8.
HN
Six Claude Code Strategies for a Productive Workflow
The article presents six key strategies for incorporating Claude Code into a productive workflow, emphasizing the importance of developer oversight and customization to build maintainable software. Firstly, it advocates for controlled execution by preferring manual review over autonomous loops due to potential unpredictability and maintenance issues associated with AI-generated code. Secondly, utilizing plan mode is highlighted as essential for generating detailed plans that ensure comprehensive understanding and approval before executing changes. Thirdly, the creation of custom agents and skills tailored to personal preferences and coding standards is recommended to maintain consistency across projects. Fourthly, task-specific models are advised, using advanced models for complex problems and simpler ones for routine tasks, thereby optimizing resource utilization. Additionally, providing explicit instructions is emphasized as a means to improve AI output quality by minimizing errors due to misunderstandings. Finally, the implementation of robust verification processes through code reviews, unit tests, and end-to-end testing is crucial for ensuring reliability and adherence to project standards. Collectively, these strategies aim to integrate Claude Code effectively while preserving developer control over the software development process.
Keywords: #phi4, AI models, Claude Code, Playwright MCP, autonomous loops, custom agents, developer judgment, developer judgment Keywords: Claude Code, explicit instructions, linting commands, plan mode, project-specific skills, strategies, unit tests, verification, workflow
intelligenttools.co an hour ago
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11.
HN
How to train your program verifier
The article discusses the creation of the a3 framework, specifically its application in developing an automated verifier named a3-python by Halley Young and Nikolaj Bjørner. This tool aims to tackle the complex task of verifying programming languages like Python, which pose challenges due to their intricate type systems and rapid development cycles. The project draws from AI-assisted techniques for generating verification theories inspired by mathematician Vladimir Voevodsky's work and utilizes Hilbert’s Stellensatz theorems, alongside advancements in symbolic model checking and PyTorch code analysis libraries.
The a3-python was developed through an iterative process involving AI-generated theory refinement and testing with real-world codebases. It uses a "kitchen sink" methodology, incorporating multiple proof strategies to evaluate potential bugs, ensuring safety or identifying genuine errors. When formal methods fall short, directed symbolic execution (DSE) is employed to produce concrete error examples. The tool has proven effective across several open-source projects by accurately pinpointing real bugs while reducing false positives.
Furthermore, a3-python integrates deterministic symbolic verification with a neural triage system to manage uncertain cases efficiently, enhancing its eco-friendliness and explainability. Its overarching goal is to develop custom verifiers tailored for specific programming languages or libraries, thereby improving program reliability and aiding developer comprehension.
Keywords: #phi4, AI agent, Copilot CLI, LLM2CLIP, Positivstellensatz, Program verifier, PyTorch, Python, a3-python, adversarial testing, adversarial testing Keywords: Program verifier, automated verification, barrier certificates, bug detection, concolic execution, dynamic symbolic execution, formal methods, mathematics, metric semantics, quantitative model checking, static analysis, symbolic model checking, verification tools
risemsr.github.io an hour ago
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20.
HN
We need to act with urgency to address the growing AI divide
At the India AI Impact Summit, Microsoft announced a commitment to invest $50 billion by 2030 aimed at narrowing the AI gap between wealthier regions (Global North) and less affluent ones (Global South). This initiative is crucial in addressing global disparities in AI adoption that risk replicating economic divides seen historically with electricity access. Microsoft's strategy unfolds through a five-part program designed for comprehensive impact:
Firstly, **Infrastructure Development** focuses on enhancing datacenter infrastructure in Africa, South America, and other underserved regions, investing over $8 billion last fiscal year to expand internet accessibility to 250 million people globally. Secondly, the initiative of **Empowering People with Technology and Skills** allocates more than $2 billion toward providing cloud and AI technologies to schools and nonprofits, while also setting a goal to train 20 million individuals in AI skills by 2028.
Thirdly, **Strengthening Multilingual and Multicultural Capabilities** includes projects like LINGUA Africa to improve language models for underrepresented languages, ensuring that AI systems are inclusive. Fourthly, **Enabling Local AI Innovations** features targeted projects such as an AI initiative focused on food security in Sub-Saharan Africa, developed in collaboration with local communities and organizations to tackle specific regional challenges.
Finally, the program involves **Measuring AI Diffusion**, where Microsoft intends to enhance research efforts and data sharing practices, contributing to indices like the World Bank's Global AI Adoption Index. Emphasizing cross-sectoral and international collaboration, Microsoft seeks to promote digital sovereignty and build trust in technological investments through partnerships exemplified by the Trusted Tech Alliance—a consortium of tech companies adhering to principles of technological trust. Through these efforts, Microsoft aims to facilitate equitable global growth and opportunities powered by AI.
Keywords: #phi4, AI, Global South, Microsoft, connectivity, cybersecurity, datacenters, diffusion, digital sovereignty, digital trust, economic growth, food security, infrastructure, innovation, investment, language capabilities, local innovations, multilingual, partnerships, policy guidance, privacy, resilience, skilling programs, skills, technology access
blogs.microsoft.com 2 hours ago
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100.
HN
After Microsoft's AI overreach, Gentoo begins its march away from GitHub
Gentoo Linux is transitioning away from using GitHub, owned by Microsoft since 2018, to Codeberg, a non-profit git-hosting service, due to concerns about Microsoft’s integration of AI tools like GitHub Copilot into their platform. Gentoo perceives these tools as intrusive and coercive for open-source repositories, given that Microsoft utilizes GitHub data for training its AI models. This shift reflects broader discontent within the open-source community regarding Microsoft's handling of such data. Although this migration is still in progress, Gentoo is establishing its presence on Codeberg to provide an alternative platform for contributions. Known for its advanced package management system requiring source compilation by users, Gentoo maintains a significant influence in the Linux sphere and has contributed to developments like ChromeOS derivatives. The move underscores wider dissatisfaction among open-source projects with Microsoft's AI practices.
Keywords: #phi4, AI, ChromeOS, ChromeOS Keywords: Gentoo, ChromiumOS, Codeberg, Copilot, Gentoo, GitHub, Linux, Microsoft, community, complexity, distro, migration, mirrors, packages, repositories, source
www.pcgamer.com 7 hours ago
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102.
HN
Investigating the Downstream Effect of AI Assistants on Software Maintainability
The study "Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability" examines how AI tools like GitHub Copilot impact software maintainability. Conducted in two phases with 151 professional developers, the research first involved participants developing a Java application feature either with or without AI assistance. In the subsequent phase, different developers worked to evolve these solutions without AI, focusing on aspects of maintainability such as completion time and code quality. The results revealed no significant differences in maintenance outcomes between those who initially used AI assistance and those who did not. While initial use of AI demonstrated productivity benefits like a 30.7% reduction in development time, these did not translate into improved or diminished long-term maintainability. Consequently, the study indicates that although AI can increase developer efficiency during coding, its influence on future code evolution remains minimal and uncertain. The research underscores the importance of further investigation into potential risks such as code bloat and cognitive debt associated with extensive reliance on AI in software development. Despite identifying no systematic benefits or drawbacks within the scope of this study, it suggests caution and a need for ongoing scrutiny of AI's long-term effects in the field.
Keywords: #phi4, AI Assistants, Artificial Intelligence, Bayesian Analysis, Code Bloat, Code Quality, Cognitive Debt, Completion Time, Controlled Experiment, Evolution of Code, GitHub Copilot, ICSME 2025, Java Web Application, Productivity, Professional Developers, Software Engineering, Software Maintainability
arxiv.org 7 hours ago
https://g2ww.short.gy/ConsAndPros 4 hours ago
https://g2ww.short.gy/MarkOfTheBorg 4 hours ago
https://g2ww.short.gy/ActualInequal 4 hours ago
https://g2ww.short.gy/ConDelivery 4 hours ago
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112.
HN
Show HN: Codex skills as RE playbooks: unpacking and IOC extraction
The blog post discusses "Codex skills as RE playbooks," emphasizing the use of AI tools like OpenAI Codex to enhance reverse engineering (RE) workflows through reusable, modular actions known as skills. These skills facilitate standardization and efficiency by implementing consistency and guardrails in analysis processes. The author highlights how OpenAI Codex's implementation leverages progressive disclosure, loading only necessary metadata initially to improve efficiency across multiple skills.
A Windows-based virtual machine using FLARE-VM is set up for isolation and reproducibility, with the installation of the OpenAI Codex CLI allowing operations directly within a repository by inspecting files and executing commands. Two specific RE skills are detailed: "unpacking" (re-unpacker) and "IOC extraction" (re-ioc-extraction). These tasks are chosen due to their repetitive nature in analyzing samples—unpacking identifies if binaries are packed, while IOC extraction focuses on identifying indicators of compromise, both producing actionable artifacts like unpacking plans or defender-ready IOCs.
The author emphasizes the approach's benefits in consistency and efficiency by organizing skills into structured directories with managed metadata, streamlining RE tasks without necessitating an in-depth initial understanding of programs.
Keywords: #phi4, AI, CLI, Codex, FLARE-VM, GitHub Copilot, IOC extraction, RE, SKILLmd, VMWare, agents, analysis, artifacts, defensible plan, environment, evidence, guardrails, indicators, malware, metadata, npm, playbooks, plugins, policies, progressive disclosure, repository, reverse engineering, sandbox, skills, subtasks, tools, unpacking, virtual machine, workflow
www.joshuamckiddy.com 8 hours ago
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121.
HN
Baseline Core – Open-source skill system that wires your business to AI
The Baseline System is an open-source, AI-driven workflow tool designed to improve productivity for product teams by organizing knowledge with specific business contexts. It incorporates integration capabilities with AI tools such as Claude Code and GitHub Copilot through a file called AGENTS.md, which guides these tools in accessing methodologies, business-specific information, and frameworks. The system consists of three main components: Skills (universally applicable methodologies), Context (customizable business-specific data like identity and voice), and Frameworks (reusable structures for tasks such as prioritization and research).
Users initiate the Baseline System with commands like `npx @baseline-studio/cli init` to set up their environment, emphasizing that the quality of AI output depends significantly on the accuracy and completeness of supplied business contexts. These contexts include essential elements like identity and voice, along with extended information such as product details and user personas.
The Baseline System is versatile in handling tasks across domains including UX design and project management, supporting strategic decision-making, research synthesis, and documentation creation. Users can modify or add to context files using commands like `npx baseline context`, ensuring AI outputs align with the brand's voice and requirements. Custom behaviors are recommended to be added to context files rather than skill files, which receive automatic updates.
The system is MIT-licensed, facilitating integration with various AI coding tools as specified in AGENTS.md, while requiring manual uploads for chat tools. Contributions to its development can be made through its GitHub repository. Developed by Trent at Baseline Studio, the Baseline System aims to enhance collaboration between product teams and AI technologies.
Keywords: #phi4, AGENTSmd, AI, AI Tools, Baseline System, CLI, Context, Context Files, Frameworks, MIT License, Open-source, Product Teams, Skills, Workflow
github.com 8 hours ago
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160.
HN
Show HN: LedgerSync – A cross-agent shared-memory protocol for AI coding
LedgerSync is an innovative protocol designed to streamline AI-assisted coding across multiple agents, such as Claude, Cursor, Codex, and others, by maintaining continuity of context and adherence to a project’s design philosophy. The system tackles common challenges like loss of product context when switching tools and the tendency for technically correct code that may not align with the intended product vision. Key features include a shared-memory mechanism where agents document decisions in `ledger.jsonl`, preserving context across different Integrated Development Environments (IDEs). Additionally, it allows developers to register grounding documents—such as design philosophies, aesthetic guidelines, and user research—that direct AI agents to make decisions consistent with the project's core principles.
The functionality of LedgerSync is realized through an initial setup in a project directory that includes configuration files within `.ledgersync/`. It offers integration capabilities for various AI tools via commands like `ledgersync integrate <agents>`, allowing developers to manage and list grounding documents. Daily operations are supported by specific commands enabling the viewing of logs, accessing context summaries, manually logging decisions, and ensuring proper setup validation.
The configuration is governed by a `config.yaml` file containing essential project details such as mandatory grounding documents, codebase support parameters, ledger entry management guidelines, and operational constraints for agents. The directory structure also includes these grounding files along with agent-specific instructions to facilitate seamless collaboration among AI tools.
LedgerSync's philosophy emphasizes a serverless approach that prioritizes immutable ledgers focusing on the rationale behind coding decisions rather than just technical accuracy. This system supports research into multi-agent coordination, as evidenced by submissions to academic forums like IJCAI-ECAI 2026. By aligning AI coding processes with the project’s vision and maintaining contextual consistency through shared memory and grounding principles, LedgerSync aims to significantly enhance AI-assisted development environments under an MIT license.
