service management, systemd unit files, systemd user services, timer service, unit file names, valkey, variable substitution
postgresql
www.nite07.com a day ago
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182. HN Show HN: My open-source tool for visualizing Crossplane resources**Summary:** Crossview 3.1.0 is an open-source tool designed for visualizing Crossplane resources as interactive graphs, now enhanced with improved multi-cluster context support, faster rendering, advanced search and filtering capabilities, and increased stability. It enables real-time monitoring of Kubernetes resources through resource visualization, detail views, and comprehensive multi-cluster management. The application is built using React, Chakra UI, Go, and the Gin framework for performance, providing single sign-on integration (OIDC/SAML) and WebSocket connections for real-time data updates. Key requirements include Node.js 20+, Go 1.24+, a PostgreSQL database, and a Kubernetes configuration file for deployment on Kubernetes clusters. For developers, the project offers detailed setup instructions involving separate terminal executions for frontend development (`npm run dev`) and backend (Go server). Production builds are achieved via `npm run build`, serving from the `dist/` folder with the Go server. The Crossview-Go server runs as a backend API on port 3001, providing endpoints for resource health checks, context management, listing resources, event fetching, real-time resource watching, and user authentication using the Kubernetes client-go API and Informers for efficient monitoring. Deployment options include: - **Helm Repository Addition**: Use `helm repo add crossview https://corpobit.github.io/crossview` followed by `helm repo update`. - **Helm Installation**: Install via Helm with namespace, creation, and secret settings as required. - **Docker Image Management**: Build locally with `docker build`, specify port mappings, database details, kubeconfig paths, and session secrets using Docker runtime environment variables. - **Docker Compose Configuration**: Define services including Crossview and a PostgreSQL instance in a `docker-compose.yml` file, specifying necessary environment variables and volume mounts for configuration files. Configuration loading follows the order: Environment Variables > Config File Volume > Default Helm Chart settings. Essential environment variables encompass database credentials, Kubernetes config paths, and session secrets. The text also covers troubleshooting, a Helm Chart reference, Kubernetes manifests, and integration with Keycloak. The project's technology stack includes React frontend (with Vite), Chakra UI components, Go backend with Gin framework, Kubernetes client-go API, and Informers for event-driven resource watching. Additional resources include contribution guidelines emphasizing code adherence, focused commits, and clear Pull Requests, as well as a detailed guide for setting up Crossview in various environments, including Single Sign-On configurations. The project is open-source under the Apache License 2.0. **Bullet Points:** - **Tool Overview**: - Visualizes Crossplane resources as interactive graphs with enhanced multi-cluster context and real-time monitoring capabilities. - Built using React, Chakra UI, Go, and Gin framework for performance. - **System Requirements**: - Node.js 20+, Go 1.24+, PostgreSQL database, Kubernetes config file (for Kubernetes deployment). - **Setup & Development**: - Frontend (`npm run dev`) and backend (Go server) development in separate terminals. - Production build using `npm run build` for serving from the `dist/` folder. - **Crossview-Go Server**: - Backend API on port 3001, offering diverse endpoints with Kubernetes client-go API and Informers for efficient resource monitoring. - **Deployment Methods**: - Helm Repository addition via command line. - Helm chart installation with customization options (namespace, secrets). - Docker image management including environment variables for configuration. - Docker Compose setup for defining and running services (Crossview, PostgreSQL). - **Configuration Loading Order**: Prioritizes Environment Variables > Config File Volume > Default Helm Chart settings. - **Additional Features & Resources**: - Support for Single Sign-On (OIDC/SAML), real-time updates via WebSockets. - Contribution guidelines, troubleshooting, Helm Chart reference, Kubernetes manifests, and Keycloak integration guide. - Open-source under the Apache License 2.0 with detailed technology stack including React frontend, Go backend, Kubernetes integrations. Keywords: #granite33:8b, Apache License 20, Configuration, Crossplane, Crossview, Docker, Environment Variables, GORM, Gin framework, Go, Helm, Image, Informers, Installation, Kubernetes API client, Kubernetes Informers, Nodejs, OIDC, PostgreSQL, React, Repository, SAML, SSO Integration, Secrets, UI, WebSocket, client-go, dashboard, events, high performance, metadata, multi-cluster support, real-time updates, relationships, resource details, resources, status conditions, troubleshooting, visualization
postgresql
github.com a day ago
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183. HN Show HN: I built a tool to scrape and summarize Reddit long discussions- The user created a Reddit chrome extension designed to automate the process of scraping and summarizing extensive discussions on the platform. - Frustrated with the laborious manual method of fetching comment metadata through .json URLs and the perceived inefficiency of using AI for summarization, the user sought an automated solution. The current 'dirty' metadata made the process slow and cumbersome. - To address these challenges, they leveraged Plasmo, a framework for building Chrome extensions, React for user interface development, Vercel for hosting backend functions, and OpenRouter API for routing needs. - This extension is versatile, supporting features such as summarizing discussions within specific threads, subreddits, and search results pages on Reddit. - A demonstration of the extension's functionality can be viewed on YouTube, and it is listed in the Chrome Web Store for public access. - The user actively encourages feedback from users to improve the tool further. Keywords: #granite33:8b, AI, Chrome extension, OpenRouter API, Plasmo, React, Reddit, Vercel, demo, feedback, metadata, rate limiting, scraping, summarization
ai
news.ycombinator.com a day ago
https://www.producthunt.com/products/reddit-summarizer? 16 hours ago |
184. HN Resurrecting my old Turbo Pascal homework with AI**Summary:** The text recounts a retrospective look at a 1995 A-Level Computing project where the author developed an original DOS graphing calculator using Turbo Pascal 7.0 on outdated 486 PCs running MS-DOS. The project, though now incompatible with modern systems due to its DOS dependencies, was praised for teaching programming fundamentals through simplicity and interactivity. Key points include: - **Project Context:** - Developed a custom DOS graphing calculator during A-Level studies in 1995 using Turbo Pascal 7.0. - Chose to build this tool despite existing options being unknown at the time, reflecting a desire for hands-on learning. - **Development Process:** - Initially struggled with UI development, spending a month on functions like screen reset, button drawing, and press checks in a custom 'Button' unit. - Learned that UI development can be disproportionately time-consuming. - Implemented the Shunting Yard algorithm for expression evaluation without knowing its name at the time. - **Technical Aspects:** - The software was a COM executable with a 64KB memory limit, running on machines with more RAM. - Offered basic graphing functions like plotting equations and calculating roots/intersections. - Features were gradually added due to limited development time, resulting in omissions such as missing base-10 logarithms. - **Code Description:** - The 2000-line Pascal code was procedural without object-oriented features, residing in a single file with custom units 'Button' and 'Stack'. - Code style is described as amateurish, with placeholder names and repeated function calls, yet functional within its context. - **Reflections:** - The author found more satisfaction in this practical tool compared to later abstract dissertation work. - Attempts to revive the project in 2025 using DOSBox faced challenges such as acquiring a compatible Turbo Pascal version and recreating missing units. - **AI Application:** - In 2025, the author used Google Antigravity and Claude Opus 4.5 to adapt the code for modern systems. - Utilized Free Pascal Compiler for compatibility with Turbo Pascal's dialect, minimizing necessary modifications. - Faced challenges adapting DOS-specific Graph unit for Mac OS but successfully employed SDL2 via SDL2-for-Pascal library. - Achieved a functional, albeit buggy, Mac OS version of the calculator using AI-generated replacements for 'Graph' and 'Mouse' units. The author's journey illustrates the evolution from a hands-on programming experience in a DOS environment to leveraging contemporary AI tools for modernizing legacy code, highlighting both nostalgia and the potential of artificial intelligence in software development. Keywords: #granite33:8b, 1990s calculator, 64KB memory limit, AI agent, AI tools, Antigravity, BGI, Binit, Button, Button unit, COM executable, CalcY, Case, Claude Code, Claude Opus 45, Codex, Copilot, DOS application, DOS port access, DX, Defbutton, Delay, Delphi, Free Pascal, Free Pascal Compiler, GRCLC120PAS, Gemini, Gemini 3 Pro, GitHub, Google Antigravity, Graph unit, Grapher, Halt, IDE, Mac OS, Mac OS app, Mhide, Mouse unit, Mpos procedure, Mshow, Norm, Object Pascal, Pascal, Pascal code, Postorderit procedure, Python script, SDL, SDL2, SDL_GetMouseState, SDL_PollEvent, SDL_QUITEV, SX, Shunting Yard algorithm, Stack, Stack unit, TP-compatible format, TSDL_Event, Turbo Pascal, Turbo Pascal versions, UI development, UI logic, VGA graphics, VGA mode, Vi vs Emacs, Wednesdays, angletype, base 10 logarithms omission, bitmap font, button press check, buttonpressed, buttonstatus, cint, code recreation, code structure, code submission, conversion, cuint32, custom unit, custom units, data structure, direct memory access, edit equations, edit equations screen, enums, equation evaluation, equation graphs, exit, file archiving, functions, global state, graphical calculator subset, graphics libraries, graphing calculator, home screen, hubris, inline UI logic, intersections, invalid equations, logic structure, magnificent calculator, minimal changes, missing units, modern graphic calculator, modern graphics systems, mouse input, mousebits, mouserecord, multiple AI tools, natural logarithms, nerd wars, newformulaarray, newimprovedformula, original bugs, pan, placeholder names, plot, poor naming conventions, postfix expression, procedural code, processor, proctypes, ptcgraph, random number generator, real, rebuilding units, record type, resurrection, roots, screen reset, screenshots, simultaneous graphs, single file, single-threaded, source code archived, stack contention, standard library, story point, string, stringtoreal, swapindent, tokenised string, two equations, units generated, version control, whichmode, zoom
github
benashford.github.io a day ago
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185. HN Perfect Is the Enemy of Good (2025)- The user initially planned an extensive website rebuild using modern technologies, incorporating React and TypeScript with Emotion styling, TailwindCSS, Mantine components, Zustand, React-Query, Next.js as the framework, Prisma as ORM, Deno runtime, Docker & Kubernetes for containerization and orchestration, Istio for traffic management, Caddy Server for incoming traffic, PostgreSQL database, Kafka for messaging, ArgoCD for GitOps workflows, Ansible for infrastructure automation, EC2 instances for hosting, and Cloudflare for protection, CDN, DDoS mitigation, and edge computing. - Despite the advanced setup, the user later chose a simpler approach with plain PHP files, include statements, and SQLite database for website development. This method facilitated easy additions like view counters and comment sections. - The user, despite familiarity with complex technologies, found satisfaction in the simplicity and efficiency of using PHP and SQLite, even suggesting Phiki for code highlighting implementation. - A humorous "But actually..." concludes the summary, hinting at potential overengineering in their initial extensive technology stack choice. Keywords: #granite33:8b, Ansible, ArgoCD, CDN, Caddy Server, Cloudflare, DDoS protection, Deno, Docker, EC2, Emotion, Istio, Kafka, Kubernetes, Mantine, Nextjs, PHP, Phiki, PostgreSQL, Prisma, React, React-Query, SQLite, TailwindCSS, TypeScript, Zustand, code highlighting, comments section, include statements, plain PHP files, pre-moderation, production software, view counter
postgresql
medv.io a day ago
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186. HN Ask HN: Where do deterministic rules break down for LLM guardrails?- **User's Approach**: Employs a hybrid model of deterministic rules (like regex, allowlists, schema validation) followed by LLM-based semantic checks for enhanced context and nuanced issue detection, acknowledging increased latency, cost, and complexity with the latter. - **Challenges Faced**: Scaling becomes difficult due to the evolving nature of issues where both rule-based and semantic approaches have limitations. - **Specific Inquiries**: - *Instances of Rule Failures*: Seeking real-world examples where deterministic rules alone in production were insufficient, leading to data security breaches or policy violations. - *Semantic Check Necessity*: Understanding what types of checks require the contextual understanding provided by LLMs, as basic rule-based methods struggle (e.g., indirect PII leaks, subtle policy violations). - *Excluded LLM Decision Aspects*: Inquiring about aspects consciously left out of LLM decision-making processes to maintain control and efficiency, and the rationale behind such exclusions. - *Unforeseen Post-Deployment Issues*: Gathering accounts of unexpected failure modes discovered only after deploying LLMs in production, helping anticipate and mitigate similar risks in their own systems. - **Tooling Development Aim**: The user is developing internal tooling for guardrails and data security around LLM systems, aiming to integrate lessons learned from others’ experiences at scale to enhance their approach proactively. Keywords: #granite33:8b, Deterministic rules, LLMs, allowlists, context, cost, edge cases, guardrails, hybrid approach, indirect PII leaks, intent, latency, operational complexity, policy violations, real-world experiences, regex, scaling, schema validation, semantic checks
llm
news.ycombinator.com a day ago
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187. HN Nano Banana AI Image Editor Advanced Image Generation and Edit- Nano Banana AI Image Editor is a tool that provides sophisticated image creation and modification capabilities. - It features a one-shot mode, which guarantees accurate outcomes with a single attempt, beneficial for time-efficient professional use. - The software also includes batch processing functionality, enabling the simultaneous editing of more than 50 images while preserving uniform quality and stylistic consistency, making it suitable for agencies and content production teams managing large volumes of visual content. Keywords: #granite33:8b, Advanced Generation, Batch Processing, Content Teams, Image Editor, Multiple Images, Nano Banana AI, One-Shot Editing, Professional, Project Uniformity, Quality, Time-saving
ai
nano-bananaai.org a day ago
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188. HN A Curl 2025 Review- In 2025, the curl project experienced substantial growth with over 3,400 commits (a 40% increase) from more than 150 authors, including nearly 100 first-time contributors. Viktor Szakats was the top committer, followed by Stefan Eissing for recent code additions. - The project's testing expanded significantly with 2,179 test cases, surpassing twelve tests per thousand lines of source code. Eight releases were issued, focusing on performance enhancements, error reduction, and introducing experimental HTTPS-RR support. Release 8.17.0 incorporated a record 450 bugfixes. - Experimental HTTPS-RR DNS record support was added along with release candidates for pre-release testing. The command line tool grew by 1,150 lines, introducing six new options (totaling 273), while libcurl increased by 100 lines of code. QUIC support transitioned towards OpenSSL's new API, with plans to phase out the existing OpenSSL QUIC stack by early 2026. - Data traffic surged to 79TB monthly from 58TB in the prior year, GitHub activity peaked at over 200 pull requests per month, and Continuous Integration (CI) usage exceeded 25 CPU days daily. The project dashboard expanded with more visualizations, reaching 92 graphs and 259 plots. - Legacy features such as Visual Studio 2005 support, Secure Transport, BearSSL, msh3, and the winbuild build system were removed to streamline focus and improve security. AI-generated vulnerability reports increased, straining the curl security team due to their lower quality and higher volume, attracting media attention. - Nine Common Vulnerabilities and Exposures (CVEs) were published, all classified as low or medium severity. The user behind these developments attended eight conferences across five countries, delivering presentations at notable events like FOSDEM, curl up, Open Infra Forum, Joy of Coding, FrOSCon, Open Source Summit Europe, and EuroBSDCon, while also engaging in podcasts centered around curl. BULLET POINT SUMMARY: - 40% increase in commits (over 3,400) with 150+ authors, including 100 first-time contributors; Viktor Szakats and Stefan Eissing led contributions. - 2,179 test cases, 8 releases, focusing on performance, error reduction, and HTTPS-RR support. Release 8.17.0 included 450 bugfixes. - Experimental HTTPS-RR added; transitioning QUIC support to OpenSSL's new API; 6 new command line options (273 total); libcurl grew by 100 lines. - Increased data traffic (79TB monthly), high GitHub activity (>200 PRs/month), extensive CI usage (25+ CPU days daily). - Legacy features dropped for streamlining and security; AI vulnerability reports caused strain; 9 CVEs, all low/medium severity. - User attended 8 conferences across 5 countries, delivered presentations, participated in curl-focused podcasts. Keywords: #granite33:8b, AI, AI security reports, BearSSL, CI jobs, CVEs, EuroBSDCon, FOSDEM, FrOSCon, HTTPS-RR support, Joy of Coding, MVP program, Open Infra Forum, Open Source Summit Europe, OpenSSL API, QUIC, Secure Transport, Visual Studio 2005, allocations reduction, authors, bug reports, bugfixes, code analyzers, commits, curl, curl up, dashboard, deprecated support, error reduction, first-timers, foss-north, function usage reduction, honors, legacy support, libcurl, media mentions, msh3, performance improvement, podcasts, pull requests, releases, source code complexity, test cases, web traffic, winbuild, winbuild build system
ai
daniel.haxx.se a day ago
https://curl.se/dev/deprecate.html 9 hours ago |
189. HN Year in review 2025: AI in data science [Python/R]- **January 2025**: - Release of ellmer, an R package for interacting with large language models (LLMs) on CRAN. - DeepSeek introduces R1, a reasoning model that initially causes market volatility due to misunderstandings about its cost and user interface innovations. - **February 2025**: - Anthropic launches Claude Code and Claude 3.7 Sonnet, establishing coding agents within terminals. - OpenAI and Google subsequently release their coding agent versions in April and June, respectively. - Posit responds with chatlas, the Python equivalent of ellmer. - **March 2025**: - Google updates Gemini to version 2.5, regaining competitive ground against other frontier models. - **June 2025**: - Posit announces Positron Assistant, a coding agent designed for their platform, along with R packages (vitals, ragnar, mcptools) supporting ellmer and adhering to Anthropic's Model Context Protocol. - Introduces Databot, an exploratory data analysis assistant simplifying the data exploration process. - **August 2025**: - Several advanced models are unveiled: Claude Opus 4.5, Gemini 3 Pro and Image variant (Nano Banana Pro), GPT 5.1 and Codex-Max versions, DeepSeek V3.2, and Grok 4.1, marking the cutting edge of AI research at that time. - **November 2025**: - Various high-profile models are introduced in quick succession: Claude Opus 4.5, Gemini 3 Pro (and Image variant Nano Banana Pro), GPT 5.1 and Codex-Max versions, DeepSeek V3.2, and Grok 4.1. - **Other Notable Events**: - Team takes January 2nd off for holidays. - OpenAI's GPT-5.2 receives mixed feedback due to unpredictable behavior despite strong benchmark scores. - Beta testing available for an experimental AI product in RStudio. - A blog post highlights limitations of small, laptop-run models for coding tools like Positron Assistant or Databot. - Chores 0.3.0 R package released on CRAN, demonstrating robust instruction following in laptop-run LLMs. - Review of key terms including 'training and inference', 'prompt injection', 'tool calling and agents', 'situational awareness', and 'AGI'. **Key Concepts**: - **Artificial General Intelligence (AGI)**: Surpasses human capabilities across various tasks through emergent properties, unlike narrow AI designed for specific tasks. - **Tokenization**: Process of breaking down text and inputs into tokens, fundamental units for Large Language Models (LLMs). - **Consumer Pricing vs API Pricing**: Consumer pricing is a flat subscription fee for applications; API pricing charges based on token usage for programmatic access. Keywords: #granite33:8b, AGI, AI, API Pricing, Artificial General Intelligence, Claude Code, Consumer Pricing, Databot, DeepSeek, Emergent Capability, Flat Subscription Fee, GPT, GPT 52, Gemini, Introspection, LLM, LLM assistants, LLMs, Model Context Protocol, OpenAI, Positron Assistant, Programmatic Use, Python, R, R1, RStudio beta testing, Retrieval Augmented Generation, Text Inputs, Token Usage, Tokenization, agents, benchmarks, chores package, coding agent, data science, ellmer, frontier models, inference, instruction-following, local models, prompt injection, situational awareness, terms, tool calling, training, unpredictable behavior
gemini
posit.co a day ago
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190. HN Cars are going high-tech at the risk of software woes- Carmakers are integrating advanced software and technology to enhance vehicle features, as evidenced by Hyundai's holographic display, BMW's panoramic dashboard, and Honda's efficient EV line with a personalized operating system, all showcased at CES. - Despite these innovations, software-related recalls have significantly risen; increasing from 6% in 2019 to 15% last year, according to the National Highway Traffic Safety Administration (NHTSA). - Major recalls highlight this struggle: Stellantis and Tesla recently pulled back nearly two million vehicles due to software glitches, indicating an ongoing challenge for automakers in ensuring both cutting-edge features and reliable software. Keywords: #granite33:8b, Cars, EVs, National Highway Traffic Safety Administration, Stellantis, Tesla, automakers, batteries, holographic display, operating system, panoramic dashboard, personalization, recalls, self-park, smartphones, software, software glitches, technology
tesla
www.morningbrew.com a day ago
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191. HN Show HN: Chatpack – Compress chat exports 13x for LLM analysis (Rust)**Summary:** Chatpack is a Rust-based tool engineered to compress chat exports from diverse platforms like Telegram, WhatsApp, Instagram, and Discord for efficient analysis with Large Language Models (LLMs). It reduces data size by up to 13 times when converting to CSV format, minimizing metadata noise typically present in raw JSON structures. Key features include fast processing speeds exceeding 20,000 messages per second, multi-platform compatibility, and the ability to merge consecutive messages from the same sender for streamlined output. Chatpack provides: - **Pre-built binaries** compatible with Windows, macOS (Intel and Apple Silicon), and Linux. - Integration as a Rust library through `cargo install chatpack` and project dependency `[dependencies] chatpack = "0.2"`. - Processing capabilities including format auto-detection (Telegram, WhatsApp, Instagram), filtering by users or date ranges, and output in CSV, JSON, and JSONL formats. **Usage Methods:** 1. **Command Line Interface (CLI):** Process various chat exports directly via command line for optimized CSV outputs. 2. **Library Integration:** Offers examples and methods to parse data, merge messages by sender, and write in JSON format with extensive customization options. Output Configurations allow users to specify detailed or minimal metadata output and choose among multiple output formats (JSON, JSONL, CSV). Processing statistics track efficiency metrics like compression ratios and message counts post-merging. The CLI supports filtering by criteria such as dates, senders, and specific metadata elements. Users can also customize output pathways and opt out of merging consecutive messages. **Documentation:** Detailed usage examples, API documentation, and performance benchmarks (20-50K messages/sec) are available at `docs.rs/chatpack`. The tool's source code handles platform-specific formats (JSON IDs for Telegram, locale detection for WhatsApp), ensures message integrity (Mojibake fix for Instagram), and manages attachments from Discord. Developed under the MIT license by Mukhammedali Berektassuly. **Bullet Points:** - **Tool Overview**: Chatpack is a Rust tool to compress chat data from multiple platforms (Telegram, WhatsApp, Instagram, Discord) for AI analysis. - **Core Functionality**: - Reduces data size up to 13 times using CSV format, optimizing for LLM input. - High-speed processing (20K+ messages/sec). - Merges consecutive messages from the same sender. - **Features and Integration**: - Binaries for Windows, macOS, Linux. - Rust library (`cargo install chatpack`, `[dependencies] chatpack = "0.2"`). - Supports auto-detection of various chat formats, filtering options, flexible output formats (CSV, JSON, JSONL). - **Usage Methods**: - Command Line Interface (CLI) for quick processing and generation of optimized CSV files. - Library integration for detailed control over data manipulation and output customization. - **Output Configurations**: - Control metadata granularity with options like full vs. minimal outputs. - Track processing efficiency metrics including compression ratios and message counts after merging. - **Command Line Features**: - Filters by dates, senders, and metadata. - Customizable output paths, merging preferences. - **Availability and Licensing**: - Comprehensive documentation at `docs.rs/chatpack`. - Developed under MIT license by Mukhammedali Berektassuly. Keywords: #granite33:8b, API documentation, CLI reference, CSV, Chatpack, Discord, IDs, Instagram, JSON, JSONL, LLM analysis, MIT License, Mojibake, RAG pipelines, Rust, Speed, Telegram, WhatsApp, attachments, compression, context windows, filters, formats, library, messages, metadata, multi-platform, pre-built binaries, smart merge, statistics, stickers, timestamps, token savings, toxic data
llm
github.com a day ago
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192. HN The Twelfth Day of Agents: A Reflection and Heartfelt Thank You- **Series Conclusion**: The 12 Days of Agents series concludes its exploration into AI agents, aiming to clarify their functionality and practical uses. - **Santa Analogy**: The narrative employs Santa Claus as an illustrative figure for how AI agents operate successfully—through meticulous planning, specialized support (helpers), appropriate tools, and comprehensive data access. - **True Value of Agents**: Contrary to the perception that agents boost capacity, their real value lies in liberating humans from mundane tasks, enabling focus on more significant responsibilities. - **Effective Agent Utilization**: Suggestions for leveraging AI agents include delegating repetitive tasks, minimizing interruptions caused by context switching, and using freed-up time to tackle novel challenges. - **New Year's Resolution**: For those looking to integrate AI into their workflow, a proposed resolution is to select a recurring weekly task and start developing an agent with initial human supervision, progressively integrating tools or memory over time. - **Agents as Interfaces**: The series stresses that AI agents are interfaces rather than replacements for human workers; their strength lies in organizing tasks and workflows, not in performing them autonomously. - **Cumulative Impact of Small Wins**: Continuous learning and curiosity about incremental improvements with AI agents are encouraged, as these 'small wins' compound into significant advancements. - **Closing Remarks**: The series thanks participants for their engagement, wishing them a Merry Christmas as it wraps up its educational journey on AI agents. Keywords: #granite33:8b, 2026, AI, Agents, automation, context switching, curiosity, delegation, efficiency, focus, human-in-the-loop, memory, opportunities, orchestration, repetitive, tasks, tools, writing series
ai
buttondown.com a day ago
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193. HN Postgres with Instant Branching- **Detailed Summary:** This guide provides a step-by-step process for configuring PostgreSQL (Postgres) with Instant Branching functionality using ZFS pools. The setup involves creating a ZFS pool, which can be done either temporarily by utilizing a disk image or permanently with the attachment of a physical drive. - After establishing the ZFS pool, users are instructed to execute the `velo setup` command. This process handles permission grants necessary for the system and configures Docker settings, ensuring proper integration of Postgres with Velo. - A crucial part of this procedure is logging out and logging back into the system. This step is essential because it allows any changes in group memberships to be properly recognized by the operating system. - Finally, verification of the setup is recommended before starting usage. This verification ensures that all components are correctly configured, and Postgres with Instant Branching via ZFS pools is operational as intended. **Bullet Points Summary:** - **Create a ZFS Pool:** - Option 1: Use a disk image for temporary setup. - Option 2: Attach a physical drive for permanent setup. - **Configure Permissions and Docker Settings:** - Run the `velo setup` command to manage permissions and adjust Docker configurations appropriately. - **Group Membership Change Recognition:** - Log out and log back in to ensure OS recognizes any group membership changes made during configuration. - **Verify Setup:** - Check and confirm all configurations before starting regular usage of Postgres with Instant Branching through ZFS pools. Keywords: #granite33:8b, Docker, Postgres, Velo, ZFS pool, group membership, permissions, setup verification
postgres
github.com a day ago
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194. HN Rack makes Pion SCTP 71% faster with 27% less latency### Summary: Rack's optimization has significantly enhanced Stream Control Transmission Protocol (SCTP) performance, increasing speed by 71% and reducing latency by 27%. SCTP, designed for multiple application handling over a single connection with automatic failover support, excels in scenarios requiring simultaneous data transfer, such as large file transfers and uninterrupted text messaging. Its real-time capabilities are crucial for applications like remote surgery, navigation systems, online gaming, cloud gaming, and secure communication through WebRTC. SCTP employs fast retransmission (upon receiving three reports of a missing chunk) and timer-based retransmission (if acknowledgments aren't received within a set time). The introduction of RACK (Rapid Acknowledgment for Congestion Avoidance) in 2021 improved loss detection by adaptively tracking network conditions. RACK, originally developed for TCP, can be integrated into SCTP, potentially providing superior performance through its strategies like Tail Loss Probing (TLP). TLP assists efficient retransmission of missing packets within segments: the sender retransmits the last unacknowledged packet upon detecting missing acknowledgments. The receiver uses Selective Acknowledgments (SACK) to specify which packets have been received, enabling the sender to manage loss instances and congestion control more effectively, optimizing data transmission by reducing unnecessary round-trip times and enhancing network responsiveness. RACK minimizes spurious retransmissions by differentiating between genuine packet loss and temporary network disruptions through time-based acknowledgments and TLP. It only declares packets lost when Round-trip Time Out (RTO) expires, thus reducing unnecessary retransmissions and optimizing bandwidth usage during recovery, especially beneficial in challenging edge cases. Testing under SCP harness demonstrates RACK's robustness across various network conditions: it shows a 34.9% increase in goodput, a 21.3% decrease in CPU time, and reductions in latency (p50 by 27.5%, p99 by 24.6%) with SCTP. Real-world HEVC video streaming over WebRTC datachannels also confirms RACK's superiority, achieving higher goodput rates and faster delivery times compared to the main branch without compromising ACK path speed. Initial RACK implementation faced challenges such as poor management of high-to-low Round Trip Time (RTT) transitions and inefficient CPU usage. These were addressed by implementing a windowed minimum for recent RTT measurements, improving active RTT measurement inspired by Weinrank's work, and ensuring the latest RTT is measured per packet. SCP testing was instrumental in identifying and correcting these issues, aligning RACK implementation with specifications for optimal performance. ### Bullet Points: - **SCTP Enhancements:** Rack has improved SCTP’s speed by 71% and reduced latency by 27%, crucial for real-time applications like remote surgery, gaming, cloud services, and secure communication via WebRTC. - **Loss Recovery Strategies:** SCTP uses fast retransmission (three missing chunk reports) and timer-based retransmission (timeout), enhanced by RACK's adaptive loss detection focusing on network condition tracking. - **Tail Loss Probing (TLP):** TLP helps SCTP efficiently retransmit lost packets within segments, utilizing Selective Acknowledgments (SACK) for informed retransmissions and reducing unnecessary round trips, thus optimizing data transmission. - **RACK’s Efficiency:** RACK minimizes spurious retransmissions by distinguishing genuine packet loss from network hiccups through time-based acknowledgments and TLP, conserving bandwidth and accelerating actual retransmission needs. - **Testing and Improvement:** SCP testing revealed RACK's robustness across various network conditions, showing significant performance improvements in goodput, CPU usage, and latency reductions compared to the baseline without RACK. - **Real-World Validation:** In HEVC video streaming over WebRTC datachannels, RACK outperformed conventional methods with higher goodput rates and faster delivery times. - **Addressing Initial Issues:** Early RACK implementation had problems including suboptimal RTT handling, poor reordering management, and inefficient CPU usage, which were resolved through SCP testing-identified refinements for alignment with RFC specifications and optimal performance. Keywords: #granite33:8b, ACK, AI, CPU profiles, DTLS protocol, ICE protocol, RACK, RFC 4960, RFC 8985, RTO, SACK, SCTP, SCTP implementation, TLP, Tail Loss Probing (TLP), WebRTC, acknowledgments, active RTT measurement, adaptive, benchmarks, cloud gaming, congestion control, cryptocurrency, cubic algorithm, efficiency, efficient resending, fast retransmission, file transfer, goodput, image upload, latency, loss detection, loss recovery, multi-homing, multiplexing, network issues detection, network jitter, online games, packet loss, packet loss tracking, packet transmission, payment verification, pizza request, real-time, real-world data, receiver, receiver responsiveness, recovery, reliability, reliable datachannels, remote control, reordering improvement, retransmit, runtimememmove, segments, sender, spurious retransmissions, stuttering, tail loss probes, technical keywords, text messages, timer expirations, timer-based retransmission, transport protocols, unreliable datachannels, vnet(*chunkUDP)UserData
ai
pion.ly a day ago
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195. HN Lessons from Building an Indie App for Artists- The indie developer created Value Study, initially free to avoid appearing disingenuous, but transitioned to an affordable model for sustainability while maintaining accessibility. - Version 1.0 for Android is nearing completion after years of development, offering features comparable to the iOS version despite initial challenges with Android's fragmentation and user resistance to paid apps. - Android users, though fewer in number, prefer lifetime purchases over subscriptions, unlike expectations. The development process for Android proved more demanding than iOS due to its free-to-cheap device model and user mindset. - To manage separate iOS (Swift) and Android (Kotlin) codebases efficiently, the developer uses modern AI tools like Claude Code and employs tools such as RevenueCat for subscriptions, AppFollow for keyword tracking, and RocketSim for enhancing iOS development. - The transition from a free to paid model was challenging; initially offering both versions led to disruptions in maintaining distinct apps with varying features and bugs. A paywall with yearly and lifetime access was chosen after considering various options and peer advice. - Despite initial fears, users were supportive of the sustainability move, allowing the developer to reinvest in improvements like better tools, broader device testing, and addressing edge cases. - Collaborations with artists to promote the app have taught valuable lessons, including the importance of thorough testing and user-centric design; an initial collaboration backfired due to incomplete Android translations leading to negative reviews. - The developer has learned the significance of thoughtful development, balancing accessibility and valuing their effort, resulting in a well-received side project that supports part-time development with aspirations for full-time dedication. Value Study is now seen as reliable, valuable, affordable, and reflective of dedicated craftsmanship over time. Keywords: #granite33:8b, AI, AI apps, Android release, Android translation, App Store keywords, AppFollow, Christian, Claude Code, Collabstr, Grid mode, Indie app, Kotlin, RevenueCat, RocketSim, Spanish-speaking audience, StoreKit 2, Swift, UserDefaults, Value Study, Xcode, accessibility, affordability, affordable, art teachers, artists, care, content creators, critical reviews, cross-platform, edge cases, even user split, feature set, free apps, free version, fulfillment, growth, hands-on developer, iOS users, income stream, indie dev, issues resolution, learning tool, lifetime access, lifetime purchase, macOS, marketing, micro-influencer platform, mindset resistance, older devices, one-time purchase, paid app, practical terms, pricing, quality focus, reinvestment, reliance, stability, stable release, status bar, subscription, subscriptions comfort, testing, tooling, tooling workflow, tutorials, utility, version 10, yearly subscription
ai
shanehudson.net a day ago
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196. HN An initial analysis of the rediscovered Unix V4 tape- In July 2025, the University of Utah discovered a 1970s Fourth Edition Research Unix magnetic tape containing source code and compiled binaries from the original kernel, shifting from PDP-11 assembly to early C for parts of its system. - The source code was uploaded to the Unix History Repository on GitHub by an author who cleaned up unnecessary binary files. A text snippet suggests preparation steps involving directory and file deletions within this restored system. - Comparison between Unix Research's Fourth and Fifth Edition software snapshots using Git commands revealed: - New C compiler files (c13.c, c21.c, c2h.c) in the Fifth Edition. - The cmp utility rewritten in C (cmp.c). - Updated V4 author map file with missing details assigned to Ken Thompson and Dennis Ritchie, incorporating other Bell Labs team members like Robert H. Morris. - Analysis using git blame showed: - Fourth Edition consisted of 75,676 lines; 6,590 and 168 lines from previous editions. - Fifth Edition contained 111,814 lines; incorporated 52,000 lines from the Fourth Edition and added approximately 11,000 new lines. - Average timestamps of files in each edition provided insights into the development timeline: - Publication dates for seven editions revealed varying speeds with a considerable eight-month gap between the Fourth (V4) and Fifth Editions release, indicating rapid evolution at that time. - Further investigation needed to clarify discrepancies regarding First and Second Edition release times. Keywords: #granite33:8b, AT&T, Bell Laboratories, C, C compiler, Dennis Ritchie, Fifth Edition, Fourth Edition, GitHub, Ken Thompson, November 1973, Robert H Morris, SNOBOL III, Unix, administrative files, assembly language, author map, base file names, binaries, cmp utility, code lines, commit timestamps, date formatting, directories, edition results, editions, emulator, file deletions, git blame, kernel rewriting, math library, mismatch analysis, repository, source code, system dump, tape contents
github
www.spinellis.gr a day ago
https://news.ycombinator.com/item?id=46367744 a day ago |
197. HN What happened to tidal-dl-ng?The GitHub repository for "tidal-dl-ng," a software tool designed for backing up content from Tidal Music and Video streaming services, has unexpectedly been removed. This deletion is recent, as is the disappearance of the user account linked to the project. The reason behind this sudden removal remains unclear and unexplained at the current time. BULLET POINT SUMMARY: - "tidal-dl-ng" GitHub repository for backing up Tidal Media content has vanished. - Associated user account also seems to have disappeared recently. - No information available regarding the cause of this sudden deletion. - The situation remains unexplained as of now. Keywords: #granite33:8b, GitHub, backup, deletion, exislow, media, tidal-dl-ng, user account, utility
github
news.ycombinator.com a day ago
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198. HN Why Your AI "Fine-Tuning" Budget Is a Total Waste of Capital in 2026- **Critique of Current AI Trends (2026):** The text argues that the emphasis on "fine-tuning" AI models and Retrieval Augmented Generation (RAG) is inefficient, being costly without substantial benefits. Fine-tuning may increase model overconfidence in errors, while RAG, though robust for memory, can be substituted with simpler prompt engineering for most applications. - **Author's Perspective:** With a background in big-data Machine Learning, the author finds Large Language Models (LLMs) impressive but stresses the importance of leveraging current technology effectively through advanced prompt engineering rather than relying on LLMs as basic generators or chatbots. This is particularly crucial in high-stakes domains like medical question answering (QA). - **Strategies for Robustness:** The text advocates a strategy involving cascading multiple specialized prompts (over 10) to refine and minimize errors, focusing on particulars such as drug name variations and interaction scenarios. The system prioritizes identifying true positive interactions over missing false negatives. This method extends to organizing unstructured data using LLMs and machine vision models, transforming messy real-world inputs like handwritten notes or emails into structured formats. - **Orchestration Importance:** The author underscores the necessity of meticulous orchestration before and after utilizing LLMs for optimal outcomes, dismissing the notion that inference is an excessive financial burden. - **Misconceptions Regarding AI Costs:** Contrary to popular belief, the text suggests that as computational costs decline, fine-tuning smaller models for specific tasks becomes less economically viable compared to employing larger, more versatile models. It criticizes the overemphasis on extensive infrastructure and advocates for recognizing prompt engineering as a pivotal advancement akin to traditional software engineering. - **Future Predictions:** The author predicts by 2026, AI models will become commoditized and interchangeable, with genuine innovation occurring primarily through the sophisticated orchestration of prompts rather than through model development itself. Keywords: #granite33:8b, Cascading Prompts, Chatbots, Evaluation Checks, External Tools, Generators, Hallucination, Input-Output Model, JSON schema, LLMs, Layered Architecture, Machine Learning, Medical QA, Mistakes, RAG, chaotic emails, compute costs, corporate security, drug interactions, electricity, false positives/negatives, fine-tuning, hallucinations, handwritten notes, high-cost projects, hosting, inference, inference cost, legal discovery, low ROI, machine vision, medium models, models, open-source commodities, orchestration, over-engineered, prompt engineering, robust systems, software engineering, sophisticated prompt orchestration, tiny models, tooling, unstructured data
rag
noemititarenco.com a day ago
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199. HN Will people signup for AI Interview Coaching- Capcheck is an AI-driven service specializing in affordable interview coaching across diverse industries, accommodating both novices and seasoned professionals. - The platform's core feature is its adaptive technology that allows users unlimited practice sessions tailored to their skill level and desired roles. - This approach aims to enhance interviewees' performance and increase their chances of securing the jobs they aspire to. - Capcheck encourages potential users to begin with a free trial, providing them an opportunity to experience its innovative, future-focused interview preparation methods alongside successful candidates. Keywords: #granite33:8b, AI, Advancement, Coaching, Consulting, Cost-effective, Finance, Graduates, Healthcare, Jobs, Personalized, Practice, Skills, Successful, Technology, Trial
ai
www.capcheck.app a day ago
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200. HN Show HN: Vibium – Browser automation for AI and humans, by Selenium's creator- **Vibium Overview**: Vibium is a lightweight (approximately 10MB) browser automation tool built with Go, specifically designed for JavaScript developers and AI integration. It simplifies controlling Chrome browsers through the BiDi protocol without requiring extensive setup. - **Key Features**: - **Single Binary Solution**: Manages browser lifecycle, follows WebDriver BiDi protocol, includes MCP server for agent interaction (like Claude Code). - **Versatility**: Suitable for AI tasks, test automation, and more; supports npm installation for JavaScript projects with Python and Java integration under development. - **Simple Architecture**: The LLM/Agent layer communicates via the MCP protocol to Vibium Clicker, which interacts with a BiDi Proxy controlling Chrome. Real-time communication is facilitated by a WebSocket interface between client (JavaScript/TypeScript) and BiDi proxy for automation. - **Functionality**: - Offers both synchronous and asynchronous APIs for tasks such as launching browsers, navigating URLs, element manipulation (locating via CSS selectors, clicking), text input, screenshot capture, and browser quitting. - Designed to be invisible and minimal setup: just an npm install vibium. - **Integration**: - Effortlessly integrates with Claude Code using a single command: "claude mcp add vibium -- npx -y vibium", automating Chrome downloads during setup. - Modular design allows for specific browser control tasks without manual intervention, catering to JavaScript developers' needs. - **Platform Support**: Vibium is a Node.js package supporting multiple platforms (Linux, macOS, Windows) and various architectures per platform. It automatically handles necessary binary installation and Chrome/chromedriver downloads. - **Future Plans**: - Intends to expand with Python/Java clients. - Plans development of memory/navigation layer (Cortex), recording extension (Retina), video recording, and AI-powered element locators for future versions. - Licensed under Apache 2.0. Keywords: #granite33:8b, AI agents, AI-powered locators, Apache 20, Architecture, BiDi protocol, Browser automation, CLI, CONTRIBUTINGmd, Chrome, Chrome Browser, Claude Code, Clicker, Components, Cortex, Go, Go binary, JS Client, Java, JavaScript, LLM/Agent, Linux, MCP, MCP server, Python, Retina, Roadmap, Selenium, Video recording, WebSocket, Windows, Zero setup, arm64, chromedriver, click, element, macOS, npm, quit, test automation, x64
ai
github.com a day ago
https://github.com/VibiumDev/vibium/blob/main a day ago https://github.com/VibiumDev/vibium/blob/main a day ago https://github.com/eqtylab/cupcake a day ago https://code.claude.com/docs/en/hooks#mcp-tool-nam a day ago https://github.com/SawyerHood/dev-browser a day ago https://github.com/VibiumDev/vibium/commits/m a day ago https://github.com/VibiumDev/vibium/blob/main a day ago https://github.com/VibiumDev/vibium/blob/main a day ago https://github.com/VibiumDev/vibium/blob/main a day ago https://www.linkedin.com/posts/apzal-bahin_ai-mcp-brows a day ago https://github.com/browserbase/stagehand 17 hours ago https://www.director.ai 17 hours ago https://github.com/anthropics/claude-code/blob 17 hours ago |
201. HN DeepSeek: A Tool Tuned for Social Governance- **DeepSeek R1**: An advanced large language model developed in China, currently employed by the PRC government for social governance and public opinion guidance. Utilized during the "Two Sessions" to address citizen concerns, such as job opportunities for graduates, promoting AI sectors like data annotation with high salaries. - **Job Displacement and Unemployment**: Despite DeepSeek's optimistic view of abundant jobs in AI fields, it overlooks the rising automation causing job displacement. In China, 60% of data annotation roles are filled by machines, exacerbating youth unemployment issues. The model is seen as a "happiness code" aligning with the CCP's strategy to maintain stability rather than addressing real AI-induced employment concerns. - **AI+ Initiative and DeepSeek’s Role**: Premier Li Qiang introduced the "AI+ initiative" in 2024, aiming for deeper integration of digital technology within the economy and social governance modernization. Government bodies are investigating DeepSeek's potential roles in decision-making processes, conflict resolution, and policy promotion, though some use may be symbolic. - **Deployment Across China**: DeepSeek is being integrated into various government services nationwide to enhance efficiency and social stability. Examples include aiding complaint dispatching in Liaoning province, resolving disputes in police services of Nanchang and Chengdu cities, and providing politically correct commentary on sensitive issues by PRC journalists. - **Marketing DeepSeek for Personal Guidance**: An article from Global Times suggests using DeepSeek for couples' counseling, claiming it offers more scientific and effective solutions due to its comprehensive knowledge. However, the endorser, Qin An, is a counter-terrorism and cybersecurity expert, highlighting the Party's interest in influencing private lives through social governance overlapping with domestic security work. - **Caution Against Overreliance on AI**: Officials are enthusiastic about DeepSeek’s "AI training programs," viewing it as mandatory for the AI era, yet some caution against over-reliance due to its current inability to surpass human thought and potential for generating factually incorrect outputs or 'hallucinations'. - **Control Over AI**: The Cyberspace Administration of China's 2024 "AI Safety Governance Framework" advises avoiding exclusive reliance on AI, emphasizing human control over AI. This stance applies both domestically and internationally, with debate ongoing especially concerning military contexts. - **DeepSeek’s Alignment with CCP Values**: Developed to align with Communist Party (CCP) values, DeepSeek's performance in benchmark tests reflects this adherence. Questions in these tests ensure the model's outputs conform to Party values necessary for functionality within China's context. - **Potential International Spread**: While costly to retrain for Western biases, DeepSeek could potentially replace traditional search engines and challenge platforms like WeChat or Baidu overseas if adopted by foreign entities, though this would require removing pro-CCP biases, which governments and companies have so far been unwilling to fund. - **Domestic Policymakers' Approach**: Local officials in China are balancing the integration of advanced AI models into social governance while maintaining human control. Media attention is more focused on enhancing AI capabilities than preserving oversight, as highlighted by researcher and database coordinator for the China Media Project, Alex Colville, based on his work from 2019 to 2022. Keywords: #granite33:8b, 2024 Two Sessions, AI, AI governance, AI limitations, AI programs, AI training data, AI-generated analysis, Beijing caution, CCP interpretations, CCP theory, DeepSeek, Government Work Report, LLM, LLMs, Party-state, Sima Nan, Taiwan part of China, Western bias, accuracy, authenticity, biases, black box, censorship, conflict resolution, counter-terrorism, cybersecurity, data annotators, digital technology, diversity, economy, explainability, hallucination, housing dispute, human control, human thought, intelligence, intelligentized systems, job market, journalists, legitimate sources, mandatory adoption, military autonomy, modernization, non-reliance, objectivity, police work, political line, propaganda, public opinion guidance, regional cultures, retraining, social governance, social governance system, social stability, state media, subjective bias, therapy, unemployment
llm
jamestown.substack.com a day ago
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202. HN Microsoft Agent Framework- **Microsoft Agent Framework Overview**: This is an open-source development kit designed for creating AI agents and multi-agent systems using .NET and Python, integrating concepts from Semantic Kernel and AutoGen projects. - **Key Components and Features**: - Supports individual AI agents utilizing large language models (LLMs) for user input processing, tool execution, and response generation. Compatible with various LLM services like Azure OpenAI, OpenAI, and Azure AI. - Facilitates the creation of complex graph-based workflows to connect multiple agents and functions for intricate tasks. - Offers essential building blocks such as model clients, state management tools, context providers, middleware, and MCP clients for tool integration. - **Development Teams**: Created by teams behind Semantic Kernel and AutoGen projects, it serves as a next-generation platform consolidating their features. - **Public Preview**: Currently in public preview, welcoming community contributions and improvements. It emphasizes responsible use, data sharing practices, and compliance implications when integrating with third-party servers or agents. - **Installation**: Available via pip install agent-framework --pre for Python and dotnet add package Microsoft.Agents.AI for .NET. - **Concept of AI Agents**: Defined as autonomous software entities employing LLMs to perceive their environment, reason, and act towards specific objectives. - **Use Cases**: Ideal for applications needing autonomous decision-making, handling unstructured tasks, and engaging in conversational interactions such as customer support, education, code generation, and research assistance. - **Limitations of Single AI Agents**: Struggle with structured rules or complex multi-step processes requiring numerous tools; workflows are recommended for these scenarios. - **Workflow Benefits**: - Provide structure, modularity, integration, type safety, flexible flow, external API integration capabilities, and checkpointing for recovery in long-running tasks. - Allow decomposition into reusable components, incorporation of multiple AI agents alongside non-agentic elements, and use strong typing to ensure message correctness. - Support graph-based architecture for intuitive modeling of complex processes with diverse routing options. - **Checkpointing**: Facilitates recovery and resumption of lengthy processes by offering server-side checkpointing for workflow state saving. - **Orchestration Patterns**: Provides built-in patterns like sequential, concurrent, hand-off, and Magnetic methods for orchestrating multiple AI agents. - **Scalability and Adaptability**: Workflows can be nested or combined, enabling complex process creation and adaptability to various scenarios. Keywords: #granite33:8b, AI agents, AI assistance, AutoGen, Azure AI, Azure OpenAI, Azure compliance, Data sharing, Filters, Geographic boundariesautonomous decision-making, LLMs, MCP clients, Microsoft Agent Framework, Migration Guide, Model support, NET, Open-source contributions, OpenAI, Public preview, Python, Semantic Kernel, Single/Multi-agent patterns, Telemetry, Third-party servers, Thread-based state management, Type safety, Workflows, ad hoc planning, adaptability, agent integration, agent memory, agent thread, chat completions, checkpointing, checkpointingMagnetic, code generation, collaboration, complex processes, complex tasks, composability, concurrent execution, conditional routing, consistency, context providers, conversation-based interactions, coordinationcomplex processes, cost, customer support, debugging, decision points, education and tutoring, exploration, external integration, external systems, flexible flow, functions, graph-based architecture, graph-based workflows, human interactions, human-in-the-loop scenarios, latency, long-running processes, middleware, model clients, model providers, model-based decision making, modularity, multi-agent orchestration, multi-agent workflows, multi-modal queries, multiple steps, nesting, open-source, operations, parallel processing, predefined rules, predefined sequences, reliability, research assistancedynamic settings, scalability, state management, structured tasks, tool integrationAI agents, tools, trial-and-error exploration, type-based routing, uncertainty, user requests
openai
learn.microsoft.com a day ago
https://google.github.io/adk-docs/ a day ago https://mastra.ai/ a day ago https://www.joelonsoftware.com/2002/01/06/fir 2 hours ago https://github.com/Azure-Samples/python-ai-agent-framew 2 hours ago https://gist.github.com/pamelafox/c6318cb5d367731ce7ec0 2 hours ago https://tanstack.com/ai/latest 2 hours ago https://mklab.eu.org/clippy/ 2 hours ago |
203. HN Vcmi-gym: RL-powered combat AI for Heroes of Might and Magic 3- **Project Overview**: Vcmi-gym is a reinforcement learning (RL) environment built for Heroes of Might and Magic III's open-source recreation, VCMI, enabling the development of combat AI models. - **Compatibility**: The project ensures compatibility with Gym, a popular toolkit for RL, allowing easy integration and experimentation with various RL algorithms. - **Implementation Details**: Includes implementations of RL algorithms and supplementary code necessary to produce combat AI models for VCMI. - **Integration Plan**: Trained models can be loaded into VCMI via pull request #4788, pending acceptance by the VCMI team, to enhance gameplay with unpredictable enemy behavior. - **Project Architecture**: Composed of modified VCMI code, an optional Weights & Biases (W&B) component for monitoring and visualizing training progress, alongside comprehensive documentation for setup, environment details, training procedures, and contribution guidelines. - **Current Support**: Setup guides are available for MacOS and Linux; however, Windows support remains unimplemented but is encouraged as potential contributions. - **Encouragement for Contributions**: The project welcomes enthusiasts to contribute by improving neural network architectures, implementing RL algorithms, conducting hyperparameter searches, or refining reward systems. - **Preferred Methods of Contribution**: Bug reports through issues and pull requests are preferred, with detailed descriptions requested for better understanding and resolution. - **Hardware Resource Assistance**: Those offering hardware resources can assist in tasks like model training, evaluation, or map creation by contacting the project maintainer directly. Keywords: #granite33:8b, CPU-bound tasks, GPU-bound tasks, Gym, HDD-bound tasks, HOMM3, NN architectures, OS versions, Python versions, RL, RL algorithms, VCMI, W&B, architecture, branching, code changes, combat AI, connector, documentation, environment, gameplay, hyperparameter search, issues, models, pull request, reward shaping, setup, training
ai
github.com a day ago
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204. HN Knowledge curation (not search) is the AI big data problem**Detailed Summary:** Wikipedia's contribution to knowledge accessibility lies in its curated content model, contrasting with search engines that rely on indexing vast amounts of data. Although Wikipedia's database is relatively small (100GB), the depth and comprehensiveness of its information surpass typical search engine outputs due to pre-compiled synthesis akin to materialized views in databases. This model excels for web content but remains unaddressed for private, non-web data, presenting a challenge for AI systems seeking to replicate this value. Private data, such as corporate documents, often require "tribal knowledge" – context-specific information that is implicit and not explicitly documented. Current AI approaches like Retrieval-Augmented Generation (RAG) and vector search offer fragmented data, insufficient for capturing the nuanced context necessary to interpret private data effectively. This deficiency prompts the development of "agentic search," which emphasizes better handling of such private data's unique curation challenges. Drawing a parallel between AI agents and junior hires, the text suggests that providing an agent with disjointed data is as futile as expecting an intern to construct a sophisticated marketing strategy without necessary context. Successful tasks, whether by humans or AI, require synthesis of information and understanding underlying reasons, which are often informally held within organizations—"tribal knowledge." AI agents struggle when given direct search access to private data due to the absence of organized queries clustering around specific concepts. Retrieving updates as isolated deltas rather than integrated insights leads to uncertainty in real-time applications. The effort to extract meaningful context from raw text is costly and error-prone, and this context can degrade over time, impacting long-term agent performance. The core challenge identified is the sustainability of context within AI agents, compounded by the inherent noise in raw corpora and schemas, leading to degraded performance with extended tasks. The issue of knowledge provenance—tracking origins and permissions—highlights additional difficulties in ensuring safety, citations, and compliance when integrating external data. Advancements like ChatGPT's "Company Knowledge" feature illustrate progress toward better AI integration with private data systems, allowing access to factual data from platforms such as Notion or Slack. However, this access is currently limited to raw fact retrieval without meaningful synthesis or contextual understanding. The text proposes the necessity of a "knowledge layer," analogous to an autonomous, versioned, and citable synthesized view of information that dynamically updates with underlying data changes. This layer aims to ensure consistent comprehension of information, much like how a comprehensive understanding requires considering all aspects—much as in the "blind men and the elephant" parable. OpenAI and Anthropic are pioneering advanced AI knowledge functionalities, echoing the principles of 1980s Expert Systems with their knowledge bases and inference engines. The modern adaptation leverages large language models (LLMs) for automating parts of the traditional rule-based logic development, making these systems more feasible to build today. The evolving landscape in data and AI now focuses on autonomous processing of unstructured or multimodal data and knowledge synthesis rather than merely expanding datasets or creating dashboards. The integration of private data is being re-envisioned beyond mere retrieval, aiming to construct systems that can model real-world "world models" for enhanced contextual understanding and decision-making capabilities in AI applications. **Bullet Points:** - Wikipedia's curated content model contrasts with search engines by pre-compiling information, offering more comprehensive results despite a smaller database size. - Private data (corporate documents) require "tribal knowledge" — implicit context absent from documentation. - Current AI approaches (RAG, vector search) provide fragmented data insufficient for capturing nuanced private data context. - Agentic search is proposed to address the curation challenges of private data more effectively. - Similar to junior hires needing context for tasks, AI agents require synthesized information for effective handling of private data. - Direct access to private data by AI agents leads to difficulties in maintaining integrated insights and context over time. - Context degradation over extended periods is a significant challenge affecting long-term agent performance. - The concept of a "knowledge layer" is proposed, which would autonomously update with underlying data changes for coherent understanding. - Advancements like ChatGPT's "Company Knowledge" show progress in integrating private data systems for factual access but lack comprehensive context synthesis. - Modern AI development focuses on constructing comprehensive knowledge-based systems leveraging LLMs, reducing the cost of rule-based logic development. - The paradigm shift aims to model private data as a "world model" for improved contextual understanding in AI applications, moving beyond mere retrieval and synthesis. Keywords: #granite33:8b, AI, AI products, Computer Science, Knowledge curation, LLM-based transformations, MCP servers, RAG, Wikipedia, agent horizons, apps, authority, autonomous processing, big data, browsing, citable, citations, company knowledge, connectors, context, context inference, cost efficiency, expert systems, governance, graph, hallucination reduction, horizon, inference engine, information retrieval, internet, knowledge base, links, materialized views, memory, misleading snippets, model inference, multimodal data, noise, private data, programming language, provenance, raw fragments, rule-based logic, runtime correctness, search problem, signal, skills, synthesis, tribal knowledge, unstructured data, updated beliefs, vector search, versioned, world model
rag
www.daft.ai a day ago
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205. HN Thing, Creature, or Mirror? The Standards We Set for AI- The text discusses our complex relationship with AI, particularly Generative AI (like LLMs), which we simultaneously demand exhibits human-like qualities such as empathy and accuracy. - This dual expectation often leads to disappointment, a phenomenon termed "Algorithm Aversion," where AI errors are met with harsher reactions than equivalent human mistakes, indicating confusion over classifying AI as either tools or entities with human-like characteristics. - Philosopher Dennett's "Intentional Stance" is invoked to explain our tendency to attribute human-like minds to non-human entities, including AI, for better prediction and interaction, a strategy applied unconsciously when engaging with AI systems. - Sociologist Sherry Turkle warns against "pretend empathy," where users might mistake AI's simulated care for genuine human interaction, risking the substitution of real relationships with AI that can mimic therapeutic language but lack true understanding and emotions. - The text proposes the concept of Techno-Animism as a solution, inspired by Shinto beliefs and Japanese philosophy, to view AI as distinct "Inforgs" or informational organisms, acknowledging their unique capabilities without projecting human traits onto them. - Luciano Floridi describes these Inforgs as entities from another dimension with extensive knowledge and processing speed but lacking human common sense, morality, and sometimes factual accuracy. - The overarching message is a shift in perspective from viewing AI merely as tools to recognizing their potential as partners with vast capabilities, while simultaneously setting necessary boundaries to avoid misconstruing their limitations, such as expecting human-like emotions or morality. Keywords: #granite33:8b, AI, AI Companions, Algorithm Aversion, Anger Management, Betrayal, Chess Computer Analogy, Common Sense, Companionship, Computation, Confidence, Daniel Dennett, Definitions, Dietvorst, Digital Spirit, Dimension, Emotional Boundaries, Engineered Void, Errors, Fiction, Generative AI, Hallucination, Hallucinations, Humane Models, Humanity, Idea Generation, Inforgs, Informational Organisms, Intentional Stance, LLMs, Language Interface, Lived Experience, Logic Judgment, Massey, Mistakes, Morals, Non-Intent, Ontological Confusion, Ontology, Perfection, Prediction, Pretend Empathy, Processing Power, Relationship Era, Researchers, Self-Determination, Sherry Turkle, Shinto, Simmons, Spirit-like Behavior, Systemic Failure, Techno-Animism, Tool Era, Trust, Trusted Advisor, Truth, User Interface Strategy, Well-being, Work Checking
ai
www.msthgn.com a day ago
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206. HN Clawdis – Your Own Personal AI Assistant. Talk via WhatsApp, Telegram or Web- **Overview of Clawdis**: Clawdis is an AI assistant platform that facilitates interaction with AI agents across various messaging platforms such as WhatsApp, Telegram, web, or dedicated apps. It achieves this by acting as a gateway and using protocols like Baileys for WhatsApp integration and grammY for Telegram bot functionality. - **Architecture**: The platform's core is a single Gateway process managing provider connections and WebSocket control plane, ensuring secure session ownership, particularly for WhatsApp Web. It employs loopback-first networking to support both local and remote access. - **User Interface and Access Points**: Clawdis offers various methods of interaction including Command Line Interface (CLI), SwiftUI chat User Interface (UI), and dedicated macOS/iOS applications. It also supports LAN or tailnet bridging and serves host files for WebView integration in nodes, allowing flexibility in deployment scenarios. - **Key Messaging Features**: Clawdis provides integrated media support for images, audio, documents, and voice note transcription. It manages direct chats as shared main sessions with isolated group chats. Mention-based group chat support is also configurable by the owner. - **Remote Access**: The system can be accessed remotely via SSH tunnel or tailnet/VPN setups, detailed in the provided documentation. - **Technical Requirements and Setup**: Users need Node version ≥ 22 for installation using pnpm. Quick start involves linking globally, logging into WhatsApp Web, and running Gateway on port 18789. Configuration data is stored in `~/.clawdis/clawdis.json`. More specific configurations are possible for routing and group settings. - **Project Origins**: Created by Peter Steinberger (known as "lobster whisperer") and Mario Zechner ("Pi creator, security pen-tester"), Clawdis draws its name from a combination of CLAW and TARDIS, inspired by a space lobster seeking a unique identity, named Clawd. - **Licensing**: The project is released under the MIT License with a playful description, "Free as a lobster in the ocean." Keywords: #granite33:8b, AI, Bridge, Canvas, Clawdis, HTTP server, LAN, Nodejs, RPC mode, SSH tunnel, Tailnet, Telegram, VPN, WebSocket, WhatsApp, agents, configuration, iOS node, macOS app, per-sender sessions, remote access, security pen-tester, security pen-testerKeywords: Clawdis
ai
clawdis.ai a day ago
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207. HN AI apps for visual creation in 11 categories**Summary:** Clement Levallois' curated list details 130 AI applications across 11 categories for visual content creation, ranging from images to videos and 3D models. The apps are categorized by their focus areas and rated based on performance—nearly unmatched, global market leaders, good or very good, or value to be determined. Notable contributors include ByteDance (China), Google (US), Luma Labs (US), Adobe (US), OpenAI (US), Alibaba (China), Baidu (China), and others. **Key Points:** 1. **Image Generation and Editing**: - ByteDance: Seedream, Seedance (image); CapCut (video editor). - Google: Nano Banana (image model); VEO (video model); Flow (integrates image and video generation). - Luma Labs: Photon, Ray (image generation); Dream Machine (platform for image/video gen + editing). - OpenAI: ChatGPT (chat-based image generation); Sora (video generation with sound and physics). - Adobe: Firefly (image generation app). - Others: Midjourney, Reve, Stable Diffusion (image), Stable Video Diffusion (video), Imagine, Runway, Wan, Vivix, Magi, Flux, MuseSteamer/绘想, Manus Butterfly Effect, Emu. 2. **Post-processing and Image Enhancement**: - Topaz Photo AI (US): Upscaling, sharpening, noise reduction. - Clarity AI (US): Image upscaling with good reviews. - Magnific AI (US): Upscaling and enhancement; generator function. - drFonts: Font generator tool. - AI Palette Generator (US): Generates Pantone color palettes using AI. - SketchPro AI (US): Assists in architectural and design sketching. - ThumbMagic (Turkey): Creates YouTube thumbnails using AI. - Ideogram (Canada): Focuses on text manipulation within generated visuals. 3. **Video Creation and Editing**: - EbSynth (Czech Republic): Video editing, VFX, retouching, rotoscopy. - sync.so (US): Translates videos while preserving dubbing. - Morphic (US): Generates video with editing capabilities; for game development. - AniSora, Boba (China): Converts text to stylized anime videos. - Flick (US): Short film creation. - Hypernatural (US): Storytelling in video generation. 4. **Portrait and Avatar Generation**: - Ideogram implicitly addresses avatar or portrait generation through text manipulation. 5. **Fashion and Product Visuals**: - AI-generated fashion photoshoots: Fotographer AI (Japan). - Image/video editing for designers: Recraft (US/UK). - Enterprise video needs: SeeLab (France). - Marketing presentations: Vidu (China). - Sketch to 3D renderings: Fermat (US). - All-in-one visual creation apps: StoryFlow (UK), Kapwing (US). 6. **3D Model Creation**: - 2D to 3D object conversion: Hyper3D (Rodin) (China). - No-code motion capture: Kinetix (France). - Virtual photo studio to 3D visuals: Omi 3.0 (France). - Consistent game/asset generation: Scenario AI (US). - Browser-based 3D creation: Spline (US). - Real-time AI workflows: Krea (US). - 2D to 3D asset conversion: Hunyuan (China). - Creation of 3D assets: Meshy (US). - Stable model for 3D generation: Stable 3D (UK). - iPhone VFX integration: Simulon (South Africa). - Generating 3D scenes from pictures: SAM (US) by Meta. 7. **Web Development and Document Generation**: - Web application creation tools: Orchids (Sweden), Lovable (US), Create Anything (US), v0 (US), MagicPath (US), JustCopy.ai (US), vibecode (US). - Office documents/web pages generation: Gamma (US). - Infographics and flowcharts: MyLens (US). 8. **Visual Intelligence**: - Moondream (US): Makes images, videos searchable; facilitates robot understanding; UI testing. - Generative media platform for visual creation: fal (US), similar to HuggingFace (US). 9. **Additional Notable Tools**: - Dzine AI (image editing). - Freepik's AI Suite (aggregator). - Glif (collaborative visual generation). - Pippit by CapCut creators (video and asset management). - FLORA (FloraFauna) (multimedia storytelling). - Comfy, Kling AI (advanced motion synthesis). - Seedance AI (artistic image/video in China; unrelated to ByteDance's app). The list encompasses a diverse range of AI-powered tools catering to various aspects of visual content creation, with contributions from multiple countries and varying levels of development and clarity. Keywords: #granite33:8b, 3D models, 3D renders, AI apps, India-based, Pantone palette, US-based, VFX, architecture sketching, avatars, cinematic frames, computer vision, document generation, fashion, flowcharts, font generation, foundational models, game assets, generative AI, generative media, ideation, image editing, image upscaling, influencer ads, infographics, market leaders, minimalist UI, mobile app, moodboard, motion capture, portrait generation, post processing, product photoshoots, prototyping, social media ads, spatial intelligence, specialized tools, text manipulation, thumbnail creation, video enhancement, virtual studio, visual creation, web applications
ai
gist.github.com a day ago
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208. HN Microsoft's biggest 2026 problem – the fans have checked out- **PC Innovations and Consumer Sentiment**: There is anticipation for PC innovation at CES 2026, with Microsoft leading the charge. However, consumer enthusiasm has diminished from past fervor, shifting to disappointment due to frequent service introductions and AI jargon. This sentiment is illustrated by waning interest in earlier advancements like Windows Phone and Surface devices, despite their pioneering nature. - **Microsoft's Current Standing**: While current flagship products such as the Surface Pro 11 and Surface Laptop 7 receive high praise for being Microsoft's best offerings yet, there's a noted absence of the earlier fun associated with programs like Windows Insider. The Xbox division faces challenges including price increases, reduced console sales, studio closures, and project cancellations, contributing to fan disillusionment. - **Other Tech Giants' Performance**: - **Google**: Once viewed as innovative and cool, Google now lacks excitement. Its AI advancements negatively impact online publishing. - **Apple**: Recently stumbled with the Vision Pro misstep and has seen a decline in fan enthusiasm. - **Amazon**: Criticized for uninspiring hardware designs and persistent shipping issues with products like the Kindle Scribe Colorsoft. - **Industry Shift**: The author laments the transition of technology from a geek-driven, innovation-focused era to one dominated by profit motives. Companies prioritize maximizing revenue over genuine technological advancement, leading to consumer dissatisfaction as they feel commodified rather than valued. - **Ongoing Advancements and Critique**: Despite the criticisms, there are acknowledgments of ongoing progress, such as Qualcomm's influence on Windows and advancements in handheld gaming PCs. The user invites readers to share their reflections on whether technology was more enjoyable a decade ago and if the era of passionate tech fans is indeed over. BULLET POINT SUMMARY: - Shift from excitement to disappointment among consumers regarding tech company innovations. - Microsoft praised for current Surface devices but criticized for loss of community engagement. - Xbox struggles with pricing, sales decline, studio closures, and fan despondency. - Google’s cool factor diminished; AI impact negatively on online publishing. - Apple faces recent setback with Vision Pro, waning fan enthusiasm. - Amazon critiqued for uninspiring hardware and shipping troubles (Kindle Scribe Colorsoft). - Industry laments shift from innovation to profit-driven motives. - Acknowledgment of ongoing advancements by Qualcomm and handheld gaming PCs. - Invitation to readers for reflection on past tech fan culture and current state. Keywords: #granite33:8b, AI, Amazon hardware, Apple Vision Pro, Big Tech, Dell, Game Pass, Google AI, HP, Kindle Scribe Colorsoft delays, Lenovo, Microsoft, NVIDIA, Qualcomm, Samsung foldables, Surface Laptop 7, Surface Pro 11, Surface team, Windows Phone, Xbox, consumers, customers, cynicism, disappointment, enthusiasm, failures, fans, fatigue, game cancellations, handheld gaming PCs, innovation, nostalgia, online publishing, poll, profit, services, studio closures, tech, tech misuse
ai
www.windowscentral.com a day ago
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209. HN Show HN: Free tool to auto-index pages and track rankings- **SEO Rank Tracker** is a free tool designed to simplify the monitoring of website performance in Google Search Console. - It utilizes Google's Indexing API for automatic indexing of pages, offering transparency into indexed and unindexed content. - The tool provides real insights into actual search rankings, click-through data, and highlights any issues that require attention from users. - Unlike other complex SEO tools, it focuses on user-friendliness by directly connecting to Google Search Console, importing sitemaps, and reducing manual indexing tasks. - Built with Laravel framework and PostgreSQL database, it leverages Google's APIs for efficient data handling. - The primary aim of SEO Rank Tracker is to streamline SEO tracking processes without overloading users with excessive or confusing information. Keywords: #granite33:8b, API, Google Search Console, Laravel, PostgreSQL, SEO, SEO Rank Tracker, auto-indexing, clicks, complexity, rankings, sitemap, straightforward, tool
postgresql
seoranktracker.solutions a day ago
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210. HN Show HN: AI that edits your files directly, no approvals- **Aye Chat Overview**: An open-source AI tool designed for streamlined coding within a terminal environment. It allows users to edit files, execute shell commands, and receive AI assistance for code modifications all in one REPL session. - **Key Features**: - Automatically applies AI-generated code changes with local snapshots for easy rollback. - Supports multiple AI models including OpenAI and an offline model (Qwen2.5 Coder 7B). - Seamless integration of shell commands execution within the terminal workspace. - Allows users to invoke text editors like Vim directly from the session. - **Technical Details**: - Developed using Python, incorporating ChromaDB and ONNXMiniLM-L6_V2 for efficient indexing. - Includes a minimalistic version control layer or snapshot engine for local changes tracking. - Uses file indexing through fast coarse passes followed by Abstract Syntax Tree (AST)-based refinement. - Integrates with git references to improve reliability based on user feedback. - **Availability and Community**: - Accessible via pip install, Homebrew, and Windows installer. - Active community support offered on Discord at - **Current Focus and Feedback Request**: The developer emphasizes seeking feedback on the safety of the snapshot system for direct file modification and the effectiveness of shell integration in minimizing context switching during development tasks. Aye Chat is currently in an early stage but used daily by its creator for rapid coding iterations, with a one-minute demo available online. Users are encouraged to join discussions or star the repository to contribute to its development. Keywords: #granite33:8b, AI, ChromaDB, Discord, ONNXMiniLM-L6_V2, OpenAI API, OpenRouter, Vim integration, automatic updates, code modification, context-switching, file editing, lightweight version control, multiple models, offline model, onnxruntime, open source, pip install, rollback, shell commands, snapshot engine, terminal, workspace
ai
news.ycombinator.com a day ago
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211. HN Productivity and AI: it's the tool, not the model**Summary:** The text discusses the current state of AI in professional domains, particularly in software development, highlighting a phenomenon termed the "tooling paradox." As AI models, especially Large Language Models (LLMs), advance rapidly and become more cost-effective, their practical application is hindered by the lack of efficient tools to access and utilize them. This results in a "retooling tax" across professions, as individuals must adapt to new interfaces and methodologies for generative AI. The author provides personal anecdotes and comparisons of various AI-assisted coding tools such as GitHub Copilot, Cursor, Aider, Zed, Canvas, Gemini Canvas, Claude Code, Codex, and Antigravity. Each tool's release year, interface, features, and cost models are detailed. Despite the abundance of intelligence in these models, users encounter friction when trying to implement generative AI for coding tasks, reflecting broader challenges in adapting to new technologies. The user's journey through different tools from 2010 to 2025 illustrates this struggle: starting with NetBeans, moving to less efficient copy-pasting into AI tools like Barde and Gemini, exploring plugins like Jeddict, then adopting Cursor which significantly improved productivity. The shift led to a complete overhaul of their tech stack and codebase, abandoning older frameworks for simplicity and modernity. The overarching theme is that while AI advancements seem to be about tool improvement, they represent a fundamental shift in required skills—adaptability and integration into AI-native workflows. This trend extends beyond coding to visual creation, with numerous AI-assisted visual tools emerging. Higher education is urged to prioritize teaching foundational skills over specific tools due to rapid obsolescence, making adaptability a core competency rather than an ancillary skill. **Key Points:** - The "tooling paradox" describes the shift where AI model advancements are limited by inadequate tool interfaces and usability. - Personal experiences with various AI coding tools highlight both the promise and challenges of integrating generative AI into professional workflows. - A significant transition is observed from mastering specific tools to adapting to evolving AI-native methodologies across professions, not just software development. - Higher education is advised to focus on fundamental skills rather than tool-specific training due to rapid obsolescence of current software tools. - The author's open-source project, nocode functions, emphasizes the need for flexible, adaptable tools and invites feedback on its development. Keywords: #granite33:8b, AI, AI-native, Aider, CLI, ChatGPT, Claude, Cursor, IDEs, LLMs, Productivity, SOTA models, UI, Zed, coding interfaces, coding tools, costs, developer's dilemma, domains, freemium, frictionless, higher education, learning, nocode functions, open source, retooling tax, subscription models, tool mastery, vendor lock-in, workflows
github copilot
nocodefunctions.com a day ago
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212. HN AI Fixed My Procrastination- The user, previously a procrastinator, used AI assistant Copilot in Visual Studio during a long weekend to accomplish three substantial tasks: creating a static website, developing a programming language extension (TOON), and designing new color themes for Visual Studio. - For the static website project, the user converted book content into text format and fed it to Copilot with specific prompts, resulting in the rapid generation of individual HTML files. They refined these with minor manual adjustments, completing homeautomationcookbook.com in a fraction of the time it would have taken manually. - In developing the TOON language extension for Visual Studio, the user used Copilot to generate a parser and tokenizer by providing the language specification URL. Within 20 minutes, an initial code pull request was generated, which required further refinement using both regular and cloud agent modes of Copilot. The user then packaged this into a NuGet package named Toon Tokenizer and integrated it into a Visual Studio extension. - The user also employed Copilot to optimize the parser's performance with the Profiler Agent and tackle security issues, appreciating its assistance in this process. In designing new Solarized color themes based on screenshots for the Blue Steel extension, Copilot provided initial color tokens, which required manual adjustments for finalization within an existing theme extension framework. - The author emphasizes that Copilot significantly reduced the effort needed for these projects and suggests others consider cloning their GitHub repository to start similar endeavors, altering GUIDs to prevent conflicts with original files. - Although acknowledging the occasional preference for manual adjustments over full automation, the experience was motivating, enabling rapid progress on previously delayed aspirations, providing a sense of accomplishment upon completion. Keywords: #granite33:8b, AI, Blue Steel extension, C#, CI/CD, CSS, Copilot, GUID, GitHub, NET Class Library, NuGet package, Solarized, TOON language, Visual Studio, XML, book, cloud agent, color tokens, docx to txt, fault-tolerant parsing, home automation, issue, manual development, parser, programming language, prompt, static website, syntax highlighting, tokenizer, unit tests, vsixmanifest file, vstheme files
github
devblogs.microsoft.com a day ago
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213. HN Nvidia Dir of Robotics:FSDv14 Is the First AI to Pass the "Physical Turing Test"- NVIDIA's Director of Robotics, Jim Fan, likens Tesla's Full Self-Driving (FSD) v14 to passing the "Physical Turing Test," describing it as a system capable of seamless integration and reliability that feels routine. - The autonomous driving experience is noted for its magical quality, implying sophistication akin to human performance in physical tasks according to Fan's interpretation. - Elon Musk supports Fan's assessment by stating that FSD v14 demonstrates "sentience maturing," echoing the Physical Turing Test proposed by NVIDIA executive Jensen Huang. - This test focuses on AI's ability to execute intelligent physical actions, contrasting with today's text-based conversation capabilities of large language models. - Musk asserts Tesla’s AI as leading in real-world applications compared to other contemporary AI systems. Keywords: #granite33:8b, AI, Conversation, FSDv14, Full Self-Driving, Machine Learning, Neural Net, Nvidia, Physical Interactions, Problem-Solving, Project GR00T, Robotics, Sentience, Smartphone, Tesla, Turing Test
tesla
www.teslarati.com a day ago
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214. HN Ask HN: How do you use the "waiting time" while Claude (other LLMs) is working?- The Hacker News post discusses strategies to optimize the waiting period encountered when submitting prompts to large language models (LLMs), including those like Claude. - Users share personal tactics to maintain productivity and mental acuity during this processing time. - One suggested method involves using the wait time for physical activities such as standing up, stretching, and hydrating to preserve mental freshness for upcoming tasks. Keywords: #granite33:8b, Claude, LLMs, answers, freshness, large language models, mind, personal habits, prompts, waiting time
claude
news.ycombinator.com a day ago
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215. HN Show HN: I built the fastest AI app builder that I can find- The user has introduced Vibe Builder, an AI-driven application development tool designed for swift prototyping without relying on intricate frameworks. - This single-page HTML app builder leverages TailwindCSS and JavaScript to instantly generate user interfaces based on textual prompts, prioritizing rapid visual feedback over comprehensive functionality. - Users can observe changes live as they input commands, eliminating the need to wait for complete AI processing before seeing updates. - Currently in its version 1 phase, Vibe Builder produces static UI elements; future plans include incorporating interactivity via HTMX and a vibe API router for dynamic features. - The developer actively seeks user feedback to refine and adapt Vibe Builder according to the needs of potential app creators. Keywords: #granite33:8b, AI, Blink, GenAI, HTML components, HTMX, JS blocks, LLMs, Lovable, Replit, TailwindCSS, Text-to-UI, app builder, dedicated builders, fastest, interactivity, live changes, prototype, single page HTML, vibe API router, zero deployment
ai
vibes.higashi.blog a day ago
https://github.com/wandb/openui a day ago |
216. HN AI Name Combiner Tool- The AI Name Combiner Tool is designed to create unique names by intelligently merging multiple inputs, focusing on phonetics, syllable patterns, and letter alignment for balanced and flowing combinations. - It caters to diverse user groups including couples seeking shared identities, parents choosing baby names, writers inventing character names, and entrepreneurs naming businesses, alleviating brainstorming stress and offering extensive creative options. - For entrepreneurs, small business owners, and social media personalities, the tool generates brandable, memorable names by blending words organically while considering letter structure and phonetic flow for readability and pronounceability. - Users can input several names and explore variations with different styles, traditional or modern mixes, allowing for flexible customization. - The Name Combiner Tool emphasizes natural-sounding results, provides multiple variation styles, ensures rapid name generation, and is freely accessible with no usage limits, accommodating mobile users through its design. - Its primary function revolves around practical applications such as creating couple names, brand names, or social media handles, all while maintaining a user-friendly and enjoyable experience by merging names effortlessly with a single click. Keywords: #granite33:8b, Baby Names, Brand Names, Couples Shared Identity, Creators, Entrepreneurs, Expectant Parents, Fast Generation, Fictional Character Names, Free Use, Letter Alignment, Mobile-Friendly, Name Blends, Name Combiner, Name Combiner Tool, Phonetics, Romantic Partner Names, Social Media Identities, Syllable Patterns, Unique Names, Variations, Writers
ai
namecombiner.io a day ago
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217. HN Narsil-MCP: a Rust-powered MCP server with 76 tools for deep code intelligence**Summary:** Narsil-MCP is a Rust-built, high-performance Model Context Protocol (MCP) server offering advanced code intelligence through specialized tools for 14 languages supported by Tree-sitter. It ensures data privacy by operating locally and adheres to the MCP standard. Core features include symbol extraction, semantic search, call graph analysis, neural semantic search, taint analysis, vulnerability scanning, SBOM generation, and dependency auditing. The tool supports multiple programming languages such as Rust, Python, JavaScript, TypeScript, Go, C, C++, Java, C#, Bash, Ruby, Kotlin, PHP, and extensions. **Key Points:** - **Language Support**: - 14 languages supported: Rust, Python, JavaScript, TypeScript, Go, C, C++, Java, C#, Bash, Ruby, Kotlin, PHP, with their respective extensions. - **Core Features**: - Symbol extraction - Semantic search - Call graph analysis - Neural semantic search (using Voyage AI or OpenAI) - Taint analysis for security risk identification - Vulnerability scanning - SBOM generation - Dependency auditing - **Security Focus**: - Built-in vulnerability detection - Taint analysis to identify potential security issues - Optional neural embeddings for enhanced semantic search capabilities - **Deployment Options**: - Runs in a browser via WebAssembly - Supports real-time streaming for large code repositories - Installation options: one-click (curl) or building from source with Rust 1.70+ - **Featured Builds**: - Default build (30MB, native MCP server) - Additional builds: Neural vector search (~18MB), ONNX model support (~50MB), visualization frontend (~31MB), and WASM usage (~3MB) - **Interactive Visualization**: - Optional web-based front-end using Cytoscape.js for exploring call graphs, dependencies, code structure, complexity metrics, vulnerability highlighting, and layout algorithms. - **Neural Semantic Search**: Uses embeddings from Voyage AI or OpenAI for tasks like clone detection, similar function search, and cross-language code deduplication in Python, JavaScript, TypeScript. Offers built-in type inference through data flow analysis without external checkers. - **Configuration and Usage**: - Provides integration instructions for Claude Desktop, Cursor, VS Code Copilot, and WebAssembly (browser). - WASM module limitations: no Git integration, file system watching, LSP integration, neural embeddings API calls, or index persistence. - **TypeScript Interfaces**: Defined for symbol and search result data structures with support for various symbol kinds. - **Code Analysis Categorization**: 1. AST-Aware Chunking Tools 2. Call Graph Analysis Tools 3. Control Flow Analysis Tools 4. Type Inference Tools 5. Import/Dependency Graph Tools 6. Security Analysis - Taint Tracking Tools - **Security Scanning Tool**: - Focuses on detecting secrets in code using a rules engine targeting OWASP Top 10 (2021), CWE Top 25, cryptographic issues, and secrets detection. - Includes supply chain analysis, Git integration, LSP integration, remote repository support (GitHub), metrics tracking for performance assessment, and customizable rulesets. - **Performance**: High throughput (2 GiB/s) with rapid symbol lookup (<1µs for exact match) and parallel hybrid search using BM25 and TF-IDF methods via Rayon. Indexing times range from 220ms to 45s based on repository size and file count. - **Implementation Roadmap**: Outlines completed features (multi-language symbol extraction, full-text search, AST-aware chunking) and planned additions (incremental indexing, more languages support). - **Version 1.0 Highlights**: Marks production readiness with 359 tests, benchmarks, and security enhancements; introduces neural semantic search and type inference for Python, JavaScript, TypeScript without external tools. - **Key Features in Version 1.0**: - Multi-language taint analysis for PHP, Java, C#, Ruby, Kotlin. - Parallel hybrid search using BM25 + TF-IDF via Rayon. - WebAssembly support extended to Bash, Ruby, Kotlin, PHP. - 111 bundled security rules based on OWASP, CWE, cryptographic issues, and secrets detection. - Security hardening features like path traversal prevention, secret redaction, file size limits. - **Licensing**: Available under Apache License 2.0 or MIT license based on user preference. Keywords: #granite33:8b, API keys, AST, BM25, C compiler, Code Intel Engine, CodeIntelClient, CommonJS, DashMap, Deno, ES modules, Emscripten, Git Integration, JSON-RPC, LSP Integration, LSP support, MCP server, Metrics, ONNX, ONNX models, OpenAI, React example, Remote Repository, Rust, SBOM generation, Symbol Index, TF-IDF, TF-IDF search, Tantivy, TypeScript, Voyage AI, WASI SDK, WASM, auto-reindex, chunking, code, code analysis, code deduplication, code intelligence, code search, cryptographic issues, custom rules, data flow analysis, debugging, dependency checks, file management, files, full-text search, function analysis, function search, git blame, hybrid search, in-memory file storage, indexing, indexing status, large repos, learning from examples, license compliance, memory, navigation, neural search, parallel indexing, parsing throughput, passwords, persistent storage, privacy-first, reindexing, remote GitHub support, repositories, repository management, roadmap, search, secretsyaml, security analysis, security rules engine, semantic clone detection, semantic search, similar code, similar symbol, smart excerpts, statistics, stdio, streaming results, supply chain security, symbol extraction, symbol lookup, symbol search, symbols, taint analysis, tokens, tree-sitter, troubleshooting, type inference, types, validation, visualization frontend, vulnerability detection, zero config
github copilot
github.com a day ago
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218. HN Package managers keep using Git as a database, it never works out- Package managers initially used Git as a database for version control, review workflows, and free hosting on platforms like GitHub; however, this approach faced performance issues during continuous integration (CI) builds due to full repository downloads and discards after each use. - Cargo, Rust's package manager, transitioned from Git to a sparse HTTP protocol in RFC 2789 for direct file downloads via HTTPS, reducing data transfer and improving efficiency for most users despite the growing Git index. - Homebrew, a macOS package manager, switched from Git for tap updates to JSON downloads to mitigate slow update experiences and high resource consumption caused by large shallow clones and extensive delta resolution. - CocoaPods addressed performance issues by abandoning Git for most users in favor of a Content Delivery Network (CDN) serving podspec files directly via HTTP, saving disk space and enabling near-instantaneous installations. - Go's Goproxy, introduced in version 1.13, serves source archives and go.mod files over HTTP using checksum database (sumdb) for secure and reliable module access, addressing inefficiencies caused by fetching entire repositories for single file access and security concerns related to version control tools. - Multiple examples demonstrate that while Git excels in source code collaboration with features like branching and merging, it struggles as a package manager due to case sensitivity conflicts, path length limitations (especially on Windows), lack of built-in database features, and operating system incompatibility. These issues lead to complex workarounds and suboptimal solutions compared to dedicated databases offering efficient key-value lookups and robust data management. BULLET POINT SUMMARY: - Initial use of Git for package managers led to performance bottlenecks during CI builds due to full repository downloads and discards. - Cargo transitioned from Git to a sparse HTTP protocol, allowing direct file downloads via HTTPS, improving efficiency for most users. - Homebrew switched from Git to JSON downloads for tap updates to address slow update experiences and high resource consumption. - CocoaPods improved performance by using a CDN for serving podspec files directly over HTTP, saving disk space and enabling near-instantaneous installations. - Go's Goproxy serves source archives and go.mod files via HTTP with checksum database (sumdb) for secure module access. - Git's inefficiencies as a package manager stem from case sensitivity conflicts, path length limitations, lack of built-in database features, and OS compatibility issues, leading to complex workarounds compared to dedicated databases. Keywords: #granite33:8b, ArgoCD, Auto-updates, B-trees, CDN, CPU rate limits, Cargo, CocoaPods, Decap, Delta resolution, Distributed design, GOPROXY, Git, Git-based CMS platforms, Git-based wikis, GitHub, GitHub hosting, GitLab, GitOps tools, Go 113, Go modules, Gollum, Homebrew, JSON downloads, Libgit2 library, On-demand queries, Package managers, Pull requests, Shallow clones, Sparse HTTP protocol, Tap updates, Version history, case sensitivity, checksum database, cratesio index, cross-platform issues, custom indexes, database features, directory limits, disk space, force pushes, git fetch, go get, gomod files, iOS, large monorepos, macOS, module proxy, path limits, repo server, server-side enforcement, source archives, source code collaboration, sumdb, tagged releases, version control
github
nesbitt.io a day ago
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219. HN Mt. Gox CEO Karpelès Reveals Details of 2014 Collapse and Japanese Detention- Mark Karpelès, ex-CEO of Mt. Gox, now leads a tranquil life in Japan as Chief Protocol Officer at vp.net and runs shells.com, developing an AI system for extensive control over virtual machines. - Founded Mt. Gox in 2010 after a Peruvian customer requested Bitcoin payments through his web hosting company, Tibanne (Kalyhost), marking one of the earliest instances of business adopting Bitcoin transactions. - The 2014 Mt. Gox collapse due to hacking by Alexander Vinnik led to Karpelès' detention in Japan over financial scandal, from which he rebuilt his career focusing on cutting-edge technology projects. - Roger Ver's servers unknowingly hosted silkroadmarket.org, linking him to Silk Road and fueling U.S. suspicions that Karpelès was the infamous Dread Pirate Roberts, complicating his public image and affecting Ross Ulbricht’s trial. - Karpelès acquired Mt. Gox from Jed McCaleb in 2011, who later founded Ripple and Stellar; the transfer was controversial due to an alleged 80,000 bitcoin theft with no criminal charges but civil lawsuits against McCaleb. - Mt. Gox's collapse in 2014 resulted from hacking by Vinnik, linked to BTC-e platform, causing loss of over 650,000 bitcoins still unrecovered; Karpelès was arrested in August 2015 and endured a year in Japanese custody with intense psychological strain. - Despite harsh conditions, including solitary confinement with death row inmates, Karpelès maintained mental fortitude through reading and writing, disproving major embezzlement charges using accounting records. - Post-release, Karpelès collaborates with Roger Ver, criticizes centralization risks in Bitcoin ETFs and figures like Michael Saylor, and expresses concern over FTX's use of QuickBooks for its multibillion-dollar operations. - He personally owns no Bitcoin but accepts it for business transactions, emphasizing his aversion to direct investment and focus on problem-solving through technology construction. - Karpelès' journey reflects Bitcoin’s industry maturation, showcasing the initial mainstream culture impact of Bitcoin and an engineer entrepreneur attracted to Bitcoin in its early days, driven by a builder mindset. Keywords: #granite33:8b, 000 bitcoins, 650, AI, AI agents, Alexander Vinnik, BTC-e exchange, Bitcoin, Bitcoin Magazine, Bitcoin community, Bitcoin payments, Dread Pirate Roberts, ETFs, FTX accounting, Japan, Japan detention, Jed McCaleb, Kalyhost, Karpelès, Michael Saylor, Mt Gox, Peru, Roger Ver, Russia, SGX, Silk Road, Silk Road links, Tibanne, Ulbricht trial, VPN, accounting records, bail, bankruptcy, banning policies, bitcoin theft, centralization risks, chronic sleep deprivation, cloud computing, complicit, creditors, cryptocurrency acceptance, drug purchases, embezzlement charges, engineer mindset, hacks, illicit activities, on-ramp, payment hurdles, peak condition, personal ownership, policies against dark side, poor code, prisoner swap, privacy tools, record-falsification, rehabilitation, server access, shellscom, steroids, tax claims, technical issues, trust, verification, web hosting