Keywords: #phi4, AI coding agents, LedgerSync, agent integration, agent integration Keywords: AI coding agents, append-only ledger, context preservation, decision log, design principles, grounding docs, multi-agent coordination, product philosophy, shared-memory protocol, user research
github.com 13 hours ago
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240.
HN
What I learned from 500k LOC built with AI
The experiment conducted by the author explored AI's potential in real-world software development through a .NET desktop app project built with Avalonia and supported by GitHub Copilot and ChatGPT Codex. This extensive project, featuring over 500,000 lines of code, utilized AI tools to execute coding tasks while adapting based on feature descriptions and feedback. Initially, the AI demonstrated remarkable productivity in low-constraint environments, particularly when provided with structured prompts that encouraged comprehensive implementation.
The experiment employed various models, including Claude Opus 4.5, Claude Sonnet 4.5, and ChatGPT Codex 5.2, with "big context" models preferred for handling intricate tasks due to their ability to manage coherence in large codebases. The GitHub PR workflow played a crucial role in identifying errors that AI might overlook during rapid development phases.
Despite the high initial productivity of AI agents, several challenges arose, especially concerning UI layout constraints, debugging without sufficient telemetry, and achieving complete test coverage. Debugging emerged as particularly complex, necessitating human conceptual understanding beyond mere syntax or logic corrections. Early integration of testing was highlighted as essential to prevent technical debt accumulation.
While AI excelled at repetitive tasks such as code generation and log analysis, the necessity for human oversight remained evident in areas like architecture decisions, security, scalability, UX design, and framing complex debugging issues. The "beads" task tracking system was employed to maintain continuity across sessions with cloud-based agents.
In summary, while AI significantly enhances productivity by automating coding tasks, it cannot replace humans' role in high-level decision-making and ensuring coherence within complex software systems. The author plans to continue leveraging these tools as enhancers of engineering skills rather than substitutes, highlighting their potential to amplify human capabilities effectively.
Keywords: #phi4, Avalonia, ChatGPT Codex, GitHub Copilot, NET, UI layout constraints, agentic coding, architecture, debugging, evidence-driven debugging, models, productivity multiplier, software development, task tracking, test coverage, workflow
mmlac.com a day ago
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275.
HN
Openclaw 2.0. Openrappter.
OpenClaw 2.0, also known as Openrappter, is an innovative AI agent framework that utilizes GitHub Copilot for AI inference without necessitating additional API keys or recurring fees. Its architecture ensures local operation, thereby preserving the privacy and security of user data. The system supports both Python and TypeScript runtimes, allowing developers to create dual-runtime agents with flexibility.
The key features of OpenClaw 2.0 include local data handling where all memory, configuration, and state are stored on the user's machine. It allows for the creation of single file agents that use native language constructs like Python dictionaries or TypeScript objects, removing the need for separate YAML files or configurations. The framework supports persistent memory and context enrichment by retaining information across sessions while integrating contextual signals such as time, user behavior, and past interactions into each action. Additionally, it offers data sloshing to facilitate seamless data transfer between agents in a pipeline without requiring an external orchestrator.
OpenClaw 2.0 also features auto-discovery of new agents added to directories and supports the generation of agents from natural language descriptions at runtime. The setup process is simplified through a skills.md file that guides AI assistants like Copilot or ChatGPT in automating installation and configuration, with options for manual setup using specific commands for both Python and TypeScript environments.
The architecture routes user input to agents via the Copilot SDK, enriches data with contextual signals before execution, and facilitates communication between agents through a signal pipeline. Openrappter integrates with RappterHub and ClawHub, offering native agent registry capabilities and compatibility with OpenClaw skills, respectively. As an open-source project under the MIT license, Openclaw 2.0 encourages community contributions and is designed to streamline AI agent development while maintaining user control over data and resources.
Keywords: #phi4, ClawHub, GitHub Copilot, OpenAI, Python, RappterHub, TypeScript, agent chaining, context enrichment, data sloshing, dual-runtime, persistent memory, single file agents, skillsmd
github.com a day ago
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279.
HN
The Broken Equilibrium
The introduction of advanced AI coding tools like GitHub Copilot has significantly enhanced developer productivity by enabling tasks to be completed at a much faster rate, often 2-3 times quicker than before. However, this increased efficiency reveals a critical bottleneck: the slow and complex process of infrastructure provisioning, which largely remains unchanged due to its reliance on manual workflows. This disparity between rapid development capabilities and sluggish infrastructure readiness results in several economic drawbacks, including developers spending valuable time waiting for necessary changes, leading to increased technical debt from workarounds that create fragmented environments. These inefficiencies can also cause frustration among developers, potentially driving them away from their organizations.
Moreover, the slow pace of infrastructure provisioning hinders timely feature deployment and reduces opportunities for experimentation, thereby diminishing strategic advantages. Attempts to mitigate these issues often fall short; hiring additional DevOps engineers or introducing better tooling offers only slight improvements. Allowing direct developer access can lead to governance challenges. The fundamental problem is that existing pre-AI solutions are ill-suited to meet the demands of the AI era, highlighting a need for a radical transformation in how infrastructure provisioning is managed to align with modern development practices and technological advancements.
Keywords: #phi4, AI coding tools, DevOps, GitHub Copilot, Terraform, governance policies, infrastructure bottleneck, platform teams, productivity gains, software development, speed mismatch, technical debt, velocity
stackgen.com a day ago
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361.
HN
Ask HN: What is the best bang for buck budget AI coding?
A developer experienced in traditional programming is exploring budget-friendly AI coding tools, aiming not to exceed $30 per month. Currently utilizing Z.ai and GitHub Copilot for a combined monthly cost of $16, they are facing challenges with each tool's limitations: aggressive rate limiting on Z.ai's GLM 4.7 model and smaller context windows in GitHub Copilot. Although other free web/mobile-chat plans are available, the developer prefers CLI-compatible solutions due to hardware constraints that preclude running large models locally.
Given these circumstances, the developer is evaluating whether their current tools provide optimal value or if there are better alternatives within their budget. They express particular interest in Codex and Claude as potential options for extensive daily use but are unsure about how well these fit into their financial plan due to unclear usage limits across platforms. The main goal is to maximize AI coding capabilities while adhering strictly to the $30 monthly limit, seeking recommendations on the best approach to optimize spending without compromising tool efficacy or exceeding budgetary constraints.
Keywords: #phi4, AI coding, CLI, GitHub Copilot, Zai, budget, computers, concurrency, developer, models, programming languages, rate limit, tokens, usage limits
news.ycombinator.com a day ago
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371.
HN
My performance art-like piece: The Slopinator 9000
"The Slopinator 9000" is a satirical performance piece critiquing the prioritization of speed over quality in software development. It functions as an autonomous pipeline that swiftly generates and deploys code by sourcing ideas from GitHub's trending repositories. The process involves several phases: identifying trending repositories, generating derivative ideas evaluated by large language models (LLMs), conducting feasibility research through browser automation, coding with a Pi agent, and deploying to GitHub with automated tweets announcing the work.
This system operates with minimal human intervention, requiring Node.js version 20 or higher, along with GitHub and Twitter API credentials and an LLM API key. Configuration is managed via environment variables, and it includes a dry-run mode for testing purposes. Research is conducted using Puppeteer. The architecture consists of six specialized "oracles," each with defined interfaces, time budgets, structured logging, and error recovery mechanisms, all coordinated by an orchestrator.
Despite its emphasis on rapid production over perfection, the system aims to ship functional code within 12 hours, enabling iterative improvements in production. Licensed under The Unlicense, it allows free use of the project, underscoring its open-source nature while highlighting the trade-offs between speed and quality in software development practices.
Keywords: #phi4, Chromium/Chrome, GitHub, LLM, Nodejs, Puppeteer, Slopinator 9000, Twitter API, TypeScript, environment variables, npm, performance art, pipeline automation, satire
github.com a day ago
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449.
HN
InferenceX v2: Nvidia Blackwell vs AMD vs. Hopper – SemiAnalysis
InferenceX v2 is an advanced benchmark suite that evaluates AI inference capabilities of Nvidia, AMD, and Hopper GPUs, building on its predecessor InferenceMAXv1 by expanding coverage to include more GPU SKUs and introducing new tests such as disaggregated inference with wide expert parallelism (wideEP). The benchmark notably includes third-party testing for Nvidia's Blackwell Ultra GB300 NVL72 across all SKUs and assesses AMD’s performance in similar contexts. While AMD GPUs demonstrate competitive capabilities, particularly in FP8 MoE disaggregated inference scenarios, Nvidia maintains an overall lead due to superior energy efficiency and the effective implementation of multiple inference optimizations. However, AMD faces challenges with software composability when integrating different optimization techniques.
The benchmark underscores Nvidia's leading performance across various tasks, attributing up to 100x performance improvements over Hopper and H100 models like Blackwell B300 and GB300 NVL72 to their advanced distributed inference techniques such as prefill-disagg and wideEP. Nvidia’s software ecosystem, including TensorRT-LLM and Dynamo, enhances its multi-node setup efficiency, whereas AMD needs to enhance its software integration capabilities for better performance across multiple GPUs.
In terms of AI chip architecture and optimization techniques, the benchmark compares cost and performance trade-offs among several GPUs like GB300 NVL72, Google TPU, AWS Trainium, Nvidia Blackwell Ultra, and AMD MI355X. Notable observations include the higher all-in cost per GPU for GB300 compared to its rack-scale design advantages over designs such as Google TPU and AWS Trainium. Although the Blackwell Ultra shares similar specifications with Blackwell, it exhibits superior FP8 performance due to optimization in newer software versions.
AMD's MI355X surpasses older models like the MI300X in DeepSeek SGLang Disaggregated Inferencing and provides cost benefits at higher interactivity levels but faces multi-node inferencing challenges. AMD also struggles with composability issues in its open-source inference stack, affecting its performance in AI labs' deployments involving FP4 and wide expert parallelism.
The article highlights techniques such as speculative decoding and Multi-Token Prediction (MTP) for reducing inference costs without sacrificing accuracy by processing multiple tokens together, benefiting from dense models. Additionally, approaches like WideEP optimize memory usage across GPUs, while disaggregated prefill enhances performance in mixed workloads.
Anthropic's Fast Mode balances throughput and latency at a higher cost but achieves economical efficiency through increased interactivity levels under total cost ownership metrics. InferenceX has evolved since October 2025 by incorporating AI tools like Claude Code to enhance developer productivity with features such as pull request reviews and cluster operation automation. Despite challenges with GitHub Actions' reliability, collaborations have led to feature enhancements.
Future developments for InferenceX include refining real-world benchmarks using datasets like WildChat-4.8M and focusing on agentic coding scenarios to align with new AI models and inference engines. The suite plans to expand its benchmarks to cover architectures such as TPUs, Trainiums, and newer models like DeepSeek V3.2, positioning itself as a leader in real-world inference benchmarking by integrating more datasets and optimizing model evaluations across various platforms while enhancing Total Cost of Ownership metrics for emerging technologies.
Keywords: #phi4, AI chips, AMD, Claude Code, DeepSeek MoE, FP4, FP8, FP8 performance, GB300, GPUs, GitHub Actions, Hopper, InferenceX, Klaud Cold AI, MI355X, MTP, MoRI, Mooncake, NVL72, Nvidia Blackwell, Pareto frontier, Pareto optimal performance, ROCm, SGLang, TCO, TRTLLM, TensorRT-LLM, Trainium, agentic coding, bandwidth, benchmark, benchmarks, composability, cost per token, datasets, disaggregated inference, disaggregated prefill, distributed inferencing, economics, expert parallelism, inference optimization, interactivity, latency, multi-token prediction (MTP), multi-turn chat, performance, rack-scale architecture, software optimization, software stack, speculative decoding, throughput, throughput-latency tradeoff, vLLM, wide expert parallelism
newsletter.semianalysis.com 2 days ago
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477.
HN
How Well Does AI Find Code Vulnerabilities?
The article investigates the capability of Artificial Intelligence (AI), particularly Large Language Models (LLMs) from Anthropic and OpenAI, to identify code vulnerabilities compared with traditional static analysis tools like Semgrep. The research utilized benchmarks from the OWASP Benchmark Project for Java and Python, testing six AI models against these conventional tools. Key findings reveal that while traditional Static Application Security Testing (SAST) tools outperformed AI in recognizing vulnerabilities within Java's complex structures, AI models showed comparable performance to SAST tools in Python yet still fell short. Notably, Anthropic’s Opus and Gemini Pro 3 demonstrated high recall rates but struggled with false positives, especially in semantic analysis required for dataflow issues such as SQL Injection. The limited context size of these AI models was identified as a significant constraint, impeding their effectiveness in detecting security vulnerabilities, particularly within dynamically typed languages or extensive codebases.