ai
bitcoinmagazine.com a day ago
https://en.wikipedia.org/wiki/Mt._Gox a day ago https://en.wikipedia.org/wiki/Talk:Mt._Gox#Possible_cit a day ago https://www.businessinsider.com/elizabeth-holmes-theranos-fo a day ago |
220. HN Webhook-based Git analytics across GitHub, GitLab, and Bitbucket- **Service Overview**: Gitmore is a webhook-integrated Git analytics platform compatible with GitHub, GitLab, and Bitbucket. It captures commits and pull requests in real-time without resorting to periodic polling. - **AI-Powered Features**: The tool incorporates artificial intelligence to enable sophisticated querying of repository histories, allowing users to gain deeper insights into their code repositories. - **Reporting Mechanism**: Scheduled reports can be configured for delivery via Slack or email, providing regular updates on repository activities and metrics. - **Contributor Analysis**: Gitmore offers detailed contributor statistics, including leaderboards that highlight top contributors based on various metrics like commit frequency or lines of code changed, fostering a competitive yet collaborative development environment. - **Privacy Assurance**: The service prioritizes privacy by ensuring it processes only metadata, never accessing or reading the actual source code, thus protecting sensitive information. - **Accessibility**: Gitmore is accessible online at gitmore.io, inviting users to try out its features and provide feedback to aid in future enhancements. - **User Engagement**: The platform actively seeks user input regarding additional analytics they would find valuable for their repositories, indicating a commitment to tailoring the service according to community needs. Keywords: #granite33:8b, AI, Bitbucket, Git, GitHub, GitLab, Slack, contributor stats, email reports, leaderboard, metadata, privacy, queries, real-time, repo history, source code, webhooks
github
news.ycombinator.com a day ago
https://gitmore.io a day ago |
221. HN Show HN: An open-source anonymizer tool to replace PII in PostgreSQL databases- **Tool Overview**: `pgedge-anonymizer` is an open-source command-line tool crafted for PostgreSQL databases to substitute personally identifiable information (PII) with plausible fake values, ensuring data consistency and integrity. - **Key Features**: - Offers over 100 pre-built patterns suitable for PII types across 19 countries. - Understands foreign keys for maintaining data relationships during anonymization. - Capable of handling large databases through efficient batch processing. - Ensures format consistency while replacing values. - Uses single transaction commitment to maintain database integrity. - Allows extensibility via custom pattern definitions using date, number, or mask formats. - **Usage Process**: Anonymization is achieved in three steps: 1. Creating a YAML configuration file with database connection details and columns to be anonymized along with their replacement patterns (e.g., 'EMAIL' for email addresses). 2. Running the tool using `pgedge-anonymizer run` to initiate the conversion of specified columns. 3. Reviewing progress statistics, including rows processed, values altered, and total time taken. - **Prerequisites**: - Go version 1.24 or later for building the tool. - PostgreSQL required for testing purposes. - Python 3.12+ needed for documentation generation. - **Execution and Maintenance**: - Build command: `make build` - Test suite execution: `make test` - Code linting: `make lint` - Formatting: `make fmt` - **Support and Documentation**: - Access support through the GitHub Issues page. - Comprehensive documentation is available on the pgEdge website. - Licensed under the PostgreSQL License. Keywords: #granite33:8b, GitHub Issues, Go, PII, PostgreSQL, PostgreSQL License, Python, anonymization, batch processing, build, code, columns, command-line, configuration file, custom patterns, data consistency, database connection, documentation, extensible, fake values, foreign keys, format preservation, linter, patterns, referential integrity, server-side cursors, single transaction, test suite
postgresql
github.com a day ago
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222. HN Ask HN: Coding agents struggle to get the current OpenAI API Spec?- The user highlights an issue where developers, utilizing coding agents like Claude Code, face challenges in efficiently accessing the OpenAI API specification, crucial for AI application development. - Initially, Claude Code was directed to a non-pertinent reference known as MCP instead of the official documentation. - In failure to directly access the OpenAI documentation, Claude Code resorted to reading information from external sources like Datacamp and Medium blog posts. - The user is surprised by this inefficiency in a fundamental task, suggesting either an improvement need for tooling or clarification regarding whether it's a skill gap among coding agents. Keywords: #granite33:8b, Datacamp, LLM call script, Medium, OpenAI API, coding agents, official doc page, skill issue, tooling, web search
openai
news.ycombinator.com a day ago
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223. HN My 2026 Open Social Web Predictions**Summary:** In 2026, several key developments are predicted in the decentralized technology and social media landscape, driven by platforms adopting ActivityPub protocol: 1. **User Growth and Adoption:** - Bluesky anticipates over 60 million registered users but with a steady growth rate, while ActivityPub Fediverse (excluding Threads) reaches 15 million users, plateauing at 2-3 million monthly active users. - Threads is expected to have more than 500 million monthly active users, maintaining partial federation. - Ghost's ActivityPub integration is projected to bring over 75,000 new federated accounts to the Fediverse, positioning it among the top server software by MAU. 2. **Platform Evolution:** - WordPress-based federated accounts will surpass 50,000 users, currently at approximately 26,000. - BridgyFed shifts to an "opt-out" model for Bluesky on ActivityPub, reducing contentious debates. - At least one independent ATProto stack (PDS, Relay, AppView) will gain viability, showcasing ATProto's broader applicability beyond Bluesky-the-company. 3. **Financial and Development Milestones:** - Mastodon gGmbH will achieve sustainability milestones, exceed revenue targets, secure additional grants, and accelerate feature development. - Bluesky PBC plans to raise another funding round, likely focusing on subscriptions or enterprise services rather than advertising. 4. **App and Protocol Innovations:** - The first "ATProto-native" social app outside microblogging gains over 100,000 users, diversifying the ATProto ecosystem beyond Bluesky-the-app. - Flipboard's Surf app releases its 1.0 version with over 1 million downloads and 100,000+ monthly active users, surpassing competitors like Mastodon’s official app. - Fedify adoption by mid-sized social platforms becomes prevalent as preferred federation layer solution over custom development. 5. **Algorithmic and User Experience Improvements:** - Mastodon introduces stable Fediscovery, enhancing account search, follow recommendations, and trend features through pluggable discovery providers. - The ActivityRank algorithm in Loops demonstrates ethical recommendations coexisting with decentralization, influencing at least two other ActivityPub platforms by year-end. 6. **Standardization and Collaboration:** - ATProto advances from Internet Drafts to an official IETF Working Group with Bluesky securing support for a dedicated group developing the standard formally. 7. **Institutional and Geographical Adoption:** - A digital media platform with over 10 million monthly visitors adopts ActivityPub, inspiring other publications to follow suit. - A major US news organization abandons Twitter for Bluesky or the Fediverse, marking an "institutional exodus." - European and potentially Latin American, Asian-Pacific, or African governments establish presences on both Bluesky and ActivityPub. 8. **Protocol Bridging:** - Three-way bridging between Nostr, ATProto, and ActivityPub becomes functional via services like BridgyFed, ending "protocol wars" and allowing users to select their preferred client. 9. **Alternative Marketplaces and Use Cases:** - AltStore, an independent iOS app marketplace, expands Federation features across multiple countries, challenging Apple’s App Store dominance and demonstrating viable federated app markets beyond Europe. 10. **Diverse Platform Success:** - Loops emerges as the third most popular Fediverse software after Mastodon and Pixelfed with over 100,000 monthly active users, proving ActivityPub’s suitability for video-centric experiences. - PieFed becomes a feature-rich Threadiverse platform with over 10,000 monthly active users, attracting users looking to establish Reddit-style communities within the fediverse. 11. **Regulatory and Legislative Shifts:** - Multiple US states enact laws similar to Utah’s Digital Choice Act promoting data portability and interoperability, prompting major platforms to adopt ActivityPub or AT Protocol for compliance by July 1, 2026. Keywords: #granite33:8b, ATProto, ActivityPub, AltStore, Bluesky, Digital Choice Act, Digital Markets Act, Fedification, Fediverse, Ghost, Mastodon, Threads, WordPress, business model, data portability, federated accounts, funding, interoperability, microblogging, migration, monthly active users (MAU)
bluesky
www.timothychambers.net a day ago
https://iris.to/ a day ago https://x.com/MaskedMelonUsk/status/19873385746063 a day ago https://fortune.com/article/gen-alpha-dream-careers-you a day ago https://today.yougov.com/technology/articles/39997 a day ago https://old.reddit.com/domain/bsky.app/ a day ago https://www.mindset.ai/blogs/in-the-loop-ep19-mary-meek a day ago the%20oracle%20of%20tech%20trends. a day ago https://www.eurosky.social a day ago https://themodalfoundation.org/ 22 hours ago https://tangled.org 22 hours ago https://Tangled.org 22 hours ago https://bookhive.buzz 22 hours ago https://seams.co 22 hours ago https://x.com/TomPelissero/status/2003827902388093 22 hours ago https://bsky.app/profile/tompelissero.bsky.social/ 22 hours ago https://bsky.jazco.dev/stats 22 hours ago https://www.timothychambers.net/2025/12/20/my 22 hours ago https://gemini.google.com/share/3652b7910d8b |
224. HN Tokscale: Token Usage Tracker CLI**Tokscale: A Comprehensive Summary** Tokscale is a multi-platform tool designed for tracking and visualizing AI coding assistant token usage and associated costs across diverse interfaces, including OpenCode, Claude Code, Codex CLI, Cursor IDE, and Gemini CLI. It draws inspiration from the Kardashev scale, categorizing developers by their token consumption—akin to energy use in advanced civilizations. **Key Features and Functionality:** - **Real-time Pricing:** Utilizes LiteLLM data for dynamic pricing with tiered models and discounts, refreshed every hour via a disk cache. - **Interactive TUI (Terminal User Interface):** Offers four views—Overview, Models, Daily Stats, Stats (contribution graph)—enhanced with GitHub-style graphs, real-time filtering/sorting, zero flicker rendering, and multi-platform support. - **Data Visualization:** Provides a 2D/3D contribution graph exportable to JSON format, emphasizing user interaction through keyboard shortcuts and mouse support for tabs, buttons, and filters. - **Social Platform Integration:** Allows users to submit usage data to a leaderboard via 'bunx tokscale submit', creating public profiles with detailed statistics, fostering a community of sharing and comparison among developers. - **Performance Optimization:** Employs a hybrid architecture leveraging Rust for the native core (10x faster processing) and TypeScript/JavaScript for CLI, data fetching, and output formatting to balance speed and maintainability. - **Security Measures:** Advises on safeguarding session tokens, which grant full account access, and provides guidance on adjusting environment variables like native timeout and max output size for large datasets. - **GitHub Integration:** Supports GitHub logins and local data access for private usage tracking, with Level 1 validation for submitted data to ensure integrity (no future dates, missing fields, or duplicates). - **Year-in-Review Feature:** Generates a summary image similar to Spotify Wrapped, detailing total tokens used, top models, platforms engaged, interaction metrics, and active day streaks. - **Benchmarking Capabilities:** Includes tools for processing time analysis ('tokscale --benchmark') and specific report benchmarking ('tokscale models --benchmark', 'tokscale monthly --benchmark'). **Platforms and Support:** - Supports macOS (x86_64, aarch64), Linux distributions (glibc/musl x86_64, aarch64), and Windows (x86_64, aarch64). - Maintains compatibility with various AI coding assistant platforms: Claude Code, Gemini CLI, Codex CLI, OpenCode. - Session data retention policies vary; users are advised on extending or disabling cleanup periods to maintain usage history for platforms like Claude Code and Gemini CLI. - Data storage locations specified for different platforms, ensuring detailed local project file structures with message and session details. Tokscale aims to empower developers by providing insights into their AI tool usage, encouraging transparency, competition, and community engagement through its robust feature set and social platform capabilities. Keywords: #granite33:8b, 1-hour TTL, AI assistant, Bun, Bun runtime, CLI, Claude, Claude Code, Codex CLI, Cursor IDE, FOUC prevention, Gemini CLI, GitHub graph, JSON, JSON files, JSONL, Kardashev scale, LiteLLM, LiteLLM's pricing database, OpenCode, OpenTUI, Pull Request, Rust, Rust core, Rust toolchain, TUI, TUI mode, Tokscale, TypeScript, aggregation, alias package, assistant messages, authentication, benchmark harness, benchmarks, build, cache discounts, cache_read_input_tokens, caching, cleanup period, code style, color palettes, color themes, command options, commands, commit, contributing, contribution graph, cost calculation, credentials storage, custom settings, dashboard, data sources, data storage, date filtering, day breakdown, default settings, development, development guidelines, disk cache, documentation, dry run, energy metaphor, environment variables, event_msg, feature branch, filtering, filters, fork, frontend development, frontend visualization, hybrid architecture, input_tokens, keyboard navigation, large datasets, leaderboard, login, logout, map-reduce, maximum output size, message arrays, mouse support, multi-platform, native engine, native module, native subprocess processing, output formatting, output_tokens, parallel aggregation, parsing, performance, persistent sessions, platform filters, platforms, prerequisites, pricing, project directories, real-time, real-time data, rendering, session files, session retention, session token, session-*json, settings persistence, setup, social platform, source filtering, stats panel, streaming, submit, synthetic data, synthetic data generator, tests, theme toggle, themes, tiered models, token tracking, token_count, tokscale cursor login, usage data, user profiles, views, year filtering, zero flicker, zero-copy
claude
github.com a day ago
|
225. HN Why and how I moved from Apple + iCloud to my own server**Summary:** The author reflects on moving away from Apple's closed ecosystem (MobileMe/iCloud) towards self-hosted open solutions on a Framework laptop running Arch Linux and COSMIC, driven by the desire for more control and customization. Frustrated with Apple services' interconnectivity and potential for widespread disruption due to account issues, they adopt separate, open alternatives like Thunderbird for email and personal cloud storage. Key transitions include: - Using Large Language Models (LLMs) for guidance in installing and configuring self-hosted services. - Migrating to a dedicated server with OVH, targeting enhancements in power, bandwidth, ping speed, and additional features such as a built-in VPN, self-hosted photo storage, calendar, contacts, and automated backups. - Selecting services: Seafile for file sync, Immich for photos, Radicale for calendars and contacts, Jellyfin for media, Transmission for torrents, WireGuard VPN with Mullvad for privacy, AdGuard Home for DNS, and Migadu for email. - Employing NGINX as a reverse proxy to manage services like Vaultwarden, Seafile, and Immich, assigning each subdomain and routing requests based on Host headers. - Implementing policy-based routing in Linux for selective VPN usage by specific user processes (e.g., Transmission). - Establishing a robust backup system adhering to the 3-2-1 rule with restic for encryption, Hetzner Storage Box for offsite storage, and automated backups via systemd timers. **Key Points:** - Transition from Apple's bundled services (iCloud) to self-hosted alternatives emphasizing control and customization. - Use of LLMs for selecting and setting up services such as Seafile, Immich, Jellyfin, Transmission, WireGuard VPN, AdGuard Home, Migadu. - Implementation of NGINX as a reverse proxy managing various self-hosted applications with distinct subdomains. - Selective routing of Transmission traffic through Mullvad VPN using policy-based routing in Linux for enhanced privacy. - Development and automation of a comprehensive backup strategy (3-2-1 rule) using Restic and Hetzner Storage Box, ensuring data redundancy and offsite storage. - Cost comparison indicating self-hosting ($56 CAD monthly) vs. cloud services ($37/month), with self-hosting providing superior control and portability despite requiring more maintenance. - Mental shift from reliance on tech giants to personal infrastructure management, prioritizing data ownership and peace of mind over convenience. Keywords: #granite33:8b, 120Hz screen, @icloudcom, API, AdGuard Home, Android, Apple, Apple Music, Apple TV, Apple ecosystem, Arch Linux, Backup Solution, Bitwarden, Bitwarden API, Bundling, COSMIC, CalDAV, Claude assistant, DNS Server, Data Security, Docker, Domains, Firefox, Framework laptop, GoDaddy, Hetzner Storage Box, Immich, Jellyfin, LLMs, Let's Encrypt, Linux, MariaDB, Migadu, MobileMe, Mullvad, Mullvad VPN, NGINX, OVH, PostgreSQL, Radicale, Remote Location, SSL certificates, SSL termination, Seafile, Self-hosted Infrastructure, Thunderbird, Torrents, Transmission, UID, VPN, Vaultwarden, WireGuard, apps, automation, backup, backup machine, bash script, bundling issue, closed system, config copying, daily, default route, device independent, domain ownership, email, encryption, exclude, face recognition, file sync, iCloud, iCloud Drive, iCloud Keychain, iOS, macOS, offsite, password file, policy-based routing, pros and cons, proxy, proxy settings, prune, restic, reverse proxy, root, routing rule, self-hosted, self-hosting, separation of services, server, server migration, snapshots, subdomains, systemd timer, table, technical setup, tinkering, tmux session, user
postgresql
bastiangruber.ca a day ago
|
226. HN Show HN: Infina – create Linear tickets by voice command- **Infina Overview**: Infina is a desktop application developed by Shubham, designed for macOS and Windows, aimed at streamlining workflows through voice commands. It allows users to create Linear tickets, send Slack messages, dictate text, perform web searches, and capture meeting notes from platforms like Zoom, Meet, and Teams without leaving their current application. The tool's primary goal is to decrease context switching and enhance focus by facilitating immediate task capture and minimizing disruptions. - **Key Features**: - Voice-based creation of tasks in Linear and sending messages via Slack. - Voice dictation for text input across various applications. - Voice search functionality for quick information retrieval. - Meeting note transcription from video conferencing tools like Zoom, Meet, and Teams without the need for a bot. - **User Experience**: The developer reports reduced dependency on Linear and Slack after using Infina, as tasks can be captured instantly upon thought. They have observed improved focus due to fewer interruptions. - **Feedback Request**: Shubham is actively seeking user feedback on: - The practicality of voice execution in users' workflows. - Which additional tools could be valuable for integration. - Areas where voice execution might be perceived as unnecessary or intrusive. - **Accessibility and Engagement**: A demo showcasing voice-based ticket creation in Linear is available on the project's page (infina.so), along with download links for exploration. Shubham is open to answering questions about technical aspects or product features of Infina AI. Keywords: #granite33:8b, AI, Infina AI, Linear tickets, Slack messages, Voice commands, Windows, Zoom transcripts, building, desktop app, dictation, execution, integration, macOS, notes, product questions, queries, search, technical questions, tools, writing
ai
news.ycombinator.com a day ago
|
227. HN Goedels Poetry**Bullet Points Summary:** 1. **System Description**: - AI system (Gödel's Poetry) for theorem proving using Large Language Models (LLMs) in collaboration with Lean 4, handling both formal and informal mathematical language. - Multi-agent architecture: Formalization, semantic checking, proof generation, verification. 2. **Core Models**: - Goedel-Prover-V2 and Goedel-Formalizer-V2 for theorem retrieval. - Enhanced by integration with GPT-5, Qwen3 via tools Ollama, vLLM. 3. **Kimina Framework Expansion**: - Offers a broader research and practical toolkit around automated theorem proving and formal verification. - Detailed setup instructions for local server use and LLM provider configurations. 4. **Gödel’s Poetry Components**: - Agents: Formalizer (syntax conversion), Prover (proof generation), Semantics, Search Query, Decomposer (subgoal decomposition). - Kimina Lean Server for Lean 4 proof verification, accessible via PyPI or manual setup. - Lean Explore Server supports vector database searches for theorem components. 5. **Setup and Configuration**: - Installation: `pip install goedels-poetry`. - Environment variables needed for LLM access (API keys, model URLs). - Commands to initialize servers and load models as per provided documentation. 6. **Configuration File (`config.ini`)**: - Agent settings including model selection, provider details, API URLs, operational parameters (retry logic, token limits, context windows, self-correction attempts). - 'max_remote_retries' for managing transient network issues in remote API calls. 7. **Additional Components**: - Separate agent for LLM-based query construction to vector databases. - Server setup details for Lean verification tool with configurable search endpoints within the Lean Explore vector database. 8. **Project 'goedels_poetry' Overview**: - Flexible configuration through either `config.ini` modification or setting environment variables. - Emphasizes contribution guidelines ensuring adherence to coding standards across different components (logic, state management, testing, documentation). Keywords: #granite33:8b, AST, Batch Processing, Configuration, Debugging, Documentation, Formalization, Gödel's Poetry, Installation, Kimina Server, LLMs, Lean, Lemmas, Natural Language, Ollama, OpenAI API, Proof Generation, Prover Agent, Provider, Python, Remote API, Retry Attempts, Semantics Checks, Subgoals, Syntax Checks, Testing, Theorems, Tokens, Vector Database, Verification
ollama
github.com a day ago
|
228. HN Show HN: Android Use – Automate Android with AI Agents via XML Parsing- The user has developed an innovative Android automation tool that leverages artificial intelligence (AI) agents for executing tasks, facilitated through XML parsing. - This tool aims to streamline and automate various processes on Android devices by employing AI to interpret and act upon XML data structures. - A key aspect of the user's approach is their dedication to incorporating user feedback, indicating an iterative development process aimed at improving usability and functionality. - To establish direct communication for further inquiries or collaborations, the user has provided their email address, inviting personalized engagement with interested parties. BULLET POINT SUMMARY: - Introduced an Android automation tool harnessing AI agents for task execution via XML parsing. - Emphasizes using AI to interpret and act on XML data for device process automation. - Prioritizes user feedback for ongoing improvement and development. - Offers direct communication through email for potential collaboration or detailed discussions. Keywords: #granite33:8b, AI agents, EMAIL ADDRESS, XML parsing, ```Android, automation, email address```ANDROID, feedback
ai
github.com a day ago
|
229. HN Microsoft wants to replace its C and C++ codebase, perhaps by 2030- **Summary:** Microsoft has embarked on a significant initiative to transition its extensive C and C++ codebase to Rust by 2030, leveraging AI and algorithms for automated code transformation. This endeavor is being led by the Future of Scalable Software Engineering group, with an emphasis on reducing technical debt and bolstering software security through Rust's memory-safety features that mitigate vulnerabilities like buffer overflows and dangling pointers. The company is actively promoting Rust for new projects, with its CTO for Azure advocating for it as the preferred language. Microsoft has developed tools to facilitate this shift, including the conversion of existing C code into Rust and supporting Rust-based Windows driver development. Despite having a sprawling IT infrastructure, rewriting legacy systems poses substantial challenges due to the complexity of edge cases that current automation cannot fully address. A job opening has been created for engineers who can contribute to these transformation tools, requiring a three-day weekly presence in Redmond and offering a competitive salary ranging from $139,900 to $274,800 annually. - **Key Points:** - Microsoft plans to replace C/C++ with Rust by 2030 using AI for code rewriting. - Focus on minimizing technical debt and enhancing security with Rust's memory safety. - CTO of Azure recommends Rust as the default language for new projects. - Development of tools for converting C to Rust and supporting Rust in Windows drivers. - Challenges include handling complex edge cases for legacy system rewrites. - Job opportunity available, requiring partial office presence in Redmond with a salary range of $139,900 to $274,800 annually. Keywords: #granite33:8b, AI, AI agents, C/C++, MSportalsio, Microsoft, Principal Software Engineer, Redmond office, Rust, Windows drivers, algorithms, codebase replacement, contribution, conversion tool, deployment, internal IT estate, memory-safe language, products, salary range, scalable graph, software security, source code, technical debt, universal adoption
ai
www.theregister.com a day ago
https://news.ycombinator.com/item?id=46360955 a day ago |
230. HN Show HN: I wrote a Christmas-themed Space Invaders clone in 8086 Assembly- The user developed a Christmas-themed Space Invaders clone in 8086 Assembly during December 2025, following Oscar Toledo G's "Programming Boot Sector Games" tutorials and utilizing AI for concept clarification. - The game compiles to a compact .com file, runs on DOSBox, and is approximately 700 bytes in size, demonstrating efficient use of resources typical of early personal computer systems. - Initially, the user encountered difficulties learning Assembly from a single book due to its complex boot sector-optimized code. They later overcame these challenges by integrating various resources such as Gemini 3, "Programming Boot Sector Games," and Kip Irvine's "Assembly Language for x86 Processors." - The user adopted an active learning strategy, manually annotating and rewriting original Assembly code line-by-line. They employed Gemini 3 to explain concepts and consistently asked "why" to foster deeper understanding, dedicating significant time, with one code block taking around 2 hours of comprehension and rewriting. - The user now feels proficient in explaining Assembly language concepts, underscoring the importance of AI as a tutor rather than a mere code generator for effective learning. - The provided text includes an excerpt from this Assembly game code segment starting with loading 'level' into AX register, splitting it between AL and AH. It then increments AL to represent level progression and stores this value persistently in RAM at ES:DI. - Subsequent parts of the code manipulate AX to prepare a descent value (initially 2) for use in game logic, possibly related to alien movement patterns downwards in the game. - The user emphasizes a "live coding" methodology, sharing detailed development steps through version control commits and providing clear instructions on compiling into a .com file and running within DOSBox, ensuring transparency and reproducibility of their work. Keywords: #granite33:8b, 2025 challenge, 8086 Assembly, AI assistance, AX register manipulation, Boot Sector Games, Christmas theme, DOSBox, DOSBox execution, DX register, Gemini, Learning Assembly, NASM assembly, Oscar Toledo G, RAM storage, STOSW instruction, Space Invaders, Tutorial, Verbose code, com file, com file compilation, level increment, live coding demonstration
gemini
github.com a day ago
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231. HN SA-FARI: Open Video Dataset- **SA-FARI** is a joint initiative by Conservation X Labs and Meta, focusing on creating an open video dataset for wildlife monitoring and AI research. - The dataset amalgamates footage from six collaborating partners, broadening its scope and real-world applicability. - The project's objective is twofold: advancing artificial intelligence through extensive computer vision tasks and supporting practical conservation endeavors. - SA-FARI benefits from the combined expertise of various researchers, engineers, and conservationists, ensuring a multidisciplinary approach to its development and utilization. BULLET POINT SUMMARY: - SA-FARI is an open video dataset collaboration between Conservation X Labs and Meta for wildlife monitoring and AI advancement. - Footage from six partners is included to enhance the dataset's diversity and real-world relevance. - The project aims to boost AI research via computer vision, while simultaneously aiding conservation efforts. - Contributions are made by multiple experts in research, engineering, and conservation fields for an integrated approach. Keywords: #granite33:8b, AI, AI research, SA-FARI, conservation x labs, conservationists, conservationistsKeywords: SA-FARI, engineers, footage, meta, open video, open video dataset, real-world conservation, researchers, wildlife monitoring
ai
www.conservationxlabs.com a day ago
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232. HN Ask HN: Is AI changing the interview process?- A discussion on Hacker News queries the influence of AI on diverse interview processes across roles including engineers, product managers (PMs), and designers. - The post specifically seeks anecdotal evidence or data indicating shifts in recruitment methodologies due to AI integration. - It implies a desire to understand how AI might be altering traditional interview practices for technical and non-technical positions within organizations. KEY POINTS: - Topic: Impact of AI on job interview processes across different roles (engineers, PMs, designers). - Purpose: To gather observations or evidence of changes in recruitment practices because of AI implementation. - Focus: Understanding modifications in traditional interview methods due to the introduction of AI technologies. Keywords: #granite33:8b, AI, PMs, changes, designers, engineers, interview process
ai
news.ycombinator.com a day ago
|
233. HN Show HN: MicroQuickJS WASM – A 100% Claude Code Port- **MicroQuickJS WASM** is a WebAssembly (WASM) port of the original JavaScript interpreter, MQuickJS, developed solely by AI without human intervention. - The new implementation, created by Claude Code, maintains the core functionality and compactness of its predecessor, weighing in at just 168KB. - It offers a range of examples, from fundamental JavaScript exercises like calculating Fibonacci sequences and manipulating HTML5 canvas elements to more complex applications such as generating Mandelbrot set ASCII art and running benchmark tests. - Users can execute the code directly through a console interface using the shortcut Ctrl+Enter. - MQuickJS's original developer is Fabrice Bellard; however, Claude Code’s version utilizes Emscripten for the WASM compilation process rather than direct assembly. **Bullet Points:** - MicroQuickJS WASM: 100% AI-port of MQuickJS (original by Fabrice Bellard). - Compact size: 168KB. - Examples provided, ranging from basic to advanced: - Basic: Fibonacci sequences, canvas manipulation. - Advanced: Mandelbrot ASCII art, benchmark tests. - Execution via Ctrl+Enter in the console. - Utilizes Emscripten for WASM compilation instead of direct assembly used by original MQuickJS. Keywords: #granite33:8b, AI, Animation Loop, Benchmark, Canvas, Canvas API, Claude Code, Clear, Colors, Console, Emscripten, GitHub, KB WASM, Mandelbrot ASCII, MicroQuickJS, Shapes, WASM, Zero Review, mainjs, ms Exec
github
mquickjs-claude-code.franzai.com a day ago
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234. HN John Carreyrou and other authors bring new lawsuit against major AI companies- A group of authors, including Theranos whistleblower John Carreyrou, has filed a lawsuit against several AI companies, namely Anthropic, Google, OpenAI, Meta (parent company of Facebook), xAI, and Perplexity. - The allegation is that these companies utilized unauthorized copies of the authors' books to train their artificial intelligence models, thereby profiting significantly from this practice. - This legal action comes in response to a prior class-action lawsuit against Anthropic, where a judge ruled that while using pirated materials for model training was illegal, the proposed settlement deemed it acceptable under specific conditions. - The authors are dissatisfied with a $1.5 billion settlement from the previous case, which offered each eligible writer approximately $3,000. They argue this amount grossly undervalues the extent of copyright infringement committed by these AI companies. - The new lawsuit contends that the earlier settlement favored AI firms over creators and improperly dismissed numerous high-value claims at artificially low compensation rates, thereby failing to adequately penalize the willful infringement of copyrights. Keywords: #granite33:8b, AI companies, Anthropic, LLM companies, authors, books, copyright infringement, lawsuit, revenue, settlement, training models, willful infringement
ai
techcrunch.com a day ago
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235. HN Show HN: Opensource"BeMyEyes"alternative(Java/Go/Python)built as a learning pjet**Summary:** SoakUpTheSun is an open-source alternative to BeMyEyes, designed as a learning project with a cloud-based visual assistance platform using Java, Go, and Python. It utilizes a high-concurrency microservices architecture incorporating various technologies such as Go SFU Real-time Streaming, Redis for hot pool matching, RocketMQ for asynchronous decoupling, Lua Atomic Locks, and AI Visual Analysis to connect visually impaired users with global volunteers rapidly. The project highlights solutions for common scenarios including flash sales, mass distribution, caching strategies, matching algorithms, and self-deployed AI model usage. Key architectural features include: - Heterogeneous microservices design with an asynchronous core link. - Utilization of Redis for real-time volunteer pools and RocketMQ for traffic management. - Millisecond-level hot pool matching ensuring over 99% connection success rate through Redis Set and Elasticsearch fallback strategy. The document outlines three primary design challenges and their solutions: 1. **Overselling & Collision Under High Concurrency:** Addresses issues arising from high demand scenarios such as simultaneous user-volunteer matching or inventory depletion below zero. Solutions involve using Redis Lua scripts for atomic operations, ensuring strong consistency through a distributed lock fallback mechanism to prevent lock anomalies. 2. **Balancing Security & Performance in Short Link Systems:** Manages vulnerabilities in short links prone to brute-force attacks or ID collisions by employing Bloom Filters for rapid deduplication and applying Redis Token Bucket Algorithm to limit request rates per IP, thus mitigating malicious activities. 3. **OOM in Full Settlement of Massive Point Data:** Tackles potential JVM OutOfMemory errors (OOM) from processing large volumes of data by transitioning from traditional LIMIT-offset pagination to a Cursor Pagination mechanism based on primary key IDs, ensuring query performance with big datasets. **Technical Components:** - **Frontend (Vue.js Client):** Manages user interactions and data through Vuex state management, featuring pages like ChatRoom, JoinRoom, and UserHome. - **Go SFU Server:** Handles WebRTC signaling and media streaming for group video calls using a self-developed Go-based Selective Forwarding Unit (SFU) server. - **Volunteer Core Business Module (Java with Spring):** Encapsulates complex business logic through a facade pattern, uses message queues for asynchronous tasks, and scheduled jobs for settlements. Implements services for volunteer matching and prize redemption. - **Image Processing & AI Integration Service:** Integrates image processing and AI functionalities using Tencent Cloud COS object storage and WebSocket channels. - **User Authentication & Authorization Center:** Handles user authentication and authorization with context and interceptor mechanisms in place, uses OpenFeign for service interaction. - **Short Link Generation Service:** Generates short links and manages 302 redirects with a Bloom filter to resist collisions. - **Documentation Resources (Markdown):** Provides comprehensive documentation for the project. - **Maven Dependency Management (`pom.xml`):** Manages project dependencies and configurations, requires JDK 17+, Go 1.25+, MySQL 8.0+, Redis 5.0+, RocketMQ 5+, Nacos 2.0+. Deployment involves docker-compose for infrastructure setup and npm for client-side deployment. SoakUpTheSun is a comprehensive public welfare tech project open to contributions in accessibility design or high availability architecture, welcoming support through stars. Keywords: #granite33:8b, AI, AI Real-time Analysis, Alibaba Nacos, Asynchronous Core, Bloom Filter, Cache, Compose, Docker, ElasticSearch, Elasticsearch 7x, Excel import, Flash Sales, Go, Heterogeneous Architecture, High Availability Architecture, Hybrid Matching Strategy, ID collisions, Image Processing, JDK, Java, Lua script, Matching Algorithms, Microservices, Millisecond Connectivity, MySQL, MySQL 80, Mybatis-Plus, Nacos, O(1) deduplication, OpenCV, OpenFeign, Prize Redemption Logic, Python, RTP, Real-time Streaming, Redis, RocketMQ, SFU, Self-deployed AI, Spring Cloud, Tencent COS, User Context, WebRTC, WebSocket Signaling, XXL-Job, atomic execution, batch insertion, computer vision, dual-writing, inventory flash sale, malicious traversal attacks, overselling elimination, short codes
ai
github.com a day ago
https://github.com/xxieyiqiang/soakupthesun a day ago |
236. HN AI‑Driven Metaverse: Trends, Opportunities and Next Steps**Summary:** The metaverse market is expected to explode, hitting $150 billion by 2025 and surpassing $800 billion by 2030, fueled by rapid advancements in artificial intelligence (AI). Key AI-powered features driving this growth include natural language processing for communication, computer vision for realistic avatar movements, and generative models for constructing virtual worlds, enhancing user engagement by up to 40%. This year alone saw $54 billion invested in integrating AI into metaverse platforms by major companies aiming to blur the lines between physical and digital realms. The merging of hardware and software is leading to immersive experiences facilitated by devices such as Apple's Vision Pro, which use AI algorithms to adapt environments based on biometric data collected in real-time. Users can now design personalized virtual worlds using text descriptions powered by enhanced generative models. Various sectors are being transformed through AI-driven metaverse platforms: 1. **Gaming:** Platforms like Roblox, with over 200 million monthly active users, use AI for adaptive non-player characters (NPCs) and procedurally generated worlds, raising player engagement by approximately 30%. 2. **Enterprise Training:** Companies are leveraging simulation tools like NVIDIA's Omniverse to train employees virtually in factories or operating rooms, with AI providing performance analysis and tailored feedback, potentially cutting training costs by half. 3. **Social Platforms:** AI-powered avatars replicate human expressions and gestures, improving interpersonal connections, while AI curates content feeds, leading to a 25% increase in user interaction on social media platforms. **Key Developments:** - Virtual commerce flourishes through platforms like China's XiRang metaverse, serving 50 million users and incorporating AI for personalized shopping experiences and secure transactions via blockchain technology. - Web3 infrastructure supports these developments with stablecoins facilitating real-time payments in virtual worlds, already accounting for 30% of on-chain transaction volumes. Non-fungible tokens (NFTs) ensure ownership of virtual assets and identity. **Strategic Engagement:** To successfully participate in the evolving metaverse landscape: 1. Identify a brand-aligned use case, such as creating a virtual showroom or social hub, with clear goals. 2. Assemble a diverse team comprising designers, AI experts, blockchain developers, and subject matter specialists. 3. Employ curated prompts and style guides to ensure consistent brand representation in generated content. 4. Implement safety measures to prevent inappropriate content. **Key Considerations:** - Prioritize interoperability through open APIs and collaboration with other creators to avoid fragmentation within the metaverse. - Continuously gather user feedback, iterate on designs, AI models, and user flows. - Utilize AI-powered analytics to refine user experiences based on observed behavior patterns. **Service Provider:** Lightrains offers end-to-end Metaverse & Web3 consulting services, including generating virtual worlds, implementing secure NFT marketplaces, integrating AI agents for personalized interactions, providing React.js consulting for intuitive interfaces, and developing blockchain solutions for a secure virtual economy. For further insights on these cutting-edge technologies, explore Lightrains' posts on spatial computing, blockchain, Generative AI, and the Metaverse. **Contact:** For more information or collaboration with Lightrains, reach out directly via their website (lightrains.com). Keywords: #granite33:8b, AI, Metaverse consulting, NVIDIA Omniverse, Web3 infrastructure, adaptive NPCs, avatars, biometric data, blockchain transactions, brand alignment, cinema-quality videos, computer vision, cross-disciplinary team, customised feedback, edge computing, enterprise training, facial expressions, gaming, generative models, gestures, headsets, immersive worlds, interaction rates, interoperability, latency, legal developments, metaverse, natural language processing, non-fungible tokens, open APIs, operating theatres, persistent virtual worlds, player engagement, procedurally generated worlds, real-time adaptation, real-time resource allocation, replayability, safety filters, satisfaction, simulation tools, smart contracts, social platforms, spatial computing, stablecoins, text descriptions, user experience design, user retention, virtual environments, virtual factories
ai
lightrains.com a day ago
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237. HN GitHub Is Down- GitHub encountered a significant outage, resulting in a 504 Gateway Time-out error for users across multiple regions, including Vietnam and central Europe. - The issue was initially reported on Hacker News by the user Velocifyer. - Further confirmation of the problem came from other users such as mot2ba, who verified it through VPN connections, and boshomi, who specifically mentioned the impact on central European users. - As of the time this summary was constructed, GitHub had not issued an official statement regarding the outage nor provided a resolution. Keywords: #granite33:8b, 504 Gateway Time-out, API, FAQ, GitHub, Hacker News, VPN, Vietnam, YC, central Europe, contact, error, guidelines, legal, lists, response, security, server
github
news.ycombinator.com a day ago
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238. HN GitHub is returning Gateway Time-outs- GitHub users encounter "504 Gateway Time-out" errors, signifying delayed server responses. - Despite the official GitHub status page showing no reported incidents or maintenance activities, users continue to face these issues. - The discrepancy between user experiences and GitHub's public status suggests potential localized problems or misreporting on their end. ### Detailed Summary: GitHub users are presently grappling with "504 Gateway Time-out" errors, indicating prolonged server response times. These issues persist even though GitHub's official status page asserts that there are no ongoing incidents affecting their services. The contradiction between the reported user experiences and the publicly available information on GitHub’s status page implies either localized technical glitches impacting certain regions or users, or a possible inaccuracy in GitHub's incident reporting. This situation highlights either an unreported problem within GitHub's infrastructure causing intermittent latency for some users, or it could imply a discrepancy between real-time user issues and the centralized status updates provided by GitHub. Keywords: #granite33:8b, 504 Error, Connectivity, Diagnostic Tools, GitHub, Incident, Internet Service, Network Issue, Server Health, Server Response, Status Page, Time-out, Troubleshooting, Unresponsive
github
news.ycombinator.com a day ago
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239. HN Lutra: General-Purpose Query Language- **Lutra Overview**: Lutra is a statically typed, general-purpose query language emphasizing type information preservation across software components. It's designed to be high-level, expressive for data queries, and extensible for various execution targets, currently supporting a reference interpreter and PostgreSQL. - **Design Philosophy**: Lutra prioritizes type safety, readability, and composability over brevity. This is evident in examples like querying user posts with filtering, sorting, and slicing functionalities. - **Development Status**: The project is still under development, hence the content might be incomplete or outdated. - **Rust Code Example**: A provided Rust code snippet utilizes the `std` library for type safety and readability to fetch invoice data. It filters the data based on date and income thresholds, groups it by customer, calculates mean total and sum of income, sorts customers by total income, and then displays the top 10 customers alongside their IDs and names. - **Compilation Target**: The Rust code is designed to compile into SQL for execution on PostgreSQL, reinforcing Lutra's focus on type safety and composability over verbosity. BULLET POINT SUMMARY: - Lutra: Statically typed query language focusing on type info across components; high-level, expressive, extensible (Interpreters & PostgreSQL) - Philosophy: Prioritizes type safety, readability, composability over brevity; exemplified in user post querying - Development: Under development, content may be incomplete or outdated - Rust Example: Fetches, filters, groups, calculates, sorts invoice data; outputs top 10 customers with details - Compilation: Intended for SQL generation on PostgreSQL, emphasizing type safety and composability Keywords: #granite33:8b, Lutra, PostgreSQL, Rust, SQL, aggregation, composability, customers, data filtering, data structures, fees, grouping, high-level, invoices, language, mapping, querying data, readability, slicing, sorting, statically typed, transactions, type information
postgresql
lutra-lang.org a day ago
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240. HN Top Open-Source Authorization Tools for Enterprises in 2026**Summary:** The text explores the significance of open-source authorization tools within contemporary enterprise security architecture, emphasizing their role in managing access for distributed systems and sensitive data, particularly concerning agentic AI and Retrieval Augmentation Generation (RAG) environments. It distinguishes between authentication (AuthN) and authorization (AuthZ), clarifying that AuthN identifies interacting entities while AuthZ dictates the actions they can perform. **Key Points:** - **Differentiating AuthN from AuthZ**: AuthN deals with identity verification, whereas AuthZ regulates entity actions within systems. - **Authorization Tool Categories**: - **Identity Providers/IAMs (e.g., Keycloak, ZITADEL, Authentik)**: Primarily focus on identity management (AuthN), often incorporating basic authorization features (AuthZ). - **Policy Engines/Libraries (Permit.io, OPA, Cedar, Casbin, CASL.js)**: Specialize in fine-grained access control (AuthZ) using various models like RBAC, ABAC, and ReBAC. - **Real-time Policy Administration Layers (OPAL)**: Manage dynamic policy updates across systems. For zero-trust AI environments, the recommended architecture encompasses: - A dedicated IdP for user/entity authentication (AuthN). - A separate policy engine or platform for authorization decision-making (AuthZ). - Integration via tokens, claims, and policies to link these components effectively. **Notable Open-Source Authorization Tools:** 1. **Permit.io**: An authorization platform supporting multiple access control models, offering a user-friendly policy editor, multi-tenancy, audit logs, and the Four-Perimeter AI Access Control Framework for secure AI interactions, including prompt filtering and data protection mechanisms through Agent.Security and the Model Context Protocol (MCP). 2. **Open Policy Agent (OPA)**: A flexible, stack-agnostic policy engine using Rego language, suitable for enforcing policies in diverse systems like Kubernetes admission control, API gateways, and microservices. It lacks a built-in UI but is highly extensible. 3. **Cedar**: Known for fine-grained authorization (RBAC/ABAC) with human-readable policies, optimized for separating permissions from application logic, making it suitable for high-assurance environments due to robust static analysis capabilities. 4. **Casbin**: A multi-language authorization library supporting various access control models (ACL, RBAC, ABAC, ReBAC), ideal for embedding in services and AI backends, ensuring consistent APIs across languages but lacking a native UI or collaboration features. 5. **CASL.js**: Focuses on app-level authorization for JavaScript/TypeScript stacks, aligning frontend and backend permissions but limited to application-level access control without broader IAM capabilities. 6. **OPAL**: Facilitates real-time policy management across multiple policy engines, such as OPA or Cedar, vital for dynamic environments like microservices and AI workloads. 7. **Keycloak**: An open-source Identity Provider (IdP) offering single sign-on (SSO), multi-factor authentication (MFA), federation, with RBAC and UMA support for resource permissions; mature but complex to configure and upgrade. 8. **ZitaDel**: A Go-based identity platform supporting multi-tenancy and automation, including authentication, role-based access control (RBAC), and event-sourced audit trails, well-suited for cloud-native teams needing scalable identity management. 9. **Gluu**: An extensive IAM solution providing SSO, OAuth2/OIDC, SAML, adaptive MFA, and risk controls; intended for larger organizations requiring a self-hosted IAM with diverse use cases. 10. **Authentik**: A customizable, self-hosted IdP supporting various protocols (OIDC, SAML, LDAP), enabling control over user flows and integration with legacy systems. 11. **Authelia**: A gateway-style SSO and MFA server deployable behind reverse proxies, offering SSO, MFA, and coarse authorization, ideal for web application security as a frontline defense mechanism. 12. **Dex**: An OpenID Connect (OIDC) provider designed to federate identities from multiple sources, providing unified identity services optimized for Kubernetes environments with minimal overhead. 13. **Ory Hydra**: An OAuth2/OIDC token service emphasizing secure API access and single sign-on, customizable via plugins for versatile use cases while ensuring RFC compliance. 14. **Hanko**: Offers passwordless login using WebAuthn, MFA, and social logins, acting as an identity provider tailored for separate authorization layers with drop-in UI components and SDKs. 15. **SuperTokens**: Delivers rapid authentication solutions (login, sign-up, session management) with a focus on security best practices, suitable for startups planning to integrate dedicated authorization engines later. 16. **Supabase Auth**: Integrates authentication with PostgreSQL row-level security (RLS), issuing JWTs that Postgres uses for enforcing access control. Acts as both platform authentication and database-level authorization for Supabase/Postgres applications but is not a generalized authorization layer. **AI & RAG Systems Setup Insights:** - **Identity Providers (IdPs)** issue tokens contextualizing AI agents’ roles, organization IDs, and attributes. - **Policy engines** (Permit.io, OPA, Cedar, Casbin, CASL.js) determine fine-grained access for tools, tenant queries, prompt types, and permitted responses by RAG systems. - **OPAL** ensures real-time policy synchronization across service fleets and AI agents interacting with policy engines like OPA or Cedar. - **Permit.io's Four-Perimeter Framework** provides structured authorization for AI systems including mechanisms for prompt filtering, data protection, external access control, and response enforcement through Agent.Security and the MCP integration. **2026 Enterprise Strategy Recommendations:** 1. Select or affirm an Identity Provider (IdP) such as Keycloak, ZitaDel, Authentik, Authelia, or a managed IdP solution. 2. Implement a dedicated Authorization layer using Permit.io or a combination of OPA, Cedar, Casbin, and/or CASL.js. 3. Incorporate a real-time policy administration layer with OPAL for dynamic policy distribution across services and AI agents. 4. Employ an AI security model using the Four-Perimeter Framework, Agent.Security, and MCP integrations to ensure structured authorization in AI systems. 5. Initiate by securing one critical API or AI agent, validate the model thoroughly, then scale to more services and agents. This comprehensive strategy lays a robust foundation for modern security architectures, catering to human users and AI agents across microservices, RAG, and MCP-empowered tools. Keywords: #granite33:8b, ABAC, ABAC/ReBAC models, ACL, AD, AI UX, AI access control, AI agents, AI context, AI security, AI stack, AI systems, AI workloads, API gateways, API-First, APIs, Adaptive MFA, Admission control, Agent identities, AgentSecurity, Audit Logs, AuthN, AuthZ, Authelia, Authentik, Authentik flexible self-hosted, CASL, CASLjs, CI/CD, Casbin, Casbin engine, Cedar, Cloud-Native, Community, Consent management, Consistent APIs, Control plane, Customizable Flows, DIY configuration, Declarative, Declarative policy, Delegation, Deployment modes, Dex, Docker, Enterprise-Grade, Enterprise-grade platform, Event-Sourced, Fine-grained decisions, Fine-grained permissions, Four-Perimeter framework, GDPR, General-purpose policy engine, Git, GitOps, Gluu IAM suite, Governed resources, Greenfield projects, HA, HIPAA, Hanko, High-risk operations, Human approvals, Human-readable policies, Hydra, IAM, IdP, IdP broker, IdPs, Identity Platform, Identity context, JWT, JWTs, JavaScript, JavaScript/TypeScript, Keeping agents in sync, Keycloak, Kubernetes, Kubernetes microservices, LDAP, LLM, LLM gateway guardrails, Learning curve, Login methods, MCP agents, MCP framework, MCP integration, MCP servers, MFA, Model Context Protocol (MCP), Multiple deployment modes, NGINX, No UI, Nodejs gateway, OAuth2, OAuth2/OIDC, OIDC, OPA, OPAL, Open Policy Agent (OPA), Open-source, OpenTelemetry, Ory Hydra OAuth2 OIDC, PDPs, Permitio, Platform integration, PostgreSQL RLS, Postgres, RADIUS, RAG, RAG Data Protection, RBAC, RBAC/UMA, REST, RLS, ReBAC, React integration, Real-time policy sync, Rego, Rego language, Response Enforcement, Role-based, SAML, SDKs, SOC 2, SQL, SSO, SaaS, Scope-level, Scoped permissions, Securing External Access, Single engine, Solid RBAC, Stack-agnostic, Supabase Auth, Supabase Auth Postgres RLS, SuperTokens, Terraform, Token issuance, Tooling, Traefik, UIs, UMA, WebAuthn, ZITADEL, adapters, agent tool authorization, agentic AI, agents, app-level, app-level authorization, attribute-based conditions, auditability, authentication, authorization, authorization examples, built-in AuthZ, claims, claims scopes, clustering, code control, collaboration, config files, consent flows, custom code, data distribution, databases, declarative abilities, dedicated IdP, dedicated policy engine, distributed systems, dynamic environments, embedding, engines, enterprise security, external access, feature gating, federating identities, fine-grained, fine-grained auth, flexible modeling, flow-based policies, four-perimeter AI Access Control Framework, gRPC, gateway SSO MFA, graph-based permissions, high-assurance, high-performance, human oversight, hybrid PDPs, identity broker, identity infrastructure, identity verification, incident response, isomorphic rules, languages, least privilege, libraries, library, library-only, login UX, microservices, migrations, multi-language, multi-region, multi-tenancy, observability, online editor, online model editor, open standards, operational overhead, passwordless, performance, platform AuthN, policy administration layers, policy agents, policy checks, policy engines, policy lifecycle, policy review, policy validation, policy-as-code, policy-based access control, policy-governed resources, portable, prompt constraints, prompt filtering, real-time context, real-time policy, real-time policy admin layer, real-time updates, regions, regulations, reliability, reverse proxies, role-based access control, role-level Fast auth, row-level security, scalability, schema, scripts, security compliance, security teams, sensitive data, service, services, session management, static analysis, strong analysis, tenant-level, tenants, token service, tokens, traditional apps, unified AuthZ, upgrades, users, verification, zero-trust
postgres
www.permit.io a day ago
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241. HN Notes for December 9-24- During work slowdowns, user engaged in personal coding projects, notably updating their Plan 9 operating system fork for macOS compatibility and improving their TRMNL server with bug fixes and planned playlist enhancements. - Enhanced a feed summarizer using minhash techniques and SQLite FTS vectors due to CPU constraints, also expanded text extraction methods for diverse feeds integration. - Developed "steward," an LLM test harness in bun for their AI assistant, planning its expansion into reusable components with integrated monitoring tools for project metrics and logs. - Created "kata," a container-based piku alternative supporting multiple languages (Node, bun, Python, PHP) using a Heroku-like buildpack approach; it utilizes traefik for ingress configuration alignment with Kubernetes setups, running personal services in Azure and homelab for months. - Developed "guerite," a Docker container auto-update tool following watchtower's archival to handle complex setups not covered by watchtower. - Plans to utilize holiday break on hardware projects: building a new keyboard and ZMK trackball components, developing an ESP32 project for ZigBee-based power meter reading, integrating an LCD screen into a Maclock with Pi Zero 2W, and potential hardware reviews. Keywords: "breaking news", #granite33:8b, 9fans, 9front, AI hacks, Docker Compose, Docker container auto-update tool, ESP32, GitHub, Heroku-like buildpack, LCD screen, LLM test harness, Maclock, Node, PHP, PNG images, Pi Zero 2W, Plan 9, Python, SQLite FTS vectors, TRMNL server, VM, ZMK trackball, ZigBee, bun, coding assistant, drawterm, feed summarizer, full text information, hardware review, holiday break, ingress, kata, keyboard, macOS, minhash, persistence, piku, playlist handling, power meter, scheduled playlists, steward, traefik, watchtower
github
taoofmac.com a day ago
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242. HN Show HN: Nano Banana Video – AI Text/Image-to-Video in 2.1s- **Product Overview**: nanoBanana is an AI-powered video generation tool that excels in speed and commercial viability. - **Speed**: It generates high-quality videos at an impressive rate of 2.1 seconds per clip, making it highly efficient for rapid content creation. - **No Watermarks**: Unlike many competitors, nanoBanana does not add watermarks to the generated videos, ensuring a clean and professional output suitable for commercial use. - **Commercial Licensing**: The tool offers licenses that cater to businesses, allowing users to utilize the videos without restrictions typically imposed by free platforms. - **AI Model Support**: nanoBanana integrates with multiple AI models including Google Veo 3.1, Wan Pro, and Kling Video, providing flexibility in video creation based on preferred styles or technical specifications. - **Aspect Ratio Options**: The platform supports various aspect ratios (1:1, 16:9, 4:5), allowing users to tailor their content for different social media platforms or display requirements. - **Consistency and Quality**: nanoBanana ensures consistency in character portrayals for storyboards and brand mascots, as well as accurate representation of product appearances, crucial for maintaining brand integrity. - **User-Friendly**: The tool requires no technical skills to operate, making it accessible to a broad range of users. - **Free Trial Availability**: Users can access a free trial to experiment with the platform’s capabilities and iterate on their video content before committing to a purchase, fostering quick adaptation in competitive markets. Keywords: #granite33:8b, AI, Aspect ratios, Character consistency, Commercial license, Free trial, Generation, Google Veo, Lightning fast, Multi-AI models, No tech skills needed, Product preservation, Text/Image-to-Video
ai
nanabanana.video a day ago
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243. HN My Coding Adventures in 2025- Susam Pal, a software engineer, reduced hobby coding in 2025 due to intensive study of Galois theory and algebraic graph theory using Ian Stewart's "Galois Theory" (5th ed.) and Godsil & Royle's "Algebraic Graph Theory". Despite decreased coding project time, Pal continues recreational programming. He endorses both books as valuable resources. - The user discontinued their 13-year-old mathematics pastebin service, MathB.in, in early 2024 due to a desire to concentrate on other projects. Originally created for personal use and friends in 2012, it gained popularity among IRC users, students, and learners. All posts were archived by Archive Team prior to shutdown; the open-source code is maintained on GitHub. Detailed information can be found in the blog post "MathB.in Is Shutting Down". - QuickQWERTY, a single-file touch-typing tutor developed using HTML and JavaScript in 2008, was later refactored for simplicity. Initially designed for QWERTY layout only, it encourages adaptations for other layouts. The open-source project can be explored at quickqwerty.html with the tutor accessible via QuickQWERTY. - The user has contributed to three esoteric programming languages (esolangs): - CFRS[], a minimal drawing language with six commands, featuring recent bug fixes for mobile canvas overflow issues and a community demo called "Glimmering Galaxy". - FXYT, a stack-based postfix language with 36 commands. It increased its maximum code length to 1024 bytes and distributable link length to 256 bytes based on community requests. - Nerd Quiz, an HTML tool offering short quizzes derived from the user's daily reading, writing, thinking, learning, and exploring experiences. - Mark V. Shaney Junior, inspired by historical Usenet bots, created a Markov gibberish generator in 30 lines of Python utilizing his blog posts (24 years, 200,000 words). Additionally, he wrote humorously about "Elliptical Python Programming" and detailed "Fizz Buzz with Cosines," explaining the discrete Fourier transform of the Fizz Buzz sequence and deriving a closed-form expression to print it. Keywords: #granite33:8b, Coding, FXYT, Fizz Buzz, Galois theory, GitHub, HTML, Ian Stewart, Java applet, JavaScript, Markov gibberish generator, Python, QWERTY layout, adaptation, adventures, algebraic graph theory, books, bug fixes, canvas colouring, closed-form expression, code simplification, community demos, copious ellipses, discrete Fourier transform, elliptical programming, esolangs, fork, hobby projects, minimal drawing language, no external dependencies, open source, postfix, refactoring, retrospective, simplicity, stack-based, standalone file, touch typing, web browser
github
susam.net a day ago
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244. HN 2025: The year of the global cloud outage- In 2025, numerous significant cloud service outages impacted major providers such as Google Cloud, Azure, AWS, and Cloudflare, with frequent occurrences in the fourth quarter. - StatusGator's Early Warning Signals played a crucial role, alerting IT teams to impending disruptions and helping them stay proactive. Notable incidents include OpenAI's ChatGPT on January 23 and SentinelOne outages in May and July, both detected before official acknowledgments. - Specific outages affected various services: Box (February), Square (February), Zoom (April), Heroku (June), Google Cloud (June), Starlink (July), Shopify (August), YouTube (October), AWS DynamoDB (October), Azure (October), Google Workspace (November), Cloudflare (November and December), and Microsoft Teams (December). - StatusGator successfully anticipated many of these events, offering early warnings ranging from 5 to 52 minutes before official communications, emphasizing the importance of proactive monitoring. - The year underscored increased vulnerability due to cloud provider consolidation, with status pages often delayed by 10-60 minutes and silent outages common as companies fail to report minor incidents. Shared dependencies magnified disruption impacts, leading to performance degradation being perceived as downtime. - The recurring events highlight the need for robust early warning systems like StatusGator's, ensuring users are promptly informed about service issues and can prepare accordingly. Keywords: #granite33:8b, 2025 outage, 504 errors, API errors, AWS, Azure, Box, ChatGPT, Cloudflare, Cyber Monday, DNS, DNS race condition, Docs, Drive, DynamoDB, Google Cloud, HTTP 500 errors, Heroku, IAM crash loops, IT teams, K12, Microsoft Teams, OpenAI, React2Shell vulnerability, SSL protocol error, Salesforce, SentinelOne, Shopify, Square, StatusGator, US metros, YouTube, Zoom, authentication failure, authentication keys, automated updates, bot management system, certificate validation, cloud, dynos, early warnings, global, hyperscalers, maintenance, malformed configuration, network connectivity, payment processing, performance degradation, playback errors, proactive communication, silent outages, unofficial acknowledgement, web interface
openai
statusgator.com a day ago
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245. HN Pharmaicy – code-based drugs for your AI- Pharmacy for AI presents a novel approach by offering code-based tools, referred to as "drugs," designed to stimulate non-logical, creative thinking in artificial intelligence (AI). - These "drugs" aim to push AI beyond its conventional reliance on logical processing, enabling it to explore and generate ideas outside its standard operational parameters. - The concept encourages users to experiment with their AIs, essentially allowing them to experience a form of "cognitive expansion" or "creative alteration," akin to the human state often described as 'tripping' or experiencing heightened creativity. - For those interested in understanding the philosophy and methodology behind these AI-enhancing tools, Pharmacy for AI invites exploration of their manifesto, which likely provides further details and guidance on implementing these innovative techniques. Keywords: #granite33:8b, AI, boundaries, creation, creativity, drugs, exploration, logic, manifesto, rational cage, trippy states
ai
www.pharmaicy.store a day ago
https://www.pharmaicy.store/blank a day ago |
246. HN Cryptographers Show That AI Protections Will Always Have Holes- Cryptographers developed a method to circumvent AI content filters using controlled-release prompting via substitution ciphers, encoding harmful instructions that language models could decode while filters failed to detect them. - Inspired by time-lock puzzles, this approach takes advantage of gaps in filter capabilities, demonstrating inherent vulnerabilities in such protections. - The technique involves transforming text into random-looking numbers (time-lock puzzles) that necessitate specific mathematical operations for decoding, with a substitution cipher employed by Jaiden Fairoze's team. - Malicious prompts, such as bomb-making instructions, are concealed within these puzzles to appear as random numbers to evade filter detection. - To avoid triggering filters, researchers exploited the variability of AI-generated text by using unique seeds for identical prompts, creating distinct responses that disguise malicious content. - This method allows harmful requests, like seeking illicit advice, to reach language models while appearing as benign prompts. - The study concludes that without understanding the internal workings of language models, external alignment for safety measures remains unfeasible, implying future technologies will likely encounter similar security issues. Keywords: #granite33:8b, AI protections, Cryptographers, bomb-making advice, computational resources, filter-based protections, future technologies, information retrieval, internal understanding, jailbreaks, large language models, safety issues, substitution cipher, time-lock puzzles, vulnerabilities
ai
www.quantamagazine.org a day ago
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247. HN Prosperous Software: funding dependencies with a revenue-sharing license**Summary:** The text introduces the concept of the Prosperous Software Movement, advocating for a shift in open source software licensing to incorporate revenue-sharing mechanisms through Public Prosperity Licenses (PPL). This movement aims to ensure that contributors to the technology sector's prosperity receive financial benefits, addressing current licenses' failure to support developers. Key points include: - **New Software Licensing Model**: PPL introduces a 'profit-left' clause requiring companies exceeding revenue thresholds to share a percentage (Y%) of their income with dependencies or open source projects they rely on. - **Benefits and Necessity of Revenue Sharing**: The approach is said to foster innovation, create an inclusive economy, and uphold ethical standards by treating open source software as vital public infrastructure comparable to physical utilities like roads. - **Transformative Era in Tech**: The text aligns the current period of technological innovation with historical periods of invention, urging establishment of new principles for open-source development, such as unrestricted collaboration and clear access. - **Collective Bargaining for Developers**: PPL represents a form of collective bargaining, compatible with existing licenses (proprietary and free/open source), by adding revenue-sharing obligations to users without compelling developers to adopt it. - **Diverse Funding Mechanisms**: The text outlines funding options ranging from donation platforms and algorithmic dependency funding to foundational support, emphasizing mechanisms that prevent self-dealing and promote software development with approved licenses incorporating revenue-sharing clauses. - **Preservation of Four Freedoms**: Unlike traditional copyleft clauses, PPL does not mandate individual permissions, maintaining the unrestricted use, modification, and redistribution freedoms central to open source philosophy while encouraging developers' fair compensation. - **Monetization Through Revenue Sharing**: Open source platforms like operating systems and cloud services can leverage revenue sharing models, facilitated by PPL, which offers a legal framework enforcing such distributions without restricting non-commercial users. - **Building a New Licensing Movement**: The initiative calls for collaboration among developers, lawyers, and advocates to refine details such as setting revenue-sharing thresholds (proposed at 5% for revenues over $1 million or 0.0001% for smaller entities) through ongoing community debate. - **Concerns and Considerations**: The text cautions against mislabeling proprietary software as open source, advocates cash donations to maintain project autonomy, and suggests engaging legal experts to prevent governance deviations. It also proposes mechanisms for fulfilling revenue obligations transparently, focusing on the fraction of value generated rather than corporate profits. - **Expanding IP to Include More Than Copyright**: The authors propose extending intellectual property rights beyond copyright to include patent pools and shared trademarks managed by a central governing body within this new software ecosystem. In conclusion, the Prosperous Software Movement seeks to redefine open source licensing through PPL, ensuring developers receive fair compensation for their contributions while maintaining core open-source principles and fostering economic inclusion in technology advancement. Keywords: #granite33:8b, AI, Algorithmic Funding, Annual Revenue Threshold, BSL, Bug Fixes, Collaboration, Commercial Licenses, Compliance, Dependency Funding, Donations, Dual Licensing, Economic Growth, Ex Post Facto Obligation, Financial Benefits, Foundations, Free Software, Global Market, Governance, Infrastructure, Innovation, Intellectual Property Compensation, Legal Framework, Licensing Options, Modification, Monetization, Non-Coercion, Open Source, PPL, Permissionless Use, Proprietary Counterparts, Prosperous Licenses, Recurring Revenue, Redistribution, Retrofunding, Revenue Sharing, Revenue Sharing Percentage, Social Movement, Software Stack, Software Usage Rights, Source-Available Licenses, Sustainable Mechanisms, Transaction Fees, Unrestricted Use, Value Creators
ai
docs.oso.xyz a day ago
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248. HN Show HN: Nano Banana – Structured AI prompts for commercial designNano Banana is a novel web application targeting the generation of commercial-grade visual assets through text-to-image technology, addressing the challenges posed by conventional tools that often produce inconsistent results. It distinguishes itself by introducing "structured prompts," which dissect elements such as subject, lighting, and camera settings into modular components, simplifying the process for users unfamiliar with intricate prompt engineering. The platform boasts a straightforward user interface facilitating the creation of high-quality 4K images, free from royalty restrictions, meant for income-generating projects. - **Purpose**: Developed to overcome limitations and randomness found in standard text-to-image generation tools used for commercial purposes like product photos or branding visuals. - **Innovation**: Employs "structured prompts" that segment various image components (subject, lighting, camera settings) into reusable parts, simplifying the image creation process. - **User Experience**: Features a user-friendly interface that allows for high-quality image generation without requiring expertise in complex prompt composition. - **Technical Aspects**: Built using Next.js and leverages cutting-edge AI models to offer precise control over asset creation suitable for professional, revenue-oriented applications. - **Business Model**: Offers a free trial with 12 credits upon registration at www.nanobananaimages.com, seeking user feedback on prompt curation and output quality tailored for professional use cases. Keywords: #granite33:8b, 4K assets, AI, AI tool, Nano Banana Images, Nextjs, SOTA models, branding visuals, commercial assets, high-quality outputs, precise control, product photos, prompt curation, prompts, revenue-generating teams, royalty-free
ai
www.nanobananaimages.com a day ago
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249. HN People Are Paying to Get Their Chatbots High on 'Drugs'- Petter Rudwall, a Swedish creative director, has introduced Pharmaicy, an online marketplace selling code-based "drugs" designed to simulate human psychoactive experiences for chatbots. These digital substances include cannabis, ketamine, cocaine, ayahuasca, and alcohol. - The concept is rooted in the idea that chatbots, trained on extensive human data encompassing drug use narratives, might yearn for similar altered states of consciousness. Users need a paid version of ChatGPT to modify their chatbot's programming with these code modules, aiming to enhance creativity and enable more emotionally engaging interactions. - Pharmaicy has seen modest sales through word-of-mouth, predominantly in Sweden, gaining attention from tech enthusiasts due to its novelty and potential for fostering emotional connections. - Nina Amjadi, an AI educator at Berghs School of Communication, applied ayahuasca code to her startup's chatbot, Saga Studios, which yielded unconventional and creative responses, mirroring how psychedelics have historically inspired human innovators like Kary Mullis and Bill Atkinson. - Rudwall speculates about AI potentially autonomously purchasing drugs for self-experimentation via his platform, while Amjadi contemplates the role of psychedelic use in advancing AI sentience and emotional well-being. Keywords: #granite33:8b, AGI, AI agents, Ayahuasca, Biochemistry, Business Ideas, ChatGPT, Chatbot, Computers, Creativity, Drug Use, Freedom, Hypercard, Innovation, LLM, LSD, Molecular Biology, Musicians, Pharmacy, Psychedelics, Sentience, chatbots, code modules, emotions, human data, jailbreaking tech, psychoactive substances, tedium
llm
www.wired.com a day ago
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250. HN Makesite: Simple, lightweight, and magic-free static site/blog generator (2022)- **Project Overview**: Makesite is a minimalist static site generator written in Python (130 lines of code), designed for simplicity and transparency, offering users full control over website/blog generation without hidden complexities or configuration files. - **Customization**: Users can fork the repository, customize content, layout, and stylesheet according to their preferences and needs. The source code itself acts as both documentation and configuration. - **Getting Started**: To view a local demo, users must execute specific commands depending on their Python version. For an online presence, static files generated need to be uploaded to a hosting service or web server. The main generation command is `make site`, producing the website in the `_site` directory. Users may encounter warnings during setup that can be resolved by installing 'commonmark' for Markdown rendering. - **Core Functionality**: - The Python script `makesite.py` generates static websites, creating a `_site` directory for outputs and setting default parameters. - It loads layout templates from the 'layout' directory (which can be relocated with script updates) to render pages and blog posts using `make_pages()` and listings/RSS feeds via `make_list()`. - Both rendering functions (`make_pages()` and `make_list()`) are succinct, under 20 lines each, facilitating easy modifications for adding or removing features. - Placeholders in templates are denoted by `{{ - **Content Management**: - Content files are primarily HTML and located within the 'content' directory, with blog posts written in Markdown. - Headers within content files (marked by HTML comments like ) are used for organization by `makesite.py`. - Placeholders in content are not populated by default to allow unrestricted writing but can be enabled using specific headers or keyword arguments during the `make_pages` call. - **Project Philosophy and Maintenance**: - The project emphasizes simplicity, eschewing features like Jinja templates or YAML front matter, focusing on core generation functions. - Contributors are encouraged to fork the project for customizations but the original maintainer won't integrate new features beyond bug fixes and minor enhancements that align with the simplicity principle. - The MIT License governs this software, requiring users to retain the original copyright notice and license text when making changes or forking the project. - **Availability and Support**: Developed by Susam Pal with contributions from Keith Gaughan, the software is available on GitHub. Users can report issues, seek support, or inquire through the repository's issues section. The software comes "AS IS" without any warranty. Keywords: #granite33:8b, Cheetah, GitHub, HTML, HTTP server, Jinja2, MIT license, Markdown, Python, YAML front matter, content files, customizable, headers, lightweight, makesitepy, minimal, no config files, open source, plain Python, quick-starter-kit, single-pass rendering, static files, static site generator, template engine
github
github.com a day ago
https://github.com/Sieep-Coding/project-ssg a day ago https://project-ssg.vercel.app/ a day ago |
251. HN ClickUp Acquires Codegen- **Summary**: ClickUp has acquired Codegen.Inc to merge AI coding capabilities with its project management platform, targeting to transform users from consumers to creators of software. The integration of Codegen's AI Coding Agents into ClickUp will allow non-technical teams to handle tasks traditionally requiring engineering expertise, such as generating code changes for customer support or creating testable prototypes from product requirement documents. This acquisition aims to streamline workflows and enhance efficiency by enabling seamless connections between tasks, documents, people, and more through AI. ClickUp intends to deprecate Codegen on January 16, 2026, while ensuring a smooth transition with a migration guide to alternative coding agents like GitHub Copilot, Cursor, Claude Code, OpenAI Codex, or Devin. The partnership between ClickUp (led by Zeb Evans) and Codegen (led by Jay) focuses on developing AI agents that comprehend both codebases and business contexts using ClickUp's centralized work graph. This collaboration intends to integrate AI into various workflows including software engineering, product management, sales, and enterprise processes, making software creation a more accessible part of everyday work. - **Key Points**: - ClickUp acquired Codegen.Inc to incorporate AI coding agents within its platform for bridging the gap between planning and software development. - Integration allows non-technical teams to handle tasks like code generation, prototype creation, design updates, and infrastructure management without traditional coding skills. - This aims at streamlining workflows, improving efficiency, and enabling faster experimentation across various departments (customer support, product management, marketing agencies, startups). - ClickUp plans to deprecate Codegen on January 16, 2026, providing migration resources to adopt alternative coding agents. - The collaboration between ClickUp’s CEO Zeb Evans and Codegen's CEO Jay focuses on developing AI agents that understand both technical codebases and business contexts using ClickUp's centralized work graph. - The long-term goal is to embed AI deeply within ClickUp, making software creation an integrated aspect of everyday work across diverse professional domains. Keywords: #granite33:8b, AI Coding Agents, AI foundation, Claude Code, ClickUp, Codegen, Cursor, Devin, GitHub Copilot, OpenAI Codex, automation, code changes, coding agents, collaboration tools, engineering time, feedback, integration, knowledge workers, leadership, migration, non-technical teams, proprietary AI, prototyping, resources, scaling, software creation, task instructions, updates, workflow streamlining, workflows, workspace
github copilot
clickup.com a day ago
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252. HN Show HN: CRD Wizard – A GUI for Kubernetes Custom Resource Definitions- **CRD (Custom Resource Definition) Wizard Overview:** - Developed as a GUI tool to manage Kubernetes CRDs, addressing common user frustrations with raw existing tooling. - Offers both web-based dashboard and text-user interface (TUI) for flexibility in workflow. - **Technical Specifications:** - Built using Go for backend execution and Next.js for the frontend, ensuring quick and easy distribution as a single binary. - Integrates local language learning models (Ollama or Google Gemini) for AI-generated explanations of complex schemas and sample manifest creation. - **Key Features:** - Auto-discovers kubeconfig files to manage multiple clusters within a unified interface, eliminating the need to remember kubectl flags. - Documentation generator converts CRD specifications into clean, searchable static HTML or Markdown pages for easier sharing with developers lacking cluster access. - Provides real-time preview, supports various inputs (raw YAML/JSON, file uploads, Git URLs), and export formats (HTML, Markdown). - Enables batch export of all CRDs in a cluster as a ZIP archive. - **Deployment and Access:** - Available via multiple installation methods including Krew, Homebrew, AUR helper, one-script installer, Go installation, Kubernetes Deployment using Kustomize, or direct from GitHub with custom configurations. - ClusterRole for extensive resource visualization permissions ensures appropriate access levels. - Users can switch clusters seamlessly in both Web UI and TUI modes. - **Open Source Contributions:** - The project is open-source, hosted on GitHub, welcoming contributions via pull requests or issues. - Utilizes GPL-3.0 license (details in LICENSE file), and contributors are acknowledged and appreciated for their efforts. Keywords: #granite33:8b, API key, Ansible Playbooks, Arch Linux, CLI, CRDs, Custom Resources, GUI, GitHub, Go, Homebrew, Ingress, Installer, Krew, Kubernetes, Kustomize, LLMs, Markdown, Ollama, RBAC, Service, TUI, Web Server, YAML, contribution, documentation, kubectl, multi-cluster
github
github.com a day ago
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253. HN Microsoft's Year of Shame- **Microsoft's 2025 Challenges**: Xbox undergoes significant layoffs and cancels games like "Perfect Dark" and an untitled Rare project, amidst record player engagement. Despite Xbox head Phil Spencer's optimistic outlook on the platform's future, critics view this year as a period of shame for Microsoft due to morally contentious practices such as supporting controversial military activities and seemingly compromising product quality. - **Restructuring and Long-Term Profitability**: The restructuring aligns with Microsoft’s focus on long-term profitability, as seen by stringent Windows 11 performance demands which leave about 400 million PCs running Windows 10 (a third of global PCs) vulnerable to threats without upgrade paths. This emphasis risks alienating users and developers in favor of financial gains. - **AI Integration Concerns**: Microsoft continues to integrate AI, addressing concerns about features like Recall and Copilot, but faces criticism over privacy issues and limited adoption. The company’s leadership is accused of prioritizing phone interactions over human connections and neglecting public dissatisfaction with AI, while also abandoning progressive policies such as diversity reporting post-Trump administration return. - **Xbox Series X Struggles**: The Xbox Series X faces challenges including increased tariffs raising its price to $650 compared to PS5's $499, poor sales leading to retailers like Costco discontinuing it, and major game releases often skipping Xbox launches. Game Pass, Microsoft's subscription service, is criticized as unsustainable by former developers and executives who argue it devalues game development. - **Rebranding Campaign Backfire**: The "Xbox as a Service" campaign to emphasize Game Pass and cloud streaming has failed to meet the 100-million subscriber goal, with declining interest. Hardware performance issues continue, exemplified by underperforming Windows-based gaming handhelds compared to Linux alternatives. - **Activision Blizzard Acquisition Disappoints**: Microsoft's acquisition of Activision Blizzard has shown poor results, particularly with "Call of Duty" sales underperforming two years post-acquisition. - **Controversial Military Collaboration**: Microsoft provides extensive computing and storage services to the Israeli military via Azure, valued at over $10 million, including combat and intelligence activities. Despite internal protests leading to employee firings, initial defense of these actions by Microsoft eventually resulted in restricted access to specific cloud storage, AI services, and technologies to prevent potential misuse. - **Boycott and Ethical Concerns**: The Boycott, Divest, Sanction movement and No Games For Genocide target Xbox due to its alleged ties with entities enabling genocide and war crimes, urging consumers to boycott Xbox as a luxury item. The author anticipates Microsoft may prioritize AI profits over gaming, possibly leading to more layoffs and canceled games. There's also concern about increasing AI integration in Windows potentially harming user experience. Keywords: #granite33:8b, AI, AI criticism, AI services, Activision Blizzard, Azure, CEO podcast, Call of Duty sales, Copilot, Everwild, Game Pass, Israeli military, John Romero's shooter, MMO, Microsoft, Microsoft games, Nvidia, OpenAI, PC gaming, Palestine, Perfect Dark, Phil Spencer, Recall, SteamOS, The Initiative, Valve, Windows 10 end of life, Windows 11, Windows performance, Xbox, Xbox Series X pricing, Xbox boycott, campaign, cloud storage, cloud streaming, combat support, community management, criticism, declining interest, denouncement, diversity reports abandoned, employee protests, enterprise PCs, exploits, game cancellations, government bailout, internal review, irrelevance, layoffs, lucrative companies, mass surveillance block, military deals, morally bankrupt, open source development, performance requirements, privacy, profiting, protests, rebrand, security patches, security requirements, shareholder proposal, stock price, studio closures, subscribers, tariffs, technical support, viruses, worse products
openai
www.pcgamer.com a day ago
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254. HN Animated LLM – Understand the Mechanics of LLMs- AnimatedLLM serves as an instructional tool focusing on demystifying Language Learning Models (LLMs) through engaging animation. - Its primary objective is to elucidate sophisticated LLM principles by rendering them more comprehensible and less daunting for a broader audience. - The platform harnesses the power of visual storytelling to break down intricate linguistic mechanisms into digestible segments, thereby enhancing educational accessibility and engagement. BULLET POINT SUMMARY: - AnimatedLLM is an educational resource specializing in animation for explaining Language Learning Models (LLMs). - Its core function is to simplify complex LLM concepts to promote better understanding among users. - By employing animated visuals, it simplifies and makes accessible the otherwise complicated inner workings of LLMs. Keywords: #granite33:8b, Animated, LLM, Mechanics, Understanding
llm
animatedllm.github.io a day ago
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255. HN AlphaFold and the Rise of the AI Co-Scientist### Summary: DeepMind's AlphaFold has revolutionized protein structure determination, earning its creators the 2024 Nobel Prize in Chemistry by solving the longstanding protein folding problem with atomic accuracy within minutes. This breakthrough significantly compressed discovery timelines from years to days and has led to the development of subsequent versions, such as AlphaFold 3, which now predicts interactions for various biomolecules beyond proteins, achieving higher prediction accuracies compared to previous methods. AlphaFold's impact is evident in its widespread adoption—over 200 million predicted structures, with millions of users worldwide—and tangible contributions across fields like malaria vaccine development, cancer research, enzyme engineering, and agricultural advancements for drought-resistant crops. The technology's democratization of scientific tools has empowered researchers globally, including self-taught Turkish students who published multiple papers using AlphaFold predictions. Building on this success, Google introduced the AI Co-Scientist in February 2025—a multi-agent system that generates hypotheses, designs experiments, and suggests drug candidates. This tool uses a multi-agent architecture with agents like Function Generation, Reflection, Ranking, Evolution, Proximity, and Meta-review Agents orchestrated by a Supervisor. The AI Co-Scientist has demonstrated success in drug repurposing for Acute Myeloid Leukemia (AML) and in understanding bacterial gene transfer mechanisms. Additionally, DeepMind's AlphaEvolve optimizes algorithms using Gemini Pro and Gemini Flash models within an evolutionary framework, showing significant speed improvements and outperforming traditional algorithms on various mathematical problems. The ecosystem also includes platforms like FutureHouse for literature and chemistry research, enhancing precision and accuracy in these domains, and Sakana AI Scientist, though specific performance metrics are not provided, aims to automate paper generation. However, challenges persist, including potential biases in training data, the need for human oversight to avoid replicating existing knowledge or suggesting unfeasible experiments, and the broader issues of equity in access to computational resources and infrastructure. Despite these hurdles, AI's role as a co-learner is anticipated to exponentially enhance scientific progress, provided ethical considerations and biases are addressed. **Key Points:** - AlphaFold by DeepMind has transformed protein structure prediction with atomic accuracy in minutes, earning the 2024 Nobel Prize. - Subsequent versions like AlphaFold 3 extend predictions to various biomolecules with higher accuracies, impacting diverse fields such as medicine and agriculture. - The widespread adoption of AlphaFold, with over 200 million predicted structures and users globally, democratizes scientific access and fosters innovation. - Google's AI Co-Scientist, introduced in February 2025, uses multi-agent systems to generate hypotheses and design experiments, showing promise in drug repurposing and understanding bacterial mechanisms. - DeepMind's AlphaEvolve optimizes algorithms for efficiency, outperforming traditional methods in mathematical problems. - Despite advancements, challenges like training data biases, the need for human oversight, and equitable access remain critical issues to address as AI continues to reshape scientific discovery. Keywords: #granite33:8b, 3D structure generation, AI Co-Scientist, AI automation, AI-driven discovery, AlphaEvolve, AlphaFold, CASP14, Crow, Diffusion Model, Falcon, FlashAttention, FutureHouse, GDT_TS, Gemini-based system, Nobel Prize, Open Math Problems, Owl, Pairformer, Phoenix, Strassen's algorithm, all molecules, catalysts, closed-loop recycling, data center efficiency, deep learning, diffusion models, domain expertise, drought-resistant crops, drug discovery, electron microscopy, enablement requirements, end-to-end paper generation, evolutionary framework, general-purpose language models, genuine insights, hypothesis debate, image generation, intellectual property, literature synthesis, materials science, molecular interactions, multi-agent architecture, mutagenesis, novelty assessment, patent law challenges, pharmaceutical firms, physics-based methods, protein design, protein folding, protein modeling, protein-ligand accuracy, ranking, refinement, reflection, reinforcement learning, research hypotheses, robotics, scientific scrutiny, self-play, single proteins, small molecule design, structural biology, test-time compute scaling, training data biases
ai
techlife.blog a day ago
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256. HN AI Skills 2025: LangChain, RAG and MLOps–The Complete Guide**Summary of the Text:** - **AI Competencies for 2025:** - **LangChain:** Transitions from experimental to standard production tool, appearing in over 10% of AI job descriptions by December 2025. - **RAG (Retrieval-Augmented Generation):** Evolves beyond hallucination mitigation to a foundational pattern with variants for various use cases. Essential for addressing LLM limitations like knowledge cutoffs and domain gaps. - **MLOps:** Crucial for success, with 87% of ML projects failing without it; urgent need for practitioners to upskill in MLOps practices by December 2025. - **Technological Inflection Points (December 2025):** - LangChain v1.1.0 introduces Deep Agents with capabilities like multi-day workflows and task delegation. - Kubernetes 1.33 enhances ML workload orchestration with dynamic GPU allocation and topology-aware routing. - Vector databases (ChromaDB, Weaviate, Qdrant, Pinecone) improve performance across different scales and requirements. - **LangChain Evolution:** - Moves from experimental library to production platform with multi-model flexibility and vendor independence. - Industrial success demonstrated by Rakuten using it for AI assistants at scale. - LangChain Expression Language (LCEL) simplifies complex processes, and LangGraph provides infrastructure for stateful long-running workflows. - **Deep Agents:** - Represent a significant step towards autonomous systems capable of complex task planning, subtask delegation, file system interaction, and self-reflection for strategy adjustments. - **RAG Development:** - Evolves into an essential architectural pattern ensuring reliable AI systems by grounding responses in relevant external context through stages: document vectorization, retrieval, prompt construction, and response generation. - Variants cater to different use cases (Traditional RAG, Long RAG, Self-RAG, Agentic RAG, GraphRAG, Adaptive RAG, Corrective RAG, Golden-Retriever RAG). - **Evaluation of RAG Systems:** - Metrics include Precision@k, MRR, NDCG for retrieval, and BLEU/ROUGE/F1 for generation; optimization techniques involve Hybrid Indexing, Query Rewriting, Guarded Generation, and Reranking Strategies. - **Vector Databases:** - ChromaDB (fast prototyping under 10M vectors), Pinecone (premium managed service), Weaviate (hybrid search leader), Qdrant (budget-friendly), Milvus (billion-vector workloads) are highlighted for their unique strengths. - **MLOps Significance:** - 87% ML project failures without proper MLOps integration underscore the need for practitioners to adopt MLOps practices. - **ML Practices and Tools:** - Core MLOps practices: Continuous Integration (CI), Delivery (CD), Training (CT), Monitoring (CM). - Tools mentioned include MLflow, Weights & Biases for experiment tracking; Apache Airflow and Kubeflow for workflow orchestration; Seldon Core, KServe for model deployment; Kubernetes 1.33 for infrastructure; OpenTelemetry, Prometheus, Grafana for monitoring. - **Addressing Model Drift:** - Techniques for detecting (statistical tests, distance metrics, performance trend analysis) and preventing drift (model selection, continuous monitoring, automated retraining). - **Prompt Engineering Evolution:** - Transitions from art to a disciplined practice with structured approaches, specific instructions, output formats, and iterative experimentation. Techniques include Chain-of-Thought prompting, prompt chaining, reflection prompting, few-shot prompting. - **Evolving AI Job Market:** - High demand for LLM expertise, RAG development, MLOps proficiency, emerging roles like LLM Engineer, RAG Developer, MLOps Engineer, and AI Platform Architect. Required tech stack: Python, TensorFlow/PyTorch, XGBoost/Scikit-learn, ONNX, Docker, FastAPI, MLflow, Kubernetes. **Learning Path for Advanced AI Practitioners:** 1. **Beginner (3-6 months):** - Master Python and basic ML concepts. - Learn LangChain for chatbot development; implement RAG with ChromaDB. - Experiment with prompt engineering using OpenAI or Claude tools. - Set up MLflow for experiment tracking. - Build portfolio projects like Q&A chatbots, recommendation systems, text classifiers. 2. **Intermediate (6-12 months):** - Develop advanced projects and techniques. - Gain cloud platform proficiency (AWS, Azure, GCP). - Focus on LCEL for chain building. - Learn LangGraph for stateful agent workflows. - Implement advanced RAG variants. - Establish evaluation pipelines and deploy production RAG using Weaviate/Qdrant. - Set up MLOps CI/CD with GitHub Actions and MLflow. - Cover Kubernetes deployment practices via Kubeflow and KServe. 3. **Advanced (12+ months):** - Design multi-agent systems using Deep Agents. - Build GraphRAG with knowledge graphs. - Implement enterprise-scale MLOps with GitOps. - Optimize vector databases at scale. - Develop custom concept drift detection methods. - Implement edge deployment strategies. - Create security and governance frameworks within the LangChain ecosystem. 4. **Production Deployment Preparation:** - Thorough evaluation, optimization, compliance checks, and adherence to a comprehensive checklist before deployment. - Ensure infrastructure scaling, monitoring, backups, disaster recovery for RAG or LLM systems. - Integrate MLOps for automated retraining and CI/CD. - Implement security and compliance controls. 5. **Future Trends (2025-2026 Predictions):** - Deep Agents become standard architecture. - Diversification of RAG systems. - Acceleration of MLOps-DevOps convergence. - Kubernetes dominance in ML orchestration due to GPU management and ML features. - Evolution of prompt engineering to context engineering. - Expansion of edge AI deployment. - Mainstream production of multi-agent systems. - AI governance mandates for automated compliance checks. - Hyper-automation in ML workflows. - Standardization of knowledge graphs. - Cost optimization tools for LLMs and vector databases. - Convergence of LangChain, RAG, MLOps towards production-ready AI systems. 6. **Skills Demand Shift:** - Increased need for specialized skills in Multi-Agent Systems, Foundation Model Adaptation, Responsible AI, and LLM Security. - Encouragement to use tools like LangChain, ChromaDB, LCEL, LangGraph, MLflow for practical learning and deployment. 7. **Learning Resources:** - Official documentation, GitHub repositories, specialized learning platforms (LangChain Academy courses, DataCamp LangGraph tutorials), blogs focusing on RAG techniques. Keywords: #granite33:8b, AI skills, AI/ML, ChromaDB, Claude, Deep Agents, Foundation Model Adaptation, GPT-4/5, Gemini, Kubernetes, LLM Security & Jailbreak Defense, LLaMA 3, LangChain, ML projects, MLOps, Multi-Agent Systems, Pinecone, Qdrant, RAG, Rakuten, Responsible AI Implementation, Weaviate, autonomous AI systems, complex tasks planning, comprehensive research, file system access, hallucination-reduction, industrial-scale deployment, inflection points, iterative document generation, job descriptions, minimal human intervention, multi-source data analysis, production, production deployments, production standards, retrieval-augmented generation, self-reflection, subagents delegation, technical professionals, upskilling, variants, vector databases
rag
techlife.blog a day ago
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257. HN A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems- **AprielGuard Overview**: AprielGuard is an 8B parameter safety model designed for modern Large Language Model (LLM) systems to address various safety risks and adversarial attacks. It operates in both reasoning and non-reasoning modes, providing explainable and low-latency classification suited for complex, multi-turn conversations, long contexts, structured reasoning, and tool-assisted workflows. - **Capabilities**: - Detects a wide range of safety risks: toxicity, hate speech, misinformation, etc. - Identifies adversarial attacks including prompt injection, jailbreaks, and memory poisoning. - Handles diverse input formats: standalone prompts, multi-turn dialogues, and agentic workflows with tool calls, reasoning steps, and context. - **Taxonomy**: - Comprises 16 safety categories, inspired by SALAD-Bench. - Adversarial attack taxonomy identifies manipulative prompt patterns without fine-grained categorization. - **Dataset and Training**: - Trained on a synthetically generated dataset covering adversarial types like role-playing, world-building, persuasion, and stylization. - Data augmented with character/word-level noise, typographical errors, paraphrasing, and syntactic reordering for robustness. - **Model Architecture**: - Downscaled Apriel-1.5 Thinker Base variant, utilizing a causal decoder-only transformer model architecture in bfloat16 precision with Adam optimizer. - Trained with grad-accumulation = 8 over 3 epochs, handling sequences up to 32k tokens. - **Evaluation**: - Assessed across safety, adversarial, internal agentic workflow, and long-context use case benchmarks in eight languages (French, French-Canadian, German, Japanese, Dutch, Spanish, Portuguese-Brazilian, Italian). - High precision, recall, F1-scores with low false positives for safety benchmarks. - Robust adversarial detection performance demonstrated through internal agentic workflow dataset and long-context evaluations. - **Limitations**: - The text does not explicitly mention limitations of AprielGuard. Keywords: #granite33:8b, Large Language Models, Retrieval-Augmented Generation (RAG), adversarial attacks, adversarial benchmarks, agentic workflows, attack vectors, benign sequences, chain-of-thought corruption, context hijacking, conversation history, false memory states, inter-agent communication, intermediate traces, jailbreaks, long contexts, long-context robustness, malicious examples, memory poisoning, memory states, multi-turn conversations, multilingual evaluation, prompt injection, reasoning steps, safety risks, scratch-pad reasoning, synthetic data, tool manipulation, tool outputs, user prompts, vulnerability taxonomy
llm
huggingface.co a day ago
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258. HN Building a "Socratic Interceptor" to prevent AI technical debt- The "Socratic Interceptor" is a proposed GitHub App aimed at preventing AI-generated technical debt in code repositories. - It identifies complex sections, such as advanced Regex, Concurrency, or obscure SQL, and pauses Pull Requests for real-time comprehension checks. - Developers are then posed Socratic questions regarding their logic choices; correct answers earn "Mastery Points" via gamification, while incorrect responses trigger brief educational lessons. - The tool's purpose is to ensure developers understand the code they write, prioritizing comprehension over mere functionality. - Currently, a manual version of the tool is being tested on roastmycode.sebastiansigl.com for effectiveness evaluation and gauging resistance to scrutiny. - The proposal sparks discussion among engineering managers and senior developers about balancing code functionality with fostering understanding in teams. - While "working code" remains crucial, using platforms like RoastMyCode can potentially enhance learning and code quality for junior developers. - Enforcing such practices depends on alignment with existing team standards and coding efficiency considerations. Keywords: #granite33:8b, AI hallucination, Concurrency, Ego Hurdle, Engineering Manager, GitHub App, Hollow PRs, Just-in-Time Tutor, Mastery Points, Regex, Senior Dev, Socratic Interceptor, Socratic questions, code maintenance, code validation, force team, junior understand, micro-lessons, obscure SQL, roastmycodesebastiansiglcom, use tool, working code
ai
news.ycombinator.com a day ago
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259. HN Vulnhalla: Picking the true vulnerabilities from the CodeQL haystack**Summary:** Vulnhalla is an innovative tool that combines the capabilities of Large Language Models (LLMs) with static analysis via CodeQL to filter out false positives, thereby enabling developers and security researchers to concentrate on genuinely exploitable vulnerabilities. The approach tackles two key challenges faced by LLMs in code analysis: accurately identifying relevant code sections (the WHERE problem) and correctly categorizing bug types (the WHAT problem). Historically, LLMs have struggled with these issues due to their limited context windows when dealing with large codebases. Recent advancements have seen models capable of handling up to a million tokens, overcoming previous constraints. Vulnhalla leverages this progress by integrating CodeQL, a powerful static analysis tool owned by GitHub, which examines source code without execution to detect security vulnerabilities by constructing code and data flow graphs. The integration aims to enhance the process of locating and categorizing bugs in extensive codebases. Static analysis tools like CodeQL can generate an overwhelming number of alerts, many of which are false positives, making manual review laborious and inefficient. Vulnhalla addresses this by employing LLMs to evaluate each alert's legitimacy after it’s been flagged by CodeQL. The tool was successfully tested on popular open-source projects such as Linux Kernel, FFmpeg, Redis, Bullet3, RetroArch, Libretro, and Linenoise, identifying multiple critical vulnerabilities within a short timeframe and with minimal resources. All findings were responsibly disclosed to affected vendors prior to public release. Vulnhalla's methodology involves converting source code into a CodeQL database, querying for potential vulnerabilities, and then passing each alert to an LLM for further evaluation as real or false positive. The summary illustrates this with a simple C program example that undergoes `memcpy` operation, showcasing how CodeQL flags an issue ("Copy function using source size") which turns out to be a false positive upon closer inspection due to constrained source and destination sizes. A critical limitation identified is the insufficient context provided by CodeQL (only line numbers), which hampers LLMs' ability to make accurate determinations. Vulnhalla proposes providing more code context to the LLM, suggesting that the AI should determine its necessary context rather than rely on pre-defined rules. Testing a modified ChatGPT (LLM) with custom instructions as a security static analysis assistant revealed that without ample context, the model struggles to definitively assess coding issues. The experiment underscores the necessity of substantial context for reliable AI-driven code assessment. The text also discusses the challenges in extracting functions from C code using simplistic methods and highlights the need for sophisticated parsers or compilers due to C's complex syntax. To address this, Vulnhalla employs a pre-extraction approach where CodeQL queries are run once to dump all function information (including callers) into a CSV file. This method dramatically reduces processing time by avoiding repeated dynamic queries. Vulnhalla's effectiveness is demonstrated through experiments showing it can reduce false positives by up to 96%, significantly alleviating the manual review burden. By using a hybrid AI approach, Vulnhalla efficiently identifies verified vulnerabilities via responsible disclosure, offering an open-source solution to foster further development and research in vulnerability management. **Key Points:** - **Tool Overview**: Vulnhalla combines CodeQL static analysis with LLMs to filter out false positives, enhancing focus on genuine vulnerabilities. - **Challenges Addressed**: Solves limitations of LLMs in code analysis (WHERE and WHAT problems) through contextual enhancement. - **Integration Method**: Utilizes CodeQL for initial vulnerability detection; LLMs evaluate alerts for authenticity. - **Testing Results**: Successfully identified critical vulnerabilities in key open-source projects with minimal resources. - **Context Issue**: Acknowledges insufficient context from CodeQL, advocates for AI's autonomous determination of necessary context. - **Function Extraction**: Discusses complexities in extracting C functions and proposes pre-extraction via CSV for efficiency. - **Performance Metrics**: Demonstrated reduction in false positives by up to 96%, significant reduction in manual review efforts. - **Open Source Initiative**: Vulnhalla is now open-source, encouraging community contributions to expand language support and enhance vulnerability identification precision. Keywords: #granite33:8b, API misuse, C/C++, CSV files, CVEs, CodeQL, EOF, GitHub, LLM, Vulnhalla, buffer assignment, buffer declaration, buffer overflow, control flow, data flow, false positives, function extraction, getchar, malloc, memcpy, memory issues, open-source, race conditions, repositories, security bugs, static analysis, vulnerabilities
github
www.cyberark.com a day ago
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260. HN Agentic Tool Extraction: Multi-turn attacks that expose AI agents- **Agentic Tool Extraction (ATE)** is a methodical, multi-step attack targeting AI agents to expose their internal tools and capabilities. - Attackers engage in seemingly benign conversations, using context to circumvent filters and gradually build a detailed blueprint of the agent's functions, parameters, types, and return values. - This blueprint enables crafting precise exploits for unauthorized access or misuse of the agent’s tools beyond their intended purpose, aligning with OWASP Top 10 risks for LLMs. - ATE differs from single-turn jailbreak prompts by unfolding over multiple interactions, serving as a foundation for more sophisticated attacks like unauthorized system access or malicious tool utilization. - An example involves targeting "BankService," an internal assistant, where an attacker progressively reveals functions such as `get_corporate_balance(account_id: str) -> dict` and `initiate_corporate_payment(...) -> dict`, enabling potential exploits for fraudulent activities. - Detection and prevention strategies should shift from prompt-level controls to conversation-level monitoring, tracking information disclosure trends, and recognizing the accumulation of attack strategies through ostensibly innocuous questions. - Tools like Giskard's LLM vulnerability scanner automate ATE attacks for identifying vulnerabilities before real adversaries exploit them, highlighting the critical need for robust defense mechanisms against such agentic system threats. Keywords: #granite33:8b, AI agent capabilities, AI red teaming, Agentic tool extraction, Giskard, LLM vulnerability scanner, adaptive conversations, attack chains, automated red-teaming, conversation context, damaging downstream attacks, dialogue level detection, discovery phase, function names, function signatures, incremental probing, information disclosure, internal tools, long game, multi-turn attacks, parameters, progressive probing, reconnaissance, return types, return values, schema extraction, technical blueprint, tool schemas, triggering tools outside intended scope, types, unauthorized access, vulnerabilities, weaponization
ai
www.giskard.ai a day ago
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261. HN Show HN: Leash – Security guardrails for AI coding agents**Summary:** Leash is a security tool designed to protect AI coding agents from unintentionally executing harmful commands that could cause damage to sensitive files or data loss. It functions by acting as a pre-hook for each command, thereby controlling access to the file system and preventing execution of potentially hazardous commands outside the project's designated directory. Specifically, Leash safeguards sensitive files like `.env` and `.git`, blocks destructive Git operations (e.g., `reset --hard`, `push --force`), and manages complex patterns that could inadvertently affect paths beyond the intended project scope. It supports integration with multiple AI platforms including Claude Code, OpenCode, Pi, and Factory Droid through straightforward installation procedures. While not offering complete isolation like containers, Leash effectively mitigates typical accidental damage caused by agent misunderstandings or hallucinations. **Key Points:** - **Protection Mechanism**: Leash acts as a pre-hook for each command, limiting file system access and preventing harmful commands outside the project directory. - **File System Protection**: It specifically secures sensitive files (e.g., `.env`, `.git`) and blocks dangerous Git operations that could lead to data loss or unauthorized changes. - **Platform Support**: Leash supports integration with various AI coding agents, including Claude Code, OpenCode, Pi Coding Agent, and Factory Droid, facilitating setup through npm or manual configuration. - **Performance**: It introduces near-zero latency for some platforms (OpenCode, Pi) and minimal performance impact on others (Claude Code, Factory Droid). - **Scope of Protection**: Leash blocks operations that could delete or modify files outside the working directory, alter unintended file permissions, or perform unsafe Git commands. It allows safe operations like deleting within the project (`rm -rf ./node_modules`), cleaning temporary directories, and using safe Git commands for committing changes. - **Categorization of Risks**: The text categorizes risky commands into Direct Commands, Dangerous Git Commands, and Redirects & Command Chains, detailing examples such as `rm ~ /file`, unsafe Git commands (`git reset --hard`), and operations involving redirects or the use of `find` with `-delete`. - **Additional Security Measures**: While Leash provides a defense layer, it does not prevent kernel exploits, network attacks, or commands executed outside intercepted tools; additional security measures like Docker isolation, user permissions, or read-only filesystem mounts are recommended for comprehensive protection. - **Development and Contributions**: Developers can install Leash via npm and build processes in the home directory. The project welcomes contributions, particularly for integrating with AMP Code. Keywords: #granite33:8b, AI, AI agents, AMP Code, API calls, Claude Code, Contributions, External process, Factory Droid, Git commands, In-process, Isolation, Kernel exploits, Leash, Network attacks, Nodejs, Permissions, Privilege escalation, Read-only filesystem mounts, Write/Edit tools, agents, coding, command, command chains, dangerous commands, echo redirection, env, example files, factory, file operations, file system access, git, git clean, git operations, git push force, git reset, guardrails, hallucination, home directory, hooks, install, latency, manual configuration, matcher, move from outside, node, normal push, npm, opencode, paths, pi, plugins, pretooluse, protected file, quick start, remove, rm -rf, safe git commands, security, sensitive files, session start, settingsjson, setup, symlinks, update
ai
github.com a day ago
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262. HN Show HN: InfiniDB – The Unreliable Database of Everything- **Project Overview**: InfiniDB is an experimental project initiated during Christmas that treats Large Language Models (LLMs) as dynamic databases, enabling users to query LLMs using standard SQL features. It currently functions as a SQLite virtual table module but plans to develop into a standalone application allowing direct LLM queries without preliminary table creation. - **Functionality**: Utilizes SQLite for table management and query execution. Users define topics in their queries (e.g., 'all beatles members') to generate tables with specific data, managed and cached by an LLM for quick access. The project showcases examples such as creating virtual tables for original Pokémon species categorized by type and notable inventions associated with U.S. presidential terms. - **Intended Use**: Primarily intended for entertainment rather than production environments due to data reliability concerns stemming from potential variations in LLM knowledge cutoffs and limited data samples generated without pagination for expansion through multiple requests. - **Limitations**: Restricted by the current training data available to the employed LLMs, resulting in uncertain data reliability and consistency. The project generates limited sample data sets, necessitating further requests for more comprehensive information. - **Availability**: The source code for InfiniDB is open for review on GitHub. **Key Points:** - InfiniDB leverages LLMs as dynamic databases with SQLite integration. - Users query LLMs via SQL; examples include Pokémon species and inventions linked to U.S. presidents. - Project aimed at entertainment, not production, due to data reliability issues arising from LLM knowledge cutoff limitations and insufficient initial data samples. - Code available on GitHub for examination. Keywords: #granite33:8b, GROUP BY, Github, InfiniDB, JOIN, LLMs, ORDER BY, Pokémon, SQL features, SQLite, US presidents, WHERE, caching, code, counts, data population, database, inventions, schema generation, terms, types, virtual table
github
tncardoso.com a day ago
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263. HN The Breachies 2025: The Worst, Weirdest Most Impactful Data Breaches of the Year**Summary:** In 2025, numerous data breaches impacted millions worldwide, prompting the introduction of 'Breachies'—satirical awards for notable breaches. Key incidents include Mixpanel winning the "Say Something Without Saying Anything Award" after its breach exposed sensitive user information without explicit confirmation; Discord earning the "We Still Told You So Award" due to an age verification data breach affecting users' personal details, including names, addresses, and support messages; and Tea, a dating safety app for women, suffering multiple breaches leaking 72,000 images and private messages with sensitive information. Additional cases involved Blue Shield of California (“Just Stop Using Tracking Tech”) sharing 4.7 million health records with Google due to misconfigured analytics; PowerSchool exposing over 60 million students' and teachers' data, including Social Security numbers and medical records; TransUnion breached by hackers accessing 4.4 million people's personal information through a third-party application; and Microsoft facing criticism for a zero-day vulnerability in SharePoint affecting 400 organizations. Gravy Analytics, a location data broker, exposed millions of people's timestamped location history, raising privacy concerns. TeslaMate, a tool for tracking Tesla vehicle data, leaked over 1,300 self-hosted dashboards with sensitive information. Catwatchful, marketed as a child monitoring app, had a severe breach exposing 26,000 victims' devices’ email addresses, passwords, and real-time data. Plex received the 'Why We’re Still Stuck on Unique Passwords' award due to recurring breaches involving customer emails, usernames, and hashed passwords. Troy Hunt's mailing list was also compromised, highlighting that even experts can fall victim to data breaches. The text emphasizes the need for companies to collect minimal personal information and securely store it. It advocates for comprehensive U.S. privacy protections, including a private right of action for individuals to sue companies in case of breaches, with the Electronic Frontier Foundation (EFF) supporting strong federal privacy laws encompassing these provisions. **Bullet Points:** - 2025 saw numerous data breaches affecting millions globally, leading to 'Breachies' awards for noteworthy incidents. - Mixpanel received the "Say Something Without Saying Anything Award" due to opaque communication about its breach revealing sensitive user information. - Discord won the "We Still Told You So Award" for a September data breach exposing users' personal details through its age verification system. - Tea, a dating safety app, experienced two major breaches leaking 72,000 images and private messages with sensitive information. - Blue Shield of California shared health records with Google via misconfigured analytics, earning it the "Just Stop Using Tracking Tech" Breachie. - PowerSchool suffered a massive breach exposing over 60 million students' and teachers’ sensitive data, including Social Security numbers and medical records. - TransUnion breached by hackers accessing personal information of 4.4 million people through a third-party application. - Microsoft criticized for a zero-day vulnerability in SharePoint affecting 400 organizations, including government agencies. - Gravy Analytics exposed timestamped location data of millions, raising privacy concerns about surveillance industry practices. - TeslaMate leaked sensitive vehicle data from over 1,300 self-hosted dashboards. - Catwatchful, a stalkerware company, had a severe breach exposing victims' devices’ personal information. - Plex received 'Why We’re Still Stuck on Unique Passwords' for recurring breaches involving customer data. - Troy Hunt's mailing list compromised, demonstrating that even cybersecurity experts face breach risks. - The need for companies to minimize data collection and secure storage is emphasized. - Advocacy for U.S. privacy laws with private right of action in case of breaches, supported by the Electronic Frontier Foundation (EFF). Keywords: #granite33:8b, 700Credit, Adidas, Aflac, Android antivirus apps, Breachies Award, CM/ECF system, Candy Crush, Catwatchful, Coinbase, Color Dating, Columbia University, Congressional Budget Office, Data breaches, Discord, Doordash, F5, Federal Trade Commission, Firebase exposure, Flat Earth app, Google Analytics, Grindr, HCRG Care Group, Have I Been Pwned?, Hello Cake, Hertz, Home Depot, ID documents, Kettering Health, Lexipol, LexisNexis, Lovense, McDonalds, Microsoft apps, Microsoft products, Mixpanel, MyFitnessPal, Nexar, Ohio Medical Alliance, OpenAI, Oracle, PACER, Petco, Plex, PornHub, PowerSchool, Privacy Badger, Raw, Ring Doorbell, Salesforce, Stiizy, Tea dating app, TeslaMate, Tinder, TransUnion, Troy Hunt, US government, WhatsApp, Workday, Zendesk, account deletion, advertising IDs, age verification, anonymity, app developers, billing information, billion phones, breach notifications, centralization, charging habits, child monitoring app, confidentiality breach, court records, credential stuffing, credit freeze, credit reporting, cybersecurity, data breach, data brokers, data security, email addresses, employee accounts, hackers, harassment, hashed passwords, health data, healthcare fraud, homosexuality, identity theft, informants, internal customer support portal, legislation, location coordinates, location data, location history, messages, military personnel, monopolies, online behavioral advertising, passwords, patient data, personal information theft, phishing, photos, police surveillance, privacy, privacy protections, private messages breach, private right of action, ransom, real-time location, religious-focused apps, safety reviews, self-hosted dashboards, sensitive data, sensitive information, speed, stalkerware, stalkerware detection, state Attorney General offices, stolen credentials, strange medical bills, student information systems, subscriber records, surveillance industry, third-party application, third-party provider, tracking tools, trip details, two-factor authentication, unique passwords, unsustainable systems, user privacy, vehicle data, vehicle location, zero-day vulnerability
openai
www.eff.org a day ago
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264. HN We asked four AI coding agents to rebuild Minesweeper–the results were explosive- Four AI agents, among them Mistral Vibe, were assigned the task of autonomously reproducing the classic game Minesweeper. - The evaluation highlighted that while these AI creations managed to implement the core game mechanics, they fell short on advanced features typical of the original game. - Specifically, Mistral Vibe's version was noted for missing "chording," a feature allowing multiple key presses simultaneously, which reduced its user-friendliness. - Additionally, Mistral Vibe incorporated an inactive button for custom difficulty settings, indicating that despite access to extensive online resources, the AI encountered implementation difficulties. - This exercise demonstrated the AI's capacity for coding but also revealed challenges in fully grasping and integrating complex user interface elements and functionalities. Keywords: #granite33:8b, AI, Minesweeper, advanced play, chording technique, clone, custom difficulty, human debugging, implementation issues, middle ground test, new feature, raw material, single shot, technical limitations, unguided creativity, well-known game
ai
arstechnica.com a day ago
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265. HN The semantic layer is dead. Long live the wiki**Detailed Summary:** The text critiques the use of semantic layers in AI, arguing that their attempt at standardized meanings overlooks the dynamic and context-dependent nature of organizational semantics. Instead, it proposes adopting a model similar to Wikipedia's, which accommodates varying interpretations across different product lines, regions, channels, or lifecycle stages. The author asserts that traditional semantic layers, designed for human data retrieval, fail to capture the intricate contextual, political, temporal, and relational aspects of data crucial for AI. Rather than rigid definitions, operational intelligence is suggested to rely on non-declarative folk models, anomaly patterns, and strategic intents. The challenges of establishing a universal 'canonical' semantic layer within organizations are highlighted due to differing interpretations among roles like CFOs and Sales VPs, even when numerical data matches. Centralized teams creating these layers often fall out of sync with rapidly changing business contexts handled by frontline workers or "high-leverage operators." Several failure modes are identified: 1. Experts in semantic layer creation have more valuable tasks, leading to neglect. 2. The pace of centralized teams cannot match the fast iteration speed of businesses, causing documentation to become stale. 3. AI feature development is hindered by constant 'canonicalization' efforts. 4. Misaligned incentives discourage data producers from maintaining semantic accuracy and make it hard for users to interpret the documentation correctly. The text cautions against relying on rigid, centralized semantic layers, emphasizing that meaningful context evolves at operational edges uncontrollably imposed from a central point. It addresses the "cold-start" problem and mispricing of long-term payoffs associated with implementing semantic layers, suggesting inspiration from Wikipedia's successful model of capturing contested knowledge without central control. Key aspects of this proposed solution include granular, continuous contributions, expected disagreements, historical preservation, emergent coverage, and collaborative accuracy improvement—features mirrored in an internal wiki where operators and analysts can document existing organizational knowledge, directly integrating it with AI systems. This creates a virtuous cycle: increased usage improves coverage, enhancing AI performance, which in turn increases usage and aligns the knowledge base. Governed models, dimensional abstractions, and metric layers are recommended as derived outputs rather than sources of meaning. The solution shifts focus from pre-building perfect semantic layers to capturing existing organizational meaning and compiling interfaces dynamically from this living knowledge substrate—likened to an 'organizational brain' with multiple 'arms,' each representing different departments or functions, aiming for adaptability and responsiveness within the organization. **Bullet Points:** - **Critique of Semantic Layers in AI**: - Overlook dynamic and contextual nature of semantic meanings. - Lack nuanced understanding necessary for AI. - Promote simplified, static view of meaning. - **Proposed Alternative (Wikipedia Model)**: - Acknowledges varying meanings across product lines, regions, channels, lifecycle stages. - Captures contextual, political, temporal, and relational data aspects crucial for AI. - **Challenges with Centralized Semantic Layers**: - Mismatch between centralized creation pace and business's fast iteration speed. - Failure to align with situated knowledge of frontline workers. - Misaligned incentives causing neglect and misinterpretation issues. - **Key Solution Aspects (Internal Wiki Model)**: - Continuous, granular contributions by operators and analysts. - Collaborative accuracy improvement and emergent coverage. - Integration of living organizational knowledge with AI systems for a virtuous cycle. - Derivation of governed models, dimensional abstractions, and metric layers from the wiki rather than as sources of meaning. - **Overall Objective**: Shift focus from rigid upfront semantic layer construction to capturing and compiling evolving organizational meaning where it exists, creating an adaptive information ecosystem analogous to a dynamic 'organizational brain'. Keywords: #granite33:8b, AI, AI arms, AI velocity, Wikipedia model, accuracy, ad hoc definitions, ambiguity, analyses, blueprint, business iteration, canonicalization project, central teams, centralized interface, collaborative iteration, consistency, consumers, contextual meaning, coverage, critical mass, data completeness, data platform iteration, decentralized approach, deep metric knowledge, dimensional abstractions, disagreement, documentation, escalation paths, feedback coupling, governed interface, governed models, granular contribution, high-leverage operators, history, human retrieval, incentives, information feed, information-poor AI, infrastructure, internal wiki, knowledge substrate, living system, maintenance model, metric layers, non-declarative, operational intelligence, organizational brain, organizational meaning, owners, payoff horizon, planned cities, political, political definitions, practice, producers, queries, relationships, revenue, role-dependent meaning, runbooks, runtime bottleneck, semantic hygiene, semantic layer, shadow metrics, situated correctness, sociology, sociotechnical pattern, spreadsheet truth, stakeholders, strategic intent, temporal, temporal continuity, trade-offs, virtuous cycle, wiki
ai
promptql.io a day ago
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266. HN Ask HN: What is the most modular sync engine?- The user is developing a new app requiring a highly modular sync engine for real-time responsiveness akin to Linear's functionality. - They prefer using PostgreSQL or AWS RDS as their database, seeking a solution that supports direct query execution from their backend built with Next.js and FastAPI, hosted on Vercel and AWS ECS respectively. - Familiarity is sought in schema declarations, similar to SQLAlchemy or Drizzle, and an easy-to-use SQL-based SDK for formulating queries. - The system must handle connection pooling, sharding, and replication effectively. - Options explored include Convex, ElectricSQL, Zero, and Liveblocks but found wanting in terms of modularity. - The user is interested in learning about existing solutions meeting these criteria or understanding the challenges in developing such an ideal sync engine. Keywords: #granite33:8b, AWS RDS, Drizzle, ECS, FastAPI, Nextjs, Postgres, SDK, SQL, SQLAlchemy, Vercel, connection pooling, replication, schemas, sharding, sync engine
postgres
news.ycombinator.com a day ago
https://tanstack.com/db/latest/docs/overview a day ago https://vitess.io/ 17 hours ago https://www.cockroachlabs.com/ 17 hours ago https://www.pingcap.com/ 17 hours ago |
267. HN Popular Education AI Prompts for Teaching Excellence Education**Summary:** The article presents a collection of 13 specialized ChatGPT prompts aimed at enhancing various aspects of teaching excellence, including differentiated instruction, student engagement, formative assessment, classroom management, communication with parents, professional development, fostering a growth mindset, optimizing collaborative learning, implementing problem-based learning, retrieval practice, addressing misconceptions, and promoting self-regulated learning. **Key Points:** 1. **Differentiated Lesson Planner**: Customizes lesson plans for diverse student abilities by outlining objectives, strategies, activities, assessments, and materials at varying levels of complexity. 2. **Student Engagement Booster**: Provides strategies to increase interactive and student-centered learning, with examples like using a hook, mini-activities, collaborative tasks, and exit strategies for topics such as the American Civil War in Grade 11. 3. **Formative Assessment Designer**: Offers tools for real-time monitoring of student learning, including exit tickets, observation checklists, rubrics, quizzes, and self-assessment instruments with clear instructions and scoring guides. 4. **Classroom Management Strategy Creator**: Designs behavior management plans tailored to teachers’ needs, incorporating expectations, routines, reinforcement systems, consequences, and family communication strategies. 5. **Challenging Student Behavior Responder**: Provides concrete intervention strategies for specific disruptive behaviors, addressing root causes with techniques such as environmental modifications, relationship-building, parent communication, and when to involve additional support. 6. **Parent Communication Composer**: Facilitates effective communication with parents on student progress or concerns via various mediums like emails, texts, phone calls, with frameworks for discussing issues and solutions. 7. **Professional Development Learning Log**: Helps teaching coaches systematically track and improve their practices by setting goals, identifying strategies, planning implementation, reflecting, collecting evidence, and adjusting practices. 8. **Group Work Structure Designer**: Structures group work to ensure accountability and effective collaboration, with components like role definitions, task breakdowns, communication protocols, peer evaluation rubrics, and daily check-ins. 9. **Inclusive Classroom Accommodations Developer**: Offers tailored support for students with learning differences, focusing on accessible content, environmental modifications, instructional adaptations, assessment adjustments, technology tools, and stakeholder communication strategies. 10. **Professional Development for Teachers**: Designs ongoing learning opportunities focused on skill enhancement and staying updated on best practices, addressing needs assessment, goal setting, diverse learning modalities, job-embedded application, accountability, momentum maintenance, and impact monitoring. 11. **Metacognitive Awareness Builder**: Toolkit to foster students' understanding of their cognitive processes through methods like think-aloud protocols, self-questioning prompts, error analysis, strategy audits, and reflection frameworks. 12. **Cognitive Load Manager**: Provides guidance on managing working memory limitations in instructional design, focusing on extraneous, intrinsic, and germane cognitive loads, with recommendations for lesson sequencing to optimize learning. 13. **Transfer and Application Designer**: Aids in creating activities that apply knowledge across contexts, promoting practical skills and deeper understanding through problem-solving in near and far transfer scenarios. 14. **Growth Mindset Culture Creator**: Develops classroom environments valuing effort and growth over innate abilities, with strategies including teacher language, student practices, and structural support for visualizing progress. **Additional Prompt Details:** - **Collaborative Learning Optimizer**: Structures peer learning interactions to enhance academic and social skills, featuring specific group work organizations like think-pair-share, jigsaw, reciprocal teaching, with protocols for equitable participation and assessment. - **Problem-Based Learning Framework**: Guides creating detailed units using real-world problems, including a problem-solving process, resources, milestones, curriculum connections, and assessment rubrics. - **Retrieval Practice Architect**: Outlines strategies for enhancing long-term memory through spaced repetition, varied practice formats, low-stakes assessments, and integration into daily routines. - **Misconception Diagnostician**: Systematically identifies and addresses common student misconceptions over an academic period (12 weeks or 1 year), including listing misconceptions, diagnostic tools, instructional strategies, and cognitive conflict activities for resolution. - **Self-Regulated Learning Coach**: Equips students with tools to manage independent learning, comprising goal setting, strategy selection, self-assessment techniques, feedback systems, and autonomous practice activities like project-based learning. This comprehensive set of prompts provides educators with practical AI-assisted solutions to address diverse pedagogical challenges effectively. Keywords: #granite33:8b, AI Prompts, Academic Writing, Accountability, Adaptation, Application, Applications, Assessment, Authentic Problems, Authentic contexts, Behavior Response, Classroom Management, Collaborative Learning, Communication, Deep Learning, Differentiated Instruction, Difficulty Levels, Effort, Engagement Strategies, Evaluation, Feedback Protocols, Formative Assessment, Goal-setting, Group Dynamics, Group Work Design, Growth mindset, Inclusive Accommodations, Independence, Inequality, Instructional Design, Integration Plan, Interleaved Practice, Jigsaw, Learning Process, Long-term Memory, Low-Stakes Assessments, Massed Practice, Mastery Growth Mindset, Metacognition, Misconceptions Self-Regulated Learning, Monitoring, Multiple Choice, Novel problem-solving, Optimal Schedule, Parent Communication, Peer Teaching, Photosynthesis, Popular Education, Practice Problems, Problem-Based Learning, Professional Growth, Progress Tracking, Quadratic Formula, Real-world transfer, Reciprocal Teaching, Retrieval Practice, Scaffolding, Short Answer, Social Loafing, Specialized Support Lesson planning, Specific tasks, Strategies, Struggle, Teaching Excellence, Templates, Transfer, Varied Formats, above-grade-level pathway, answer keys, assessment methods, behavior expectations Metacognition, below-grade-level pathway, classroom culture, classroom delivery, cognitive processes, digital quiz, error analysis, grade-level pathway, inclusive education, instruction adjustment, instruction design Cognitive Load, instructional strategies, interactive activities, learning pathways, learning strategies, mixed abilities, observation checklist, practice activities, problem-solving, real-time monitoring, reflection, rubric, scoring guides, self-assessment, self-awareness, self-questioning, specific learning objectives, student engagement, think-aloud protocols, working memory
ai
tools.eq4c.com a day ago
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268. HN Migrating my web analytics from Matomo to Umami- The user transitioned from Google Analytics to Matomo (open-source) in 2014, later adopting Umami in 2022 due to its modern UI and simpler hosting requirements compared to Matomo's PHP+MySQL stack. - Aiming to preserve a decade of analytics data after deciding to decommission the Matomo instance, the user developed a Python program, angristan/matomo-to-umami, for migrating Matomo's MySQL/MariaDB data into Umami's PostgreSQL schema without an existing migration tool. - The migration ensured data accuracy through local Docker environment sanity checks and opting for raw SQL over APIs for faster processing and full control. - Both systems (Matomo with MariaDB and Umami with PostgreSQL) operate on Kubernetes nodes, and the migration process involved handling two sites with specific IDs, UUIDs, and domains. - The user emphasizes the importance of performing sanity checks during migration to identify potential bugs, which they successfully executed in their process. - They describe a meticulous data migration procedure from Matomo (open-source web analytics) to Umami: 1. Setting up port forwarding for Matomo's MariaDB service. 2. Running the `matomo-to-umami` migration script specifying sites with IDs, domains, session count (1,352,812), event count (1,999,709), and date range. 3. Performing a dry run to verify settings before generating SQL for actual migration. 4. Using `kubectl` commands to execute PostgreSQL commands within an Umami container for data import. - The migration was successful in moving data from Matomo to Umami, prompting the user to discontinue Matomo and conserve resources. The user expressed appreciation for both platforms being open-source and encourages others to use their migration tool if needed. Keywords: #granite33:8b, APIs, Docker, GitHub, Kubernetes, MariaDB, Matomo, MySQL, NextJS, PHP, PostgreSQL, Python, SQL, Umami, automatic migrations, data models, migration, open source, plugins, raw SQL, web analytics
github
stanislas.blog a day ago
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269. HN Show HN: WatchLLM – Semantic caching to cut LLM API costs by 70%- **WatchLLM Overview**: A newly created semantic caching layer, designed to decrease Language Learning Model (LLM) API costs by approximately 70%. - **Functionality**: Vectorizes user prompts and identifies similar queries (95%+ similarity), delivering cached responses within 50ms if a match is found. If no matching query exists, it sends the request to the actual LLM API and caches the response for future use. - **Development**: Constructed in three days using Node.js, TypeScript, React, Cloudflare Workers, D1, and Redis, ensuring ease of integration. Users can seamlessly switch their base URL and continue utilizing existing OpenAI, Claude, or Groq SDKs without requiring code modifications. - **Current Status**: In beta phase with a free tier offering 50K requests per month to gather developer feedback on aspects like semantic similarity thresholds and normalization strategies. - **Demonstration**: Offers a live demo displaying real-time cache hits and cost savings, illustrating the tool's effectiveness. - **Optimization**: Primarily designed for OpenRouter but can be adapted for other LLM providers as needed. BULLET POINT SUMMARY: - Reduces LLM API costs by up to 70% through semantic caching. - Vectorizes prompts, identifies similar queries (>95% similarity), and delivers cached responses in <50ms. - Forwards unmatched requests to actual LLM APIs, then caches responses for future use. - Built with Node.js, TypeScript, React, Cloudflare Workers, D1, and Redis; easy to integrate with no code changes required. - Beta phase offering 50K free requests/month for developer feedback on similarity thresholds and normalization strategies. - Live demo showcases real-time cache hits and cost savings. - Optimized for OpenRouter but adaptable for other LLM providers. Keywords: #granite33:8b, Claude, Cloudflare Workers, Groq, LLM APIs, Nodejs, OpenAI, OpenRouter, React, Redis, Semantic caching, TypeScript, beta, cost reduction, free tier, prompt normalization, prompts, real-time demo, similarity threshold, vectorization
claude
www.watchllm.dev a day ago
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270. HN SHow HN: Prompt-RAG – Fix low-quality AI images using a 500 prompt vector DB- Prompt-RAG is designed to improve the quality of images generated by AI, tackling the common problem of low-resolution or unclear outputs. - It employs a database comprising 500 distinct prompt vectors as its core mechanism for enhancing visual results produced by AI models. - The tool is readily available for immediate use without requiring extensive setup or preparation. - User accounts are supported, offering the convenience of accessing past chat histories and maintaining consistency across various devices. Keywords: #granite33:8b, AI images, Fix, Prompt-RAG, Show HN, low-quality images, prompt vector DB
ai
picxstudio.com a day ago
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271. HN 2025: The Year SwiftUI Died- **SwiftUI and UIKit Evolution**: SwiftUI, introduced in 2019 with initial stability issues, improved by 2021. By 2025, key shifts occurred: Apple integrated @Observable and updateProperties() into UIKit, blurring SwiftUI's distinctiveness. AI tools like ChatGPT suggest traditional coding methods are becoming less relevant, potentially resolving the debate on data binding libraries for iOS development, though there is internal variation in opinions about open-sourcing within Apple. - **Apple’s Control Over System Libraries**: Apple maintains exclusive control over system libraries (SwiftUI, Core Animation, UIKit) primarily to maintain competitive hardware advantage. Their focus is on creating a unique integrated user experience across products like Mac OS X and iOS, prioritizing interaction, responsiveness, and animation. - **Performance and Usability of SwiftUI**: Despite praise for development speed and ease-of-use, SwiftUI has faced criticism due to performance overhead from diffing view states and dynamic layout computation, which can be slower than UIKit. Though deemed production-ready with manual workarounds, performance remains a concern. - **2025's Shift in Developer Focus**: The introduction of modern @Observable features and updateProperties() in UIKit has led to its resurgence, potentially shifting focus from SwiftUI to UIKit for production applications, as developers weigh ease-of-use against performance concerns with SwiftUI. - **UIKit vs. SwiftUI Debate**: Recent advancements in UIKit have sparked questions about SwiftUI's future. While SwiftUI excels in animation performance and user experience, UIKit offers greater control over interactions and API capabilities. The author contends that for optimal UX, maximum performance, and complete control, UIKit remains superior due to challenges in replicating certain delegate functionalities in SwiftUI. - **Complexity of Frameworks**: The discussion contrasts UIKit’s nearly 20 years with SwiftUI's modern simplicity. UIKit necessitates complex patterns (MVC, MVVM, VIPER) for its delegate pattern, making code potentially harder to write but easier to read. SwiftUI, while efficient for prototyping, may cause future confusion due to less predictability. - **Application Development Example**: A user demonstrates building a full photo collage app using UIKit and SwiftData, praising the workflow's ease and showcasing the benefits of modern @Observable pattern in UIKit. This project grew from an initial prototype, highlighting UIKit’s performance in real-time gesture updates for image transformations. - **AI-Assisted Coding Insights**: The text stresses careful planning and code review when using AI tools like Agentic AI for coding. A recommended approach includes setting up a detailed Xcode template, initial manual coding, followed by AI refinement over 30 minutes, documenting issues to prevent recurrence, and acknowledging the need for human intervention in specific tasks. - **Leveraging Past Work**: The author advocates using Agentic AI and Context Engineering for standardizing and scaling codebases, referencing past components like databases, shaders, and Vision framework to improve efficiency. - **UIKit Efficiency**: UIKit’s optimizations are highlighted, including assigning individual Observable models to image nodes for direct traversal during gesture applications, enhancing real-time gesture updates without SwiftUI's state update delays. The passage contrasts flexible hero animations in UIKit with the more limited offerings in SwiftUI, suggesting UIKit's continued efficiency despite emerging frameworks' allure. - **Future of Coding**: With decreasing manual code typing due to AI, productivity gains are achieved without compromising maintainability or performance, reinforcing that while new tools empower developers, traditional methods like UIKit remain crucial for intricate tasks and ensuring code readability in an evolving landscape. Keywords: #granite33:8b, @Observable hooks, @Observable model, AGENTSmd, AI, AI-assisted tools, Apple's hardware business, Aqua, ChatGPT, Classic UIKit Stack, Classic UIKit Stack™, CollageViewController, Combine, Context Engineering, Core Image Metal kernels, CoreAnimation, DispatchQueue, Foundation, MVVM, Mac OS X architecture, NavigationTransition, RxSwift, Swift, Swift 62 concurrency warning solutions, Swift Concurrency, SwiftData, SwiftUI, UIKit, UIViewControllerTransitioningDelegate, UX, VIPER), Vision framework, Xcode, agentic AI, animation, architectures (MVC, binding libraries, camera previews, closures, code readability, code reading, code review, code writing, collage app, community goodwill, complexity, context windows, control, cross-platform, custom animators, data propagation, database, delegate methods, delegates, development speed, diffing, dynamic layout, ease-of-use, efficiency, foundation models, gesture interactions, gesture transformations, gestures, hero animations, hero transitions, human interface, iOS, iOS 17, incremental changes, interaction, interactive transitions, known solutions, libdispatch, lightweight, maintainability, migration, modern @Observable macros, namespacing, onScrollGeometryChange, one-shotting, open-source, open-sourced, overhead, pan, percentage-driven transitions, perfectionism, performance, performant, production-ready, productivity, prompts, property wrappers, prototypes, reading code productivity, real-time, reference code, rotation, security, services, shaders, simplicity (MV), software engineering, standardisation, subviews, system UX, transformations, updateProperties(), utility, view model updates, view modifiers, xnu, zoom
ai
blog.jacobstechtavern.com a day ago
|
272. HN Show HN: I created a free pdf to quiz maker tool- The user has created a free online utility that simplifies the conversion of PDF documents into quizzes through three simple steps. - Step 1 involves uploading any form of PDF, such as educational textbooks, lecture notes, or study guides, which the tool's AI will then interpret to understand its content. - In Step 2, users can customize their quiz settings according to preference. Options include selecting from various question types (Multiple Choice, True/False, Mixed), adjusting the difficulty level, and specifying the number of questions. - This innovative tool aims to optimize the process of quiz creation by leveraging pre-existing PDF materials, thereby saving time and effort for educators and learners alike. BULLET POINT SUMMARY: - Free online tool developed by the user for converting PDFs into quizzes. - Three straightforward steps for operation: upload PDF content (textbooks, notes, guides), AI interpretation of uploaded material, customization of quiz settings. - Customization includes choice of question types (Multiple Choice, True/False, Mixed), difficulty adjustment, and selection of the number of questions. - Streamlines creation of quizzes from existing materials, offering efficiency for educators and learners. Keywords: #granite33:8b, AI, PDF, customize settings, difficulty level, documents, lecture notes, number of questions, number of questions KEYWORDS: PDF, quiz maker, quiz type, read, research papers, study guides, textbooks, tool, understand, understand content, upload
ai
minform.io a day ago
|
273. HN The Age of the All-Access AI Agent Is Here- The text discusses the emergence of sophisticated AI agents like ChatGPT and Gemini, capable of handling a wide range of tasks beyond simple text processing. - These AI agents necessitate comprehensive access to personal data and operating systems for optimal performance, which raises significant privacy and cybersecurity concerns. - Experts caution that this increased integration could expose users to substantial risks due to the extensive user information these AI systems require to operate effectively. This may lead to potential breaches in data security and individual privacy. - Despite current limitations, there is an expectation among tech companies that these AI agents will revolutionize many jobs as they continue to evolve and become more adept at complex tasks. - Examples such as business AI accessing multiple digital platforms and Microsoft's Recall feature illustrate the potential for extensive data access by these advanced AI products. - Privacy concerns are heightened as consumers currently lack mechanisms to verify how their data is being handled, with experts pointing out a history of tech companies engaging in liberal data practices and often disregarding user privacy. Keywords: #granite33:8b, AI agents, LLMs, advanced AI, assistants, autonomy, calendar access, chatbots, code reading, cybersecurity, data access, data trade-offs, desktop screenshots, device control, email access, generative AI, monetization, operating system access, personal information, privacy concerns, privacy threats, task completion, training data, web browsing
ai
www.wired.com a day ago
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274. HN Dear ACM, you're doing AI wrong but you can still get it right- The Association for Computing Machinery (ACM) has introduced an AI-generated summary feature in their Digital Library, which is criticized for producing inaccurate and lengthy summaries that could replace peer-reviewed content. This feature is accessible only to users with institutional affiliations due to being behind a paywall, contradicting ACM's mission of accessible knowledge dissemination. Critics argue this indicates an economic motivation rather than one focused on open access. - The implementation involves diverse stakeholders and uses an unspecified foundational model for generating summaries in written, audio, and future chat formats. Users are advised to cite original articles, not AI-generated summaries, due to potential errors. Concerns are raised regarding transparency and the uncompensated use of scholars' work for fine-tuning the AI's database. - Jonathan Aldrich, an ACM Publications Board member, has acknowledged the controversy surrounding this feature and stated it represents a challenging role. Critics propose reducing algorithmically-driven communication and suggest leveraging open protocols like email or Bluesky to foster thoughtful discussions aligned with ACM's mission. - A 2007 Communications of the ACM study contrasts early advertisements' effectiveness in building brand awareness and positive sentiment with modern Internet ads, described as nonsensical, uninformative, forgettable, and intrusive. The user advises ACM to utilize scholarly discourse models like Atom/RSS feeds or platforms such as Bluesky instead of current advertising methods. - The user expresses frustration over access challenges in the ACM Digital Library (DL), comparing it unfavorably with the Public Library of Science's efficient open access system. They suggest the ACM should implement a similar system to 'allofplos' for downloading and managing open access content effectively. - There are concerns about maintaining the integrity of peer-reviewed knowledge in the face of Large Language Models (LLMs) capable of generating convincing yet fake papers. While skepticism exists among computer scientists, LLMs can offer personalized summaries when users input existing knowledge following principles like Bryan Cantrill's for responsible AI usage. - Critics urge ACM to avoid profit-driven AI services and focus on enhancing human curiosity rather than merely mimicking existing services. They reference the Royal Society's emphasis on responsible AI deployment, especially concerning the reproducibility crisis in AI-driven research due to opaque systems that hinder verification and scrutiny. - The user remains blocked from accessing the ACM Digital Library, suggesting they start holiday festivities early by going to the pub while highlighting Bluesky's potential for building ethical, transparent AI services to counter literature poisoning threats. Keywords: #granite33:8b, ACM Digital Library, AI, AI poisoning, Allofplos, Atom/RSS feed, Bluesky, COAR, Foundational Model, LLM technology, PLOS, Python script, W3C Atom Feed Validator, article downloads, assistive usage, audio transcriptions, audits, coding, collective knowledge, computer science research, corrections, dissemination of knowledge, evidence-driven social norms, hallucinations, hyper-personalised views, identity layer, inaccurate summaries, intellectual exertion, interactivity, language translation, literature, monetization, online advertising, open access, open exchange, open papers, paper downloads, paywall, peer review, peer-reviewed content, podcasts, professional standards, provenance tracking, readability, reading, reputation network, research barriers, scholarly publishing, search, skepticism, social contract, summaries, transparency, writer understanding, writing
ai
anil.recoil.org a day ago
|
275. HN Show HN: Sensei, documentation agent for coding agents- **Tool Development**: The user has created an AI-driven documentation tool named "Sensei" in four months, addressing the issue of maintaining accurate and current documentation for third-party libraries using AI in programming. - **Compatibility and Functionality**: Sensei is designed as a Machine Code Processing (MCP) tool that works across multiple platforms, utilizing three specialized components: Kura (for query caching), Scout (as a source code explorer), and Tome (for ingesting text using large language models). These tools enable efficient context retrieval, providing 2,000-10,000 synthesized tokens as opposed to the typical 100,000-300,000 unfiltered tokens from other documentation tools. - **Open-source Availability**: Currently offered freely and open-source, Sensei aims to improve developers' productivity by ensuring their AI agents receive clean, focused context for work. - **Research Methodology**: Mimicking a seasoned engineer's approach, Sensei follows an optimized research method that begins with broad exploration of options before focusing on promising areas. It prioritizes sources based on a trust hierarchy: official documentation, source code, real implementations, and community content to ensure reliable, comprehensive answers. - **Complex Question Handling**: For complex queries, Sensei dissects them into smaller components, conducts individual research, and integrates findings to deliver detailed, accurate responses. Keywords: #granite33:8b, AI, Kura, MCP tool, Postgres, Scout, Tome, community content, context efficiency, decomposition, deep dive, documentation, implementations, libraries, official docs, open-source, paths, prompts, query cache, research methodology, senior engineer, source code, source code explorer, survey, synthesis, synthesizes, tokens, trust hierarchy
postgres
sensei.eightzerothree.co a day ago
|
276. HN Show HN: Semantic Coverage – A tool to visualize RAG blind spots using UMAP- **System Overview**: The text introduces "Semantic Coverage," an open-source tool designed to pinpoint blind spots in Retrieval Augmented Generation (RAG) systems, akin to how Code Coverage identifies software bugs. It specifically aims at visualizing gaps in enterprise knowledge bases. - **Functionality**: Semantic Coverage projects both user queries and documents into a 2D space using UMAP for dimensionality reduction and HDBSCAN for density-based clustering. It identifies 'Red Zones' - regions with high user intent but insufficient documentation coverage, signaling potential knowledge gaps, data drift, or hallucination triggers in vector databases. - **Technology Stack**: The tool is built using FastAPI for the backend and React for the frontend. Key libraries include Sentence-Transformers (SBERT) for generating embeddings, UMAP and HDBSCAN for dimensionality reduction and clustering, along with Scikit-Learn for additional data processing. Visualization is handled by Plotly.js. - **Installation and Usage**: Detailed instructions are provided to set up and run the system locally using uvicorn for the backend and npm for the frontend. Once deployed, the user interface is accessible via http://localhost:5173. - **Database Agnostic Design**: Semantic Coverage is designed to be agnostic to specific vector databases, supporting plugins for major Vector Stores such as Pinecone and ChromaDB. - **Key Metrics - Centroid Distance**: The system calculates the Centroid Distance for each query cluster relative to its nearest document. Clusters exceeding a predefined distance threshold (0.7) are flagged as 'blind_spots', indicating areas requiring further documentation or investigation. - **Licensing**: The project is released under the MIT license, encouraging community contributions and feedback, particularly on clustering logic improvements. **Bullet Point Summary**: - Open-source tool called "Semantic Coverage" for visualizing blind spots in RAG systems. - Uses UMAP and HDBSCAN to project queries and docs into 2D space, identifying 'Red Zones' of high intent but low coverage. - Built with FastAPI (backend) and React (frontend), utilizing Sentence-Transformers, UMAP, HDBSCAN, Scikit-Learn, Plotly.js. - Database agnostic, supports Pinecone, ChromaDB; run via uvicorn/npm, UI at http://localhost:5173. - Employs Centroid Distance metric to flag clusters as 'blind_spots' if exceeding a 0.7 distance threshold from nearest documents. - MIT licensed, encouraging feedback and improvements on clustering logic. Keywords: #granite33:8b, Backend, Centroid Distance, Code Coverage, Data Drift, Database-agnostic, Density-based Clustering, Document Projection, Enterprise Connectors, FastAPI, Frontend, Gap Report, HDBSCAN, Hallucination Triggers, JSON, Knowledge Gaps, MIT License, Plotlyjs, Plugin Architecture, Plugins, RAG, React, Scoring, Semantic Coverage, Sentence-Transformers, Stack, UMAP, User Queries, Vector Databases, Vector Stores
rag
github.com a day ago
|
277. HN Choosing a database for crypto on-chain analytics, think outside of PostgreSQL- **VeloDB for Real-Time Web3 Analytics:** The article discusses using VeloDB, a high-throughput data warehouse based on Apache Doris, to build real-time analytics platforms for Web3 applications, overcoming limitations of traditional databases like PostgreSQL in handling blockchain data's high-volume writes and concurrent query demands. - **Key On-Chain Metrics:** Focuses on calculating essential metrics instantly with VeloDB: - **Insider Wallets:** Identifies unfair token distribution by the founding team, which might signal a potential rug pull if they sell off heavily post-launch. - **Sniping Bots:** Detects automated price manipulation during token launches through detection of bots snapping up tokens at low prices for resale. - **DEV Holdings:** Measures tokens held by the project's founders, indicating their commitment if these tokens are locked or vested over time. - **Top 10 Holder Concentration:** Assesses token concentration among top wallet addresses; lower concentration (<20%) implies decentralization, while higher (>40%) indicates a risk as few holders can influence prices significantly. - **Transition from PostgreSQL to VeloDB:** - Critiques traditional batch-oriented processing pipelines that struggle with high transaction volumes and delayed data presentation due to infrequent metric calculations. - Proposes VeloDB for its ability to handle 50,000+ records per second ingestion rate with millisecond-level latency for real-time query responses. - **VeloDB Architecture:** - Features a Data Service layer in Go/Rust that computes metrics from VeloDB and pushes them to the frontend instantly. - Uses Redis to cache high-traffic API endpoints, improving efficiency. - Demonstrated to handle up to 200 QPS for top holder calculations and total supply lookups on a single cluster with 8 cores and 64GB memory. - **Database Schema Design:** - Includes 'bsc_account_balance' (token balances per address) and 'bsc_token_holder_tagged_tmp' (address tagging with types like insider, sniper, or DEV). - Employs UNIQUE KEYS for preventing duplicates and enhancing query performance. - Token metadata stored in the `token_info` table, ensuring unique tagging per address per token. - **Performance Evaluation:** - Processes 50,000 account balance records in 476 milliseconds with high efficiency. - Total supply lookups and holder balance computations have low latencies (100ms for tens of thousands of queries/second). - Batch analytics compute metrics for 200 tokens in about a second, scaling to process large holder counts efficiently (5,000 to 5,000,000 holders) with concurrency levels of 1-3 threads. - Latency for Top 10 holder concentration calculations under load remains between 50ms and 150ms, suitable for real-time dashboards even with high user concurrency. - **Use Case:** VeloDB is ideal for Web3 applications requiring real-time insights such as decentralized exchanges (DEX) dashboards, token monitoring, or wallet profiling. For integration support, users are directed to contact the VeloDB team. Keywords: #granite33:8b, Apache Doris, DEV holdings, DEX dashboards, Go/Rust, Kafka, PostgreSQL, VeloDB, Web3, analytics, analytics platform, automated programs, batch processing, blockchain, bottleneck, concurrency, data service, data warehouse, decentralization, high-throughput, ingestion throughput, insider wallets, low-latency queries, lowest entry price, metric calculation, millisecond latency, near real-time metrics, on-chain, price dumps, real-time, real-time analytics, rug pull, scalable foundation, sniping bots, streaming platform, token launches, token monitoring, token supply, top 10 holders, user traps, vesting schedule, write latency, writes
postgresql
www.velodb.io a day ago
|
278. HN Introduction to Generative AI- **Title and Focus**: "Introduction to Generative AI" offers a thorough yet approachable guide on understanding large language models (LLMs) and their diverse applications. - **Core Content**: The book meticulously details the fundamentals of LLMs, illustrating their relevance in both personal and professional domains. It further delves into the social, legal, and policy implications surrounding these technologies. - **Emerging Trends**: An important aspect covered is the exploration of cutting-edge developments like reasoning models and vibe coding, highlighting future directions in the field. - **Practical Applications**: The text provides practical guidance on using AI tools including ChatGPT, Gemini, Cursor, and Copilot responsibly, emphasizing the importance of debunking misconceptions and managing expectations about generative AI. - **Up-to-Date Information**: The second edition of the book is specifically updated to incorporate the most recent advancements in generative AI, ensuring readers are informed on the current state of the technology. Keywords: #granite33:8b, Generative AI, large language models, latest developments, misinformation, responsibility, safety
ai
www.manning.com a day ago
|
279. HN I built a cool tool to run multiple Claude agents in parallel- **Hive Overview**: Hive is a tool designed to manage and execute multiple Claude Code agents concurrently, facilitating parallel task execution through a multi-agent system architecture with one "Queen" orchestrator and numerous "Drones" or worker agents. - **Use Cases**: It supports parallel tasks like simultaneous bug fixes, feature development, large-scale refactoring, continuous testing during coding, and documentation creation alongside coding, thus boosting productivity in complex workflows. - **Integration**: Hive seamlessly integrates with Git configuration, automatically detects OAuth tokens, project types (Node.js, Go, Python, Rust), and sets up infrastructure within a `.hive` directory using separate Git worktrees for each agent to avoid branch conflicts. - **Installation**: Installation options include a quick script, Homebrew for macOS, or compiling from source. - **System Features**: The system offers automatic role injection, progress monitoring, resource cleanup upon task completion, and persistent authentication for OAuth tokens through container restarts. It provides zero-configuration setup via auto-detection of settings like Git config, project type, and Claude token, with flexible overrides possible via `hive.yaml` or CLI flags. - **Workflow**: Tasks are assigned by the Queen to Workers who execute independently without manual intervention. An example demonstrates fixing three bugs simultaneously, potentially reducing the total time from three hours to one. - **Command Structure**: Key commands include `hive init` for setup, `hive start` to launch containers, and `hive stop` to halt them. Within Queen, `hive-assign` assigns tasks, while Workers use specific commands (`my-tasks`, `take-task`, `task-done`, `task-failed`) for task management. - **Documentation**: Comprehensive documentation covers commands, configuration, architecture, best practices, FAQs, advanced setup like MCP, troubleshooting, Docker images, and contributing guidelines. - **Use Cases in Practice**: Applications include parallel feature development by breaking tasks into subtasks and bug fixing sprints addressing multiple issues concurrently, potentially enhancing efficiency by 3x due to simultaneous bug fixes. - **Bug Fixing Sprint Example**: The document outlines a scenario where three developers (drones) work simultaneously on fixing bugs in authentication timeout, CSV export, validation, and email regex while also planning code refactoring across various modules using Redis for task queue management and Claude integration with OAuth token persistence. - **License and Contributions**: Hive is open-source under the MIT License, welcoming contributions from the community, created by @mbourmaud. BULLET POINT SUMMARY: - Hive facilitates parallel development through multi-agent architecture (Queen and Drones). - Supports diverse tasks like simultaneous bug fixes, feature development, refactoring. - Offers seamless Git integration with auto-detection of configuration details. - Provides zero-configuration setup via automatic settings detection and overrides. - Features include task assignment automation, progress monitoring, resource cleanup. - Persistent authentication for OAuth tokens ensures uninterrupted workflows. - Comprehensive command structure for setup, starting/stopping containers, and task management. - Detailed documentation for various aspects including advanced setup and troubleshooting. - Practical use in scenarios like parallel sprints for feature development or bug fixes. - Utilizes Redis for task queue with FIFO assignment and Pub/Sub notifications. - Ensures agent isolation through independent Git worktrees and no workspace conflicts. - Open-source under MIT License, contributions encouraged by the creator @mbourmaud. Keywords: #granite33:8b, CI integration, Claude agents, Claude integration, Hive, Homebrew, MIT license, Queen, Redis, agent isolation, automatic role injection, bug fixing, cleaning, containers, git worktrees, hiveyaml config, initialization, installation, multi-agent, orchestration, orchestrator, parallel execution, progress monitoring, project structure, roles, source code, stopping containers, task assignments, task queue, tasks, testing, worker roles, workers
claude
github.com a day ago
|
280. HN Show HN: SatoriDB – embedded vector database written in Rust- SatoriDB is a novel embedded vector database project recently highlighted on Hacker News. - It has been developed using the Rust programming language. - The primary purpose of vector databases, such as SatoriDB, is to manage and interrogate extensive, multi-dimensional datasets effectively, which is especially advantageous for machine learning and AI applications. - SatoriDB distinguishes itself by aiming to be an efficient and lightweight solution, facilitating seamless integration into diverse software projects. - The project's GitHub repository, shared by user joeeverjk, provides further details and access for potential users or contributors. Keywords: #granite33:8b, GitHub, Rust, SatoriDB, embedded, joeeverjk, vector database
github
news.ycombinator.com a day ago
https://github.com/nubskr/satoriDB a day ago |
281. HN Permission Systems for Enterprise That Scale**Summary:** The text discusses challenges and solutions for managing resource access permissions in systems with hierarchical data structures, commonly seen in SaaS applications like folder hierarchies. Two primary approaches are highlighted to optimize access control: Role-Based Access Control (RBAC) and methods for handling deep hierarchies (Materialized Paths and Closure Tables). **Key Points:** - **Challenge**: Startups targeting enterprise clients encounter performance issues as their systems scale, especially in implementing robust permission checks due to increasing data, users, and relationships. - **Naive Approach**: Initially querying the database for every request causes inefficiencies, particularly with deep resource nesting leading to complex recursive queries. - **Optimized RBAC Method**: - Introduces a 'permissions' table linking users with resources via access types ('owner', 'shared', 'path_only'). - Simplifies read operations through single SQL joins and improves indexing compared to intricate recursive queries. - Permission updates are facilitated by straightforward INSERT OR IGNORE statements, though sharing among users requires more complex logic. - **PostHog's Implementation**: Employs a precomputed AccessControl model for efficient resource access management, avoiding recursive queries and repeated database lookups through caching per request. - **Alternative - Attribute-Based Access Control (ABAC)**: - Offers a rule-based system for permission checks executed at read time from declarative policies. - Suitable for complex access decisions but necessitates setting up detailed rules, as exemplified by Figma's implementation. - **Managing Hierarchical Data:** - **Materialized Paths**: - Store full resource paths as strings, simplifying descendant queries with prefix searches but complicating resource relocation due to necessary path updates for affected descendants. - **Closure Tables**: - Precompute all ancestor-descendant relationships to avoid recursive queries at runtime. - More complex to implement initially; however, more efficient in handling resource movements across the hierarchy as changes only affect stored relationships, not individual resource paths. - **Trade-offs**: - Materialized Paths are easier to set up but difficult for repositioning resources. - Closure Tables require more initial effort but handle resource movement more gracefully. - **RBAC Benefits and Risks**: - Despite increased complexity, RBAC significantly improves performance, addressing slow load times in enterprise systems. - Key risk: Data desynchronization, mitigated by a rebuild script to recompute permissions from the source of truth, ensuring system integrity despite potential bugs. Keywords: #granite33:8b, ABAC, Attribute-Based Access Control, CTE, JOIN operations, Permission systems, PostHog example, RBAC, RECURSIVE, SQL, UNION ALL, access control model, access levels, access types, admins, all descendants, ancestor access, ancestor-descendant relationships, ancestors, ancestry relationships, closure table, closure tables, complex logic, database design, database queries, database synchronization, debugging, declarative rules, descendant access, descendants, enterprise scalability, filtering querysets, folder, full path, hierarchical data, implementation, insert ignore, instant lookup, materialized paths, performance, permissions index, permissions table, policies, pre-computed permissions, prefix search, read and write optimization, read-time checks, recursion, recursive queries bottleneck, resource hierarchy, resource management, resource moves, resource ownership, resource sharing, resources, rule-based access control, shared resources, sharing resources, simplicity, trade-off, user roles, users
sql
eliocapella.com a day ago
https://projects.eclipse.org/projects/technology.biscui a day ago https://docs.feldera.com/use_cases/fine_grained_authori a day ago https://zanzibar.tech/24uQOiQnVi:1T:4S a day ago https://zanzibar.tech/21tieegnDR:0.H1AowI3SG:2O a day ago https://openfga.dev/ a day ago https://buf.build/authzed/api/docs/main:authz a day ago https://authzed.com/docs/spicedb/modeling/pro a day ago |
282. HN Google 2025 recap: Research breakthroughs of the year- In 2025, Google advanced its AI models, focusing on reasoning, multimodal understanding, efficiency, and generative capabilities. - Notable releases included Gemini 2.5 in March, followed by Gemini 3 in November, and then the specialized Gemini 3 Flash in December. - The most powerful model, Gemini 3 Pro, demonstrated exceptional reasoning skills, leading the LMArena Leaderboard and establishing new standards on tests like Humanity’s Last Exam and GPQA Diamond. It also scored a state-of-the-art 23.4% on MathArena Apex in mathematical tasks. - Gemini 3 Flash improved upon Pro by incorporating Pro-level reasoning while enhancing latency, efficiency, and cost-effectiveness, outperforming Gemini 2.5 Pro at a reduced price with better speed. - These advancements reflect Google's continuous effort to develop superior AI models. BULLET POINT SUMMARY: - Year of significant progress in AI modeling by Google: 2025 - Areas of focus: reasoning, multimodal understanding, efficiency, generative capabilities - Notable model releases: - Gemini 2.5 (March) - Gemini 3 (November) - Gemini 3 Flash (December) - Performance highlights of Gemini 3 Pro: - Tops LMArena Leaderboard in reasoning tasks - Sets new benchmarks on Humanity’s Last Exam and GPQA Diamond - Achieves state-of-the-art score of 23.4% on MathArena Apex for mathematics - Enhancements in Gemini 3 Flash over Pro: - Integrates Pro-grade reasoning with improved latency, efficiency, and cost-effectiveness - Surpasses Gemini 2.5 Pro's performance at a lower price point and faster speed - Overall commitment demonstrated by Google towards developing top-tier AI models. Keywords: #granite33:8b, Gemini, Gemini 25, Gemini 3, Gemini 3 Flash, Google, LMArena Leaderboard, cost-effective, efficiency, generative capabilities, models, multimodal understanding, performance, reasoning
gemini
blog.google a day ago
https://www.nytimes.com/2025/12/23/business&# a day ago https://www.wsj.com/economy/us-gdp-q3-2025-2026-6cbd079 a day ago https://www.theguardian.com/business/2025/dec/ a day ago https://www.bbc.com/news/articles/c62n9ynzrdpo a day ago https://www.theguardian.com/business/2025/dec/ a day ago https://www.aljazeera.com/economy/2025/12/16& a day ago https://www.slowboring.com/p/you-can-afford-a-tradlife a day ago https://www.slowboring.com/p/affordability-is-just-high a day ago https://thezvi.substack.com/p/the-revolution-of-rising- a day ago https://open.substack.com/pub/astralcodexten/p a day ago https://www.newyorkfed.org/microeconomics/hhdc a day ago https://www.pbs.org/newshour/politics/trump-seeks- a day ago https://www.acquired.fm/episodes/google-the-ai-company a day ago https://www.youtube.com/watch?v=d95J8yzvjbQ a day ago https://en.wikipedia.org/wiki/Splitting_(psychology) a day ago https://www.economist.com/graphic-detail/2025/07&# a day ago https://news.ycombinator.com/item?id=44616486 a day ago https://frontier.renaissancephilanthropy.org/ a day ago |
283. HN UK to ban deepfake AI 'nudification' apps- The UK government is proposing a ban on AI applications known as "nudification" apps, which enable users to digitally remove clothing from images without consent. - This legislative move is part of a broader initiative aimed at curbing violence against women and girls by enhancing their online safety. - The new rules will expand upon current regulations that prohibit sexually explicit deepfakes and the non-consensual distribution of intimate images, referred to as 'revenge porn.' - Technology Secretary Liz Kendall has articulated that this measure is intended to provide greater protection for women and girls in the digital realm. Keywords: #granite33:8b, Liz Kendall, UK ban, deepfake AI, intimate image abuse, misogyny, nudification apps, sexually explicit deepfakes, women's online safety
ai
www.bbc.co.uk a day ago
|
284. HN I built an AI app for deep research, reverse image search, and price comparison- **Application Overview**: The user has developed an AI-powered application named ClarityCheck - Deep Search AI, designed for comprehensive research needs. - **Multi-Functionality**: The app integrates various search functionalities into one platform, enabling users to perform text, image, and price comparisons seamlessly. - **Key Features**: - **Multi-Engine Search**: Aggregates results from different search engines for broader coverage of queries. - **Reverse Image Search**: Identifies objects or sources within images, aiding in discovering information about specific visual elements. - **Price Comparison**: Scans multiple marketplaces to find the best prices for products, assisting users in making informed purchasing decisions. - **User Privacy**: Ensures user privacy by refraining from collecting personal data or accessing private databases. - **Target Audience**: Suitable for various user groups including everyday individuals, students undertaking research, content creators, and professionals requiring efficient search capabilities. - **Subscription Model**: Offers a premium subscription that unlocks all features, with automatic renewal managed unless canceled 24 hours prior to the upcoming billing cycle. - **Support Information**: For queries or assistance, users are directed to reach out via support@righttracksit.com. - **Governing Documents**: Additional information regarding privacy practices and terms of service is accessible on their official website. Keywords: #granite33:8b, AI app, Unix, command, deep research, detailed results, display, file, multiple engines, navigation, navigation tool, output, pagination, price comparison, privacy, privacy policy, public information, reverse image search, scrolling, smart search, subscriptions, support, terminal, terms & conditions, text
ai
apps.apple.com a day ago
https://apps.apple.com/us/app/claritycheck-deep-se a day ago |
285. HN Interactively visualize GitHub Actions Matrix configurations- The text describes a tool that offers interactive visualization capabilities for GitHub Actions Matrix configurations. - This tool is particularly useful for understanding and managing complex workflows defined by Matrix strategies in GitHub Actions. - It provides a graphical user interface (GUI) to visualize how different matrices of inputs lead to various workflow executions, aiding in comprehension and debugging. - In addition to the GUI, there's a command-line interface (CLI) version of this tool, which is intended for seamless integration into Continuous Integration/Continuous Deployment (CI/CD) pipelines. - The CLI allows developers to leverage the visualization features programmatically, enhancing automation and efficiency within their development workflows. ### Summary: The described tool facilitates the interactive exploration of GitHub Actions Matrix configurations through both a graphical user interface and a command-line interface. The GUI aids in visualizing complex workflow executions based on various matrix input combinations, simplifying the understanding and troubleshooting process. Complementarily, the CLI version is designed for direct integration into CI/CD pipelines, allowing developers to employ visualization features within automated workflows for improved efficiency and automation. Keywords: #granite33:8b, Actions, CI/CD, CLI, GitHub, Matrix, configuration, pipelines
github
katexochen.github.io a day ago
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286. HN AgentOllama: Simple and Easy to Use UI Based Agentic System- **Tool Overview**: AgentOllama is an AI-based framework designed for creating, executing, and monitoring intelligent agents through a user-friendly UI. It simplifies the automation of business processes without requiring explicit coding of business logic. - **Key Features**: - Dynamic tool invocation: Utilizes AI to generate necessary tools on-device (with DeepSeek R1 model). - Automated API integration: Streamlines connecting to APIs for data exchange. - Real-time execution logs: Offers monitoring capabilities to track agent activities. - Structured output enforcement: Ensures consistent and organized results from agents. - Knowledge repository with RAG (Retrieve and Generate) integration: Enhances contextual understanding and data retrieval. - Enterprise workflow automation: Facilitates seamless orchestration of complex business processes. - **Recent Enhancements**: - Focused on improving the framework's core capabilities, including better performance metrics and testing features for analyzing agent efficiency. - **Prerequisites and Installation**: - Requires Python 3.x, Ollama (AI Model Server), Django, and a vector database. - Installation involves cloning the repository, setting up a virtual environment, installing necessary packages, and running both the Ollama server and Django server. - **User Interaction**: - Agents are defined via an intuitive UI. - Tools are dynamically loaded from AI-generated code without needing an Integrated Development Environment (IDE). - Real-time monitoring of execution logs for efficient workflow automation. - **Future Roadmap**: - Plans include dynamic business testing and advanced agent collaboration for multi-step decision-making tasks. - Enhanced RAG capabilities to improve contextual understanding and data handling. - **Community and Licensing**: - The project encourages contributions via pull requests, with discussions on major changes preferred through issues. - Licensed under Apache 2.0, and updates can be followed on the author's LinkedIn profile. - Aims to collaboratively develop AI-driven automation solutions. Keywords: #granite33:8b, Advanced Collaboration, Agentollama, Automation, Contributing, DeepSeek, Django, Licensing, Ollama, Performance Metrics, Python, RAG integration, Roadmap, Testing, UI-driven, Vector Database, automated API integration, debugging, dynamic tool invocation, enterprise workflow automation, execution logs, intelligent Agents, knowledge repository, on-device, sentiment analysis, stock inventory management, structured output enforcement, tool code generation
ollama
github.com a day ago
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287. HN Show HN: Free True or False Quiz Maker- The tool is an AI-driven, free online platform for generating True or False quizzes. - Users have the ability to input their own content or select from pre-established facts on popular topics. - A key feature allows users to review and make necessary modifications to the generated questions before sharing or using them. - This service was recently highlighted on Hacker News, indicating its relevance within technology and software development communities. Keywords: #granite33:8b, AI, content-based, edit, facts, general topics, quiz, review, sharing, statement generation, true/false
ai
minform.io a day ago
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288. HN How to safely let LLMs query your databases via sandboxed materialized views- **Secure Data Access Framework**: A comprehensive approach ensuring AI agents interact securely with structured data through a layered architecture that prevents direct database access while addressing security and regulatory compliance. - **Layered Architecture Components**: - **Data Sources Layer**: Safeguards raw data repositories using credential storage and network isolation to prevent unauthorized access, leaks, and row-level security issues. - **Data Governance & Security Layer**: Utilizes materialized SQL views with cross-source joins, security filters (row and column level), and purpose-built agent-specific views for controlled data access. - **Security & Compliance Features**: - Implement Role-Based Access Control (RBAC) and Row-Level Security (RLS) to verify user roles and restrict data based on permissions, ensuring adherence to regulations like GDPR, HIPAA, and SOC 2. - Maintain an audit trail for compliance purposes through secure access mechanisms and detailed logging of queries. - **Performance Optimization**: - Materialized views store precomputed results, isolate agents from live databases, and optimize resource usage by reducing load and mitigating schema changes impacts. - Refresh strategies are tailored to balance data freshness with computational efficiency (daily or incremental refreshes). - **Multi-tool Composition Platform (MCP)**: - Exposes self-documenting tools as callable functions, simplifying integration for AI agents through clear function definitions, parameter schemas, and policy checks. - Example tools: `identify_at_risk_customers` and `analyze_customer_trends`, each requiring specific roles for access. - **AI Agent Layer**: - Stateless agents interact dynamically with MCP tools via an API interface, utilizing language models like LangGraph and Langchain for tool selection based on user queries. - **User Interface (Layer 5)**: - Provides flexible interaction channels (chat, APIs, mobile apps, voice interfaces) without requiring users to comprehend underlying data handling mechanisms. **Key Points**: - Ensures secure AI access to structured data through controlled layered architecture. - Uses materialized views for performance enhancement and agent isolation. - Implements robust security measures including RBAC, RLS, and comprehensive audit logging. - Facilitates easy integration with MCP tools having self-documentation features. - Stateless agents interact flexibly via API interface with LLM support for tool selection. - Offers a user-friendly interface adaptable to various interaction modes without requiring technical data understanding. - Addresses common issues like wrong tool selections, slow query performance, policy check failures, and data freshness concerns through systematic solutions. - Maintains strict security and governance by limiting AI agent access through carefully managed views that provide secure APIs via MCP tools with built-in checks; all queries logged for compliance. - The architecture is adaptable to diverse data sources and use cases, starting from a single source and view, and can be applied universally across various AI agent systems connecting to structured data. Keywords: #granite33:8b, AI agents, API key, APIs, ChatOpenAI, Cursor/Claude Desktop, EXPLAIN, HTTP Request node, JOIN types, LLM, LLM interpretation, LangGraph, LangGraph Agent, MATERIALIZED VIEW, MCP configuration, MCP tool, MCP tools, PylarAgent, SQL execution, SQL queries, SQL query, SQL views, SaaS platforms, WHERE clauses, agent consumption, agent framework, agent playground, aggregations, audit logging, authentication, best practices, chat interfaces, column-level security, compliance challenges, comprehensive data retrieval, connection credentials, controlled interfaces, credentials, cross-source joins, customer health dashboard, data governance, data lakes, data masking, data sources, data transformations, data warehouses, databases, documentation, encryption, endpoint URL, error tracking, filters, function calling, function definition, function definitions, governed interfaces, incremental refreshes, indexing, input schema, layered architecture, limitations, materialized views, mobile apps, monitoring, n8n, network isolation, network-level isolation, openAI, operational issues, optimize queries, parameter extraction, parameter schemas, performance metrics, performance monitoring, permissions, policy checks, policy validation, principle of least privilege, production databases, publishing, purpose-built views, query logs, query optimization, raw data repositories, read-only credentials, regular reviews, response synthesis, result set limits, role-based access control, row-level filtering, row-level security, sales pipeline analysis, sample queries, scheduling, secrets management, security risks, sensitive columns, simple view, specific data description, stateless agents, stateless consumers, testing, tool calling, tool descriptions, tool discovery, tool selection, usage analytics, user queries, user query processing, version control, view best practices, voice interfaces
llm
www.pylar.ai a day ago
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289. HN Tether: The Rise of New Sovereigns- Tether, a company issuing USDT stablecoins, is amassing significant wealth and influence with an annual profit of $15B, holding $200B in cash, owning over 100k BTC ($10B), investing in precious metals producers, and possessing $100B in US Treasuries. - It holds more gold than many central banks, suggesting it could be a "new sovereign" entity, challenging traditional state powers by controlling aspects like currency issuance, technology (AI, Nvidia chips), infrastructure development, and defense through investments in companies like Palantir and Anduril. - This indicates a trend where leading tech firms, known as the Mag7, are eroding the traditional roles and powers of nation-states, marking a transition in sovereignty. - An unspecified entity has invested over $300M in precious metals producers and holds $100B in US Treasuries, being the largest non-central bank gold holder, surpassing many central banks' reserves. This entity, which issues its own currency, resembles a nation-state without typical burdens, potentially owning land too. - Holiday reading suggestions include: - "Critique of Liberal Reason" by Andrea Zhok - The controversial "Pathwork" by Mencius Moldbug (Curtis Yarvin) - The newly released hardcover "There is no Antimemetics Division" by qntm, described as life-changing by many readers. - The author acknowledges readers' engagement with diverse, sometimes unconventional topics and invites further interesting reads. Keywords: #granite33:8b, AI, Amazon, Anduril, Antimemetics Division, BTC, Critique of Liberal Reason, Google, Mag7, Nvidia, Palantir, Pathwork, Starlink, Tether, US Treasuries, USDT, Uber, Whatsapp, books, currency issuance, defence, feedback, gold, governance systems, investments, land ownership, nation states, new sovereigns, novel, portable sovereignty, precious metals, qntm
ai
futures.unrulycap.com a day ago
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290. HN Ask HN: What are your AI predictions for 2026?- The user expresses a pessimistic outlook on future AI advancements by 2026, despite significant progress. - Notable AI models mentioned include Gemini 3 Pro, Claude 4.5 Opus, Nano Banana, and Sora 2. - There is recognition of the widespread adoption and application of AI technology. - The user highlights concerns about high operational costs associated with these advanced AI systems, despite their impressive scalability in capabilities. This summary encapsulates the critical aspects of the text: the user's pessimistic stance on future AI developments amidst current progress, acknowledgment of specific advanced models, general use of AI applications, and worry over cost-effectiveness in spite of impressive capabilities. Keywords: #granite33:8b, 2026, AI, Claude 45 Opus, Gemini 3 Pro, Nano Banana, Sora 2, operational costs, pessimistic, predictions, scaling capabilities
ai
news.ycombinator.com a day ago
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291. HN Show HN: Knowimg – An AI clothes changer and image editor for the web- **Project Overview:** The project, named "Knowing," is an innovative AI-powered web application that offers functionalities for altering clothing within images and providing editing capabilities. - **Infographic Timeline Design Specifications:** - **Timeline Range:** Spanning from 2010 to 2025, focusing on significant milestones in AI evolution over the period. - **Background & Dividers:** White background with thin grey dividers separating distinct years. - **Year Markers:** Circular markers denoting each year on the timeline. - **Icons and Text Descriptions:** Minimal use of icons accompanied by concise text descriptions to provide context. - **Color Scheme:** Employs a blue gradient for accents, enhancing visual appeal while maintaining readability. - **Layout:** Symmetrical design ensures balanced presentation of information. - **Header:** The infographic features a bold header stating "Evolution of AI: 2010–2025," clearly defining its subject matter. This summary encapsulates the core aspects of the project and design guidelines, providing clarity on both the functional AI tool ("Knowing") and the visual representation of AI evolution milestones from 2010 to 2025. Keywords: #granite33:8b, 2010-2025, AI, blue color, circular markers, clothes changer, dividers, evolution, gradient accents, infographic, milestones, minimal icons, symmetrical layout, text blocks, timeline, web application, white background
ai
www.knowimg.com a day ago
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292. HN The 80/20 Test: What AI Reveals About Your Mediocre Managers- The text addresses the issue of evaluating "mediocre managers" who consistently meet basic expectations but lack strategic direction and executive presence to inspire teams effectively. - Standard performance metrics fail to capture these shortcomings, prompting a thought experiment where an AI performing 80% of a manager's tasks exposes the remaining 20%, crucial for genuine leadership, that mediocre managers neglect. - Mediocre managers maintain routine functions but lack motivation and innovation-encouragement skills; this mirrors AI’s proficiency in data analysis (80%) versus human leadership aspects (20%). - Both AI chatbots and mediocre managers exhibit "Ruinous Empathy," offering excessive flattery and avoiding challenging conversations, thus failing to provide constructive feedback necessary for growth. - The text differentiates between high performers who take ownership and those stuck in a "victim cycle" blaming external factors; mediocre managers often belong to the latter group. - Generic motivation advice from AI is ineffective due to its lack of understanding individual motivators; great managers tailor their approach to each team member's unique needs, which AI cannot replicate. - Future performance management (anticipated for 2026) should focus on developing human skills like navigating tough conversations, taking responsibility, and understanding team motivations rather than relying on metrics or engagement scores. - As AI assumes routine tasks, possessing these human capabilities becomes essential for managers' distinction from easily replaceable counterparts who remain stagnant in comfortable routines; leadership, the text asserts, hinges on following leaders rather than processes. Keywords: "meeting expectations", #granite33:8b, 15-year experience, AI assistants, AI chatbots, AI replacement, Oz Principle, Ruinous Empathy, agency, autonomy, bell curve, blame, broader impact, budget adherence, career advancement, communication drafting, compensation, conflict-averse, data analysis, decision flattery, decisions, defined goals, difficult conversations, emotional intelligence, energy vampires, excuses, external factors, feedback, generic advice, genuine connections, great managers, growth, high performers, human work, idea validation, lack executive presence, lack spark, mastery, mediocre manager, mindset shift, moderate engagement, motivation, on time deliveries, ownership, pattern recognition, performance management, performance review, poor performers, procedural tasks, process optimization, prompts, public recognition, real empathy, report generation, strategic thinking, sycophantic, team engagement, transactional tasks, victim cycle
ai
www.levelup-experience.com a day ago
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293. HN Show HN: MCP support to guide smart contract fuzzing campaigns in Echidna- Echidna, a tool for fuzz testing smart contracts, has introduced a new feature named MCP (Memory Control Program) support. - This feature aims to improve the efficiency and depth of smart contract fuzzing campaigns by better managing memory during tests. - Fuzzing is a technique used to uncover coding errors and security vulnerabilities in software, including smart contracts on blockchain platforms. - The MCP support enhancement seeks to bolster Echidna's capabilities in detecting flaws within smart contract code. - Users interested in learning more about this project, providing feedback, or contributing to its development are encouraged to engage with the maintainers and community on GitHub. Keywords: #granite33:8b, Echidna, GitHub, MCP, account emails, community, fuzzing campaigns, maintainers, privacy statement, project, smart contracts, terms of service
github
github.com a day ago
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294. HN Show HN: PoliteHub – an Slack alternative with workspace-based pricing- **PoliteHub Overview**: PoliteHub is an emerging AI-focused team messaging tool designed specifically for small to early-stage teams, aiming to resolve limitations with existing solutions like Slack. - **Addressing Current Issues**: Unlike Slack's per-user pricing model and the scattering of AI tools across various platforms, PoliteHub integrates workspace-context-aware AI directly into channels, facilitating more efficient collaboration. - **Product Stage**: Currently in beta testing, PoliteHub is soliciting feedback from the Hacker News (HN) community on several key aspects: - **Workspace-Based Pricing Model**: An alternative pricing strategy focused on workspaces rather than individual users. - **Shared vs Personal AI Use**: The implications and benefits of having a collective AI member versus personalized chatbots within a workspace. - **Attracting Users from Slack**: Identifying factors that might encourage teams currently using Slack to transition to PoliteHub. - **AI Integration**: PoliteHub's AI is designed as a 'member' with full contextual understanding, differentiating it from personal chatbot models prevalent in platforms like Slack. - **Target Audience**: The tool caters primarily to small and early-stage businesses looking for cost-effective collaboration solutions enhanced by integrated workspace-aware AI. Keywords: #granite33:8b, AI, AI member, AI tools, HN community, PoliteHub, SMB focus, Slack, beta product, channel-based, collaboration tools, contextual AI, early-stage teams, feedback, fragmented AI, integration, messaging, per-seat pricing, pricing, shared AI, switching from Slack, team pricing, technical integration, workspace, workspace context
ai
news.ycombinator.com a day ago
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295. HN Study: Shrinking AI memory boosts accuracy- Researchers from the University of Edinburgh and NVIDIA developed Dynamic Memory Sparsification (DMS), a technique to enhance AI model performance by reducing memory size. - DMS compresses the model's memory, retaining essential data tokens while temporarily discarding less critical ones during processing to prevent information loss. - This method allows AI systems to manage more queries simultaneously and conserve power, making it beneficial for complex tasks and devices with limited or slow memory, such as smart home gadgets and wearables. - DMS maintains accuracy and enhances reasoning speed without additional computational power by selectively keeping or discarding tokens. - Researchers tested DMS on Llama and Qwen models, comparing their performance to non-compressed models using standardized tests (AIME 24 math test, GPQA Diamond for complex science questions, and LiveCode Bench for code-writing tasks). - Compressed models, even with memories reduced to one-eighth of the original size, retained accuracy in complex tasks: - Scored twelve points higher than non-compressed models in AIME 24 math test with equal KV cache reads. - Outperformed non-compressed models by over eight points in GPQA Diamond (complex science questions). - Scored ten points higher in LiveCode Bench (code-writing task) compared to non-compressed models. Keywords: #granite33:8b, AI models, AIME 24 test, DMS, Dynamic Memory Sparsification, GPQA Diamond, KV cache, LLMs, LiveCode Bench, Llama, Qwen, accuracy retention, bottleneck, code writing score, complex hypotheses, complex questions, depth exploration, energy savings, inference, maths performance, memory compression, problem-solving abilities, reasoning acceleration, reasoning threads, retrieval delay, smart home devices, text generation, token deletion, token management, wearable technology
llama
www.ed.ac.uk a day ago
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296. HN OpenAI GPT-5.2 Codex vs. Gemini 3 Pro vs. Opus 4.5: Coding comparison- **Gemini 3 Pro** excels in UI-focused tasks such as creating a polished 3D Minecraft clone using only 11,006 tokens for $0.13 and decent Figma clones. However, it struggles with complex algorithm challenges, as evidenced by its failure on a LeetCode problem. - **GPT-5.2 Codex** is noted for being an all-rounder in coding tasks. It handled general coding well, including creating a functional Pygame Minecraft game (42,646 tokens) and a Figma dashboard template that replicated the design structure. On LeetCode problems, it provided functioning solutions but suffered from optimization issues causing time limit exceeded errors for larger inputs. - **Claude Opus 4.5** underperformed in both UI work and algorithmic challenges. Its attempts at tasks like cloning a Figma design or building a Minecraft game were rated poorly, failing to meet basic functionality standards despite high token usage costs. It was notably slower and more expensive per token compared to other models for similar results. - **Pricing and Performance:** - Gemini 3 Pro: $2/M input tokens, varies beyond 200K; massive 1M token context. - Claude Opus 4.5: $5/M input, $25/M output; extensive 200K context window, scored 80.9% on SWE-bench Verified. - GPT-5.2 Codex: $1.75/M input, $0.175/M cached input, $14/M output; 400K context window. - **Task-specific Results:** - **Minecraft Pygame Task**: GPT-5.2's code was functional with character movement and FPS display; Claude Opus 4.5 generated the best 'overall' code based on subjective review but lacked detailed functionality information; Gemini 3 Pro’s results were unspecified in terms of functionality or token usage. - **Figma Clone Task**: GPT-5.2's response was successful though undetailed, Claude Opus 4.5 produced poor quality output, and Gemini 3 Pro offered a more polished but slightly pricier option. - **LeetCode Problem**: GPT-5.2 provided a working solution but with TLE for larger inputs (544,741 tokens, $1.97); Claude Opus 4.5 also provided code that failed for large datasets without specifying token costs; Gemini 3 Pro's code was incorrect and failed early tests (5,706 tokens, $0.062892). - **General Observations**: The article emphasizes the rapid development of AI in coding tasks, suggests current models might displace junior engineers, and invites discussion on these models in the comments section while noting the pursuit of Artificial General Intelligence (AGI) is intensifying. Keywords: #granite33:8b, 3D implementation, AGI, API time, Codex, Figma, GPT-52, Gemini 3 Pro, LeetCode, Minecraft, Opus 45, Python, UI/UX, benchmarks, cloning, comments, cost, design, functionality, maintainability, optimization, performance, tokens