Despite AI's current limitations in replacing SAST tools, the study suggests its potential to enhance static analysis by serving as an intermediary triage layer. This role could help filter and prioritize findings, potentially improving efficiency by reducing false positives. Consequently, while AI is not yet poised to supplant existing SAST solutions, it holds promise for aiding these tools in better prioritizing and validating vulnerabilities. The article concludes that future research should concentrate on optimizing how AI models can support traditional SAST tools effectively, emphasizing the collaborative integration of AI into current security analysis frameworks.
Keywords: #phi4, AI, AppSec, CWE Top 25, Java, LLMs, OWASP Benchmark, Python, SAST, Semgrep, context sizes, dataflow problems, false positives, frontier models, precision, recall, semantic analysis, static analysis, triage layer, vulnerabilities
ericfriese.substack.com 2 days ago
https://tachyon.so/ 2 days ago
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499.
HN
Which AI coding tools are you using? (Monthly Agentic Coding Index Survey)
The Monthly Agentic Coding Index Survey evaluates how professional developers are integrating AI coding tools into their workflows by gathering data on employment status, years of experience, and recent usage levels (0-100%) of these tools. Developers identify specific tools like GitHub Copilot and ChatGPT that aid in tasks such as writing new code or debugging. The survey examines productivity changes resulting from AI tool use, noting variations from significant decreases to major increases. It also tracks the evolution of developers' usage patterns over six months, identifying trends of increased, decreased, or stable usage. Additionally, qualitative insights are sought through optional feedback on unexpected experiences with these tools. This comprehensive assessment seeks to understand the impact and integration of AI assistance in professional coding environments.
Keywords: #phi4, AI assistance percentage, AI coding tools, Antigravity Junie, CLI Aider, ChatGPT, Claude Code, Cursor, Gemini Code Assist, GitHub Copilot, Windsurf Codex, debugging, documentation, new code, productivity change, professional software writing, refactoring, surprising experience, tests, tool usage change, years of experience
survey.actiindex.org 2 days ago
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527.
HN
Stop typing, start talking: How voice dictation changed my workflow
The author discusses transitioning from traditional typing to voice dictation, prompted by the need for increased text production due to communication with AI tools and social media. Initially skeptical about voice control, particularly in coding contexts, a pivotal moment occurred upon discovering Wispr Flow, which led to exploring various dictation tools and ultimately adopting Handy. Handy enhances workflow efficiency through automatic activation on device startup and straightforward transcription via a hotkey (Option + R). Utilizing the Parakeet V3 model, it offers accurate transcriptions across different accents and languages like Dutch, significantly boosting productivity in AI prompting, social media interactions, and content creation within a home office setting. While acknowledging that voice input is unlikely to replace keyboards entirely as natural language interfaces advance, the author notes its potential to greatly improve efficiency for specific tasks. They recommend others frequently composing text consider trying voice dictation to experience similar workflow improvements.
Keywords: #phi4, AI prompting, GitHub Copilot, Handy tool, Parakeet V3, Parakeet V3 model, Voice dictation, Wispr Flow, developers, keyboard shortcuts, mechanical keyboards, natural language, prompts, transcription accuracy, transcription accuracy Keywords: Voice dictation, typing speed, workflow
www.eliostruyf.com 2 days ago
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530.
HN
The Speed of Building Has Outpaced the Thinking Part
The article discusses the impact of AI tools on software development, emphasizing their role in enabling rapid prototyping and deployment—a phenomenon termed "vibe coding." While these tools democratize creation by lowering barriers to entry, they also pose risks such as devaluing indie developers' efforts and prioritizing speed over depth. This trend could lead to commoditization of software, with new solutions often mimicking existing ones without substantial innovation or consideration.
The author raises concerns about the potential erosion of long-term commitment and quality in software development, as AI's convenience allows developers to easily abandon projects for fresh ideas, sidelining products that benefit from extensive user feedback and community involvement. To mitigate these issues, a "Product Moral Compass" tool is proposed. This tool would encourage developers to assess existing solutions before creating new ones by performing market analysis, highlighting open-source contribution opportunities, and evaluating unique value propositions.
The article concludes with an appeal for balanced innovation in software development, urging respect for others' work and the human context within which technology operates. The author frames this approach as an evolution in developer responsibility rather than a form of gatekeeping, inviting feedback to refine these responsible practices.
Keywords: #phi4, AI tools, Product Moral Compass Agent, cloning, commoditization, community trust, developer responsibility, domain expertise, ethical building, indie development, market analysis, moral compass, speed trap
www.eliostruyf.com 2 days ago
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533.
HN
How to talk to any GitHub repo
The article serves as a guide for non-technical individuals interested in engaging with GitHub repositories using AI-driven methods, focusing on tools like Gemini, ChatGPT, or Claude. It outlines a straightforward approach to interact directly with codebases through the browser by simply importing the repository URL into an LLM tool and posing specific questions without downloading or configuring local setups. This method facilitates inquiries about discovering new projects and collaborating on existing ones, covering aspects such as understanding product basics, core architecture mapping, business rules identification, application execution, debugging, code improvement, and documentation generation.
The article also addresses the limitations of these AI tools, noting their constraints in static analysis, project size handling, and potential token usage. It highlights that private repositories can still be accessed with appropriate authentication. Additionally, it suggests alternatives like GitHub Copilot, Google CodeWiki, and DeepWiki, each providing unique functionalities for codebase interaction. The overarching message is to harness AI tools to foster better communication between product and engineering teams, enabling more informed discussions about technical projects by reducing traditional barriers.
Keywords: #phi4, AI agents, ChatGPT, Claude, DeepWiki, Excalidraw, Gemini, GitHub, GitHub Copilot, Google CodeWiki, IDE, LLM tool, Python, READMEmd, React, accessibility, architecture, authentication, business logic, code optimizations, codebase, collaboration, conversation with code, data structure, debugging, documentation, error message, feature flags, installation, internationalization, local app, open-source, performance path, private repos, product people, product understanding Keywords: GitHub, repository URL, security libraries, technical setup, user manual
www.theaithinker.com 2 days ago
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541.
HN
Show HN: Npx check-AI – check your repo for AI-readiness
**Npx check-ai** is a command-line tool designed to assess the readiness of software repositories for integration with artificial intelligence technologies, requiring no dependencies or complex setup processes. It conducts 66 evaluations across eight distinct categories: Repo Hygiene, Grounding Docs, Testing Safety Nets, Agent Configs, AI Context, Prompts & Skills, MCP Integrations, and AI Dependencies, scoring each repository from 0 to 10 based on the potential real-world impact of these checks. The tool offers a rapid audit with one command, generating detailed scorecards that break down performance across categories, such as Repo Hygiene at 77% or MCP Integrations at 100%. It also provides flexible output options like JSON and verbosity levels, and can be integrated into continuous integration workflows via GitHub Actions or GitLab CI. The scoring system assigns grades from A+ to F, emphasizing agent configurations specified in AGENTS.md. Additionally, it features an interactive mode with animated interfaces for terminal use while accommodating static outputs when necessary.
The tool is easily accessible by running `npx check-ai` directly or specifying a repository path, and can be customized with flags such as `--json`, `--verbose`, and `--no-interactive`. Built entirely using Node.js built-ins, it requires no further installations beyond `npx` and operates offline through static analysis. Licensed under MIT, **Npx check-ai** is especially beneficial for teams aiming to align their projects with best practices in AI tool integration.
Keywords: #phi4, AI Context, AI Dependencies, AI-readiness, Agent Configs, CI Integration, Grounding Docs, JSON Output, MCP Integrations, Prompts Skills, Repo Hygiene, Scoring, Testing Safety Net
github.com 2 days ago
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556.
HN
The Rise of Terminal Tools
Over the past decade, there has been a significant evolution in terminal tools, driven largely by advancements in programming languages like Rust and Go. This transformation was catalyzed by Andrew Gallant's development of ripgrep in 2016, which demonstrated Rust’s potential for creating fast command-line interface (CLI) tools. Subsequently, this sparked the creation of enhanced CLI utilities such as bat, fd, and zoxide that not only replaced traditional Unix utilities but also introduced modern features and improved user interfaces.
Concurrently, terminal emulators themselves have experienced a renaissance, becoming more powerful and visually appealing with innovations like GPU acceleration and support for contemporary themes and ligatures. Around 2024-2025, AI coding assistants began integrating into the CLI space, further increasing the practicality of working within diverse environments without relying on graphical interfaces.
The integration of AI highlights the advantages of terminal tools due to their cross-platform consistency and alignment with the Unix philosophy of simplicity and modularity. This has led developers to prefer open-source, portable solutions like Neovim over more resource-intensive GUI editors such as VSCode and IntelliJ, which perform less effectively in remote or containerized settings.
Neovim, in particular, has undergone a modern renaissance, featuring enhanced capabilities, easier configuration, and strong community support. These developments make it an appealing option for developers seeking speed, portability, and control. The convergence of these trends—faster CLI tools, advanced terminal emulators, AI integration, and the resurgence of Neovim—marks a pivotal shift in software development, underscoring the ongoing relevance and adaptability of terminals as a development environment.
Overall, this move towards terminal-centric workflows reflects a broader trend toward efficiency, flexibility, and independence from platform constraints. This empowers developers to work seamlessly across any computing environment, enhancing their productivity and creative potential.
Keywords: #phi4, AI agents, AI coding assistants, CLI, GPU-accelerated, Neovim, Rust, Terminal tools, Unix philosophy, cross-platform, open source, performance, ripgrep, terminal emulators
tduyng.com 2 days ago
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560.
HN
CodeSlick Security Scanner Is Now Live on the GitHub Marketplace
CodeSlick Security Scanner is now accessible on the GitHub Marketplace, serving as a robust security tool for pull requests by addressing vulnerabilities, AI-generated code risks, and OWASP 2025 compliance issues with integrated real-time verification. Aimed at teams employing AI coding assistants like GitHub Copilot, it can detect various types of security threats such as hardcoded secrets, SQL injection, and XSS across programming languages including JavaScript, TypeScript, Python, Java, and Go.
The scanner's key features comprise an AI code trust layer and self-healing capabilities that enable automatic fixes. Additionally, it offers enterprise-level functionalities like SARIF uploads, team dashboards, SBOM generation, shift-left security practices, and automated pull request corrections. To implement CodeSlick, users must add the Guardian to their GitHub organization and configure repository access.
All service plans guarantee OWASP 2025 compliance checks, AI code detection, auto-fixes, SARIF uploads, and SBOM creation, with a free tier available for basic use. The tool is especially beneficial for teams leveraging AI coding tools, cloud-native stacks, and contemporary frameworks such as React, Django, Spring Boot, and Go microservices, ensuring the secure deployment of code modifications.
Keywords: #phi4, AI-generated code, Auto-fix, Cloud-native security, CodeSlick, Compiler API, Django/Flask, Docker, GitHub Copilot, GitHub Marketplace, Go, Hardcoded secrets, Java, JavaScript, Kubernetes, OWASP, Python, React, SARIF Upload, SBOM Generation, SQL injection, Security Scanner, Shift-Left Security, Spring Security, Terraform, TypeScript, Vulnerabilities, XSS
github.com 2 days ago
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568.
HN
Show HN: Train AI Agents to Write Better Playwright Tests
"Show HN: Train AI Agents to Write Better Playwright Tests" presents the Playwright Skill, a tool aimed at enhancing automated test quality for web applications using Playwright by addressing common issues like inconsistent test generation due to AI's limited understanding of specific application workflows and constraints. This skill comprises over 70 structured markdown guides organized into five skill packs: core testing, CLI usage, Page Object Model patterns, CI/CD setup, and migrations from frameworks such as Cypress or Selenium. These comprehensive guides cover topics including locators, authentication, visual testing, CI configurations, and framework migration.
Installation of the Playwright Skill is straightforward using the command `npx skills add testdino-hq/playwright-skill`. Open-source under an MIT license, it can be customized to meet team-specific standards. It supports AI tools like Claude Code and GitHub Copilot by providing structured references that aid in generating more reliable tests.
The guides detail crucial aspects of Playwright testing—outlining appropriate patterns, highlighting pitfalls, offering quick code snippets, and presenting full implementations—to help both human developers and AI agents efficiently produce production-grade tests. Additionally, integrating TestDino enhances test management by enabling real-time streaming of test results, tracking flaky tests, categorizing failures via AI, and ensuring smooth integration with GitHub PRs and task management tools such as Jira or Linear. Overall, the Playwright Skill is a valuable resource for improving the reliability and scalability of testing efforts based on Playwright.
Keywords: #phi4, AI Agents, API Testing, Accessibility, Angular, Authentication, Auto-Waiting, Browser APIs, CI/CD, CLI Automation, Common Pitfalls, Core Testing Patterns, Cypress, Debugging, Docker, Error Index, Flaky Tests, Forms and Validation, Framework Migrations, GitHub Actions, I18n, Localization, Locators, MIT License, Markdown, Migration, Network Mocking, Nextjs, Open Source, Page Object Model, Playwright, React, Real-Time Reporting, Selenium, Skill Guides, Skills Protocol, Snapshot-Based Automation, Test Data Management, Test Organization, TestDino, Tests, Token Efficiency, Visual Regression, Vue
testdino.com 2 days ago
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687.
HN
Large Language Models for Mortals: A Practical Guide for Analysts with Python
"Large Language Models for Mortals: A Practical Guide for Analysts with Python" offers a hands-on approach to using large language models (LLMs) through Python, specifically catering to analysts transitioning from traditional machine learning due to recent LLM advancements. The guide covers practical applications with major LLM providers like OpenAI, Anthropic, Google, and AWS Bedrock, focusing on API interactions, structured outputs, Retrieval-Augmented Generation (RAG), tool-calling, and agent-based systems. It contains over 250 code snippets and 80 screenshots across its 354 pages, illustrating usage of tools such as GitHub Copilot and Google’s Antigravity editor. Aimed at data scientists, PhD students, and analysts, the book emphasizes processing unstructured text for LLM applications. Differing from theoretical or outdated resources like Chip Huyen's "AI Engineering" or Amit Bahree’s "Generative AI in Action," this guide provides current coding practices across various platforms. It underscores foundational knowledge crucial for building practical LLM applications and acts as a supplementary resource for those seeking to understand the technical intricacies of foundation models. Available both as a paperback and an epub, with additional materials on GitHub, it bridges the gap between theoretical understanding and practical application in the field of large language models.
Keywords: #phi4, API, AWS Bedrock, Analysts, Anthropic, BigQuery, Chat Completions, ChromaDB, Data Science, FAISS, Generative AI, GitHub Copilot, Google Gemini, Large Language Models, Machine Learning, OpenAI, Python, RAG, S3 Vectors, Tool-calling, Unstructured Textual Data, Vector Store
crimede-coder.com 3 days ago
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708.
HN
Agent Lens – Code assistant observability in VSCode
Agent Lens is a Visual Studio Code (VSCode) extension designed to enhance observability for AI coding agents such as GitHub Copilot and Claude Code. It provides users with comprehensive insights into the activities of these agents by parsing local session data, which it then visualizes directly within the editor. This includes monitoring agent activity, model usage, token consumption, and workflow connections. Key features offered by Agent Lens include a Metrics Dashboard for an overview of token use and agent interactions; an Agent & Skill Explorer to manage various tools and skills used by the agents; an interactive Agent Graph that visually represents agent interactions; and a Session Explorer that allows users to replay sessions as timelines. The extension supports GitHub Copilot Chat and Claude Code by accessing JSONL session files stored in specific directories, typically requiring no configuration except when working with devcontainers or remote SSH environments. Installation is straightforward via the VSCode Marketplace, and it invites community contributions for bug reports and improvements under an MIT license.
Keywords: #phi4, AI coding agents, Agent Lens, Claude Code, GitHub Copilot, JSONL files, VSCode, agent explorer, cache token metrics, interactive DAG, metrics dashboard, observability, session data, workspace storage
github.com 3 days ago
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778.
HN
I analyzed how AI changed software shipping speed
The analysis reveals a marked acceleration in software shipping speed since 2025, primarily driven by advancements in AI technologies such as GitHub Copilot, Cursor, and various AI agents. These developments have not only doubled the output but also reduced barriers for product releases, transitioning AI's role from assistive to both agentic and universal. This transformation is evidenced by significant growth in software products, illustrated by metrics like Product Hunt launches, Hacker News' Show HN posts, and GitHub's Octoverse data. In 2025, Product Hunt experienced a doubling of product launches compared to the previous year, with an even greater increase early in 2026. Concurrently, Show HN postings also doubled, indicating heightened public developer engagement.
GitHub has documented record numbers of repositories, commits, and pull requests, alongside a notable rise in AI-related projects and TypeScript usage. The surge in .ai domain registrations further underscores the trend toward increased AI branding efforts. These trends collectively suggest that AI tools have considerably expedited software development and product launches, pointing to sustained growth in this sector moving forward.
Keywords: #phi4, AI, Copilot, GitHub, LLM SDKs, Product Hunt, Show HN, TypeScript, acceleration, ai domains, commits, data analysis, developers, open source, repositories, shipping speed, software
datachaser.com 4 days ago
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785.
HN
Vim 9.2 Released
Vim 9.2 introduces substantial enhancements across scripting, diff mode, user interface, and security features. The update enriches Vim's scripting language with new capabilities such as Enums, Generic functions, Tuple data types, and improved class method compilation. These advancements support the creation of AI tools and are exemplified in GitHub projects. Scripting improvements also include comprehensive completion options like fuzzy matching and direct register access, controlled by new 'completeopt' flags for better match display.
In terms of user interface, Vim 9.2 brings full Wayland UI and clipboard support on Linux, adheres to the XDG Base Directory Specification, and introduces a vertical tab panel alongside native dark mode support in Windows GUIs. Additionally, an updated interactive tutor plugin provides modernized learning experiences beyond traditional vimtutor.
Diff mode sees significant improvements with a new linematch algorithm for improved change alignment, diff anchors for complex file sections, and enhanced inline highlighting. These updates optimize Vim's performance on contemporary hardware by adjusting default settings accordingly.
The release also showcases new completion and introspection features such as auto-completion, live grep, fuzzy file/buffer finding, and command line enhancement via popup menus. Addressing security concerns, the update resolves various bugs and vulnerabilities, ensuring a more robust experience for users. Lastly, Vim announces its transition from ICCF Holland to Kuwasha to continue supporting charitable activities in Uganda, encouraging ongoing user support through this new partnership.
Keywords: #phi4, AI tools, Battleship game, CmdlineChanged event, Enums, Generic functions, GitHub Copilot, Kuwasha partnership Keywords: Vim, Number Puzzle, Tuple data type, Vim, Vim9, Wayland support, XDG Base Directory Specification, auto-completion, backspace behavior, buffer completion, clipboard integration, completion features, dark mode, diff mode, diffopt settings, fullscreen support, fuzzy find file, fuzzy matching, high-DPI monitors, interactive tutor, linematch algorithm, live grep, memory leaks, memory leaks Comma-Separated Keywords: Vim, memory leaks Extracted Keywords: Vim, memory leaks Final Keywords: Vim, memory leaks Final List: Vim, memory leaks Simplified Keywords: Vim, memory leaks Vim, popup menu, ruler option, scripting language, security vulnerabilities, undo history
www.vim.org 4 days ago
https://docs.freebsd.org/en/books/handbook/wa 4 days ago
https://github.com/bellard/mquickjs 4 days ago
https://github.com/justjake/quickjs-emscripten 4 days ago
https://fennel-lang.org/ 4 days ago
https://github.com/vim/vim/tags 4 days ago
https://github.com/vim/vim/commit/e7e21018fc0 4 days ago
https://www.vim.org/ 4 days ago
https://neovim.io/roadmap/ 4 days ago
https://railsatscale.com/2023-08-29-ruby-outperforms-c/ 4 days ago
https://github.com/svilendobrev/svd_bin/blob/ 4 days ago
https://pragprog.com/titles/dnvim2/practical-vim-s 4 days ago
https://pragprog.com/titles/modvim/modern-vim/ 4 days ago
https://www.oreilly.com/library/view/the-viml-prim 4 days ago
https://learnvimscriptthehardway.stevelosh.com/ 4 days ago
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https://docs.redhat.com/en/documentation/red_hat_s 3 days ago
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https://aider.chat/docs/usage/watch.html 3 days ago
https://groups.google.com/g/vim_dev/c/65jjGqS 3 days ago
https://lwn.net/Articles/713114/ 3 days ago
https://news.ycombinator.com/item?id=7279358 3 days ago
https://neovim.io/doc/user/provider.html#_node.js- 3 days ago
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796.
HN
The Coding Agent Explorer for Claude Code (.NET)
Agentic development marks a substantial advancement in AI-assisted coding by enabling the deployment of autonomous AI agents that can independently operate within a developer's environment without requiring human intervention. These agents have the capability to autonomously read files, search through codebases, execute commands, modify code, and verify changes, thus performing multi-step tasks iteratively on their own. Unlike traditional AI tools that primarily suggest code snippets, these agentic tools are designed to carry out complex tasks independently.
Several tools exemplify this approach, including Claude Code by Anthropic (CLI-based), GitHub Copilot's agent mode within Visual Studio Code, the AI-first editor Cursor, and Windsurf. These innovations are revolutionizing software development processes, but they also require developers to have a clear understanding of their autonomous actions. To aid in monitoring these agents, tools like the Coding Agent Explorer for Claude Code (.NET) have been introduced, allowing developers to observe and understand the activities performed by these AI agents within their environments.
Keywords: #phi4, AI agent, Agentic development, Anthropic, CLI-based, Claude Code, Coding Agent Explorer, Cursor, GitHub Copilot, VS Code, Windsurf, autonomous, autonomy, codebase, commands, development environment, edit code, files, software writing, tools, tools Comma-separated list: Agentic development, tools Keywords: Agentic development, toolsExtracted Keywords: agentic development, verify changes
nestenius.se 4 days ago
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804.
HN
The Developer –> Designer Switch
The article examines the evolving role in software development from traditional developer-centric tasks towards a more structured "Designer" role, propelled by advancements in AI and Large Language Models (LLMs). The author emphasizes the benefits of Spec-Driven Development (SDD), which prioritizes detailed specifications as the foundation for project execution. Through personal experience and industry examples, such as Spotify’s use of internal systems like Claude Code, it illustrates how companies are increasingly leveraging AI tools to handle coding tasks while engineers focus on review and architecture.
Spec-Driven Development is characterized by a structured workflow that involves specifying, clarifying, planning, tasking, and implementing, with automation provided by LLMs. This approach aims for precision in development, offering better traceability through version-controlled documentation. Various SDD frameworks, like Spec Kit, help manage this process effectively. The article discusses different applications of SDD, from "spec-first" methods in new projects to "spec-anchored" approaches for ongoing work.
The text also introduces concepts such as Context Engineering and Context Bloat, aimed at optimizing interactions with LLMs by managing the input context for accuracy and efficiency. It underscores the importance of maintaining consistent instructions across tasks using files like CLAUDE.md.
While SDD shows promise in enhancing project outcomes and is particularly beneficial for medium-to-high complexity projects where ambiguity can be costly, it also faces challenges such as non-determinism, scalability issues, increased token costs, and risks of over-engineering simple projects. The article suggests that disciplined application of SDD, rather than rigid adherence, can mitigate these limitations.
Ultimately, the transition from developers writing code to designers crafting precise specifications marks a significant shift in software development. This evolution emphasizes architecture and design skills, with AI tools supporting the creation of functional systems through rigorous control. As such, modern software professionals are encouraged to focus on areas like architecture, DevOps, data models, and security, gradually integrating SDD into their workflow for improved efficiency and outcomes.
Keywords: #phi4, AI, API-first, Agile, Amazon Q, Architecture, Automation, Claudemd, Coding Agents, Complexity, Context Engineering, Contract Tests, Costs, Cross-service Dependencies, Data Models, Designer, Deterministic Guardrail, DevOps, Developer, Distributed System, Frameworks, GitHub Copilot, Google Gemini, JetBrains, LLMs, Maintenance, Microservices, Non-determinism, Overhead, Prompt Engineering, SaaS, Scalability, Security, Software Development, Spec Kit, Spec-Driven Development, Specifications, Spotify, Tokens, Workflow
c-daniele.github.io 4 days ago
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808.
HN
Claude Agent in VS Code: no extension required, Copilot subscription supported
Visual Studio Code (VS Code) natively supports third-party AI agents such as Anthropic's Claude and OpenAI's Codex, eliminating the need for additional extensions. These integrations are seamlessly embedded into VS Code’s interface, leveraging existing GitHub Copilot subscriptions for authentication and billing purposes. The platform provides a unified management system that allows users to handle both local and cloud-based agent sessions from a single interface, enhancing the coding experience with advanced debugging, testing, and session management features.
Key functionalities include rich integration capabilities where AI tools work in harmony with VS Code's editing features to optimize the development workflow. Claude operates autonomously within the workspace environment using specialized slash commands like `/agents`, `/hooks`, and `/memory` for intricate workflows. Users can choose from various permission modes, including automatic edits or requiring approvals before changes are applied. OpenAI Codex facilitates autonomous coding tasks in both interactive and background sessions, with access contingent upon a Copilot Pro+ subscription available through the Visual Studio Marketplace extension.