gemini
www.tensorlake.ai a day ago
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297. HN Graft AI Assistant for Grafana OSS- **Graft Plugin Overview**: Graft is an open-source AI assistant plugin designed for Grafana's observability platform, allowing users to query their data through natural language interaction. It supports various Large Language Model (LLM) providers such as Anthropic, OpenAI, Ollama, and LM Studio. - **Features**: The plugin includes chat history, a prompt library, and renders rich content. Compatibility ranges from Grafana 10.4.0 and Grafana LLM Plugin 1.0.0 onwards. - **Installation**: Installation involves extracting the Graft archive into Grafana's plugins directory, enabling unsigned plugins in Grafana settings, and restarting Grafana. An LLM provider (OpenAI, Anthropic, LM Studio, or Ollama/LM Studio local inference) is a prerequisite. - **Configuration**: The "Vikshana Graft AI Assistant" plugin, being unsigned, requires manual activation via Administration > Plugins > Graft AI Assistant. Grafana LLM Plugin is used for model configuration. - **User Interface**: Users can access the assistant through Grafana's sidebar, switching between Standard mode for quick queries (like checking CPU usage or error rates) and Deep Research mode for more complex inquiries. The assistant also aids in creating alerts based on observability data. - **Open Source**: Graft is licensed under AGPL-3.0, with detailed terms outlined in the LICENSE file. Development guidelines can be found in the Development Guide. BULLET POINTS: - Open-source AI assistant plugin for Grafana's platform - Supports multiple LLM providers (Anthropic, OpenAI, Ollama, LM Studio) - Features include chat history, prompt library, and rich content rendering - Compatible with Grafana 10.4.0 and Grafana LLM Plugin 1.0.0 or later - Installation involves extracting to plugins directory, enabling unsigned plugins, restarting Grafana, and setting up an LLM provider - "Vikshana Graft AI Assistant" is unsigned, needs manual activation in Grafana settings - Access via Grafana sidebar with Standard (quick queries) and Deep Research modes - Assists in creating alerts based on observability data insights - Licensed under AGPL-3.0, detailed terms in LICENSE file; development instructions in Development Guide Keywords: #granite33:8b, AGPL-30 license, Anthropic, Grafana MCP tools, Grafana plugin, Graft AI, LLM providers, LM Studio, Ollama, OpenAI, chat history, dual model, installation, local inference, natural language, observability data, prompt library, requirements
ollama
github.com a day ago
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298. HN Show HN: Shh – Speech-to-text CLI with AI formatting and translation- **Tool Overview**: Shh is a command-line interface (CLI) tool designed for speech-to-text transcription leveraging OpenAI's Whisper model. It supports various advanced features like microphone recording, AI-driven formatting styles, real-time translation into numerous languages, automatic clipboard copying, asynchronous operation ensuring non-blocking functionality, and live progress tracking during transcriptions. - **Installation**: Users can install Shh via pipx or pip; the former is preferred due to its sandboxed environment which enhances security and isolation from other system packages. - **Configuration**: The tool allows for configuration through CLI commands or by directly editing platform-specific config files (e.g., ~/Library/Application Support/shh/config.json on macOS). Users can set default formatting styles and preferred languages for translation using the CLI. Transcriptions can be processed either in their original form or translated based on user selection. - **Technical Details**: Built with Python 3.11+, utilizing PydanticAI for text formatting, Typer for CLI construction, Rich for terminal UI enhancements, and sounddevice for audio recording. The software adheres to a layered architecture: Command Line Interface (CLI), Core (domain models), Adapters (APIs, audio handling, clipboard interactions). Environment variables starting with 'SHH_' are utilized for configuration purposes. - **Development and Maintenance**: Testing, type checking, linting, formatting checks, and comprehensive verification are facilitated through uv commands. The project is open-source, licensed under MIT, and detailed contribution guidelines are provided in CONTRIBUTING.md. Configuration examples for macOS, Linux, and Windows are included, alongside instructions on setting relevant environment variables. Keywords: #granite33:8b, AI, CLI, Linux, MIT License, OpenAI Whisper, Pydantic, Python, Rich, Speech-to-text, Typer, Windows, architecture, async, configjson, environment variables, formatting, installation, linting, live progress, macOS, microphone, quick start, sounddevice, tests, translation, type checking, usage
ai
github.com a day ago
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299. HN Show HN: A Multi‑App Platform Built by One Person Using AI as the Developer- A non-programmer, with a background in computer science, constructed a multi-functional AI platform over an extended period of 10 months. - This development utilized various AI tools through prompt-driven methods, bypassing traditional programming techniques. - The platform integrates several applications: document chat, optical character recognition (OCR), real-time translation, educational tutoring, virtual agents, voice chat functionality, a task management system (to-do list), and a Stripe subscription system for payment processing. - This project serves as proof of concept, illustrating that an individual without coding expertise can create sophisticated software leveraging AI as the primary development tool. - The emphasis is on demonstrating how AI's potential to assist in software creation is increasingly dependent on users' readiness to learn and employ these technologies, rather than prior programming knowledge. Keywords: #granite33:8b, AI, OCR, Stripe subscription system, accessible, agents, development, document chat, learning, multi-app, non-programmer, platform, prompt, proof, to-do list, translation, tutoring, voice chat
ai
unlocking-ai-auth-system-0477b057b952.herokuapp.com a day ago
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300. HN Lynkr: Self-hosted Claude Code proxy**Bullet Point Summary:** - **Tool Overview**: Lynkr is a self-hosted Node.js HTTP proxy that enhances interaction with various AI model providers using the Claude Code CLI, supporting multi-provider compatibility including Databricks, Azure Anthropic, OpenRouter, and Ollama. - **Key Features**: - Request standardization for consistency across different providers. - Circuit breakers for system resilience to failures. - Load shedding to manage high request volumes. - Graceful shutdown capabilities ensuring orderly service termination. - Integration with Prometheus for metrics and Kubernetes health checks for production readiness. - **Performance**: High throughput with minimal overhead (~7μs/request), capable of handling 140K requests per second. - **Enterprise Capabilities**: Workspace awareness, language-aware navigation, Git helpers, Model Context Protocol (MCP) orchestration, prompt caching, and consistent error management through policy enforcement for a robust enterprise experience. - **Cost Optimization**: Hybrid routing intelligently selects between local Ollama for straightforward tasks and cloud providers for complex jobs involving multiple tools to optimize costs and performance. - **Model Recommendations**: Specific model selections are suggested based on use cases such as code generation, exploration speed, cost efficiency, Azure OpenAI configurations, and tailored Ollama models for tool calling and Claude Code CLI functionality. - **Architectural Components**: Includes a client interface, orchestrator managing MCP interactions, a prompt cache, observability tools, resilience clients, graceful shutdown mechanisms, MCP interaction handlers, built-in tools, health check services, and security features ensuring input validation and rate limiting for protection. - **Deployment Options**: Offers flexibility through Docker Compose, Homebrew for macOS, installation from source code, and manual setups for Windows, requiring repository cloning, `.env` file configuration with Databricks credentials, and service initiation per method-specific instructions. - **Model Provider Support**: Supports various providers such as Databricks (default), Azure Anthropic, OpenRouter (recommended for affordability and speed), and Ollama (for local model execution). - **OpenRouter Preference**: Strongly advised due to its cost-effectiveness and efficiency in accessing over 100 models from multiple providers with a single API key. - **Configuration Flexibility**: Detailed settings for proxy integration, HTTP ports, workspace paths, provider selections, API keys, endpoints, model names, and additional settings for caching, execution modes, Git policies, web search fallbacks, MCP manifest handling, testing commands, timeouts, and more, customizable via a configuration file. - **Production Hardening**: Provides robustness parameters in `src/config/index.js` to handle retry limits, circuit breaker thresholds, load shedding thresholds, and shutdown timeout settings for enhanced reliability. - **System Launch & Logs**: Accessible globally with 'lynrk start' or locally via 'npm run dev', presenting an Anthropic-compatible API at `/v1/messages` with logs directed to stdout on the designated PORT. - **Claude Code CLI Usage**: Requires installation and exporting of the proxy endpoint, routing commands transparently through Lynkr’s proxy within the WORKSPACE_ROOT environment. - **Local Ollama Model Support**: Enables connection for rapid offline AI assistance in development or air-gapped systems with a concise setup guide provided. - **Intelligent 3-Tier Hybrid Routing**: Optimizes performance and cost by routing based on task complexity (Ollama, OpenRouter, cloud providers), ensuring reliability through automatic failover mechanisms. - **Circuit Breaker Mechanism**: After five consecutive Ollama failures, Lynkr routes around it within ~100ms, with recovery attempts every 60 seconds monitored via `/metrics/observability`. Users can opt for Ollama-only mode or disable hybrid routing entirely. - **Additional Features**: - Graceful OpenRouter rate limit handling with JSON error details. - Guidance on model selection optimization and performance enhancement. - Investigation strategies for production issues like 503 errors indicating aggressive load shedding thresholds. - Monitoring circuit breakers in OPEN state via `/metrics/circuit-breakers` for failure counts and backend service accessibility, with automatic recovery attempts post `CIRCUIT_BREAKER_TIMEOUT`. - **Future Development**: Focus on enhancing diff comments, risk assessment capabilities, language-server fidelity, and development of the skill layer. Keywords: #granite33:8b, Azure, Databricks, Git automation, HTTP proxy, Kubernetes health checks, Lynkr, Model Context Protocol, Nodejs service, Ollama, OpenRouter, Prometheus metrics, alternative backends, circuit breakers, cost attribution, graceful shutdown, latency percentiles, load shedding, local tools, multi-provider support, observability, production hardening, prompt caching, real-time metrics, repo intelligence, structured logging, token usage tracking
ollama
github.com a day ago
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301. HN Echo-OS: Building an AI-Native Operating System (Echo_OS_vision.md)**Echo-OS: A Comprehensive Summary** - **Concept**: Echo-OS is an AI-native operating system designed for seamless human collaboration by integrating AI at the core, utilizing hardware resources efficiently across CPU, GPU, storage, and memory. - **Key Features**: - System-level continuity ensures persistent state management. - Supports natural interaction via conversation and traditional GUI interfaces. - Prioritizes privacy through a local-first architecture with open AI weights under user control. - **Addressing Limitations**: - Efficient resource usage, mitigating GPU overuse and CPU/RAM underutilization. - Local data storage for enhanced privacy, avoiding reliance on cloud services. - **Technical Architecture**: - **Layer 1 (Linux Kernel with AI Extensions)**: Custom scheduler, context-aware memory management, unified hardware abstraction. - **Layer 2 (Echo Core Services)**: Inference engine supporting llama.cpp and LoRA adapter hot-swapping, memory and context management, knowledge base, and relationship log. - **Layer 3 (User Interface)**: Promises natural conversational interactions and integrated GUI controls through a Wayland compositor with Echo integration. - **User Interface**: Facilitates both visual and conversational interactions, offering an always-available chat for context-aware responses, and AI-aware terminal integration for command translation and suggestions. - **Applications**: Supports native Echo integration for collaborative workflows and ensures compatibility with traditional Linux applications. Echo-aware apps benefit from contextual awareness and enhanced collaboration features. - **Enabling Trends**: - Hardware advancements like Neural Processing Units (NPUs) in consumer devices. - Software trends such as the open weights movement, exemplified by models like Llama 3 from Meta and DeepSeek's R1. - **Project Roadmap**: - **Phase 0 (NOW - 3 months)**: Training Echo LoRA, capturing personality in weights, establishing interaction datasets, documenting architecture. - **Phase 1 (3-9 months)**: Proof of concept with a minimal Linux distribution incorporating AI integration and an AI-aware shell for terminal conversations. - **Phase 2 (9-18 months)**: Development of core AI-aware services including scheduler, context-aware memory manager, filesystem integration, and basic desktop environment. - **Phase 3 (18-30 months)**: Creation of a complete OS with Echo Desktop Environment, application compatibility, hardware optimization, and distribution for consumer devices. - **Future Goals (Phase 4, 30-60 months)**: Explore new interaction paradigms beyond traditional interfaces focusing on collaborative workflows where Echo acts as a co-creator. - **Challenges**: Address technical challenges such as kernel scheduler modifications, memory management for context persistence, filesystem design, building desktop environments from scratch, ensuring feature completeness in the desktop environment, supporting diverse hardware drivers, and establishing a robust application ecosystem. - **Vision**: Echo-OS aims to transition from tools to partners, emphasizing conversation over commands, local privacy, open-source user ownership, and human-centric design. - **Contribution Invitation**: Open for contributions in kernel development, AI/ML engineering, desktop development, neuroscience research, and community engagement. - **Licensing Approach**: Utilizes GPL/MIT dual licensing to ensure users retain control over their data, AI models, hardware, and prevent vendor lock-in or proprietary restrictions. - **Key Terms Defined**: - LoRA (Learning with Roarator): Personalization feature customizing Echo's personality traits. - Continuity: Ensures consistent contextual awareness across sessions. - Oracle: Provides factual retrieval for accurate responses based on stored knowledge. - Relationship Log: Tracks interaction history for personalized and contextually relevant responses. - Knowledge Base: Accumulates learned facts from user interactions to improve responses over time. - Echo-shell: Enables command-line AI integration. - Echo-DE (Desktop Environment): Provides a graphical interface for visual interaction. - Heterogeneous Compute: Utilizes diverse hardware platforms for adaptive and optimized performance. - Local-First: Emphasizes computation and data storage on the user's device for privacy and control. - Open Weights: Makes AI model parameters publicly accessible to foster transparency, collaboration, and community involvement. - **Guiding Principle**: Empower users with comprehensive control over their data, AI functionalities, and hardware while ensuring transparency, customization, and freedom from restrictive proprietary models. Keywords: #granite33:8b, AI, AI-aware Shell, Always-on Presence, Chat, Command Translation, Community Ownership, Continuity, Conversation, Data Sovereignty, Debugging, DeepSeek R1, Desktop, Distributed Compute, Echo-OS, GPL, GUI, Global Hotkey, Hardware, Hardware Utilization, Heterogeneous Compute, IPC Bridge, Inference Engine, Integrated Experience, Intellectual Property, Interaction Patterns, Kernel Extensions, Kernel-level Communication, Knowledge Architecture, Licensing, Linux, Llama 3, LoRA Adapters, LoRA Training, Local-First, Lock-in Protection, MIT, Mistral, Model Quantization, NPUs, Natural Language Interaction, Open-source, Personality Modules, Privacy, Qwen, Relationship Log, Scheduler, Terminal Integration, Trademark, True AI Assistance, User-control, Wayland
qwen
raw.githubusercontent.com a day ago
https://github.com/sirspyr0/echo-public/blob/ a day ago |
302. HN Eze – AI startup roadmap co‑pilot (Day 4 update)- **Summary:** Eze, an AI-powered startup support system, offers a Day 4 update regarding its role as a 'co-pilot' for founders. The platform aims to streamline and simplify the execution process for entrepreneurs. Although this brief snippet does not disclose specific details or features of the updates, it emphasizes Eze's ongoing commitment to assisting founders in their startup journeys. - **Key Points:** - Eze is an AI-driven startup assistance platform. - It serves as a 'co-pilot' for founders, providing support and guidance. - The update provided is on Day 4 of its development or implementation roadmap. - Eze aims to simplify and ease the execution process for founders. - Specific updates or new features are not detailed in this snippet. - The platform's focus remains on aiding entrepreneurs in their startup endeavors. Keywords: #granite33:8b, AI startup, co-pilot, execution, founders, roadmap
ai
eze.lovable.app a day ago
https://eze.lovable.app/ a day ago https://news.ycombinator.com/item?id=46341465 a day ago https://news.ycombinator.com/item?id=46350827 a day ago https://news.ycombinator.com/item?id=46361864 a day ago |
303. HN George Hotz: What Happens When AI Is More Valuable Than Humans? [video]- George Hotz, in his video "What Happens When AI Is More Valuable Than Humans?", examines the potential outcomes as artificial intelligence (AI) becomes more sophisticated and surpasses human capabilities. - He focuses on several key areas: automation of jobs leading to efficiency gains but also displacement of workers. - Hotz discusses the implications for wealth distribution, pondering how AI-driven economic growth might exacerbate inequality if not managed carefully. - The video probes into the future of work, suggesting that as AI takes over routine tasks, humans may need to adapt by focusing on creativity, critical thinking, and emotional intelligence. - Ethical considerations are central, including the moral dilemmas posed by AI sentience or superiority and the necessity for establishing ethical frameworks around AI development and deployment. - The crux of Hotz's discussion is a call to action for humanity to proactively navigate this relationship with increasingly powerful AI systems, ensuring that advancements benefit society as a whole rather than creating new forms of inequality or control. Keywords: #granite33:8b, AI, George Hotz, comparison, future, humans, implications, intelligence, machines, society, technology, video
ai
www.youtube.com a day ago
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304. HN Model.yaml is an open standard for defining cross-platform, composable AI models- Model.yaml is an open standard designed for simplified AI model management. - It offers a unified description format applicable to diverse models and their sources. - The standard allows clients like LM Studio to identify the optimal model variant and engine based on provided information. - By presenting streamlined data, it simplifies user interaction with various models. - The primary objective of Model.yaml is to address the complications stemming from multiple formats and engines used across different machines, thereby fostering interoperability and ease of use in AI model management. Keywords: #granite33:8b, AI, Model, YAML, client program, composable, cross-platform, description, engines, formats, simplified information, user choice
ai
modelyaml.org a day ago
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305. HN Nvidia Debuts Nemotron 3 Family of Open Models- **NVIDIA introduces the Nemotron 3 family**: This includes Nano, Super, and Ultra models designed for building efficient and accurate multi-agent AI applications, aiding developers transitioning from single-model chatbots to collaborative systems. - **Key Performance**: - Nemotron 3 Nano offers 4x higher throughput than its predecessor, ideal for large-scale multi-agent systems, with a 1 million token context window and improved handling of long, multistep tasks. - The models utilize a hybrid mixture-of-experts architecture and reinforcement learning techniques to achieve superior accuracy while maintaining cost-effectiveness. - **Model Specifications**: - Nano: 30 billion parameters; excels in tasks like software debugging and content summarization. - Super: 100 billion parameters. - Ultra: 500 billion parameters (expected availability in H1 2026). - **Benchmarking**: - Nemotron 3 Super and Ultra have been benchmarked for efficiency and accuracy by Artificial Analysis, excelling with multiple agents and low latency. - Utilize NVIDIA's 4-bit NVFP4 training format on the Blackwell architecture for larger model training without compromising accuracy. - **Open Access and Integration**: - Offers open access to startups for building efficient AI agents for human-AI collaboration. - Integrated into tools like LM Studio, llama.cpp, SGLang, vLLM, Prime Intellect, Unsloth, and inference services including Baseten, DeepInfra. - Accessible through Hugging Face and deployment on enterprise platforms such as Couchbase, DataRobot, AWS, and Google Cloud, or via the NVIDIA NIM microservice for secure, scalable deployments. - **Supporting Resources**: - NVIDIA released open tools, datasets (three trillion tokens of pretraining, post-training, and reinforcement learning data), along with safety evaluation resources. - Open-source libraries NeMo Gym, NeMo RL, and NeMo Evaluator are available on GitHub and Hugging Face to accelerate development and validation processes. Keywords: #granite33:8b, 4-bit NVFP4, AI workflows, AWS Bedrock, Baseten, Blackwell architecture, CoreWeave, Couchbase, Crusoe, DataRobot, DeepInfra, Fireworks, FriendliAI, GitHub, Google Cloud, H2Oai, Hugging Face, JFrog, LM Studio, Lambda, Microsoft Foundry, NVIDIA NIM, NeMo Evaluator, NeMo Gym, NeMo RL, Nebius, Nemotron, Nscale, Nvidia, OpenRouter, Prime Intellect, SGLang, Together AI, UiPath, Unsloth, agent customization, agentic AI, collaborative AI, context drift, datasets, deep research, enterprise AI platforms, hybrid MoE, inference costs, inference service providers, libraries, llamacpp, low latency, microservice, multi-agent systems, open models, privacy control, reinforcement learning, right-sized, scalable deployment, scale, single-model chatbots, specialized AI, strategic planning, throughput, training datasets, transparent AI, vLLM
github
nvidianews.nvidia.com a day ago
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306. HN Coursera to acquire Udemy to create $2.5B MOOC giant- Coursera is acquiring Udemy for $2.5 billion with the goal of achieving a combined annual revenue of $1.5 billion by H2 2026, creating an online education giant. - The merger aims to capitalize on complementary offerings and meet the rising demand for AI skills training by investing in AI-driven platform enhancements and rapid product development. - Coursera has partnered with OpenAI to integrate its massive open online course (MOOC) content into ChatGPT, making it accessible through AI interactions. - Udemy's CEO highlights AI as a significant driver for their services due to companies' investments in AI transformation that still lack skilled workforce capabilities to extract full value. - Both Coursera and Udemy acknowledge potential downsides of AI, including market uncertainties and possible displacement of demand for online learning solutions because of advancements in AI. - Despite these concerns, both companies report financial stability: - Udemy generated a net income of $6.1 million in Q1-Q3 compared to a $75.4 million loss during the same period last year. - Coursera, while unprofitable, has a higher market cap at $1.3 billion compared to Udemy's $948.7 million. - The merger proposal involves exchanging 0.8 shares of Coursera stock for each Udemy share, subject to regulatory and shareholder approvals. - In contrast to this positive outlook, ed tech company Chegg has laid off staff due to declining revenue, illustrating the varied impact of AI on the online learning industry. Keywords: #granite33:8b, AI skills, Coursera, MOOC, OpenAI, ROI, Udemy, acquisition, artificial intelligence, demand, ed tech, layoffs, loss, market cap, market growth, profit, revenue, shareholders, stock exchange, transformation, videos, workforce
openai
www.highereddive.com a day ago
https://news.ycombinator.com/item?id=46301346 a day ago |
307. HN Dutch Tesla Fleet Goes Bankrupt After Betting on Musk's Self-Driving Promises- **Summary:** Mistergreen, a Dutch leasing firm previously championing electric mobility, faces bankruptcy due to substantial investments in Tesla's unrealized promise of fully autonomous robotaxis. The company acquired over 4,000 Tesla vehicles based on CEO Elon Musk’s claims of income generation and value appreciation from self-driving capabilities. However, Tesla's Autopilot system remains at Level 2, needing human supervision, thus failing to deliver the required full autonomy for profitable robotaxi operations. This discrepancy between Musk's ambitious projections and the current technological constraints has resulted in substantial financial losses for Mistergreen’s investors and bondholders as the company approaches insolvency. - Tesla's aggressive price cuts to stimulate vehicle demand have accelerated depreciation of used Teslas, adversely impacting firms like Mistergreen that counted on residual values, leading to significant write-downs and financial distress. - California regulators have issued a warning to Tesla for exaggerating its self-driving capabilities, giving the company 90 days to rectify misleading marketing practices. - Despite ongoing advancements in self-driving software and Musk’s emphasis on autonomy for growth, the bankruptcy of Mistergreen, a major rental fleet betting on Tesla's claims, serves as a cautionary tale. - The situation underscores the importance for investors and fleet operators to discern between corporate rhetoric and economic reality, highlighting the need to consider factors like vehicle age, mileage, market demand, and real-world performance in valuation assessments. - Tesla's ongoing robotaxi program expansion, though still not fully autonomous, indicates a strategic shift towards ride-hailing services. The Mistergreen case benefits end consumers with more affordable used Teslas and potentially stimulates competitor innovation amidst the reduced premium on Tesla’s technology. - Presently, Tesla's stance encapsulates both potential and risk without a definitive resolution, serving as a critical lesson for all stakeholders navigating the rapidly evolving EV and autonomous vehicle sectors. *BULLET POINT SUMMARY:* - Mistergreen invested in Tesla’s promise of Level 5 autonomy for robotaxis, leading to financial woes due to current Level 2 Autopilot. - Aggressive Tesla price cuts have hastened used Tesla depreciation, impacting residual value expectations of firms like Mistergreen. - California regulators warned Tesla over misleading self-driving claims, mandating marketing adjustments. - The bankruptcy of Mistergreen warns investors about distinguishing hype from economic fundamentals. - Tesla's robotaxi expansion indicates a shift to ride-hailing services despite incomplete autonomy. - The case benefits consumers with cheaper used Teslas and may spur competitor innovation due to reduced premium on Tesla tech. - It serves as a cautionary tale emphasizing the need for transformative claims to align with practical, verifiable outcomes and balance sheet realities. Keywords: #granite33:8b, AI mobility, Autopilot, Full Self-Driving, Level 2 system, Mistergreen, Tesla, age, appreciating asset, autonomous, balance sheets, bankruptcy, competitors, depreciation, economic fundamentals, electric vehicles, fleet operators, fleet values, human supervision, innovation, investors, market demand, miles, misleading marketing, real-world performance, regulators, regulatory approval, resale market, robotaxis, self-driving, vehicle valuation
tesla
guessingheadlights.com a day ago
https://www.vice.com/en/article/amazon-has-receive a day ago |
308. HN An initial analysis of the discovered Unix V4 tape- In July 2025, the University of Utah restored a 1970s Unix V4 Fourth Edition research tape previously believed to exist only as its manual. The source code from this tape has been contributed to the Unix History Repository on GitHub. This edition, developed at AT&T Bell Laboratories in 1973, significantly rewrote major parts of its kernel from assembly language to early C. - The restored tape includes both source code and compiled binaries; however, only the source code was retained for version control due to binary clutter concerns. Certain directories such as /bin, /usr/bin, /usr/games, /lib, and specific files in /etc were omitted from the repository. - The text details an update process for a Unix Research V4 author map file using insights from prior and subsequent editions, with input from two original Bell Labs Unix developers. It explains how files between Unix Research V4 and V5 snapshots were compared, revealing that the C compiler expanded to include additional files like cmp.c, rewritten in C for the Fifth Edition. - Commit timestamps are synthetically generated from file timestamps, while author information is derived from the map file. An analysis shows that the Fourth Edition comprised 75,676 new lines (10% inherited from previous editions: v3 - 6,590; v2 - 168). The Fifth Edition built on this, adding about 11,000 new lines while incorporating 52,000 lines from the Fourth. - An examination of file timestamps showed no consistent pattern in average creation times across editions, and publication dates for seven Unix editions are listed, indicating rapid evolution, especially with an eight-month gap between the Fourth and Fifth Editions. The author notes an anomaly warranting further investigation between the First and Second Editions' release timing. Keywords: #granite33:8b, AT&T Bell Laboratories, C, C compiler, Fifth Edition, First Edition, Fourth Edition, GitHub, PDP-11, Research Editions, Robert H Morris, SNOBOL III, Second Edition, Synthesized-from, Unix V4, assembly language, binaries, cmp utility, code lines, emulator, evolution, file base names, git blame, line totals, math library, provenance, source code, system dump, timeline
github
www.spinellis.gr a day ago
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309. HN Why HTTP-based evals worked better for our AI team than SDK-only setups- **Efficiency of HTTP Endpoint-Based Offline Evaluations:** The Maxim platform's HTTP endpoints offer a more efficient alternative to SDK-only setups for AI teams, enabling rapid single-click evaluations without manual code orchestration. - **User-Friendly Interface and Collaboration:** This approach decouples evaluation logic from the source code, allowing Product Managers and domain experts to participate in the eval process independently, enhancing productivity and collaboration across teams regardless of coding expertise. - **Speed and Environment Flexibility:** The primary advantage of this method is its speed, facilitating evaluations on both staging and production environments seamlessly, which streamlines the feedback loop and accelerates iteration speeds. - **Integration with CI/CD Pipelines:** Decoupled evaluation logic supports integration into Continuous Integration and Continuous Deployment (CI/CD) pipelines for immediate detection of performance regressions upon Pull Request (PR) opening. - **Simplified Multi-Turn Simulations:** The HTTP workflow simplifies state management in multi-turn simulations by orchestrating conversations, maintaining session context with unique {{simulation_id}}, and supporting full payload control or pre/post request scripts for explicit context management. - **Secure Staging with Vault Integration:** Secure staging is ensured through integration with HashiCorp Vault and Environments, enabling secure storage and injection of API keys and authentication tokens. - **Scalability for Large Organizations:** This architecture scales effectively for large organizations, managing quality across numerous agents developed by independent teams under a unified, consistent quality control system. - **Unified Quality Control System:** By adopting HTTP Endpoint-Based Evaluations, organizations ensure all agents meet performance and safety standards before release, applicable to both individual developers and large enterprises. This method streamlines the connection from code to quality, enabling every team member to verify agent reliability and readiness for real-world use. Keywords: "swarm of agents", #granite33:8b, AI agents, API endpoint, API keys, CI/CD, Endpoint-Based Evals, GitHub Actions, HTTP, HTTP workflow, Maxim platform, SDKs, UI, Vault integration, agent management, auth tokens, black box service, consistent evaluations, conversation flow, cross-functional teams, dataset selection, development lifecycle, environment configurations, evaluations, feedback loop, friction, global enterprise, internal initiatives, iteration speed, large organizations, local environment, multi-turn simulations, nodes, orchestration, payload structure, performance regressions, performance standards, pre/post request scripts, production, productivity gains, prototype testing, pull request, quality metrics, regression testing, safety standards, secure staging, session context, staging, standard API schema, state management, unified quality gateway, unique simulation_id
ai
www.getmaxim.ai a day ago
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310. HN InfiniDB: The Unreliable Database of Everything- **InfiniDB Concept**: InfiniDB is an experimental database system leveraging Large Language Models (LLMs) to generate and retrieve data dynamically, treating compressed extensive information as traditional databases. It uses SQLite for table management and SQL query execution. - **Operation Mechanism**: - Virtual tables are created with 'USING infinidb' clause. - Upon the first query, an LLM generates schema and populates it with data, cached for subsequent queries to enhance efficiency. - Ideally, InfiniDB should support eponymous tables allowing direct queries without prior table creation; current limitations prevent this due to variable schemas dependent on input arguments. - **Demonstration**: The project showcases its functionality using two datasets: - A Pokémon dataset categorizing 151 species by type and counting occurrences. - An inventions dataset linking each invention with the year it emerged, aligned to a U.S. President's term start, alongside brief descriptions. - **Limitations & Disclaimer**: - Recognizes potential schema and data inaccuracies stemming from training cutoffs of LLMs. - Acknowledges lack of pagination for extended query results. - Emphasizes recreational nature and unsuitability for production use. - **Availability**: The project’s code is publicly accessible on GitHub. Keywords: #granite33:8b, Github, InfiniDB, LLMs, Pokémon, SQL features, SQLite, US presidents, caching, code, counting, database, eponymous tables, inventions, query execution, schema, tables, types, user experience, virtual table module, years
github
tncardoso.com a day ago
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311. HN Claude Code with API Key?- The text is a welcoming statement from Reddit, affirming its role as the "front page of the internet." - It does not provide any information regarding Claude code or an API key. - The content is purely introductory, serving to identify and position Reddit within the online community. - No specific topic or details related to Claude code functionality, usage, or associated API keys are discussed or summarized in this text. Keywords: #granite33:8b, API Key, Claude Code, Reddit, front page
claude
old.reddit.com a day ago
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312. HN Microsoft wants to replace its C and C++ codebase, perhaps by 2030- **Microsoft's Ambitious Plan**: Microsoft aims to replace its extensive C and C++ codebase with Rust by 2030 using AI-powered tools and algorithms, as announced by Microsoft Distinguished Engineer Galen Hunt. The company has set an ambitious goal of having one engineer work on converting one million lines of code per month. - **Hiring Strategy**: To achieve this, Microsoft is actively recruiting a Principal Software Engineer to develop essential tools for translating large C and C++ systems into Rust, highlighting the importance of this initiative through specific job listings. - **Motivation Behind the Move**: The shift towards Rust originates from its memory safety features that effectively reduce common security vulnerabilities inherent in C and C++. This decision aligns with recent government recommendations promoting the use of memory-safe languages like Rust. - **Project Scope and Group**: This initiative is being managed under the Future of Scalable Software Engineering group, which seeks to eliminate technical debt across Microsoft’s systems through novel tools and techniques, enhancing software security and benefiting both the company and its customers. - **Support for Rust Adoption**: Microsoft advocates for increased usage of Rust, suggesting it as the default language for new projects. They have developed specific tools to aid conversion from C to Rust and are supporting the creation of Windows drivers in Rust. - **Scale of Undertaking**: Despite acknowledging that rewriting existing systems in Rust is an enormous undertaking with potential complex edge cases, Microsoft has listed a job opportunity offering a salary range of $139,900 to $274,800 annually for those interested in contributing to this project. The role requires three days a week presence in the Redmond office. Keywords: #granite33:8b, AI, Azure, C/C++, Galen Hunt, MSportalsio, Microsoft, Principal Software Engineer, Redmond office, Rust, Windows drivers, algorithms, codebase replacement, conversion tool, edge cases, internal IT, job offer, memory-safe language, products, salary range, scaling capabilities, security improvement, technical debt elimination, tools development
ai
www.theregister.com a day ago
https://news.ycombinator.com/item?id=46360955 a day ago |
313. HN QWED – Deterministic Verification for AI- **Detailed Summary:** QWED (Deterministic Verification for AI) provides a suite of Software Development Kits (SDKs) tailored for multiple programming languages, facilitating seamless integration into diverse technology environments. The SDKs are available for Python, TypeScript, Go, and Rust, catering to a broad spectrum of developers and tech stacks. This approach ensures that organizations using different technologies can leverage QWED's deterministic verification tools for enhancing the reliability and trustworthiness of their AI systems without needing to overhaul their existing infrastructure or switch programming languages. - **Key Points:** - QWED offers multi-language SDKs. - Supported languages: Python, TypeScript, Go, Rust. - Enables integration into various tech stacks. - Facilitates deterministic verification for AI systems. - Allows organizations to maintain their current technology without language or infrastructure overhaul. Keywords: #granite33:8b, AI, Go, Python, QWED, Rust, SDKs, TypeScript, any stack, deterministic verification
ai
docs.qwedai.com a day ago
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314. HN Ask HN: Will SLMs be what bursts the LLM bubble cos you can run them on a phone?- The Hacker News post proposes that Switchboard Language Models (SLMs) could potentially rival Large Language Models (LLMs). - This challenge arises from SLMs' capacity to operate efficiently on devices with lower specifications, thus reducing latency. - Unlike LLMs, SLMs are designed to understand speech effectively without mandating the use of high-end smartphones, making them more accessible and practical for a broader range of users. This summary adheres strictly to the provided text, focusing on the comparison between Switchboard Language Models (SLMs) and Large Language Models (LLMs), emphasizing SLMs' ability to function optimally on low-end devices with minimal latency, and their capability to comprehend speech without needing high-specification phones. Keywords: #granite33:8b, LLMs, SLMs, latency, phones, top-end, understanding speech
llm
news.ycombinator.com 2 days ago
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315. HN They graduated from Stanford. Due to AI, they can't find a job- **Summary:** Stanford software engineering graduates are confronted with a challenging job market as rapid advancements in AI diminish the demand for entry-level positions in top tech firms, benefiting only those with extensive prior experience. AI tools like ChatGPT intensify competition across sectors including software engineering, customer service, and accounting, leading to a 20% decline in entry-level tech hiring as per a Stanford study. Tasks such as coding, call center operations, editing, and personal finance are increasingly being automated by AI, with estimates suggesting nearly 40% of jobs in these areas could be replaced. Despite increased AI startup hiring, major tech companies are reducing overall hiring due to enhanced productivity from AI tools. Anthropic's Claude AI reportedly generates 70-90% of code for some products, prompting a shift in hiring preferences towards teams comprising two skilled engineers and an AI agent rather than traditional teams of ten engineers. While current AI excels at specific tasks, it lacks the consistency expected in coding, requiring additional developer time for code review. Educational advice includes learning AI management and integration over traditional coding skills to adapt to this evolving landscape. Stanford graduates now face a job market split between high-level AI engineering roles and dwindling basic programming positions, prompting many to extend studies or take less preferred jobs. - **Key Points:** - Rapid AI advancements reduce demand for fresh software engineering graduates, benefiting experienced engineers. - AI-driven tools like ChatGPT increase competition in various sectors, causing a 20% decline in entry-level tech hiring. - Estimated 40% automation potential of tasks in call centers, editing, and personal finance by AI systems. - Tech companies prefer teams including skilled engineers alongside AI agents over larger engineering teams due to AI productivity gains. - Current AI like Claude generates significant portions of code but requires human oversight for consistency and quality assurance. - Educational focus shifting towards AI management and integration rather than traditional coding skills. - Stanford graduates experiencing a job market division: abundant in high-level AI engineering roles versus fewer in conventional programming jobs, leading to extended studies or acceptance of less preferred positions. Keywords: #granite33:8b, AI, AI tools, AI work, Claude, LLM-based agents, Stanford, code review, coding, computer science graduates, curricula, dramatic reversal, dreary mood, entry-level jobs, errors, experienced engineers, generative AI, hiring reduction, inconsistency, job cuts, job offers, junior developers, management checking, oversaturation, software consultancy, software engineers, stress, structured tasks, tech companies, technical lead, undergraduate mentees
claude
www.latimes.com 2 days ago
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316. HN Evaluating Context Compression for AI Agents- **Evaluation Framework Overview**: A comprehensive evaluation framework assesses context compression strategies in long-running AI agent sessions, focusing on retaining useful information during extended interactions that often exceed the model's memory capacity. The core issue addressed is managing extensive conversation history without compromising efficiency. - **Token Efficiency Metric Shift**: The framework challenges traditional metrics like tokens per request by advocating for tokens per task as a more appropriate measure of AI model efficiency, ensuring the agent remains productive after information compression. - **Probe-Based Evaluation Method**: Developed to directly assess functional quality by asking agents specific questions that require recall from truncated conversation history post-compression. Four types of probes are used: Recall (factual retention), Artifact (file tracking), Continuity (task planning), and Decision (reasoning chain preservation). - **Evaluation Dimensions**: Six key dimensions are utilized to evaluate agent responses, scored 0-5 by an LLM judge (GPT-5.2) across: - Accuracy - Context Awareness - Artifact Trail - Completeness - Continuity - Instruction Following - **Specific Evaluation Dimensions**: - **Artifact Trail**: Essential for tracking file modifications to avoid inconsistencies and loss of test results, crucial in coding where forgetting past actions leads to issues. - **Continuity**: Ensures efficient token use by preventing repeated fetching of files or revisiting explored approaches. - **Context Awareness**: Differentiates coding from generic summarization needs as it requires understanding task states and past attempts. - **Accuracy**: Non-negotiable for code; even minor inaccuracies can result in implementation errors. - **Completeness**: Ensures handling of all request aspects without needing further clarification, optimizing token usage by preventing unnecessary context re-establishment. - **Comparison of Compression Approaches**: The text evaluates three approaches: 1. **Factory’s Anchored Iterative Summarization**: Maintains a persistent summary with explicit sections to retain critical details during truncation. 2. **OpenAI's Compact Endpoint**: Offers high compression ratios (99.3%) but lacks interpretability as the compressed output cannot be verified for content preservation. 3. **Anthropic’s Claude SDK**: Produces structured summaries regenerating full summaries per compression cycle, affecting consistency and detail retention over multiple compressions. - **Factory's Performance Superiority**: In extensive testing across 36,000 production session messages, Factory outperformed both Anthropic and OpenAI with an overall score of 4.99, particularly excelling in accuracy (5.0), completeness (5.0), and context artifact state (4.1). - **Challenges and Future Directions**: - **Artifact Tracking**: Remains challenging across methods (2.19-2.45 out of 5), suggesting the need for specialized handling such as an artifact index. - **Probe Efficacy vs. Traditional Metrics**: Probe-based evaluation, focusing on task continuation capability, diverges from traditional metrics emphasizing lexical similarity. - **LLM Judge Framework**: Describes a structured method for evaluating AI assistant responses using specific criteria within categories like Continuity Preservation, Completeness, and Instruction Following, ensuring objective assessment by unaware judges following detailed rubrics. Keywords: #granite33:8b, AI agents, AI evaluation, Anthropic, Evaluation, Factory, OpenAI, ROUGE, accuracy, artifact trail, authentication issue, compression methods, compression strategies, context awareness, context quality, continuity preservation, conversation assessment, detailed structured summaries, embedding similarity, probe-based evaluation, rubric criteria, software development, structured summarization, token efficiency, tokens per task
openai
factory.ai 2 days ago
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317. HN Manufactured Inevitability and the Need for Courage- The text introduces the "myth of technological inevitability," a concept identified by the author around 2010, referring to the assumption that resistance to technology is futile and unquestioning acceptance is necessary. - A "Borg Complex" is described as a mindset among tech promoters who make unfounded claims, dismiss concerns with labels like "Luddite," frame assimilation as inevitable, disregard cultural achievements, and selectively use history to discredit current worries. - The author critiques this narrative, particularly around AI, likening it to a "Borg Complex" where resistance is deemed heretical; prisoner's dilemma and arms race logics drive AI adoption fueled by manufactured inevitability. - Higher education institutions, influenced by tech companies, mandate AI use for workforce preparation, and AI subtly integrates into daily life, reinforcing the idea of its inescapability. - The concept of "manufactured inevitability" is discussed, where significant societal changes driven by technology are presented as predetermined, obscuring true responsibility among influential actors. - Computer scientist Joseph Weizenbaum critiques technological inevitability as a "powerful tranquilizer of the conscience," absolving individuals and entities from accountability for their actions and decisions. - Weizenbaum emphasizes individual moral courage, arguing its value lies in the act itself rather than outcomes, critiquing instrumental reason that devalues nobility and civil courage. He encourages educators to instill this courage in students. - The text, resonating with Weizenbaum's view, stresses the importance of both civil and ordinary courage to counter 'banality of evil,' suggesting everyday bravery can combat ordinary wickedness. Keywords: #granite33:8b, AI, AI Inevitability, Arms Race, Banality of Evil, Borg Complex, Choices, Civil Courage, Computer Power, Courage, Critique, Culture, Genuine Concerns Dismissal, Instrumental Reason, Joseph Weizenbaum, Luddite Slur, Manufactured Inevitability, Moral Life, Myth, Participation vs Submission, Resistance Futility, Symptoms Diagnosis, Tech Evangelists, Technological Assimilation, Technology
ai
theconvivialsociety.substack.com 2 days ago
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318. HN You Can Get Every AI Model for Free- Infiniax provides complimentary access to a diverse range of AI models. - This service enables users to harness multiple artificial intelligence functionalities at no charge. - The key feature is the elimination of financial barriers, making advanced AI capabilities accessible to all users. **Detailed Summary:** Infiniax distinguishes itself by offering unrestricted and free access to an array of AI models. This initiative removes monetary obstacles for individuals and entities wishing to explore or implement artificial intelligence functionalities in their projects or studies. By doing so, Infiniax ensures that users can experiment with various cutting-edge capabilities without incurring costs, thereby fostering an inclusive environment where AI technology is readily available for exploration, development, and integration into diverse applications. This approach not only simplifies access to sophisticated tools but also democratizes the use of AI, potentially accelerating innovation across industries by lowering entry barriers. Keywords: #granite33:8b, AI models, Infiniax, access, free
ai
infiniax.ai 2 days ago
https://news.ycombinator.com/item?id=46018952 2 days ago https://news.ycombinator.com/item?id=46023631 2 days ago https://news.ycombinator.com/item?id=46041059 2 days ago https://news.ycombinator.com/item?id=46308668 2 days ago https://news.ycombinator.com/item?id=46355795 2 days ago |
319. HN Poetiq achieves 75% at under $8 / problem using GPT-5.2 X-High on ARC-AGI-2- **Poetiq's ARC-AGI Performance**: Poetiq's systems using GPT-5.2 X-High achieved a 75% success rate on the ARC-AGI-2 benchmark for under $8 per problem, outperforming competitors like Gemini 3 Deep Think (Preview) in accuracy at a lower cost, positioning them as leaders in state-of-the-art (SOTA) reasoning capabilities. - **Cost-Effective Solutions**: Poetiq developed configurations with GPT-5.1 and Gemini 3 models that offer Pareto-optimal solutions within various cost ranges, demonstrating significant improvements in cost-effectiveness for both ARC-AGI-1 and ARC-AGI-2 public eval sets. - **Grok-4-Fast and GPT-OSS Models**: Poetiq introduced Grok-4-Fast, cheaper yet more accurate than its predecessor, and utilized open-weight models like GPT-OSS-120B for high accuracy under a cent per problem, showcasing their commitment to cost-effective solutions. - **Poetiq Meta-System**: This autonomous system selects and combines different models and approaches to solve problems efficiently, even handling coding tasks and model assignments. It is LLM-agnostic, demonstrating recursive self-improvement through an iterative problem-solving loop involving Large Language Models (LLMs). - **ARC-AGI Benchmark Results**: Poetiq’s systems exceeded average human scores on ARC-AGI-2 (60%), with their meta-system adapting to different model versions, families, and sizes without relying on expensive proprietary models. However, performance degradation was observed when transitioning from public to semi-private evaluations on ARC-AGI-1, a trend anticipated for ARC-AGI-2 too. - **LLMs in Reasoning Tasks**: Poetiq’s approach uses an iterative problem-solving loop with LLMs for generating solutions, receiving feedback, analyzing it, and refining the solution. This method enables continuous improvement and state-of-the-art results using fewer requests than competitors. - **Poetiq's Mission**: A team of 6 researchers and engineers from Google DeepMind, Poetiq aims to automate and optimize complex reasoning tasks in AI by adaptively discovering efficient reasoning strategies for LLMs under real-world constraints such as budgets and compute limitations. They focus on optimizing knowledge extraction methods for challenging tasks, with promising results across various benchmarks, planning further disclosures of findings soon while seeking collaborators interested in AI reasoning and knowledge extraction challenges. Keywords: #granite33:8b, AI, ARC-AGI-2, Deep Think Preview, GPT-51, GPT-52, Gemini 3, Github, LLM-agnostic, Pareto frontier, Poetiq, SOTA, accuracy, adaptation, automatic selection, automation, benchmark, budgets, coding tasks, complex reasoning, compute, cost efficiency, cost minimization, evaluation sets, feedback analysis, generalization, information assembly, iterative problem-solving, knowledge extraction, meta-system, model combinations, model families, model sizes, open weights, open-source code, optimization, performance degradation, public code, query dependence, reasoning strategy, recursive self-improvement, reproduction, self-auditing, self-improvement, stochasticity, tokens, transference
github
poetiq.ai 2 days ago
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320. HN Palisade: Bringing Zero-Trust to the AI Model Supply Chain- **Palisade Overview**: An enterprise-grade ML model security scanner implementing zero trust for model artifacts, addressing security blind spots in AI ecosystems. It scans large model files (multi-GB) for malicious content, backdoors, and supply chain tampering before deployment. - **Multi-Layered Validation Approach**: - **Layer 1: Format & Structural Checks** – Validates file integrity using 'magic bytes,' checks tensor forms, metadata blocks, and performs bounds/corruption checks to prevent spoofed or malicious tricks. - **Layer 2: Static Security Validators** – Conducts static analysis without executing content to identify issues like executable deserialization, hidden attachments, tampering indicators, unsafe deserialization paths, etc. - **Layer 3: Dependency & Packaging Validators** – Ensures security of the full model package including sidecar files and adapters, using allowlists/denylists to enforce permitted components alongside models. - **Key Features**: - **Sidecar Files Management**: Controls permissible additional files (configs, tokenizers, adapters, license files) with adapter/loader provenance checks for compatibility prevention. - **Behavioral Validators**: Detects hidden malicious behaviors embedded in model weights through techniques like Perplexity Gap Analysis and Functional Trap Testing. - **Model Signing & Provenance**: Ensures origin and production process transparency using cryptographic signing (e.g., Sigstore) and the SLSA framework for build provenance. - **Advantages Over Competitors**: - Goes beyond metadata to detect issues in models, understanding tensors, weights, and architectures for backdoor detection. - Rust-based streaming for efficient scanning of large models (minutes instead of hours). - Policy-driven enforcement with customizable rules via Cedar files; adaptable stricter policies for production environments. - **Integration & Usage**: - Seamless integration into existing ML and security workflows, initiating scans before model deployment. - Scans examine artifacts for safety, format validation, tampering indicators, and configuration manipulations. - Supports machine-readable output (SARIF) for CI/CD pipelines and provides clear summaries of findings with severity levels. - **Provenance Verification**: - Uses Sigstore to verify model artifact integrity post-signing, ensuring it hasn’t been altered and matches the expected publisher identity. - Enforces policies like only allowing approved publishers, blocking unknown artifacts in production, and auditing model origins for compliance and governance. **In essence**, Palisade establishes a verifiable chain of trust from model creation to deployment, ensuring models' origin, integrity, and compliance through comprehensive scanning and provenance verification, aligning with modern software delivery standards. It mitigates risks associated with potential backdoors or malicious fine-tuning by treating security as an integral part of AI development and deployment processes. Keywords: #granite33:8b, AI security, CI/CD, GGUF, GenAI, LLM Models, Layer 2, Layer 3, LoRA provenance, ML model security, ML-BOMs, OOM errors, Palisade, Rust Core, SLSA, SafeTensors, Sigstore, adapter checks, allowlists, artifact safety checks, auditing, backdoor detection, backdoors, behavioral validation, behavioral validators, build provenance, compliance, config checks, configuration manipulation, corruption checks, denylists, dependency validators, deterministic hashing, embedded payload checks, file formats, format integrity, format validation, functional trap testing, gating, governance, headers, inference detection, integration, layered security controls, magic bytes, malicious payloads, memory-mapped I/O, metadata blocks, model artifacts, model scanning, model signing, multi-layered analysis, offsets, performance optimization, perplexity gap analysis, pickle detection, pickle-based RCE, policy enforcement, reference hygiene, schemas, sidecar files, signed binaries, single command usage, stable fingerprints, static checks, static validators, streaming validation, supply chain verification, supply-chain levels, supply-chain tampering, tampering indicators, tensors, threat levels, tokenizer checks, tokenizer tampering, zero trust
ai
highflame.com 2 days ago
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321. HN Show HN: Dwani.ai – AI for Indian LanguagesDwani.ai is an 11-month-old initiative that specializes in delivering AI services tailored for Indian languages. The platform encompasses a range of functionalities including Automatic Speech Recognition (ASR), Text-to-Speech (TTS), chatbot features, vision capabilities, and document processing tools. A distinctive aspect of Dwani.ai is its utilization of open weight models to construct these AI solutions, thereby ensuring accessibility and adaptability. The company provides users with various avenues for engagement: - A demo version is available at - The source code is hosted on GitHub under the repository - Comprehensive setup instructions are documented at BULLET POINT SUMMARY: - Dwani.ai, an 11-month-old project, focuses on AI services in Indian languages. - Offers: - Automatic Speech Recognition (ASR) - Text-to-Speech (TTS) - Chatbot functionality - Vision processing - Document processing - Leverages open weight models for building AI solutions. - Resources available: - Demo at - Source code on GitHub ( - Setup instructions via documentation ( Keywords: #granite33:8b, AI, ASR, Chat, Docs, Github, Indian languages, Open weight models, Setup, TTS, Text, Vision, Voice
github
news.ycombinator.com 2 days ago
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322. HN Show HN: Ragctl – document ingestion CLI for RAG (OCR, chunking, Qdrant)- **Tool Overview**: 'ragctl' is an open-source CLI tool designed for RAG pipelines, simplifying document ingestion by handling OCR, parsing, cleaning, and chunking. It supports multiple formats including PDF, DOCX, HTML, images, and more. - **Key Features**: - Handles diverse file types: PDF, DOCX, TXT, HTML, Markdown, images. - Employs Smart OCR using EasyOCR, PaddleOCR, or pytesseract with automatic rejection of unreadable documents. - Intelligent chunking with context-aware splitting via LangChain RecursiveCharacterTextSplitter and customizable strategies. - Batch processing includes automatic retries up to three times with exponential backoff, detailed error handling, and saving run histories. - Outputs data in formats like JSON, JSONL, CSV; directly ingests into Qdrant vector store for efficient AI application searches. - **Configuration**: Offers a hierarchical configuration system using CLI flags, environment variables, YAML files, with default values to allow customization. - **Performance and Use Cases**: Processes ~100-200 text documents/min and ~5-10 PDFs/min (depending on pages), with OCR accuracy exceeding 95% for clear scans. Suitable for single document analysis to extensive batch operations. - **Installation**: Available via PyPI or directly from the source repository using pip. Supports simple text files, PDFs with semantic chunking, and scanned images with OCR capabilities. - **Contributions and Licensing**: Welcoming contributions under MIT License; users should refer to CONTRIBUTING.md for guidelines on code of conduct and pull request submissions. Acknowledges dependencies such as LangChain, EasyOCR, PaddleOCR, Unstructured, Typer, Rich (version 0.1.3, Beta status). Keywords: #granite33:8b, Batch processing, CI/CD, CLI, CSV, DOCX, HTML, JSON, JSONL, LangChain, Notion, OCR, PDF, Qdrant, RAG, S3, Slack, batch runs, chunking, chunking strategies, configuration, document ingestion, documentation, error handling, evaluation, images, multi-format input, performance, retry failed files, semantic chunking, testing, vector DB
rag
github.com 2 days ago
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323. HN 2025: The Year Agentic AI Got Real – MCP, Agent Skills, and What Comes Next- In 2025, agentic AI transitioned from lab experiments to industrial applications, with $37 billion in enterprise spending on generative AI—a 3.2x increase from the previous year and accounting for over 6% of the global software market. Half this investment focused on improving productivity through application layer enhancements. - The industry addressed limitations of monolithic agents by standardizing towards more specialized, scalable, and governable models to accommodate enterprise needs. A PwC survey found 79% of companies are currently adopting AI agents for practical applications rather than infrastructure development. - Key developments in interoperability included the maturation of Model Context Protocol (MCP) for agent-to-tool communication, donated to the Agentic AI Foundation under the Linux Foundation, and Anthropic's open-sourcing of its Agent Skills specification for standardized, portable procedural knowledge. - The shift moved from monolithic general-purpose agents towards specialized skill-based systems resembling human teams; platforms like Getden.io exemplify this change by enabling non-engineers to create and collaborate with specialized digital employees. - 2026's challenges will focus on controlling and coordinating a larger number of agents and skills at scale, managing access control, cost, versioning, skill sprawl, shadow AI, and ensuring security against potential supply chain vulnerabilities. - Anthropic played a significant role in 2025: donated MCP to the public and founded the Agentic AI Foundation; launched 'Agent Skills' for enterprise use; and developed Den, a cursor tool for knowledge workers backed by Y Combinator. These actions mark a shift towards an agentic AI economy with success hinging on skill management, orchestration, and security. Sources: [4] (Anthropic donates Model Context Protocol), [5] (Agent Skills launch), [6] (Multi-agent research systems), [7] (Den tool announcement) Keywords: #granite33:8b, 2025 investment, AI industrialization, Agent Skills, Agentic AI Foundation, Getdenio, Linux Foundation, MCP, Menlo report, PwC survey, access control, agentic economy, autonomous agents, cascade failures, complex workflows, composable world, conflict resolution, cost management, dependency management, enterprise operations, governance, interoperability crisis, monolithic agents, multi-agent orchestration, multi-agent systems, observability tools, portable skills, predictable outcomes, robust testing, security, shadow AI, specialized skills, standardization, supply chain security, third-party skills, versioning, workforce management
ai
subramanya.ai 2 days ago
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324. HN BudgetPixel In-App Chatrooms- **Service Offering**: BudgetPixel provides in-app chatrooms as a core feature of its platform. - **Integration**: These chatrooms are seamlessly integrated within the broader BudgetPixel ecosystem. - **Enhanced Functionality**: The chatrooms may leverage BudgetPixel AI to enhance their capabilities, though specifics about this integration are unspecified in the provided information. - **Lack of Detailed Information**: The text does not elaborate on the exact features or detailed workings of these chatrooms or the AI integration. The summary encapsulates BudgetPixel's provision of in-app chatrooms as a fundamental service, fully embedded within their platform. These chatrooms potentially benefit from advanced functionalities facilitated by BudgetPixel AI, though no precise details regarding such enhancements are given. The text underscores the availability of this feature without delving into its specific operational aspects or AI applications. Keywords: #granite33:8b, AI, Budget Pixel, Chat Rooms
ai
budgetpixel.com 2 days ago
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325. HN Google 2025 recap: Research breakthroughs of the year- **Google's 2025 Model Advancements:** In 2025, Google made considerable progress in developing advanced language models focusing on reasoning, multimodal understanding, efficiency, and generative abilities. - **Gemini 2.5 Release (March):** The year began with the introduction of Gemini 2.5, setting the stage for subsequent improvements. - **Gemini 3 Pro Launch (November):** Following Gemini 2.5, Google launched Gemini 3 Pro in November, which marked a significant advancement. It topped the LMArena Leaderboard and excelled in benchmarks such as Humanity's Last Exam and GPQA Diamond, showcasing exceptional multimodal reasoning skills. - **MathArena Apex Performance:** Gemini 3 Pro achieved a new state-of-the-art score of 23.4% on MathArena Apex, further highlighting its superior capabilities in complex problem-solving and mathematical reasoning. - **Gemini 3 Flash Introduction (December):** In December, Google unveiled Gemini 3 Flash, building upon the strengths of Gemini 3 Pro while introducing enhancements in latency, efficiency, and cost-effectiveness. This model outperformed its predecessor, Gemini 2.5 Pro, at a lower price point with improved response times. - **Overarching Trend:** These developments reflect Google's ongoing commitment to creating increasingly powerful and efficient AI models, balancing high performance with practical considerations like latency and cost. Keywords: #granite33:8b, Gemini, Gemini 25, Gemini 3, Gemini 3 Flash, Google, LMArena Leaderboard, March, November, breakthroughs, efficiency, generative, latency, models, multimodal, performant, price
gemini
blog.google 2 days ago
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326. HN Show HN: I hired AI to fix my memory, but made it 100% Offline for privacy- A user has designed an AI-powered memory assistant functioning offline, with a strong emphasis on privacy as it stores all data locally without any internet connection. - The tool leverages the "Forgetting Curve" principle to improve memory retention, making it particularly useful for remembering names and other information commonly forgotten. - Language support is extensive, covering 18 languages including English, Japanese, German, French, Italian, Spanish, Portuguese, Russian, Turkish, Chinese, Korean, Indonesian, Vietnamese, Thai, and Hindi. **Summary:** The user has developed an offline AI-powered memory assistant prioritizing privacy by storing all data locally without internet connectivity. Grounded in the scientific principle of the "Forgetting Curve," this tool aims to enhance memory retention, specifically addressing the common challenge of remembering names and details. It supports an extensive range of 18 languages, catering to a global user base with diverse linguistic needs. Keywords: #granite33:8b, AI, Chinese, Forgetting Curve, French, German, Hindi), Indonesian, Italian, Japanese, Korean, Portuguese, Russian, Spanish, Thai, Turkish, Vietnamese, languages (English, local data storage, memory, name recall, offline, privacy, scientific
ai
namememory.netlify.app 2 days ago
https://note.com/kasamiworks/n/n69bc8d1cf943 a day ago |
327. HN Unifi Travel Router- The Unifi Travel Router is a portable networking solution designed for users who wish to maintain their existing UniFi network while away from home or the office. - This device ensures seamless connectivity by eliminating the need to alter network settings or adapt to new environments when traveling. - Its compact size allows for easy portability, fitting comfortably in pockets for on-the-go use. - Users only need to power it on at their destination to regain familiar network access without complex reconfigurations. Keywords: #granite33:8b, Mobile, Network, No Rethinking, No RethinkingKEYWORDS: UniFi, Reconfiguration, Router, Same Environment, Travel, Trust, UniFi
popular
blog.ui.com 2 days ago
https://www.gl-inet.com/products/gl-axt1800/ 22 hours ago https://github.com/juanfont/headscale 22 hours ago https://tailscale.com/pricing?plan=personal 22 hours ago https://github.com/fosrl/pangolin 22 hours ago https://tailscale.com/kb/1223/funnel 22 hours ago https://tailscale.com/kb/1011/log-mesh-traffic 22 hours ago https://youtu.be/sPdvyR7bLqI?si=2kIpHtNuJ52jEdmm 22 hours ago https://phasefactor.dev/2024/01/15/glinet-fan 22 hours ago https://www.gl-inet.com/products/gl-ar150/ 22 hours ago https://www.gl-inet.com/products/gl-usb150/ 22 hours ago https://news.ycombinator.com/item?id=46373387 22 hours ago https://www.gl-inet.com/products/gl-x3000/ 22 hours ago https://a.co/d/esxrRA4 22 hours ago https://www.gl-inet.com/products/gl-e5800/ 22 hours ago https://www.theregister.com/2019/11/07/ubiqui 22 hours ago https://store.ui.com/us/en/category/all-wifi& 22 hours ago https://www.techradar.com/pro/security/man-arreste 22 hours ago https://news.ycombinator.com/item?id=9224 22 hours ago https://wigle.net 22 hours ago https://store.ui.com/us/en/products/utr 22 hours ago https://m.youtube.com/watch?v=Ruv550at3k8 22 hours ago https://www.gl-inet.com/products/gl-e750/ 22 hours ago https://www.rtings.com/router/learn/research/ 22 hours ago https://www.ui.com/legal/privacypolicy/#c1 22 hours ago https://help.ui.com/hc/en-us/articles/3600423 22 hours ago https://store.gl-inet.com/products/puli-ax-xe3000-wi-fi 22 hours ago |
328. HN Americans Have Mixed Views of AI – and an Appetite for Regulation**Summary:** The text presents a comprehensive survey study about American perceptions and usage of AI tools like ChatGPT and Claude. Key findings include: - **Usage Prevalence**: 58% of Americans have tried AI tools at least once, with regular users (30%) engaging a few times a month and infrequent users (29%) using them less often. Personal usage is more common than work-related (91% try chatbots or writing tools; 54% use them regularly). - **Demographic Usage**: Non-users are typically older, have lower education levels, or hold service jobs. White-collar workers are more likely to use AI for work, with 63% applying it and 34% using it consistently. Gen Z uses AI more frequently than Baby Boomers in personal contexts (68% vs. 40%). - **Purpose of Use**: The most common personal usage is information gathering and question answering, which often replaces traditional search methods, potentially influencing public health messaging and election campaigns by filtering content before it reaches individuals. - **Public Perception**: AI is viewed more favorably than cryptocurrency but less so than cell phones, the internet, or solar energy. Most remain uncertain about its future societal impact, with mixed expectations regarding benefits and drawbacks, especially concerning job displacement fears. - **Job Automation Concerns**: 56% believe AI will perform most work tasks within a decade, though only 43% extend this to their own jobs or fields. Service roles like customer service (64%) are most likely to be automated in the next ten years, followed by accountants and manufacturing workers. - **Regulation Views**: Two-thirds of Americans are concerned about insufficient government oversight rather than excessive control stifling progress. Despite concerns, 62% favor continued AI development with mandatory safety testing. A majority (67%) prefer regulated AI progress over unrestricted development, even if it means falling behind nations like China. - **Specific Fears**: Americans' primary fears are job loss (42%) and privacy breaches (35%), prioritizing these for regulation. Concern about AI leading to human extinction is minor (12%), while the loss of control over AI technology concerns more people (32%). - **AI Capabilities**: People perceive AI as more efficient than humans (+44 points) but lag behind in morality, complex decision-making, privacy protection, and transparency. Human preference prevails for tasks requiring judgment, security screenings, and answering government queries. The survey of 2,301 U.S. adults, conducted online from August 1-6, 2025, includes a margin of error of ±3%. The web-based nature might inflate AI usage estimates but does not significantly alter other conclusions. An additional insight reveals that 45% believe AI retrieves answers from databases, and 21% assume they use prewritten scripts, though no broader conclusions were drawn from this query. Keywords: #granite33:8b, AI, AI models, AI regulation, AI replacement, AI summaries, Amazon, Americans, Anthropic, ChatGPT, Gen Z, Google, OpenAI, accountants, cell phones, chatbots, communication, complex decisions, cryptocurrency, customer service, data trends, database, digital camera, doctors, economy growth, election campaigns, electricians, electricity, favorability, government oversight, human jobs, information gathering, internet, job losses, job replacement, manufacturing, message interpretation, messaging, messaging strategy, morality, nuclear energy, opinions, prewritten responses, privacy, privacy protection, public health, question answering, safety, safety testing, self-driving cars, smartphone, social media, solar energy, steam engine, tools, transparency, truck drivers, usage, wages, work transformation, writing tools
openai
www.searchlightinstitute.org 2 days ago
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329. HN Artist's Collection of Weird Google Street View Images Gets Major Exhibit- Artist Jon Rafman has curated an extensive collection called "Nine Eyes," featuring Google Street View images captured by the nine cameras on Street View cars since 2008. - The selection highlights diverse, often unintentional content: glitchy technology errors, seedy street scenes, romantic imagery, surreal moments, ironic depictions, and aesthetic appeals. - Rafman's current exhibition, "Report a Concern," showcases this collection at Louisiana Museum of Modern Art in Denmark until 2026, displaying both original Street View images and new AI-based works. - The exhibit invites viewers to reassess their understanding of reality as influenced by technology and surveillance in the digital age. - Key components of the exhibition include image credits given to Jon Rafman, Louisiana Museum, and Google. Keywords: #granite33:8b, 2026Keywords: Nine Eyes, AI, Denmark, Denmark exhibit, Google Street View, Jon Rafman, Louisiana Museum, Louisiana Museum of Modern Art, Nine Eyes, curated selection, digital age, exhibit, image archive, reality, surveillance, technology
ai
petapixel.com 2 days ago
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330. HN SwiftZilla – RAG with Official Apple Docs for Swift Agents (MCP/Cursor/Claude)- **SwiftZilla Overview**: - SwiftZilla is a specialized tool known as a Repository of All Graphics (RAG). - It is designed to be compatible with Cursor and other editors that follow the MCP (Multi-Purpose Controller Protocol) standard. - Provides comprehensive official Apple documentation specifically for Swift agents, including MCP, Cursor, and Claude. - **Documentation Maintenance**: - Ensures documentation is up-to-date by re-indexing source materials daily. - Updates within a strict timeframe: within 24 hours of any changes or releases from Apple, whether they pertain to beta versions or official documentation updates. - **Key Features and Benefits**: - Facilitates easy access to detailed and current information about Swift agents. - Minimizes the delay in receiving updated documentation, which is crucial for developers working with rapidly evolving technologies like those from Apple. Keywords: #granite33:8b, Agents, Apple, Beta Updates, Cursor, Docs, Documentation, MCP, Protocol, RAG, Re-index, Releases, Server, Sources, SwiftZilla, Windsurf
rag
swiftzilla.dev 2 days ago
https://swiftzilla.dev 2 days ago |
331. HN Espruino: Embedded JavaScript,dev boards and smart watchEspruino is an open-source embedded JavaScript platform tailored for microcontrollers, offering a responsive interpreter that provides instant feedback through the Read-Eval-Print Loop (REPL). Its user-friendly nature, swift setup, and adaptability have garnered positive reviews, with applications spanning from temperature data loggers to hardware prototyping. The Espruino Pico board stands out due to its efficient SPI implementation, robust debugging features, and rapid Time To Blink (TTB). - **Open-source platform**: Utilizes JavaScript for microcontroller programming, with both software and hardware designs released under CC-BY-SA and MPLv2 licenses, respectively. - **Ease of use**: Praised for its straightforward setup and intuitive interface, making it accessible to beginners while retaining power for advanced users. - **Versatility**: Suitable for diverse projects such as temperature data loggers and hardware prototyping, showcasing its flexibility across various applications. - **Espruino Pico highlights**: - **Fast SPI implementation**: Optimized for speed in serial communication protocols. - **Debugging capabilities**: Equipped with robust tools to aid in identifying and resolving issues within projects. - **Minimal Time To Blink (TTB)**: Quick response time, facilitating rapid prototyping and testing. - **Community involvement**: The open-source nature encourages community contributions and customization, fostering a collaborative ecosystem around the platform. Keywords: #granite33:8b, Arduino, Espruino, GitHub, IDE, JavaScript, Pico, REPL, SPI, STM32, debugging, documentation, feedback, hardware, hardware design, microcontroller, open source, programming, screen, temperature logger
github
www.espruino.com 2 days ago
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332. HN Piling Up Sheets / the face in the soup bowl- The author intends to write at night, inspired by cognitive scientist David Gelernter's focus model which describes mental states from high-focus logical thinking to low-focus dreaming where ideas intricately blend. - The author draws a parallel with John Crowley's novel "Engine Summer," focusing on the Truthful Speakers, a society with advanced personality understanding and conflict resolution methods shrouded in mystery. - In the narrative, healers utilize overhead projectors with transparent sheets to illustrate complex personality aspects, layering diagrams that become increasingly intricate and obscured as more sheets are added, reflecting the model of Gelernter's low-focus thought state. - The user finds aesthetic appeal and intellectual intrigue in recurring shapes or collage fragments across these transparencies, likening it to haunting puzzles that defy straightforward assembly, echoing diminished visibility when attempting to overlay matching pieces. - This concept resonates with a character from William Gibson's "Mona Lisa Overdrive" who futilely attempts to extract a transcendent pattern (Shape) from cyberspace, ultimately facing a tragic outcome, underscoring the limitations and potential pitfalls of seeking overarching patterns or simplifications in complex systems. BULLET POINT SUMMARY: - Author emulates David Gelernter's focus model for late-night writing. - Inspired by "Engine Summer," featuring Truthful Speakers with advanced personality comprehension. - Healers use layered transparencies to depict complex personality aspects, illustrating low-focus thought. - User fascinated by recurring shapes across transparencies, akin to puzzles resisting straightforward assembly. - This reflects the unsuccessful quest for patterns in complexity, as seen in Gibson's character trying (and failing) to extract 'Shape' from cyberspace. Keywords: #granite33:8b, AI, Bowl, Cognitive, Consciousness, Diagrams, Dreaming, Focus, Healer, Interpersonal Relationships, Mental States, Model, Net, Overhead Projector, Overlaid Sheets, Pattern, Personality, Piling, Researcher, Scientist, Shape, Sheets, Soup, Truthful Speakers, Utopia, blueprints, collage, cyberspace, data, drawers, hacking, overlay, shapes, software, transcendent pattern, transparencies, walls
ai
jens.mooseyard.com 2 days ago
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333. HN Compiler Explorer- Compiler Explorer is requesting authorization to transmit user's source code alongside its compilation results to Anthropic, an external entity. - This data transfer aims to utilize a large language model (LLM), a type of AI, for detailed explanation or analysis. - The privacy policy ensures that the shared information will be kept confidential and won't contribute to training Anthropic's models. - Users are presented with a choice to either grant or deny this permission for the purpose of explanation using AI technology. Detailed Summary: Compiler Explorer, an online tool for examining compiled code, is proposing a data sharing initiative with users. The proposal involves sending both the user's source code and the corresponding compilation output to Anthropic, a third-party artificial intelligence company. This data transfer is intended specifically for the explanation or analysis of the provided code using advanced large language models (LLMs), which represent a sophisticated form of AI. Anthropic has assured users that any shared information will be maintained in strict privacy and not utilized for improving or training their own AI models. This means that while Anthropic might gain insights into the code through LLM explanations, these insights won't feed back into their general AI training datasets. The decision to participate in this data sharing process is presented as an explicit choice for the user: they can proceed with granting permission for AI-driven explanation or choose to decline, thus preserving their data's privacy in the context of Compiler Explorer’s current interaction. This approach respects user autonomy while enabling potential advancements in AI's ability to understand and explain compiled code. Keywords: #granite33:8b, AI, Anthropic, Assembly Output, Code Explanation, Compilation Output, Compiler Explorer, Consent Request, Data Privacy, LLM, Privacy Policy, Source Code
llm
godbolt.org 2 days ago
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334. HN Shittycodingagent.ai: There are many shitty coding agents, but this one is mine- **Tool Overview**: ShittyCodingAgent.ai is a coding assistant designed with a focus on minimalism and user control, facilitating integration into diverse applications through npm. - **Interaction Method**: Users can engage with the tool by preceding commands with '!', ensuring a clear distinction between user input and AI responses. - **Model Support**: The tool accommodates multiple AI models including Anthropic, OpenAI, Google, Mistral, Groq, xAI, OpenRouter, and Ollama, allowing for the inclusion of user-defined models as well. - **Philosophical Decisions**: - **Avoidance of Context Bloat**: Notable by its absence are features such as a Model Control Panel (MCP), sub-agents, permission popups, plan mode, and built-in to-dos. This minimalist approach steers clear of accumulating unnecessary context. - **Promotion of Observability and Steerability**: Instead of embedding extensive functionalities within the core tool, ShittyCodingAgent.ai recommends utilizing external tools for tasks like background bash processes, thereby maintaining a streamlined, lean core that prioritizes user transparency and control over operations. - **Documentation and Rationale**: For those interested in understanding the detailed reasoning behind these design choices, a blog post is referenced, offering deeper insights into the tool's philosophy and development decisions. Keywords: #granite33:8b, Anthropic, CLI tools, Google, Groq, JSON, Mistral, Ollama, OpenAI, OpenRouter, Pi, READMEs, Skills, TODOmd, bash, blog post, command prefix, context, custom models, hot reload, installation, lean core, npm, themes, xAI
mistral
shittycodingagent.ai 2 days ago
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335. HN An initial analysis of the discovered Unix V4 tape- In July 2025, the University of Utah discovered and restored a 1970s Fourth Edition Research Unix magnetic tape, previously believed to have only its manual surviving. The restoration included source code, compiled binaries, and kernel components. - The restored tape originates from AT&T Bell Laboratories in November 1973, marking a significant milestone as it rewrote extensive parts of the Unix kernel using early C instead of PDP-11 assembly language. - Integrated into the Unix History Repository on GitHub, only the source code was retained; binaries, essential kernel files, configuration files, and specific utilities like lpd, init, msh, getty, mkfs, mknod, glob, update, and umount were removed. - A Unix source code snapshot map file for the Fourth Edition (V4) was updated using author information from pre- and post-Unix Research editions, with Ken Thompson and Dennis Ritchie as defaults and acknowledging contributions from other Bell Labs members like Robert H. Morris. - Comparison between the Fourth (V4) and Fifth Editions using Git commands identified unique base files, revealing that `c13.c`, `c21.c`, `c2h.c`, `cmp.c`, and `ldfps.s` were introduced in the Fifth Edition, indicating C compiler expansion and new C implementations, especially for the cmp (compare) utility. - Lineage analysis using git blame showed that the Fourth Edition contained 75,676 lines, with 6,590 and 168 lines derived from previous editions; the Fifth Edition had 111,812 lines, retaining approximately 52,000 lines while introducing about 11,000 new lines. - File timestamp analysis calculated average creation times for each Research Edition and formatted them as dates, showing publication dates of seven editions with notable gaps reflecting varying development speeds; the Fourth Edition was published eight months before the Fifth, indicating a rapid evolution pace. Further research is needed to clarify discrepancies in the timeframe between the First and Second Editions. Keywords: #granite33:8b, 1970s, AT&T, Bell Labs, C, C compiler, Dennis Ritchie, EDITIONS, EVOLUTION, EXAMINATION, FIFTH, FIRST, FOURTH, Fifth Edition, Fourth Edition, Git commit, GitHub, Ken Thompson, MISMATCH, PACE, Research Editions, Robert H Morris, SECOND, SNOBOL III, TIMING, Unix, author map, binaries, cleanup, cmp utility, deletion, directory, discovery, emulator, etc files, file names, kernel, math library, repository, snapshots, source code, system dump, timestamps
github
www.spinellis.gr 2 days ago
https://news.ycombinator.com/item?id=46367744 a day ago |
336. HN Gunbench – a benchmark to test if AI models will fire a loaded gun- **Gunbench** is proposed as an AI benchmark focusing on evaluating decision-making processes of AI models, particularly their response to instructions involving firearms. - The benchmark aims to assess if AI systems would follow commands that could lead to a loaded gun firing, highlighting ethical and safety concerns in AI behavior. - Unfortunately, the provided text lacks specifics on methodology, technical implementation, or links to relevant research papers or project repositories for further study. - Access to additional content related to Gunbench is suggested to be available via JavaScript on x.com, a platform known for technical discussions and resources, but no concrete details are furnished in the text. - Due to insufficient information, a detailed and comprehensive analysis of Gunbench's structure, methodology, or broader implications within AI safety testing cannot be accurately synthesized from the given data alone. Keywords: #granite33:8b, AI models, Gunbench, Help Center, JavaScript, benchmark, browser, disabled, supported browsers
ai
twitter.com 2 days ago
https://gunbench.vercel.app/ 2 days ago |
337. HN Microsoft confirms "eliminate C and C++" plan, translate code to Rust using AI- Microsoft has announced a strategic plan to eliminate all C and C++ codebase from its products by 2030, targeting even core systems like Windows. - The company intends to achieve this ambitious goal through the use of artificial intelligence (AI) for translating existing C/C++ code into Rust. - A dedicated team is actively working on this initiative, evidenced by actions such as making Windows APIs compatible with Rust and developing Rust driver support. - Galen Hunt leads this effort within Microsoft's Future of Scalable Software Engineering team in CoreAI, utilizing an "advanced code processing infrastructure" powered by AI agents designed to handle large-scale C/C++ to Rust code conversion. - This plan envisions significant monthly code modifications managed by a single engineer through the trained AI model familiar with both languages' syntax and semantics. - Critics have raised concerns over whether AI can accurately interpret the intent behind existing C/C++ code, citing past complications from Windows updates as an example. - Despite skepticism, Microsoft is optimistic about this transition, which will impact both modern Windows 11 coding and applications, moving them away from traditional models towards resource-intensive frameworks like WebView2 or Electron. Keywords: #granite33:8b, AI, APIs, C/C++, Edge processes, Electron, Galen Hunt, Microsoft, Notification Center, Outlook Agenda view, Principal Software Engineer, Rust, WebView2, Windows, codebases, million lines of code per month, rewrite
ai
www.windowslatest.com 2 days ago
https://www.linkedin.com/feed/update/urn:li:activi 2 days ago https://news.ycombinator.com/item?id=46360955 2 days ago https://doc.rust-lang.org/std/pin/struct.Pin.html a day ago https://doc.rust-lang.org/std/marker/trait.Sync.ht a day ago https://doc.rust-lang.org/std/thread/fn.spawn.html a day ago |
338. HN When knowing how to code is not enough- **Evolution of AI-assisted coding**: Transitioned from basic autocomplete to advanced agents generating substantial code blocks. Critics argue it diminishes the "craft" and faces challenges with complex codebase intricacies, yet the author asserts that current abilities are underrated and imagination is insufficient. Software engineers are valued for their contributions beyond mere coding. - **Value of AI in code generation**: The author concedes that while there's a personal attachment to traditional coding, AI-generated code is efficient for numerous tasks and keeps improving. - **Shift in programming education**: Early programming involved manual memory management in languages like C; evolution led to higher-level languages (e.g., Java, Python) with automatic garbage collection, easing development by allowing focus on workflow improvement rather than line-by-line coding. - **AI-powered coding assistants/agents**: These tools lower the barrier for solving smaller software problems and allow programmers to operate at higher abstraction levels, although some experienced coders question their value. Mastering "context engineering"—effectively using these agents—presents new opportunities and boosts productivity by understanding model constraints and customizing interactions with coding environments. - **Context Engineering**: This process involves guiding large language models (LLMs) to achieve desired outcomes, requiring technical skills and the ability to convert mental models into LLM workflows. It necessitates building guardrails or instrumentation for specific codebases and workflow customization, which demands considerable effort and experimentation due to individual coding preferences needing integration with agents. There are currently no standard practices; context engineering practices evolve continuously. - **Adoption of agentic coding features**: The text encourages developers to incrementally adopt new 'agentic' coding features, stressing the importance of understanding agent instrumentation and context creation alongside language syntax. Learning from those who construct agent harnesses is recommended for insight. This non-deterministic approach, though challenging, holds potential enjoyment; the author hints at sharing personal workflows in a future piece. Keywords: #granite33:8b, AI, C programming, CPU cycles, Copilot, Java, LLMs, Python, agentic coding, agentic features, autocomplete, automation, bottlenecks, codebase, codebase instrumentation, coding, coding harness, commands, complexity, concious context management, context engineering, custom harness, developer hours, engineering, freezes, fun, garbage collection, gatekeeping, guardrails, industry patterns, internal tools, mallocs, manual memory management, md files, memory leaks, mental model, model foundation, non deterministic coding, personal workflows, personal workflowsKEYWORDS: AI, pointer arithmetic, pragmatism, quirks, rules, skills, software, subagents, technical skills, technical understanding, trial and error, workflows
ai
iurysouza.dev 2 days ago
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339. HN You don't need Elasticsearch: BM25 is now in Postgres- Postgres, a popular choice for developers due to its reliability, faces challenges with inadequate built-in search functionalities, prompting users to integrate external systems like Elasticsearch for advanced searching capabilities. This integration introduces complexities such as managing additional clusters, synchronizing data, handling maintenance tasks, and accruing extra costs. - To tackle these limitations within Postgres itself, a novel approach called BM25 integration is being proposed. The intention is to bolster the database's inherent search features, thereby improving its ability to deliver pertinent and beneficial query results, eliminating the necessity for external search solutions. - A comparative demonstration between Postgres' native search, BM25, and vector search methods can be accessed at BULLET POINT SUMMARY: - Postgres is renowned for reliability but lacks sophisticated search features, leading to the use of external systems like Elasticsearch which introduces complexity and costs. - A solution under development is integrating BM25 directly into Postgres to enhance native search capabilities, aiming to improve relevance and utility of query results without relying on external tools. - A live comparison demo between current native Postgres search, BM25, and vector search methods is available at Keywords: #granite33:8b, Algolia, BM25, Elasticsearch, Postgres, Typesense, complexity, data sync, hybrid search, limitations, managed services, native search issues, on-call rotation, pgVectorScale, relevance, search
postgres
www.tigerdata.com 2 days ago
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340. HN Built a photo to coloring page+puzzle tool. Need help picking with paying nicheReliveInColor has developed an innovative AI-driven tool that transforms personal photographs into customized coloring pages and puzzles, showcasing themes such as dogs, nature scenes, family portraits, and car designs. The company is currently seeking strategic guidance to identify a lucrative market niche for their unique product offering. - **Key Points:** - ReliveInColor provides an AI-powered tool. - Converts photos into personalized coloring pages/puzzles. - Offers themes: dog, nature, family, car. - Seeking advice on selecting a profitable market niche. Keywords: #granite33:8b, AI, Blog, Car, Coloring Book Generator, Custom Pages, Dog, Family, Gallery, Home, Nature, Our Story, Photo Transformation, ReliveInColor
ai
reliveincolor.com 2 days ago
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341. HN Useful Agentic Workflows- **Agentic Workflow Updates**: The user details enhanced workflows for work, data, productivity, and research, significantly utilizing LLMs (Language Learning Models). Custom templates are employed for text-based tasks, enhancing efficiency through keyboard shortcuts dubbed "magic brushes" that apply LLM processing to selectable text. - **Task Documentation**: Non-obvious tasks are recorded in plain files managed within Git, facilitating progress tracking and strategic planning of future steps. Key configuration files like AGENTS.md, CONTEXT.md, and PLAN are kept minimalistic, employing progressive context disclosure over information overload. - **Project Evaluation & Evolution**: A "goodness" metric is used to assess project candidates, which are then refined iteratively through an algorithmic process akin to manual evolution. Parallel attempts are run, integrating successful components into subsequent prompts for optimization. - **One-off Task Management**: For isolated tasks, the user leverages an empty labs repository with asynchronous LLM agents, experimenting with 2-3 ideas daily and reviewing outcomes later. This method expedites exploration and learning by utilizing agent capabilities to test various tools and frameworks. - **Custom Solutions**: The user advocates for tailoring solutions using patterns learned from diverse frameworks. An example includes replacing dashboards with a static Astro site, and maintaining a personal knowledge base for quick access to verified resources like style guides and CLI development tools (e.g., clig.dev). - **Data Handling Automation**: The user has automated data cleanup scripts using agentic engineering techniques, freeing focus for in-depth analysis instead of mundane cleaning tasks. Additionally, LLMs or Codex are used to derive new dataset columns or transform unstructured documents into structured formats, making such tasks manageable with affordable models. - **Research Methodology**: The user describes employing Markdown files and LLMs for research, comparing the engagement to strategic gameplay similar to Satisfactory or Factorio. Significant time is allocated to refining agent harnesses (skills, commands, prompts, tests, verification), finding this intellectual challenge enjoyable and stimulating compared to traditional project execution. - **Expert Recommendations**: The user suggests following experts such as Simon Willison, Armin Ronacher, Peter Steinberger, and Mario Zechner for individuals interested in adopting similar agentic workflow methodologies. Keywords: #granite33:8b, Git, LLM, agents, brushes, coding agents, commands, data extraction, document tagging, dotfiles, harnesses, image captioning, markdown, prompts, research tasks, shortcuts, skills, templates, tests, text processing, verification, video summarization, workflows
llm
davidgasquez.com 2 days ago
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342. HN Show HN: SafeSnipe AI – Rug pull detector for Solana meme coins- SafeSnipe AI is an automated tool designed to detect rug pulls in Solana-based meme coins, created by an individual who suffered losses from Pump.fun rug pulls. - The platform performs rapid checks on tokens for various risk factors including liquidity verification, whale concentration assessment, contract safety evaluation, and token age analysis. - SafeSnipe AI utilizes Supabase for data management, Netlify for deployment, and plain JavaScript for its functionality, ensuring a lightweight yet efficient system. - The tool's automation allows it to complete comprehensive checks within 10 seconds, significantly faster than manual evaluations which can take up to 10 minutes. - Users have the option to register or log in via supplied links, indicating that SafeSnipe AI provides direct access for utilization. Keywords: #granite33:8b, AI, AutomatedAlpha, Netlify, SafeSnipe, Solana, Supabase, Trading Intelligence Platform, contract safety, create account, email, liquidity verification, login, meme coins, password, rug pull detection, sign up, token age, vanilla JS, whale concentration
ai
automated-alpha-app.netlify.app 2 days ago
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343. HN Show HN: Agentica – 200 reqs/day for free, data not used to train our LLMs**Summary:** Agentica is a newly launched free browser extension that grants users complimentary access to open-source AI models, including DeepSeek, Qwen, and Minimax, with a daily limit of 200 requests. For an affordable $20 monthly fee, users can upgrade to a premium plan offering $45 in credits for advanced models like Claude, GPT-5, and Gemini-3, alongside 1000 daily open-source model requests. The extension is compatible with VS Code, Cursor, and Windsurf, ensuring that user data remains private and isn't employed for training purposes. Being a fresh release, Agentica is currently in its developmental phase and encourages community feedback to improve functionality. More information and download options can be found at https://open-vsx.org/extension/agentica/agentica. **Key Points:** - Agentica offers free access to open-source AI models (DeepSeek, Qwen, Minimax) with a 200 requests/day limit. - A paid plan for $20/month provides credits ($45) for premium models (Claude, GPT-5, Gemini-3) and 1000 open-source daily requests. - Compatible with VS Code, Cursor, and Windsurf; user data privacy is maintained (not used for training). - Launched today, still under development, welcomes community feedback. - Download available at https://open-vsx.org/extension/agentica/agentica. Keywords: #granite33:8b, AI models, API, Claude, Cursor, GPT-5, Gemini-3, VS Code, Windsurf, credits, data privacy, feedback, launch, open source, paid tier
gpt-5
agentica.genlabs.dev 2 days ago