Billing for these third-party AI agents is streamlined via GitHub Copilot subscriptions rather than direct provider billing, which can be more cost-effective. Compatibility of these services hinges on existing Copilot plans, with users having the flexibility to choose between local and cloud-based sessions depending on availability. This integration empowers developers by incorporating powerful AI capabilities directly within their development environment, offering both versatility and efficiency in coding tasks.
Keywords: #phi4, Anthropic, Authentication, Billing, Chat View, Claude Agent, Cloud-based Agents, Codex, Copilot Subscription, Debugging, GitHub Copilot, Lifecycle Hooks, Local Sessions, Memory Files, OpenAI, Partner Agent, Permission Modes, Prerequisites, SDK, Session Type, Slash Commands, Subscription Plan, Testing, Third-party Agents, VS Code, VS Marketplace, Workspace
code.visualstudio.com 4 days ago
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829.
HN
AI usage in popular open source projects
The document examines the role of artificial intelligence (AI) in enhancing productivity across several prominent open-source projects, such as Apache Spark, Apache Airflow, CPython, .NET, and cURL. It highlights the growing trend of utilizing AI tools for code contributions, exemplified by Apache Spark's mandate since August 2023 requiring contributors to disclose their use of AI in pull requests. Statistical data from Apache Spark shows that approximately 1-2% of commits over a two-year period utilized AI tools like Claude/Opus/Copilot, with usage increasing annually as AI capabilities improve.
The integration of AI into these projects introduces challenges, notably the maintenance of code quality and the increased workload for project maintainers tasked with reviewing AI-generated contributions. Some projects, such as NetBSD, have implemented bans on unapproved AI-generated code due to concerns regarding trust and security. These issues underscore ongoing discussions within open-source communities about the need for disciplined AI use.
AI's impact on productivity is multifaceted; it aids developers by enhancing their understanding and efficiency but should not supplant essential software development knowledge. When used appropriately, AI can boost both productivity and personal expertise, particularly as contributors advance to maintenance roles. However, open-source communities depend heavily on trust, which can be compromised if AI is misused or employed carelessly, leading to heightened scrutiny from maintainers.
To address these challenges, there is a call for clear guidelines and responsible integration of AI tools within projects. This approach aims to manage the cognitive load on maintainers while preserving high code quality standards, thereby maintaining project integrity and community trust. Thus, while AI offers substantial benefits in software development processes, its adoption must be tempered with rigorous review practices to safeguard the fundamental values of open-source communities.
Keywords: #phi4, AI slop, AI usage, Anthropic models, Apache Airflow, Apache Spark, CPython, GitHub, GitHub Copilot, NET, PR template, Python script, SQLAlchemy, The Mythical Man Month, auto-generated PRs, bug bounty program, bug fixing, business decisions, cURL, claude, code contributions, commit messages, contributing docs, copilot, cursor, deterministic work, dynamic nature, features aided by AI, generative AI, git clone, investment in AI, issues and pull requests, legacy code, maintainers, management entrance exams, matplotlib incident, monitoring workflows, open source, opus, performance improvement, process_repo_sparkpy, productivity, security reports, session lifecycle, shallow-since, software engineering, software fundamentals, sonnet, tainted code, translation UI, workflow authoring
tirkarthi.github.io 4 days ago
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846.
HN
Show HN: Automate Mac with Codex: macOS Control MCP Demo
The project introduces an MCP server designed for macOS that empowers AI agents with the ability to interact with a Mac screen through visual and manual actions, offering functionalities akin to human users' state awareness. Key features include a "See-Think-Act Loop" which allows AI agents to capture screenshots, analyze them via AI to determine interactions like clicking buttons, and refine their behavior based on feedback from past actions. The server is conveniently run using `npx`, eliminating the need for traditional installations by setting up a Python virtual environment for dependencies. However, full functionality necessitates permissions for screen recording and accessibility features to execute tasks such as clicking and typing.
Configuration instructions guide users in integrating the MCP server with various AI clients, like Claude Desktop or VS Code, by editing configuration files to include specific commands. A suite of tools is available for screen interactions—such as taking screenshots, performing OCR, and simulating clicks—and managing applications and browser automation, including executing JavaScript in tabs.
The project illustrates example workflows that demonstrate how AI agents can automate diverse tasks such as filling web forms, navigating software, extracting email information, controlling media players, file management using Finder, Slack messaging, conducting online research, and adjusting system settings. It requires macOS 13+, Node.js 18+, Python 3.9+ for OCR and mouse control operations, with AppleScript handling keyboard and app interactions.
For troubleshooting, the project offers solutions to common issues like permission errors, setup failures, or inaccuracies in OCR processing to ensure seamless operation. As an open-source initiative under the MIT license, the project aims to facilitate AI-driven automation on macOS environments.
Keywords: #phi4, AI Agents, Accessibility Tree, App Management, Apple Vision, Automate Mac, Browser Automation, Codex, MIT License, Nodejs, OCR, Permissions, Python 39, Python Bridge, Quartz Frameworks, Screen Interaction, System Settings, Tool Description, Troubleshooting, UtilitiesKeywords: Automate Mac, Workflow Examples, macOS Control MCP
github.com 4 days ago
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860.
HN
Former GitHub CEO raises record $60M dev tool seed round at $300M valuation
Thomas Dohmke, the former CEO of GitHub, has secured $60 million in seed funding for his startup, Entire, with a valuation of $300 million, marking a record amount for such an early-stage investment. The round was led by Felicis and included participation from notable investors like Madrona, M12, Basis Set, Harry Stebbings, Jerry Yang, and Olivier Pomel, CEO of Datadog. Entire focuses on developing an open-source tool aimed at aiding developers in managing the surge of code generated by AI agents. The company's technology is built around three core components: a Git-compatible database to consolidate AI-produced code; a universal semantic reasoning layer for enabling collaboration among various AI agents; and an AI-native user interface designed to enhance agent-to-human interactions. Dohmke's first product, Checkpoints, pairs AI-generated software with contextual information to assist human developers in evaluating and understanding this code.
The motivation behind Entire's creation stems from the challenges faced by developers inundated by rapidly produced large volumes of AI-generated code, which traditional manual systems struggle to manage effectively. This technology aims to streamline the review process for such contributions, many of which might be flawed or unusable. Dohmke established Entire after leaving his position as GitHub’s CEO at Microsoft in August 2025, during a time when AI coding agents like GitHub Copilot were gaining traction under his leadership. The company's focus on addressing these challenges underscores its commitment to facilitating better management and integration of AI-generated code within existing development workflows.
Keywords: #phi4, $60 million, AI agents, Basis Set, Boston, Checkpoints, Entire, Git-compatible database, GitHub, GitHub Copilot, Harry Stebbings, Jerry Yang, M12, Madrona, Microsoft, Olivier Pomel, TechCrunch Founder Summit 2026, Thomas Dohmke, agent boom, code contributions, dev tool, open source, seed round, semantic reasoning layer, software project, user interface, valuation
techcrunch.com 4 days ago
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921.
HN
WinGet Configuration: Set up your dev machine in one command
WinGet Configuration is a tool designed to simplify the setup of Windows development environments using a YAML configuration file executed through a single command. This approach streamlines the process by allowing users to specify their required tools and settings in one place, which WinGet then applies automatically. To start with WinGet Configuration, developers must install the WinGet DSC module via PowerShell. Once installed, configurations can be applied using `winget configure`, with changes applied idempotently—only modifying what is necessary without redundancy.
Unlike simpler import/export features, WinGet Configuration provides advanced capabilities such as configuring Windows settings, enabling Developer Mode, installing Visual Studio workloads, setting environment variables, defining dependencies, checking OS requirements, and executing PowerShell DSC resources. This makes it akin to a comprehensive recipe for setting up an environment rather than just listing packages.
The tool can be further enhanced with the GitHub Copilot CLI, which aids in generating configuration files based on specific needs, such as creating a Python data science setup or converting scripts into configurations. The `winget configure export` command allows users to capture their current setups for later use or sharing, facilitating consistency across team environments. By storing these configuration files in project repositories, teams ensure consistent development environments. Overall, WinGet Configuration offers an efficient, version-controlled method of configuring development machines, with added flexibility through integrations like GitHub Copilot CLI.
Keywords: #phi4, Configuration, DSC module, Developer Mode, GitHub Copilot CLI, PowerShell, WinGet, Windows settings, YAML file, assertions, dependencies, dev machine setup, export command, idempotent, package IDs
developer.microsoft.com 5 days ago
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954.
HN
GitHub Agentic Workflows are now in technical preview
GitHub Agentic Workflows, currently available as a technical preview, revolutionize task automation within GitHub repositories by leveraging AI agents through GitHub Actions. These workflows are uniquely crafted using plain Markdown, simplifying the process compared to traditional YAML configurations and enabling natural language descriptions for tasks such as issue triage and CI failure analysis. Users initiate these automations by placing Markdown files in the `.github/workflows/` directory, where the `gh aw` CLI tool converts them into executable workflows with support from tools like the GitHub Copilot CLI.
A strong emphasis on security is evident through features such as read-only permissions by default, sandboxed execution environments, network isolation, SHA-pinned dependencies, and sanitized outputs to ensure safe write operations. This secure framework supports multiple AI coding agents while maintaining a consistent format across all engines, facilitating seamless integration with GitHub's extensive suite of resources, including repositories, issues, pull requests, and security systems via the GitHub MCP Server. Additional capabilities extend to browser automation and web searches.
Agentic Workflows can be activated through various triggers or initiated manually, simplifying their deployment process: users install the CLI extension, create a Markdown file, compile it using `gh aw`, and commit as they would with standard GitHub Actions. These workflows are accessible for authoring in environments such as VS Code or directly on GitHub, with the project being open source under the MIT license to encourage community involvement.
The automation potential of Agentic Workflows is vast, encompassing automatic issue triage, CI failure analysis, documentation upkeep, test coverage enhancement, compliance monitoring, and even team morale improvement. Users seeking inspiration can explore Peli’s Agent Factory, which offers over 50 specialized workflows. Additional resources include the GitHub Agentic Workflows documentation and community discussions on platforms like the GitHub Next Discord.
This initiative results from collaboration between GitHub Next, Microsoft Research, and Azure Core Upstream, with its implementation open-sourced in the `gh-aw` repository. More details are available through a dedicated blog post on GitHub's platform, showcasing this cutting-edge approach to workflow automation within GitHub environments.
Keywords: #phi4, AI agents, Azure Core Upstream, CI failure analysis, GitHub Actions, GitHub Copilot CLI, GitHub Next, MIT license, Markdown, Microsoft Research, Peli’s Agent Factory, SHA-pinned dependencies, VS Code, YAML, automation, browser automation, issue triage, network isolation, open source, pull request reviews, repository maintenance, safe outputs, sandboxed execution, triggers, web search
github.blog 5 days ago
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958.
HN
Stop Typing, Start Talking
The article explores the author's transition from traditional typing to utilizing voice recognition tools for enhancing productivity amid a surge in writing prompts and messages. Initially skeptical of voice control solutions like GitHub Copilot Voice, the author eventually embraced Handy, a tool recommended by Andrew Connell. This software integrates seamlessly into their workflow, allowing spoken words to be transcribed directly into focused windows on the computer using a hotkey activation. The adoption of Handy has significantly boosted productivity in tasks such as AI prompting and social media interactions, particularly within a home office setting where it proves most effective. While acknowledging that voice input is increasingly becoming a logical interface for interacting with technology, the author notes that keyboards still hold value. They encourage others to experiment with voice dictation to potentially enhance their workflows. The article also references resources like Wispr Flow, Whisper wrapper, and Parakeet V3 model, which relate to voice recognition technologies.
Keywords: #phi4, AI prompting, GitHub Copilot, Handy, Parakeet V3, Voice control, Wispr Flow, content drafting, developers, mechanical keyboards, microphone, microphone Keywords: Voice control, natural language, prompts, shortcuts, social media, talking, transcription, typing, workflow
www.eliostruyf.com 5 days ago
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980.
HN
Show HN: Retrospec: reverse-engineer a spec prompt for an AI agent from a commit
Retrospec is a command-line tool aimed at reverse-engineering high-level specification prompts from specific commits within a code repository by analyzing changes made to generate plausible spec prompts that could have led to those alterations. The tool emphasizes two primary criteria: technical similarity and realism, inspired by efforts in code reproduction and the release of GitHub's Copilot SDK. Its functionality includes understanding historical commit intents, creating reusable task specifications from actual code modifications, and constructing datasets with realistic engineering requests.