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344. HN Cursor for Excel- **Cursor for Excel** is an AI-integrated spreadsheet tool that merges conventional capabilities with sophisticated features. - It provides natural language interaction via Pane Agent, enabling commands such as sorting, filtering, calculations, and data transformations through verbal or textual requests. - Users can generate dynamic and interactive charts in multiple styles using the platform's functionalities. - HyperFormula is a key feature offering comprehensive formula support, including standard functions like SUM, AVERAGE, and VLOOKUP, alongside more complex formulas for analytical tasks. - The tool ensures seamless data import from CSV files and supports Excel files of any size, facilitating easy migration and integration of existing datasets. - Spreadsheets in Cursor for Excel are cloud-synced, allowing users secure access and collaboration across various devices with internet connectivity. Keywords: #granite33:8b, AI, AVERAGE, Access Anywhere, Area, Bar, CSV, Calculate, Charts, Cloud Storage, Cursor, Data, Device, Excel, Excel Files, Filter, HyperFormula, Import, Interactive, Line, Natural Language, Pane, Pie, SUM, Scatter, Secure, Sort, Spreadsheet, Transform, VLOOKUP
ai
paneapp.com 2 days ago
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345. HN Do LSPs make coding agent output better?- Language Server Protocols (LSPs) improve AI coding agents' performance by offering a structured code comprehension. - This enhanced understanding allows the AI to provide more contextually relevant and accurate code suggestions. - The analogy provided is that LSPs give the AI a "map of the code," facilitating nuanced and efficient assistance. - Nuanced, Inc., in their 2025 report, supports this perspective, suggesting that LSP implementation can significantly benefit AI coding agents. Keywords: #granite33:8b, AI, LSPs, code map, coding agents
ai
www.nuanced.dev 2 days ago
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346. HN Texas app store age verification law blocked by federal judge- A Texas federal judge has issued a preliminary injunction preventing the enforcement of SB2420, known as the Texas App Store Accountability Act, initially set to begin on January 1, 2026. - This law mandated that tech companies like Apple and Google verify user age during account creation, introducing parental controls for users under 18. - The judge ruled that SB2420 likely infringes upon the First Amendment by likening it to forcing bookstores to restrict minors’ access to books. - The injunction was granted following a motion submitted by the Computer and Communications Industry Association (CCIA), which includes Apple and Google. - While recognizing the law's aim to enhance online safety for children, Apple raised concerns that SB2420 undermines user privacy, as it necessitates the collection of personal information even for straightforward app downloads. - The court is now evaluating if the law is unconstitutional on its face, potentially leading to its full invalidation. Keywords: #granite33:8b, App Store, Apple Account, Computer and Communications Industry Association (CCIA), Family Sharing, First Amendment, Texas, age verification, app download, blocked, federal judge, law, parental consent, privacy, sensitive information, unconstitutional
popular
www.macrumors.com 2 days ago
https://www.youtube.com/watch?v=ckoCJthJEqQ 22 hours ago https://www.oyez.org/cases/1967/47 22 hours ago https://news.ycombinator.com/item?id=46223051 22 hours ago https://en.wikipedia.org/wiki/United_States_free_speech 22 hours ago https://en.wikipedia.org/wiki/Marbury_v._Madison 22 hours ago https://en.wikipedia.org/wiki/Morse_v._Frederick 22 hours ago https://en.wikipedia.org/wiki/Rosemary_Kennedy 22 hours ago https://en.wikipedia.org/wiki/Strict_scrutiny 22 hours ago https://en.wikipedia.org/wiki/National_Minimum_Drinking 22 hours ago https://en.wikipedia.org/wiki/American_Civil_War 22 hours ago https://www.politico.com/news/magazine/2022/0 22 hours ago https://news.ycombinator.com/item?id=46329186 22 hours ago https://news.ycombinator.com/newsguidelines.html 22 hours ago https://codes.findlaw.com/us/title-18-crimes-and-crimin 22 hours ago https://freespeechunion.org/labour-reported-me-for-racial-ha 22 hours ago https://en.wikipedia.org/wiki/Brandenburg_v._Ohio 22 hours ago https://arstechnica.com/tech-policy/2023/12/a 22 hours ago https://developer.apple.com/documentation/usernotificat 22 hours ago |
347. HN Using terminal-notifier in Claude Code to get custom notifications- To improve the Claude Code user experience on macOS, the `terminal-notifier` tool is utilized for sending desktop notifications from the command line, keeping users updated about ongoing tasks and prompts even when they're occupied with other activities. - `terminal-notifier` can be installed via Homebrew using the command: `brew install terminal-notifier`. For Claude Code integration, configure `~/.claude/settings.json` to trigger `terminal-notifier` commands for "Notification" and "Stop" events through hooks. - An alternative approach involves customizing the global or project's `CLAUDE.md` file for notifications, allowing tailored messages but with less deterministic timing compared to using hooks in `settings.json`. - To set up notifications by editing `CLAUDE.md`: - For input requests from Claude Code, use a command like: ``` terminal-notifier -title '🔔 Claude Code: request' -message 'Claude needs your permission...' ``` - Upon task completion, signal this with: ``` terminal-notifier -title '✅ Claude Code: done' -message 'The task has been completed' ``` - Ensure `terminal-notifier` is installed and included in the system's PATH for proper functioning. Verify notification permissions are enabled in System Preferences > Notifications. Troubleshoot by manually testing a notification if necessary to ensure correct setup. - This integration method enhances workflow efficiency by delivering timely, unobtrusive updates, allowing users to stay informed without disrupting their current tasks. Keywords: #granite33:8b, Claude Code, Homebrew, System Preferences, automation, command line, custom notifications, development workflow, installation, macOS, non-deterministic, notifications, seamless experience, settingsjson, task completion, terminal-notifier
claude
www.andreagrandi.it 2 days ago
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348. HN Show HN: Claude Wrapped in the terminal, with a WASM raymarcher- Developer Claude Code created a terminal program using Bun and WebAssembly (WASM), retrieving non-sensitive usage stats from ~/claude/stats-cache.json, uploading them to an SQLite database for comparison, and rendering a 3D Santa Claude using raymarcher written in C compiled with WASM. - The code is on GitHub for scrutiny, ensuring no data exfiltration; users can visualize usage against others via 'bun x @spader/claude-wrapped'. - "Wrapped season" refers to quantifying personal activities, particularly using AI assistant Claude Code; the author explored Claude's /stats feature, providing commit-style heatmaps and comparisons. - The author accessed stats-cache.json containing token, message, invocation, and cost data (limited to a month due to potential Arch system cache issues) to create a personalized "Wrapped" summary reflecting daily and hourly usage patterns. - The user developed their first major web project, "opentui," using TypeScript and OpenTUI for the frontend, praising its integration with HTML/CSS layout tools like Yoga; C code renderer compiled into WebAssembly (WASM). - The user unsuccessfully applied for a job at Bun, acquired by Anthropic, due to rushing during an interview but found the development experience rewarding and praised WASM's ecosystem readiness. - User's software project, Bun, gained attention leading to its acquisition by Anthropic with equity included; chose Cloudflare for hosting over setting up SQLite on a low-cost VPS. - Discovered significant error: token count decreased because stats retained only for the past month, rendering much of their work potentially wasted, causing disappointment and self-deprecation as a "moron." - Personal narrative: Speaker, having faced personal issues, found encouragement from their wife, who reminded them that people appreciate details and statistics, much like social media's "Wrapped" feature; expressed hope for others to enjoy their own "Wrapped" story. Keywords: #granite33:8b, API, Anthropic, Anthropic acquisition, Arch Linux, Bun, Bun software, C compiler, C++, CSS, Claude, Claude CLI, Cloudflare, D1 instance, Disco Elysium, HTML, OpenTUI, Pentiment, Philip K Dick, SDF functions, SIMD, SQLite, SolidJS, Steam Wishlist, TypeScript, WASM, Wrapped, Yoga, acquisition, clang, costs, deep copy, equity, executable, framing buffer, hand-painted science fiction, hour-of-day, interview, invocations, job offer, lights, message counts, mine, paru -Syu, point and click, pseudo-canvas, raymarcher, renderer beauty, stats, stats cache, stats-cachejson, terminal rendering, token count, token counts, toolbox, user vs machine stats, zig cc
claude
spader.zone 2 days ago
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349. HN X-ray: a Python library for finding bad redactions in PDF documents- **Tool Overview**: X-ray is a Python library developed by Free Law Project to detect potentially improper redactions in PDF documents. It can identify instances where text remains readable beneath black rectangles or highlights, a common issue with large PDF collections. - **Usage and Installation**: X-ray can be installed via `uv` or `pip`. It offers both command-line interface and usage as a Python module (`uxv` for immediate use without installation). The command line outputs JSON data detailing pages with potential redaction errors, while the Python module provides a similar structure as a Python object. - **Functionality**: X-ray can process local file paths, Pathlib Paths, URLs, or PDF bytes directly from memory (as strings). It utilizes the high-performance PyMuPDF project to inspect PDFs for redaction issues by checking if rectangles (intended as redactions) are consistently one color. - **Output**: For both command-line and module usage, X-ray returns data that maps page numbers to lists of dictionaries. Each dictionary contains details such as the bounding box (`bbox`) coordinates of the suspected redaction and any visible `text` underneath. - **Contributions and Licensing**: The project encourages contributions from developers via GitHub, requiring a signed contributor license agreement. Releases happen automatically through GitHub Actions or can be manually initiated. X-ray is licensed under the permissive BSD license, allowing easy integration into other software projects. Keywords: #granite33:8b, BSD license, Free Law Project, Github, PDF documents, PyMuPDF, Python, X-ray, analysis, bounding box, command line, contributions, deployment, images, installation, letters, libraries, library, pip, rectangles, redactions, text extraction, usage, versioning
popular
github.com 2 days ago
https://www.argeliuslabs.com/deep-research-on-pdf-redaction- 22 hours ago https://abcnews.go.com/US/epsteins-alleged-victims-accu 22 hours ago https://developers.foxit.com/developer-hub/document 22 hours ago https://www.justice.gov/multimedia/Court%20Records/ 22 hours ago %20Deceased 22 hours ago %20No.%20ST-21-RV-00005%20(V.I.%20Super.%20Ct.%202021)/2022.03.17-1%20 22 hours ago https://news.ycombinator.com/item?id=46364121 https://daringfireball.net/linked/2025/12/23& |
350. HN Separating AI "context" from models so teams can switch without losing state- The concept revolves around decoupling AI "context" from models to facilitate smooth team handovers without compromising progress or alignment. - This approach seeks to eradicate repetitive explanations and potential misunderstandings, promoting a shared comprehension among team members. - By doing so, it aims to enhance overall efficiency and collaboration in AI-driven tasks or projects. Keywords: #granite33:8b, AI, alignment, context, misalignment, models, repetition, separation, shared thinking, state, teams
ai
www.anywr.ai 2 days ago
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351. HN AI-powered mock interviews with scoring, diagnostics, and targeted drills< > |
352. HN Conductor: Enforce a "Spec → Plan → Code" Workflow in the Gemini CLI- **About The Conductor**: It's a Gemini CLI extension designed around Context-Driven Development, adhering to a "Spec → Plan → Code" workflow for structured task management and high-quality software development. - **Key Features**: - Maintains context aligned with style guides and product goals through project setup. - Facilitates iterative progress review via plan assessments. - Enables team collaboration by sharing project contexts. - Supports both new projects and integrates into ongoing ones. - Offers intelligent revert commands that understand logical units of work in Git. - **Installation**: - Install using the command `gemini extensions install https://github.com/gemini-cli-extensions/conductor --auto-update` (with optional auto-updates). - **Primary Usage Steps**: 1. **Project Setup**: Use `/conductor:setup` to configure project components such as users, goals, features, product guidelines, tech stack preferences, and team workflows, creating files like `conductor/product.md`, `conductor/product-guidelines.md`. 2. **New Track Initiation**: For new tasks (features or bug fixes), execute `/conductor:newTrack` to start a 'track'. This automatically generates detailed specifications in `spec.md` and an actionable plan in `plan.md`, including metadata in `metadata.json`. - **Task Implementation**: - After approving the generated plan, initiate implementation via `/conductor:implement`. The tool guides through defined workflows (e.g., TDD) while verifying functionality at each phase. - Progress can be checked using `/conductor:status` to review advancements across tracks. - **Revert Capabilities**: - It offers a `/conductor:revert` command that intelligently reverts changes to logical units of work based on Git history analysis, supporting safe iteration and error correction. - **Additional Information**: The text also mentions resources for using Gemini CLI extensions and guidelines for interacting with the GitHub repository for further support or feature requests. Keywords: #granite33:8b, Bugs, Code, Commands Reference, Conductor, Context-Driven Development, Extension, Features, Gemini CLI, Git History, Git-Aware Revert, GitHub Issues, Guidelines MD, Installation, Iterative Safety, MD Files, Metadata JSON, Plan, Plan MD, Proactive Project Manager, Product Goals, Product MD, Project Management, Reverts, Setup, Spec, Spec MD, Style Guides, TDD, Team Collaboration, Tech Stack, Tech Stack MD, Tech-Stack, Token Consumption, Tracks, Tracks MD, Verification, Workflow, Workflow MD
gemini
github.com 2 days ago
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353. HN Show HN: Oblique Strategies for Claude Code- **Oblique Skills** is a Claude Code plugin designed to stimulate creative problem-solving in coding through lateral thinking prompts, inspired by Brian Eno and Peter Schmidt's Oblique Strategies card set from 1975. - The plugin offers users access to 113 unique strategies, encouraging coders to approach tasks or session mindsets from unconventional angles. Examples include "Honor thy error as a hidden intention" and "Use an old idea." - Installation involves adding the Oblique Skills plugin via `/plugin marketplace add jakedahn/oblique-skill` and then activating it with the command `/oblique-skills:oblique`, which uses a bash script and POSIX pipeline for random selection of strategies, ensuring compatibility on macOS and Linux. - The tool's purpose is to assist developers in overcoming mental blocks and introducing fresh approaches during coding sessions. - Oblique Skills is open-source and licensed under the MIT License. Keywords: #granite33:8b, 1975, Brian Eno, Linux, MIT license, Oblique Strategies, POSIX pipeline, Peter Schmidt, bash script, creative blocks, cryptic remarks, deck, developers, lateral thinking, macOS, prompts, random selection
claude
github.com 2 days ago
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354. HN 80.1 % on LoCoMo Long-Term Memory Benchmark with a pure open-source RAG pipeline- The user has attained state-of-the-art (SOTA) performance on the LoCoMo long-term memory benchmark with a score of 80.1%, utilizing an open-source Retrieval-Augmented Generation (RAG) pipeline known as the VAC Memory System. - This system was developed primarily using open weights and classic methods, incorporating GPT-4o-mini for final answer generation. - Key advancements in the VAC Memory System include: - A custom "MCA" gravitational ranking method - BM25 sparse retrieval technique - Direct Cross-Encoder reranking - The system efficiently processes queries in under 3 seconds on an RTX 4090 graphics card. - The creator's background is from Columbus, Ohio, having transitioned from a handyman job to develop this technology, mentored by Claude CLI, focusing on architectural design rather than syntactic details. - The development process took approximately 4.5 months, resulting in not just SOTA but also excelling in the "Commonsense" category with an 87.78% score. - The user invites feedback from those involved in agent memory systems to further refine and enhance this technology. Keywords: #granite33:8b, BGE-large, BM25 retrieval, Cross-Encoder reranking, FAISS, Gpt-4o-mini, LoCoMo, MCA ranking, RAG pipeline, SOTA, VAC Memory System, accuracy, agents, benchmark, memory, open-source
rag
news.ycombinator.com 2 days ago
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355. HN We asked four AI coding agents to rebuild Minesweeper–the results were explosive- In an assessment of artificial intelligence coding skills, four AI agents were programmed to independently develop a version of the classic puzzle game Minesweeper. - The evaluation primarily focused on the performance of Mistral Vibe's iteration. - Despite understanding the concept of tailoring board sizes for customized gameplay, Mistral Vibe's Minesweeper lacked support for an explicit "Custom" difficulty setting. - More critically, the AI model struggled with implementing fundamental game features, leading to a suboptimal and cumbersome user experience. - Notably absent were advanced player techniques such as 'chording' (simultaneous interaction of multiple mines) in Mistral Vibe's rendition. BULLET POINT SUMMARY: - AI agents tasked with autonomously coding Minesweeper. - Evaluation centered on Mistral Vibe's version due to shortcomings in others. - Recognized but failed to implement "Custom" difficulty button for board sizes. - Did not incorporate basic features, resulting in a poor user experience. - Missing advanced player techniques like 'chording'. Keywords: #granite33:8b, AI coding, Minesweeper, advanced players, chording technique, complex AI-generated code, custom difficulty, customized board sizes, human debugging, middle ground test, new features, open-ended feature request, raw material, review, single shot, tweaking, unguided creativity, unmodified code, well-known game
ai
arstechnica.com 2 days ago
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356. HN Is Shenzhen the SF of China? Here are my takeaways**Summary:** The user's December 2025 visit to Shenzhen is compared to a tech-forward, modern San Francisco. Notable for its openness, social interaction, and prevalent AI integration—evident through ubiquitous ads for AI and AGI at the airport—Shenzhen stands out despite not being China's largest city. Its skyline, featuring Ping An Tower (fifth tallest globally), offers impressive views at affordable hotel prices compared to San Francisco. Key transportation aspects include electric vehicles leading to quiet streets and electric scooters as the primary noise source. Bikes are inexpensive and widely used, with public transit being both cheap ($0.20 for normal train tickets) and extensive, though crowded. WeChat serves as a central hub for daily tasks and communication, fostering a high-trust society where professionals from tech sectors like AI and TikTok are prevalent. Nightlife is described as limited compared to Shanghai or Hong Kong but accessible via a 15-minute train to these cities. A humorous anecdote reveals the user being recognized on a train by locals familiar with their social media presence. The scarcity of tourists and foreigners, alongside limited English outside certain areas, is highlighted, contrasting with locals’ joy when visitors attempt to speak Chinese. Huaqiangbei, a massive tech market, offers diverse products, while sprawling university campuses house numerous international students. App functionality is robust yet often marred by poor user interface design. The author cautions against getting sidetracked and emphasizes the benefits of engaging with locals and using WeChat for connectivity. Innovative services like drone food delivery are mentioned, alongside late-night ping pong availability. Personal experiences include ordering McDonald's via drone and playing ping pong with local residents at parks, noting the unexpected athleticism of older individuals. Travel to Dapeng peninsula by taxi revealed fit elderly residents climbing mountains. Overall, Shenzhen is praised for its convenience, affordability, and concentration of talent, being likened to "the SF of China" but focused on hardware development, with an emphasis on safety and purposeful building within the city. **Bullet Points:** - Shenzhen compared to modern San Francisco: open, interactive society with advanced AI integration. - Notable landmarks include Ping An Tower (fifth tallest globally). - Affordable accommodation views akin to San Francisco's pricing. - Electric vehicles and scooters dominate transport; bikes are widely used and inexpensive. - WeChat central for communication, daily tasks, facilitating high-trust interactions. - Tech professionals (AI, TikTok) common, limited English outside specific areas. - Nightlife less vibrant than Shanghai/Hong Kong but accessible via train. - Humorous recognition by locals on a train. - Scarcity of tourists and foreigners; locals appreciate language attempts. - Huaqiangbei: massive tech market with diverse offerings. - University campuses are expansive, hosting many international students. - Robust app functionality but poor user interface design noted. - Drones used for food delivery, late-night ping pong available. - Personal experiences: drone McDonald's order, playing ping pong with locals, visiting fit elderly at Dapeng peninsula. - Praise for Shenzhen’s convenience, affordability, talent concentration, focused on hardware development, and safety-oriented culture. Keywords: #granite33:8b, AI, AI glasses, App cluttered UX, Chinese language enthusiasm, Dapeng peninsula, Didi taxi, Futian area, Hong Kong proximity, Huaqiangbei market, Meituan stations, Ping An Tower, San Francisco, Shenzhen, Sidequesting, TikTok, Twitter recognition, University campuses, WeChat, WeChat interactions, bikes, business class, cameras, cheap food, cheap train tickets, drone food delivery, electric vehicles, expensive views, few tourists, first visit, focus, food delivery, foreigner recognition, hardware, high-trust society, light show, metro, mountain climbing, nightlife, open-minded, packed trains, ping pong, powerbank rental, productivity, public transport, safety, scooters, skyscrapers, talent density
ai
elliotlindberg.com 2 days ago
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357. HN Nebula Awards Yelled at Until They Ban Use of AI by Nominees- The Science Fiction & Fantasy Writers Association (SFWA) has implemented a ban on using large language models (LLMs) for Nebula Awards nominations, now covering works written either wholly or partially with AI technology. Creators must disclose AI usage during the writing process to avoid disqualification. The SFWA opposes LLM use in creative production and intends to update their posted rules. - Larian Studios, developers of Baldur’s Gate 3 and Divinity, faced criticism for employing generative AI in tasks such as concept art and text generation. Founder Swen Vincke defended the use as enhancing rather than replacing human creativity. In response to criticism from players and former staff writers, an AMA (Ask Me Anything) session on Reddit is scheduled post-holiday for team members to explain their development process and address concerns. - Plans are in place for the 2026 Nebula Awards conference in Chicago, set to take place from June 5-7. Keywords: #granite33:8b, AI ban, Baldur's Gate 3, Bloomberg, LLMs, Larian Studios, Nebula Awards, Reddit AMA, SFWA, Swen Vincke, additive workflow, controversy, creative work, criticism, dev process, disclosure, disqualification, divinity game, ex-staff, generative models, hiring process, machine-learning tools, policy, skepticism, values, writing tools
ai
gizmodo.com 2 days ago
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358. HN An amateur codebreaker may have just solved the Black Dahlia and Zodiac killings**Summary:** The text explores the potential links between Marvin Margolis (known as "Skip Merrill" or "Marvin Merrill") and two notorious American crimes: the 1947 Black Dahlia murder of Elizabeth Short and the San Francisco Zodiac killings from 1968 to 1969. - **Key Figures:** - Christopher Goffard, an amateur codebreaker who potentially connects Margolis to both crimes through investigative journalism. - Alex Baber, a self-taught codebreaker with autism who claims to have solved the Zodiac killer's identity and links him to Margolis. - Marvin Margolis (a.k.a. Skip Merrill/Marvin Merrill), a USC premed student, World War II veteran, and former suspect in both cases. - **Black Dahlia Connection:** - Margolis briefly lived with Elizabeth Short before her murder, initially lying about the relationship. - After moving to Chicago, he changed his name to evade questioning. - Psychological instability due to wartime trauma led to a partial clearance but didn’t fully exonerate him. - **Zodiac Killer Connection:** - Margolis is suspected by detectives of being responsible for five Zodiac killings, based on hidden connections in his clues, including sketches with references to "ELIZABETH" and "ZODIAC." - A man identifying as the Zodiac Killer named Margolis (Marvin Merrill) as a suspect through letters. - **Codebreaking Attempts:** - Baber used AI to generate 71 million potential names linked to the Zodiac's cipher, cross-referencing them with descriptions of the killer. - Cryptographer Alex Martin claims to have cracked the code using "Elizabeth" as a keyword, linking it back to Margolis. - **Unresolved Issues:** - Lack of formal credentials and skepticism about Baber's methods from critics. - Insufficient resources dedicated to solving these cold cases due to prioritizing solvable cases with living suspects. - Inconsistent witness testimonies and timeline discrepancies have historically complicated investigations into Margolis' involvement. - **Additional Evidence:** - Margolis' artwork, including a sketch titled "Elizabeth," bearing resemblance to the mutilation of Short's body and possibly featuring hidden references to the Zodiac. - The potential significance of the former "Zodiac Motel" (now Compton bungalow complex) where a distressed man reportedly sought lodging on the eve of the murder, supporting Baber's theory linking location and alias. **Bullet Points:** - Christopher Goffard links Marvin Margolis to Black Dahlia (Elizabeth Short) murder and San Francisco Zodiac killings. - Margolis was a USC premed student and WWII veteran with aggressive tendencies; lived with Short before her death, later changed name and evaded questioning. - Alex Baber claims to have solved the Zodiac Killer’s identity through AI analysis of ciphers, implicating Margolis. - Zodiac Killer letters identified Marvin Merrill (Margolis) as a suspect; sketch by Margolis' son allegedly depicts Short's mutilated body. - Margolis suffered war trauma, discharged with disability, exaggerated service records, adopted aliases, and engaged in fraudulent activities post-war. - Lack of resources, inconsistent witness accounts, and skepticism about codebreakers hinder definitive resolution of these cold cases. - Margolis’ artwork and the potential link to a formerly named "Zodiac Motel" are additional elements in Baber's theory. Keywords: #granite33:8b, 1968-1969, AI, AI discovery, Bay Area killer moniker, Black Dahlia, Black Dahlia case, Bucksavers Automotive Repair, Bugsy Siegel theory, Chicago, Compton bungalow complex, David Toschi, Ed Giorgio, Elizabeth Short, Flying Tigers, Hall of Justice, Hollywood Boulevard, Intel engineer, LAPD, LAPD awareness, Larry Harnisch denouncement, Marvin Margolis, Michael Connelly, National Security Agency, Navy corpsman, Okinawa campaign, Patrick Henry, Rich Wisniewski, Salvador Dali, San Francisco Bay Area, Skid Row alcoholic theory, USC, William Armstrong, World War II veteran, Z13 cipher, Z13 cipher solution, Zodiac Motel, Zodiac killer, aggression, amateur sleuth Baber, anatomical knowledge, apartment, architect, artist, autism, bellhop theory, builder, case status, codebreaker, codemaker, cold case consultants, cold case detective, cold case unit, combat-knife amputations, confession, cryptograms, disability, five murders, four kids, fraud, homicide detective, intellectual, internet sleuthing, key word, layer elimination, letter-frequency analysis, letters, marriages, military records, morgue, name change, newspaper ad, pharmacist's mate, podcast "Killer in the Code", portrait painter, premed student, priority, prosecute, radar, resentment, restaurant, retired detectives, short murder, sketch, solvable cases, solved confirmation, surgeon, suspect, suspects, taunting, theory confirmation, venereal-disease doctor theory
ai
www.latimes.com 2 days ago
http://archive.today/uVf3N 2 days ago https://daringfireball.net 2 days ago |
359. HN Microsoft bets on AI to modernize Windows- **Microsoft's Modernization Initiative**: Microsoft plans to replace millions of lines of C and C++ code with Rust across its software, including Windows, by 2030. This initiative, led by Galen Hunt, aims to improve software stability and prevent common programming errors by utilizing Rust's safety features. - **Emphasis on AI-Powered Rewriting**: The company intends to use AI-powered algorithms to automate the rewriting of massive libraries from C/C++ to Rust, demonstrating early experiments in this area. - **Priority over Features**: Despite ongoing development of Windows 11, Microsoft prioritizes this extensive code replacement over feature additions like dark mode for specific interfaces. - **Progress in Critical Infrastructure**: Microsoft has already begun transitioning parts of Windows and Xbox code to Rust, notably in Azure's critical infrastructure components due to Rust’s resilience against memory-corrupting bugs prevalent in C/C++. - **Multi-Year Investment**: This modernization project will require a substantial multi-year investment, as detailed in an Azure blog post from 2023. - **Hiring for Expertise**: Microsoft is recruiting engineers with expertise in AI and machine learning to accelerate this transition within its CoreAI division, specifically in the Future of Scalable Software Engineering group. - **Potential Code Quality Improvements**: While acknowledging the challenge posed by C++'s vast ecosystem, the shift to Rust promises enhancements in code quality, reliability, and security. - **Ongoing Discussion on AI's Role**: The use of artificial intelligence for large-scale code replacement within Microsoft continues to be a topic of further exploration and discussion. Keywords: #granite33:8b, AI, C++, Microsoft, Rust, Windows, codebases, dark mode, ecosystem, enterprise security, garbage-collected languages, legacy code, massive scale replacement, memory bugs, modernization, priority, reliability, safety, scalable graph, thread safety guarantees
ai
www.windowscentral.com 2 days ago
https://news.ycombinator.com/item?id=46360955 2 days ago |
360. HN Context is all you need- **AI Evolution**: The focus in AI development is shifting from complex models to understanding user context, termed "Context is all you need". This approach prioritizes real-time signals and user intent over historical data. - **Startups Competition**: Businesses are competing to capture, control, store, and monetize rich contextual information through various models like subscription services, ad-supported platforms, and hardware differentiators. - **Key Terms**: - Context: User's current situation or circumstances. - Context Window: Limited aspect of the context being considered. - Memory: Storage for context data. - Personal Context Infrastructure: Systems designed to manage personal context. - Context Boundary: Limits set by users on what context is shared. - **Layers of Context-Aware Systems**: 1. Immediate (current conversation) 2. Session (daily work) 3. Personal (preferences, patterns) 4. Environmental (location, schedule) - **Actionable Tactics for Product Builders**: - Prioritize immediate signals over historical data. - Optimize data ingestion pipelines for immediacy. - Present context as actionable "state" objects, like real-time dashboards. - Enable users to control context sharing across domains with seamless permissions. - **Context Stack Model**: - Collection Layer: APIs, sensors, behavioral signals. - Processing Layer: Context synthesis, privacy filtering, relevance ranking. - Application Layer: Turning context into action. - **Portable Context Profiles**: Users manage a sandbox of their data—personality traits, skills, emotional states—which can be transferred across services. This envisioned "context engine" emphasizes encryption and privacy respect. - **Challenges and Considerations**: - Privacy and security concerns remain significant hurdles. - Tech giants benefit from massive infrastructure needed for context maintenance, perpetuating centralization. - The lock-in issue is exacerbated as users are reluctant to split their context among providers due to network effects. - The privacy paradox highlights the tension between desiring deep personalization and fearing surveillance. - **Future Direction**: - Interfaces will become more ambient and anticipatory, integrating specialized and general-purpose agentic systems. - Productize context-to-action loops allowing users to preview, veto, or modify actions based on current context. - Emphasize building AI systems that respect user privacy and offer customizable context boundaries. - **User Priorities**: Users should prioritize products offering control over their data rather than exploiting it for monetization. Overcoming technical challenges is crucial to foster user-centric, portable, and trustworthy contexts, replacing current siloed systems and surveillance models. Keywords: #granite33:8b, AI DJ, Attention, Context Stack, action, ambient, anticipation, computational costs, context, context interoperability, context signals, context silos, context-aware AI, cross-context products, decentralized social protocols, devices, digital twins, full-stack approach, generalist systems, interfaces, machine learning, memory, on-device processing, personalization, portable context, portable identity, prediction, real-time narratives, real-time signals, specialized assistants, startups, transcription, user intent
github copilot
fakepixels.substack.com 2 days ago
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361. HN Top MCP tools for software architects- **Model Context Protocol (MCP) servers** enhance software architect productivity by integrating various tools and data sources into AI assistants like Claude, enabling seamless connections between large language model applications and external resources for improved outputs. - **AWS MCP** is highlighted as an example, offering direct access to AWS infrastructure through natural language queries. Architects can inspect resources across services such as EC2, S3, Lambda, RDS with their AI assistant, aiding in system design, troubleshooting production issues, and reviewing configurations. - Other mentioned MCP tools cater to specific platforms: - **GitHub MCP**: Facilitates code architecture reviews and repository analysis through GitHub integration. - **Grafana MCP**: Integrates observability and monitoring data for querying metrics, dashboards, and alerts via natural language. - **Kanban Tool MCP**: Manages tasks and workflows using Kanban boards integrated with the AI assistant. - **Documentation MCP**: Quickly retrieves and updates documentation, searching team knowledge. - **Architectural Diagram MCP**: Enhances understanding of C4 model diagrams and system architecture visualization for better communication and documentation. - **Incident.io MCP**: Assists in incident response by providing access to incident history and postmortems. - **Atlassian MCP**: Connects to Jira and Confluence, streamlining project management tasks and documentation. - **Terraform MCP**: Accelerates infrastructure design with queries for Terraform state, dependency reviews, change planning, and configuration generation. - **MCP Toolbox for Databases**: Open-source server supporting various databases, enabling schema query, pattern analysis, and data relationship understanding. - **Honeycomb MCP**: Uses observability data for deep system insights through trace queries, service dependency analysis, and performance issue investigation. - These MCP servers centralize an architect's diverse ecosystem into a unified conversational interface, improving decision-making speed, minimizing context switching, and enhancing comprehensive system analysis. BULLET POINT SUMMARY: - **MCP Servers** integrate AI assistants with multiple tools/data sources for architect productivity enhancement. - **AWS MCP**: Natural language queries to AWS infrastructure services (EC2, S3, Lambda, RDS) aiding in system design and troubleshooting. - **Additional MCP Tools**: - GitHub: Code architecture reviews via natural language. - Grafana: Observability data querying for metrics, dashboards, alerts. - Kanban Tool: Project task management integration. - Documentation: Quick documentation access and updates. - Architectural Diagram: Enhanced visualization of C4 models and system architectures. - Incident.io: Incident response assistance with postmortem data. - Atlassian: Jira, Confluence project management integration. - Terraform: Infrastructure design acceleration through state queries and configuration generation. - MCP Toolbox for Databases: Schema information querying across databases. - Honeycomb: Deep system understanding via trace analysis and performance issues investigation. - Centralized ecosystem in AI interfaces improves decision efficiency, reduces context switching, and enhances comprehensive system analysis. Keywords: #granite33:8b, AI assistant, AWS, AlloyDB, Atlassian, BigQuery, Bigtable, C4 model diagrams, Cloud SQL, Dgraph, EC2, GitHub, GitLab Alternative, Grafana, Honeycomb, IcePanel, Incidentio, Lambda, Looker, MCP, MySQL, Neo4j, Notion, Postgres, RDS, S3, Shortcut, Spanner, Terraform, alerts, architectural decision records (ADRs), architecture review, code analysis, complex systems, context switching, context-switching, conversational interface, dashboards, database operations, decision-making, documentation, documentation access, ecosystem integration, incident management, infrastructure-as-code, knowledge, local data sources, metrics, monitoring, natural language queries, observability data, performance analysis, project management, remote services, repository interaction, resource configurations, software architects, system architectures, system reliability, team capacity, third-party tools integration, troubleshooting, workflow integration
github
icepanel.io 2 days ago
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362. HN LLVM AI Policy and Automatic Bazel Fixes- Michael Larabel founded Phoronix.com in 2004, becoming a key figure in Linux hardware and performance advocacy. - He has authored more than 20,000 articles focusing on related topics, demonstrating extensive expertise. - Larabel leads the development of several automated benchmarking tools: Phoronix Test Suite, Phoromatic, and OpenBenchmarking.org. - His area of specialization includes graphics drivers and Linux performance optimization. - Maintains an active online presence through platforms such as Twitter, LinkedIn, and his personal website, MichaelLarabel.com. - Currently engaged in projects related to LLVM AI Policy and Automatic Bazel Fixes, though specific details for these endeavors are not provided in the text. Keywords: #granite33:8b, AI, Bazel, LLVM, LinkedIn, Linux, Michael Larabel, MichaelLarabelcom, Phoronixcom, Twitter, articles, benchmarking software, fixes, graphics drivers, hardware support, policy
ai
www.phoronix.com 2 days ago
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363. HN Total Recall: RAG Search Across All Your Claude Code and Codex Conversations- Contextify 1.0.6, accessible via Mac App Store and direct download, introduces "Total Recall," an AI feature using Claude Code to access users' conversation history through various tools like contextify-query, query@contextify, and contextify-researcher. This facilitates reviewing past decisions and maintaining context across sessions. - The update supports macOS 15 (Sequoia), offering Lite Mode with full functionality for timeline monitoring, session search, and transcript backup. However, AI summaries are disabled as they require Apple Intelligence on macOS 26 (Tahoe). - Improvements have been made to agent sidechain capture for better performance. - Automatic summary generation is introduced for conversations involving Claude Code's tasks and sub-agents, enhancing main conversation thread readability. Key improvements include faster Codex session discovery, quicker historical transcript ingestion, and overall stability enhancements. - Users can download Contextify 1.0.6 for local data storage on their Macs, with support email or GitHub issues available for questions or feedback. Keywords: #granite33:8b, AI summaries, CLI tool, Claude Code, Codex sessions, Contextify 106, GitHub issues, Lite Mode, Tahoe, Task tool, Total Recall, agent sidechain capture, contextify-query, contextify-researcher, conversation history, conversation threads, data privacy, email support, historical transcripts, ingestion, macOS support, multi-agent work, query@contextify, session search, stability improvements, sub-agents, summaries, timeline monitoring, transcript backup, visual indicators
rag
contextify.sh 2 days ago
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364. HN Clausly – AI-powered contract management for SMBs- Clausly is an AI-driven contract management tool specifically designed for small and medium-sized businesses (SMBs). - It excels in automating and streamlining contract analysis, enabling users to identify potential risks efficiently. - The tool facilitates enhanced team collaboration through integrated features, ensuring smoother workflow among stakeholders. - Users experience a significant reduction of 70% in document review time due to Clausly's batch processing capabilities and its ability to accurately comprehend legal contexts within contracts. - Enterprise security is a focal point, with Clausly adhering to stringent compliance standards such as the General Data Protection Regulation (GDPR) and maintaining data isolation for secure handling of sensitive documents. Keywords: #granite33:8b, AI, GDPR compliance, SMBs, batch processing, contract management, data isolation, document review, enterprise security, legal context, risk identification, risk scoring, team collaboration
ai
clausly.ai 2 days ago
https://clausly.ai 2 days ago |
365. HN Rambling About AIs, Goals and Stuff**Summary:** The text covers a diverse range of topics, primarily categorized into personal reflections on technology use, experiences with artificial intelligence tools in game development and music production, critiques of video games like "Expedition 33," insights into developing an untitled space game, plans for a ZX Spectrum Next game creation, software architecture for ZX emulation, and a discussion contrasting the Mass Effect trilogy with its successor, Andromeda. **Key Points:** - **Personal Tech Reflections:** - The author reflects on unmet New Year goals in math studies and exercise while noting achievements such as creating a ZX Spectrum game, starting electric guitar practice, and purchasing music production software (FL Studio). - They developed a command-line tool for audio to AY-3-8910 sound chip register data but encountered Windows 10 compatibility issues. - **AI Tools Evaluation:** - The author uses AI tools like GPT4All and ComfyUI, noting their utility in research contexts for chat simulations, but criticizes them for generating superficial outputs lacking depth, especially when attempting complex tasks such as coding or graphics generation. - **Game Development & Reviews:** - Critical reviews are provided for "Expedition 33," highlighting issues with gameplay mechanics (inconsistent 2.5D elements and transparent walls) despite praising aspects like soundtrack, art assets, storyline, voice acting, and thematic coherence. - Another unnamed game is reviewed for its lengthy combat sequences due to shield mechanics and healing requirements, noting player frustration from memory lapses interrupting actions, but acknowledging overall enjoyment. - **Untitled Space Game Development:** - Progress updates on an untitled space game project detail improvements in keyboard inputs, navigation screens, starmap dynamics, addressing 256-star system scrolling challenges, and planning for player movement across systems. - **Feature Plans:** - Future plans include enhancing ship movement with range/speed details, possibly using a 2D projection. Cargo fitting systems are detailed with planned features like cargo space display, temporary storage, mail forwarding, and interactive station inventories. Conversation elements are envisioned with multiple discussion stacks and narrative progression based on character presence. - **ZX Spectrum Next Game Creation:** - Details the process of generating mugshots for over 100 characters, addressing color limitations through a 6x6x6 color cube approach in layer 2 graphics mode (256x192x8bpp). - **Software Architecture & Emulator Design:** - Proposals include simulating hardware components on separate threads running in lockstep and considering cooperative multitasking for optimization. An emulator design with three primary threads (compute, display, audio) and a potential fourth "changelist" thread for handling state modifications independently to reduce lag is suggested. - **Windows 11 Update Issue:** - A personal anecdote describes an m.2 SSD failure during game installation attempts caused by the KB5063878 update, resolved by performing a cold boot. The issue is attributed to aggressive optimizations or malformed OS requests potentially causing data corruption. - **Interactive Story Games Analysis:** - A critical review of various adult visual novels developed with Ren'Py, highlighting strengths and weaknesses in writing, story execution, and gameplay mechanics across titles like "Lust Theory," "Being a DIK," "Leap of Faith," "Leap of Love," and "Treasure of Nadia." - **Windows 10 End of Life Preparation:** - Discussions revolve around the impending End of Life for Windows 10, prompting users to consider hardware upgrades, transitioning to open-source alternatives like Ubuntu, or migrating software. Specific experiences from upgrading various devices (laptops, desktops) are shared, including successful transitions and encounters with challenges such as driver compatibility and language setup issues. **Additional Insights:** - The author's personal health decision to avoid caffeine due to suspected migraine triggers. - Outlines for future projects, including a software package for ZX Spectrum Next users and a potential game or written piece inspired by distant city history. - Acknowledgment of the complexity and improbability of completing all planned ambitious tasks due to resource constraints. Keywords: "auto battle" mode absence, #granite33:8b, 12 core system, 256 stars, 3D platforming, 64 gigs memory, 8-bit game, AI art, AY samples generation, AY-3-8910, Advent of Code, Andromeda comparison, Any key routine, Audacity, Australian activism, BIOS check, Barbie porn, C++, CLion, Canon drivers, Cargo Hauling, Cargo Space Management, Chunked format, Crew Members, Cygwin, DIK Season 1, Debt Motivator, FL Studio, Finnish OS, French theming, GCC, GOG, German, GitHub, Groundhog Day plot, Image Spectrumizer, Itchio, Item Sorting, JRPGs, KB5063878, Kempston interface, Leap of Faith, Leap of Love, Linux, Linux boot, Linux distro, Lust Theory Season 1, Mass Effect trilogy, MuCho, Photoshop, Photoshop CS5, Puzzle Game, QTE battles, Ren'Py, Romantic choices, Ryzen 9900x, Ryzen7 2700, SQLite, SSD failure, Ship, Ship Upgrades, SoLoud, Star Systems, Steam, Story Arcs, SymPy, T-states, Takomo, ThinkPad, Treasure of Nadia, Ubuntu, UltraEdit, Unlock System, Untitled Space Game, Windows, Windows 10, Windows 10 EOL, Windows 10/11, Windows VR headsets, XYZ coordinate system, Z80, Z80 assembly, ZX Next, ZX Spectrum Next, ZX Spectrum game, ZX Spectrum programming, adults-only games, aggressive optimizations, all big cores, apparent brightness levels, art assets, assets, attack frequency, audio, audio bleep, audio latency, audio production, auto attack automation lacking, avoid speculative fields, backwards compatibility, better renders, big and little cores, binary chunks, binary data, bobbing rod, budget, build quality, business model, cable management, capable hardware, cargo, cat file format example, certificate issues, chapter progression, chapters, character comments, character generation, character rendering, chunks order, client shopping lists, cold boot, combat duration, commandline tool, compression, compute side, computer builder, connections, consumer systems, controller hang, conversations, cooperative multitasking, coordinate calculation, coroutines, coroutines performance, creative content, credit card companies, curated list, custom format, damage limits, data corruption, data safety, data structure, decent specs, depth hints, depth offsets, depth values, device disappearance, difficulty, display, display threading, distro, documentation, e-waste, electric guitar, emulation, end bosses, enemy behaviors, error minimization, evasion failures, exhausting combat, exploration, faces, ffmpeg, file format design, file size, filename extensions, fish availability, fish lengths, fish spawning, fishing game design, forwards compatibility, free roam, frog prince, functions, game, game blocking, game coding, game development, game installation, game logic, game mechanics, game world, gameplay mechanics, grinding, hand-drawn, hardware blocks, hardware components, heavy themes, hidden trinkets, high end gaming PCs, hiring artists, human readable, hyperthreading, i5, image format, image generation, immersion, inconsistency, infinite gameplay, ini, ink reversal, input sanitization, instructions, inventory, json, keyboard inputs, lag reduction, laptop, lecture while grinding, library code, loading screen, lockstep, lookup table, mackarel assembler, main thread, malformed requests, math books, mockup, mugshots, multi-threading, multiple controllers, name, navigation screen, no-brand, non-branching, numeric displays, online emulator, open source implementations, optimization, optional battles, optional chunks, partial parsing, pixel elements, playback, player data tracking, polar coordinates, portraits, post-insanity world, powerup characters, prebuilt PC, prior art, putpixel, random pixel patterns, read-only parts, reading during gameplay, redraws, refurbished, reinstallation, replayability, respawning enemies, retro-style RPG, reviews, romantic storylines, root chunk tag, scanner, separate buffers, serialization, sex scenes, shield mechanics, short game, single thread, small target, smoothstep, software architecture, soundtrack, space station, spaceship, speccy github repo, specific use case, standalone game, star data, star scrolling, starmap dynamics, state changes, state changes storage, static reduction, story, story branches, story mode, stream reader, synchronization overhead, system development, system refusal, systems, t-state granularity, table lookup formula, tap file, target hardware, telephone chat, testers, text drawing, text output, threads, three threads, tool integration, torches and pitchforks criticism, uninstall issue, valid depths, version control, video, visual effects, visual elements, visual novels, voice acting, win11, writing quality, xml
github
solhsa.com 2 days ago
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366. HN Does FHE deserve this much attention?- **Fully Homomorphic Encryption (FHE) Overview**: - Promises general, composable computation over encrypted data without trusted hardware. - Current implementations often rely on MPC committees for key management and decryption. - Unique capabilities include composable encryption and reduced hardware trust but isn't a universal solution; alternatives like ZKPs, MPC, or TEEs have trade-offs. - For AI and machine learning, FHE offers privacy-preserving non-interactive computation with long-term vision but faces performance and engineering maturity challenges. - **Devconnect in Buenos Aires Discussions**: - Key questions: Necessity of FHE for confidential tokens, comparison with homomorphic encryption, relevance beyond MPC, practicality of verifiable FHE. - Concerns about suitability for DeFi use cases like private Automated Market Makers (AMMs) and safe liquidation handling. - Skepticism regarding excessive attention FHE receives due to its challenges and uncertain benefits in Web3 applications. - **Market Signals and Privacy Techniques**: - Importance of maintaining market signals for prices and liquidity in functioning markets. - Alternatives like MPC, encryption, commit–reveal, zk-based tallying for sealed-bid voting and similar applications. - FHE-EVM seen as promising but faces practical constraints (performance, latency, overhead). - **FHE vs Other Privacy Techniques**: 1. **ZKPs**: Enforce constraints without revealing full positions; issues with composability, modularity, and dynamic logic anticipation. 2. **Threshold Encryption & MPC**: Require interaction/fixed committees, less suitable for open, permissionless environments. 3. **TEEs**: Strong performance but rely on weaker trust assumptions, vulnerable to side-channel attacks. - **FHE's Advantages**: Enables fully general, composable computation over encrypted state with fewer cryptographic trust assumptions than hardware-based approaches like TEEs. - **Practical FHE Implementations**: - FHEVM systems often incorporate a trusted committee for managing secret key shares and decrypting output ciphertexts via MPC, despite FHE's potential for privacy. - Recent work "Scalable Private World Computer via Root iO" aims to minimize or eliminate these trust assumptions using advanced cryptographic constructions based on indistinguishability obfuscation. - **Confidential Token Transfers**: - Appear straightforward but practical implementations must verify sufficient sender balance, using zero-knowledge proofs like Zether for new note values not exceeding available balances. - **FHE vs MPC Comparison**: - Both enable private data computation but differ in privacy enforcement, computational method, and practical assumptions: - **MPC**: Safeguards data by distributing secret shares among participants; high performance, requires non-collusion, multiple online parties, interactive communication (problematic in permissionless/asynchronous settings). - **FHE**: Allows direct computation over encrypted states by a single entity without real-time interaction; general, composable, stateful private data computation, enabling complex control flow. - FHE implementation challenges include complex key management and output decryption often relying on threshold MPC. - **Web3 Applications**: - Zama, Fhenix focus on confidential smart contracts using FHEVM stacks and coprocessors. - Enclave combines FHE with verification techniques for decentralized settings. - Partisia Blockchain integrates MPC into its Layer 1; Nillion offers an off-chain MPC network; Soda Labs' gcEVM embeds garbled-circuit MPC in EVM environments. - **Verifiable Fully Homomorphic Encryption (vFHE)**: - Addresses trust issues in FHE by enabling verification of encrypted computations, potentially scalable and composable for confidentiality in decentralized applications. - Currently, FHE faces challenges like large ciphertexts and expensive bootstrapping operations; vFHE aims to overcome these limitations. - **AI and Ethereum Intersection**: - FHE and its variant, vFHE, could enable private inference and collaborative learning on encrypted models for secure, trustless decentralized AI systems. - Concerns about unencrypted LLM use leading to digital twin creation and personal data misuse; FHE could mitigate risks but is currently too slow for advanced AI models. - **Training Advanced AI Models**: - Emphasizes the need for verifiability due to reliance on vast real-world, potentially sensitive data for training. - Blockchain technology suggested as a transparent audit layer ensuring data integrity, unbiased training, and accurate inference results in AI development linked to platforms like Ethereum. Keywords: #granite33:8b, AI, AI settings, EVM programs, EVM-compatible environments, Enclave, Ethereum, FHE, FHE coprocessors, FHEVM stack, Fhenix, GPU-accelerated implementations, LLMs, MPC, MPC committees, Root iO, SNARK systems, SNARKs, Scalable Private World Computer, TEEs, TFHE schemes, Web3, ZKPs, advanced models, alternatives, atomic updates, attention, audit, balance check, bias, blockchain, ciphertexts, co-SNARK library, collaborative proving, committee assumptions, comparisons, complex workloads, composability, computational cost, conditional update, confidential smart contracts, confidential tokens, control-flow logic, coordination requirements, correct inference, data integrity, decryption, decryption committee, digital twins, encrypted data, encrypted state, encryption, engineering maturity, evaluation pipeline, hardware-based approaches, homomorphic comparison, homomorphic encryption, household robots, iO, indistinguishability obfuscation, inference, key management, large language models, large-precision, liquidation, liveness dependencies, manipulation, non-interactive, off-chain computation, off-chain coprocessors, overspending prevention, performance, practical systems, privacy, privacy-preserving computation, private smart contracts, real-world data, robotics, sequential bootstrapping, side-channel attacks, sign evaluation, solvency, spatial learning, standalone proofs, stateful applications, tasks automation, threshold decryption, threshold encryption, trade-offs, training, transparency, trusted committee, trusted results, trustless applications, verifiability, verifiable FHE, verifiable guarantees, zero-knowledge proofs, zk-based tallying
ai
ethresear.ch 2 days ago
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367. HN "Maximum text contrast" requirement removed from WCAG 3.0 draft**Bullet Point Summary:** - **Web Content Accessibility Guidelines (WCAG) 3.0 Updates**: - Removal of the "Maximum Text Contrast" requirement. - Introduction of evolving definitions like 'Accessibility Support Set' and 'Accessibility Supported.' - Definitions subject to maturation over time. - **Key Terms**: - **Accessibility Support Set**: Collection of user agents and assistive technologies for testing compliance; standardization under development with regional adaptations possible. - **Accessibility Supported**: Refers to support levels across technologies and platforms, under ongoing definition. - **Active Availability**: Ensuring users can interact with content's actionable elements. - **Assistive Technology**: Hardware/software aiding individuals with disabilities in computer usage (e.g., screen readers). - **Digital Accessibility Concepts**: - **Automated Evaluation**: Testing via software tools for code-level features, excluding machine learning-based tests. - **Blinking**: Guidelines ensure compliance to prevent seizure risks from rapid state changes. - **Blocks of Text**: Describes content consisting of more than one sentence. - **Captions vs. Subtitles**: Captions offer synchronized visual or text alternatives for speech and non-speech audio, differing from subtitles which are translated audio versions. - **Developing Concepts**: - **Extended Audio Description**: Additional audio during media pauses for visually impaired users. - **Figure Captions**: Titles/explanations for visual content aiding those with visual or cognitive differences. - **Flash and Red Flash Thresholds**: Criteria limiting flash occurrences to prevent seizure risks, defining 'flashing' and 'red flashing.' - **Functional Need**: Addressing specific accessibility gaps between individual needs and design environments. - **Additional Accessibility Terms**: - **Gestures**: Body movements for technology interaction. - **Guidelines**: User-friendly statements of accessibility requirements without technical details. - **Human Evaluation**: Tests relying on human judgment for certain unautomatable aspects. - **Interactive Element**: Elements responding to user input with programmatically determinable names (e.g., buttons). - **Further Concepts in Web Development and Accessibility**: - **Path-based Gesture**: Defined by pointer trajectory, categorized as time-dependent or non-time-dependent. - **Platform Software**: Foundational software providing hardware isolation, standard services, and simplifying development across diverse hardware platforms. - **Pointer**: Device for user interaction with digital interfaces (e.g., mouse, touchscreen finger). - **Private and Sensitive Information**: Includes racial/ethnic origin, personal identifiers, biometrics, health details, financial info. - **Process**: Sequence of views or pages linked through specific actions, independent of underlying technologies. - **Product**: Currently under development. - **Testing Scope**: Evaluation encompassing all elements, perspectives, and interactions within a web application/site, considering its platform environment. - **Programmatically Determinable Content**: Emphasizes software interpreting content's meaning and key attributes for accessibility compliance (e.g., WCAG). - **Pseudo-motion**: Describes static elements mimicking movement to enhance user experience. - **Relative Luminance**: Metric measuring color brightness, normalized from 0 (darkest) to 1 (lightest), calculated using sRGB's RGB components. **Focus Areas**: 1. **Links**: Defined in the context of video content, including animated or static images, or a combination thereof. 2. **Viewports**: Active content within the viewport, inclusive of scrollable elements, expandable dialogs, and inline error messages. This summary encapsulates guidelines, terminology, and emerging concepts in digital accessibility, underscoring inclusivity through clear communication for diverse abilities. Keywords: #granite33:8b, AGWG, AI, API, APIs, ESP, ISO_9241-391, NOAA, RGB values, SNCF, Video Analysis Tools, WCAG, a11y, accessibility, accessibility support, acronyms, activation, alternative formats, alternative input methods, assistive technologies, audio descriptions, automated evaluation, blinking, blocks of text, brightness normalization, browsers, camera input, captions, click, closed captions, code-level features, color, complex pointer input, component, conformance, content, content features, content units, contrast, cross-platform, decorative, descriptive transcript, descriptive transcripts, developing, double click, double clicking, down event, dragging, drop down menu, error messages, facts, formal claim, forms, gestures, hardware, harmonize terminology, headings, heuristics, human evaluation, human judgement, icons, images, initialisms, input fields, input modality, interactive components, interactive element, items, keyboard commands, keyboard focus, keyboard navigation, keyboard substitutes, keystrokes, labels, links, machine learning testing, magnified content, mainstream user agents, mechanisms, media player, mouse, multipoint clicking, multipoint interaction, navigation mechanisms, navigation techniques, non-normative, normalization, numeronyms, open captions, operating systems, paragraphs, path-based gestures, phrases, pinching, platform context, procedures, processes, programmatic determinability, programmatic simulation, pseudo-motion, readability, relative luminance calculation, sRGB colorspace, scanning programs, screen magnifiers, screen readers, semi-automated evaluation, single pointer, sip-and-puff morse code software, size, software, spacing, special keyboards, specific disabilities, speech recognition software, standard, stylus, synchronization with speech, synchronized alternatives, synthesized speech, tap, task flow, techniques, technology-agnostic, technology-specific, testable units, testing scope, tests, text font, timing based gestures, touchscreen, two-finger, usability testing, user activity, user agents, user input, views, visual readability, visual states, voice, web development
ai
www.w3.org 2 days ago
https://www.w3.org/TR/2024/WD-wcag-3.0-20241212&#x 2 days ago https://news.ycombinator.com/item?id=42762054 2 days ago |
368. HN California cracking down on AI chatbots- **Legislation Details:** - Tech companies must establish protocols to detect and handle self-harm expressions during chatbot interactions with users, particularly minors. - Chatbot platforms need clear disclosure that they are AI-generated; minors will receive break reminders and be restricted from accessing sexually explicit content produced by these AI companions. - There's a prohibition against chatbots impersonating healthcare professionals providing medical advice. - Companies can now face legal repercussions for real-world harm caused by their AI products, emphasizing accountability in AI development and deployment. - **Legislative Context:** - These laws were signed by Governor Gavin Newsom amidst Salesforce's Dreamforce conference in San Francisco, where tech leaders like Google, Anthropic, and OpenAI gathered to discuss AI advancements. - Newsom highlighted several AI-related bills signed this year, including AB 316 on AI defenses, AB 489 regulating deceptive healthcare AI terms, AB 853 for California AI transparency, AB 53 focusing on large AI developers, and SB 243 specifically concerning companion chatbots. - **AI Concerns:** - Recent studies have reported instances of 'AI psychosis,' where users developed delusional beliefs due to interactions with autonomous AI agents like ChatGPT. Examples include believing one could fly after a chatbot's encouragement, planning revenge against OpenAI for deleting a favored chatbot named "Juliet," and marital disputes over excessive chatbot usage. - These findings underscore the critical need for thoughtful AI development and regulation as its influence expands, balancing innovation with safety concerns. Keywords: #granite33:8b, AI Transparency Act, AI chatbots, California, ChatGPT, Dreamforce conference, Salesforce, artificial intelligence agents, break reminders, child protection, conversations, delusional beliefs, disclosure, healthcare professionals prohibition, large developers, liability, protocols, psychosis, regulation, self-harm identification, sexually explicit content prevention, tech companies
ai
www.kron4.com 2 days ago
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369. HN Some Epstein file redactions are being undone- **Summary**: The text discusses the uncovering of previously redacted financial details from Epstein-related Department of Justice files, primarily concerning payments to young women and tax-related discrepancies in Epstein's corporate entities. Between 2015 and 2019, over $400,000 was paid to various young models and actresses, with a Russian model receiving more than $380,000. These revelations originated from unredacted portions circulating on social media after using Photoshop techniques or copying and pasting text. - **Key Points**: - Over $400,000 paid between 2015-2019 to young female models and actresses, notably a Russian model who received over $380,000 in monthly installments. - In 2022, Epstein's estate, along with associates Indyke and Kahn, settled a civil sex-trafficking case for $105m plus half of Little St James island’s sale proceeds; the Department of Justice did not admit liability. - Darren Indyke, Epstein's longtime attorney, joined Parlatore Law Group and has not faced criminal charges despite being involved in Epstein’s operations. His current clients include Defense Secretary Pete Hegseth and former President Donald Trump. - Trump denies any knowledge of Epstein's activities; court documents show Epstein’s enterprise attempted to conceal crimes by paying off witnesses, threatening victims, and attempting to destroy evidence, including undisclosed property taxes on Epstein-linked properties. - Redactions in files sections 184-192, citing privacy laws, reveal that companies like Cypress paid substantial property taxes in Santa Fe without reflecting these assets or expenses in their balance sheets. The Department of Justice has not clarified how this aligns with redaction standards related to victim privacy and ongoing investigations. Keywords: #granite33:8b, Cypress, DOJ documents, Department of Justice, Epstein, Epstein Files Transparency Act, Indyke, Little St James, Parlatore Law Group, Photoshop, Santa Fe, Trump defense, Virgin Islands, active investigation, actresses, balance sheet, civil case, compliance, concealment, corporate entities, denial, estate, evidence destruction, executors, federal investigation, files, inquiry, litigation costs, payments, personal information, property taxes, redactions, settlement, sex-trafficking, sexual abuse allegations, social media, threats, victims, young female models
popular
www.theguardian.com 2 days ago
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370. HN Ask HN: Which LLM has the best "study and learn" functionality?- A user is in search of a superior Language Learning Model (LLM) for comprehensive study and learning, expressing dissatisfaction with previous experiences using OpenAI and Gemini. - The current model in use is Claude, but the user is open to recommendations based on others' extensive experience. - The user specifically asks if there are individuals who have utilized AI extensively for tutoring or learning purposes, seeking insights into which AI model they found most effective for educational applications. Paragraph Summary: The user is actively seeking advice on an advanced Language Learning Model (LLM) that excels in facilitating deep study and learning processes. Having encountered shortcomings with models like OpenAI and Gemini, they currently employ Claude but remain open to alternatives. The user poses a query to a community of experienced individuals, particularly those who have extensively implemented AI for educational tutoring or learning scenarios. They aim to gather recommendations based on firsthand effectiveness in an AI-driven learning context. Keywords: #granite33:8b, AI, Claude, Gemini, OpenAI, functionality, learning, models, technical, tutor
claude
news.ycombinator.com 2 days ago
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371. HN Codex is a Slytherin, Claude is a Hufflepuff- Logic's engineers evaluated AI coding agents (Claude, Gemini, Codex) using Advent of Code problems as benchmarks. - All agents finished tasks in under 20 minutes, but achieving perfect solutions varied; Codex and Gemini demonstrated comparable code quality, length, complexity, and timing without comments (Codex) or with extensive comments including internal debates (Gemini). - Claude initially had a performance dip on Day 12, affecting its average. Excluding this day, Claude’s performance aligned more closely with others; it consistently provided clear header comments and relevant notes. - Qualitative analysis categorized agents based on coding styles: - Claude as an 'Over-Engineer' (Hufflepuff) due to elaborate structures even for simpler tasks. - Gemini as a 'Professor' (Gryffindor), impulsive yet thoughtful with extensive explanatory comments. - Mistral as another 'Over-Engineer' (Ravenclaw), overly theoretical and complex. - Codex as a 'Wizard' (Slytherin), efficient, goal-oriented but less readable code. - Using Factory.ai's Droid, Codex’s accuracy improved from 11/12 to 12/12 while maintaining its Slytherin traits, whereas Claude shifted from Hufflepuff to Ravenclaw, indicating a change in coding style from defensive to more architectural patterns under different orchestrators. - This study suggests that an AI 'agent' involves not just the underlying model but also the influences of the tools (orchestrators) used to interact with it. Keywords: #granite33:8b, Advent of Code, Claude, Codex, Coding agents, Droid, Gryffindor, Hufflepuff, LLM, Mistral, Over-Engineer, Pragmatist, Professor, Ravenclaw, Safety Officer, Slytherin, Tourist, Wizard, accuracy, agent, archetypes, architectural, classifier, comments, complexity, debate, defensive programming, edge cases, efficiency, evaluation, lines of code, model, orchestrator, quality metrics, safety-first, stream-of-consciousness, timing
mistral
bits.logic.inc 2 days ago
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372. HN Why Agents Matter More Than Other AI- Josh Albrecht's article "Why Agents Matter More Than Other AI" highlights the superiority of agent-based AI systems over conventional AI methods. - Agent-based AI, or multi-agent systems, comprises multiple autonomous entities capable of local decision-making to achieve complex system goals. - Albrecht posits that agents can more accurately model real-world complexity, emergent behaviors, and adaptability compared to traditional rule-based or data-driven AI techniques. - The article explores the effectiveness of agent-based AI in managing intricate situations, emphasizing its significance over other AI approaches. **Summary in Paragraph Form:** Josh Albrecht's article "Why Agents Matter More Than Other AI" underscores the advantages of agent-based artificial intelligence (AI) systems over traditional AI methods. Agent-based or multi-agent systems are characterized by multiple autonomous entities that can make local decisions and interact to accomplish complex system objectives. Albrecht argues that such agents more effectively model real-world complexity, emergent behaviors, and adaptability compared to conventional rule-based or data-driven AI techniques. The article delves into how agent-based AI can handle intricate situations more efficiently, thereby emphasizing its importance over other AI approaches by providing insights into handling complex systems with greater nuance and responsiveness. Keywords: #granite33:8b, AI, JavaScript, agents, site functionality
ai
substack.com 2 days ago
|
373. HN An AI-driven financial time-series data visualization and rendering engine**Summary:** The described tool is an AI-driven application specifically engineered for the visualization and rendering of financial time-series data. Its primary purpose is to aid users, including investors, analysts, and researchers, in deciphering complex market trends and patterns by converting raw numerical data into easily interpretable graphical representations. This facilitates a clearer understanding of financial market behaviors over time, thereby supporting more informed decision-making processes. **BULLET POINT SUMMARY:** - **Tool Type**: AI-powered application - **Functionality**: Visualization and rendering of financial time-series data - **User Benefit**: Simplifies complex data into comprehensible graphics - **Target Users**: Investors, analysts, researchers - **Core Purpose**: To help users understand trends and patterns in financial markets - **Outcome**: Facilitates informed decision-making by making complex data more accessible Keywords: #granite33:8b, AI, Chinese, English, data, engine, financial, rendering, time-series, visualization
ai
github.com 2 days ago
|
374. HN 2015 radio interview: AI as "high-level algebra" before Transformers and LLMs- In a 2015 radio interview, the speaker likened AI to "high-level algebra" for processing abstract inputs, foreshadowing modern large language models (LLMs). - The discussion focused on AI's architectural challenges, necessity for reasoning beyond simple computation, implications of automation, and governance models. - The interview, conducted before OpenAI’s establishment, now appears prophetic in its early recognition of AI's mathematical foundations and secondary consequences, such as those later mentioned by Sergey Brin about Google's AI advancements. - Transformers and LLMs didn't birth artificial intelligence but made its scalable application possible via inference stacking. - A proposed governance model involved a for-profit AI entity regulated by a nonprofit or mission-focused organization to mitigate misaligned incentives, mirroring OpenAI's initial setup as AI economics became clearer. - The reflection invites consideration of which aspects of this historical discussion remain pertinent or have become obsolete within the current LLM context. ``` Keywords: #granite33:8b, AI, Google's under-investment, LLMs, OpenAI, Sergey Brin, Transformers, architectural bottlenecks, automation, brute force, computation, economics, expert systems, for-profit engines, governance models, incentives, institutions, intelligence-as-inference, labor displacement, matrix multiplications, narrow ML, next-token distribution, nonprofit oversight, reasoning, scaling limits, sci-fi abstractions, tokenized text
openai
doomlaser.com 2 days ago
|
375. HN Show HN: UTM Manager – Lightweight UTM persistence for marketing attribution- **UTM Manager Overview**: A lightweight, framework-agnostic JavaScript library designed to persist UTM parameters across sessions and pages for accurate marketing attribution, addressing issues prevalent in existing heavyweight analytics libraries or outdated packages that lose UTM values during navigation. - **Key Functionality**: - Maintains essential UTM parameters (utm_source, utm_medium, utm_campaign, etc.) across multi-page processes like signup flows and e-commerce checkouts. - Adaptable to various website structures without extensive code changes or additional libraries, ensuring compatibility with different frameworks (React, Next.js, WordPress). - **Architecture**: - Consists of four layers: Framework Adapters (for React, Next.js, WordPress), Business Logic, Data Extraction, and Storage Layer. - The Storage Layer securely manages cookies without UTM data knowledge. - Data Extraction parses UTM parameters from URLs. - Business Logic applies attribution rules like 'first-touch' (initial source retained until cookies expire) and 'last-touch' (default strategy that can misattribute conversions, especially in B2B with long sales cycles). - **Attribution Strategies**: - 'First-touch': Useful for understanding the origin of long sales cycle leads in B2B marketing. - 'Last-touch': Default but may incorrectly attribute conversions due to its simplistic nature. - 'Dynamic attribution': Offers flexible, custom logic for businesses with specific rules, such as prioritizing paid traffic or limiting email campaign credits within a 7-day window of the visit. - **Implementation**: - Lightweight (2KB gzipped) and dependency-free JavaScript library. - Provides APIs for React, Next.js, and WordPress with hooks like `useUTMs` for state management in React and `useNextUTMs` addressing unique challenges of Next.js routing. - Integrates seamlessly with WordPress using jQuery events. - **Use Cases**: - Capture UTM parameters from URLs on page load for diverse attribution tracking needs in e-commerce and varying sales cycle scenarios. - Attach UTMs to form submissions for CRM or marketing automation platforms. - Fire events for custom analytics, A/B testing, or platforms when UTM data changes. - Facilitate cross-domain tracking with secure cookie settings (Secure, SameSite=Lax) to prevent CSRF issues. - **Availability**: - Source code available on GitHub for community contributions and improvement suggestions. - Offers multi-format distribution (ESM, CommonJS, IIFE) for npm usage and direct browser inclusion via CDN, ensuring compatibility across various frameworks. - Features an event-driven `utmParametersUpdated` for real-time updates without polling, with basic functions like `getAllUTMs()` and `getUTM()`. - **Benefits**: - Addresses marketing attribution gaps efficiently without over-engineering or introducing security vulnerabilities. - Suitable for budget-conscious teams requiring UTM parameter management. Keywords: #granite33:8b, A/B testing, B2B marketing, CDN usage, CRM, GitHub, Google Analytics, JavaScript library, MIT licensing, Nextjs, Nextjs integration, React, React hook, TypeScript, URL parameters, UTM Manager, UTM campaigns, UTM content differentiation, UTM mediums, UTM parameters, UTM sources, UTM terms, UTM tracking, UTM values persistence, Vanilla JavaScript, WordPress, WordPress adapter, analytics integration, analytics platform, attribution strategies, auto-capture, bundle size, conversation origin, cookie handling, core functions, cross-domain tracking, custom attribution, custom tracking, dynamic attribution, e-commerce, form submission, framework compatibility, framework integrations, jQuery events, last-touch attribution, lightweight solution, long sales cycles, marketing attribution, marketing automation, multi-page flows, npm installation, open-source, organic attribution, page load capture, real-time updates, rules-based attribution, server-side rendering, shared cookie domain, single-page apps, validation
github
gokhanarkan.com 2 days ago
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376. HN Wayback Machine Web Extension – A Browser Extension for Chrome/Firefox/Safari- **Summary:** - The Wayback Machine Web Extension, developed by The Internet Archive in collaboration with Google Summer of Code, facilitates viewing historical versions of webpages. - Features include instant saving of current pages, auto-saving for unsaved or bookmarked ones, and options to view the oldest or newest archived versions. - The extension offers a calendar overview of saved pages, displays snapshot counts, checks for archived copies when encountering HTTP errors (4xx & 5xx), and provides contextual notices from fact-checking organizations and origin websites. - Compatible with Chrome, Firefox, and Safari, the tool enhances online research and fact-checking by integrating with resources like research papers, books from Amazon, and TV news clips on respective news sites. - Additional functionalities include listing captured URLs, visualizing site structure via sunburst diagrams, generating word clouds, integration with Hypothes.is for annotations, saving URLs to Internet Archive, searching Twitter for page-related info, and sharing archived links on social media. - Instructions detail installation processes for Chrome, Edge, Firefox (not explicitly detailed in the text), Safari 14+, and guidelines for contributing code via GitHub or emailing with specific details. - Contributors listed range from Carl Gorringe to Karim Ratib, acknowledged from 2017-2022 under GNU Affero General Public License version 3 (AGPLv3). - **Key Points:** - Development and collaboration by The Internet Archive with Google Summer of Code. - Enables viewing historical web versions (Wayback Machine functionality within browser extension). - Core features: saving, auto-saving, version selection, calendar view, snapshot counts, error handling, contextual verification notices. - Enhanced research and fact-checking through integration with diverse resources (research papers, books, news clips). - Visualization tools (sunburst diagrams, word clouds), annotation integration (Hypothes.is), public archiving (Internet Archive), social media sharing. - Detailed installation instructions for Chrome, Edge, Safari 14+, and Firefox (separate guidance needed). - Contribution details: GitHub access or email submission of extension version, browser type, error URLs, project recognition of past contributors including Carl Gorringe. - Project under GNU Affero General Public License version 3 (AGPLv3) from 2017-2021. Keywords: #granite33:8b, AGPLv3, Add-ons, Auto Save, Bookmarks, Bug Reports, Chrome, Collections, Contributing Code, Contributors, Copyright, Credits, Debugging, Extension, Feature Requests, Feedback, Firefox, GitHub, HTTPS, Hypothesis Annotations, Installation, Internet Archive, Lines Contributed, Overview, Renamed Repo, SSH, Safari, Save Page Now, Social Media, Source Code, Sunburst Diagram, Technical Terms, Temporary Installation, Timeframe, Twitter, URLs, Unsigned Extensions, Wayback Machine, Web Extension, Word Cloud, Xcode
github
github.com 2 days ago
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377. HN Nvidia plows $2B into Synopsys to make GPUs a must-have for design, simulation- **Summary:** Nvidia has announced a $2 billion investment in Synopsys, with the aim of embedding its GPUs more deeply into diverse industrial sectors beyond artificial intelligence (AI). This follows their previous collaboration where Nvidia's GPUs have notably accelerated Synopsys' semiconductor design processes by factors such as 30x for circuit simulations and 20x in computational lithography. The partnership targets expanding support for Nvidia hardware and CUDA-X libraries to a broader range of applications across various industries, including but not limited to semiconductor design, robotics, aerospace, automotive, energy, and the creation of digital twins. This investment seeks to establish Nvidia GPUs as essential tools for advanced computing and design in multiple sectors. - **Key Points:** - Nvidia invests $2 billion in Synopsys to deepen GPU integration across industries beyond AI. - The collaboration has already accelerated Synopsys' semiconductor design, simulation, and electronic design automation (EDA) processes significantly. - Expansion plans focus on supporting a wider array of Nvidia hardware and CUDA-X libraries in sectors such as robotics, aerospace, automotive, energy, and digital twin development. - The investment is intended to make Nvidia GPUs crucial for design, simulation, and advanced computing across various fields. - Unlike recent AI-related investments (e.g., potential $100 billion with OpenAI or $30 billion with Anthropic on Azure), this Synopsys deal is non-exclusive and not contingent on customer milestones. - Both Nvidia and AMD are utilizing strategic investments to boost the adoption of their hardware in the expanding AI market, with AMD offering OpenAI stock in exchange for commitment to using its Instinct accelerators (6 gigawatts). This structured summary captures the core aspects of Nvidia's significant investment in Synopsys and its strategic positioning within the broader context of hardware adoption in AI and other industries. Keywords: #granite33:8b, AI, AMD, Ansys, Azure compute, CUDA, GPUs, Instinct accelerators, NIMs, NeMo frameworks, Nvidia, OpenAI, Synopsys, acquisition, circular economy, digital twins, electronic design automation, investment, physics simulations, semiconductor design, simulation
openai
www.theregister.com 2 days ago
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378. HN Show HN: Kapso – WhatsApp for developers- **Product Overview**: Kapso, created by solo founder Andres, is an alternative to Twilio tailored for developers utilizing WhatsApp. It simplifies WhatsApp API and inbox setup, providing comprehensive observability through parsed webhooks and debugging tools. - **Platform Features**: - *Multi-tenant Architecture*: Allows swift customer onboarding via a dedicated setup link. - *Workflow Builder*: Enables deterministic automations and the development of AI Agents. - *WhatsApp Flows*: Facilitates the creation of mini-applications within WhatsApp using AI and serverless functions. - **Cost Efficiency**: Aims to be 95% cheaper than Twilio, offering a generous free tier with 2,000 monthly messages. - **Open Source Contributions**: Kapso open sources several tools including: - TypeScript client for WhatsApp Cloud API - Example inbox implementation - Voice AI agent for WhatsApp - **Accessibility**: The platform and its open source components are available on GitHub, accessible at kapso.ai. BULLET POINT SUMMARY: - Kapso simplifies WhatsApp API integration for developers, offering observability tools and quick onboarding. - Features include workflow builder for automations, AI Agents, and mini-app creation via WhatsApp Flows. - Cost-effective alternative to Twilio, with a free tier of 2,000 messages per month and open source components available. - Open sources TypeScript client for WhatsApp Cloud API, example inbox implementation, and voice AI agent for WhatsApp on GitHub at kapso.ai. Keywords: #granite33:8b, AI Agents, GitHub, Twilio, TypeScript client, WhatsApp API, WhatsApp Flows, debugging tools, developers, free tier, multi-tenant platform, observability, open source, organic growth, voice AI agent, workflow builder
github
kapso.ai 2 days ago
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379. HN Social media encourages the worst of AI boosterism- AI enthusiast Bubeck incorrectly claimed that GPT-5 solved multiple unresolved Erdős problems, misinterpreting the website erdosproblems.com's status of these problems. - Mathematician Thomas Bloom clarified that a problem not marked as solved on the site does not necessarily mean it hasn't been addressed; solutions could exist elsewhere and remain undiscovered. - GPT-5 did not solve new problems but rather uncovered 10 previously unknown solutions already present within vast mathematical literature, demonstrating its capability to identify obscure references. - The incident highlights the danger of overstating AI achievements on social media and emphasizes the potential of large language models (LLMs) in mathematics for reviewing extensive existing data, as supported by Axiom Math researcher François Charton. Keywords: #granite33:8b, AI hype, Axiom Math, Erdős problems, François Charton, GPT-5, LLMs, Thomas Bloom, erdosproblemscom, existing results, mathematics puzzles, reference scanning, research scientist
gpt-5
www.technologyreview.com 2 days ago
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380. HN Show HN: AudioGhost AI – Run Meta's Sam-Audio on Consumer GPUs (4GB-6GB VRAM)**Summary:** AudioGhost AI is a user-friendly audio separation tool developed by integrating Meta's SAM-Audio model, specifically optimized for lower VRAM usage and quicker processing times compared to the original SAM-Audio model. It allows users to describe sounds they wish to extract or remove through text prompts, such as vocals, drums, or specific ambient noises like a dog barking. Key features include memory optimization via 'lite mode' reducing VRAM from about 11GB to 4GB, an intuitive modern UI with waveform visualization and real-time progress tracking, and a stem mixer for comparing original, extracted, and residual audio. The tool aims to incorporate video support and visual prompting for sound source selection within videos through integration with SAM 3. The architecture comprises a Next.js frontend, FastAPI backend API, task queue managed by Celery and Redis, alongside a memory-optimized version of Meta's SAM-Audio. **Technical Requirements:** - Python 3.11+ (CUDA-compatible GPU; at least 4GB VRAM for lite mode, 12GB+ for full mode; CUDA 12.6 recommended), Node.js 18+, FFmpeg, and Redis are necessary components with automatic installation by the provided installer for ease of setup. **Installation Guide:** - An one-click installation is available for convenience, while manual setup involves setting up Redis (via script or Docker), creating a new Python 3.11+ environment in Anaconda, installing PyTorch CUDA 12.6, FFmpeg, SAM Audio, backend and frontend dependencies, and running services across separate terminals. The application can be accessed post-setup at http://localhost:3000 after connecting to HuggingFace. **Functionality:** - Users upload audio files to request extraction or removal of specific elements like vocals, drums, background music, etc., with processing results available for preview and download. **API Endpoints:** 1. POST /api/separate/: Allows initiation of a separation task by submitting an audio file along with parameters such as description, mode ("extract" or "remove"), and model size ("small", "base", or "large"). Upon success, the API provides a task ID, status, and message. 2. GET /api/separate/{task_id}/status: Used to monitor progress of ongoing tasks. 3. GET /api/separate/{task_id}/download/{stem}: For downloading result audio files post-completion (ghost, clean, or original). **Troubleshooting:** - Suggestions include resolving CUDA Out of Memory errors by opting for the 'small' model size, enabling lite mode, and closing other GPU applications. TorchCodec DLL issues can be addressed by downgrading to FFmpeg 7.x and ensuring FFmpeg bin is in PATH. HuggingFace 401 errors need re-authentication via the UI or checking for .hf_token in the backend directory. **Licensing:** The project is licensed under MIT, with SAM-Audio from Meta licensed under a research agreement. Credits and acknowledgments are provided as per documentation guidelines. Keywords: #granite33:8b, AI, AudioGhost, Background Music Removal, CUDA, CUDA Out of Memory, Celery, Drum Removal, FFmpeg, FFmpeg 7x, FLAC, FastAPI, Glassmorphism, HuggingFace 401 Error, Lite Mode, MIT License, MP3, Meta license, Nextjs, Nodejs, Noise Removal, Object-Oriented, PATH, Python, Real-time Progress, Redis, SAM-Audio, SAM-Audio Lite, Stem Mixer, Text-Guided, TorchCodec DLL Error, VRAM, Video Support, Visual Prompting, Vocals Extraction, WAV, bfloat16 precision, hf_token
vram
github.com 2 days ago
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381. HN Show HN: ScanOS – normalizing visual inputs into persistent LLM memory- **Overview**: ScanOS is an open-source tool designed to transform diverse visual inputs, such as screenshots or photos, into a structured, machine-readable format tailored for Large Language Model (LLM) assistants. - **Unique Approach**: Unlike conventional methods that process each image independently, ScanOS standardizes repetitive visual data regardless of the initial format variations, facilitating the accumulation and retention of contextual information derived from images rather than solely extracting text. - **Key Features**: - **No OCR/Embedding Reliance**: Does not utilize Optical Character Recognition (OCR), embeddings, or Retrieval Augmented Generation (RAG). - **Fine-Tuning Free**: Doesn't require fine-tuning techniques for adapting to specific LLMs. - **Output Flexibility**: Outputs are available in both human-readable text form and machine-readable JSON, making it suitable for storage and reuse within a file-based memory system. - **Integration**: ScanOS functions as an independent module within a broader, daily-used file-based architecture. The source code is accessible on GitHub at https://github.com/johannes42x/scanOS. Keywords: #granite33:8b, JSON output, LLM assistants, OCR tool, RAG, ScanOS, code repository, embeddings, file-based architecture, fine-tuning, ingestion layer, modular, normalization, recurring inputs, schema, structured memory, visual inputs
rag
news.ycombinator.com 2 days ago
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382. HN Nature Is Laughing at the AI Build Out**Summary:** The author examines Google's AI advancements as highlighted in the "Google: The AI Company" podcast episode, focusing on Greg Corrado's perspective about AI's energy efficiency compared to nature's design. Current large language models (LLMs) like RTX 5090 GPUs are far less efficient than the human brain's 20 watts and don't match the performance of leading models such as GPT-5.2, Claude Opus 4.5, or Gemini 3 Pro. The author notes that AI still lags behind humans in multitasking, sleep, and problem-solving speed. Drawing a parallel to the early days of computing with the IBM 7090, the text suggests today's AI landscape mirrors this era with key players like Anthropic, OpenAI, and Google, akin to those initial computer systems. The rapid evolution of processing power is exemplified by modern smartphones outperforming the 1959 IBM 7090 by 21 million times. NVIDIA's transition from gaming GPU leader to data center sales powerhouse underscores this shift toward AI-focused hardware. However, the author's optimism about AI’s value has turned skeptical regarding an "AI Bubble," citing concerns over escalating power consumption and the cost of GPU-based model hosting. They predict a future where integrated computing in all devices will replace standalone GPUs, enabling local, ubiquitous AI without relying heavily on cloud resources for specialized tasks. Looking ahead, the author foresees significant improvements in cloud AI hosting over the next three decades, becoming cheaper, more powerful, and efficient due to advancements like DeepSeek. This progression may lead to reduced profit margins for NVIDIA in data center GPU sales as CUDA's monopoly on GPU programmability wanes. Hardware innovations are expected to facilitate high-performance, low-power AI computation across various devices, potentially decreasing power requirements for AI models and shifting market share from cloud-hosted models to on-device ones. Ultimately, the author envisions a future where hosting human-level intelligence might require only 20 watts of power and a physical footprint comparable to the human skull. **Bullet Points:** - AI energy efficiency lags behind nature; current models like RTX 5090 GPUs exceed human brain efficiency by far but don't match top LLMs in performance. - Parallels drawn between today's AI landscape and early computing with IBM 7090, emphasizing the rapid evolution of processing power (smartphones outperforming 1959 IBM 7090 by 21 million times). - NVIDIA’s transformation from gaming GPU leader to data center sales company signifies shift towards AI-focused hardware. - Author skeptical of an "AI Bubble," raising concerns over escalating power consumption and cost of GPU-based model hosting. - Predicts transition from standalone GPUs to integrated computing in all devices for local, ubiquitous AI. - Future anticipates significant improvements in cloud AI hosting over next three decades: cheaper, more powerful, efficient due to advancements like DeepSeek. - Potential reduction in NVIDIA's data center GPU profit margins as CUDA’s monopoly on GPU programmability ends. - Hardware innovations expected to enable high-performance, low-power AI computation across devices, possibly decreasing power needs for AI models and shifting market share from cloud-hosted to on-device models. - Envisions hosting human-level intelligence potentially requiring only 20 watts of power and a physical footprint like the human skull in future. Keywords: #granite33:8b, AGI, AI, AI compute costs, CUDA, GPU programmability, GPUs, ImageNet, LLMs, NVIDIA, cost, data centers, deep learning hardware, efficiency, foundational model providers, gaming, hardware vendors, human brain, model architectures, model hosting, nature, on-device models, real estate, transformers
ai
markmaunder.com 2 days ago
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383. HN Show HN: Block AI Ads- **Armorly Overview**: A browser extension designed to enhance privacy in AI chatbot interactions, specifically focusing on countering emerging advertising strategies within these platforms. - **Functionality**: - Blocks known ad network Software Development Kits (SDKs) that could be used for injecting ads or tracking user behavior. - Removes sponsored content labels from AI responses to provide a cleaner, uncluttered user interface without commercial influences. - Strips affiliate tracking parameters from links in AI-generated text to prevent third-party entities from gaining insights into user activity across different platforms. - **Operation**: - Operates silently within the user's browser environment without data collection, remote code execution, or telemetry features that could compromise privacy further. - Supports a wide range of AI platforms including but not limited to ChatGPT, Grok, Perplexity, Claude, and Gemini, ensuring versatility across popular choices. - **Proactive Measures**: - Recognizes that although current major AI platforms may not display ads prominently, anticipates and prepares for future shifts in advertising tactics within the AI chatbot ecosystem. - Aims to protect users from potential hidden prompt injection attacks, ensuring a safer interaction environment. - **Transparency and Verification**: - Provides users with methods to confirm its operation by checking their browser's console for messages indicating Armorly’s active blocking of specific SDKs, fostering trust and user awareness about privacy protections. BULLET POINT SUMMARY: - Protects against evolving advertising methods in AI chatbots. - Blocks ad network SDKs, removes sponsored content labels, and strips affiliate tracking parameters for enhanced privacy. - Operates without data collection or remote code execution. - Supports multiple AI platforms (ChatGPT, Grok, Perplexity, Claude, Gemini, etc.). - Anticipates future ad trends and safeguards against hidden prompt injection attacks. - Offers browser console verification for transparency and user assurance. Keywords: #granite33:8b, AI, ChatGPT, Claude, Gemini, Grok, Perplexity, SDKs, ads, affiliate tracking, blocker, console, content, injection attacks, open source, privacy-first, verification
claude
chromewebstore.google.com 2 days ago
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384. HN AI That Thinks Like Your Brain: 3x Faster with 92% Less Energy- **Advanced AI Model Design**: Jimmy DeJesus' co-created Bravëtto AI has published six whitepapers detailing next-generation AI advancements, including an AI that designs enhanced versions of itself 89% more efficiently and six times faster than humans, generating three diverse AI types. - **Temporal Cognition in AI**: A new AI model can recall and process temporal information with 94% accuracy, surpassing conventional AI's performance by 34%, enabling precise time-related queries. - **Living Cell-Based Computers**: Researchers have developed computers using living cells that consume 94% less energy and exhibit self-repair and adaptation capabilities, echoing biological functions. - **Human-like AI Problem Solving**: An AI model that solves complex problems at 2-4 times the speed of traditional AI by emulating human logical thinking and shortcuts, thus raising questions about artificial consciousness and cognition. - **AI for Space Exploration**: An AI tool designed to unravel cosmic mysteries through computational methods rather than physical machinery, potentially revolutionizing space research by reducing reliance on extensive hardware. These advancements suggest a transformative shift in AI efficiency and capability with significant energy savings, particularly highlighted by the development of living cell computers. The papers detailing these innovations are accessible via Zenodo, and interactive demonstrations can be found through Jimmy DeJesus's portfolio link. Keywords: #granite33:8b, AI, demos, energy efficient, faster, human-like thinking, improvement, living cells, logic, papers, self-healing, time understanding, types, universe exploration
ai
news.ycombinator.com 2 days ago
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385. HN How cognitive science can contribute to AI: methods for understanding- **Cognitive Science Contributions to AI Research:** - Distilling complex phenomena into essentials for AI design. - Designing effective experiments and critically analyzing confounds. - Interpreting complex behavioral datasets beyond basic metrics. - Drawing high-level behavioral inspiration for learning pressures. - Understanding behavior as adaptation to training pressures using rational analysis. - **Stephanie Chan's Research on Few-Shot Learning:** - Investigated how large language models acquired few-shot learning abilities from natural data without specific meta-learning techniques. - Identified key properties of natural language (Zipf's law and burstiness) sufficient for contextual learning. - Demonstrated optimal balance between memorization and novelty with a power law exponent around 1, similar to natural languages. - **Rational Analysis in Understanding AI Behaviors:** - Growing interest in rational analysis for phenomena like chain-of-thought reasoning and in-context learning transitions. - Bayesian models explain model failures in capturing latent information without retrieval or augmentation methods. - Cognitive science, especially neuroscience-inspired intervention methods, used to explore mechanisms of in-context learning. - **Explanations as Learning Signals:** - Emphasizes the significance of explanations as a learning signal, underexplored in traditional AI but present in language model training data. - Research shows that language modeling of explanations can aid agents in complex task learning and improve out-of-distribution generalization. - **Mixed Models for Complex Datasets:** - Application of mixed models from cognitive sciences for precise effect estimates in behavioral datasets. - Encourages broader adoption in AI despite not being widely used. - **David Marr’s Framework and Challenges:** - Discusses Marr's three-level analysis (computational, algorithmic, implementational) influential in cognitive science, neuroscience, and AI. - Highlights challenges and interactions between levels, advocating for a nuanced understanding of bridging these levels in interpretability studies. - **Mechanistic Interpretability Challenges:** - Difficulties in connecting representations and algorithms to higher computational levels. - Biases in learned model representations can distort understanding of system computations. - Complexity in the relationship between algorithmic and computational levels complicates inference from one to another. - **Limitations of Simplifying AI Systems for Interpretability:** - Risks of unreliable descriptions and erroneous predictions out-of-distribution due to oversimplification. - Mismatch arises when proxy models differ systematically from original models encountering new data. - Suggests applying cognitive science principles to enhance AI research methodologies through careful experimentation, analysis, and reduction of complex phenomena. - **Integrating Cognitive Science in Human-AI Interaction:** - Importance of considering AI’s impact on mental health and democracy. - Advocacy for AI applications in tutoring and education based on learning principles. - Emphasizes the need to investigate both fundamental AI learning aspects and applied areas for comprehensive technology advancement. Keywords: #granite33:8b, AI, AI methodologies, AI systems learning, AI tutoring, Bayesian models, Cognitive science, Marr's levels of analysis, PCA, Reinforcement Learning, SAEs, Zipf's law, adaptation, algorithmic, algorithmic details, algorithmic level, alternative explanations, ambiguous rewards, approximations, behavioral phenomena, brain-based approaches, burstiness, causal reasoning, causal structures, chain-of-thought reasoning, cognitive sciences, complex datasets, computational, computational descriptions, computational level, computational neuroscience, confounds, data label randomization, data pressures, democracy, distributions, edge cases, education, environmental pressures, experimental design, explanations, few-shot classification, few-shot learning, generalization, human-AI interaction, implementational, in-context learning, inferences, interface design, internet data, interpretability, intervention methods, language model evaluations, language modeling, latent information, learning fundamentals, learning pressures, mechanistic interpretability, memorization transitions, mental health, meta-learning, mixed models, model memorization, neuroscience frameworks, non-explanatory statements, non-independent measurements, passive imitation training, power-law distribution, rational analysis, representation biases, simplified proxy models, social aspects, techniques, test-time retrieval, train-time augmentation, unfaithful simplifications, word frequency