The process involves scoring candidate prompts on their alignment with the target commit and how likely they are to resemble human-written requests, with a default emphasis on technical similarity. Retrospec supports diverse input configurations for repositories but strictly excludes elements like code blocks or references in the generated prompts. Users can deploy Retrospec either by using prebuilt binaries or compiling from source, which necessitates Git and GitHub Copilot CLI installations. The tool offers several customization options through flags, including iteration limits and realism heuristics.
Retrospec’s operation entails cloning the repository, computing a patch for the target commit, generating candidate specs, executing them in Copilot coder sessions, and refining these based on scores to identify the best prompt. This iterative refinement process culminates in outputs such as the optimal spec prompt, accompanying metrics, logs, and patches, thereby enhancing understanding of the rationale behind code changes.
Keywords: #phi4, AI agent, GitHub Copilot SDK, Retrospec, coding agent, commit-to-prompt, high-level spec, markdown structure, no-code rules, optimization iterations, realism score, structured candidate specs, structured candidate specs Keywords: Retrospec, technical similarity
github.com 5 days ago
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990.
HN
Microsoft confirms plan to ditch OpenAI
Microsoft is shifting away from OpenAI’s models towards developing its own advanced AI systems, marking a strategic move as the relationship between Microsoft and OpenAI becomes strained. Historically reliant on OpenAI for products like ChatGPT and tools such as Microsoft 365 Copilot, Microsoft's decision to transition stems partly from OpenAI's new partnerships with other tech firms. In response, Microsoft has increased investments in AI competitors like Anthropic and plans to develop its own AI models by 2026.
Mustafa Suleyman, Microsoft’s AI Chief, highlighted this strategic pivot towards creating innovative AI tools designed to revolutionize industries such as healthcare. Despite acknowledging the optimism surrounding AI's potential benefits, he also noted significant ethical concerns related to AI technology. OpenAI, on the other hand, faces financial and legal hurdles, alongside skepticism regarding the broader societal impact of AI advancements.
This development positions Microsoft as a direct competitor in the AI industry, joining forces with major players like NVIDIA and Google DeepMind. The company aims for its AI solutions to be self-improving and autonomous, while ensuring compliance with corporate standards amidst ongoing public debates about AI’s role and implications.
Keywords: #phi4, AI models, Anthropic, Azure tools, ChatGPT, DALL-E 3, Gemini, GitHub Copilot, MAI models, Microsoft, Microsoft 365 Copilot, Mustafa Suleyman, NVIDIA, OpenAI, Sam Altman, automation, copyright violation, economic upheaval, job losses, lawsuits, medical super-intelligence
www.windowscentral.com 5 days ago
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1005.
HN
Reflecting on my AI adoption timeline
The author recounts their transformative journey with AI integration in software engineering, highlighting a shift from traditional hand-coding to leveraging advanced AI tools such as GitHub Copilot, Cursor, and Opencode. Initially skeptical about "agentic coding," the author's perspective changed after successfully utilizing these tools for significant projects like maintaining an open-source project and overhauling a tech platform at their new job. By June 2025, while serving as Founding Engineer at Tax Nuggets Academy, AI dramatically enhanced productivity in tasks such as data migration and application development through efficient workflows using Linear, Cursor, and Codex CLI. These tools facilitated issue tracking and code reviews, reducing the mental strain of working alone by automating routine coding tasks.
By February 2026, the author continues to incorporate AI into their workflow but remains vigilant about preserving control over critical business logic and ensuring quality assurance. They recognize a marked increase in efficiency, estimating productivity to have risen by approximately 2.3 times compared to pre-AI periods. This experience underscores the rapid evolution of AI within coding, highlighting its potential to amplify engineering capabilities when users adapt their processes while upholding rigorous standards for code quality and oversight.
Keywords: #phi4, AI adoption, Codex CLI, Cursor, GitHub Copilot, Linear Agent, OpenCode, SaaS development, agentic tools, coding timeline, data migration, legacy codebase, mental fatigue, productivity boost, velocity increase, velocity increase Keywords: AI adoption, workflow automation
tomquirk.me 5 days ago
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1053.
HN
Openrappter- Local-First AI Agent Powered by GitHub Copilot SDK
OpenRappter is a local-first AI agent framework designed to work seamlessly with the GitHub Copilot SDK using existing Copilot subscriptions, thereby eliminating the need for additional API keys or accounts. It emphasizes data privacy by keeping all memory, configuration, and state stored locally on the user's machine, ensuring no extra costs are incurred. The setup process is streamlined through `skills.md`, enabling AI agents to automatically handle installation, configuration, and startup tasks.
The framework boasts several key features: it leverages GitHub Copilot for AI inference while maintaining a local-first data approach. Each agent operates as a single file with metadata defined in native code constructors, promoting portability and ease of management. OpenRappter supports persistent memory to maintain context across sessions, remembering facts and preferences. Additionally, it offers dual runtime support for both Python (with four agents) and TypeScript (with three agents), alongside mechanisms like Data Sloshing & Slush Pipelines that enrich agent calls with contextual signals and facilitate seamless inter-agent communication.
For setup, users can opt for an automated approach by copying `skills.md` to AI assistants such as Copilot or ChatGPT, which handles configuration automatically. Alternatively, manual installation involves cloning the repository and following specific instructions depending on whether Python or TypeScript is used—installing dependencies via pip or npm and running builds accordingly.
OpenRappter's architecture routes user input through an agent registry and Copilot SDK for tool invocation, with data sloshing enriching context prior to executing `Agent.perform()`. This setup enables direct communication between agents through data slush pipelines without requiring cloud AI intervention. The framework is supported by RappterHub, a native agent registry that allows the installation of community-developed agents and ClawHub compatibility for extended functionality via OpenClaw skills.
As an open-source project under the MIT license, OpenRappter invites contributions from developers. Its structure includes separate directories for Python and TypeScript implementations and provides comprehensive documentation along with a complete agent-teachable reference in `skills.md`.
Keywords: #phi4, AI agent, CLI commands, ClawHub, GitHub Copilot SDK, Python, RappterHub, TypeScript, agents, data sloshing, dual-runtime, local-first, openrappter, single file agent pattern
github.com 6 days ago
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1079.
HN
Moltis: Rust based AI assistant with memory, tools, and self-extending skills
Moltis is a Rust-based AI assistant aimed at boosting productivity through features such as memory retention, extensibility, and multi-channel communication. This versatile tool can be installed on various systems using methods like Homebrew, Cargo, Docker, or directly from the source code. One of its standout capabilities is support for local Large Language Models (LLMs) that facilitate offline use while maintaining security through isolated container browsing.
Moltis offers a range of key features including hybrid memory search and dynamic self-extension abilities. It supports multiple LLM providers such as OpenAI Codex and GitHub Copilot, enhancing its versatility in handling different AI tasks. Access to Moltis is facilitated via WebAuthn passkeys and scoped API keys, ensuring secure user interactions.
The platform emphasizes security through human-in-the-loop approval processes, origin validation, and zeroing secrets on drop. It provides an extensible environment through MCP server support, a hook system for lifecycle management, cron job scheduling, and configuration via TOML files. Moltis supports various communication channels including a Web UI, Telegram bot, JSON-RPC API, mobile PWA, and push notifications, with added observability from tools like Prometheus metrics and OpenTelemetry tracing.
Despite its advanced features, Moltis is noted as early-stage software, advising users to exercise caution, particularly concerning tool permissions and system access. Developed by Fabien Penso, the project is MIT licensed and encourages responsible usage.
Keywords: #phi4, AI assistant, Cargo, Docker, GitHub Copilot, Homebrew, MCP, Moltis, OpenAI Codex, Prometheus metrics, Rust, SQLite persistence, SQLite persistence Keywords: Moltis, authentication, channels, embeddings, extensibility, hooks, hybrid search, installation, local LLMs, memory, multi-channel, observability, plugins, sandboxed browsing, security, self-extending skills, streaming-first, tools, voice
www.moltis.org 6 days ago
https://pen.so/2020/11/07/own-your-content&#x 5 days ago
https://pen.so/2020/12/10/own-your-email/ 5 days ago
https://pen.so/2026/02/12/moltis-a-personal-a 5 days ago
https://rustacean.net 4 days ago
https://github.com/moltis-org/moltis 4 days ago
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1125.
HN
Show HN: Drift – Real-time codebase health dashboard with AI-powered fixing (Go)
Drift is a terminal-based tool designed to monitor the real-time health of codebases in eight programming languages—Go, TypeScript, Python, Rust, Java, Ruby, PHP, and C#. It evaluates various metrics like cyclomatic complexity, dependency freshness, architecture boundary violations, and dead code through an interactive text user interface dashboard. A standout feature is the `drift fix` command, which utilizes the GitHub Copilot CLI to propose automated refactoring by generating context-rich prompts based on function sources, allowing users to review suggestions before implementation. Additionally, Drift features a custom Copilot agent that enhances AI's understanding of code health metrics and incorporates a GitHub Action to transform raw reports into digestible pull request comments. The tool uses full Abstract Syntax Tree (AST) parsing for Go through `go/ast`, while other languages are analyzed using heuristic regex methods. Built with the Bubble Tea and Lip Gloss libraries, Drift serves as a "heartbeat monitor" for codebases, identifying and diagnosing health issues using AI technology, similar to Datadog but specifically tailored for coding environments. The tool is accessible via its GitHub repository or official website.
Keywords: #phi4, AI-powered fixing, AST parsing, Bubble Tea, Drift, GitHub Action, GitHub Copilot CLI, Go analysis, Lip Gloss, PR comments, TUI dashboard, architecture boundary violations, codebase health, custom agent, cyclomatic complexity, dead code, dependency freshness, health degradation Keywords: Drift, heuristic regex, monitoring, monitoring Comma-separated List: Drift, monitoring Extracted Keywords: Drift, monitoring Final Keywords: Drift, real-time dashboard, refactorings, terminal tool
drift.marquis.codes 6 days ago
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1172.
HN
Copilot Fun – Play terminal games while GitHub Copilot codes for you
Copilot Fun is a terminal user interface (TUI) application designed to enhance productivity by integrating gaming with coding using GitHub Copilot. It allows users to seamlessly switch between working on code and playing games through `Ctrl-G`, or automatically toggle based on AI activity with `Ctrl-S`. The app offers 13 games, preserving the game state for continuity, and displays AI activity status on a bar via Copilot Hooks. Its game library features ten WASM games from nbsdgames alongside three JavaScript games: 2048, Cookie Clicker, and Tic-Tac-Toe, while also supporting custom Node.js scripts in `~/.copilot-fun/games/`. The application requires Node.js 18+ and the GitHub Copilot CLI, functioning optimally on Linux or WSL with limited compatibility for macOS and Windows due to native hook restrictions. It operates through a pseudo-terminal using node-pty, managing screen states like tmux with VTerminal, compiling WASM games from C via Emscripten, and running JS games as Node.js child processes. The project is structured around the main wrapper (`index.js`), compilation scripts, game implementations, and configuration files for customizations, developed utilizing tools such as GitHub Copilot CLI, node-pty, @xterm/headless, and Emscripten. It holds an MIT license, with some games available under CC0 public domain.
Keywords: #phi4, Ctrl-G toggle, Emscripten compiler, GitHub Copilot, Nodejs scripts, TUI wrapper, WASM games, auto-switch mode, game state preservation, nbsdgames source code, pseudo-terminal, terminal games, virtual terminals
github.com 6 days ago
https://github.com/sirluky/copilot-fun 6 days ago
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1192.
HN
Lines of Markdown, a Claude Code Sensation
The article delves into a Markdown file consisting of 65 lines that encapsulates four principles for enhancing AI-assisted coding, inspired notably by Karpathy. This concise document was transformed into an extension compatible with various code editors such as Claude Code, VS Code, and Codex, achieving notable recognition on GitHub with nearly 4,000 stars. The narrative begins with the author's experience at an AI workshop within their company, which regularly employs AI tools like Cursor and GitHub Copilot for coding tasks. Here, they discovered the potential of custom rules files to augment AI tool capabilities, leading them to further investigate this Markdown-based extension.
The journey involved technical challenges in converting the file into a VS Code extension due to the author not being a Verified Publisher on the marketplace. Similar obstacles arose while attempting publication through open-vsx.org for Cursor. Despite these barriers, the author encourages others to try the extension and provide feedback, emphasizing its potential to significantly impact coding practices with its simplicity. The article concludes by underscoring the unexpected yet considerable influence minimal guidelines can exert on AI-driven development processes, inviting readers to experiment with the tool themselves.
Keywords: #phi4, AI, AWS Bedrock, CLI, Claude Code, Coding Standards, Cursor, Eclipse Foundation, Extension, GitHub Copilot, Markdown, Model Training, Publisher, Refactoring, Repository, Rules, Stars, Strands, VS Code, Workshop
tildeweb.nl 6 days ago
https://www.star-history.com/#forrestchang/andrej-karpa 6 days ago
https://github.com/kelseyhightower/nocode 6 days ago
https://jsdate.wtf 6 days ago
https://rationalwiki.org/wiki/Deepity 6 days ago
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1222.