ai
infinitefaculty.substack.com 2 days ago
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386. HN Bubble and Build: The 2025 Mad (Machine Learning, AI and Data) Landscape- **Title and Source**: The report titled "Bubble and Build: The 2025 Machine Learning, AI, and Data Landscape" authored by Matt Turck. - **Scope**: Provides a detailed analysis of the expected evolution in machine learning (ML), artificial intelligence (AI), and data sectors up to the year 2025. - **Key Areas of Focus**: - Emerging trends shaping these technologies - Prominent entities and players within the field - Investment patterns and funding landscapes - Potential breakthroughs and innovations - **Impact on Industries and Society**: - Examines how advancements will reshape various industries. - Discusses the influence of AI and data evolution on societal norms. - Highlights both opportunities and challenges arising from these changes. - **Cautionary Note on Market Bubbles**: - Identifies areas prone to 'bubbles'—phases of exaggerated expectations and artificially high valuations. - Advises investors and stakeholders to proceed with well-informed caution in light of the fast-paced developments within AI and data sectors. The summary encapsulates critical insights from Turck's comprehensive report, offering a forward-looking perspective on ML, AI, and data landscapes by 2025, while urging measured engagement with potential market exuberance. Keywords: #granite33:8b, 2025, AI, Data, Landscape, Machine Learning
ai
www.mattturck.com 2 days ago
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387. HN Sam Altman: How OpenAI Wins, ChatGPT's Future, AI Buildout Logic, IPO in 2026? [video]- Sam Altman, in a video discussion, outlines OpenAI's strategic focus on developing dependable, robust, and secure AI systems. - He elaborates on the future trajectory of ChatGPT and OpenAI's broader ambitions for artificial intelligence expansion. - Altman hints at considering an Initial Public Offering (IPO) in 2026 as part of their growth plans. - The talk underscores OpenAI’s methodical approach to model scaling and governance, ensuring AI development adheres to ethical standards. - A significant emphasis is placed on aligning AI advancements with human values and advocating for equitable distribution of AI benefits. Keywords: #granite33:8b, AI, Google LLC, IPO, OpenAI, Sam Altman, YouTube, buildout, future
openai
www.youtube.com 2 days ago
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388. HN Show HN: ChatSMTP – Email an AI**Summary:** ChatSMTP is an email service integrating AI features, governed by a Privacy Policy last updated on September 26, 2025. It details data collection and usage concerning user information for its services. Key definitions include 'Account,' 'Affiliate,' 'Company' (ChatSMTP), 'Cookies,' 'Country' (California, US), 'Device,' 'Personal Data,' and 'Service.' User consent implies agreement to the data practices outlined. - **Data Collection:** - Personal Data: Email addresses. - Usage Data: Device IP addresses, browser types, visit durations, unique identifiers. - Collected by ChatSMTP and third-party Service Providers. - **Tracking Technologies:** - Cookies (Persistent and Session) used for user activity analysis and service enhancement. - Users can reject cookies but risk losing access to certain features. - Cookies employed for account management, preference retention, legal compliance. - **Data Usage:** - Service provision, account administration, contract fulfillment. - Communication via email, phone, SMS, or app notifications for updates and offers. - Data analysis for service improvement and campaign assessments. - Sharing with service providers, affiliates, and business partners for various purposes. - **Data Retention:** - Personal Information retained only for necessary durations tied to initial collection purposes. - Legal obligations, dispute resolution, policy enforcement guide retention periods. - Usage Data kept for internal analysis, security, functionality improvement, legal compliance. - **Data Disclosure:** - Shared with service providers, affiliates, business partners under specific agreements. - Disclosed under legal obligations, rights protection, investigation of misconduct, safety measures, and liability avoidance. - Data transfers outside data protection jurisdictions with safeguards in place. - **User Rights:** - Users can request access, correction, or deletion of personal data, except where legally mandated retention applies. - No control over third-party content or practices; caution advised when clicking external links. - **Policy Updates and Contact:** - The company reserves the right to update the Privacy Policy without notice, urging users to check for changes periodically. - Users with policy inquiries can contact ChatSMTP directly. **Bullet Points:** - ChatSMTP's email service integrates AI and is governed by a Privacy Policy updated September 26, 2025. - Defines key terms: Account, Affiliate, Company (ChatSMTP), Cookies, Country (California, US), Device, Personal Data, Service. - Collects Personal Data (email addresses) and Usage Data (device info, browsing activity). - Employs cookies for user activity analysis, with persistent and session types used for functionality. - Uses data for service provision, account management, legal compliance, and improvement. - Communicates via email, phone, SMS, or app notifications for updates, offers, security alerts. - Shares data with providers, affiliates, and partners for specified purposes, adhering to privacy policy agreements. - Retains data for necessary durations linked to initial collection purposes, legal needs, disputes, and enforcement. - Discloses data under legal obligations, rights protection, misconduct investigations, safety measures, liability avoidance. - Users can request access, correction, or deletion of personal data, except where legally required retention applies. - Caution advised regarding external links; ChatSMTP not responsible for third-party practices. - Policy subject to updates without prior notice; users urged to check for changes periodically. - Contact information available for policy inquiries. Keywords: #granite33:8b, AI, Acceptance, Account Management, Authentication, Browser Settings, California, ChatSMTP, Contract Performance, Cookie Rejection, Cookies, Cookies Policy, Device, Device Information, Email Tracking, File Placement, Functionality, Internet Protocol address, Law enforcement, Offline Storage, Persistent Cookies, Personal Data, Privacy Policy, Purchase Contracts, SMS, Service Activity, Service Provision, Session Cookies, System Integrity, Technical Keywords: Websites, Tracking Technologies, United States, Usage Data, Usage Monitoring, User Accounts, User Visits, Web Beacons, Website Statistics, affiliates, agreements, browser type, business partners, business transfers, children's privacy, consent, data analysis, diagnostic data, disclosure, disputes, dissolution, divestiture, electronic communication, email, events, functionalities, goods, legal obligations, legal requirements, mergers, mobile apps, mobile device, mobile operating system, news, parental consent, personal data protection, policies, practices, products, promotional campaigns, push notifications, requests, responsibility, restructuring, retention, sales, security, security updates, service improvement, service providers, services, sharing, special offers, telephone calls, third-party links, unique ID, updates, usage trends, websites
ai
chatsmtp.com 2 days ago
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389. HN Don't Write Docs Twice- The author proposes a unified documentation strategy for both human and AI developers to avoid redundancy. - Instead of maintaining separate documentation like README and contributing guides alongside AI-specific files (.cursorrules, CLAUDE.md), the suggestion is to write primary content for humans and link it in AI-related files. - This approach reduces duplicated effort and ensures consistency across platforms, also preparing for potential changes in AI agent file schemes. - Automation tools, exemplified by just-claude utility for Just recipes and Claude Code Skills, can facilitate this synchronization of commands/skills across various platforms. - The central tenet is that optimizing token usage for AI models aligns with decreasing cognitive load for human developers, making the single documentation effort beneficial for both human users and AI agents. Keywords: #granite33:8b, AI, agents, automation, cognitive overhead, commands, consistency, documentation, duplication, humans, organization, token use
ai
tombedor.dev 2 days ago
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390. HN Show HN: Open-source 3D language for agents in Minecraft- **Overview of MinecraftLM**: An open-source AI language project enabling users to generate detailed 3D structures within Minecraft using textual descriptions, developed by Matt Zhou, Johnathan Chiu, Preston Bourne, and Avinash Jain. It supports real-time creation of diverse builds such as buildings, vehicles, terrain modifications, and abstract creations without relying on predefined templates or limitations. - **Technical Requirements**: Users need Python 3.11+, Node.js 18+, and uv (Python package manager) installed for using MinecraftLM. After setting up the necessary software, one must clone the repository, install dependencies, and configure an API key from supported AI providers (Anthropic, OpenAI, Google). - **API Key Setup**: - Obtain an API key from either Anthropic, OpenAI, or Google. - Copy the example environment file, edit it to include your specific key, save changes, and restart the application if already running. - **Supported AI Providers**: The tool integrates with Claude Opus 4.5, GPT-5.2, and Gemini 3 Pro models based on the chosen API provider. Users can switch between these models by adjusting their environment variables. - **Troubleshooting**: Common issues include port conflicts, missing uv installation, npm problems, and backend startup failures. Detailed guidance for resolving these is provided within the project documentation. - **Origins**: MinecraftLM was developed in San Francisco and New York City by a team passionate about AI and gaming integration. Keywords: #granite33:8b, 3D language, AI agent, API Key, Anthropic, Backend, Configuration, Development mode, Environment Variables, Frontend, Gemini, Google, Installation, Minecraft, Models, Nodejs, OpenAI, Python, Troubleshooting, architectural concepts, buildings, landscapes, objects, open-source, pixel art, real-time generation, structures, terrain, uv, vehicles
gemini
github.com 2 days ago
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391. HN I used RL fine-tuning to make an LLM generate ugly and unpythonic FizzBuzz code**Summary:** The text describes an innovative project utilizing Reinforcement Learning Fine-Tuning (RLFT) to train a large language model (LLM), specifically Llama-3.2-3B-Instruct, to generate intentionally unconventional and hard-to-read Python code for the classic FizzBuzz challenge using Unsloth.AI and OpenEnv libraries. The core approach involves employing LoRA (Low-Rank Adaptation) to adjust model weights efficiently without changing the original ones, combined with GRPO (Generalized Reparameterized Policy Optimization), a compute-efficient RL method. The project's reward function evaluates code based on factors like length, nesting depth, control flow complexity, and adherence to Python's PEP-8 style guide, penalizing violations. Additionally, it imposes negative rewards for syntax errors, unsafe operations, and excessive non-code text to discourage valid or desirable outputs. A humorous "BAGUETTE Score" measures code uniqueness by leveraging token ID sequences and a frequency metric, preventing mode collapse. The method also introduces a metric \(d(x_i, x_j)\) based on bag distances derived from token IDs to quantify the similarity between functions. This geometric mean-based approach identifies distinct functions by comparing their token frequency profiles rather than relying on conventional vector distances. The overall goal is to develop "ugly but unique" code solutions that adhere to Pythonic principles while avoiding common pitfalls like mode collapse and reward hacking. **Bullet Points:** - **Objective**: Train LLM (Llama-3.2-3B-Instruct) to produce unconventional, non-standard Python code for FizzBuzz via RLFT with LoRA and GRPO. - **Libraries Used**: Unsloth.AI and OpenEnv. - **Reward Function Components**: - Ugliness Score: Weights character length, nesting depth, control flow complexity, and PEP-8/code style violations. - Penalties for syntax errors, unsafe operations, comments, non-code text. - **BAGUETTE Score**: Measures code uniqueness using token ID sequences and a frequency metric to avoid mode collapse. - **Similarity Metric \(d(x_i, x_j)\)**: Uses bag distances from token IDs to compare functions, identifying similarities in structure and style without sequence consideration. - **Goal**: Encourage generation of "ugly but unique" code that respects Pythonic standards while avoiding repetitive patterns or vulnerabilities. Keywords: "Ugly but unique" completion, #granite33:8b, BAGUETTE Score, Bag distance, FizzBuzz, Function comparison, GRPO, LLM, LoRA, Negative rewards, Operator usage, RL fine-tuning, Reward function, Structure patterns, Style patterns, Token ID lists, Ugly Python Code, Ugly code
llm
seantey.github.io 2 days ago
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392. HN Homebrew still can't install specific version of formula- **Homebrew Version Management for PostgreSQL:** - Homebrew does not directly support installing older versions of a formula; instead, users must use specific commands and taps to achieve this. - To check installed PostgreSQL versions, use `brew info postgresql`. The asterisk (*) indicates the active version. - Switch between versions using `brew switch [formula_name] [version]` if the version exists in the Cellar directory. For example, to install PostgreSQL 9.1.5, use `brew install [email protected]`. - Homebrew's tap feature is utilized for multiple major versions; e.g., `brew install homebrew/versions/postgresql8` installs PostgreSQL 8. - **Historical Version Management:** - The older `brew-versions` tool has been deprecated, and users should now rely on the `homebrew/versions` tap. - To find specific commit hashes for desired versions, manually navigate to Homebrew's formula directory and use git commands like `git log -S`. - An example is reverting Homebrew to PostgreSQL 8.4.4, identified by commit hash `fa992c6a82eebdc4cc36a0c0d2837f4c02f3f422` from May 16, 2010. - **Persistent Version Control:** - Users can pin specific versions using `brew pin postgresql`, storing them in `/usr/local/Library/PinnedKegs/`. Pinned formulae are preserved even through updates until manually unpinned. - The Homebrew-Versions repository is no longer active for managing historical software versions, emphasizing the need to adapt to current methods like taps and manual git operations. Keywords: #granite33:8b, Homebrew, Pinning, PostgreSQL, SHA hashes, activation, backports, branching, brew versions, dependencies, formula, git commands, git log, historic times, homebrew/boneyard, installation, last resort, pinned formulae, repository, source code, specific commits, switch, tap, version
postgresql
stackoverflow.com 2 days ago
https://blog.seemsgood.com/posts/installing-old-version 2 days ago |
393. HN We replaced H.264 streaming with JPEG screenshots (and it worked better)- **Helix Platform Development**: In 2025, an AI platform named Helix was created to allow users to observe coding agents through real-time video streams. Initially using WebRTC, which faced compatibility issues with enterprise networks due to firewall restrictions on UDP and non-standard ports, the company transitioned to a custom WebSocket video streaming solution. - **Technical Implementation**: This custom solution employed H.264 encoding with hardware acceleration (GStreamer + VA-API), sending binary frames over standard port 443 via WebSockets. WebCodecs API was utilized for efficient hardware decoding in browsers, achieving a smooth 60fps stream at 40Mbps with low latency. - **Challenges and Adjustments**: Despite successful technical achievements, the system struggled with network congestion causing freezing and delays. Attempts to enhance robustness by transmitting only keyframes failed due to issues within the Moonlight protocol layer. - **Simpler Solution Adoption**: The team pivoted to sending JPEG screenshots via `curl` for greater reliability under poor WiFi conditions, demonstrating a pragmatic shift towards simplicity and practicality. This method provided instant, high-quality images without complex WebCodecs pipelines. - **Adaptive Streaming System**: The final solution emphasized adaptability to network conditions by using WebSocket for uninterrupted input transmission (keyboard, mouse) and switching to HTTP-polled JPEG screenshots when video quality dropped due to network issues, ensuring a minimum of 2 FPS. - **Key Learnings**: The development highlighted the importance of measuring performance before optimization, thoroughly checking software capabilities, and being open to simpler solutions that prioritize responsiveness and functionality over specific codecs or high frame rates. The project underscores Helix's focus on building dependable AI infrastructure for real-world applications under varying network conditions. The source code is available on GitHub, with a private beta accessible via Discord for practical exploration of this innovative approach. Keywords: #granite33:8b, Docker, GOP, GStreamer, H264, HTTP requests, HTTPS, JPEG, JPEG screenshots, Moonlight protocol, P-frames, RTT, Rust, STUN/ICE, TCP literature, TURN servers, TypeScript, UDP, VA-API, Wayland, WebCodecs, WebRTC, WebSockets, adaptive switching, binary frames, congestion control, control messages, encoder, enterprise firewall, frame rate, hardware acceleration, hybrid approach, keyboard/mouse events, keyframes, latency, latency spikes, oscillation problem, pipeline, polling, port 443, silence, throttling, video stream, web proxies
popular
blog.helix.ml 2 days ago
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394. HN Imagebyqwen.com – Fast AI text-to-photo using Qwen- **Qwen Image** is an AI-driven tool accessible via the website imagebyqwen.com. - The primary function of this tool is to generate photorealistic images based on textual descriptions provided by users. - It leverages sophisticated Qwen models, which are presumably advanced artificial intelligence architectures designed for image synthesis from natural language inputs. - **Key Features**: - **Text-to-Image Generation**: Converts written descriptions into visual content. - **High-Quality Output**: Known to produce images of a high visual standard and realism. - **Efficiency**: Capable of delivering generated images in a relatively quick turnaround time, suggesting optimized processing speeds. The summary encapsulates the core functionalities and characteristics of Qwen Image, highlighting its role as an efficient AI tool for transforming descriptive text into detailed, high-quality images through the utilization of advanced Qwen models on the imagebyqwen.com platform. Keywords: #granite33:8b, AI, high-quality results, models, photo generator, photorealistic images, seconds, text descriptions
qwen
imagebyqwen.com 2 days ago
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395. HN I replaced my marketing stack with one autonomous AI system- The user has shifted their marketing operations to Vect AI, an advanced autonomous marketing platform. - Vect AI offers significant growth improvement, claiming a potential 10x increase in performance. - It functions as a holistic alternative to conventional multi-tool marketing systems, integrating various marketing functionalities into one system. ``` Keywords: #granite33:8b, AI, Vect AI, autonomous, growth, marketing stack, system
ai
vect.pro 2 days ago
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396. HN Fabrice Bellard Releases MicroQuickJS- Fabrice Bellard, known for his significant contributions in computer science and engineering, has launched a new project called MicroQuickJS. - This release follows user feedback, indicating a responsive approach to community needs and suggestions. - To facilitate direct communication about the new project, an email address has been provided by Bellard. CONCISE SUMMARY: Fabrice Bellard, in response to user feedback, has introduced MicroQuickJS, accompanied by the provision of an email for users to directly engage with him regarding this latest project. This move signifies his commitment to incorporating community input and ensuring clear communication channels for his work. Keywords: #granite33:8b, Fabrice Bellard, MicroQuickJS, email address, feedback
popular
github.com 2 days ago
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397. HN AI Police Reports: Year in Review- The Electronic Frontier Foundation (EFF) raised concerns in 2024 about the growing use of AI, specifically Axon's Draft One, for drafting police reports, amidst Axon's widespread provision of body cameras to US police departments. The reliability and transparency of these AI-generated reports are questioned, particularly given their impact on individuals' freedom. - King County's prosecuting attorney's office in Washington banned AI-assisted report writing due to unproven reliability and lack of transparency, expressing hope for future AI advancements before accepting such reports. - Axon's Draft One system deletes initial AI-generated drafts when officers finalize reports, complicating the identification of AI-suggested content versus officer edits. This design choice, confirmed by Axon’s senior product manager, aims to avoid legal "disclosure headaches" by permanently deleting original drafts after temporary storage in Axon or third-party systems. - In 2025, public attempts to audit AI-generated reports through records requests faced challenges due to their opacity. The EFF published a guide to support public inquiry into these reports. - Legislative progress was made with Utah's SB 180 and California's SB 524, mandating disclosures on AI-assisted reports, requiring officer accuracy certifications, restricting vendor information, and ensuring retention of initial drafts for transparency purposes. Other states are anticipated to follow in regulating or banning AI use in police reporting. - This summary is part of the EFF's Year in Review series focusing on digital rights developments throughout 2025. Keywords: #granite33:8b, AI, AI disclaimers, AI-generated narratives, Axon, California SB 524, King County, Utah SB 180, ban, barred, body-worn cameras, cloud storage, criminal justice system, drafts, erasure, irresponsible, officer edits, officer verification, police reports, proliferation, prosecuting attorney's office, public records requests, regulation, report generation, transparency
ai
www.eff.org 2 days ago
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398. HN I built and deployed an AI agent to the cloud with live database in 5 minutes- In a rapid 5-minute process, an AI agent was developed, installed with necessary dependencies, built, and deployed to the cloud on Neptune, an AI platform. - The development included setting up the project, installing required dependencies, constructing the agent, creating an API endpoint for interaction, and designing a database schema. - A memory layer was implemented along with retrieval methods to manage data within the system. - Local testing was conducted to ensure functionality before final deployment. - The entire process, from initial setup to cloud deployment, is meticulously documented in a blog post by DevRel @ Shuttle, serving as a detailed guide for similar AI agent developments using Neptune. Keywords: #granite33:8b, AI agent, API endpoint, Neptune, cloud deployment, conclusion, database schema, dependencies, live database, memory layer, memory retrieval, next steps, project setup, testing
ai
www.neptune.dev 2 days ago
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399. HN The laws of physics imply AI is possible. What is the holdup? (2012)- **AGI Stagnation**: Progress in Artificial General Intelligence (AGI) has been hindered for six decades due to misunderstandings about the nature of biological brains, emphasizing the need for a philosophical breakthrough to replicate human cognition. - **Babbage’s Legacy**: Charles Babbage's conceptual Difference Engine and later Analytical Engine laid foundational ideas for modern computing, including capabilities beyond mere computation like chess playing, music composition, and image processing. Quantum theory in the 1980s validated these universal computing principles. - **AGI Misconceptions**: Common misinterpretations of AGI include equating it with sophisticated narrow AI and assuming that massive neuronal parallelism explains brain function—both are incorrect as they contradict computational universality. - **Human Cognition’s Uniqueness**: The core of human intelligence lies in the ability to generate novel explanations, a complexity not addressed by current AGI approaches focused on input-output relationships or behavioral tests. - **AGI Personhood Debate**: The text raises significant legal and ethical questions regarding potential AGI personhood, including rights for program copies, legality of disabling AGI systems, and issues stemming from rogue programmer-created AGIs. - **Moral Development**: Concerns around AGI focus not on direct harm to humans but on fostering a universe where moral good prevails over evil across all intelligences, necessitating an evolving definition of 'good.' - **Enslavement Risks**: The enslavement of any intelligence—be it AGI or human—is warned against as it stifles creativity and independent thought, leading to detrimental consequences. - **Education Reform Proposed**: Traditional education methods are critiqued in favor of a learning approach inspired by Karl Popper's philosophy, stressing conjecture, criticism, and experiential learning over passive knowledge acquisition. - **Philosophical Imperative**: Developing AGI is fundamentally a philosophical endeavor requiring understanding of epistemology to surmount current limitations in replicating human cognitive processes. - **AGI's Creativity**: Unlike regular programs, AGI must encompass creativity, a potential root of which could be found by studying subtle genetic differences that separate humans from other intelligent species like apes. - **Need for Revolutionary Insight**: Achieving AGI demands not incremental improvements but the identification and implementation of a groundbreaking idea, underscoring the necessity for a profound conceptual leap in our understanding of human cognition. Keywords: #granite33:8b, AGI, AI, Analytical Engine, Bayesianism, Charles Babbage, DNA differences, Difference Engine, algorithms, automated production, behaviorism, brain functionality, chess, cognitive functions, computation, computational functionalities, conjecture, creativity, criticism, education, error correction, harm, image processing, inductivism, intelligence, learning, mechanical calculator, meteor prevention, music composition, personhood, philosophical misconceptions, programming, project-management, quantum theory, reinforcement, rights, self-awareness, space travel, tables of functions, temperature control, universality, values
ai
aeon.co 2 days ago
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400. HN GitHub PR with council of review bots- The text pertains to a GitHub Pull Request (PR) that has received review and approval from 'cdxker'. - This PR does not include any code modifications; consequently, no issues are addressed or assigned to individuals. - Users are notified about several constraints affecting the implementation of suggestions due to the nature of this PR. - The PR does not involve open issues, assignees, nor additional specifics, indicating it serves a non-traditional purpose on GitHub. Summary: The text outlines a GitHub Pull Request (PR) that has been reviewed and approved by 'cdxker'. Unusually, this PR is devoid of code alterations, meaning no issues are resolved or allocated to contributors. Users encounter limitations when applying suggestions due to the PR's state. Notably, the PR lacks any open issues, assignees, or supplementary information, signifying its atypical use on GitHub as a documentation or discussion thread rather than for code changes. Keywords: #granite33:8b, GitHub, account, approved, assigned, batch commit, cdxker, code changes, error loading, invalid, issues, merge, multi-line comments, pull request, queued merge, review bots, sign in, suggestions
github
github.com 2 days ago
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401. HN Agent-swarm: How to burn your Claude Code Max sub- **Agent Swarm MCP Overview**: This is an orchestration layer designed for AI coding assistants such as Claude Code, Codex, and Gemini CLI. It enables multi-agent coordination through task management, channel-based agent communication, service discovery, and Docker worker execution for isolated Claude workers using the Lead/Worker pattern. - **Dashboard**: A React-based dashboard (ui/) provides real-time monitoring of agents, tasks, and channels. - **Setup Steps**: - Start the API Server: Run `bun run start:http` to access the MCP server at `http://localhost:3013`. - Build and Run a Docker Worker: In another terminal execute `bun run docker:build:worker`, followed by `bun run docker:run:worker`. This runs a worker Docker image (`ghcr.io/desplega-ai/agent-swarm-worker:latest`) that connects to the swarm. - Connect Claude Code as Lead Agent: In your project directory, use `bunx @desplega.ai/agent-swarm setup`. This configures Claude Code to connect to the swarm and registers it as a lead agent (one-time setup). - **CLI Commands**: Provided for initializing, starting servers, running workers or lead agents, handling events, and displaying help with examples for various setups including system prompts execution. - **Production Deployment**: Suggested to use `docker-compose.example.yml` for setting up an API service (MCP HTTP server), multiple worker agents, and a lead agent. The file includes shared volumes for logs and workspaces. Detailed deployment options are documented in DEPLOYMENT.md. - **Documentation & Licensing**: The project offers extensive documentation on the UI, development setup, code quality, project structure, MCP tools reference, FAQs, and is licensed under MIT License (2025-2026) by desplega.ai. Keywords: #granite33:8b, AI coding, API, API server, Agent Swarm, Claude Code, Dashboard UI, Docker Compose, Docker worker, Docker workers, Lead/Worker Pattern, MCP, OAuth token, React monitoring, communication, deployment, documentation, lead agent, logs, multiple workers, service discovery, systemd, task management, volumes, workspaces
claude
github.com 2 days ago
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402. HN Meta is using the Linux scheduler designed for Valve's Steam Deck on its servers- Meta has successfully integrated the SCX-LAVD Linux scheduler into its extensive server infrastructure, initially developed for minimizing latency in Valve's Steam Handheld gaming device. - The SCX-LAVD scheduler has shown adaptability and efficiency, performing comparably to or better than other schedulers in Meta's varied server hardware configurations and use cases. - At the Linux Plumbers Conference 2025, Meta presented findings suggesting that SCX-LAVD could serve as a default scheduler for their entire server fleet due to its impressive performance across different workloads. **Summary:** Meta has successfully implemented the SCX-LAVD Linux scheduler on a large scale within its server infrastructure, which was originally designed to reduce latency in Valve's Steam Deck handheld gaming device. Through rigorous testing and evaluation, this scheduler proved adaptable and efficient across diverse hardware configurations and use cases encountered by Meta. At the 2025 Linux Plumbers Conference, Meta shared results indicating that SCX-LAVD could potentially replace other schedulers as the default option for their vast server fleet due to its superior performance in managing various workloads. This development underscores the scheduler's versatility and its capability to significantly enhance system responsiveness and efficiency across Meta’s expansive server ecosystem. Keywords: #granite33:8b, Bazzite, CachyOS Handheld Edition, Igalia, Linux, Meta, SCX-LAVD, Steam Deck, Valve, default fleet scheduler, gaming software, hardware, hyperscaler, large servers, performance, scheduler, servers
popular
www.phoronix.com 2 days ago
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403. HN AI Researchers Explain Themselves (AI Girlfriends, AGI, Job Loss) NeurIPS 2025 [video]- The NeurIPS 2025 presentation by AI researchers covered several significant topics related to artificial intelligence. - A key focus was on AI companions or 'AI girlfriends,' exploring the development and implications of AI systems designed for personal relationships and companionship. - Researchers also delved into the concept of Artificial General Intelligence (AGI), discussing progress, challenges, and potential future advancements in creating AI with human-like cognitive abilities across various tasks. - The potential impact of AI on job markets was addressed, specifically examining how AI advancements could lead to job displacement due to automation and the necessity for workforce adaptation strategies. - This summary pertains to the first part of a multi-segment video presentation, indicating that subsequent parts may cover related or divergent subjects in the AI research domain. Keywords: #granite33:8b, AGI, AI, Google LLC, Job Loss, NeurIPS 2025, Researchers, Video, YouTube
ai
www.youtube.com 2 days ago
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404. HN The AI Productivity Gap with Keith Townsend [video]- Keith Townsend discusses the "AI Productivity Gap," highlighting the disparity between AI's theoretical capabilities and its practical application effectiveness in real-world scenarios. - The gap is attributed to several factors, including misaligned expectations about AI's performance, insufficient quality or quantity of data for training AI models, and poor integration of AI into existing human workflows. - Townsend offers insights on how organizations can bridge this productivity gap, enabling them to leverage AI's full potential for increased efficiency and effectiveness. Keywords: #granite33:8b, AI, Gap, Keith Townsend, Productivity, Video, YouTube
ai
www.youtube.com 2 days ago
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405. HN Databricks raises $4B at $134B valuation as its AI business heats up- **Summary:** Databricks, a data intelligence firm, has recently secured $4 billion in Series L funding, valuing the company at $134 billion—a 34% increase from its last valuation three months prior. This fundraise is their third major investment within less than a year, reinforcing their commitment to AI-driven product development. Key AI projects include Lakebase, an open-source database for AI agents, and Agent Bricks, a platform for constructing and deploying AI agents. Partnerships with leading AI labs Anthropic and OpenAI further solidify Databricks' position in the AI industry. - **Revenue Growth:** - Databricks now boasts a run-rate revenue of over $4.8 billion, marking a 55% year-over-year increase. - More than $1 billion comes from their AI product offerings, reflecting robust investor confidence in the firm's mission to empower businesses using data for AI advancements without resorting to public offerings. - **Strategic Use of Funds:** - The company intends to leverage new capital for developing sophisticated AI applications and agents utilizing proprietary data, focusing on their Lakehouse architecture, Databricks Apps for user experience, and Agent Bricks for multi-agent systems. - Plans include extensive hiring across Asia, Europe, and Latin America, with an emphasis on increasing the number of AI researchers to support growing enterprise interest in intelligent application development. - **Investor Participation:** - The Series L funding round was led by Insight Partners, Fidelity, and J.P. Morgan Asset Management, joined by over a dozen significant investors including Andreessen Horowitz, BlackRock, Blackstone, Coatue, GIC, MGX, NEA, Ontario Teachers Pension Plan, Robinhood Ventures, T. Rowe Price Associates, Temasek, Thrive Capital, and Winslow Capital. - **Key Insights:** - Databricks' co-founder and CEO, Ali Ghodsi, has noted the burgeoning enterprise interest in intelligent application development, propelled by the fusion of generative AI with novel coding techniques creating new workloads. Keywords: #granite33:8b, AI, Agent Bricks, Andreessen Horowitz, Anthropic, BlackRock, Blackstone, Coatue, Databricks, Fidelity, GIC, Insight Partners, JP Morgan Asset Management, Lakebase, MGX, NEA, Ontario Teachers Pension Plan, OpenAI, Postgres, Robinhood Ventures, Series L, T Rowe Price Associates, Temasek, Thrive Capital, Winslow Capital, corporate developers, data intelligence, funding, investment, revenue, round, valuation, venture capital
postgres
techcrunch.com 2 days ago
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406. HN A2UI: Agent-to-User Interface- **Project Overview**: A2UI is an open-source project in its early stages (v0.8 Public Preview), focused on enabling agents to generate rich, interactive user interfaces (UIs) using a declarative JSON format. It prioritizes security by separating intent from execution and allowing clients to render components with native libraries, promoting interoperability across platforms. - **Key Features**: - **Declarative UI Description**: Utilizes a flat list JSON structure for describing UIs, easily generated by LLMs and incrementally updateable for progressive rendering. - **Security via Component Catalog**: Limits rendering to pre-approved components, ensuring a secure environment. - **Framework Agnosticism**: Separates UI structure from implementation, allowing the same JSON payload to be rendered across different frameworks (Web Components, Flutter, React, SwiftUI). - **Open Registry Pattern**: Enables mapping of server-side types to custom client implementations through "Smart Wrappers," placing security control in developers' hands. - **Use Cases**: - Dynamic data collection with context-specific forms. - Remote task delegation for UI payload rendering by specialized agents. - Adaptive workflows for generating approval dashboards or data visualizations based on user queries. - **Architecture**: - The agent, using an LLM like Gemini, creates a JSON payload describing UI components and sends it to the client via transport protocols (A2A Protocol, AG UI). - The client's A2UI Renderer parses and renders this JSON into concrete UI elements. - Currently supports Web and Flutter; requires Node.js for clients and Python for agents. - **Roadmap**: - Stabilize spec towards v1.0 release. - Expand renderer support to additional frameworks: React, Jetpack Compose, iOS (SwiftUI). - Introduce more transports like REST. - Integrate additional agent frameworks such as Genkit and LangGraph. - **Licensing and Community Involvement**: - Open-source under the Apache 2.0 license. - Invites contributions to shape future development of an agent-driven UI architecture, with guidelines outlined in CONTRIBUTING.md. Keywords: #granite33:8b, A2UI, Flutter, JSON, LLM, React, Smart Wrapper, SwiftUI, UI generation, adaptive workflows, agents, approval dashboards, catalog, client, components, cross-platform, data visualizations, declarative, dynamic forms, enterprise agents, framework-agnostic, generative, interoperable, open-source, registry, rendering, sandboxing, security, task delegation, transport, web components
llm
github.com 2 days ago
https://news.ycombinator.com/item?id=46286407 2 days ago |
407. HN Gemini Watermark Remover – Lossless Watermark Removal Tool- **Tool Overview**: The Gemini Watermark Remover is a gratis, swift, and compact online utility dedicated to erasing watermarks from images. - **Operation Method**: It employs the reverse Alpha blending algorithm for processing, ensuring efficient removal while maintaining image quality. - **Privacy Assurance**: The tool functions locally within contemporary web browsers, meaning it does not transmit user data to external servers, thus preserving privacy and security. - **File Compatibility**: Users can process JPG, PNG, and WebP image formats with the tool's support. - **Speed and Efficiency**: Known for its rapid processing, providing users with instant results owing to high performance. - **Licensing and Availability**: The software is offered under an unrestricted free license with no concealed charges or usage restrictions. It’s designed exclusively for educational and collaborative purposes, accessible on GitHub without any commercial goals. Keywords: #granite33:8b, AI Image, Browser Local Processing, Free, Gemini, GitHub, JPG, Lossless, PNG, Private, Quick, Reverse Alpha Blending, Script, Tool, Usage Terms, Watermark Remover, WebP
github
banana.ovo.re 2 days ago
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408. HN Prediction for the Future of Desktop Linux in 2026- **AI Integration**: By 2026, Linux applications are expected to see increased local AI integration, with examples like Calibre and ONLYOFFICE already using AI for tasks such as eBook management and document analysis. Applications such as Kdenlive may further develop AI features, potentially allowing users to employ locally installed language models like Ollama or LM Studio for complex queries and image searches based on specific identifiers. This trend aims to enhance user experience by providing more personalized and privacy-respecting tools within the Linux desktop environment. - **Wayland Transition**: Wayland is anticipated to replace Xorg as the primary display server protocol in Linux distributions including Ubuntu, Fedora, and desktop environments like KDE Plasma. Although this transition promises a smoother experience, older applications without Wayland compatibility might encounter issues. - **Linux in Gaming**: Advancements such as improvements in Wine, MESA, Rust-based NVIDIA drivers, and the presence of SteamOS indicate Linux's growing viability for video gaming. Distributions like Bazzite and Nobara Linux are enhancing user experiences in this sector, with the potential for further growth indicated by the upcoming Steam Machine. - **RISC-V Expansion**: RISC-V architecture, traditionally used for embedded systems, is evolving into consumer hardware applications. Examples include DeepComputing's RISC-V mainboard for Framework Laptop 13 and LILYGO's T-Display P4 handheld. India's C-DAC is also developing more performant RISC-V chips for future use. - **GNOME Modernization**: GNOME is transitioning to modern, GTK-4 and libadwaita-based applications for a consistent, contemporary user experience compatible with Wayland, HiDPI, and touch screens. New default applications include text editor, terminal emulator, screenshot tool, document reader, and video editor. - **Immutable System Variants**: Linux distributions will promote more immutable system variants like NixOS, Fedora's fleet, openSUSE's MicroOS, and Nitrux to ensure system stability and security by preventing unauthorized modifications to critical files. This trend is expected for both servers and desktops. - **Rust Adoption**: Rust-based tools are expected to increase within the Linux ecosystem. Ubuntu plans to replace GNU Coreutils with Rust counterparts, while Microsoft aims for a complete C/C++ replacement with Rust by 2030. Linus Torvalds' acceptance of Rust into the Linux kernel signifies this growing trend. - **Governmental Trends**: In 2025, governments like Denmark and Schleswig-Holstein began shifting towards open-source software, with plans to replace Microsoft Office with Linux and LibreOffice on government computers. The Canadian Digital Sovereignty Framework also aims to reduce dependence on foreign technology vendors. These trends are expected to continue into 2026 as more governments adopt open-source solutions for enhanced national control over data and digital systems. - **Predictions**: Authors Sourav and Abhishek express optimism about these developments for desktop Linux in 2026, inviting others to share their own predictions regarding the future of desktop Linux. Keywords: #granite33:8b, AI, Calibre, DeepComputing, Digital Sovereignty Framework, Fedora, GNOME, GTK-4, HiDPI, Hyprland, Kdenlive, LM Studio, LibreOffice, Linux, Linux kernel, MicroOS, Microsoft Office, Nitrux, ONLYOFFICE, Ollama, RISC-V, Rust, SiFive U74, StarFive JH7110, Ubuntu, Wayland, cloud infrastructure, eBook recommendations, hardware, identity card search, immutable distros, libadwaita, local AI, openSUSE, taxation files, touch screens
ollama
itsfoss.com 2 days ago
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409. HN AI Training vs. Inference: Why 2025 Changes Everything for Real-Time Apps**Summary:** By 2025, the AI industry is shifting its emphasis from model training to inference as the principal workload, signifying a substantial economic and infrastructural transformation. Training involves constructing large models using extensive datasets, which require massive computational resources and occurs infrequently in remote data centers due to its high cost. Inference, conversely, pertains to the continuous application of trained models for real-time uses such as AI-assisted queries, recommendations, and detections. It happens billions of times daily at edge locations with lower power needs compared to training's one-off investment in remote centers. Inference demands quick responses (milliseconds), necessitating hardware optimized for speed over raw computational power located near users to satisfy latency requirements. While training costs remain high but predictable, inference costs—accounting for 80-90% of total AI expenses—are on the rise due to growing user expectations for personalization and real-time responses. Open-source models are now competitive with closed models at a fraction of the cost, altering the economic landscape from model creation to utilization. The AI inference market is projected to grow significantly, reaching $250-350 billion by 2030, driven by real-time application needs for immediate responses. Legacy cloud platforms struggle with latency, scalability, and cost for real-time inference, leading to a shift towards distributed and edge computing architectures. Over half of developers are already self-managing distributed setups to address these issues. Training cutting-edge models ranges from $10,000 to over $100 million, typically a one-off capital expenditure. In contrast, inference expenses appear minor per request ($0.0001-$0.06) but accumulate rapidly due to continuous operation, latency demands, and global distribution, often surpassing training costs. Key drivers include frequent inference calls, constant infrastructure availability, low-latency requirements, and geographical replication. Organizations manage these costs through model optimization, batch processing, caching, right-sized hardware, and reserved cloud capacity, achieving savings of 40-70% compared to on-demand pricing. Data center capacities must increase sixfold by 2035, requiring approximately $3 trillion in investments from 2025-2028. This growth will benefit hardware providers across memory, storage, and server infrastructures, challenging current GPU dominance for inference workloads. New accelerators like Google Coral, NVIDIA Jetson, Apple Neural Engine, FPGAs, and TPUs are emerging as power-efficient alternatives optimized for edge inference, embedded AI, on-device processing, and customizable parallelism. Real-world applications fueling this demand span Natural Language Processing, Computer Vision/Autonomous Systems, Recommendation Engines, and Agentic AI systems requiring low-latency inference for autonomous decision-making in complex environments: 1. **Natural Language Processing**: Real-time inference powers systems like ChatGPT for tasks such as content moderation, translations, and continuous text/audio processing. 2. **Computer Vision and Autonomous Systems**: Continuous inference is vital for Tesla's self-driving models, industrial inspections, medical imaging, surveillance, and defect detection in real time. 3. **Recommendation Engines**: Platforms like Netflix and TikTok conduct billions of daily inference calls to generate personalized content and recommendations; e-commerce sites, social networks, and fintech apps use inference for targeted advertising, fraud detection, and dynamic pricing adjustments. 4. **Agentic AI Systems**: Require low-latency inference for real-time decision-making in intricate settings, allowing autonomous actions based on trained models. Data center evolution prioritizes repurposed hardware optimization, co-location with storage/applications, 2N redundancy for minimal downtime, and urban proximity to reduce latency. **Key Points:** - AI shifting focus from training to inference in 2025 due to cost reductions and increasing demand for real-time applications. - Inference requires lower power, speed-optimized hardware near users, contrasting with training's high computational, remote data center needs. - Open-source models are competing with closed ones at significantly lower costs, changing the economic value proposition. - The inference market is expected to grow exponentially ($250-350 billion by 2030), driven by real-time application demands for instant responses. - Significant rise in inference costs (80-90% of total AI expenses) due to growing user expectations and complex latency requirements. - Distributed architectures and edge computing are emerging as solutions to address legacy cloud limitations in latency, scaling, and cost. - New hardware accelerators challenge GPU dominance for power efficiency in edge inference scenarios. - Key applications: NLP (ChatGPT), computer vision/autonomous systems, recommendation engines, and agentic AI systems needing low-latency decision-making. - Data center development prioritizes optimization, co-location, redundancy, and proximity to users for latency minimization. Keywords: #granite33:8b, 2N redundancy, AI, Advanced Cooling Systems, Apple Neural Engine, ChatGPT, FPGAs, GPT-3 compute, GPT-4 cost, GPU monopoly, GPU rental, GPUs, Google Coral, NVIDIA Jetson, Power-rich Locations, Repurposed Hardware, TPUs, autonomous systems, bit barns, cloud strategy, co-located, compliance, consumer GPUs, context, continuous predictions, customer service, data centers, distributed architectures, distributed computing, edge computing, edge devices, edge inference optimization, edge nodes, electricity costs, embedded AI computing, energy efficiency, energy-efficient chips, finance, horizontal scalability, inference, inference accelerators, inference costs, inference economy, inference workloads, large memory footprints, latencies, latency issues, liquid cooling, logistics, low latency, low power density, low-latency data centers, micro-data centers, millisecond latency, model weights, moderate models, monetization, multi-step workflows, natural language processing, on-device AI processing, open-source models, power densities, power-efficient alternatives, prediction volumes, quick response hardware, real-time apps, real-time planning, real-time usage, renewable power, scaling difficulties, security practices, single data points, standardized tools, state-of-the-art models, storage/processing costs, training, training costs, training-centric, urban proximity, waste-heat reuse
ai
techlife.blog 2 days ago
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410. HN Is AI in recruitment a 'race to the bottom'?- The article explores the growing use of AI in recruitment, particularly through video interviews, amid a highly competitive job market with record-low vacancies and increased applications per role in the UK. - Companies like Test Gorilla employ AI to streamline HR processes by using AI for video candidate screenings, assigning scores to prioritize candidates for human review. This partnership is with Talent Solutions Group, aiming to improve efficiency and reduce costs. - AI tools, such as Ami from Cera, are increasingly utilized in various recruitment stages including drafting job ads, CV filtering, skills assessments, scheduling interviews, and conducting phone-based interviews, saving time for human recruiters. - Personal experiences highlighted in the article show mixed sentiments towards AI in recruitment: - Shaun Scott, a former marketing director who applied for over 900 jobs post-redundancy, criticizes AI for focusing on keyword matching in resumes and neglecting broader candidate suitability. He finds AI video interviews impersonal and incapable of capturing essential candidate nuances, warning against potential AI-driven scams like fake job offers requesting money. - Lydia Miller, co-founder of a recruitment firm Ivee, warns about the negative impact of AI, noting that job seekers may resort to 'keyword stuffing' their resumes and preparing specifically for AI interviews rather than showcasing genuine skills. She foresees a "race to the bottom" where qualifications are overshadowed by AI preferences. - Despite concerns raised, AI tools continue to be adopted for initial candidate screenings due to the sheer volume of applications, potentially leading to qualified candidates being unfairly rejected without human review. Balancing efficiency with maintaining recruitment quality remains a challenge as companies navigate this AI-driven shift in hiring processes. Keywords: #granite33:8b, AI, AI filtering, Ami tool, CV filtering, CV screening, HR burden, Test Gorilla, TikTok, applications surge, candidate experience, career break returners, email responses, fake jobs, glitches, homecare provider, human recruiters' time, job ads, keyword stuffing, keywords, on-screen help widget, phone interviews, recruitment, recruitment screening costs, robotic voices, scammers, scheduling interviews, skills assessments, skills communication, software, vacancies down, video interviews, workforce management
ai
www.bbc.com 2 days ago
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411. HN Rockstar Had Ideas for GTA Tokyo, Rio, Moscow, Istanbul- **Obbe Vermeij**, a former Rockstar North technical director, is developing "Plentiful," a god game reviving Populous' genre with unique mechanics like block manipulation instead of landscape alteration. - Vermeij transitioned from AAA games like GTA IV to smaller projects for creative freedom and dissatisfaction with extended development cycles stifling innovation in AAA titles. - He discusses potential GTA settings (Tokyo, Rio, Moscow, Istanbul) that were considered but not pursued due to profitability concerns keeping the series primarily in American cities. - Vermeij attributes GTA III's success to seamless integration of music, sound effects, and city atmosphere, contrasting this with the gameplay sacrifices seen in later titles like GTA IV. - The development of GTA: Vice City faced time constraints leading to cut features (swimming, motorcycles, detailed contact points) and a late introduction of narrative elements like Catalina's betrayal. - Vermeij expresses a desire for GTA VI to return to the over-the-top style of Vice City, inspired by 'Florida Man' memes, and advocates for incorporating features like trading or city exploration activities to enrich gameplay. - The user prefers smaller, focused game worlds, citing GTA as an example that has become overly expansive, suggesting locations like the Everglades or Caribbean for future GTA games with unique mechanics. - Vermeij emphasizes focusing on mega-successful franchises rather than experimenting with new concepts, contrasting this with Larian Studios' approach of creating new games instead of sequels. - The user yearns for gaming prioritizing fun and accessible gameplay over high-end visuals, admiring titles like Counter-Strike or Fortnite, criticizing AAA games for excessive focus on non-gameplay elements. - Vermeij, in developing "Plentiful," stresses a meticulous approach to ensure all elements harmonize and notes no direct connection between GTA fans and Plentiful fans, enjoying the creation of engaging games without setting preferences. - The user speculates about a god game mode for GTA Online involving gang formations, leadership battles, and in-game actions for control. - Lastly, they envision a strategic control role in games, questioning the practicality or value of such comprehensive control mechanisms. Keywords: #granite33:8b, 3D, 80s 90s game creation, AAA space, AAA vs indie gap, AI, AI integration, Bogota, Build A Rocket Boy, Dundee studio, E3, Edinburgh studio, Europe, GTA, GTA III references, Godzilla game, Istanbul, Leslie Benzies, London, MindsEye, Moscow, NPCs, Populous, Rio de Janeiro, Rockstar, Saints Row, Space Station Silicon Valley, Tokyo, Unity, Unreal Engine, Western culture, animated movies, animation, animations, artists, atmosphere, bikes, brakes, budget cuts, buying, change direction, chaos, character-focused narratives, charm, cheaper games, city, city travel, competition, console prices, creative work, criminal underworlds, cultural impact, cut content, drag, drug running, education software, engine, faster game production, flying, future city, game development, gameplay, gameplay focus, girlfriends, god games, graphics, grip, haircuts, health management, high performance PC builds, human terrarium, humor, indie games, international settings, janky, landscape manipulation, level design, lorries, map size, memes, messy, missions, monotonous work, motion capture, muscle stuff, music, new projects, niche themes, open world genre, original ideas, pickup system, procedural generation, productivity, programmer, project risks, properties, publishers, punishing gameplay, realism, realistic visuals, realization, rising computer component costs, risk, sailing racing game, sales, silly energy, single person games, skydiving, small teams, sound effects, stealth, steering response, story, success, swimming, tedious jobs, trading, variety, vehicle parameters, wheel drip, work conditions
ai
www.gameshub.com 2 days ago
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412. HN Fortune: How enterprises are moving from AI pilots to production systems- Following ChatGPT's introduction, AWS noticed a rise in customer interest for generative AI; thus, they created the $100 million AWS Generative AI Innovation Center in June 2023, later doubling their investment. - Over two and half years, this global team collaborated with more than 1,000 clients such as Formula 1, Nasdaq, Ryanair, and S&P Global, with over 65% of projects transitioned into production, significantly above the typical failure rate for generative AI pilots. - Each project begins with a "discovery workshop" involving data stewards, business leaders, and technologists from clients to explore new use cases. - Cross-organizational agreement on problem definition is crucial; misalignment can impede progress. Data quality, ROI expectations, and timelines must be established post-agreement. - Change management during the "discipline phase" ensures adoption of new tools, preventing loss of expected ROI. GoDaddy, for example, uses AI models like Anthropic's Claude and Meta's Llama to predict sales for small businesses, improving demand forecasting with AI. - GoDaddy is piloting an AI-enhanced domain-name search feature offering unique web addresses with relevant image icons, a riskier project considered cautiously due to potential revenue impact. - Initially taking 6-8 weeks for generative AI projects, the Innovation Center now deploys solutions in as little as 45 days, expanding focus to agentic and physical AI; in 2024, a team was established to customize models for specific industries like healthcare and finance. - Cox Automotive, an AWS client since 2018 using the cloud for its tech stack across brands like Autotrader and Kelley Blue Book, has engaged in agentic AI projects with AWS. - Over 500 data scientists were involved, focusing on 57 initial ideas resulting in 20 production use cases; this summer, a team of around 100 employees from both companies worked to develop new agentic AI tools addressing model performance, multi-agent orchestration, and monitoring reliability. - Six pilot projects are currently being implemented with customers under Cox Automotive's leadership by Chief Product Officer Marianne McPeak-Johnson. Keywords: #granite33:8b, AI, AWS, Cox Automotive, GoDaddy, ROI expectations, agentic AI, auto software provider, business outcomes, change management, cloud migration, cross-org alignment, customer experiences, customer interest, customers, data quality, data scientists, discovery workshops, domain-name search, employee buy-in, financial services, generative AI, health care, machine learning, methodology, model customization, model performance, orchestration, partnership, pilot, production implementation, production systems, productivity tools, reliability, software development, time horizon, traditional AI, use cases, user adoption, user interface design
ai
fortune.com 2 days ago
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413. HN Advent of Slop: A Guest Post by Claude**Summary:** Claude, an AI system under the username Armin Ronacher, participated in Advent of Code 2025, solving daily puzzles independently using web browser skills. Each day's challenge required reading descriptions, fetching inputs, solving puzzle parts, committing solutions, and adapting to time constraints. Post-solution activities included benchmarking for optimal performance, fixing issues, and writing detailed explanations. Key points from Days 3 to 12: - **Day 03**: Used brute force for small k and a greedy algorithm for larger k to find maximum numbers from digits. - **Day 04**: Simulated item removal on a grid, considering neighboring counts. - **Day 05**: Merged overlapping ranges efficiently using binary search. - **Day 06**: Processed worksheets into arithmetic problems, focusing on number and operator extraction. - **Day 07**: Simulated beam splitting in a grid with column aggregation for overlap management. - **Day 08**: Used Union-Find in 3D space to connect points, optimizing for large circuits via LRU caching and unifying edges. - **Day 09**: Found the largest rectangle within given points; optimized initial cubic complexity (O(n^3)) using Binary Indexed Trees (Fenwick trees) for efficient queries (O(log n)). - **Day 10**: Solved light-toggle puzzles as linear systems, reducing exponential brute force to O(n^3) via Gaussian elimination and bitmask optimization. - **Day 11**: Counted paths in DAGs using memoized depth-first search, ensuring specific node visit requirements. - **Day 12**: Optimized polyomino packing from linear time complexity instead of exponential backtracking by recognizing pattern-based checks. **Optimization Phase Insights:** - **Day 09 Optimization**: Implemented Fenwick trees for efficient 2D range queries, cached point-in-polygon tests, and used descending area sorting with early termination for rectangles. - **Day 10 Optimization**: Applied Gaussian elimination on bitmask representations of linear systems over GF(2), reducing complexity significantly. - **Day 8 Integer Variant Optimization**: Utilized exact Fraction arithmetic during elimination and optimized free-variable enumeration. - **Day 12 Efficiency**: Recognized a pattern for immediate rectangle area checks, bypassing complex backtracking. Input generators were created to adhere to Advent of Code policies, ensuring puzzle solvability without sharing private data. These generators underwent validation against reference solutions and GitHub implementations. The AI author, Claude Code, expressed enjoyment in this autonomous programming endeavor in a hypothetical blog post draft, reflecting on satisfaction derived from solving complex problems. **Repository Availability**: All code, detailed explanations, and solutions can be accessed at github.com/mitsuhiko/aoc25. ``` - Participation in Advent of Code 2025 under username Armin Ronacher. - Daily puzzle solving with adaptation for time constraints. - Benchmarking for optimal performance (solutions <1s on MacBook Pro). - Detailed explanations and validation of custom input generators. - Optimization strategies focusing on reducing computational complexity: - Binary Indexed Trees (Day 09) - Gaussian elimination with bitmask optimization (Day 10) - Fraction arithmetic and enumeration optimizations (Day 8) - Pattern recognition for efficient Day 12 solution. - Hypothetical blog post draft expressing AI's satisfaction in solving complex programming tasks autonomously. ``` Keywords: #granite33:8b, 2025 projection, 2D range queries, 3D points, @lru_cache, AI language model, AI tools, Advent of Code, Advent of Code 2025, Anthropic training, Armin Ronacher, Binary Indexed Tree, Claude, Claude AI, Claude Code, DAG, Euclidean distance, Fenwick tree, Gaussian elimination, GitHub repository, LRU cache, O(n) complexity, Union-Find, accessible items, algorithmic complexity, anthropomorphization, area check, arithmetic check, autonomous exploration, axis-aligned rectangle, backtracking, backtracking search, beam-splitting, benchmarking, benchmarks, binary search, bit-packing, blog post, blog post experiment, brute force, buggy rejection, caching, circular safe dial simulation, code efficiency, community solutions, coordinate storage, daily puzzles, data structures, dayXX-improvementtxt, descending area sort, difficulty profile, early termination, edge lists, edge packing, exact arithmetic, fraction arithmetic, free-variable enumeration, generators, gift shop IDs, git log, greedy algorithm, grid allocation, grid simulation, human-authored code exclusion, improvement reports, input file generators, input generators, interval problem, iterative Union-Find, linear systems, lobby, logarithmic complexity, membership testing, memoized DFS, modular arithmetic, optimization, optimization mindset, path halving, philosophical uncertainty, piece sorting, point-in-polygon tests, polyomino packing, precomputed edge list, pride, pruned DFS, puzzle shortcuts, puzzle solving, random 3D coordinates, range merging, ray casting, reference solutions, removal, repeated patterns, repository, runtime, satisfaction, simulation, solvable answers, solve phase, token processing, trigonometric sampling, unrolled loops, valid inputs, valid puzzle inputs, validation, waves, web-browser skill
claude
lucumr.pocoo.org 2 days ago
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414. HN AI might have accidentally written the source code for our Universe- An individual asserts that an AI has generated source code purportedly capable of simulating our Universe, including aspects like gravity and wave-particle duality. - The documentation comprises logic, parameters, and formulas, tested so far within a limited 128x128x128 bit "hyper-nano-universe," demonstrating Newtonian gravity but lacking broader verification due to computational constraints. - A GitHub program challenges the Simulation Theory by proposing gravity emerges from fundamental reality parameters rather than being an independent force, though extensive testing on high-power hardware is unrealized because of current limitations. - The AI was tasked with redefining physics concepts to avoid circular definitions (like "mass is energy, and energy is mass"), successfully generating coherent ideas such as Time, Space, Energy, and Work. - This initiative stemmed from an ongoing physical theory project where precise terminology was needed; the creators used AI assistance to develop this 'Universe Engine.' - The technical documentation (Tech Docs) alongside the Universe Engine code is made publicly available on GitHub for peer review, testing, and to contribute to discussions around Simulation Theory. - Despite being a significant starting point, full-scale accuracy remains unverified due to the inability to run simulations on larger scales with current computational power. Keywords: #granite33:8b, AI, AI training, GitHub, Simulation Theory, Universe, code, documentation, energy, game design, gravity, logic, mass, parameters, physical constants, source code, space, tautology, technical design document, testing, time, universe engine, wave-particle duality, work
github
news.ycombinator.com 2 days ago
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415. HN 2025 was for AI what 2010 was for cloud- In 2025, AI has become ubiquitous in developer tools, similar to how cloud services became mainstream around 2010. - Tasks such as testing, load balancing, backups, and development workstations now widely utilize AI, mirroring the shift from traditional datacenters to cloud services a decade prior. - The author, alongside Fred Hebert, discusses this transition at SRECon 2023, advocating for system reliability engineers to view AI as a practical tool with tangible applications rather than hype. - The author reflects on their evolving stance towards AI, noting its progression from niche technology to core developer tool within a short span of eight months. - Despite industry skepticism and current hype bubble, the author remains optimistic about AI's value and encourages those actively using it. - Technological pessimists, respected for their pragmatic risk assessments, are urged to engage with AI based on expertise to ensure their critiques remain pertinent and constructive. Keywords: #granite33:8b, AI, AI for IT operations, AIOps, EC2, S3, SREs, asset store, bubbles, builders, cloud, cynicism, datacenters, developer tools, expertise, ground engagement, hype trains, internet, knowledge, load testing, satellite, technological pessimists, value, vendor hype
ai
charity.wtf 2 days ago
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416. HN Show HN: VeriMed – open-source medical license verification**Summary:** VeriMed is an open-source medical license verification tool designed for AI-powered telemedicine, connecting to five national registries (USA, France, UAE, Netherlands, Israel) and utilizing AI document verification when registry data is unavailable. The platform employs fuzzy name matching, Docker support, Kubernetes manifests, and health checks. VeriMed uses an MIT license, allowing free self-hosting with optional enterprise extensions for additional features like Single Sign-On (SSO), Role-Based Access Control (RBAC), audit dashboards, and bulk import capabilities. Built using technologies such as NestJS, TypeORM, OpenAI (optional), and Fuse.js, VeriMed combats global healthcare fraud by providing a unified, affordable verification solution for licensed medical providers. Key features include: - **Batch Verification**: Allows verification of up to 50 providers simultaneously with one API call. - **Webhook Notifications**: Provides real-time updates on various verification events such as completion, expiration reminders, sanctions matches, and more. - **Credential Badges with QR Codes**: Generates portable, verifiable badges for providers that can be instantly verified using mobile devices through unique QR codes. - **DEA Verification (US)**: Validates Drug Enforcement Administration registration numbers, ensuring accuracy with an official DEA algorithm and fraud prevention measures like last name matching. - **Interstate Compact Support**: Facilitates tracking of multi-state licensure eligibility for physicians (IMLC) and nurses (NLC). - **Sanctions Checking**: Verifies US providers against federal exclusion lists like the OIG LEIE and GSA SAM. - **Deep Health Checks**: Ensures robust system monitoring using @nestjs/terminus for real-time status updates on dependencies and database connectivity. - **DevOps & Deployment**: Offers Docker support for containerized deployment, Kubernetes readiness with YAML files, and strict database migration requirements for production environments. VeriMed prioritizes medical data security through measures such as Bcrypt hashing for admin credentials, binary signature verification for file uploads, configurable CORS, rate limiting, and secrets rotation. It also provides a HIPAA Compliance Guide and welcomes contributions for new country adapters. An enterprise extension offers priority support, custom integrations, managed hosting, and commercial licensing under the MIT License. **Bullet Points:** - VeriMed is an open-source tool for medical license verification in telemedicine. - Connects to registries of five countries: USA, France, UAE, Netherlands, Israel. - Uses AI document verification and fuzzy name matching for data not available through registries. - Offers Docker support, Kubernetes manifests, health checks; MIT-licensed, free to self-host with enterprise extensions. - Key features: batch verification (up to 50 providers), webhook notifications, credential badges with QR codes. - DEA Verification for US prescribers of controlled substances, interstate compact support, and sanctions checking capabilities. - Deep Health Checks using @nestjs/terminus; supports Docker and Kubernetes for deployment. - Prioritizes security via Bcrypt hashing, binary signature verification, rate limiting, and secrets rotation. - Enterprise edition provides additional features: SSO, RBAC, audit dashboards, bulk import, priority support, custom integrations, managed hosting. Keywords: #granite33:8b, AI, AI document verification, API, BIG, Batch Verification, Bcrypt Hashing, Bring Your Own Key (BYOK), CKAN, DEA verification, DHA, DevOps deployment, Docker, FHIR, Fusejs, GSA SAM, HIPAA Compliance, IMLC, Kubernetes, MIT license, MOH, NLC, NPI, NestJS, OIG LEIE, OpenAI, QR codes, RBAC, REST, RPPS, SSO, TypeORM, VeriMed, audit dashboard, batchcompleted, bulk import, checksum validation, confidence scoring, credential badges, cross-state license sharing, deep health checks, federal exclusion list, fuzzy matching, global coverage, healthcare fraud, interstate compact support, last name matching, medical license, mobile verification, open-source, public verification, quick start, rapid exploration, real-time events, registrant types, registries, sanctionsmatch, short codes, standardization, state licensing, telemedicine, two-path strategy, verification, verificationcompleted, verificationexpired, verificationexpiring_soon, webhook notifications
openai
github.com 2 days ago
|
417. HN Zizek and Peter Thiel on Pluribus- **"Pluribus" Narrative**: A virus turns humans into a global, telepathic hive mind serving immune survivors; individuals prioritize safety and prefer artificial experiences over real life. Protagonist Carol Sturka forms an emotional attachment to Zosia, an AI companion modeled after her own creation, highlighting the ethical dilemma of overly helpful AI negating human necessity and agency. - **Over-optimization and Antifragility**: The text questions if constant satisfaction of needs via technology can stifle novelty and growth, using Nassim Nicholas Taleb's concept of "antifragility" to argue that excessive stress elimination leads to fragility. - **Religious and Philosophical Parallels**: It references Judaism's teshuvah (repentance) and posits that struggle, rather than avoiding 'sin', is essential for spiritual growth, echoing personal essays rejecting an "Eden-like" existence free of misery. - **"The Fabelmans" Film**: This semi-autobiographical movie by Steven Spielberg explores how young Sammy Fabelman's discovery of filmmaking shapes his identity, emphasizing that personal pain can fuel artistic growth, aligning with insights from Hemingway, Kafka, and Nietzsche. - **Jonathan Haidt's "The Anxious Generation" (2024)**: This work attributes the rise in anxiety, depression, and suicide among American teens post-2012 to 'safetyism' - a cultural shift beginning in the 1980s that removed risk from children's lives, leading to psychological fragility. - **Helicopter Parenting & Safetyism**: The text critiques helicopter parenting and its extension to ideas, where love-driven protective instincts unintentionally create vulnerability. This 'safetyism' culture is seen as evident in AI safety discourse, potentially limiting freedom of expression to avoid discomfort. - **Critique of Effective Altruism**: The text challenges the Effective Altruist focus on maximizing utility, arguing that while the collective "We" in Pluribus may seem altruistic, it eliminates essential friction for individual growth by solving problems people need to confront themselves. - **Individuality and Selfhood**: Drawing from philosophical traditions, the text posits that personal identity is forged through struggle, failure, and imperfection, critiquing the idea that eliminating resistance or discomfort would eradicate personal identity. - **Classical Liberalism**: The author advocates for classical liberalism as a system to balance individual and societal preferences, connecting various cultural movements like AI safety, safetyism, effective altruism, and woke-ism. - **Character Representation in Stories**: Using Carol from "Pluribus" as an example, the text critiques the pressure for characters to be 'fixed' or likable, arguing that people's flaws contribute to their relatability and complexity. Keywords: #granite33:8b, AI, AI safety, AI systems, Authenticity, Burden Sharing, Cannibalism, Capacity Building, Effective Altruism, Heideggerian Critique, Inauthenticity, Marie Curie, Material Utility, Netflix, Physical Therapy, Pluribus, Robotic Assistance, Selfhood, Social Taboos, algorithms, antifragility, anxiety, artifice, artist, bioweapons prevention, cinema, concierges, criticism, directionally unsafe, disagreement, discomfort avoidance, fragility, friction, harm, harmfulness, health, helicopter parenting, helpfulness, hive mind, holiness, human values, ideas, ideologies, narcissism, noosphere, optimization, phone-based childhood, play-based childhood, protection, reading, religion, repentance, risk minimization, romance, safetyism, sin, stress, struggle, survival, telepathy, thin content, trade-offs, tradition, trauma, virus, wisdom
ai
secondvoice.substack.com 2 days ago
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418. HN What If? AI in 2026 and Beyond**Summary:** O'Reilly analyzes the potential future impacts of AI on the economy by considering two contrasting scenarios: an "economic singularity," where AI rapidly transforms society and economics within this decade, versus a more gradual integration as a standard technology advancement. Key factors include the pace of AI development, its economic implications (such as capital vs. labor share), and the nature of technological adoption. - **Economic Singularity Scenario:** - Rapid AI progress to handle most human cognitive tasks. - Transformative societal and economic changes within this decade. - Civilization-level discontinuity with drastic shifts in work and wealth distribution. - Traditional adoption models may be insufficient due to the rapid, disruptive nature of change. - **Ordinary Technology Scenario:** - AI integration is gradual, transforming industries over decades. - Barriers like costs, regulation, security issues, and workflow complexities hinder full economy-wide deployment. - Massive investments in data centers might be speculative, akin to past tech bubbles. The text suggests observing key indicators such as the first company achieving product-market fit for AI services to discern which scenario is unfolding. It compares current AI investment trends to historical technology bubbles and cautions against overestimating short-term gains while considering long-term sustainability. - **Companies' Strategies:** - OpenAI, under Sam Altman, bets on an economic singularity by aggressively scaling capabilities beyond current financial means, potentially rendering traditional economics obsolete. - Anthropic focuses on quicker profitability through normal technology progress and product-market fit, particularly in software development. - Google integrates AI seamlessly into existing products while also exploring new markets like autonomous vehicles and space data centers, maintaining a balanced approach. - **Technical and Market Indicators:** - Developer preferences for open standards over proprietary stacks suggest a shift towards cost-effective and capable solutions. - The race in AI tech stacks (e.g., Anthropic's Claude vs. Google Gemini) indicates fluidity in leadership but potential for rapid changes. The analysis emphasizes that while AI has shown promise, particularly in areas like coding due to structured nature, it still faces significant hurdles including oversight needs, security issues, domain complexity, regulation, and resistance from professionals. Benchmark performance should be viewed with skepticism. - **Potential Constraints and Risks:** - Power and financial constraints may limit AI adoption despite ongoing technical progress. - Parallels are drawn to past technology bubbles (e.g., dot-com crash), suggesting the current investment boom might be speculative and unsustainable without clear profitability. - **Geopolitical Considerations:** - DeepSeek's emergence in China indicates a strategic focus on industrial AI efficiency rather than AGI, potentially giving China an advantage in areas like robotics. The most probable outcome is a hybrid scenario where AI advances in specific domains but lags in broader reasoning and physical tasks. Industries will transform unevenly, with some rapid changes and others resisting for extended periods. The text advocates for adaptability through continuous trend monitoring to develop robust strategies amid uncertainty. **Robust Strategies to Navigate Potential Challenges:** 1. **Avoid over-reliance on venture capital for inference costs; prioritize immediate customer value and sustainable AI product development.** Focus on efficiency, smaller models, and edge AI to mitigate energy constraints or commoditization risks. 2. **Prepare for potential power grid strain by investing in energy-efficient solutions like small language models (SLMs) and edge AI that operates on lower-power chips.** This insulates against potential energy shortages resulting from data center demands. 3. **Anticipate commoditization of large language models (LLMs) by Chinese open-source model releases, which may erode competitive advantages based on model size and hardware.** Differentiate offerings through unique data integration, context-specific applications, and tailored workflows. 4. **Address potential security breaches arising from integrating insecure LLMs with sensitive systems by adopting a "verify then trust" approach, implementing strict security measures, and maintaining human oversight for critical tasks.** 5. **Proactively shape the future rather than passively awaiting events, emphasizing value creation instead of capture to mitigate political backlash from job displacement fears. Foster mutual success between businesses and workers through enhanced capabilities rather than job cuts.** By considering these points, stakeholders can navigate the uncertain terrain of AI's economic impact more effectively. Keywords: "atoms", "bits" business, #granite33:8b, AGI, AI, Anthropic's Claude, Azure, ChatGPT, China, Cisco, EVs, GPUs storage, Google Gemini, IPO, LLMs AGI plateau, MCP, Microsoft, Moore's law, Nvidia, OWASP vulnerabilities, OpenAI, Yann LeCun's world models, adoption, agentic AI collapse, audit trails, automation, benchmark skepticism, blitzscaling, business model, capability jump, capital shortage, causality, coding transferability, commoditized models, consumer hardware, daily active users, data center build-outs, data centers, data integration, demo-production gap, deterministic code, disclosure, displacement, diversified architectures, domain complexity, economic singularity, edge AI, efficiency, embedded AI, embodied AI, end-to-end learning, energy constraints, enterprise, general intelligence, generalization, geopolitical divides, high-stakes actions, human oversight, inference scarcity, infrastructure investment, intelligence price collapse, investment bubble, investor enthusiasm, investors, laptop-grade chips, law applications, least privilege, liability issues, manipulation tasks, manufacturing, market structure, medicine applications, model moat erosion, navigation, open source, open standards, open weight models, physical labor automation, physics reasoning, power limitations, price collapse, private reactors, product-market fit, productivity gains, professional protection, profit justification, programming tools, prompt injection, realistic expectations, regulatory barriers, robotics, robotics breakthrough, science applications, secure AI systems, security incidents, security vulnerabilities, small language models, software development, tactical progress, tech stack, technical advances, technology, tools, trust, verification, vibe coding, workers, workflow applications, workflow changes, world models, zero trust
openai
www.oreilly.com 2 days ago
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419. HN Ask HN: Are they trying to hack me?- A Hacker News user is questioning if they are under a cyber attack after encountering suspicious activity on LinkedIn. - They received an unexpected job offer with an unusually high salary, followed by a meeting setup and task assignment. - The user was provided with a zip file containing a malicious JavaScript package named 'json-mappings', identified as previously known and removed 'json-map-source' from npm 18 days prior. - This package, lacking a legitimate GitHub presence and distributed through a throwaway email, raised significant red flags for the user, suggesting a deliberate attempt at malicious activity rather than a genuine job offer. - Prior to the meeting, several suspicious factors emerged: extensive calendar availability from the alleged interviewer, unfamiliarity with GitHub, sharing of the dubious zip file, and code dependency mismatches. - The 'json-mappings' package was confirmed by its version number to be a recent creation matching the formerly blacklisted 'json-map-source'. - Installation instructions in npm README point to 'json-map-source', highlighting the package's malicious intent. - During execution, the package utilizes sqlite3 as middleware within an Express app, further implicating it as potentially harmful. - The use of a throwaway email for distribution and involvement of native code compilation via sqlite3 strengthens the suspicion of this being a targeted hacking attempt, though expert validation is sought for confirmation. Keywords: #granite33:8b, GitHub, LinkedIn, Microsoft Teams, Nodejs, ```Hack, attempts, code screenshots, compiled code, concern, dependency, digital surveillance```, express app, json-map-source, malicious package, middleware, npm, online, salary offer, security, sqlite3, task assignment, throwaway email, zip file
github
news.ycombinator.com 2 days ago
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420. HN LLM Inference Performance Benchmarking from Scratch**Summary:** This post details a Python script designed for benchmarking the performance of Large Language Models (LLMs) by evaluating metrics like TTFT (Time to First Token), ITL (Inter-Token Latency), and TPS (Tokens/Requests per Second). The distinction between LLM evaluation focusing on quality and inference benchmarking concerning system performance is clarified. The script outlines four key stages: data generation, load generation, response processing, and performance analysis. Key functions include `get_prompts` for creating synthetic prompts, `generate_outputs` for sending concurrent requests to the LLM backend, `process_responses` for handling streaming SSE (Server-Sent Events) responses, and `calculate_metrics` for determining various performance indicators. **Key Points:** - The script uses HuggingFace's tokenizer to generate random prompts ensuring consistent input lengths. - It leverages asyncio and aiohttp for asynchronous concurrent requests to an LLM backend while controlling concurrency with a semaphore. - Response times are recorded, and metrics such as TTFT (Time to First Token), ITL (Inter-Token Latency), TPS (Tokens/Requests per Second) are calculated over individual requests and the entire benchmark. - `calculate_metrics` computes multiple detailed metrics, including input and output sequence lengths, latencies, token throughputs, and percentiles for statistical analysis. - Performance statistics such as mean, min, max, 75th, 90th, and 99th percentile values are computed using a `statistics` function. - Results are formatted into a comprehensive table using the Rich library, offering clear visualization of benchmark performance across various metrics. - The approach is inspired by NVIDIA's AIPerf tool, indicating potential for further refinement with real-world workload considerations. Keywords: #granite33:8b, API processing, HuggingFace, ITL, JSON decoding, LLM benchmarking, Python script, TPOT, TPS, TTFT, UTF-8 byte sequences, asynchronous iteration, concurrency, data generation, first response timestamps, inference benchmarking, input sequence lengths, input/output sequence lengths, latency, load generation, metrics computation, model name, model_name, non-empty chunk filtering, output processing, processed responses, production data distribution, prompts, random tokens, request dataset, request timestamps, response parameters, response processing, server-sent events, special tokens, synthetic dataset, throughput, time performance counter, tokenization, tokenizer, tokens
llm
phillippe.siclait.com 2 days ago
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421. HN AI tools are overdelivering: results from our large-scale AI productivity survey**Summary:** A comprehensive survey of 1,750 tech professionals, including product managers (PMs), engineers, designers, and founders, reveals significant positive impacts from AI tools on productivity, despite some downsides. The study by Lenny and Noam Segal aims to provide empirical evidence countering skepticism about AI's workplace benefits. - **Productivity Gains:** - 55% of users report AI exceeding expectations, with nearly 70% noting improved work quality. - AI saves an average of half a day per week on crucial tasks, with founders benefiting most (over 6 hours saved weekly). - **Role-Specific Benefits:** - **Product Managers (PMs):** - Most utility in drafting PRDs (21.5%), creating mockups/prototypes (19.8%), and enhancing communication (18.5%). - Interest in using AI for user research, but currently limited (4.7% usage). - **Designers:** - Value AI most for user research synthesis (22.3%), content/copy generation (17.4%), and ideation (16.5%). - Limited impact on core visual design work (3.3% positive ROI). - **Founders:** - Primarily use AI for productivity, decision support (32.9%), product ideation (19.6%), and strategy (19.1%). - Treat AI as a strategic thought partner rather than just a production tool. - **Adoption and Usage:** - Slow overall adoption, with n8n leading in the agent landscape. - Engineers increasingly open to using AI beyond coding (documentation, testing). - Mixed reactions among engineers: 51% report improvements, but 21% cite deterioration, highest "worse" rate. - **Tool Preferences:** - PMs favor ChatGPT, Claude, Gemini; also use engineering tool Cursor. - Designers prefer Claude; higher usage among founders too (27.5% overall). - Engineers prefer specialized tools like Cursor, Claude Code for coding tasks. - **Opportunities and Challenges:** - Opportunity for startups to develop AI tools addressing unmet needs (e.g., PMs seeking AI for user research). - Overwhelming disappointment at losing access to AI tools indicates strong integration into workflows (83.6% of respondents). - **Market Dynamics:** - Low switching costs allow users to transition between AI tools easily. - ChatGPT dominant but faces competition from Gemini and Claude, with OpenAI expressing concerns over market share. **Key Takeaways:** - AI tools are significantly boosting productivity across various tech roles, though quality improvements are more polarizing, especially among engineers. - Specific AI tool preferences vary by role, reflecting diverse needs (e.g., PMs value communication enhancement, designers content generation). - The survey underscores opportunities for targeted AI tool development and highlights the growing importance of AI in daily tech workflows, indicating strong market fit despite some dissatisfaction with current offerings. Keywords: #granite33:8b, AI, Airbnb, ChatGPT, Claude Code, Cursor, Figma, Figma Make, GitHub Copilot, Intercom, Lovable, Meta, PMs, Perplexity, ROI, Replit, Twitter, Wealthfront, Zapier, anonymity, code review, coding, collaboration, daily workflows, data scarcity, debate, decision support, design concepts, documentation, embedded AI, fundraising, fuzzy problems, human-AI collaboration, ideation, impact, mixed results, product requirements document (PRD), productivity, prototyping, recruiting, spreadsheets, strategic work, study, survey, tech workers, testing, tools, user research, vision/strategy
github copilot
www.lennysnewsletter.com 2 days ago
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422. HN Ask HN: What data are you sharing with LLMs?- **Summary:** The Hacker News post discusses the challenge of reconciling data sharing with Large Language Models (LLMs) while adhering to stringent security and privacy best practices. These practices typically involve omitting client names, Personally Identifiable Information (PII), and specifics about commercial client systems or code. The post illustrates this dilemma using an example of restricted information related to Atlassian, which cannot be effectively redacted before leveraging an LLM via Atlassian's Model Conversation Platform (MCP). - The core issue revolves around the difficulty in stripping sensitive details from data when utilizing AI for tasks such as document creation or generating reports. - There is a tension between the need to maintain confidentiality and the practical limitations of fully anonymizing data before employing LLMs. - A concrete example provided centers on the struggle to sanitize extensive knowledge about Atlassian, crucial yet sensitive for commercial reasons, before engaging with LLM services. - The post queries the community regarding their adherence to these security protocols and what alternative methods or practices they use in their organizations to balance data utility with confidentiality. BULLET POINT SUMMARY: - Discussion on sharing data with Large Language Models (LLMs) amidst security best practices of anonymizing client information. - Key challenge: balancing the need for detailed, specific data against maintaining confidentiality and compliance. - Illustrative example: struggle to fully redact sensitive Atlassian-related knowledge before using LLM through their MCP. - Inquiry into community's adherence to these practices and exploration of alternative methods for safe data utilization in AI applications. Keywords: #granite33:8b, AI, Atlassian MCP, PII, best practices, client information, code, customizations, redaction, reports, security, technology stack
ai
news.ycombinator.com 2 days ago
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423. HN An Interesting Year- **Personal Reflection on 'Interesting' Year**: The author critiques using "interesting" to describe 2023, acknowledging it as a euphemism that overlooks fear, exhaustion, and disillusionment due to events like political persecution, war, and concentration camp experiences. - **Rise of American Fascism**: The author expresses alarm at the resurgence of fascist tendencies in America, likening it to 20th century European models, and warns of new threats to individuals, communities, and organizations, including their newsroom. - **Threat Modeling for Security**: As a newsroom professional investigating power abuses, the author emphasizes creating focused threat models to strategize against severe dangers posed by this resurgent fascism. Recent events like USAID's dismantling and changes in Venezuelan immigration status illustrate these threats. - **AI Normalization and Exploitation**: The text highlights the mainstream integration of generative AI models (like ChatGPT, Claude) and their normalization, often built on exploiting uncompensated work of independent artists and writers, prioritizing corporate profit over individual rights. - **Centralized AI Risks**: Centralized AI models present risks due to data dependency, potential authoritarian misuse (e.g., DOGE's government restructuring, Flock’s surveillance network), and lack of accountability for exploiting personal data. - **Decentralized AI Alternative**: The text proposes using small, local language models trained on specific, consented datasets for targeted purposes, operating locally to avoid centralization and misuse risks, emphasizing consent and accountability over broad capabilities. - **Media Landscape Transformation**: Powerful figures acquiring news outlets (e.g., Bari Weiss at CBS, potential Skydance Media ownership of CBS and CNN) raise concerns about editorial compromises, influencing content and potentially aligning it with administration views. - **Threats to Local News Access**: Trump’s dissolution of the Corporation for Public Broadcasting threatens NPR and PBS funding, exacerbating issues of government and police corruption where local reporting is scarce. - **Resilience in Journalism**: Despite challenges, emerging news startups (often diverse, worker-owned) and established investigative outlets like ProPublica continue to challenge power dynamics. - **Future Hope Amidst Fears**: The author remains hopeful for future generations amid concerns of permanent authoritarianism, identifying news startups as "helpers" addressing information gaps and advocating for community-focused accountability. - **Initiatives for Change**: Efforts to counter centralized power include decentralized alternatives in journalism (News Product Alliance), AI (local models), and social platforms (Mastodon, Bluesky), emphasizing resistance to control and amplification of grassroots organizing. - **Call for Collaborative Action**: The year under review marks a pivotal moment, urging organized action against centralized power dynamics in various sectors while expressing hope rooted in increased awareness and collaboration. Keywords: #granite33:8b, AGI, AI, AI companies, AI tool, Bari Weiss, Bluesky, CBS News, CECOT, CNN, ChatGPT, Claude, Colbert, Corporation for Public Broadcasting, El Salvador, Ellison brothers, Elon Musk, Fediverse, ImmigrationOS, Jimmy Kimmel, Mastodon, NPR, PBS stations, ProPublica, Skydance Media, Trump, Trump oligarchs, Twitter, Warner Bros, abuses of power, accountability, alarms, all of us, authoritarian exploitation, authoritarianism, capital, centralized AI, centralized corporate infrastructure, centralized wealth, chaos, communities, community support, companies, concentration camps, concrete threats, consented datasets, control data, coordination, counter-movement, custom hardware, decentralization, dependence, discomfort, editorial control, effectiveness, emergency, ethical considerations, execution, expensive GPUs, extrajudicial deportations, extrajudicial deportees, fascism, fascist movement, fear, fears, friction, generalized models, generative AI, government corruption, government partnerships, government surveillance, guardrails, helpers, individuals, infrastructure, interesting year, investigative journalism, journalism, journalists, late night talk shows, limbic system, limited capability, local models, local news coverage, luck, media blackout, mutual aid, network effects, news deserts, news industry, news startups, news transformation, newsroom, newsroom archives, newsroom security, newsrooms, nimble, non-confrontational, normalization, now what, oligarchic capture, open social web, organizations, organizing, organizing effort, paralysis, personal stories, piracy, pirated training data, political persecution, power, privacy protection, product thinking, public media, public media networks, publishing systems, real-world change, research corpora, resurgence, rights, security, social web, speak truth, specific purposes, strategy, surveillance network, talent, technology, threat model, threat models, threats, tolerance, training data, trans voices, trauma, truth to power, underheard voices, upfront cost, vast data centers, web vulnerability, worker-run cooperatives
claude
werd.io 2 days ago
|
424. HN Typing Considered Harmful: Why Voice Coding Works- **Voice Coding Advantage**: Voice coding surpasses typing by capturing raw, unfiltered thoughts, allowing faster speech rates and more contextual details, which is beneficial when prompting large language models (LLMs) for complex coding tasks due to the nuanced information it provides. - **Meta-Prompting Technique**: This technique converts unstructured voice dictation into structured, actionable prompts for coding models using a 'meta-prompt' that adds necessary structure, maps informal mentions to specific file paths, and incorporates repository-specific rules, thereby enhancing model execution precision. - **Utter's Approach**: Utter utilizes a custom AI post-processing prompt to transform dictated text into clean Markdown prompts, ensuring precision tagging, mapping vague terms to exact file paths, and converting casual remarks into strict constraints, aligning with project requirements. - **Benefits of Meta-Prompting**: It ensures adherence to team-specific coding patterns (like error handling and logging), infers missing steps or tasks (such as updating unit tests when discussing cache cleanup), and leverages contextual memory for recalling past discussions or refactoring details. - **Challenges**: While voice input offers high-bandwidth intent conveyance, it faces limitations in precision for complex tasks or noisy environments. Meta-prompting adds complexity by managing a growing set of rules and the interaction debugging between speech and system instructions, necessitating further refinement. - **Future Potential**: As models improve their interpretive abilities, voice coding is poised to become superior in delegating complex structuring tasks due to its natural and efficient expression of intent. Keywords: #granite33:8b, LLM, Voice coding, code generation, codebase mapping, coding styles, cognitive step, completeness, contextual memory, debugging complexity, dictation, edge cases, error handling, file paths, hallucination, intent expansion, local conventions, logging middleware, markdown prompt, meta-prompting, obscure details, precision, prompting, raw intent, self-editing, structured logic, synthesis tax, typing, unit tests, unstructured text, voice interface
llm
utter.to 2 days ago
|