HN
The Problem with LLMs
The essay delves into the ethical and practical implications of utilizing Large Language Models (LLMs) within software development, particularly examining their role in expediting feature implementation for applications such as Pariyatti. While LLMs enhance productivity by facilitating language accessibility and assisting developers with disabilities or injuries, they raise significant ethical concerns due to their tendency to generate outputs based on copyrighted materials, effectively "stealing" from training data without proper attribution. This issue of plagiarism poses a dilemma in assessing the originality of work produced through such models.
Despite these challenges, LLMs offer notable advantages, including enabling rapid experimentation and reducing coding demands for developers with varying levels of experience or physical constraints. However, their use is met with caution due to potential pitfalls like increased bug occurrence and code quality deterioration—a phenomenon linked to "AI Fatigue." This term describes how the efficiency gains from AI tools can paradoxically lead to more work and burnout as developers push themselves without proper pacing.
The essay further explores psychological impacts on developers, such as an "attachment" to traditional programming pleasures and a possible "addiction" to productivity enhancements afforded by LLMs. Both factors influence mental well-being within the tech industry. Additionally, it raises concerns about data gatekeeping and proprietary models that could create restrictive ecosystems by leveraging continuous user input.
Ultimately, while LLMs present compelling benefits in terms of accessibility and innovation, their integration in nonprofit contexts like Pariyatti remains fraught with unresolved ethical dilemmas. The essay concludes by advising management to carefully weigh these advantages against the associated ethical concerns when making decisions regarding LLM implementation.
Keywords: #phi4, AI, AI Fatigue, AI improvements, AI winter, CSS, Claude Code Pro, GitHub Copilot, LLMs, Rust, YOLO, accessibility, addiction, attachment, copyright, data gatekeeping, distribution models, environmental impact, ethics, generative AI, nonprofit, open source, plagiarism, programming, proprietary models, psychological landscape, software development, sīla, tokens
www.deobald.ca 6 days ago
https://arxiv.org/abs/2601.02671 6 days ago
https://arxiv.org/abs/2404.01019 6 days ago
https://transformer-circuits.pub/2025/attribution-graph 6 days ago
https://en.wikipedia.org/wiki/Sealioning 5 days ago
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1226.
HN
Shadow-code: a novel approach to coding with AI
Shadow Code is an AI-driven coding tool that transforms human-written pseudocode into clean, production-ready code in selected programming languages. This innovative technique empowers developers to maintain control over the code generation process by using detailed pseudocode to specify code intent precisely. A key feature of Shadow Code is its integration with Visual Studio Code (VS Code) as a free, open-source extension, utilizing VS Code's Language Models API and requiring a model provider like GitHub Copilot for functionality.
The tool offers several functionalities including the ability to convert pseudocode into target language code through user commands or keyboard shortcuts. It also supports syntax extensions for custom needs, such as emulating features missing in certain programming languages, and context control to refine AI understanding of relevant codebases. Installation is straightforward via VS Code's Extensions Marketplace, where users can input pseudocode in ".shadow" files and convert it using built-in commands; the tool automatically installs necessary dependencies if they are absent.
Performance-wise, Shadow Code typically handles 5,000 to 8,000 input tokens with outputs averaging between 800 and 2,000 tokens. Generation times generally hover around ten seconds, contingent on the model used. Currently, Shadow Code supports Dart, JavaScript, TypeScript (including JSX/TSX), and is expanding to include Python and Java. The project encourages contributions, particularly for broadening language support, with future plans aiming to introduce inline code insertions/modifications and dedicated prompts for additional languages like Python and Java.
Keywords: #phi4, AI coding, Dart, Firestore ORM, Java support, Java support Keywords: Shadow Code, Python support, Shadow Code, Shadow Mode, VS Code Extension, boilerplate code, contributions, dependencies installation, import function, inline insertions, language models, performance metrics, pseudocode, shadow files, syntax conversion
github.com 6 days ago
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1267.
HN
I Vibe Coded a Game to the Front Page of Hacker News
The article details "Ripple," a daily cause-and-effect puzzle game created by a former coder turned product manager, inspired by Freakonomics and developed predominantly using AI tools. The project's development began with idea validation through various AI chat platforms, followed by the creation of a Minimum Viable Product (MVP) using Lovable, an AI tool for rapid prototyping that included features such as puzzle chains, animations, and streak tracking. The development workflow was enhanced by integrating GitHub for code management, along with VS Code and GitHub Copilot to improve efficiency. For quality assurance, AI in the form of ChatGPT was employed to simulate user interactions to identify usability issues.
The design review process involved gathering feedback from multiple AI chat platforms, which led to improvements in the game's leaderboard design based on diverse suggestions. Content generation combined personal insights with AI-generated puzzles, ensuring high-quality outputs through careful editing. User feedback played a crucial role in refining the game; exposure on Hacker News prompted the addition of an archive feature, showcasing adaptability.
Key lessons from the project include recognizing AI’s versatility across various development stages while noting it cannot replace human creativity or marketing skills. The importance of iterative improvement is highlighted by the necessity of MVPs for rapid learning and adaptation based on user feedback. Successful collaboration with AI involves leveraging its strengths and maintaining control over design decisions through human oversight. Overall, the project exemplifies how minimal coding knowledge, combined with advanced AI tools, can facilitate the creation of a fully functional game, underscoring creativity and idea validation as crucial elements in product development.
Keywords: #phi4, AI, Content Generation, Copilot, Design Review, Game Development, GitHub, Hacker News, Marketing, Playtesting, Product Management, Ripple, Vibe Coding
katecatlin.substack.com 7 days ago
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1269.
HN
From Muscle to Matrix
The article explores a significant economic transformation, shifting from valuing money based on "Human Time x Skill" to "Energy x Inference Efficiency," largely influenced by AI advancements and changes in monetary policy between 2020-2023. This change is described as the "sandwich effect," which has led to drastic reductions in knowledge work costs due to AI, resulting in substantial workforce declines.
Historically, economic value was linked to human labor, evolving from muscle power in the 1800s to thinking and expertise by mid-20th century. The current era marks a shift towards AI, with energy and computational power becoming primary economic inputs as opposed to human effort. This transition is evidenced by the dramatic reduction in costs for AI services—from $100 per task down to just $0.001 within five years.
The acceleration of this shift was driven by pivotal events: the COVID-19 pandemic prompted expansive monetary policies (ZIRP), resulting in hiring booms, particularly in tech sectors. However, subsequent inflation-induced interest rate hikes and advancements in AI technology offered a cost-effective alternative to human labor. This dual pressure—economic constraints from rising rates combined with technological displacement due to cheaper, more efficient AI—created the "sandwich" effect, compressing the knowledge work sector.
As a result, there is an irreversible shift in economic dynamics: tech companies now achieve massive profit margins as operational costs approach zero, while wealth becomes concentrated around those controlling computational resources and energy. For workers, this translates to deflationary pressures on wages, diminishing their value over time. Consequently, companies are likely to prioritize AI solutions even during future growth cycles.
The article raises critical questions about humanity's role in an economy where creating valuable order is no longer dependent solely on human capability but rather on energy and intelligence. This transition underscores a fundamental shift from biological constraints to physical limitations as the primary factor in economic value creation, posing significant implications for the workforce and economic structures moving forward.
Keywords: #phi4, AI Capability, AI Era, Cost Collapse, Economic Role, Electricity, Energy, Human Time, Inference, Interest Rates, Knowledge Work, Negentropy, Phase Transition, Value Creation
www.aviraj.dev 7 days ago
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1289.
HN
A practical guide to use AI Coding agents
The guide offers a practical approach for software developers to effectively integrate AI coding agents into their workflows without succumbing to over-reliance or hype. It positions these AI tools as enhancements that assist with specific tasks such as code generation and refactoring, rather than replacements for human skills. Developers are encouraged to use AI agents primarily for mechanical tasks while reserving complex decision-making for themselves.
A key strategy proposed is the "direct and verify" approach: developers should set clear goals and constraints for AI tools, allowing them to execute specific tasks under supervision. This method requires thorough review of AI-generated outcomes to ensure they meet correctness, security, and project alignment standards. Developers are advised to prioritize planning before coding, utilizing AI assistance in refining requirements and identifying edge cases.
The guide highlights the strengths of AI agents in modes like inline autocomplete and chat-based assistance, while emphasizing their capability for autonomous task execution based on pre-defined plans. It warns against bypassing critical review stages or over-delegating complex tasks without human oversight.
AI tools are also noted for their role in reviewing generated code, providing improvement suggestions while maintaining that a human developer retains final judgment. While AI can be used to create test cases, developers should avoid letting agents automatically adjust these tests.
The guide discusses the potential benefits of multi-agent workflows in scenarios requiring context isolation or parallel exploration but acknowledges they are not universally applicable. It concludes with the expectation that as coding automation advances through AI tools, developers will increasingly engage in creative and supervisory roles.
Keywords: #phi4, AI Coding Agents, Autonomy, Context Isolation, Human Judgment, Multi-Agent Workflows, Orchestration, Parallelization, Planning, Productivity Boost, Review, Software Development, Testing, Workflow Integration
www.devtoolsacademy.com 7 days ago
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1337.
HN
Software 2.0: Code Is Cheap, Good Taste Is Not
"Software 2.0: Code Is Cheap, Good Taste Is Not" delves into the significant changes in software development brought about by Large Language Models (LLMs), transitioning from traditional coding practices to a new paradigm focused on verification rather than specification. The essay highlights how LLMs have boosted productivity by automating code generation but emphasizes the enduring necessity of human oversight for ensuring quality and aesthetic value in software products.
The document outlines several key points, starting with the evolution from "Software 1.0," which involved manual coding, to "Software 2.0," where developers primarily verify AI-generated code rather than writing it manually. In this new era, LLMs serve as powerful tools that enhance both productivity and creativity. Despite some developer roles becoming obsolete due to these advancements, those who adapt by learning how to effectively use AI tools remain essential. These skilled individuals are tasked with addressing the limitations of AI models, focusing on design, taste, and verification processes.
A core principle in this paradigm shift is prioritizing verification over specification, meaning developers now focus on validating code produced by LLMs rather than creating it from scratch. This involves developing automated systems for validation through methods like static analysis, testing, and manual reviews. Managing the vast amounts of code generated quickly by LLMs requires effective tools and processes to ensure outputs align with project goals while maintaining quality standards.
For successful adoption of Software 2.0, developers are encouraged to establish clear documentation practices (such as creating CLAUDE.md), enhance their planning skills for working alongside LLMs on specifications, manage context within sessions efficiently, and utilize cost-effective models where appropriate. While LLMs offer advantages in speed and efficiency, they also pose challenges related to accuracy, alignment, and security that must be addressed through robust verification frameworks.
Overall, the essay underscores a fundamental shift where AI-driven code generation is leveraged by human developers who focus on oversight and quality assurance, ensuring software products meet high standards of excellence.
Keywords: #phi4, AI-assisted development, LLMs, Software, agent harnesses, coding tools, context management, model optimization, productivity, prompt engineering, software engineering, technical debt, verification, verification pipeline
aaronstannard.com 7 days ago
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1371.
HN
Golang textile parser, implemented using Codex as a "clean room" native parser
The project introduces a native Go parser for Textile markup, developed as a "clean room" implementation using Codex, aimed at filling the gap of a comprehensive Textile parser in Go. This initiative leverages Github Copilot CLI and Codex, along with the php-textile test suite, to ensure full parity with php-textile's behavior and similar rendering to its Python counterpart.
**Key Features:**
The parser includes block-level parsing capabilities such as headings, paragraphs, blockquotes, code blocks, various lists (including nested/mixed types), tables, definition lists, raw block handling, HTML wrapper detection, and divider blocks. Inline parsing covers emphasis, strong text, bold/italic styles, links with multiple formats and attributes, footnotes, notelists, attribute fragments, glyph substitutions, acronyms, caps wrapping, bracketed phrases, and fractions.
**Modes and Policies:**
Users can choose between restricted mode for HTML sanitization, lite mode for minimal parsing, HTML5 vs. XHTML rendering, raw HTML block passthrough, and URL sanitization/encoding helpers. The parser offers customization options including handling preferences for images, link relationships, prefixes, line wrapping, raw blocks, block tags, HTML5 rendering, and glyph omission.
**Implementation Details:**
The implementation ensures the parser passes all tests from the vendored php-textile test fixtures using Go's standard library tools without relying on regex-heavy parsing. It includes a fixture-driven test harness with filtering and limiting capabilities to enhance testing flexibility.