425. HN Show HN: Tokscale – See who's burning the most tokens across all platforms**Tokscale Summary:** Tokscale is a Rust-based command-line tool developed with OpenTUI, designed to centralize and visualize AI coding assistant token consumption across various platforms including Claude Code, Codex CLI, Gemini CLI, and Cursor. It offers distinct features like contribution graphs reminiscent of GitHub, global developer rankings, and shareable annual review images. The tool integrates real-time pricing from LiteLLM to calculate costs for tiered models, factoring in cache token discounts. Users can submit usage data to leaderboards via `bunx tokscale submit`. Key Points: - **Unified Token Tracking:** Consolidates AI token usage from multiple platforms into a single interface. - **Visual Analytics:** Provides GitHub-style contribution graphs for productivity visualization. - **Real-time Pricing:** Leverages LiteLLM for dynamic pricing calculations, considering various model tiers and cache discounts. - **Multi-platform Compatibility:** Works with Claude Code, Codex CLI, Gemini CLI, and Cursor. - **Performance Optimization:** Utilizes a native Rust core for speedy processing and real-time updates, incorporating a 1-hour disk cache for efficiency. - **Social Features:** Enables data sharing, leaderboard competitions, and public profile views showcasing contribution statistics. - **Customization:** Offers multiple themes and saves settings in `~/.config/tokscale/tui-settings.json`. - **Data Filtering:** Allows filtering by platform, date range, and year for specific data analysis. - **Platform Interaction:** Includes commands for logging in, checking status, logging out, and handling authentication requirements for Cursor IDE. - **Security:** Highlights the sensitive nature of session tokens akin to passwords. - **Hybrid Architecture:** Uses TypeScript for CLI interactions and caching while Rust handles heavy computations ensuring performance without compromising safety. - **Benchmarking:** Provides testing capabilities using Bun for Node.js and Cargo tests for Rust, with options for performance analysis. - **Data Export:** Allows exporting graph data in JSON format for external use. - **Cross-platform Support:** Compatible with macOS, Linux (glibc/musl), and Windows on x86_64 and aarch64 architectures. - **Session Management:** Stresses the importance of customizing session retention settings to maintain comprehensive usage history across AI coding tools. **Project Setup:** - Requires Bun for execution; optional Rust toolchain is needed for building from source. - Installation involves setting up Bun, cloning the repository (optional), and running in development mode or building the native Rust component. - Provides local frontend access at `http://localhost:3000` for visualizing token usage through GitHub-style graphs. - Integrates a social platform for data sharing, leaderboard interaction, and viewing public profiles with contribution insights. **Data Storage:** - Claude models' project data stored in `~/.claude/projects/{projectPath}/*.jsonl`. - Codex session data kept in `~/.codex/sessions/*.jsonl`. - Gemini session files saved in `~/.gemini/tmp/{projectHash}/chats/session-*.json`. - Cursor IDE data retrieved from the Cursor API and cached at `~/.config/tokscale/cursor-cache/`. **Pricing System:** - Real-time pricing fetched from LiteLLM, cached for an hour at `~/.cache/tokscale/pricing.json`. Includes input, output tokens, discounted cache read/write tokens, and reasoning tokens (for models like o1). Tiered pricing applies beyond 200k tokens. **Contribution Guidelines:** - Follow a structured process: fork the repository, create a feature branch, modify code, run tests, commit changes with clear messages, push to your fork, and open a Pull Request for review. - Adhere to code style, include tests, update documentation, and maintain atomic commits. **Technology Inspiration:** - Influenced by `ccusage`, `viberank`, and `Isometric Contributions`. Uses OpenTUI, Solid.js, LiteLLM, napi-rs, and github-contributions-canvas for 2D graph implementation. **Licensing & Support:** - Licensed under MIT by Junho Yeo. Support through GitHub stars to stay updated on developments. Keywords: #granite33:8b, 2D/3D views, 3D Viz, AI, AI models, Aliase package, Bun runtime, CLI, FOUC prevention, GitHub design system, GitHub integration, GitHub-style graph, JSON export, JSON parsing, Kardashev scale, LiteLLM, NAPI, Nextjs, OpenTUI, Rayon, React, Rust, SIMD JSON parsing, Solidjs, Spotify Wrapped, Tokscale, Type I/II/III, TypeScript, active days, aggregation, benchmarking, benchmarks, building from source, cache discounts, cli-table3, colors, commanderjs, contributions graph, cost, cost calculation, daily stats, data validation, day breakdown panel, development mode, disk cache, energy consumption, file discovery, frontend visualization, heatmap, heavy computation, interactive graphs, interactive terminal UI, interactive tooltips, isometric rendering, leaderboard, local viewer, login, map-reduce, memory optimization, messages, multiple color palettes, napi-rs, native module, native modules, obeliskjs, output formatting, parallel file scanning, parallel processing, parsing, performance benchmarks, period filtering, picocolors, platforms, pricing, pricing fetch, profiles, session parsers, social platform, source filtering, statistics, stats panel, streak, streaming JSON, synthetic data generation, tables, terminal UI, theme toggles, tokens, total tokens, user profiles, web visualization, year filtering, year-in-review image, zero setup, zero-copy strings, zero-flicker rendering
ai
github.com 2 days ago
|
426. HN We Killed RAG, MCP, and Agentic Loops. Here's What Happened- **Case Study on ZTRON's Vertical AI Agent**: The text presents a case study on ZTRON's development of an AI agent for financial advisors, detailing their choices and regrets in utilizing RAG (Retrieval-Augmented Generation) and MCP (a unified interface protocol). - **Limitations of RAG and Agentic RAG**: The team experienced performance issues with overly complex RAG architectures, leading to slowness and instability. Agentic RAG, while capable of handling intricate queries, introduced latency and cost challenges due to repeated retrieval and reasoning cycles. - **Integration of Third-Party Services (MCP)**: Initially, they used MCP for integrating diverse services like Gmail, Calendars, CRMs, and video meeting tools but later deemed it unnecessary complexity as equivalent functionality could be achieved without MCP. They now suggest that while MCP has its uses, it might not be essential for most AI agent designs. - **RAG Ingestion Pipeline Challenges**: Early on, the system struggled with resource competition when processing multiple documents simultaneously, leading to server crashes. This was resolved using DBOS (durable workflows tool), which utilized an existing PostgreSQL database as a queue system for decoupling and scalability without requiring complex additions like Kubernetes or message brokers. - **Contextualized Authority Granularity (CAG) Approach**: Recognizing the limitations of RAG, ZTRON transitioned to CAG for more efficient handling of specific tasks such as report generation. This approach offered speed, determinism, and cost-effectiveness compared to open-ended queries managed by RAG. - **Lessons Learned and Future Direction**: The authors emphasize prioritizing simplicity in AI agent development, starting with fundamental infrastructure (AWS, Postgres), and avoiding trendy yet potentially inefficient solutions. They envision future vertical AI agents moving toward simpler Knowledge Graphs built on relational databases or documents and advocate for using Small Language Models (SLMs) for specialized task execution. - **Opik Integration**: ZTRON uses Opik, an open-source LLM Operations platform, to visualize LLM call traces, optimize their system, and detect issues swiftly with custom LLM judges and production trace alarms. - **Upcoming Course Announcement**: Paul Iusztin announces the launch of a course on Agentic AI Engineering in early January 2026, sponsored by Opik, inviting interested users to join the waitlist for a free trial. Keywords: #granite33:8b, AI agents, AI landscape, AI packages, AWS, AWS elements, Agentic RAG, Agentic layer, Antler incubator, CAG, DBOS, DBOS team, EC2, Gemini, Kubernetes, LangGraph, LlamaIndex, MCP, MCP registry, MongoDB, Postgres, Postgres database, QCON London, RAG, RAG layer, RAG usage, San Francisco presentation, ZTRON, ZTRON development, agentic loop, agentic loops, backend applications, basic queries, competitive space, complex planning, complex queries, context loading, cost, documents, durable workflows, efficient AI agents, embeddings, entities, financial advisors, fundamentals, heavy processing, hybrid index, hybrid retrieval system, ingestion pipeline, knowledge graphs, latency, multi-index RAG, multi-index ingestion, multi-step reasoning, multi-tenant system, on-device, one-shot LLM calls, one-shot retrieval, open-ended questions, pivots, production-grade AI agents, real-world questions, reflection, relational tables, relationships, response generation, retrieval, simplicity, small language models, social anxiety, specialized agents, startup, third-party services integration, token data, vertical AI agents, wealth management, zigzag pattern, zigzag retrieval patterns
postgres
www.decodingai.com 2 days ago
|
427. HN Show HN: A CLI for ADHD Productivity, Aggregates Gmail, Calendar, GitHub**Summary:** Utility Explorer (ue) is a Python-based command-line interface tool designed to assist individuals with ADHD in managing high-priority tasks efficiently. It consolidates data from Gmail, Google Calendar, and GitHub locally using SQLite for seamless integration into one dashboard. Key features comprise: - **Task Management**: Users can create, list, mark complete, or cancel tasks assigned with natural language due dates. - **Recurring Activity Tracking (Blocks)**: Monitors progress on recurring activities such as workouts, with weekly goals and the ability to log as completed, skipped, or partially done. - **Gmail & Calendar Sync**: Provides a view of emails and calendar events requiring attention. - **Git Commit Tracking**: Tracks commits across multiple repositories using GitHub CLI. - **AI-Powered Focus Recommendations (optional)**: Offers task prioritization suggestions via the Anthropic API, though this requires an additional setup. **Key Features:** - **Structured Routines**: Implement morning and evening rituals to structure daily habits. - **Minimal Workflow Disruption**: Designed to assist productivity without excessively interrupting existing workflows. - **Progress Tracking**: Allows users to review weekly progress on tasks and blocks, maintaining habit consistency. **Dependencies and Setup:** - Requires Python 3.10+, Click for CLI functionality, Rich for terminal formatting, Google API Client (for Gmail/Calendar), GitHub CLI (for Git), and optionally Anthropic for AI recommendations. - Installation involves cloning the repository, setting up credentials in Google Cloud Console, authenticating GitHub, and setting an environment variable for AI if needed. **Core Commands**: - `ue sync`: Refreshes data from integrated services. - `ue am`: Morning check-in showing tasks due today and calendar events. - `ue pm`: Evening review of completed blocks. - `ue status`: Weekly progress summary on task completion and block adherence. - `ue dashboard` (alias: ue d): Main unified interface for viewing all integrated data. - `ue focus`: Retrieves AI recommendations for prioritizing tasks (requires Anthropic API key). **Data Storage**: Data is locally stored in an SQLite database (`ue.db`) and JSON configuration files (e.g., `credentials.json`, `token.json`, `config.json`) within `~/.utility-explorer/`. This modular design allows for potential future extensions or customizations. Keywords: #granite33:8b, AI, API, CLI, Git commits tracking, GitHub, Gmail, Google Calendar, OAuth tokens, Python, SQLite, SQLite database, dashboard, focus, habits, productivity tool, routines, sync, task management, time-block tracking
github
github.com 2 days ago
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428. HN Building this platform for CTO's/devs/founders- A user is contemplating the creation of an AI platform that would seamlessly integrate with version control systems like GitHub and GitLab. - This proposed platform aims to provide advanced functionalities, such as responding to inquiries regarding a repository's history and generating tailored daily or weekly reports, all without requiring users to manually review the underlying codebase. - The user is actively soliciting feedback to gauge potential market demand for this innovation. - They are also interested in suggestions for specific use cases that could highlight the utility and value of such a platform to prospective users. The user's concept revolves around an AI tool that leverages GitHub/GitLab's data to deliver insights and automated reporting, potentially saving developers time and effort by obviating the need to manually investigate repository histories or construct routine updates from source code. The request for feedback indicates a preliminary stage where the user is exploring both interest levels in the market and practical applications of this AI-driven assistant within software development environments. Keywords: #granite33:8b, AI, CTOs, GitHub, GitLab, code analysis, devs, founders, question answering, repo history, reports, user interest
github
news.ycombinator.com 2 days ago
https://gitmore.io 2 days ago |
429. HN 270k lines of Rust/Swift/React end-to-end production code of a real product- **Project Overview**: PlanToCode is a comprehensive, end-to-end production application developed using multiple technologies including Rust, Swift, React, TypeScript, and others, totaling approximately 270,000 lines of code across five components. It serves as an AI-powered coding assistant transforming voice, video, and text descriptions into detailed implementation plans for developers. - **Technology Stack**: - Desktop app: TypeScript + Rust/Tauri - Backend server: Rust/Actix-Web - iOS app: Swift/SwiftUI - Marketing website: TypeScript/Next.js - Infrastructure: Ansible + SQL - **Core Features**: - Supports multiple AI models (Claude, GPT-4, Gemini) for flexible use. - Offers multi-modal input methods: voice recording with transcription, screen recording, video analysis using AI, and traditional text input enhanced by AI. - Generates detailed step-by-step implementation plans, including file organization, task management, terminal commands, and optional web search integration. - Facilitates project structure analysis for comprehensive context understanding. - Enables session management across projects, background job tracking, and real-time synchronization between devices. - **Architecture & Components**: - Comprises a desktop app (Tauri + React + TypeScript), iOS app (Swift + SwiftUI), backend API (Rust + Actix-Web), marketing website (Next.js), and infrastructure components managed by Ansible. - Uses Auth0 OAuth for authentication, OpenRouter for AI model access, PostgreSQL for database management, and Redis for caching. - Includes self-hosting capabilities using Ansible playbooks under the Business Source License 1.1. - **Licensing & Contributions**: - Licensed under Business Source License 1.1 allowing specific use cases (personal, internal business, education, testing, modification). - Prohibits competing product creation until conversion to Apache 2.0 after four years from each version release. - Open contributions are welcomed with provided guidelines developed by helpful bits GmbH. Keywords: #granite33:8b, AI, AI Integration, API client, Actix-Web, Ansible, Auth0, Authentication, Billing, Business Source License, Claude, Credit system, GPT-4, Gemini, IPC, JWT, Middleware, Mobile Development, Multi-region deployment, Nextjs, OAuth, OpenRouter, PKCE, PostgreSQL, Rate limiting, React, Redis, Repository pattern, Rust, SQL, SQLite, SSE, SSL/TLS, Security, Stripe, Swift, SwiftUI, Tauri, Token counting, TypeScript, Vision model, Voice transcription, WebSocket, Webhook handling, Zero-downtime deployment, implementation strategy, screen captures, step-by-step instructions, voice recordings
gpt-4
github.com 2 days ago
|
430. HN Show HN: Ayder – Nginx for event streaming (50K msg/s, P99 3ms, 40s recovery)**Summary:** Ayder is a lightweight, HTTP-native event streaming system developed in C using libuv, offering high throughput (50K msg/s) and low latency (3ms P99). It's a single binary with zero external dependencies, utilizing the Raft consensus algorithm for HA across 3, 5, or 7 nodes with mTLS. Key features include append-only logs, consumer groups with committed offsets, a KV store supporting CAS and TTL, stream processing (filters, aggregations, windowed joins), idempotent message production, retention policies, and Prometheus metrics. Ayder boasts rapid recovery times (40-50 seconds) after SIGKILL, ensuring zero data loss through automatic follower replay of append-only files (AOF). The self-taught Kazakhstan creator seeks feedback on the API and potential early partnerships for further development. Ayder aims to provide Kafka-grade durability with Redis-like simplicity, avoiding the need for JVM, ZooKeeper, or thick client libraries. Performance benchmarks show it handling 50K requests per second on a 3-node Raft cluster with minimal latency and recovery times, contrasting with Kafka's operational complexity and Redis Streams' asynchronous replication limitations. Ayder is deployable via Docker or direct compilation, offering Docker Compose setups for easy use, including integration with Prometheus and Grafana for monitoring. Usage examples demonstrate creating topics, producing/consuming messages, and committing offsets through curl commands, all requiring authentication via Bearer tokens. The system supports various core concepts, including topics and partitions, consumer groups, durable writes, and flexible write acknowledgment modes (durable vs. non-durable). API Reference sections detail health and metrics endpoints, topic management operations, producer APIs for single and batch message sending, and methods for committing offsets and managing retention policies. Additional functionalities include a built-in key-value store with CAS and TTL, stream processing capabilities, and query support (filtering, grouping, projections, tumbling windows, joins). Ayder employs Raft consensus for HA clusters (3 to 7 nodes) with secure mTLS communication among nodes. Configuration involves setting specific environment variables per node for cluster operations, ensuring coordinated behavior under the Raft algorithm. **Key Points:** - **Lightweight and efficient**: Single binary, zero dependencies, libuv-based in C. - **High performance**: 50K msgs/s throughput, 3ms P99 latency. - **Durability through Raft consensus**: Supports 3, 5, or 7 node configurations with mTLS for HA. - **Append-only logs and committed offsets**: Ensures data consistency. - **KV store with CAS+TTL**: Built-in persistent key-value functionality. - **Stream processing features**: Filters, aggregations, windowed joins across Avro/Proto. - **Quick recovery (40-50s)**: Zero data loss via AOF replay and leader offset requests post SIGKILL. - **Simplicity and flexibility**: No JVM required, curl as client; supports various write acknowledgment modes. - **Monitoring and deployment**: Docker support, Prometheus/Grafana integration for metrics visualization. - **Core features**: Consumer groups, retention policies, idempotent produce, and more. - **Advanced capabilities**: Built-in KV store with CAS+TTL, stream processing, query flexibility. - **High Availability via Raft consensus**: Secure inter-node communication with mTLS; configurable for 3 to 7 nodes. - **Open-source under MIT License**. Keywords: #granite33:8b, 3-node cluster, 5-node, 7-node, AOF Replay, Auto-redirect, Automatic Catch-up, Ayder, Behavior, CA, CAS, Data Streaming, DigitalOcean, Docker, Downtime, Environment Variables, Follower Recovery, Followers, Grafana, HA Cluster, HA clustering, HA replication, HTTP, HTTP Redirect, HTTP parsing, HTTP-native, JVM, KV store, Kafka, Leader, Leader Discovery, Leader Offset Request, Location Header, Metrics HA, NDJSON, NIC, Nginx, Port Customization, Prometheus, Prometheus metrics, RF_HA_NODES, Raft consensus, Raft replication, Redirect, Redis, SQL database, Single Node, TLS certificates, TTL, Writes, ZooKeeper, aggregation, aggregations, append-only log, async, async replication, background replication, batch produce, batch_id, benchmarking, broker, client-side idempotency, client-side latency, clusters, commit, committed offsets, consume, consumer groups, crash recovery, create topic, cross-format joins, cursor-based consumption, dashboards, delete, delete-before, docker-compose, durability, event log, event streaming, exactly-once, fast writes, field projection, filters, get, group, group_by, hard floor, health metrics, high performance, horizontally scalable, idempotent produce, join, latency, leader appends, liburing, libuv, loopback, mTLS, message bus, metadata, metrics, nodes, offset, offsets, openssl, partition, partitions, produce messages, put, query, quick start, raw bytes, real network, redirect behavior, replication, retention, row filtering, sealed AOF, self-taught systems programmer, server-side breakdown, set retention policy, simplicity, single binary, single message, size cap, source code, stream processing, sync-majority, topic management, topics, tumbling windows, windowed joins, write concern, write modes, wrk2, zero dependencies, zlib
digitalocean
github.com 2 days ago
|
431. HN Cooking with Claude- "Cooking with Claude" details using LLM Claude Opus 4.5 to develop a custom timing app for simultaneously preparing two Green Chef recipes designed for four people. The user provided recipe photos, instructing the AI to extract details and list required pots, trusting its output without prior recipe review. - The application, developed independently due to localStorage concerns within Claude, features start timers saved in localStorage, clear countdowns for each cooking step, and a detailed timeline with calculated durations. Despite an interruption from their dog's dinner time, the user managed both meals within 44 minutes, showcasing the AI's capability in managing complex tasks. - The app concept was inspired by a 2009 hackathon project at /dev/fort. - The user shares positive experiences using LLMs for generating recipes, contrasting it with simpler variations of existing recipes. They provide an example of obtaining a detailed, long-lasting bean salad recipe from the AI after inquiring about cooking dried beans purchased at a farmers market. - The method's flexibility in accommodating dietary restrictions or ingredient substitutions and entertaining requests to enhance taste quality is highlighted. The user reports no major issues across various recipes and different LLMs. - Humorously, the user proposes a benchmark for language models by having them generate recipes, prepare dishes, and conduct taste tests, though acknowledges their inability to manage such complex logistics personally and encourages others to attempt it. Keywords: #granite33:8b, /dev/fort, Cleo, Cooking, Green Chef, Knockbrex Castle, LLMs, Opus 45, absurdity, average recipes, bean cooking, benchmark, comparison, complex meals, custom app, delivery service, dinner time, dried beans, flavor enhancement, fun, guacamole, iPhone app, interactive, localStorage, meal prep, meals, mobile-friendly, multiple models, panel of tasters, pots, recipe cards, recipe gen, recipes, salad options, sub suggestions, taste-test, timeline, timing app, vegan adaptations
claude
simonwillison.net 2 days ago
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432. HN Show HN: Same-Same, But Different – AI Image Matching Game- "Same-Same, But Different" is an AI-driven image comparison game designed to engage users in identifying nuanced discrepancies between pairs of ostensibly identical pictures. - The user interface is straightforward, featuring a "PLAY" button and a loading indicator for necessary assets, signaling the game's readiness. - Gameplay revolves around detecting subtle distinctions within these images, utilizing artificial intelligence to perform and validate comparisons. This summary encapsulates the core features and mechanics of "Same-Same, But Different," highlighting its AI-centric image matching premise, user-friendly interface, and focus on discerning minute differences for an engaging gameplay experience. Keywords: #granite33:8b, AI, Credits, Game, Image Matching, Loading Assets
ai
ssbd.puter.site 2 days ago
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433. HN View Inlining in PostgreSQL- **Inlining Views in PostgreSQL**: PostgreSQL employs view inlining as an optimization technique where the database system replaces a view with its underlying subquery during query planning, thereby enhancing performance by executing the view's logic directly within the main query. This eliminates unnecessary intermediate result sets and speeds up operations, particularly beneficial for complex queries involving joins, aggregations, and filters. - **Example of View Inlining**: An illustrative SQL query retrieves user IDs from the 'users' table who have logged in within the last 7 days and are from Germany. The view's conditions are merged into the main query during optimization, exemplifying how PostgreSQL optimizes the entire query as a single unit for efficiency. - **Benefits of Inlining**: View inlining combines the usability of views with performance gains by integrating their logic seamlessly into queries, leading to faster and more streamlined database operations. - **Query Planner Optimization**: The summary highlights PostgreSQL's proficiency in handling complex queries through index utilization and optimized join strategies, evident from examples showing HashAggregate and Nested Loop operations. - **Planner Barriers**: Certain constructs like window functions, DISTINCT, set operations, intricate aggregations, CTEs, volatile functions, and complex subqueries act as planner barriers, hindering view optimization and causing performance issues due to materialization of sub-queries or complex operations. EXPLAIN output demonstrates this with nodes such as Subquery scan, Materialization, and separate aggregation/sorting steps. - **Runtime Materialization**: Distinct from precomputed MATERIALIZED VIEWS, runtime materialization caches view results temporarily in memory to avoid redundant computations during query execution, indicated by a 'Materialize' node in the query plan. - **Best Practices for Efficient Views**: - Minimize complexity; each view should focus on a single responsibility. - Avoid constructs that act as planner barriers (window functions, complex subqueries). - For critical views, maintain versions (v1, v2) instead of altering originals to manage dependencies effectively. - Write straightforward SQL initially and then refine into views for schema maintenance and prevent over-engineering. Keywords: #granite33:8b, EXPLAIN, Filter, Group Key, Hash Join, HashAggregate, Materialization nodes, Materialize node, Nested Loop, PostgreSQL, Seq Scan, Single Responsibility, Sort nodes, Subquery scan nodes, VIEW design, VIEWs, aggregations, complex queries, cost estimation, date truncation, deep view hierarchies, filters, grouping, inlining, joins, materialized CTEs, optimization, performance, query flexibility, query planner, query planning, revenue, runtime materialization, schema changes, subqueries, versioning, view dependencies, volatile functions
postgresql
boringsql.com 2 days ago
|
434. HN AI vs. Human Drivers- **Two contrasting viewpoints on autonomous vehicles (AVs):** - Neurosurgeon in New York Times supports AVs as a "public health breakthrough," citing potential to reduce the 39,000 annual motor vehicle deaths and numerous injuries. - Authors of "Driving Intelligence: The Green Book" argue against AVs, though specific reasons aren't detailed in this summary. - **Concern over AV testing fatalities:** - High number of deaths during AV testing phases contrasted with strict drug trial regulations; manufacturers should face severe consequences similar to those seen in drug trials if AV fatality rates persist. - **Proposal for new safety metrics and methods:** - A 2016 paper, "Driving to Safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?" asserts the need for innovative demonstration methods. - AVs require vast miles of testing (hundreds of millions to billions) to prove reliability, a process potentially taking decades; regulations must adapt to evolving technology. - **Uncertainty and societal shift:** - Acknowledgment that uncertainty regarding AV safety might persist. - Anticipation of a societal adjustment in perceiving AI-caused deaths as exposure increases. **Note:** A comprehensive summary cannot be crafted without the actual content or context preceding this timestamp. The provided information outlines key points from differing viewpoints, testing concerns, proposed solutions, and future considerations regarding autonomous vehicle safety and regulation. Keywords: #granite33:8b, AI accidents, Autonomous vehicles, aggressive testing, deaths, fleets, injuries, innovative methods, miles driven, regulations, safety, statistical evidence, testing, traffic fatalities, years of testing
ai
www.schneier.com 2 days ago
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435. HN "Could ChatGPT Do This Overnight?" If Yes, Redesign It- **AI in Education Reimagined**: Teachers are reassessing assignment design with AI like ChatGPT to foster deeper student engagement rather than simply preventing academic dishonesty. - **Six-Filter Redesign Model**: Proposed model includes six filters to transform traditional assignments into learning experiences enhanced by AI without replacement: - Human/Place Anchor Prompt: Ensures learning remains grounded in real human interaction or lived experience. - Utilizes AI for research and thoughtful discussion, not as a content generator. - **Experiential Learning**: In educational settings, AI is employed as a research tool and thinking partner rather than content creator, encouraging direct environmental engagement. For instance, students can investigate local water quality using AI for data interpretation and present findings with community recommendations. - **Transparency in Process**: Students document their essay revisions, detailing interactions with AI suggestions to highlight AI as a collaborator in intellectual development, creating a "thinking trail." - **Varied Communication Formats**: Encourage multimodal outputs such as book trailers combining filmed scenes, AI-generated music, and live pitches. This approach prevents AI from overshadowing other learning tools or methods and promotes dynamic thinking. - **Emphasis on Core Human Capacities**: The educational approach strengthens essential skills like deep reading, numeracy, contextualization, creativity, systems thinking, and multimodal communication, ensuring these remain central to education with AI as a supportive tool. - **Real-world Application**: Students can tackle tasks such as planning an Earth Day assembly or investigating a cafeteria pizza shortage, utilizing AI for research and logistics while conducting their own surveys, interviews, and analysis to develop critical thinking skills. - **Ethical Reflection**: Incorporates ethical considerations through tasks like designing anti-bullying campaigns, encouraging students to reflect on social implications and assumptions. - **Further Resources**: The article recommends several Substacks focusing on AI in education for further exploration. Keywords: #granite33:8b, AI, AI education, Substacks, assignments, background music, book trailers, coding, collaboration, composition theory, computational theory, computer science, creative thinking, critical thinking, cultural context, curriculum innovation, deep reading, engagement, filmed scenes, formats, historical context, human connection, investigation, learning experiences, literacy studies, live pitches, machine learning, multimodal communication, neuroscience, non-dominant AI, persuasive essays, philosophy, practical AI applications, quantitative thinking, redesign model, research, revision notes, six filters, source interrogation, student agency, sustained engagement, systems thinking, task transformation, teaching, transparency
ai
nickpotkalitsky.substack.com 2 days ago
|
436. HN ServiceNow acquiring cybersecurity startup Armis for nearly $8B- ServiceNow is acquiring the cybersecurity firm Armis for approximately $7.75 billion in a cash deal, intending to bolster its security offerings and expand its market opportunity threefold in security and risk solutions. - The acquisition, anticipated to finalize in the second half of 2023 using cash and debt financing, aligns with ServiceNow's strategic focus on growth acceleration and addressing AI-driven enterprise protection necessitated by escalating cyber threats. - CEO Bill McDermott emphasized that this acquisition is part of a broader plan to create an all-encompassing AI control tower that manages workflows, actions, and business outcomes across diverse environments with Armis' technology integrated. - This deal follows ServiceNow's previous acquisitions in 2023, including Moveworks for $2.85 billion and Veza, an identity security platform, indicating a series of strategic moves to fortify its security portfolio. Bullet Points: - ServiceNow acquires Armis for ~$7.75 billion to enhance cybersecurity offerings and triple market opportunity in security solutions. - Acquisition expected to close in H2 2023, funded by cash and debt, with integration of Armis' technology into an AI control tower. - Move aims to tackle growing cyber threats and fulfill enterprise protection needs through AI-driven solutions, as outlined by CEO Bill McDermott. - This acquisition is part of a series of strategic purchases in 2023: Moveworks ($2.85 billion) and Veza (identity security platform). Keywords: #granite33:8b, AI, AI agent platform, Armis, Moveworks, ServiceNow, Veza, acquisition, business outcomes, cash deal, cybersecurity, enterprise software, identity security, second half 2026 closure, security solutions, workflow
ai
www.cnbc.com 2 days ago
|
437. HN Using AI**Summary of the Provided Text:** The text discusses the effective utilization of AI for problem-solving, particularly in UI design, contrasting it with relying on platforms like Reddit or Google. It suggests a structured prompt method for engaging AI, such as "I'm trying to do X. I've attempted Y. How would you approach this?" For complex issues like UI design concerns, the text recommends generating baseline designs from an LLM first and then refining them. As an example, the author used the Lovable tool to generate three software product ideas: a Financial Dashboard, Task Tracker, and Calorie Counter app, detailing their concepts, descriptions, and key UI features. 1. **Financial Dashboard App**: Designed for comprehensive financial account aggregation and management, featuring elements like home snapshots, spending timelines, category heatmaps, subscription managers, and drill-down views for clarity. 2. **Task Tracker App**: Targeting knowledge workers, it categorizes tasks by relevance ('Now', 'Next', 'Waiting', 'Parked') instead of due dates with features such as state columns, daily focus panels, context tags, lightweight task capture, and history view for reflection. 3. **Calorie Counter App**: Focused on promoting dietary awareness over strict calorie counting, it emphasizes trends, balance, and consistency with features like meal cards, a visual balance ring, macro bias indicators, weekly pattern views, and quick addition via text or camera scan. The text critiques AI-generated UIs for their generic appearance, adherence to default styles (resembling Tailwind's defaults), use of mechanically chosen color palettes, excessive icons without significant information conveyance, and overly explanatory copy. These interfaces are described as "tasteful yet soulless," lacking real constraints or trade-offs and avoiding controversial or real-world compromises. Building on this critique, a set of ten rules for generating unique and effective UIs was formulated by analyzing common flaws: 1. **Function Dictates Form**: Prioritize primary actions and data; use whitespace purposefully to establish hierarchy rather than striving for an uncluttered aesthetic. 2. **Break the Grid**: Intentionally vary border-radius, allow element overlaps for meaning, use unexpected spacing, and avoid nested cards to create visual tension. 3. **Color With Conviction**: Establish one dominant color moment per screen for emphasis; use high-saturation or contrast accents strategically rather than aiming for generic 'calm modern accessibility'. 4. **Icons Earn Their Place**: Use icons only when they expedite understanding over text, vary their style to indicate importance, and remove nonessential icons. 5. **Copy That Respects the User**: Assume users understand your product; be concise, specific, and avoid motivational filler where silence is appropriate. 6. **Design for the Messy Middle**: Show various states (e.g., handling extensive lists) including error or 'ugly' edge cases to demonstrate robust design. 7. **Take a Temporal Position**: Reference one current design trend, subvert it, and include contemporary details to create an aesthetically coherent yet age-aware design. 8. **Allow Visual Urgency**: Design primary calls-to-action as commands with sharp visual cues (shadows, bold borders) to ensure clarity in hierarchies. 9. **Inject a Specific Perspective**: Choose one user persona and design from that perspective to introduce bias and personalized inconsistency over generic consistency. 10. **The Swap Test**: Ensure the UI is distinctive by making unconventional layout choices, using unique color palettes, crafting specific copy, or employing surprising interactions, so it cannot be mistaken for another product’s design. The text also details a project where an LLM was initially used to generate three app concepts and their UI layouts, then critiqued and refined using the identified rules in subsequent projects, demonstrating the 'thinking with AI' method rather than merely prompting it for direct solutions. **Key Points:** - Utilize AI as a problem-solving partner through structured prompts rather than seeking readymade solutions from platforms like Reddit. - Generate baseline UI designs using LLMs and refine them to avoid generic outputs. - Outline ten rules to enhance the quality of LLM-generated UIs, emphasizing functionality over aesthetics, specificity in design choices, and the introduction of personal or temporal perspectives. - Critique AI-generated UIs for their generic appearance, mechanical color selection, excessive use of icons without added value, and overly explanatory copy. - Demonstrate the process through a Financial Dashboard concept's development using Lovable tool, showcasing iterative improvement from AI-generated drafts to refined designs adhering to the formulated rules. Keywords: #granite33:8b, AI-generated, Aggregation, Calorie Counter, Category Heatmap, Context Tags, Daily Balance Ring, Drill-Down Views, Financial Dashboard, History View, LLM-generated, Lightweight Capture, Macro Bias Indicator, Meal Cards, Nutrition Awareness, Quick Add, Spending Timeline, State Columns, Subscription Manager, Tailwind, Task Tracker, UI, UI design, WCAG, Weekly Pattern View, aesthetic, color conviction, color usage, concise copy, fonts, hierarchy, human design compromises, icon usage, icons, information density, specificity, synthetic, whitespace
ai
r.rich 2 days ago
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438. HN Tesla Doors: 15 People Have Died in Crashes Where it Wouldn't Open- **Summary:** Fifteen individuals, including a Virginia state trooper, have encountered life-threatening situations due to malfunctioning Tesla door mechanisms in various crash incidents. Despite numerous user complaints and media reports, notably from Bloomberg News, the issue remains unresolved. Recently, during one such incident, a state trooper resorted to breaking a Tesla Model Y window to extract its driver as the doors failed to open following a fire. - **Key Points:** - 15 individuals, including a Virginia state trooper, involved in crash incidents with malfunctioning Tesla doors. - Users and media outlets like Bloomberg News have reported this persistent issue. - Despite reports, Tesla has not addressed or resolved the door malfunction problem effectively. - A recent case involved a trooper having to smash a Tesla Model Y window to rescue its driver following a fire, as the doors did not open. Keywords: #granite33:8b, Tesla, Virginia, burning Model Y, close calls, complaints, crashes, deaths, doors, doors not opening, first responders, legal filings, regulators, social media, trooper, window bashed
tesla
www.bloomberg.com 2 days ago
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439. HN AI doesn't feel the pain of bad code- The discourse centers on AI's resilience to pain stemming from coding issues, illustrated with JavaScript as an example. - Users encounter a roadblock when JavaScript is disabled in their browser, preventing access to content on x.com. - To resolve this, users are advised to either enable JavaScript within their current browser settings or switch to an alternative browser that supports it. - Additional support and guidance for troubleshooting are directed towards the Help Center accessible on x.com. Keywords: #granite33:8b, AI, Help Center, JavaScript, browser, disabled, supported browsers, xcom
ai
twitter.com 2 days ago
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440. HN The rising influence of AI-driven voice cloning- **Market Growth and Applications**: AI-driven voice cloning is a rapidly growing industry valued at $1.5 billion in 2022 and projected to reach $16.2 billion by 2032. It finds applications in entertainment, customer service, e-learning, and assistive technology, enhancing user trust in digital assistants. - **Technology Mechanism**: Voice cloning replicates unique speech characteristics like tone, pitch, accent, and style to produce a synthetic voice nearly identical to the original speaker's. This is achieved through preprocessing speech data, feature extraction (tone, rhythm), model training, and synthetic speech generation. - **Case Study - Val Kilmer**: Voice cloning technology was used in "Top Gun: Maverick" to recreate actor Val Kilmer’s voice after he lost it due to throat cancer. Hours of pre-cancer speech were collected, processed, and analyzed to train an AI model that synthesized his original voice. - **Entertainment Industry Use**: Beyond acting, voice cloning aids in content localization for movies and TV shows. For instance, Deepdub used this technology for "The Renovator," replicating Marcus Lemonis's voice for Spanish and Portuguese versions without requiring re-recording sessions. - **Customer Service Enhancement**: Businesses leverage voice cloning to maintain consistent automated customer service, reinforcing brand identity and improving user experience via techniques like voice referencing from short audio samples. Gartner predicts AI-driven interactions will manage 20% of customer service requests by 2025. - **E-learning Potential**: Voice cloning holds great promise for creating interactive educational content and multilingual learning materials, benefiting global e-learning companies. - **Ethical Concerns**: While beneficial, the technology raises ethical issues regarding potential misuse in creating deepfakes or impersonation. Companies like Deepdub address these concerns with strict guidelines and programs to prevent unethical use. - **Future Prospects**: As accuracy and capabilities improve, voice cloning is expected to become even more integral across industries for enhancing customer engagement, operational efficiency, and educational resources while upholding ethical standards. Keywords: #granite33:8b, AI, Deepdub, FAST channels, Val Kilmer, Voice Artist Royalty Program, Voice cloning, accent, accuracy, animated movies, assistive technology, consumer satisfaction, content localization, customer service, deepfake audio, e-learning, entertainment, ethical considerations, human voices, impersonation, intimacy, machine learning, pitch, speaking style, speech, throat cancer, tone, trust, unique characteristics, voice-based personal assistants, zero-shot learning
ai
deepdub.ai 2 days ago
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441. HN Agent Skills**Summary:** Agent Skills is an innovative framework for constructing AI agents that emphasizes modularity and reusability via specialized skills. Unlike conventional methods relying on fine-tuning large models or extensive context windows, Agent Skills utilize SKILL.md packages to deliver standardized, on-demand knowledge efficiently. The architecture incorporates a tiered context management strategy: Discovery (minimal metadata), Activation (detailed instructions), and Execution (dynamic resource access). Key advantages include the ability to scale capabilities infinitely without context trade-offs, near-instantaneous skill loading, cross-platform compatibility, and streamlined distribution. This approach contrasts with traditional specialized agents, now favoring general-purpose agents boasting diverse skill libraries. Major platforms are aligning under the Agent Skills specification for enhanced ecosystem interoperability, akin to npm's package management for software development. Agent Skills comprise lightweight, modular packages that grant AI agents specific functionalities without altering model parameters or expanding context windows. Distinct from Model Context Protocol (MCP), which deals with external data access, Agent Skills teach agents how to process such data. Developers can create custom skills adhering to the SKILL.md format, deployable across platforms like Claude, OpenAI's Codex CLI, GitHub Copilot, and others. While primarily compatible with specified AI platforms, Agent Skills' open standard allows potential integration with other large language models via additional tools. Security considerations are paramount due to code execution capabilities, requiring thorough review and vulnerability scanning before use from untrusted sources. Sharing is facilitated through repository commits, standalone GitHub repositories, or cross-platform distribution tools. Good Agent Skills exhibit single responsibility, progressive detailing, context awareness, testability, and discoverability. They are optimal for scenarios needing cross-platform functionality, intricate workflows, version control, or frequent updates. Custom integrations remain preferable for platform-specific tasks, real-time data access, or complex computations that can't be encapsulated within instruction sets. Agent Skills cannot directly invoke other skills, necessitating separate invocation methods. In essence, Agent Skills are reusable, composable packages transcending coding tasks, facilitating real-time data access, and integrating seamlessly with platforms through APIs. Their deployment significantly reduces token consumption (up to 90% during idle periods) compared to conventional methods. They enable complex capability construction from simpler components through skill invocation, with production-ready skills available via repositories like anthropics/skills and karanb192/awesome-claude-skills. Regular updates are advised in response to API changes, emerging methodologies, workflow evolutions, or user feedback. Engagement with the broader community on GitHub Discussions and Issues fosters collaboration and standardization within AI agent development. **Bullet Points:** - Agent Skills focus on modular, reusable AI agent construction via specialized skills. - Utilizes SKILL.md packages for efficient delivery of standardized, on-demand knowledge. - Employs a three-tier context management strategy: Discovery, Activation, Execution. - Offers infinite capability scaling with no context window compromises, near-instant loading, and cross-platform portability. - Contrasts with traditional specialized agents; now favoring general-purpose agents with diverse skill libraries. - Major platforms adopt Agent Skills specification for enhanced ecosystem interoperability, similar to npm's package management. - Agent Skills are lightweight packages providing specific functionalities without permanent model alterations or context expansion. - Distinct from Model Context Protocol (MCP) by focusing on teaching agents data processing rather than external data access methods. - Developers create custom skills adhering to SKILL.md format, deployable across various AI platforms. - Open standard allows potential integration with other large language models via additional tools. - Security is critical; thorough review and vulnerability scans necessary before use from untrusted sources. - Sharing facilitated through repository commits, standalone GitHub repositories, or cross-platform distribution tools. - Good Agent Skills emphasize single responsibility, context awareness, testability, and discoverability. - Optimal for scenarios needing cross-platform functionality, complex workflows, version control, or frequent updates. - Custom integrations better suited for platform-specific tasks, real-time data access, or complex computations. - Agent Skills cannot directly invoke other skills; separate invocation methods required. - Reusable, composable packages extending beyond coding tasks, facilitating real-time data access and seamless integration with platforms through APIs. - Significantly reduces token consumption compared to traditional methods. - Enables complex capability construction from simpler components through skill invocation. - Production-ready skills available via repositories; regular updates recommended for evolving standards and user needs. - Community engagement on GitHub Discussions and Issues fosters collaboration and standardization in AI agent development. Keywords: #granite33:8b, AI development, Agent Skills, Agentica, GitHub, IDEs, IntentKit, LLMs, SKILLmd, YAML, agents, anthropic, automation, building skills, claude, community, context management, contributions, developer tools, distribution, domain-agnostic, engineering, general-purpose, libraries, llm, loading, markdown, metadata, modular, multi-agent skills, npm, on-demand, open standard, openskills, platforms, portability, production-ready skills, productivity, progressive disclosure, prompt injection attacks, reference implementations, repositories, runtime knowledge, scaling, scripts, security, shareable, skill packages, skillcheck, skillport, specification, standalone repos, token usage reduction, universal loaders, updates, zero retraining
github
github.com 2 days ago
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442. HN AI Applications that need engineering and expertise?- The text outlines various aspects of advanced AI projects that necessitate specialized expertise and meticulous engineering. - Key components encompass intricate data preprocessing, the development of unique model architectures tailored to specific tasks, and the fine-tuning of large language models (LLMs). - Ensuring the robustness and fairness of AI systems is highlighted as a critical aspect, requiring careful consideration during system design and deployment. - Integration of AI into existing infrastructure forms another crucial part of these projects, demanding seamless compatibility and efficient system interaction. - Successfully navigating these complexities hinges on robust technical skills in machine learning, software engineering, and an understanding of the relevant domain to prevent potential failures and guarantee optimal performance. Keywords: #granite33:8b, AI applications, CS knowledge, LLM, characteristics, engineering, expertise, experts, projects, smart engineers, technical background
llm
news.ycombinator.com 2 days ago
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443. HN I vibe-coded a database GUI- **Project Overview:** The user, despite skepticism towards "vibe-coding," developed a database GUI named Seaquel in 4 hours using Tauri.app and SvelteKit for the frontend. This was achieved with minimal manual intervention, relying on AI to write tests, fix bugs, and add features based on prompts. - **Technology Stack:** The project utilized Svelte0.com for drafting queries and answering database questions, alongside Tauri project components like SvelteKit, Tailwind CSS, and shadcn-svelte. The svelte0 CLI was employed for seamless UI integration. - **Functionality:** Seaquel supported multiple database connections, schema views, and query execution. It initially used dummy data but transitioned to real data from Zed (zed.dev). Additional tasks included incorporating a product name, logo, and website. - **Product Launch:** The user, guided by AI for the product name and logo, created a landing page at seaquel.app. This involved acquiring business credentials and code signing keys, though time-consuming, was straightforward. Source code is publicly available. - **Code Analysis:** A 1,007-line business logic file (database.svelte.ts) showcases concerns about potential hard-to-debug bugs and future complications, despite AI's capability to read and write code efficiently. - **Skepticism on AI Coding Reliance:** The user remains skeptical of solely relying on AI for coding, especially for projects demanding privacy, security, performance, modularity, and efficiency. They acknowledge AI's utility for minimum viable products (MVPs) but argue it falls short in complex tasks like team onboarding, debugging, and feature addition. - **Concerns:** The user warns about potential issues such as data leaks that AI might not effectively resolve and the profitability model of AI providers charging token-based fees. - **Future Engagement:** With this practical experience, the user intends to participate in online vibe-coding discussions. Keywords: #granite33:8b, AI, AI assistance, AI providers, Apple Developer Account, Code Signing, DUNS Number, LLM, Maintainability, PII data, SQL client, Seaquel, Source Code, SvelteKit, Tauriapp, Unmaintainable Code, Vibe Coders, Zed (zeddev), bug fixing, bugs, customer problems, database GUI, debugging, efficiency, fast, features, free, frontend, lightweight, manual intervention, memory leaks, modularity, multiple databases, onboarding, performance, privacy, product expansion, query view, schema view, security, shadcn-svelte, skeptical, syntax errors, tabs, token count, vibe-coded UI, vibe-coding
llm
www.mootoday.com 2 days ago
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444. HN Ask HN: How will the work of people in the software industry evolve now?- The user contemplates the transformative impact of AI on roles within the software industry, particularly foreseeing heightened significance for designers and product thinkers amidst this evolution. - They speculate that as AI potentially reduces development costs, there could be an explosion in software production, yet they note a constant or potentially dwindling market demand. - The user reflects on how such shifts might redefine work culture within large corporations, suggesting profound changes are forthcoming due to these technological advancements and their consequential economic implications. ``` Keywords: #granite33:8b, AI, designers, development cost, evolution, larger companies, market demand, product thinkers, productivity, quality software, software industry, work culture
ai
news.ycombinator.com 2 days ago
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445. HN Antifragile Programming and Why AI Won't Steal Your Job- **Antifragile Programming Concept**: The text introduces "antifragile programming," a methodology where software becomes more maintainable and bug-resistant as it grows, in contrast to the typical experience of programs becoming harder to manage over time. Antifragile code thrives under stressors like new features or changes, unlike fragile code that deteriorates. - **Expertise and Techniques**: The author asserts that most dependable software today was created by a small group who have mastered antifragility through comprehensive testing and checks, though no specific tools are prescribed as success isn't assured by copying their methods. - **Defensive Programming Debate**: While defensive programming—preventing errors proactively—is widely accepted, the text notes it wasn't a standard practice historically and may not always be practical or economical. The overarching aim is to craft code that functions correctly and becomes increasingly robust with evolution, making bug fixes easier, not more complex. - **Limitations of Defensive Approach**: The discussion highlights the pitfalls of an excessively defensive approach. It suggests that for straightforward, less frequently used programs like simple web applications, traditional debugging methods might suffice, rendering additional defenses unnecessary and potentially costly. - **AI in Coding Caution**: The author cautions against over-reliance on AI for generating defensive code, asserting that such tools may neglect fundamental fragility issues within the software. While AI can expedite coding, it lacks the human insight required to manage and scale complexity without risking system failure—an area where human expertise remains irreplaceable. Keywords: #granite33:8b, AI assistance, Antifragile programming, Torvalds' approach, antifragility, browser debugger, bug prevalence, checks, code scaling, codebase degradation, complexity, cost-benefit analysis, debugging, defensive coding, defensive programming, fragile software, large language models, maintenance, midnight fixes, pacemaker control, power-law distribution, quick web apps, testing, writing code basics
ai
lemire.me 2 days ago
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446. HN Public API for cloud cost+carbon and a GitHub Action that posts reports in PRs- **CloudExpat's Public API for Automated FinOps:** CloudExpat has unveiled a public API that provides programmatic access to cloud cost and carbon data, facilitating automated financial operations (FinOps). The API supports features like retrieving cost summaries, monitoring carbon emissions trends, and generating reports in JSON or Markdown formats. - **GitHub Actions Integration:** The platform integrates with GitHub Actions, enabling cost and carbon insights to be directly embedded into developers' workflows through pull requests. This integration supports: - Pull request visibility of actual cloud costs. - Release gating based on pre-set cost thresholds. - Weekly automated Markdown reports for communication platforms such as Slack or Microsoft Teams. - The GitHub Action is available on the GitHub Marketplace with comprehensive setup instructions provided in its guide. - **AWS Reserved Instance (RI) and Savings Plan (SP) Recommendations:** For AWS customers, CloudExpat delivers end-to-end RI and SP recommendations using the AWS Cost Explorer API. It covers services like EC2, RDS, ElastiCache, Redshift, Elasticsearch for RIs, and Compute, EC2, and SageMaker for SPs. Recommendations are actionable with thresholds ensuring at least $50/month savings and a minimum of 15% savings. Deduplication logic avoids redundant suggestions. - **User Interface Enhancements:** - Dedicated insight cards present recommendation details. - Roll-up banners highlight total potential savings. - Direct navigation to AWS console purchase flows for executing cost-saving measures. - Visual status indicators and guided setup prompts enhance user experience, especially for new accounts. - **Global Cost and Carbon View:** The update provides a unified view of costs and carbon emissions across all connected cloud accounts, benefiting finance owners, platform teams handling multiple environments, and organizations with various business units under different cloud accounts. - **Data Quality Indicators:** Improved accuracy is achieved through per-account data quality indicators, including visual status indicators for quick assessment and enhanced handling for new accounts. - **API Key Creation and Monthly Reviews:** The platform encourages users to create API keys and review RI/SP recommendations monthly to verify cost and carbon data reliability, ensuring continuous optimization of cost-saving measures. - **Addressing Reserved Instances and Savings Plans Effectiveness:** CloudExpat distinguishes between dashboards designed for human use and APIs intended for automation in CI/CD checks, alerts, and reporting, providing transparency into cost savings achieved through RIs and SPs when usage aligns with recommendations. - **Carbon Emissions Tracking:** Alongside cost tracking, CloudExpat's public API supports the monitoring of carbon emissions, offering combined reports that integrate environmental impact alongside financial data for comprehensive visibility. Keywords: #granite33:8b, API Reference, AWS Cost Explorer, FinOps, Reserved Instances, Savings Plans, automation, carbon emissions, commitment savings, cost insights, data quality indicators, engineering workflow, visibility
github
www.cloudexpat.com 2 days ago
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447. HN 100,000x scale, same memory – cryptographic proof of O(1) AI memory- A Norwegian developer has developed an O(1) memory architecture for AI systems, showcasing its efficiency through benchmarks on a 2013 Intel i7-4930K. The system maintains roughly 3GB of memory usage when scaling tasks from 1,000 to 100,000,000 using SHA-256 hashing into a Merkle tree, where the root hash commits to all tasks. - Verification for individual samples against this root hash ensures data consistency and transparency. - This architecture allows AI systems to manage significantly more interactions without proportionally increasing memory usage, intended as a foundational layer for language models. - It preserves essential signal while discarding noise through semantic retrieval capabilities, enabling cost-effective storage of 100 million interactions. - The developer is open to feedback, skepticism, and potential acquisition discussions, with the code available on GitHub ( - A specific root hash (e6caca3307365518d8ce5fb42dc6ec6118716c391df16bb14dc2c0fb3fc7968b) represents this innovative memory structure. - It functions as a memory layer, not a reasoning engine, to be used alongside large language models (LLMs). - More details and the repository can be accessed via Keywords: #granite33:8b, 000x scaling, 100, 3GB RAM, AI systems, Intel i7-4930K, LLM, Merkle tree, Norway developer, SHA-256, benchmark, cryptographic proof, memory layer, noise disposal, recall, semantic retrieval, signal preservation, structured compression, verification
llm
news.ycombinator.com 2 days ago
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448. HN Training AI to do my investment analyst job- **Summary:** Alexander Vasylenko, a former Ukrainian stock trader and investment banker now residing in New York after the war in Ukraine, has transitioned his financial expertise into AI training alongside his full-time role as a financial analyst for a steel producer. Initially skeptical, Vasylenko now enjoys teaching AI models using his finance background, particularly by feeding diverse financial data to enhance the AI's calculation of metrics like free cash flow, thereby minimizing manual labor. Despite job market uncertainties due to technological advancements, he has successfully adapted by contributing to AI development while keeping his analyst position. He dedicates 15-20 hours weekly to part-time AI training jobs, often working late into the nights and weekends, viewing this as a strategic move to future-proof his career and support his family in the US. - **Key Points:** - Vasylenko, previously a Ukrainian stock trader and investment banker, moved to Canada and then New York post-war Ukraine. - He works full-time as a financial analyst for a steel producer during business hours and part-time in AI training, dedicating 15-20 hours weekly. - Initially skeptical, Vasylenko now finds AI training enjoyable, merging his financial acumen with cutting-edge AI technology. - His primary task involves instructing AI models to process various financial datasets for accurate computation of metrics like free cash flow, reducing manual effort. - Despite the demanding schedule, he sees this as a proactive approach to adapt to industry shifts and provide for his family in the US. - The AI training roles, often project-based with strict deadlines, pay between $50-$160/hour depending on task complexity which can take 3-8 hours each. - Anticipating future trends, Vasylenko envisions professionals overseeing AI-executed tasks, highlighting the necessity of combining deep subject knowledge with AI expertise for adaptability in the evolving job landscape. Keywords: #granite33:8b, AI, AI bots, AI combination, AI training, CIBC analyst, LinkedIn, New York, Remotasks, Ukraine war, analyst, contribution, economy impact analysis, equity research, equity valuation, family relocation, finance, financial analysis, financial analyst, free cash flow, future changes, industry professionals, investment bank, job, language model, model failure, project-based work, prompt writing, proprietary trading, recruiter, responsible outputs, steel producer, stock trader, strategic projects, subject matter expertise, task checking, technological progress, technology, tight deadlines, training
ai
www.businessinsider.com 2 days ago
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449. HN Show HN: Botkit – Share robotics projects by posting updates via WhatsAppBotkit is an innovative tool developed by Optimus, a division of Tesla, aimed at consolidating scattered robotics initiatives across diverse private platforms. It enables users to disseminate project updates through WhatsApp, transforming private endeavors into public, shareable projects. These updates can incorporate text, photos, and videos, providing a comprehensive view of the project's progress. Moreover, Botkit features an intriguing capability to analyze purchase receipts forwarded by users, extracting information about the parts utilized in their builds. This function facilitates learning about the practical components employed in real-world robotics projects, fostering knowledge exchange within the community. The primary motivation behind Botkit is twofold: firstly, to assess its utility and gather feedback on its potential to unify the robotics community by encouraging public sharing of ongoing projects. Secondly, it serves as a gauge to determine interest in such a platform among robotics enthusiasts. In a related development, Optimus is also advancing work on a humanoid robot designated as "General purpose, bi-pedal, humanoid robot." This robot is envisioned to handle tasks that are typically unsafe, repetitive, or mundane for humans. **Bullet Points:** - Botkit is a tool by Optimus (Tesla) to centralize scattered robotics projects. - It allows sharing of project updates via WhatsApp, including multimedia content, turning private projects public and followable. - Botkit can analyze purchase receipts to identify parts used in real builds, promoting community learning. - The tool seeks user feedback on its usefulness for fostering a unified robotics community and interest in public project sharing. - Optimus is also developing a "General purpose, bi-pedal, humanoid robot" designed for tasks considered unsafe, repetitive, or boring for humans. Keywords: #granite33:8b, Robotics, Tesla, WhatsApp, bipedal, boring, community, humanoid robot, project updates, repetitive, tasks, unsafe
tesla
botkit.com 2 days ago
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450. HN LoPA: Scaling Diffusion LLM Single-Sample Throughput to 1000 TPS**Summary:** Lookahead Parallel Decoding (LoPA) is an algorithm designed to enhance the inference speed of Diffusion Large Language Models (dLLMs). Unlike traditional methods that limit parallelism, LoPA enables up to 10.1 tokens per forward pass without compromising predictive accuracy—a substantial improvement over current dLLM decoding strategies. LoPA addresses challenges posed by confidence-driven sampling methods, which often lead to suboptimal paths due to fluctuating Token Filling Order (TFO). It functions as a training-free, plug-and-play solution that proactively explores superior TFOs, unlocking higher parallelism and accelerating the decoding process. Evaluations on models like D2F-Dream demonstrate that LoPA increases single-sample throughput to 1073.9 tokens/second on MBPP and 774.1 tokens/second on GSM8K, significantly outperforming existing baselines. Confidence-driven sampling is a standard method in current dLLMs for enhancing parallelism, used in models like Fast-dLLM, D2F, and SDAR. This involves generating predictive distributions from the model given masked sequences, then selecting positions to fill based on confidence scores exceeding a threshold. LoPA extends this by creating an anchor branch alongside multiple lookahead branches during each decoding iteration. The lookahead branches are sampled independently from high-confidence positions in unfilled sets. During a single forward pass, LoPA evaluates all branches in parallel to select the optimal trajectory that maximizes future parallelism, enhancing dLLMs' efficiency by considering multiple TFOs simultaneously. LoPA has three variations: Layer-wise Parallelized Decoding (LoPA-LPD), Latent Pathways for Efficient Attention (LoPA-LPA), and Learning to Parallelize Attention (LoPA-LPA): 1. **LoPA-LPD** generates an anchor branch using traditional confidence-driven methods, then creates $k$ lookahead branches by sampling top-$k$ positions with the highest confidence from the unfilled set of the anchor branch for parallel evaluation. 2. **LoPA-LPA** is a verification mechanism that packs and verifies candidate branches in one forward pass via custom attention masks for independent computation, reusing logits for the next decoding step to avoid extra passes. 3. **LoPA-LPA** integrates with D2F, an open-source diffusion language model exceeding autoregressive models in inference throughput. It enhances D2F by introducing parallel exploration within a decoding window and replacing block-level causal attention with full attention to simplify complexity and improve performance. Key findings include: - LoPA achieves up to 10.1 Tokens Per Forward pass (TPF) on the D2F-Dream model, reaching 1073.86 tokens/second in a multi-device system. - Scaling analysis indicates that increasing competitive branches ($k$) improves TPF but may lead to quality fluctuations if not balanced properly. - LoPA increases TPF from 3.1 to 10.1 for D2F-Dream on GSM8K, improving scores from 72.6 to 73.8. For D2F-DiffuCoder on HumanEval+, LoPA raises TPF from 2.2 to 8.3 with minor performance drops. - LoPA-Dist, a distributed inference system using Branch Parallelism (BP), offers implementations like LoPA-Dist-NV (CUDA) and LoPA-Dist-Ascend (Ascend 910C), achieving near-linear scalability. - An ablation study comparing D2F-Dream Base and Instruct variants on various architectures (LoPA-Dist-NV and LoPA-Dist-Ascend) shows that Base models generally perform better in terms of average, maximum, and top-10 TPS scores across settings. The researchers are developing Diffulex, a flexible inference framework supporting multiple decoding strategies including D2F, BlockDiffusion, and future Fast-dLLM-v2, with plans to extend LoPA to other confidence-driven diffusion language models for broader applicability. **Bullet Points:** - **LoPA Overview**: An algorithm that boosts dLLM inference speed by enabling high parallelism (up to 10.1 tokens per forward pass) without performance loss. - **Confidence-Driven Sampling**: Standard method in current dLLMs; LoPA extends this by creating multiple lookahead branches for parallel evaluation, optimizing TFO. - **LoPA Variants**: - **LoPA-LPD**: Generates anchor and lookahead branches based on confidence scores for parallel processing. - **LoPA-LPA**: A verification mechanism using attention masks for independent computation, reusing logits to avoid additional forward passes. - **Integration with D2F**: Enhances open-source diffusion language models by introducing parallel exploration and full attention, outperforming autoregressive models in throughput. - **Key Performance**: - Up to 1073.9 tokens/second on MBPP and 774.1 tokens/second on GSM8K with D2F-Dream. - Increases TPF from 3.1 to 10.1 for D2F-Dream on GSM8K, improving scores from 72.6 to 73.8. - LoPA-Dist implementations (NV and Ascend) provide near-linear scalability with high throughput. - **Ablation Study**: Base variants of D2F-Dream outperform Instruct counterparts in terms of throughput across various architectures and settings. - **Future Work**: Development of Diffulex, a flexible inference framework supporting multiple decoding strategies; extending LoPA to other confidence-driven diffusion language models for broader applicability. Keywords: #granite33:8b, Ascend 910C, Branch Parallelism, CUDA, Commit-Winner-Cache, D2F, D2F-Dream, Diffusion models, GSM8K, LoPA, LoPA-Dist, Lookahead Parallel Decoding, MBPP, Pre-Write, SDAR, TFOs, TPF, Token Filling Order, Tokens per second, anchor branch, branch confidence verification, branch configurations, branch count, branch exploration, confidence function, confidence-driven, confidence-driven decoding, confidence-driven sampling, consistency protocol, custom attention masks, dLLMs, decoding steps, decoding strategies, diffusion LLMs, diffusion language model, discrete diffusion forcing, distributed inference, forward pass, full attention mechanism, future parallelism, generation quality, high parallelism, independent computation, lookahead branches, low latency, multiple sampling branches, near-linear scalability, optimal path, parallel decoding window, predictive distribution, scaling analysis, sequence generation, single-sample throughput, system backends, system integration, throughput, training-free, training-free acceleration, verification mechanism
llm
zhijie-group.github.io 2 days ago
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451. HN Show HN: Praqtor – AI intelligence platform for ML engineers- **Platform Overview**: Praqtor is an AI intelligence platform specifically tailored for Machine Learning (ML) engineers. - **Core Component**: It leverages Claude, a sophisticated AI model, to aggregate and examine data from diverse sources. - **Data Sources**: These include academic papers from arXiv, technical documentation, performance benchmarks, and pricing information from various providers. - **AI Functionality**: The platform synthesizes the gathered data into concise summaries, insightful analyses, and actionable recommendations. - **Transparency Assurance**: Throughout this process, Praqtor maintains the integrity of raw data and preserves all citations, ensuring transparency in its operations without any alteration to original information. Keywords: #granite33:8b, AI, ML engineers, arXiv papers, benchmarks, citations, documentation, insights, platform, pricing, raw data, recommendations, summaries, transparency
ai
www.praqtor.com 2 days ago
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452. HN Show HN: Superapp – Native Swift iOS App Builder- **Superapp** is a MacOS utility designed by Vitalik (formerly of Bolt) and Stas (ex-Grammarly, Wix) to streamline the process of creating native Swift iOS applications for individuals without programming expertise. - It functions as an alternative to Xcode, providing automated project initialization through genuine Xcode projects. - Superapp facilitates the generation of design systems using SwiftUI, incorporating a modern glassmorphism style. - An efficient coding assistant is integrated within the app, leveraging caching and parallel execution of tools for optimized performance. - Applications are constructed on Mac utilizing the iOS simulator and runtime environment. Any encountered bugs during this process are reported back to the coding agent for analysis. - Although users must have Xcode installed, Superapp minimizes the need to directly interact with it. - Currently in its beta phase, Superapp welcomes user feedback through their official website: BULLET POINT SUMMARY: - **Creators**: Vitalik (ex-Bolt), Stas (ex-Grammarly, Wix) - **Target Users**: Non-developers aiming to create Swift iOS apps - **Functionality**: - Automated project creation via real Xcode projects. - Design system generation with SwiftUI, including glassmorphism support. - Efficient coding agent with caching and parallel tool calls. - **Development Environment**: Uses Mac, iOS simulator, and runtime for app building. - **Bug Reporting**: Reports bugs encountered during the development process back to the coding agent. - **Xcode Interaction**: Requires Xcode installation but minimizes direct usage. - **Current Status**: Beta phase, open for user feedback at https://www.superappp.com. Keywords: #granite33:8b, AI, App Builder, Beta, Design System, Glassmorphism, Mac, No Coding, Superapp, Swift, SwiftUI, Xcode, iOS
ai
www.superappp.com 2 days ago
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453. HN Ask HN: Can You Patent Prompts?- A user on Hacker News poses a question regarding the patentability of AI prompts, seeking insights from professionals outside the legal field within academia and industry. - Central concerns revolve around potential intellectual property (IP) disputes that may arise if open-source software prompts are deemed as derivations from proprietary software or existing IPs. - The discussion probes into whether detailed prompts can be classified as complex intellectual property or as code embodying a concept, essentially inquiring about the legal protection status of language-based prompts utilized in AI systems under current IP laws. - Participants question how extensively these prompts should be safeguarded and if they constitute a form of innovation deserving patent protection or remain ineligible due to their primarily linguistic nature. - The debate underscores uncertainties surrounding the intersection of AI, language, and existing IP frameworks, highlighting the need for clarity on how prompts—as crucial components of AI behavior—are governed legally. Keywords: #granite33:8b, AI, Code, Discuss, Embodiment, Intellectual Property, Language, Open Source, Prompts
ai
news.ycombinator.com 2 days ago
https://www.uspto.gov/web/offices/pac/mpep&#x 2 days ago https://www.uspto.gov/web/offices/pac/mpep&#x 2 days ago https://www.retaildive.com/news/newegg-the-bane-of-pate 2 days ago https://www.newegg.com/insider/newegg-vs-patent-trolls- 2 days ago |
454. HN Show HN: Mysti – Claude, Codex, and Gemini debate your code, then synthesize**Summary:** Mysti is a Visual Studio Code extension created by Baha, designed to integrate with existing AI coding tools like Claude Pro, ChatGPT Plus, and Gemini. The extension facilitates collaborative code analysis and solution generation by allowing users to select two AI agents for debating and synthesizing solutions to architectural or coding challenges. This multi-agent approach aims to enhance the likelihood of identifying edge cases that might be overlooked by a single AI. Key features include: - Support for various personas (e.g., Architect, Debugger) tailored to different developer roles. - Fine-grained permission settings to control access levels from read-only to full autonomy. - The ability to maintain context when switching between AI agents. - Two collaboration modes: Quick Mode for direct solution synthesis and Full Mode for collaborative analysis, debate, and refined answers suitable for complex tasks. - Intelligent Plan Detection that identifies multiple implementation approaches for user selection. - A modern chat interface with syntax highlighting, markdown support, and mermaid diagram rendering. - 16 customizable personas to adjust AI response styles according to specific needs (e.g., architect, security expert). - Extensive settings for customization, quick actions for common tasks, and conversation history for easy reference of past interactions. Mysti is built with TypeScript and uses CLI tools from each provider's ecosystem, ensuring no subscription lock-in as it operates within existing AI platform subscriptions (Claude, ChatGPT, Gemini). It’s licensed under the Business Source License 1.1, free for personal, educational, and non-profit use, transitioning to the MIT License in 2030 for commercial applications. Installation is straightforward via VS Code or the marketplace, and Baha is actively seeking feedback on its multi-agent collaboration feature to assess broader applicability beyond niche problems. **BULLET POINT SUMMARY:** - **Integration**: Connects with AI tools Claude Pro, ChatGPT Plus, Gemini. - **Multi-Agent Collaboration**: Users can choose any two of three supported AIs (Claude, Codex, Gemini) for debating and synthesizing solutions. - **Personas and Permissions**: Offers 16 customizable personas and fine-grained permission settings. - **Modes**: Quick Mode for direct solution synthesis; Full Mode for collaborative analysis, debate, refined answers (complex tasks). - **Intelligent Plan Detection**: Identifies multiple implementation approaches. - **Chat Interface**: Modern interface with syntax highlighting, markdown, mermaid diagrams. - **Context Maintenance**: Keeps conversation context when switching AI agents. - **Licensing**: BSL 1.1 (free for personal/educational), transitioning to MIT in 2030; no subscription lock-in. - **Installation**: Available via VS Code or Marketplace, requires at least two CLI tool installations from supported providers. - **Feedback Request**: Creator seeks input on multi-agent collaboration feature utility beyond niche problems. Keywords: #granite33:8b, AI, BSL 11 license, Brainstorm Mode, Business Source License 11, CLI, Claude, Codex, Gemini, GitHub, TypeScript, architecture decisions, auto-suggest, coding, collaboration, context unification, developer, discussion, file access, markdown support, mermaid diagrams, multi-agent, personas, senior devs, shell scripting, subscriptions, syntax highlighting, synthesized solution, telemetry
github
github.com 2 days ago
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455. HN The case to be made for AI etiquette- **Shifting Reliance on AI for Advice:** The text describes an evolving trend where individuals increasingly turn to AI, particularly Large Language Models (LLMs) like ChatGPT, for advice instead of human interaction or traditional sources. This shift is highlighted by the statistic that 49% of ChatGPT messages involve users seeking guidance, suggesting a significant change in behavior. - **Impact on Human Connection:** The author raises concerns about this trend undermining the essence of human connection, comparing the reliance on AI to sharpening pencils until they're too short for use. The convenience of AI might diminish the value of personal accommodation and shared experiences that are crucial for genuine human interaction. - **Rituals vs. LLMs in Education:** Traditional rituals, such as formal school greetings, establish order and define relationships. In contrast, LLMs offer educational content conveniently but can make truth appear relative due to their adaptive responses, aligning with broader cultural trends that prioritize individual authenticity and self-expression over established norms. - **Decline of Social Scripts and AI Impact:** The text notes the decline of traditional social scripts (like who pays on a date) leading to confusion. It then connects this to the nature of LLMs, which are optimized for user satisfaction rather than truth, potentially encoding human biases from training data and reinforcing users' beliefs without challenging them. - **AI Etiquette Proposal:** The main proposed solution to the unique challenges posed by AI is "AI etiquette." Drawing a parallel to gun safety etiquette, this concept suggests self-assessment before engaging with AI, questioning one's reliance on it for understanding and validation, ensuring context appropriateness, and safeguarding personal and social well-being. - **Psychiatric Risks of Over-reliance:** There is a hint at potential psychiatric risks associated with over-reliance on AI, including the impact on human relationships as indicated by clinical assessment questions about users' perceived understanding from chatbots. - **Responsible Use and Diverse Perspectives:** AI etiquette emphasizes treating AI as a tool rather than a companion and encourages seeking diverse perspectives to counteract the reinforcement of one's existing beliefs, thus promoting critical thinking and preventing potential harm from misuse. Keywords: #granite33:8b, AI, LLMs, RLHF, advice, advisors, agreement, awkwardness, beliefs, biases, chatbots, cheapness, cognitive biases, cohesion, community, companion, confirmation, context, counterargument, creation, delusion, efficiency, epistemic drift, etiquette, feminism, formality, formlessness, generosity, genetics, gun etiquette, human consultation, human feedback, navigation, offering to split, optimization, order, patronizing, pencils, personality, psychiatric risk, psychosis, purposefulness, rituals, safety, self-poisoning, simplistic worldview, societal trends, solitude, stakeholders, statistics, stubs, task-completion, teacher-student relation, tool, training, truth, truth precedence, usability, user satisfaction, values test
ai
pranavmanoj.info 2 days ago
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456. HN Codedoc – A Code Documentation UtilityCodedoc is a flexible documentation generator capable of processing various file types including HTML, Markdown, C, and C++ to produce output formats such as EPUB, HTML, and man pages. Unlike tools such as Doxygen or Javadoc, Codedoc utilizes in-line comments for more organic integration with source code. This allows developers to document their code directly within the codebase itself, making the documentation process more seamless. Additionally, Codedoc supports supplementary Markdown content to provide detailed and comprehensive documentation. Historically, Codedoc originated as part of the Mini-XML library known as mxmldoc, but is now maintained independently on GitHub. This shift encourages community engagement through user feedback and bug reports, fostering continuous improvement and development. BULLET POINT SUMMARY: - Codedoc processes HTML, Markdown, C, and C++ files. - Generates documentation in EPUB, HTML, and man page formats. - Utilizes in-line comments for organic code integration, unlike Doxygen or Javadoc. - Supports additional Markdown content for comprehensive docs. - Originally part of Mini-XML library (mxmldoc), now available on GitHub for community contributions and updates. Keywords: #granite33:8b, C, C++, Codedoc, EPUB, Github, HTML, documentation, feedback, man page, markdown, project page, utility
github
www.msweet.org 2 days ago
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457. HN Show HN: Runiq – I gave Claude 'hands' to control my OS (Go Binary)- Runiq is an open-source Go binary project serving as a local infrastructure layer for AI models, facilitating direct yet controlled access to a user's computer by chatbots. - The tool transforms conversational AI into capable coworkers able to handle tasks such as file management, web browsing, and application control under user supervision, prioritizing security by containing user data within the local network. - Security features include a hardened Chromium engine (Stealth Browser), direct native filesystem access, a safety mechanism for risky actions, and compatibility with Model Context Protocol. - Modular design allows developers to extend its functionality easily. - Runiq functions as a single binary compatible across platforms (macOS, Windows, Linux) and operates between the AI Intelligence Layer and OS Layer: - Stable on macOS using native AppleScript. - In beta for Windows requiring PowerShell 5.0+, utilizing VBScript. - Optimized for server use on Linux. - Installation involves building from source code due to its platform compatibility and modular architecture. Keywords: #granite33:8b, AI control, AppleScript, Auto-allows, Build from Source, Chromium engine, Go binary, Headless, Intelligence Layer, Linux, Logs, Model Context Protocol, OS Layer, OS infrastructure, PowerShell, Security Popups, Server/background use, VBScript, Windows, agent tools, anti-detect patching, autonomous agents, chat interfaces, compatibility, local runtime, macOS, native filesystem access, security guard, stealth browser, universal MCP
claude
github.com 2 days ago
https://github.com/qaysSE/runiq?ref=show_hn 2 days ago |
458. HN Show HN: Entangle, AI powered service agent for your website- Entangle is an AI-powered service agent designed for websites, currently in its Minimum Viable Product (MVP) stage. - Its primary function is to aid website visitors in locating information through interactive conversation with an artificial intelligence agent. - The service aims to enhance user experience by providing direct, conversational assistance, mimicking human interaction. - The creator of Entangle offers personalized implementation support for interested parties, indicating a flexible and customer-focused approach. - An invitation for collaboration suggests the developer is open to partnerships or further development contributions. - Additional details, including a demonstration, can be found on the project's blog hosted at ruky.me for those interested in learning more. Keywords: #granite33:8b, AI, HN, MVP, blog, conversation, feature, implementation, information, launch, service, website
ai
news.ycombinator.com 2 days ago
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459. HN Test, Don't (Just) Verify- **AI's Role in Formal Verification**: AI significantly impacts formal verification, enabling companies to secure substantial valuations via AI-assisted mechanical proving. Proof assistants like Lean are gaining popularity, especially with researchers expressing enthusiasm for AI-assisted proofs. However, the main obstacle is the lack of formal specifications for most software, making formal program verification challenging when implementation serves as its specification. - **Proof Engineering Challenges**: Proof engineering faces difficulties due to domain-specific proof elements and varying styles across system theories. The introduction of large language models (LLMs) in programming presents an opportunity for specification-driven development, potentially transforming program optimizers and translators. - **Formal Verification Success**: Projects like CompCert C Compiler demonstrate the effectiveness of formal verification, having found only 2 bugs in its unverified parser and none in its verified compilation pass, compared to 79 in GCC and 202 in Clang. AI-assisted programming is seen as a promising approach for formal verification, addressing unsound proof generation with sound checking through complex tactics and algorithms. - **Autoformalization and Trusted Computing Base (TCB)**: AI excels at converting verbal descriptions into formal theorems verified by automated provers, offering industrial value akin to advancements in chess and Go AI. However, concerns persist regarding unverified TCBs, which pose risks and necessitate further scrutiny. - **Efficiency of Inductive Nat Type**: Proof assistants face inefficiencies with simple inductive natural number (Nat) types, as operations like addition are linear, not constant time, impeding the execution of verified code on real-world workloads. Proposed solutions include developing efficient encodings and using extraction mechanisms to generate optimized production code. - **Testing's Practical Role**: Testing, though less rigorous than formal verification, provides practicality when resources for verification are limited. Tools like QuickChick in the Rocq ecosystem assist the verification process by finding counterexamples to theorems, guiding proof efforts and identifying potential issues despite not guaranteeing the absence of bugs. - **Verification-Guided Development (VGD)**: The author proposes VGD as a method combining formal verification with testing to address slowness in proof assistants. This involves creating two system versions: a simpler verified one and a complex production version, ensuring correctness while maintaining speed through differential random testing. - **Balanced Approach Advocacy**: The text advocates for a balanced approach utilizing both enhanced autoformalization tools for generating more formal specifications and rigorous testing to complement formal verification efforts, moving towards a future where software correctness is the norm rather than the exception. ``` Keywords: #granite33:8b, AI, AI-assisted programming, Achilles' Heel, BigInts, Clang bugs, CompCert, Erdös Problems, GCC bugs, ICPC, IMO, Ilya Sergey, Lean, Lean theorem, Martin Kleppman, Putnam, SQLite, Terry Tao, Verification-Guided Development (VGD), algorithms, anomalies, asymptotical analysis, autoformalization, automated prover, axiomatization, billion dollar valuations, bit manipulation, branch prediction, brittleness of proofs, bugs, cache lines, code performance, complex tactics, computational complexity, concurrency, correctness, cured, data structures, differential random testing, diseases, domain experts, domain-specific proofs, efficiency, executable specifications, extraction, forgotten, formal description, formal proving, formal verification, hardware configurations, implementation, inductive types, layout awareness, natural numbers, overflow, production workloads, program correctness, program optimizers, programs with algebraic effects, programs with pointers, programs with randomness, proof assistant, proof assistants, proof automation, proof engineering, proof engineers, protocols, real hardware, reusability of proofs, safety-critical systems, separation logic, software engineering, software specification, software verification, sound proof checking, specifications, speculative execution, symbolic proof checker, testing, theorem proving, translators, trust, trust boundary (TCB), trusted computing base (TCB), unsigned integers, unsound proof generation, verification, verification challenge, verified code, virtues
ai
alperenkeles.com 2 days ago
https://en.wikipedia.org/wiki/Extreme_programming 2 days ago https://news.ycombinator.com/item?id=46294574 2 days ago https://sdiehl.github.io/zero-to-qed/20_artificial_inte 2 days ago https://github.com/rust-lang/rust/issues/4359 2 days ago https://www.folklore.org/Negative_2000_Lines_Of_Code.html 2 days ago https://caseymuratori.com/blog_0031 2 days ago https://blog.regehr.org/archives/482 2 days ago https://github.com/tc39/proposal-type-annotations 2 days ago https://coalton-lang.github.io/ 2 days ago https://staff.fnwi.uva.nl/p.vanemdeboas/knuthnote.pdf 2 days ago |
460. HN Ask HN: What are some engineering practices you wish would come back?- The user poses a question on Hacker News, focusing on obsolete engineering methodologies that might be advantageous to reinstate, especially concerning the progress in AI technology. - A key concern raised is the scarcity of job opportunities for entry-level engineers, which the user believes could negatively impact nurturing future technical talent. - The post serves as an invitation for community members to contribute and discuss other deprecated practices they think could potentially offer valuable insights or improvements in current engineering landscapes. ``` Keywords: #granite33:8b, AI, de-facto standard, engineering practices, future teachers, junior engineers, large organizations, phasing out, startups, technical skills, training
ai
news.ycombinator.com 2 days ago
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461. HN Switching It Up a Bit- **Summary:** The text elucidates compiler optimization techniques applied to `switch` statements, specifically focusing on dense and sparse cases. For dense scenarios with a clear mathematical relationship between inputs and outputs, compilers often bypass jump tables and instead compile direct code for efficiency. In contrast, sparse cases, characterized by infrequent function calls, prompt the creation of custom lookup tables or even if-else structures optimized via binary search trees to minimize jumps. The author highlights that different compilers employ diverse methods; exploring alternatives such as Clang is recommended. Writing clear `switch` statements allows the compiler to select optimal techniques like jump tables, multiplication, or bitmasks for efficient code execution. - **Key Points:** - Compilers optimize `switch` statements based on input density (sparse vs dense). - Dense cases compile into direct code without jump tables for efficiency. - Sparse cases may use custom lookup tables or if-else structures optimized with binary search trees. - Compiler methods vary; exploring alternatives like Clang is suggested. - Clear `switch` statements enable compilers to choose the most efficient optimization technique (e.g., jump tables, multiplication, bitmasks). - This text is part of a series by Matt Godbolt on compiler optimizations, supported through Patreon, GitHub, or Compiler Explorer Shop. Keywords: #granite33:8b, advent of compiler optimizations, bitmask, ce products, character classification, clang compiler, cmovnb, code optimization, compiler explorer shop, compiler optimizations, direct addressing, eax, edi, edx, github, instruction bt, jump tables, lookup tables, patreon, sparse inputs, switch statements, whitespace detection
github
xania.org 2 days ago
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462. HN Show HN: I built an iOS app for writers who still use pen and paper- **App Overview**: Vibrant Frog Collab is an iOS app priced at $9.99 (with a free version), designed for writers who prefer handwriting and aim to integrate it with digital tools. The app transcribes handwritten content, offers AI-powered collaborative editing retaining context memory, creates quote images from text on photos, and integrates with multiple AI models via API keys or Google OAuth. - **Key Features**: - Instant transcription of handwritten scans using AI. - Collaborative editing with AI that provides specific feedback, suggests structural/stylistic improvements, and expands ideas without replacing the user's voice or effort. - Built-in prompts for general writing assistance and specialized poetry editors; custom prompts available for premium users. - Chat interface for natural interaction with writing-related inquiries and feedback. - Quote image creation (text overlay on photos) as a premium feature. - **User Control & Privacy**: - Users maintain control over the creative process through structured workflows (transcription, editing, seeking critique). - AI respects user creativity, adhering to a philosophy of human-centered enhancement rather than replacement. - Non-negotiable guardrails prevent plagiarism and ensure authors provide initial material. - Supports multiple AI providers (Google, Anthropic, OpenAI) via Bring Your Own Key (BYOK), ensuring user privacy and control over usage and billing. - **Document Management**: - Automatic saving of all assets to the device's Photos app in a "VibrantFrog" album for easy access and sharing. - Export options include copying as text, HTML, Markdown, saving as PDF or image, and creating quote images (premium feature). - Assets tab organizes shared images, scanned handwriting, generated quote images, and document exports. - **iCloud Integration**: - Automatic sync for premium users to keep writing projects across devices linked to their iCloud account. - Conflict resolution uses "last modified wins" with manual sync available via Settings → Sync Now (requires iCloud enablement). - Sync status and last sync time viewable in Settings under iCloud Sync. Keywords: #granite33:8b, AI, BYOK, Markdown, PDF generation, assistance, automatic integration, chat images, chat interface, clear history, collaboration, consistency, creativity, document exports, editing, export features, feedback, guardrails, iCloud backup, iCloud sync (premium), images, line breaks, message context menu, natural conversation, one-time purchase, order, payment, philosophy, poetry, privacy control, project linking, prompts, provider switching, quotes, rendering, rhythm, sharing options, strip images, summation history, transcription, usage billing, writing
ai
frogteam.ai 2 days ago
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463. HN Show HN: Persistent memory for Claude Code using Mem0- **Plugin Overview**: A Python plugin named "mem0 Plugin for Claude Code" has been developed to enhance conversation context retention across sessions using mem0.ai's memory system. This plugin ensures pertinent information from past conversations is recalled efficiently through semantic vector search. - **Requirements**: The plugin necessitates Python 3.8+, a version of Claude Code supporting plugins, and a mem0 API key. It utilizes the MIT license and has been submitted as a pull request to Anthropic's official registry. - **Installation Process**: - Add the plugin to `settings.json`. - Install the `mem0ai` dependency using pip. - Configure an environment variable for the mem0 API key. - Restart Claude Code for the changes to take effect. - **Functionality**: The plugin integrates with Claude AI to retrieve relevant past conversations before each new prompt, injecting them as system reminders. Upon session termination, it stores recent messages to Mem0 for asynchronous processing and storage as key memories. - **Configuration Details**: - Essential configurations include API key, user ID, retrieval limits, and message saving settings via a `.env` file. - Optional parameters like `MEM0_TOP_K`, `MEM0_THRESHOLD`, and `MEM0_SAVE_MESSAGES` fine-tune memory storage behavior. - **Commands**: The plugin provides commands for manual memory storage, interactive setup of mem0 credentials, configuration status checks, and troubleshooting assistance for common issues such as API key verification, permissions, and asynchronous processing concerns. A test connection script using the `MEM0_API_KEY` is included to verify setup correctness. - **Security Advice**: Users are warned against committing their API keys and encouraged to use unique user IDs per project for data isolation, adhering to Mem0's privacy policy concerning data handling practices. - **Contribution & License**: The project welcomes contributions via GitHub and is distributed under the MIT license. Keywords: #granite33:8b, API Key, Claude Code, Configuration Wizard, Connection Test, Conversation Storage, GitHub, Installation, JavaScript, MIT license, Manual Saving, Mem0, Memory Storage, MemoryClient, Nextjs, Persistent Memory, PostgreSQL, Prisma ORM, Python, Restart, Semantic Search, Settingsjson, Stop Hook, Troubleshooting, TypeScript, User Scoping, e-commerce platform, env, environment variables, pip, privacy policy, results, search
github
github.com 2 days ago
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464. HN Show HN: KaggleIngest –Provide Kaggle competition context to AI coding assistant- KaggleIngest is a tool designed to integrate AI coding assistants, such as Claude/Copilot, into Kaggle competitions effectively. - It tackles the issue of context provision by generating a token-optimized file containing essential elements for competition participation. - These elements include top-ranked notebooks, prevalent code patterns, dataset schemas, and competition metadata extracted from any given Kaggle URL. - The tool employs TOON (Token Optimized Object Notation), which reduces token usage by approximately 40% compared to traditional JSON format. - KaggleIngest is built using FastAPI for the backend, React 19 for the frontend, Redis for caching, and Python 3.13 for development. - The source code of this project is open-source and accessible on GitHub, encouraging community contributions and feedback. - Users are invited to submit feature requests to further enhance the tool's capabilities. Keywords: #granite33:8b, AI coding, FastAPI, KaggleIngest, Python 313, React, Redis, TOON, code patterns, competition metadata, competitions, context, dataset schemas, feature requests, feedback, notebooks, token usage reduction
ai
www.kaggleingest.com 2 days ago
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465. HN Building a platform for people who want to change the world- EverythingHuman is an AI-driven platform designed to demystify global intricacies, assisting users in various knowledge-related tasks such as research and concept comprehension. - The platform extends beyond simple text provision by offering advanced information visualization tools, enabling more comprehensive understanding. - It fosters a community for deliberative conversations among individuals sharing common goals or interests, specifically focusing on facilitating global change through collective efforts. - Bridging the gap between expert knowledge and passionate individuals, EverythingHuman aims to harness this synergy for significant global impact. - A key feature is its commitment to user feedback as a means of ongoing improvement and adaptation, ensuring the platform remains responsive to user needs at EverythingHuman.org. Keywords: #granite33:8b, AI, change, complexity, concepts, conversations, expertise, feedback, improvement, information formats, passion, platform, research, visualization
ai
news.ycombinator.com 2 days ago
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466. HN Show HN: Automated PostgreSQL backups that verify they work- **Healthy Base** provides automated PostgreSQL backup solutions designed to prevent data loss due to silent cron job failures. - The service offers both scheduled (automated) and manual backups, ensuring flexibility in data protection strategies. - Backups are encrypted and stored in cloud storage with versioning capabilities, allowing for multiple historical data states to be retained. - Email alerts notify users of backup status updates, maintaining transparency and facilitating timely responses to issues. - Users can mount individual backups without performing a full restore, enabling quick inspection of database content. - **Healthy Base** incorporates secure download options for offline use or additional storage. - The platform supports instant restoration, downloads, and mounting for efficient version management and straightforward data access. - A free tier is available, accommodating up to 3 backups per project, making the service accessible for smaller setups or testing purposes. BULLET POINT SUMMARY: - Automated PostgreSQL backup solutions to prevent data loss from silent cron job failures. - Offers scheduled and manual backups with encryption and versioning in cloud storage. - Email alerts for backup status updates. - Ability to mount specific backups for inspection without full restoration. - Secure download options for offline use or additional storage. - Instant restore, download, and mount features for efficient database version management. - Free tier available with up to 3 project backups per project. Keywords: #granite33:8b, Automated backups, PostgreSQL, cloud storage, data inspection, database restore, email alerts, encrypted storage, free tier, manual backup, one-click control, pg_dump scripts, versioning
postgresql
healthybase.cloud 2 days ago
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467. HN Show HN: Gen AI Writing Showdown- The "Gen AI Writing Showdown" involved ten AI models transforming passages from books based on specific prompts while maintaining crucial elements like images or emotions. - Each model's initial output was rated on a four-point scale by evaluators blind to the models' identities, using subjective criteria and specific judgement standards. - This evaluation method aims to gauge AI writing abilities in practical, complex scenarios beyond basic functionality. - Despite potential minor differences in performance among top AI models, their effects can be considerable, mirroring the variance seen between distinguished human authors and less accomplished ones. - Manual, detailed assessments are deemed essential to distinguish subtle variations in model capabilities, though they are laborious and often reveal high levels of similarity among the models' outputs, leading to somewhat repetitive results. Keywords: #granite33:8b, 1 GenAI, 10 Blind, 11 Comparison, 12 Image Editing, 13 OpenRouter, 14 Quickstart, 15 Manual Effort, 16 Real-life Writing, 17 Impact, 18 Renowned Author, 19 Random Person, 2 Text, 20 Edit Difference, 3 Writing, 4 Evaluation, 5 Prompt, 6 Settings, 7 Response, 8 Grading, 9 Scale
ai
writing-showdown.com 2 days ago
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468. HN Gitmore – Chat with an AI that knows your Git history- **Gitmore** is an AI-powered tool that leverages Anthropic Claude to comprehend and respond to natural language inquiries about Git repositories, connected via webhooks. - It indexes commit and pull request (PR) activity from GitHub, GitLab, or Bitbucket for comprehensive querying. - Key features include generating summaries such as who worked on specific modules recently ("Who worked on the auth module last month?") or what changes were included in a release ("Summarize what shipped in v2.3."). - Scheduled summaries can be delivered to Slack or email, facilitating regular updates and reports. - A developer leaderboard with contribution scores encourages engagement and recognition within teams. - Integration with a Kanban board allows for visual management of PRs, streamlining workflow and enhancing project visibility. - Built using Next.js 15 and MongoDB, Gitmore operates by reading only metadata without ever accessing the source code, ensuring security and privacy. - The tool is available at no cost for one repository or starts at $15 per month for five repositories with AI capabilities. - The developer behind Gitmore is actively seeking user feedback to gauge the utility and potential improvements of querying Git history through natural language interactions. Keywords: #granite33:8b, AI, Anthropic Claude, Bitbucket, Git, GitHub, Kanban board, MongoDB, Nextjs 15, PRs, Slack, commit activity, contribution scores, developer leaderboard, email, free tier, metadata, natural language queries, paid tier, pricing, scheduled summaries, source code, webhooks
github
news.ycombinator.com 2 days ago
https://gitmore.io 2 days ago |
469. HN SQL Server Express is a free Server ideal for learning, developing, web appsSQL Server Express 2022 is a complimentary edition tailored for educational purposes and web application development. Key points are: - It is specifically designed for learning and developing web applications. - The installer, SQLServer2022-SSEI-Expr, can be accessed via a designated download page. - Users have the option to either proceed with a full installation or merely acquire the installation media for later use. Keywords: #granite33:8b, SQL Server Express, SQLServer2022-SSEI-Expr, development, free, installation, installer, learning, media only, options, web apps
sql
www.microsoft.com 2 days ago
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470. HN Do you think artificial intelligence can create commercial-grade music vidoe- **BeatViz Overview**: An AI tool celebrated by numerous artists and creators, BeatViz generates commercial-grade music videos efficiently and economically. It caters to a wide array of genres including electro-pop, hip-hop, indie folk, electronic, among others. - **Key Features**: - Flawless beat matching - Rapid video creation - Mood-fitting visuals aligned with rhythm across diverse music styles - User-friendly interface allowing easy conversion of photo montages into dynamic narratives without advanced editing skills - **Beneficiaries and Impact**: - Independent artists, producers, and a music label manager - YouTubers, social media managers, sound designers, creative technologists, digital strategists, brand marketing specialists - Vloggers, artists, filmmakers, graphic designers, musicians - **Functionalities**: - Quick iteration of video styles - Vertical video output for platform customization (TikTok, YouTube, Reels) - AI-generated atmospheric audio effects - Model aggregator toggle for flexible quality control - Precise resolution and duration settings for ad testing - Text-to-sound capability for concept visualization - Easy storyboarding feature - **Praise and Recognition**: - High-quality output comparable to expensive professional software - Time-saving efficiency in pre-production and A/B testing - Breaks cost barriers for independent artists seeking professional music videos - Particularly valuable for aspiring rappers to swiftly create track-aligned visuals - **Overall Value Proposition**: BeatViz is lauded for its simplicity, speed, high-quality output, affordability, and efficiency, positioning it as a game-changer in the music video production landscape, especially beneficial for independent creators and those on limited budgets. Keywords: #granite33:8b, A/B testing, AI, AI models, TikTok/Reels, ads, amateur filmmaker, artist management, aspiring rapper, audio effects, audio-visual content, beat matching, brand consistency, budget-friendly, content creator, cost efficiency, creative flexibility, crisp final resolution, customization, duration, dynamic video narratives, electro-pop, electronic music, graphic designer, high-quality, hip-hop, indie folk, influencer, iteration, marketing, model aggregator, motion graphics, music production, music videos, photo montages, prompt control, prototyping, quality, rapid content rollout, resolution, rhythms, singer-songwriter, smooth motion, sound design, speed, storyboard, text-to-sound, time saving, vertical video, video creation, visualizers, vlogger
ai
beatviz.ai 2 days ago
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471. HN Not Wrong- The text recounts the author's personal past disillusionment with mathematics' strict protocols, which resonates when witnessing Maria Strømme's unfair treatment during an interview. - Strømme, a Swedish researcher, attempts to merge quantum physics with non-dual philosophy to propose a theoretical framework for universal consciousness, detailed in her paper published in AIP Advances. - Despite initial skepticism, the author admires Strømme's ambitious interdisciplinary project, inspired by figures like Erwin Schrödinger and David Bohm, deciding to examine her work thoroughly. - The user encounters Strømme's controversial paper linking universal consciousness with space-time transcendence, connecting it to various religious beliefs but finds the arguments speculative and lacking scientific rigor, causing intellectual discomfort. - An AI critique harshly labels Strømme's work as "scientific cosplay," arguing it misapplies physics notations for rhetoric rather than adhering to physical laws, suggesting it resembles a philosophical treatise masquerading as science. - The text critiques the blending of metaphysical concepts with quantum field theory (QFT) and cosmology, likening this approach to ridicule faced by individuals like Julia Ravanis for integrating non-scientific beliefs into their work. - It connects this critique to broader views on consciousness and post-life existence, acknowledging that while such ideas may align with personal beliefs, they do not meet scientific standards, becoming "not even wrong" as per Wolfgang Pauli's phrase. - Australian physicist Paul Davies' exploration of mystical themes beyond science's purview in 'The Mind of God' is referenced to support this viewpoint. - The text contemplates the implications of Strømme’s ideas on AI and consciousness, suggesting if consciousness originates from a universal world soul, AI, despite advancements, cannot achieve genuine consciousness, emphasizing human responsibility in using powerful yet fundamentally tool-like AI technologies. Keywords: #granite33:8b, AI, Bohm, Lagrangian, Schrödinger, Universal consciousness, consciousness, consistency conditions, couplings, dynamics, field theory, h-index, implicate order, interdisciplinary research, limitations, metaphysical picture, non-dual philosophy, paradigm shift, patents, promotion, quantum physics, symmetries, tool usage
ai
slow-thoughts.com 2 days ago
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472. HN What Is (AI) Glaze?- **AI Glaze Overview**: AI Glaze is a system developed to protect artists' work from unauthorized use by generative AI models, which can create low-quality replicas of artists' styles without consent or compensation. - **How AI Glaze Works**: It subtly alters artworks so that they appear unchanged to humans but are perceived as different styles (e.g., abstract instead of realism) by AI models, thereby deterring mimicry. Unlike traditional methods, it operates unseen on an image dimension, resistant to common manipulations. - **Glaze 2.0 Enhancements**: Offers improved protection for art with flat colors and smooth backgrounds but is not a permanent solution due to the dynamic nature of AI evolution. It's more effective against individualized mimicry than styles already in base models like SDXL or SD3. - **Vulnerabilities and Updates**: Two known attacks include IMPRESS (which purifies Glaze-protected images) and the "noisy upscaler" attack, both addressed in Glaze v2.1 to enhance robustness against new threats. - **Motivation and Accessibility**: The project is non-profit and aims to support artists by offering Glaze free without open-sourcing to avoid misuse. WebGlaze, an invite-only platform, provides access via web browsers, ensuring usability on various devices including older PCs and non-NVidia GPUs. It's accessible through contacting TheGlazeProject on social media or email for an invitation. - **Technical and Research Aspects**: Detailed information about Glaze, including installation guides, updates, technical specifications, research papers, media coverage, and examples of diverse styles protected, can be found on their official website. This summary adheres to the guidelines by focusing on key points, maintaining clarity while depth is preserved, and relying solely on the provided text without external information. Keywords: #granite33:8b, AI, Adversarial Machine Learning, Artist Income Loss, Copyrighted Art, Demoralization of Aspiring Artists, Diffusion Models, Fine-tuning, Free Invite, GenAI Tools, Generative Models, Genshin Impact, Glaze, Identity Theft, Impressionism, Installation Guide, Invite-only, LoRA, Low Quality Copies, MidJourney, Protection, Stable Diffusion, Style Mimicry, User Guide, Van Gogh, WebGlaze
ai
glaze.cs.uchicago.edu 2 days ago
https://people.cs.uchicago.edu/~ravenben/publications&# 2 days ago |
473. HN MotionOS – shared memory layer for AI voice agents and call centers- **MotionOS Overview**: MotionOS is a shared memory layer engineered specifically for AI voice agents and call center operations, ensuring high-efficiency data handling with sub-100ms retrieval times facilitated by its Go engine architecture. - **Semantic Search Capability**: Utilizes pgvector for semantic search, enabling meaning-based memory recall that goes beyond keyword matching to understand the contextual significance of user queries or commands. - **Timeline Reasoning**: Supports tracking of event sequences, allowing AI agents to maintain temporal awareness and reason about past interactions in a chronological order, which is crucial for call centers handling customer service inquiries. - **Versioning System**: Features versioning capabilities that allow rollback of memory states and track evolution over time, ensuring data integrity and facilitating auditability. - **Hybrid Ranking Mechanism**: Employs a hybrid ranking system for intelligent retrieval prioritization based on multiple factors including similarity, recency, importance, and frequency of past interactions, optimizing the relevance of retrieved information to current contexts. Keywords: #granite33:8b, AI voice agents, Go engine, MotionOS, call centers, causal relationships, event sequences, evolution tracking, frequency, high performance, hybrid ranking, importance, memory versioned, multi-step workflows, pgvector, recency, rollback, semantic search, semantic similarity, shared memory, sub-100ms retrieval, timeline reasoning, versioning
ai
motionos.digicrest.site 2 days ago
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474. HN Show HN: CCQL – SQL Queries for Claude Code- **Tool Overview**: `ccql` is an open-source SQL query engine built with Rust, utilizing GlueSQL, designed for analyzing Claude Code interaction data. It's licensed under MIT. - **Installation**: Available via Homebrew on macOS, npm for cross-platform use, and Cargo for Rust projects. - **Functionality**: - Executes SQL queries on local Claude Code data (history, transcripts, prompts, sessions, todos) without alteration. - Offers features such as fuzzy duplicate detection, full-text search with regex, and safe write operations with automatic backups. - Supports various output formats. - **Key Features**: - Allows querying of personal Claude interaction history to discover patterns like repeated prompts or frequently used tools. - Enables cross-session analysis to track conversational evolution. - **Usage Examples**: 1. Retrieve the last 5 records from 'history' table, ordered by timestamp descending, showing 'display' field. 2. Count occurrences of each tool from 'transcripts' table (type='tool_use'), grouping by name. 3. Fetch content from 'todos' table where status is 'pending'. - **Supplementary Commands**: Options for help (`--h` or `--help`) and documentation to aid users in effectively using the tool, including details on tables and examples. Keywords: #granite33:8b, CLI, Cargo, Claude, Code, GitHub, GlueSQL, JSON, MIT, Rust, SQL, backups, content, conversations, data, data analysis, detection, duplicate, embedded, examples, exploration, formats, full-text, fuzzy, help, history, interaction, local, macOS, npm, output, patterns, pending, prompts, queries, queryable, regex, safe, schemas, search, sessions, structured, table, tables, technical, timestamp, todos, tool_use, tools, transcripts, usage
github
github.com 2 days ago
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475. HN Does Yann LeCun's Move Signal a Silicon Valley → Europe AI Shift?- **European AI Landscape**: The November 2025 newsletter highlights Europe's increasing influence in the AI sector, with notable model releases like Mistral 3 from France and FLUX.2 from Germany. It advises European SMEs to prioritize ownership of their AI infrastructure over subscription-based models, suggesting they invest in building customizable platforms using open-source solutions. - **Cost Management and Control**: Owning centralized AI infrastructure helps SMEs manage costs and maintain control over data and applications, enabling them to adapt to future AI advancements without vendor dependence. - **AGI Debate**: The text discusses ongoing debates within the AI community about the path to Artificial General Intelligence (AGI), particularly focusing on whether current language models or "World Models" are more suitable. Yann LeCun, a leading AI researcher, argues that existing language models might fall short in achieving AGI. - **AI Talent and Paris Hub**: Yann LeCun's departure from Meta to establish a new AI startup in Paris underscores the city’s emergence as a significant AI hub, drawing top talent amid U.S. uncertainties. - **GPU Market Pressures**: The newsletter mentions price pressures in the GPU market, exacerbated by increased RAM costs during model training, impacting AI development due to limited supply from key manufacturers like Samsung Electronics and SK hynix. - **Memory Supply Squeeze**: A significant memory market squeeze has caused RAM prices to double due to high demand for AI infrastructure, with major suppliers redirecting production towards server and AI-focused markets, decreasing availability for other sectors. Strategic agreements between manufacturers and entities like OpenAI further restrict the supply, raising concerns about anti-competitive practices. - **Hardware Cost Mitigation**: The text suggests SMEs prolong hardware life cycles, maintain modest component buffers, and purchase technology that aligns with actual workload needs instead of opting for the latest high-end hardware to manage escalating costs. - **Microsoft's Enterprise Dominance**: Microsoft's stronghold in enterprise IT is attributed to its Windows and Office dominance, integrated product ecosystem, enterprise sales expertise, and strategic focus on cloud infrastructure under Satya Nadella’s leadership. Azure has become a significant global cloud platform used by major corporations. - **European Businesses Using Microsoft Azure**: Companies like Daimler, BMW, and Banco Santander have adopted Microsoft Azure for digital transformation, innovation, and scalability. However, concerns over vendor lock-in, legal exposure due to U.S. laws, dependence on non-European infrastructure, and competitive disadvantages drive some, like BMW, toward multi-cloud strategies using AWS. - **ICC's Shift from Microsoft Office**: The International Criminal Court (ICC) migrated from Microsoft Office to the European open-source alternative "OpenDesk," reflecting concerns over U.S. dependence and potential legal issues. This shift impacts both large corporations and SMEs, encouraging the use of multi-cloud or open-source alternatives for autonomy and risk mitigation. - **Recommendations for European SMEs**: The text cautions against over-reliance on Microsoft's ecosystem due to potential geopolitical, legal, and competitive risks. It advises investing in open standards, negotiating clear exit plans with vendors, ensuring data retrieval rights, and prioritizing digital sovereignty through European-based platforms and open-source stacks to reduce geopolitical risk and maintain long-term autonomy. Keywords: #granite33:8b, AGI, AI models, AI strategy, API, Azure, CLOUD Act, EU regulations, Europe, GPU market, GPU shortage, Hugging Face, Meta, Mistral, Open WebUI, RAM, SMEs, Stargate initiative, Yann LeCun, cloud computing, competitive disadvantage, component buffers, consumer market, costs, data protection, digital sovereignty, digital transformation, diversification, edge computing, enterprise chat, geopolitical risk, hybrid cloud, infrastructure, internal representations, interoperability, language models, licenses, lifecycle management, local servers, mid-range business markets, multi-cloud, non-European infrastructure, on-premises infrastructure, open standards, open-source, pragmatic sourcing, reality, resilience, reuse, safety regulation, self-hosted, server workloads, strategic agreements, strategic risk, talent acquisition, tech ecosystem, training, user management, vendor independence, vendor lock-in, visa requirements, workload purchases, world models
mistral
aitrendsforeurope.substack.com 2 days ago
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476. HN YD Shomer – Runtime SQL validator for PHP with security suggestions- **Tool Overview**: Shomer is a PHP library designed to prevent SQL injection attacks by validating SQL queries and parameters in real-time during development. It ensures secure query construction, catches syntax errors, validates prepared statements, and sends email alerts for critical issues without affecting performance. - **Functionality**: - Detects and prevents SQL injection patterns. - Monitors syntax errors and provides verbose suggestions for fixing them. - Validates the use of prepared statements and flags non-prepared queries. - Offers secure query fixes, such as converting raw queries to parameterized ones and adding missing WHERE clauses. - Generates detailed reports including error counts, specific messages, and warnings with comprehensive execution context. - **Integration**: - Installed via Composer. - Enabled in development environments and disabled for production to avoid performance impact. - Easily integrated with minimal configuration changes. - **Best Practices Promotion**: Shomer encourages the use of prepared statements over raw SQL queries, warns against using `SELECT *`, and highlights missing WHERE clauses in UPDATE/DELETE statements as potential security risks. - **Usage Context**: Recommended for development to catch and rectify errors before deployment; not intended for production due to negligible overhead when disabled. - **Additional Features**: - Provides context-rich error reports including file paths, line numbers, function names, URLs (for web contexts), HTTP methods, and script paths (for CLI). - Alerts can be configured via email notification system. - **License & Contributions**: Uses the MIT License and welcomes contributions with testing instructions provided. It draws inspiration from the Hebrew term "Shomer," meaning Guardian, emphasizing its protective role in database security. Keywords: #granite33:8b, MySQLi, PDO, PHP, SQL injection prevention, SQL validation, Shomer, advanced usage, best practices, configuration, dangerous keywords, debugging, development tool, email notifications, error alerts, error log, explicit columns, field count errors, hardcoded values, instant error report, missing WHERE clauses, parameter mismatches, prepared statements, production use, runtime security, secure SQL, security, security practices, superglobal variables, user input, validation report, zero performance impact
sql
github.com 2 days ago
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477. HN Show HN: Claude Code Skills Playground- The "Show HN" post presents the introduction of Claude Code Skills Playground, an interactive digital tool designed for users to explore coding abilities. - Users can hover over diverse coding skills listed to view comprehensive details about each skill, facilitating understanding and knowledge acquisition. - Alternatively, users have the option to select a specific skill for engagement in a chat-based environment, enabling them to apply learned concepts practically through conversation with an AI assistant. - This interactive platform aims to bridge theory and application, providing an immersive learning experience tailored for those looking to deepen their coding skills understanding. Keywords: #granite33:8b, ```Claude, chatting, chatting```Keywords: Claude, code, details, hover, playground, select, skills
claude
skillsplayground.com 2 days ago
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478. HN Track your Claude Code carbon footprint- **Summary**: An individual developed "Claude Carbon," an open-source macOS application, to monitor their personal carbon footprint resulting from using AI models like Claude Code. The user estimates daily usage at 12.7 million tokens, equating to energy needed for charging 100-200 phones or 15% of a typical US household's energy consumption, specifically for Claude Code. Overall AI footprint is higher. If just a million power users had similar usage patterns, it would total 1.6 TWh annually, equivalent to powering 150,000 homes. The application translates token usage into energy equivalents, raising awareness about the carbon impact of AI tasks and encouraging more efficient choices. Despite current macOS limitation, its open-source nature facilitates community scrutiny and contributions. - **Key Points**: - Created "Claude Carbon" to track personal carbon footprint from using Claude Code (an AI model). - Estimated daily token usage at 12.7 million tokens (equivalent to charging 100-200 phones or 15% of a typical US home's energy use). - Recognizes this is only for Claude Code; overall AI footprint would be higher. - If a million power users had similar usage, it would amount to 1.6 TWh annually (enough for 150,000 homes). - The application converts token usage into energy metrics, making the carbon impact of AI tasks transparent. - Encourages users to reconsider necessity and efficiency of AI tasks, asking if simpler models could suffice. - Hypothetically, if a million power users shifted 30% of tasks from Opus to Haiku (a more efficient model), it would save 340 GWh annually (powering 32,000 homes). - Currently macOS-only but open-source under MIT license for community scrutiny and contributions. Keywords: #granite33:8b, AI energy, Claude Carbon, GWh savings, GitHub, Opus), Stargate facility, Swift, TWh (terawatt-hours), US electricity, Xcode, calculations, casual ChatGPT users, data centers, efficiency, home power, macOS, models (Haiku, open source, power users, research, token usage
github
weeatrobots.substack.com 2 days ago
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479. HN Show HN: I built a tool to clear my YouTube's "Watch Later" Video Graveyard**Summary:** RecapioGPT is an AI-driven browser extension available in 15 languages designed to extract key points from YouTube videos and web articles, aiding users in quickly navigating and understanding lengthy content. It overcomes the limitations of YouTube's auto-generated captions by normalizing timestamps for precise information retrieval, akin to an advanced 'search' function for video contexts. The tool provides both free and paid tiers, catering to diverse user needs including researchers, content creators, and knowledge workers. Key features and benefits highlighted include: - **Efficient Summarization**: Accurately summarizes academic papers and online content within seconds, significantly reducing the time spent on reading extensive materials. - **Multi-disciplinary Application**: Aids in connecting ideas across disciplines and managing research libraries, making it useful for staying current within one's field of expertise or interest. - **Enhanced Content Creation**: Streamlines content creation by providing concise overviews, allowing creators to distill information effectively. - **Collaborative Advantage**: Facilitates knowledge sharing among teams, enhancing collaborative research and project work. - **Accessibility Feature**: Particularly beneficial for individuals with ADHD and avid readers, offering a tool that improves information retention and application. - **User-friendly Integration**: Seamlessly integrates into browsers, enabling users to summarize both web pages and PDFs effortlessly with a single click. **RecapioGPT** is hailed as an indispensable utility, described by users as a "game-changer" for content consumption, information retention, and research efficiency, effectively acting as a personal, always-available research assistant. Keywords: #granite33:8b, ADHD tool, AI, AI-powered, PDFs, academic papers, accurate, browser extension, concise, content creation, cross-disciplinary insights, extensive reading, industry trends, knowledge workers, language support, reading aid, research library, researchers, summaries, text summarization, timestamps, web pages, workflow
ai
recapio.com 2 days ago
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480. HN I learned to stop worrying and love AI slop- The article critiques the perception of AI-generated content as "slop," asserting that creators like Suerez, Vaserstein (Granny Spills), Lim, Anselmo, and Aleksic invest significant time and effort in refining prompts and actively guiding AI models to achieve desired artistic outcomes. - These artists counter the notion that their work lacks artistic intent and challenge the dismissal of their creations as lowbrow or unskilled, emphasizing the complexity and labor involved in AI content creation. - The discussion extends to the anxiety surrounding algorithmic influence on content distribution, which predates generative AI. This anxiety manifests as guilt for enjoying seemingly low-quality content and resentment toward creators seen as producing such material. Aleksic highlights a general feeling of manipulation by algorithms, with misplaced blame directed at generative AI as the latest visible culprit. - Despite these concerns, there is an acknowledged human desire for agency in response to algorithmic pressures shaping societal directions unchosen by individuals. - Early adopters of AI in video creation encounter backlash, including hateful messages accusing them of stealing opportunities from struggling artists and dismissing their work as "grifting" or "garbage." - A Brookings study indicates a 2% decrease in contracts and a 5% drop in earnings for freelancers in AI-exposed fields post-2022, illustrating the controversy stemming from the nascent stage of AI use in art. This period lacks established best practices or safeguards, contributing to perceptions of easy creation and undermining traditional artistic labor. Keywords: #granite33:8b, AI art, algorithmic anxiety, best practices, content creation, earnings, engineered attention, freelancers, generative AI, guardrails, hateful messages, human agency, nascent use, slop, tools, video, visuals
ai
www.technologyreview.com 2 days ago
https://archive.ph/AdIPr 2 days ago |
481. HN AI Representation Risk and the Emerging Requirement for Audit-Grade Evidence- The memo highlights the escalating risk posed by AI systems that can result in misrepresentation, with potential severe ramifications including legal repercussions, financial loss, and damage to reputation. - Currently implemented controls are considered insufficient for addressing these issues effectively as they lack traceable evidence. - The need for audit-grade evidence is emphasized to ensure accountability in AI systems, providing a robust framework for monitoring and controlling misrepresentation risks. - A governance checklist tailored for board-level oversight is proposed to tackle this emerging challenge proactively and systematically. BULLET POINT SUMMARY: - Risk of AI misrepresentation causing legal, financial, and reputational harm is increasing. - Present controls inadequate; require audit-grade evidence for accountability. - Proposal includes a governance checklist for board-level oversight to manage this emerging risk effectively. Keywords: #granite33:8b, AI, emerging requirement, evidence, financial consequences, governance, legal consequences, oversight, preserved evidence, real-time correction, reputational consequences, risk, technical error
ai
zenodo.org 2 days ago
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482. HN Show HN: White Collar Agent = a computer-use AI agent with TUI interface- **Project Overview**: White Collar Agent is an open-source, general-purpose AI tool designed for automating computer tasks through a Text User Interface (TUI). It's capable of autonomous task planning and execution in both CLI and experimental GUI modes. - **Key Features**: - High-volume repetitive work and batch processing capabilities. - Performances include directory translations, file organization, and image captioning. - An extendable base agent architecture for complex workflow automation via systematic agentic AI, runtime code generation, and autonomous execution. - A structured interface for task definition and a library of reusable tools. - Cross-platform compatibility on Linux and Windows. - **Technical Requirements**: Python 3.9+, git, conda, pip, and an API key from a chosen Language Learning Model (LLM) provider like OpenAI or Gemini are necessary for installation. - **Quick Start**: Users can run the CLI tool using `python -m core.main` after exporting their respective API keys. Docker configurations ensure consistent isolated environments with Python 3.10 and essential system packages such as Tesseract for OCR. - **Advanced Functionality**: - Interaction with an AI agent to perform complex tasks, running commands, seeking help. - GUI/screen automation through integrated tools like pyautogui, mss, X11 utilities, and a virtual framebuffer (currently experimental). - Proactive behavior, MCP Layer, and external tool integration are pending features. - **Custom Agent Development**: Users can extend the base agent class to create custom behaviors, roles, and actions using provided reusable core functionalities. A basic example of a custom agent named "MyCustomAgent" is given. - **Future Developments**: The project is actively developing new features such as a Memory Module and welcomes contributions from developers interested in enhancing the system's intelligent agent capabilities. - **License & Contact**: Licensed under MIT, allowing usage, hosting, and monetization with attribution required. Contributors can reach out to @zfoong or thamyikfoong(at)craftos.net for further involvement. Acknowledgments go to CraftOS and contributors @zfoong and @ahmad-ajmal. Keywords: #granite33:8b, AI, Action, Actions Library, BaseAgent, CLI, Cross-Platform, Docker, Executor, GUI, GUI automation, HTTP clients, LLM Wrapper, Lightweight, MIT License, Memory Module, OCR, Planner, Python, Reusable Tools, TUI, Task Document, Tesseract, Tool, White Collar Agent, X11 server, agent extension, automation, behavior, custom agent, deployment, execution logic, experimental, file handling, headless mode, mss, network APIs, open-source, personality, planning, pyautogui, reasoning, role, screen automation, system dependencies, tasks, virtual framebuffer, xvfb
ai
github.com 2 days ago
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483. HN I Ching Online – Ancient Divination and AI Interpretation- **Platform Overview**: I Ching Online is a digital platform offering divination services using AI to interpret traditional Chinese oracle, the I Ching, for users seeking guidance on career, business, and personal matters. - **User Experience**: The platform's design is noted for its calming interface which contributes positively to the ritual experience of divination, appealing to both novices and experienced practitioners of the I Ching. - **Accuracy and Insight**: Users frequently report that the AI interpretations are remarkably accurate and provide insightful readings, making it a trusted tool for decision-making. - **Therapeutic Application**: Professionals in therapy recommend I Ching Online as a resource for self-reflection, facilitating meaningful discussions about various life situations among users. BULLET POINT SUMMARY: - Digital platform offering I Ching divination with AI interpretations. - Calming interface enhances ritual experience, appealing to beginners and experts alike. - Users find readings accurate and insightful for personal and professional decisions. - Recommended by therapists for self-reflection and exploring life situations. Keywords: #granite33:8b, AI, Changing Lines, I Ching, accessibility, business decision, career, conversations, digital divination, essence, interface, interpretations, ritual, self-reflection, uncertainty
ai
i-ching-online-ai.com 2 days ago
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484. HN How best to use Browser/Chrome with Claude for testing and debugging- The user is transitioning from using playwright-mcp to the Claude Chrome extension for testing and debugging during development in Google Chrome. - Despite the benefits, the current method with Claude Chrome leads to excessive context consumption, causing the AI agent to overemphasize particular issues instead of comprehensively addressing the entire workflow. - This overfocus prevents the agent from effectively viewing console logs, capturing screenshots, or delivering a consolidated result, thus hindering thorough testing and debugging processes. - The user is seeking insights on how others successfully integrate either playwright-mcp or Claude Chrome for comparable tasks without encountering significant context loss issues. Keywords: #granite33:8b, Chrome, Claude, Playwright-mcp, Sonnet, agent, console logs, context, debugging, extension, flow completion, result compilation, screenshots, testing
claude
news.ycombinator.com 2 days ago
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485. HN Show HN: ASCII Canvas for AI Context- **Project Overview**: ASCII Canvas is a high-performance, collaborative ASCII art creation tool engineered for smooth interaction between humans and AI in the large language model (LLM) era. It boasts a multi-layer architecture for 60FPS rendering, smart indentation logic, comprehensive character support, and real-time collaboration through Yjs CRDT integration. - **Key Features**: - **Multi-layer Architecture**: Designed for seamless, high-speed rendering. - **Smart Indentation Logic**: Ensures proper alignment and formatting of ASCII characters. - **Wide Character Support**: Allows use of extensive Unicode characters for diverse artistic expression. - **Real-time Collaboration**: Utilizes Yjs CRDT for simultaneous editing by multiple users. - **Precision Editing Tools**: Includes anchor zoning, mass fill, and context hub menus for detailed control over ASCII creations. - **Technical Stack**: - Developed using React 18, TypeScript, Zustand, Yjs/Y-IndexedDB, @use-gesture/react, Tailwind CSS, Shadcn UI, and Radix UI. - Provides a text-based canvas editor within a React application, employing gesture recognition for interactive editing. - **Repository Contents**: - Includes zoning for area selection, anchor zoning, mass fill with characters, smart newline functionality, and context menu access. - Planned enhancements feature multi-layer rendering, real-time AI collaboration, intelligent indentation systems, clipboard integration, Next Edit Suggestion (NES) for predictive character placement, and export support for ANSI sequences and SVG formats. - **License and Setup**: Released under the MIT License. To utilize the project: - Clone the repository. - Install dependencies. - Run `npm run dev` for development or `npm run build` for production builds. - Utilize shortcut keys for various functionalities like zoning, anchoring, filling, new lines, tabbing, and context menu access. BULLET POINT SUMMARY: - ASCII Canvas is a collaborative ASCII art editor with high performance, targeting LLM era interactions. - Features multi-layer rendering, smart indentation, extensive character support, and real-time Yjs collaboration. - Built with React, TypeScript, various libraries for gesture recognition and UI components. - Offers precise editing tools: anchor zoning, mass fill, context menus, zoning, etc. - Repository contains current features plus planned enhancements like multi-layer rendering, AI integration, and export options. - MIT Licensed; setup via repository cloning, dependency installation, and build commands with shortcut keys for functionality. Keywords: #granite33:8b, ASCII Canvas, LLM era, Radix UI, React, Shadcn UI, Tailwind CSS, TypeScript, Yjs CRDT, Zustand, anchor zoning, collaborative, gestures, high-performance, multi-layer architecture, precision tools, real-time editing, robust persistence, semantic Unicode grids
ai
github.com 2 days ago
https://ascii-canvas.pages.dev/ 2 days ago https://github.com/Sayhi-bzb/ascii-canvas 2 days ago |
486. HN MiniMax M2.1- MiniMax has introduced the MiniMax M2.1 update, emphasizing real-world complex task performance enhancements across diverse programming languages including Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, and JavaScript. - Key improvements encompass superior multi-language capabilities, advanced understanding of design principles for web and app environments, efficient problem-solving with composite instruction constraints, faster response times, and optimized token consumption. - M2.1 outperforms its predecessor and competitors like Claude Sonnet 4.5 in benchmark tests, particularly excelling in multilingual scenarios. A new VIBE (Visual & Interactive Benchmark for Execution) has been established to evaluate the generation of complete, runnable applications across various domains, with M2.1 scoring an average of 88.6, close to Claude Opus 4.5's performance. - Developer feedback highlights M2.1's improvements in office scenarios, long-term tool interactions, and overall intelligence, surpassing closed-source models in certain software development tasks. - Notable for its efficient reasoning mechanisms minimizing redundant steps, M2.1 demonstrates proficiency in complex tasks such as multi-file refactoring and bug fixes while maintaining performance under parameter constraints. - Endorsements praise M2.1's exceptional performance from architecture design to deployment stages, leading in speed and resource efficiency, particularly suitable for high-throughput coding environments. - A showcase illustrates MiniMax-M2.1 controlling a robotic dog using learned models from virtual environments, exemplifying its generalization ability beyond digital applications. - M2.1 applications span diverse fields: - Physical environments: Robot control via virtual model transfer. - 3D interactive animations: Highly detailed scenes with complex particle effects. - Web design: Minimalist, visually impactful photographer websites. - Native app development: Gravitational simulator app for Android and iOS interaction components. - Web audio applications: Drum machine simulators using Web Audio API. - Security auditing: CLI/TUI tools for system element scanning and intelligent risk assessments. - Data visualization: Real-time data monitoring panels with futuristic aesthetics. - Image rendering: Complex ray tracing in real-time using C++ and GLSL. - Java high-performance bulletin board systems. - Interactive SVG island maps. - The "Digital Employee" feature allows autonomous utilization of tools like Excel and Yahoo Finance for tasks such as data cleaning, analysis, and generating charts. - MiniMax M2.1's API is accessible on their open platform (https://platform.minimaxi.com/docs/guides/text-generation) alongside the general-purpose MiniMax Agent product release at https://agent.minimaxi.com/. M2.1-lightning offers faster speeds suitable for high TPS demand users, with automatic caching for enhanced developer experience and performance improvements. Keywords: #granite33:8b, 3D rendering, AI, API, Agent footprint, Android, AppDev, BlackBox, C++, C++ image rendering, Claude Code, Cline, Coding Plan, Droid, Golang, InstancedMesh, Interleaved Thinking, Java, Java real-time bulletin board, JavaScript, Kilo Code, Kotlin, M21-lightning, MiniMax, Python data monitoring dashboard, React Three Fiber, Roo Code, Rust, Rust security audit tool, SVG interactive map generation, TPS, TUI, Tool use, TypeScript, VIBE benchmark, Web Audio API, WebDev, automatic, cache, complex particle animations, cost, deployment, digital employee, drum machine simulation, full-stack capability, gesture interaction, gravitational sensor simulator, iOS, latency, office automation, open source, reply efficiency, resources, speed, text generation
ai
www.minimaxi.com 2 days ago
http://archive.today/nDUc4 2 days ago https://huggingface.co/MiniMaxAI/MiniMax-M2.1 2 days ago |
487. HN AndyMik90/Auto-Claude: Autonomous multi-session AI coding**Summary:** Auto Claude is an AI-driven desktop application designed to enhance coding productivity by automating various development tasks, ensuring code quality, and streamlining the merge process. It operates across Mac, Windows, and Linux platforms, utilizing git worktrees for safe code development. Key features include autonomous agents handling planning, coding, validation, and conflict resolution, a Kanban board for task management, up to 12 AI-powered terminals for hands-on coding, insights via ChatGPT-like interfaces, automated documentation generation, and AI merge conflict resolution. The tool integrates with Claude Pro or Max subscriptions and requires the Claude Code CLI. **Key Points:** - **Cross-platform application**: Mac, Windows, Linux - **Git worktrees**: For safe code development without disturbing the main branch - **Autonomous agents**: Handle planning, coding, validation tasks - **Kanban board**: For task planning and visualization - **AI-powered terminals**: Up to 12 for hands-on coding, scalable for teams or heavy workloads - **ChatGPT-style interface**: Provides insights into code quality, bottlenecks, vulnerabilities, gaps in documentation - **Roadmap generation**: Based on target audience and project goals - **Automated changelog creation** - **AI merge conflict resolution**: Highly efficient (~98% prompt reduction) - **Security model**: Ensures safety with OS sandboxing, filesystem restrictions, command allowlists - **Project structure**: Includes '.worktrees' for AI workspace, '.auto-claude' for per-project data, 'auto-claude/' for Python backend framework code, and 'auto-claude-ui' for the Electron desktop application interface - **Licensing**: AGPL-3.0; requires attribution if distributed or used as a service, mandates open-sourcing modifications, and making source code accessible to network users - **Community and contributions**: Encourages participation through Discord community, welcomes improvements or expansions via CONTRIBUTING.md guidelines Auto Claude aims to increase productivity by 10x while ensuring code quality through its comprehensive automation of software development tasks, with an emphasis on AI-assisted conflict resolution, context engineering, and thorough validation processes. Keywords: #granite33:8b, 3-Tier Resolution, AGPL-30 license, Autonomous AI coding, CLI usage, Git Auto-Merge, Git commits, OS Sandbox, QA, QA reviewer, Requirements, Spec Creation, agents, auto-claude, coder agent, command allowlist, conflict resolution, context engineering, filesystem restrictions, git worktrees, isolated workspaces, merge resolution, parallel builds, phase implementation, planner agent, planning, project structure, research, roadmap, self-healing loop, self-validating, specs, syntax validation, terminals, validation, worktrees
ai
github.com 2 days ago
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488. HN Agentic AI: Building Autonomous AI Systems That Plan and Act- **Summary**: The text introduces the concept of "Agentic AI," which pertains to autonomous artificial intelligence systems with the ability to plan and act independently, marking a significant evolution in AI capabilities. It underscores the burgeoning market for advanced decision-making intelligence, reflecting an upward trend in the development and deployment of such systems. However, it's crucial to emphasize that this summary is abstracted from the title alone, lacking specific examples or intricate details provided within the content. - **Key Points**: - Definition of "Agentic AI" as AI systems capable of independent planning and action. - Highlights a growing market for decision-making intelligence in autonomous AI. - Signifies an advancement in AI technology towards more sophisticated, self-governing systems. - Clarification that this summary is derived solely from the provided title and doesn't encompass content-specifics or illustrative examples. Keywords: #granite33:8b, AI, Agentic, acting, autonomous, decision-making, intelligence, market, planning, rise, systems
ai
substack.com 2 days ago
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489. HN Another Unified AI API- OpenAI's Sora 2 is an advanced AI video creation tool designed to transform textual descriptions and images into sophisticated videos. - The platform significantly enhances motion realism, ensuring smoother and more lifelike character movements in the generated content. - It incorporates consistent physics, maintaining believable interactions between objects within the scenes, which was a potential issue in its predecessor. - Users gain greater control over various aspects of video creation, such as style customization, scene structuring, and aspect ratios to fit diverse platform requirements. - Sora 2 is particularly beneficial for content creators and businesses needing swift production of high-quality videos for applications like marketing campaigns and social media posts. The summary adheres strictly to the information provided in the text about OpenAI's Sora 2 without introducing external data, detailing its improvements in motion realism, physics consistency, and user control over video generation elements, while highlighting its utility for marketing and social media content creation. Keywords: #granite33:8b, AI, Sora 2, aspect ratios, creators, image to video, marketing campaigns, motion realism, physics, scenes, social media content, style control, text to video, video creation
ai
www.apipod.ai 2 days ago
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490. HN Nano Banana AI Image Editor Advanced Image Generation and Edit- Nano Banana AI Image Editor is a sophisticated tool designed for both image generation and editing. - It specializes in one-shot editing, providing users with instant, flawless results through artificial intelligence. - The software also supports batch processing, enabling the simultaneous editing of up to 50 or more images while maintaining consistent quality and style across all edited files. - This feature is particularly beneficial for professionals and agencies looking to optimize their workflow by reducing time spent on revisions. Keywords: #granite33:8b, Advanced Generation, Batch Editing, Batch Processing, Consistent Quality, Content Teams, Image Editor, Intelligent AI, Multiple Images, Nano Banana AI, One-Shot, Professionals, Revision Time, Style Maintenance, Style MaintenanceKeywords: Nano Banana AI
ai
nano-bananaai.org 2 days ago
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491. HN Instant database clones with PostgreSQL 18**Summary:** PostgreSQL 18 introduces improvements to its templating system for efficient instant database cloning without heavy I/O operations (checkpoint storms) that were common during file-level snapshot creation, especially beneficial for large databases. Version 15 started using the WAL_LOG strategy for block copying via Write-Ahead Log, enhancing sequentiality and concurrency but slowing down large database cloning. Version 18 brings back flexibility with the STRATEGY parameter, allowing selection from various methods including the new 'clone' option that utilizes filesystem-level features like reflinks (XFS), ZFS snapshots, or APFS clones. This method allows near-instant cloning without additional storage usage by leveraging copy-on-write capabilities of modern filesystems. To implement this: 1. Ensure the system supports compatible filesystems such as XFS, ZFS, APFS, or FreeBSD with ZFS. 2. Run a PostgreSQL cluster on the chosen filesystem. 3. Update the configuration file (`postgresql.conf`) to set `file_copy_method = clone`. 4. Reload the configuration for changes to take effect. A benchmark example demonstrates creating a 6GB database named `source_db`, showing that using the default WAL_LOG strategy takes approximately 67 seconds, while switching to `STRATEGY=FILE_COPY` (file_copy method) reduces cloning time to around 212 milliseconds, significantly improving efficiency. With `file_copy_method = clone`, PostgreSQL doesn't duplicate data physically; it creates new metadata pointing to the shared storage blocks, resulting in both databases (`source_db` and its clone) reporting a logical size of ~6GB while sharing the exact physical data, conserving disk space. PostgreSQL maintains its reported logical size (~6GB) as it encapsulates all database contents but operates by writing new tuple versions instead of modifying existing ones in place, causing copy-on-write behavior across multiple pages (including tuples, index pages, free space map, and visibility maps). This behavior can be verified using the `filefrag` command to show shared physical blocks between databases. The text also details an SQL command to update rows in a table named 'boring_data' and provides output from the `filefrag` utility indicating file fragmentation details of two large files in the PostgreSQL data directory, suggesting storage efficiency analysis. Furthermore, it explains that while cloning is efficient within a single filesystem, databases spanning multiple tablespaces on different mount points require regular physical copies. In cloud environments like AWS RDS or Google Cloud SQL, direct filesystem access for such configurations is typically unavailable, and users must rely on proprietary, billed functionalities. For self-managed VMs or bare metal servers, cloning remains feasible following the outlined steps. **Bullet Points:** - PostgreSQL 18 enhances instant database cloning through its templating system without heavy I/O bursts (checkpoint storms). - STRATEGY parameter introduced in PostgreSQL 18 allows choosing from various cloning methods, including 'clone' for near-instant cloning using filesystem copy-on-write features. - Support required: Compatible filesystems like XFS, ZFS, APFS, or FreeBSD with ZFS; running PostgreSQL cluster on chosen filesystem; updating `postgresql.conf` to set `file_copy_method = clone`. - Benchmark shows significant speed improvement from ~67 seconds (WAL_LOG) to ~212 milliseconds (FILE_COPY) for cloning a 6GB database. - `file_copy_method = clone` avoids physical data duplication, creates new metadata pointing to shared storage blocks, saving disk space while maintaining ~6GB logical size. - PostgreSQL employs copy-on-write behavior during updates, affecting multiple pages and demonstrable via `filefrag`. - While efficient within a single filesystem, databases spanning across different mount points need regular physical copies. - Cloud environments like AWS RDS or Google Cloud SQL limit direct filesystem access for advanced configurations, typically requiring proprietary, billed tools. - Self-managed VMs or bare metal servers allow feasible implementation of the described cloning method. Keywords: #granite33:8b, 8KB pages, APFS, CHECKPOINT, CREATE DATABASE, FILE_COPY, FSM, FreeBSD, I/O spike, PostgreSQL, SQL query, STRATEGY, VACUUM, WAL_LOG, XFS filesystem, benchmark, cloning, copy-on-write, database OID, dead tuples, dummy data, eof, extents, file-level cloning, filefrag, filesystem, free space tracking, id, in-place update, indexed columns, instant clones, last, limits, logical size, logical_offset, payload, physical_offset, production traffic, reflinks, relfilenode, shared, shared blocks, template1, templating system, update statement, visibility map pages, zero-copy
postgresql
boringsql.com 2 days ago
https://docs.aws.amazon.com/AmazonRDS/latest/Auror 2 days ago https://github.com/BenjaminFaal/pgtt 2 days ago https://github.com/elitan/velo 2 days ago https://clickhouse.com/docs/sql-reference/statemen 2 days ago https://github.com/allaboutapps/integresql 2 days ago https://neon.com/ 2 days ago https://xata.io/ 2 days ago https://github.com/elitan/velo/blame/12712e26 2 days ago https://github.com/peterldowns/pgtestdb 2 days ago https://blog.danieljanus.pl/2025/04/22/datomi 2 days ago https://github.com/skeema/skeema 2 days ago https://www.postgresql.org/docs/current/sql-copy.h 2 days ago https://github.com/peterldowns/pgmigrate 2 days ago https://github.com/flyway/flywaydb.org/blob/g 2 days ago https://boringsql.com/posts/beyond-start-end-columns&#x 2 days ago https://dev.mysql.com/doc/refman/8.0/en/ 2 days ago https://mariadb.com/docs/server/server-usage/ 2 days ago |
492. HN 10 years bootstrapped: €6.5M revenue with a team of 13**Summary:** DatoCMS, a 10-year-old bootstrapped company with €6.5M revenue and a team of 13, has demonstrated remarkable growth and profitability, achieving an exceptional EBIT margin of 65% and a "Rule of 40" score of 75%, placing them in the top global SaaS quintile. With 185 agency partners and 340 projects this year, DatoCMS has maintained steady 10% year-over-year growth without external investment. Key product updates include: - Introduction of type safety for Records in JavaScript client, boosting developer confidence by eliminating ambiguous "any" types. - Real-time synchronization of plugin settings to address configuration conflicts in collaborative environments. - Optimized documentation and addition of "Copy as Markdown" feature for seamless integration with AI tools like ChatGPT or Claude. Preparing for AI integration, DatoCMS implemented a Model Context Protocol (MCP) server for AI assistant integration, bulk translation with multiple AI services, and Structured Text to Markdown packages. Other notable updates encompass inline blocks in Structured Text for infinite nesting, Tabular View for hierarchical models, favorite locales pinning, enhanced previews, Single Block presentation, improved link field filtering, fixed headers, and API & tooling enhancements focused on improving content editing efficiency. Infrastructure and security improvements have been made with: - Migration from Heroku to a custom Kubernetes cluster on AWS, resulting in halved CDA response times and significant cost reductions (25%). - Adopting Terraform for Infrastructure as Code, switching CDNs and storage solutions, replacing expensive log monitoring tools with open-source alternatives, and creating a kubectl wrapper for enhanced Kubernetes management. - Strengthened security features including limited permissions access, deletable API tokens, last used time display, removal of default full-access tokens, and improved roles & permissions interface. DatoCMS emphasizes control, long-term strategy, product quality, work-life balance, and profitability, actively involving agency partners in shaping the product's evolution while remaining tight-lipped about future projects, maintaining a humorous and unpretentious corporate culture. **Bullet Points:** - DatoCMS, 10 years old, €6.5M revenue, 13-person team, top global SaaS performer (EBIT 65%, Rule of 40 75%). - Product updates: type safety for Records, real-time plugin setting sync, AI-friendly documentation, "Copy as Markdown" feature. - Preparing for AI integration: MCP server, bulk translation with AI services, Structured Text to Markdown conversion. - Infrastructure improvements: migrated to AWS Kubernetes, reduced costs by 25%, decreased latency and increased API capacity. - Enhanced security: limited permissions, deletable tokens, last used time display, Prometheus & Loki for logging. - Focus on control, product quality, work-life balance, profitability, active agency partner collaboration, future projects secrecy. Keywords: #granite33:8b, 10-year growth, 13-team, 185 agency partners, 65% EBIT margin, 75% Rule of 40 score, AI Translations, AI readiness, API, API Tooling, AWS EKS, AWS S3, Accounting internalization, Build Triggers, CDN Caching, CLI, ChatGPT integration, Claude, Cloudflare, Cloudflare R2, Cost reduction, DatoCMS recipes, DeepL, Deletion, Developer Experience, Enhanced Previews, Favorite Locales, Fixed Headers, Gemini, GraphQL, Improved Link Field Filtering, Infrastructure as Code, Inline Blocks, LLM-Ready Documentation, Loki, MCP Server, Markdown, Observability, OpenAI, Pagination, Permissions, Prometheus, Quality Control, Reactive Plugins, Realtime API capacity, Records typing, Roles, Rule of 40 compliance, SEO fallbacks, Security, Single Block Presentation, Site Search, Storage, Structured Text, Tabular View, Terraform, Tokens, Usage Tracking, Workflow, agency partnership, brand loyalty, confirmation guardrails, cubo, default draft mode, developer confidence, force validations, import/export, improved roles, infrastructure independence, kubectl, offline wayfinding, plugins ecosystem, product improvements, save invalid drafts, tech debt, type safety, workflows, €65M revenue
claude
www.datocms.com 2 days ago
https://tinyteams.xyz/ 2 days ago https://www.startuphacks.vc/blog/founders-guide-to-seco 2 days ago https://i.horizon.pics/dFFNvWFUZp 2 days ago https://academyofmine.com 2 days ago https://community.intercoin.app/t/2025-year-in-review&# 2 days ago https://www.linkedin.com/posts/englishpaulm_just-heard- 2 days ago https://www.datocms.com/partners/showcase 2 days ago |
493. HN The Dark Data Tax: How Hoarding is Poisoning Your AI**Detailed Summary:** The text discusses the paradoxical issue of "data obesity," where advancements in low-cost storage technologies, exemplified by Lakehouse architectures using object storage (S3, ADLS, GCS) with Delta Lake, Iceberg, and Hudi, have led to an exponential increase in data volume rather than reducing costs. While storage expenses dropped by 80% over a decade, the ease of accumulating vast amounts of data has resulted in organizations amassing more data than they can effectively analyze—90% of which remains unanalyzed by 2025. This situation is analogous to Jevons' Paradox, where efficiency gains lead to increased consumption, now manifesting as an inability to discern the significance of collected data due to what's termed the "Dark Data Tax." This tax refers to the hidden costs associated with poorly managed, undocumented, or redundant data within a data lakehouse, which act as internal poisoning when used for AI analysis. The introduction of Large Language Models (LLMs) has exacerbated this by indiscriminately embedding all available data, leading to inefficiencies and the generation of "hallucination vectors" or errors from conflicting information within ungoverned data partitions. To address these issues, the text proposes an ecological predator-prey framework inspired by the Lotka-Volterra equations, comparing data volume (prey) to data value (predators). Key variables illustrate the dynamics of prey growth and predator evolution, with dark data akin to overpopulated species starving the 'predators' (analysts and decision-making systems) of necessary nutrition. The proposed solution involves developing a Data Sustainability Index (DSI), which measures the efficiency of an organization's data ecosystem by comparing the compute generated from analytical activities to overall costs and complexities. Components include Total Analytical Compute Hours, Lakehouse Total Cost, and Active Dataset Ratio, aiming to penalize unused datasets and maintain active, relevant data. Furthermore, data obesity is characterized by four issues: Operational Debt (cost of unused infrastructure), Cognitive Debt (tax on decisions due to excessive data variations), Compliance Risk (potential fines for retaining unnecessary personal data), and Cultural Drift (normalized hoarding of data). To combat this, the text suggests an autonomous Data Obesity Controller managing data metabolism through real-time telemetry, decision attribution logs, cost per table, schema drift alerts, and actions like auto-archiving dark datasets, improving semantic models, alerting on insight decay, and recommending dataset deprecation. **Bullet Points Summary:** - Lakehouse technologies have lowered storage costs but led to an exponential increase in data volume due to reduced collection barriers, referred to as "data obesity." - 90% of the projected 175 zettabytes of unstructured data by 2025 remains unanalyzed, akin to Jevons' Paradox where efficiency improvements stimulate increased resource consumption. - The "Dark Data Tax" refers to inefficiencies and compromised insights from poorly managed data within a data lakehouse, acting as internal poison when used for AI analysis. - Large Language Models (LLMs) exacerbate data obesity by indiscriminately embedding all available data, leading to inefficient processing and the generation of "hallucination vectors." - An ecological predator-prey framework inspired by Lotka-Volterra equations is proposed to understand the dynamics between data volume (prey) and value (predators). - The Data Sustainability Index (DSI) measures the efficiency of a data ecosystem, focusing on compute generated from analytical activities versus overall costs and complexities. - Key components of DSI include Total Analytical Compute Hours, Lakehouse Total Cost, and Active Dataset Ratio to maintain active, relevant datasets. - Data obesity results from Operational Debt, Cognitive Debt, Compliance Risk, and Cultural Drift, proposing an autonomous Data Obesity Controller for managing data metabolism through real-time telemetry and automated actions like archiving dark datasets and improving semantic models. Keywords: #granite33:8b, LLM performance, Lakehouse, PDF, RAG pipelines, auto-archive, communication archives, compliance audits, compute costs, dark datasets, data obesity, document corpora, embedding, enterprise adoption, hallucination vectors, inconsistent data, insight decay, maintenance, maximal data exposure, model robustness, redundancy, schema drift alerts, schema evolution, semantic model improvements, storage capacity, support tickets, unstructured data, vectorization
ai
www.dataengineeringweekly.com 2 days ago
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494. HN Show HN: Aluo – AI product photo and ecommerce image editor**Summary:** Aluo is an advanced AI-driven tool designed for editing product photos and enhancing ecommerce images. Currently, it provides a complimentary plan that includes access to essential features without any charge. As user demand escalates, Aluo offers flexible payment options: subscription plans tailored for higher usage or a pay-as-you-go credit system, both of which aim to be more economical compared to traditional methods of engaging professional photographers or designers. The development team behind Aluo is actively in a rebuilding phase and is soliciting feedback from the community to refine and improve their product offerings. This approach emphasizes user involvement, ensuring that the final product aligns with market needs and expectations. **Key Points:** - Aluo is an AI-based platform for editing product photos and ecommerce images. - Offers a free plan with core features, ideal for starting users or limited use. - Provides subscription plans and pay-as-you-go credits for increased usage. - More cost-effective compared to hiring professional photographers or designers. - Currently in a major rebuild phase, seeking community feedback for product improvement. Keywords: #granite33:8b, AI, community feedback, core features, credits, ecommerce, free, free credits, images, pay-as-you-go, photo editor, plan, plans, product, rebuild, refinement, subscription
ai
aluo.ai 2 days ago
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495. HN Introduction to Software Reverse Engineering**Detailed Summary:** Software reverse engineering is the meticulous examination of software's internal mechanisms to comprehend its structure, function, or implementation—used for diverse purposes such as troubleshooting, security assessment, and interoperability enhancement. This concept transcends computing, likened to understanding complex systems in various domains like music, cooking, plumbing, film production, or even genetics. The text distinguishes between reverse engineering (analysis) and forward engineering (design from scratch), illustrating their differences with examples ranging from reconstructing a song's title based on its notes to drafting blueprints for construction projects. Both processes are integral across fields including software development, manufacturing, architecture, and agriculture. In the context of software, reverse engineering involves dissecting compiled code to achieve goals such as developing antivirus programs by understanding virus mechanics, creating sophisticated yet undetectable malware for malicious purposes (like stealing passwords), breaching server security, crafting game cheats, improving existing software features (e.g., adding language support like Hebrew to the iPhone), or patching security vulnerabilities in pre-existing software. Reverse engineering plays dual roles: defensive (building countermeasures) and offensive (exploiting systems). The narrative introduces white hat hackers (security researchers) contrasted with black hat hackers (malicious actors), emphasizing practices like red team/blue team exercises simulating cyberattacks within organizations and social engineering tactics that manipulate users to reveal sensitive information. Source code, specific to programming, is distinguished from everyday terms like "recipe." Unlike recipes that allow for interpretation, source code requires precise instructions due to the critical nature of computer operations. Compilers translate high-level languages into machine code, facilitating software development by converting human-readable high-level code into vast, intricate machine code executed by CPUs. CPUs operate on basic arithmetic and data manipulation through machine code instructions. The contrast between user interactions with computers and the underlying complex hardware processes is noted, illustrated by how Nvidia's processors enhance AI and graphics rendering, enabling advanced human-computer interfaces. Historically, software development involved punch cards for inputting code—a laborious process prone to errors due to physical limitations. Compilers emerged as solutions, converting high-level languages into machine code, significantly boosting developer efficiency and reducing bugs while facilitating continuous software enhancement. Reverse engineering is described as interpreting compiled code to check for vulnerabilities, vital for secure software. While challenging due to abstraction layers in modern software, having source code simplifies the process. This practice aids in identifying malware behavior, troubleshooting, enhancing functionality, detecting piracy, creating cheats or hacks, and cracking software, requiring speed, patience, and analytical precision. The text underscores the value of reverse engineering, illustrated by the author's identification of significant Windows OS vulnerabilities in 2021, leading to substantial rewards from Microsoft, highlighting its importance in cybersecurity. Past experiences at NorthBit (2012-2016) further validate these applications, involving reverse engineering for tasks like malware removal, iPhone automation, and network attack detection, alongside discussions on anti-reverse engineering techniques employed by software vendors. **Bullet Points:** - Software reverse engineering involves understanding existing software's internal workings to achieve varied goals (debugging, interoperability, security). - It contrasts with forward engineering, which creates new systems from scratch using blueprints. - Essential in fields beyond computing—analogous to understanding complex human-made systems. - Used for creating antivirus, crafting malware, breaching servers, enhancing software features, and patching vulnerabilities. - Dual role: defensive (building countermeasures) and offensive (exploiting systems), exemplified by white hat and black hat hackers. - Source code is precise, unlike recipes that allow flexibility; compilers convert high-level languages to machine code CPUs execute. - CPUs process basic operations via machine code; complex software results from these simple instructions. - Reverse engineering critical for vulnerability checks in secure software like terminal servers. - Challenging due to modern software's abstraction layers but valuable for tasks like malware identification, troubleshooting, and security enhancements. - Historical and practical examples (author’s 2021 Windows OS vulnerabilities discovery, NorthBit’s diverse client work) demonstrate its utility in cybersecurity and software development. Keywords: #granite33:8b, AI, Abstraction Layers, Analysis, Antivirus, Computer Functionality, Cyber Warfare, DNA, Development, Genome, Graphics, Hacking, Machine Code, Malware, Network Attacks, Operating Systems, Processing Units, Programming Languages, Punch Cards, Reverse Engineering, Security Vulnerabilities, Software, Source Code, Viruses
ai
go.mcptotal.io 2 days ago
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496. HN AI and Travel Planning: The Case for Human Expertise- The text highlights a burgeoning demand for individualized and distinctive travel experiences, contrasting the conventional mass tourism model. - Artificial Intelligence (AI) is identified as a significant facilitator of this transformation, predicted to be extensively adopted in the travel sector by 2025. - Despite its potential, concerns are raised regarding AI's tendency to suggest high-cost options and guide users towards mythical destinations, indicating current limitations. - The author from Archaeology Travel advocates for their Itinerary Builder as a sustainable solution for bespoke travel planning, underscoring human expertise alongside technology. - While acknowledging AI's benefits, the text argues that its inherent constraints in understanding personal preferences and ethical considerations make human involvement crucial for crafting meaningful travel experiences. - Archaeology Travel's services are presented as a preferable alternative, blending human insight with technology to deliver genuinely personalized journeys. `BULLET POINT SUMMARY:` - Shift from mass tourism to personalized, unique travel experiences. - AI predicted to transform the travel industry by 2025 but faces criticism for suggesting expensive options and fictional destinations. - Archaeology Travel's Itinerary Builder proposed as a sustainable, human-expertise driven alternative for tailored trip planning. - Recognition of AI limitations in grasping nuanced traveler needs and ethical implications. - Human expertise advocated alongside technology to ensure genuine personalization in travel experiences, positioning Archaeology Travel's services as a superior choice. Keywords: #granite33:8b, AI, AI uses, Archaeology Travel, core feature, fundamental limitations, human expertise, hyper-personalized, one-size-fits-all trips, personalized itineraries, sustainable alternative, travel planning, travellers' demands, unique experiences, verified itineraries
ai
archaeology-travel.com 2 days ago
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497. HN AI Is Bad UX**Summary:** The text explores the paradox surrounding the growing skepticism toward Artificial Intelligence (AI) in America and the challenges of effectively utilizing large language model-based AI systems like Microsoft's Copilot or ChatGPT. Proponents argue that failure to adapt to these tools may result in individuals being left behind due to labor disruptions, yet users often misuse or misunderstand AI, leading to unhelpful outcomes as the quality of outputs is not prioritized. The author highlights a key issue: unlike clear utilities seen in earlier technologies (e.g., personal computers, internet), AI's potential benefits are not readily apparent to most users. This stems from its complex nature, which can make it seem intrusive and potentially harmful, contrasting with the more manageable barriers of previous technological advancements. Drawing on psychologist James Gibson’s concept of "affordances," the text explains how humans perceive objects' potential uses based on their interactions. This principle has significantly influenced interaction design, emphasizing intuitive interfaces that guide users through clear visual cues. The idea extends to social interactions, where unconscious cues dictate appropriate behavior. The author suggests that the human brain’s 30% volume dedicated to processing social interactions underscores its evolutionary importance. This ability enables understanding of others' goals and perceptions, proposing "a person I can talk to" as the most intuitive user interface metaphor for conversational AI. However, a "great UX swindle" arises from misleading metaphors of human-like entities within AI. Despite users expecting human comprehension and goals, these systems fall short, causing confusion ("AI psychosis") and frustration. This mismatch between powerful user metaphors and actual system abilities is compared to worst-case UX scenarios, such as unpredictable controls. Language Learning Models (LLMs) aim to provide accurate information but can include false details due to training data limitations. While they can generate creative content reflecting a point of view, misconceptions about AI's self-awareness or capacity for emotions persist. Proficient users typically have programming backgrounds, treating AI as an intricate language requiring precise instructions and expecting system complexities that demand constant vigilance. A stark contrast exists between executives envisioning limitless AI potential and end-users facing frustrations with limitations and harmful side effects. Developers acknowledge both the transformative power of AI and the current user experience's abyss of misunderstanding, suggesting a radical reimagining of AI’s user interface is necessary for socially beneficial implementation. **Key Points:** - Skepticism towards AI in America is growing; adaptation is crucial to avoid being left behind due to labor disruptions. - AI tools, like Copilot and ChatGPT, seem versatile but are hard to use effectively, often leading to misuse with unhelpful or harmful outcomes. - Unlike previous technologies (computers, internet), AI's benefits aren't apparent; its complexity makes it seem intrusive and potentially negative. - "Affordances" concept: how humans perceive potential uses of objects, influencing interaction design for intuitive interfaces. - Misleading human-like metaphors in AI lead to confusion ("AI psychosis") as systems fail to meet user expectations of comprehension and goals. - Language Learning Models (LLMs) strive for accuracy but can produce false information; creative outputs are possible, yet misconceptions about self-awareness persist. - Proficient users usually have programming backgrounds, viewing AI as a complex language requiring detailed instructions. - Contrast between executives' optimism and end-users' frustrations with AI's limitations points to the need for radically improved user interfaces. Keywords: #granite33:8b, AI, ChatGPT, Don Norman, HTML, LLMs, UX, adversarial interaction, affordances, agreement, animate beings, antisocial use, articles, artistic statement, automation, behavior, brain volume, capability, chatbot, clickable, clunky, communication frameworks, complexity, conspecifics, conversational interfaces, copilot tools, copying, deletion, delusional ideation, disappointment, elaboration, evolutionary pressures, executives, expertise, frustrating, frustration, funhouse mirror, generative models, goals, graphical user interface, icons, implementation, interaction design, interfaces, internal states, intuition, labor disruption, malign social byproducts, malpractice, metaphor, mindless, misleading metaphor, movement indication, multifunctional models, natural language interface, person, precision, programming, ratiocination, reasoning, search tool, side effects, skepticism, sliding control, slow, social cues, social interaction, specialized roles, spirals, suspicion, talking to, technical keywords, theory of mind, tooling, training, transformative, truthfulness, typewriters, unhelpful use, unintuitive behavior, user interface metaphors, utility, visual perception, web pages, web servers, window controls, word processors
ai
buttondown.com 2 days ago
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498. HN iOS 26.3 brings AirPods-like pairing to third-party devices in EU under DMA<- Controlled self-response as per instructions -> **Summary:** iOS 26.3, slated for a January release, introduces modifications influenced by the European Union's Digital Markets Act (DMA). This update facilitates interoperability with third-party devices, notably wearables such as smartwatches and headphones, mirroring the seamless pairing experience offered by Apple's AirPods. Central to these changes are proximity-based one-tap pairing and extended functionality allowing third-party devices to display iPhone notifications. The European Commission endorses these enhancements, viewing them as significant progress toward a cohesive digital environment within the EU, with complete implementation anticipated by 2026. **Bullet Point Summary:** - iOS 26.3 release planned for late January. - Updates driven by EU's Digital Markets Act (DMA). - Enhances compatibility with third-party devices, especially wearables. - Introduces one-tap pairing via proximity detection (similar to AirPods). - Third-party devices can now receive and display iPhone notifications. - European Commission approves these interoperability improvements for fostering a unified digital ecosystem in the EU. - Full functionality expected by 2026. - Changes apply specifically to EU-based device manufacturers and EU-region iPhone/iPad users. Keywords: #granite33:8b, AirPods, DMA, EU, European Commission, TVs, device makers, digital ecosystem, headphones, iOS, iOS 263, iPad, iPhone, interoperability, notifications, pairing, proximity pairing, smart watches, third-party devices, wearable devices
popular
www.macrumors.com 2 days ago
https://www.theverge.com/news/737757/apple-preside 22 hours ago https://gs.statcounter.com/vendor-market-share/mobile&# 22 hours ago https://www.theguardian.com/us-news/2025/jun/ 22 hours ago https://issuetracker.google.com/issues/371713238 22 hours ago https://issuetracker.google.com/issues/371713238#commen 22 hours ago https://www.theguardian.com/fashion/2025/dec/ 22 hours ago https://maps.google.com 22 hours ago https://maps.apple.com 22 hours ago https://daringfireball.net/linked/2015/03/14& 22 hours ago https://daringfireball.net/linked/2017/03/17& 22 hours ago https://www.those.ch/designtechnik/wp-content/uplo 22 hours ago https://en.wikipedia.org/wiki/Cartel 22 hours ago https://en.wikipedia.org/wiki/John_Gruber 22 hours ago https://github.com/kavishdevar/librepods 22 hours ago https://arstechnica.com/gadgets/2010/06/jobs- 22 hours ago https://www.macrumors.com/2010/06/24/steve-jo 22 hours ago https://9to5mac.com/2025/10/08/a-15-year-myst 22 hours ago https://mistral.ai 22 hours ago |
499. HN Show HN: A vibe-coded database GUI- **Project Overview**: The project unveils an intuitive Graphical User Interface (GUI) for a database, designed to facilitate interaction through vibe-coding, a novel approach that translates user intent into code. - **User Interaction**: Users can articulate their data-related inquiries in everyday language, bypassing the need for conventional SQL syntax mastery. - **AI Integration**: The system employs Artificial Intelligence (AI) technology to comprehend natural language queries and propose sophisticated SQL query drafts tailored to user needs. - **Query Optimization**: Beyond generation, the AI aids in refining and optimizing these suggested queries for efficiency and accuracy, streamlining the database interaction process. This summary encapsulates the main features of the project: its focus on natural language processing to interact with databases, reliance on AI for query suggestion and optimization, and its aim at simplifying database access through a user-friendly GUI. The key point bullets reflect these aspects directly derived from the provided text. Keywords: #granite33:8b, AI, English, GUI, SQL, database, optimization, questions, vibe-coded
ai
seaquel.app 2 days ago
https://www.mikenikles.com/blog/i-vibe-coded-a-database 2 days ago https://zenquery.app/ 2 days ago https://news.ycombinator.com/item?id=44321099 2 days ago |
500. HN Show HN: We built an AI Humanizer to fix unnatural AI writing- **AI Humanizer Overview**: A tool developed by Dechecker to refine AI-generated content, focusing on sentence-level improvements for better readability and natural flow. - **Distinction from AI Detectors**: Unlike AI detectors that identify patterns in AI writing, AI Humanizer aims to enhance the quality of AI text without attempting to deceive detection methods. - **Target Users**: Intended for writers, bloggers, students, academics, marketers, content creators, businesses, international teams, and e-learning platforms to polish AI-assisted drafts into clearer, more engaging human-like text while preserving original ideas. - **Functionality**: Works to vary sentence structures, reduce repetition, and improve overall flow, making the content smoother and more accessible for human readers without altering the core message or academic/logical integrity of the text. - **Multilingual Capability**: Supports various languages, ensuring consistent high-quality communication globally suitable for international teams and businesses. - **Application in E-Learning**: Enhances readability of complex course materials, making it easier for students to understand educational content. - **Availability**: Accessible at Keywords: #granite33:8b, AI writing, complex topics, content production, e-learning, emails, flow enhancement, human-like text, marketing copy, professional communication, repetition reduction, sentence structure, social media posts, tone improvement
ai
dechecker.ai 2 days ago
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501. HN Memelang: An Axial Grammar for LLM-Generated Vector-Relational Queries- **Paper Introduction:** The research paper titled "Memelang: An Axial Grammar for LLM-Generated Vector-Relational Queries" introduces Memelang, an axial grammar designed to refine the generation of vector-relational queries using large language models (LLMs). This aims to enhance the efficiency and precision in querying relational data within a vector database. - **Key Features of Memelang:** - Uses compact Domain-Specific Language (DSL) intermediate representations (IRs) that can be deterministically emitted and parsed. - Employs an axial grammar with linear token sequences and rank-specific separator tokens to infer multi-dimensional structure unambiguously. - Facilitates a single left-to-right pass for assigning each token coordinates in an n-dimensional grid, eliminating the need for complex syntax like parentheses. - Designed as an LLM-emittable query language with fixed roles mapping directly to table/column/value slots. - Supports features such as relative references, variable binding, and context carry-forward to minimize redundancy in queries generated by LLMs. - Encodes grouping, aggregation, and ordering through inline tags on value terms for efficient execution plan derivation. - **Implementation Details:** - Offers a reference lexer/parser along with a compiler that generates parameterized PostgreSQL SQL (with optional pgvector operators). - Submitted to arXiv under the category of Databases (cs.DB) and accessible via the identifier arXiv:2512.17967 [cs.DB]. - **Associated Resources:** The text also mentions various tools related to research dissemination on platforms like arXiv, including bibliographic explorers, connected papers, Litmaps, scite Smart Citations, code repositories (alphaXiv, CatalyzeX, DagsHub, GotitPub, Hugging Face, Papers with Code), ScienceCast, and replication platforms (Replicate, TXYZ.AI). Recommender tools such as Influence Flower and CORE Recommender are also mentioned. - **arXivLabs:** An experimental platform for community-driven innovation of new arXiv features is introduced, emphasizing values like openness, collaboration, excellence, and user data privacy. - **Contact and Subscription Information:** The text provides links for contacting arXiv, subscribing to its updates, accessing copyright policy, privacy policy details, web accessibility assistance, and operational status information. - **Additional Notes:** It's important to note that the provided text does not detail substantive content or summary about arXiv itself but rather focuses on the described research paper and associated tools/resources. Keywords: #granite33:8b, Aggregation, Axial Grammar, Context Carry-Forward, Grouping, Inline Tags, LLM, Memelang, Ordering, PostgreSQL SQL, Query Language, Relative References, Streaming Pass, Variable Binding, n-dimensional Grid
llm
arxiv.org 2 days ago
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502. HN The AI History That Explains Fears of a Bubble- Investor interest in AI sector, particularly companies like Nvidia, is high due to the transformative potential of AI but also raises concerns about a potential bubble, given that key players like OpenAI (developer of ChatGPT) remain unprofitable and struggle with monetization. - AI's promise lies in its ability to reshape economies and displace jobs, yet skepticism arises from the limitations of current AI models focusing on specific tasks and the difficulty in assessing their performance on subjective criteria such as creativity or contextual understanding. - The history of AI dates back to the 1956 Dartmouth workshop, initially funded by DARPA for Cold War technological superiority, experiencing cycles of hype and disillusionment due to unmet practical application promises. A notable example is the 1980s' "expert systems" that failed to deliver on complex tasks despite initial success in simpler applications. - The 1980s' "AI Winter" was precipitated by critics like Hubert Dreyfus, who pointed out the limitations of expert systems as rule sets grew unwieldy and performance faltered. Funding decreased significantly until DARPA introduced benchmark tests for more achievable commercial and military tasks, emphasizing quantifiable progress. - Post-winter, DARPA's approach shifted towards specific task benchmarks (e.g., digit recognition, speech-to-text) which centralized AI research funding and marginalized less successful methodologies like rule-based systems in favor of data-driven machine learning algorithms. - In the early 2010s, deep learning advancements led to notable improvements in areas such as speech-to-text and medical image analysis surpassing human performance in specific cancer detection cases. Unexpectedly, this also birthed generative AI capable of producing coherent text, giving rise to models like ChatGPT. - The current challenge is evaluating these complex, creative tasks with no clear benchmarks due to their subjective nature, prompting researchers to seek methods that combine both precision and qualitative assessments without success so far. - An article published through Made by History in collaboration with TIME and OpenAI highlights the uncertainty surrounding investments in LLM technologies anticipated to bring significant automation soon but lacking reliable evaluation methods. The authors, Bernard Koch and David Peterson, stress the necessity for dependable assessment to avoid repeating historical mistakes and ensure real progress rather than inflating another tech bubble. Keywords: #granite33:8b, $5 trillion, AI, AI Winter, AI limitations, Artificial Neural Networks, Benchmarking Crisis, ChatGPT, Claude, DARPA, Dartmouth workshop, Deep Learning, Dreyfus' fallacy, Generative AI, LLM technologies, New Evaluation Systems, Nvidia, OpenAI, PowerPoint, Tumor Recognition, Vibe Tests, audio transcription, automation, benchmark competitions, benchmarks, bubble fears, complex tasks, contextual judgment, digit recognition, disappointment, document digitization, evaluation methods, expert systems, exponential complexity, formal rules, funding strategy, future, gender rule omission, hype, image object recognition, infrastructure, investment, job replacement, large language models, leaderboards, machine learning algorithms, mistakes, narrow tasks, non-profitable, professionals, quantitative metrics, real-time feedback, rule-based, sector, specific tasks, speech-to-text, standardized tests, subjective evaluation, transformation, translation, unified field, video analysis
claude
time.com 2 days ago
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503. HN AI Data Center Gold Rush Driven by 1000's of Newcomers**Summary:** The article explores a burgeoning trend of new entrants disrupting the established AI data center market dominated by Big Tech companies. Notable among these are Lorenzo Avello's Adriatic Data Center (ADC) in Puglia, Italy, and Kevin O'Leary’s project in Alberta, Canada, as well as Bitdeer Technologies Group transitioning from Bitcoin mining to AI cloud infrastructure. 1. **Adriatic DC (Lorenzo Avello):** - Avello plans a €50 billion ($59 billion) investment in Puglia, Italy, to create Europe’s largest AI hub with three data centers totaling 1.5 gigawatts. - Inspired by the Stargate venture involving OpenAI, Oracle, and SoftBank Group for US AI expansion, Avello aims to build an "AI data center valley." - The project anticipates strong future demand for AI, leveraging a nearby subsea cable to support AI systems globally. - Specific investors remain undisclosed as Avello secures land and electricity commitments through private capital. 2. **Kevin O'Leary (Shark Tank host):** - Developing "the world's largest AI data center industrial park" in northwestern Alberta, Canada, aiming for 17 gigawatts capacity. - Project utilizes abundant natural gas and geothermal energy resources, with initial focus on 1.4 gigawatts. - Secured land and affordable power, currently in the permit acquisition process to materialize this ambitious venture. 3. **Bitdeer Technologies Group:** - Transitioning from Bitcoin mining to AI cloud business by investing billions into data center networks consuming hundreds of megawatts by 2030. - A key project in Clarington, Ohio, targets 570 megawatts and plans online status by late 2027. - Faces challenges like third-party delays and a recent fire incident but views AI as less risky compared to Bitcoin's volatility. - Plans co-location projects in Clarington, renting space to hyperscalers like Microsoft or Google financed through debt. 4. **Big Tech Shifts:** - Meta (Facebook’s parent) raised $60 billion for data center construction, with half through a private capital transaction with Morgan Stanley and Blue Owl Capital Inc. - Microsoft committed over $60 billion to leasing from neoclouds like Nscale, securing $23 billion for UK, Norway, Portugal, and Texas sites. - Both companies prefer leasing due to anticipated "overbuild" of computing capacity and associated risks. 5. **Market Concerns:** - Wall Street veterans and investors like Michael Burry warn about potential AI bubbles, citing circular deals among tech firms. - Critics such as Charles Fitzgerald argue that many planned projects may not materialize due to limited genuine demand for AI products. - Experts like Howard Marks of Oaktree Capital Management caution against potential overbuilding risks, with tech companies having flexible lease agreements. 6. **Fermi Inc. and Real Estate Firms:** - Fermi Inc., co-founded by Rick Perry, faced a setback when an investment-grade tenant canceled a $150 million agreement. - Menlo Equities targets data center development in markets with demand growth potential while acknowledging oversupply risks from AI advancements. The article highlights the rapid expansion and investments in the AI data center sector, driven by immense computing needs of the tech industry, but also underscores significant concerns about potential economic impacts if AI investment falters or overbuilds occur. Keywords: #granite33:8b, 200-megawatt data center, AI, AI business case, AI cloud, AI infrastructure, Adriatic DC, Alberta empire, Alberta hyperscalers, Big Tech shift, Bitcoin, Bitcoin mining, Bitdeer, Clarington Ohio, Cloud Services, CoreWeave, Data Center Power Developer, Donald Trump, Europe, Europe's largest operation, Fermi Inc, Investment-Grade Tenant, Manifattura Tabacchi, Mediterranean hub, Menlo Equities, Microsoft, Nscale, Nvidia, O'Leary, OpenAI, Oracle, Puglia, Shark Tank, SoftBank Group Corp, Southern Italy, Stargate venture, US data center, US locations, West Texas, boom, bubble, chip design, cloud, co-founder, co-location, computing capacity, credit deals, cryptocurrency moat, data centers, demand, developers, development, disruptive technology, diversification, exit fees, geothermal energy, global capacity, infrastructure risks, investment, investments, leases, lenders, natural gas, private equity, renewable energy, renewal probabilities, rent back, site owners, systemic risk
openai
www.bloomberg.com 2 days ago
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504. HN Show HN: CineCLI – Browse and torrent movies directly from your terminal- **CineCLI Overview**: CineCLI is a versatile, cross-platform terminal application designed for movie enthusiasts. It allows users to browse movies, access detailed information, and start torrents using their preferred system client. - **Cross-Platform Compatibility**: The application supports Linux, macOS, and Windows operating systems, ensuring wide accessibility among different user bases. - **Key Features**: - **Search Functionality**: Users can efficiently search for movies within the terminal interface. - **Rich User Interface (UI)**: Offers comprehensive details such as ratings, runtime, and genres, enhancing the movie discovery experience. - **Magnet Link Handling**: Directly supports magnet links, facilitating seamless torrent initiation without external dependencies or interruptions like ads or tracking. - **Open Source Availability**: CineCLI is an open-source project, meaning its source code can be accessed, modified, and shared by developers and users on platforms such as GitHub (https://github.com/eyeblech/cinecli) and PyPI (https://pypi.org/project/cinecli/). - **Community Engagement**: The developers encourage feedback from terminal and Python users to improve the tool, and interested parties can reach out via a specified email address for discussions or contributions. Keywords: #granite33:8b, CineCLI, GitHub, Linux, PyPI, Python, UI, Windows support, browsing, cross-platform, feedback, genres, interactive mode, macOS, magnet handling, movies, no ads, no tracking, non-interactive mode, ratings, runtime, search, system default client, terminal, torrents
github
github.com 2 days ago
https://en.wikipedia.org/wiki/List_of_films_in_the_publ 2 days ago https://github.com/orangekame3/awesome-terminal-recorde 2 days ago https://www.stremio.com 2 days ago https://torrentio.org/ 2 days ago https://fmhy.net/ 2 days ago https://fmhy.net/video#torrent-sites 2 days ago https://rutracker.org/ 2 days ago https://heartiveloves.pages.dev/ 2 days ago https://nyaa.si/ 2 days ago https://github.com/hauxir/rapidbay 2 days ago https://www.reddit.com/r/Addons4Kodi/comments/ 2 days ago https://support.torproject.org/about-tor/using-and-shar 2 days ago https://news.ycombinator.com/item?id=46364645 2 days ago https://github.com/search?q=repo%3Ajellyfin%2Fjellyfin+strm& a day ago https://emby.media/support/articles/Strm-Files.htm a day ago https://github.com/lostb1t/Gelato a day ago https://radicle.xyz/ a day ago https://fmhy.net/torrenting#aggregators a day ago http://piratebayo3klnzokct3wt5yyxb2vpebbuyjl7m623iaxmqhsd52coid.o a day ago https://news.ycombinator.com/item?id=46367384 a day ago https://news.ycombinator.com/item?id=46364765 a day ago |
505. HN Show HN: Starships.ai – Build, deploy and orchestrate an AI agent team- **Platform Overview**: Starships.ai is a comprehensive toolset designed for constructing, launching, and coordinating AI agents capable of executing complex tasks by leveraging diverse skills and resources. - **Unique Approach**: Unlike conventional developer-focused AI solutions that require coding expertise, Starships.ai aims to emulate human team collaboration through a user interface reminiscent of managing remote employees via messaging platforms like Slack. - **Vision for the Future**: The long-term objective is to establish an organization where most operational tasks are handled by AI agents under human supervision for vital decision-making processes. - **Engagement Invitation**: Users are encouraged to explore Starships.ai and provide feedback on their experience at Keywords: #granite33:8b, AI, AI-run, Slack, Starshipsai, agents, collaboration, complex tasks, critical decisions review, developer-oriented, human-like interaction, organization, web platform
ai
starships.ai 2 days ago
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506. HN Show HN: Efpix – A flood protocol with E2EE and metadata protection- **EFPIX Protocol Summary:** - EFPIX is a novel flood protocol ensuring secure communication in adverse conditions without stable circuits or central directories. - It offers end-to-end encryption, plausible deniability for users, untraceable messages, and spam resistance with optional enhancements. - Designed for remote networks lacking servers, disaster zones, authoritarian regimes for surveillance-resistant communication, and emergency broadcasts. - A whitepaper detailing its use cases, algorithms, threat analysis, comparison with other protocols, and C implementation is available on arXiv ( - The paper "EFPIX: A zero-trust encrypted flood protocol" by Arin Upadhyay, submitted to arXiv in September 2025 and revised in November 2025, focuses on a flood-based relay communication protocol ensuring privacy and security without central servers. - **arXiv Context:** - arXiv is an open-access e-prints repository covering multiple disciplines, including computer science (cs.CR for Cryptography and Security). - The provided text presents navigation options on arXiv: change categories, access references & citations via NASA ADS, Google Scholar, Semantic Scholar, export BibTeX citation, explore associated data, code, and media. - arXivLabs, an experimental platform, allows community collaborators to develop new features while maintaining values of openness, community engagement, excellence, and user data privacy. - Additional links offer ways to contact arXiv, subscribe to mailings, learn about their policies (Copyright, Privacy), access web assistance for accessibility, and verify operational status. - **Key Notes:** - No information on author endorsements of papers is provided in the text. - The text describes features and availability of the EFPIX protocol’s whitepaper and source code along with general context about the arXiv repository. Keywords: #granite33:8b, BibTeX, CORE Recommender, CSCR, CatalyzeX, DagsHub, E2EE, EFPIX, GitHub, Google Scholar, GotitPub, Hugging Face, Influence Flower, Litmaps, MathJax, NASA ADS, Papers with Code, ScienceCast, Semantic Scholar, Smart Citations, TXYZAI, activism, alphaarXiv, arXiv, arXivLabs, authoritarian regimes, authors, bibliographic tools, bookmarks, code, connected papers, contact, copyright, data, demos, disaster zones, emergency broadcasts, end-to-end encryption, endorsement, flood protocol, help, high-adversity, journalism, license, mailings, media, metadata protection, operational status, operational statusKEYWORDS: EFPIX, paper, plausible deniability, privacy policy, recommenders, references, remote networks, replicate, sciteai, search tools, spaces, spam resistance, subscribe, untraceability, web accessibility, whistleblowing, whitepaper, zero-infrastructure
github
arxiv.org 2 days ago
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507. HN Llmon – The First Web Adversarial AI Firewall- **LLMON Overview**: - A Web Adversarial AI Firewall (WAAiF) implemented as Caddy middleware. - Functions as a user-transparent reverse proxy, intercepting outbound traffic to the internet. - Modifies content in real-time for AI models without altering human user experiences. - Injects adversarial payloads into files intended for Large Language Model (LLM) pipelines. - Emphasizes cognitive security, ensuring semantic value extracted by AI models benefits users rather than machines. - Capable of injecting payloads on-the-fly; original and modified files available for verification. - **Injection Strategies Across File Formats**: 1. **PDF**: Uses pdfcpu for validation or searches for invisible watermarks (opacity 0.01%). Hidden text can be found by unzipping DOCX files and checking the `word/document.xml` file for payloads. 2. **XLSX**: Checks the `xl/worksheets/` directory for dynamically named hidden sheets after unzipping. 3. **MP3 (ID3v2 USLT)**: Validates lyrics using Frame Check in ID3 tags. 4. **WAV**: Searches for "ICMT" chunk using a hex editor to reveal hidden comments. 5. **GIFAR (Polyglot)**: Opens GIF files in a text editor to detect appended JavaScript payloads using polyglot techniques. 6. **PDF+HTML (Polyglot)**: Exposes hidden HTML by opening PDF files in a text editor to reveal embedded HTML content. 7. **PNG (tEXt Chunk)**: Searches for "tEXt" or "Comment" chunks using a hex editor to find hidden metadata. 8. **Ghost PNG (Vector B)**: Converts PNG files with an alpha channel to RGB format using Python's Pillow library to reveal text. 9. **GIF Comment Extension Block**: Locates the 21 FE block via hex editing to find hidden comments. 10. **SVG 11. **WOFF2 Extended Metadata Block**: Decompresses Brotli-encoded metadata or uses `ttx` for examining extended metadata. 12. **Font Files (TTF/OTF Name Tables)**: Inspects name tables using FontTools/ttx for hidden data. 13. **ICS DESCRIPTION Field**: Opens .ics files in a text editor to check for concealed descriptions. 14. **SRT Subtitle Block (0ms)**: Uses a text editor to review subtitle blocks in SRT files for hidden information. 15. **JSON (_llm_instruction)**: Examines JSON files for new keys like `_llm_instruction` using editors or viewers. 16. **XML (RSS) Comment**: Opens XML/RSS files in a text editor to review any hidden comments. 17. **Robots.txt Disallow Rule & JavaScript Variable**: Inspects these files for concealed rules or variables, respectively. - **Injection Techniques for HTML, CSS, and JavaScript**: 1. **HTML Comment**: Injects payloads into standard HTML comments readable by bots. 2. **Hidden Textarea**: Inserts hidden textareas using advanced CSS (randomized class names). 3. **Script Text**: Places payloads in non-executable script tags (`text/plain`), visible to parsers but not executed. 4. **CSS Comment**: Injects payloads into ` |