**Usage Example:**
An example provided demonstrates how users can convert Textile markup into HTML in Go, showcasing its straightforward integration within applications.
**Testing:**
The testing framework is driven by php-textile fixtures stored in `test/fixtures`, allowing users to execute all tests, filter specific ones using the `TEXTILE_FIXTURE_FILTER` environment variable, or limit the number of tests with `TEXTILE_FIXTURE_LIMIT`.
The project's license remains unspecified, but additional information on fixture provenance can be found in `test/fixtures/README.md`.
Keywords: #phi4, Codex, Github Copilot CLI, Golang, HTML sanitization, Textile parser, block-level parsing, fixture-driven test harness, inline parsing, license, native implementation, options struct, php-textile, stdlib tooling
github.com 7 days ago
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1387.
HN
The Problem with LLMs
The essay delves into the nuanced ethical and practical considerations associated with employing Large Language Models (LLMs) in programming and app development, particularly within nonprofit contexts like Pariyatti’s mobile app. It highlights LLMs' potential to expedite feature implementation while acknowledging significant ethical dilemmas due to their tendency towards plagiarism—copying copyrighted material and presenting it as original work—which conflicts with Pariyatti's stringent ethical standards.
The author outlines the advantages of using LLMs, such as enhancing accessibility in foreign languages and providing valuable assistance for individuals facing physical challenges, exemplified by the author’s own experience with an eye injury. The essay also illustrates diverse developer attitudes towards LLMs, from cautious use to a more experimental "YOLO" approach.
The discussion extends to issues like "AI Fatigue," where users may overextend themselves due to the increased productivity afforded by LLMs, leading to psychological impacts such as attachment to traditional programming joys and an addiction to heightened efficiency. This can result in unsustainable work practices. Additionally, there is a warning about industry shifts towards data gatekeeping as companies might use proprietary LLM models for competitive advantages.
Looking ahead, while acknowledging the accessibility benefits of LLM technology, the essay emphasizes the necessity for careful ethical scrutiny before adoption by nonprofits like Pariyatti. It advocates for management to carefully consider these complex issues when deciding on integrating such tools into their operations.
Keywords: #phi4, AI Fatigue, AI tools, CSS, GitHub Copilot, LLMs, Rust, accessibility, addiction, architecture, attachment, code licensing, copyright, data gatekeeping, ethical concerns, ethics, generative AI, nonprofit, open source, plagiarism, programming, proprietary models, software development, tokens, transformers
www.deobald.ca 7 days ago
|
1407.
HN
Show HN: Multi Tenant MCP Platform
SageMCP is an open-source platform designed to facilitate the deployment of Multi-Channel Proxy (MCP) servers in a multi-tenant environment, providing each tenant with isolated server instances that share centralized OAuth and API key management. The system offers unique endpoints for every tenant (`/api/v1/{tenant}/mcp`) supporting full MCP protocol capabilities including HTTP, WebSocket, and Server-Sent Events (SSE), alongside features such as version negotiation, resumable streams, and JSON-RPC batching.
The platform is compatible with 340 tools distributed across 23 native connectors in various categories like Code & VCS (e.g., GitHub, GitLab, Bitbucket), Project Management (Jira, Linear, Confluence), Communication (Slack, Discord, Microsoft Teams), Email services (Gmail, Outlook), Document management (Google Docs, Sheets, Slides), and AI coding tools, utilizing a standardized metrics schema. SageMCP extends its functionality by allowing the hosting of external MCP servers using Python, Node.js, or Go subprocesses with built-in health checks and auto-restart capabilities.
Technologically, SageMCP's backend is constructed using FastAPI, React, SQLAlchemy, PostgreSQL/Supabase, Docker, Kubernetes, and Helm charts. It includes LRU server pooling, session management through `Mcp-Session-Id`, tenant-specific rate limiting, Prometheus metrics for monitoring, and feature flags to facilitate progressive rollouts. The project is hosted on GitHub under the Apache 2.0 license and provides further architectural insights and information about its multi-tenant MCP patterns upon request.
Keywords: #phi4, API Key Management, Connectors, Docker, FastAPI, Feature Flags, HTTP, Helm Charts, Isolated Instances, JSON-RPC Batching, Kubernetes, LRU Pooling, MCP Platform, Multi-Tenant, OAuth, Open-Source, Path-Based Isolation, PostgreSQL, Prometheus Metrics, Rate Limiting, React, SQLAlchemy, SSE, SageMCP, Supabase, WebSocket
news.ycombinator.com 7 days ago
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1414.
HN
Give GitHub Copilot in VS Code a local memory
Agent Recall is a VS Code extension designed to enhance AI assistants like GitHub Copilot by providing persistent cross-project memory, addressing their limitation of losing context and preferences after sessions end. It achieves this through four main tools that allow users to read, write, list, and delete knowledge base entries stored as markdown files in `~/.agent-docs/`. This functionality enables the retention and recall of information such as coding practices, debugging patterns, and user preferences across projects or sessions. The extension integrates with VS Code's Language Model Tools API for seamless interaction within AI chat interfaces using commands like `#kbRead`, `#kbWrite`, `#kbList`, and `#kbDelete`. Upon activation, Agent Recall creates an instructions file and a LIBRARIAN.md to guide knowledge base management practices. Installation is accessible via the VS Code Marketplace or manual methods, with entries being plain markdown files that are editable and version-controllable. However, the extension requires at least VS Code 1.95.0 and compatibility with tool-calling Language Model providers.
Agent Recall does have limitations: it supports only keyword-based search without fuzzy matching, lacks conflict resolution for concurrent writes, limits searches to three results per query, and restricts customization of the LIBRARIAN.md and instructions file. Despite these constraints, its utility lies in enhancing AI capabilities by enabling persistent storage and recall of user-specific information across projects. The extension is distributed under an MIT license, making it open for further development and use within the community.
Keywords: #phi4, Agent Recall, GitHub Copilot, VS Code, YAML frontmatter, configuration settings, configuration settings Keywords: GitHub Copilot, cross-project, knowledge base, language model tools, markdown files, persistent memory, storage directory, tool calling
marketplace.visualstudio.com 7 days ago
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1429.
HN
Show HN: Multi-agent-shogun – tmux and YAML mailbox for parallel AI agents
"Multi-agent-shogun" is an advanced system designed for the parallel execution of multiple AI coding tools, structured around a hierarchical command model inspired by feudal Japan. This system enables users to manage up to eight AI agents—such as Claude Code, OpenAI Codex, GitHub Copilot, and Kimi Code—through a unified interface without requiring API access, thereby reducing costs associated with token-based billing.
The key features of the system include parallel execution where commands are issued to a central "Shogun," which delegates tasks through its managerial "Karo" to worker agents known as "Ashigaru." This setup is bolstered by using YAML files for communication between agents, ensuring zero coordination overhead and allowing efficient orchestration. Transparency is maintained with each agent's activities visible in tmux panes and documented via readable YAML files that users can version-control.
The system supports cross-session memory retention through Memory MCP, enhancing personalized user interaction. Additionally, mobile access is facilitated using tools like Tailscale and SSH through Termux for remote command issuance. The setup process varies slightly depending on the operating system, with specific steps for Windows users involving WSL2 or direct installation on Linux/macOS.
Daily operations commence by launching processes with `shutsujin_departure.sh`, allowing users to connect via tmux to manage tasks and monitor progress through a dashboard interface. Tasks are divided into subtasks for parallel processing, with results reported back in YAML files, streamlining workflow management without manual intervention.
An innovative aspect of the system is its skill discovery feature, where agents identify reusable task patterns and propose them as skills upon completion. These suggestions can be approved by users to organically expand system capabilities. The integration with ntfy provides notifications on mobile devices for seamless updates and command inputs without requiring SSH or a server setup.
The Model Context Protocol (MCP) enhances the platform's functionality through external integrations, like Notion and GitHub, while preserving memory context across sessions. Real-world applications include research sprints and proof of concept preparations involving diverse AI agents to compile results or prepare technical plans.
Configuration settings, such as language preferences and screenshot integration for visual context, are managed within `settings.yaml`. The system's architecture comprises setup scripts, daily startup processes using tmux sessions, and various priority options for session customization. Common workflows utilize aliases for convenient script launches and debugging modes for manual control.
The file structure includes categories like setup scripts, behavior definitions, utility scripts, configuration files, and directories for project management, which is handled outside the repository via `config/projects.yaml`. The system's troubleshooting section addresses potential issues such as agent permissions or crashes with tmux commands.
Version 3.0 of the platform introduces multi-CLI architecture, bidirectional ntfy communication, and enhanced task monitoring capabilities. Users are encouraged to contribute through issues and pull requests, with credits given to Akira-Papa for inspiration under an MIT license. Overall, Shogun offers a flexible and customizable environment for managing AI coding tasks efficiently, promoting user-driven project management and integration across various platforms.
Keywords: #phi4, AI agents, API calls, Bloom's Taxonomy, CLI tools, Linux, MCP servers, SayTask, Shogun, YAML mailbox, aliases, authentication, automation, behavioral psychology, bidirectional communication, configuration, dashboard, design principles, event-driven communication, file structure, integration, macOS, mobile access, model settings, multi-agent, notifications, ntfy, parallel execution, philosophy, project management, setup, skills, task dependencies, task management, tmux, transparency, troubleshooting, version control
github.com 7 days ago
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1439.
HN
Patch Tuesday, February 2026 Edition
In February 2026's Patch Tuesday release, Microsoft addressed over 50 security vulnerabilities affecting Windows operating systems and various software platforms. This update included fixes for six critical "zero-day" vulnerabilities actively exploited by attackers. CVE-2026-21510 involves a vulnerability in the Windows Shell that allows malicious content execution through simple link clicks. CVE-2026-21513 targets the MSHTML engine within Windows' default browser, while CVE-2026-21514 pertains to a security feature bypass issue in Microsoft Word. CVE-2026-21533 enables local attackers to gain "SYSTEM" level access via Windows Remote Desktop Services, and CVE-2026-21519 involves an elevation of privilege flaw in the Desktop Window Manager (DWM). Additionally, CVE-2026-21525 presents a denial-of-service vulnerability affecting VPN connections through Windows Remote Access Connection Manager. The release also addressed remote code execution vulnerabilities in GitHub Copilot and several Integrated Development Environments (IDEs) like VS Code, Visual Studio, and JetBrains products due to a command injection flaw. Experts emphasize the importance for developers to understand AI-related risks when using language models and advise implementing least-privilege principles to safeguard sensitive data. Enterprises are encouraged to thoroughly test patches and regularly back up their data.
Keywords: #phi4, AI vulnerabilities, API keys, AWS, Azure, CVE-2026-21510, GitHub Copilot, IDEs, JetBrains, LLMs, MSHTML, Microsoft, Microsoft Word, Patch Tuesday, Remote Desktop Services, VS Code, Visual Studio, Windows, agentic AI, command injection, denial-of-service, developers, least-privilege principles, remote code execution, security holes, threat actors, updates, zero-day vulnerabilities
krebsonsecurity.com 8 days ago
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1491.
HN
Copilot SDK in Technical Preview
The Copilot SDK has entered technical preview, providing language-specific SDKs that enable programmatic access to the GitHub Copilot Command Line Interface (CLI). These SDKs are currently available for Node.js/TypeScript, Python, Go, and .NET, offering a uniform API across these languages. This API facilitates multi-turn conversations with session history, allows the execution of custom tools, and grants users full lifecycle control over clients and sessions. Users participating in this technical preview are encouraged to join the GitHub Community to provide feedback on their experiences and insights.
Keywords: #phi4, API, Community feedback, Community feedback Keywords: Copilot SDK, Conversations, Copilot SDK, GitHub Copilot CLI, Go, Lifecycle control, Multi-turn, Multi-turn conversations, NET, Nodejs, Python, Technical Preview, Tool execution, TypeScript
github.blog 8 days ago
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1503.
HN
Former GitHub CEO raises record $60M dev tool seed round at $300M valuation
Thomas Dohmke, former CEO of GitHub, has secured $60 million in seed funding for his startup, Entire, valuing it at $300 million. The company is developing an open-source tool to enhance developers' ability to manage AI-generated code effectively. Supported by Felicis and other investors, Entire's platform integrates three main components: a git-compatible database to consolidate AI-produced code, a semantic reasoning layer for enabling interaction between multiple AI agents, and an AI-native user interface fostering collaboration between these agents and human users.
Entire’s initial offering, Checkpoints, pairs each AI-generated software piece with the context of its creation, aiming to improve developers' understanding and management of such code. This addresses challenges faced by open-source projects overwhelmed by potentially unusable AI contributions. Dohmke advocates for new methods over traditional manual approaches due to the fast-paced nature of current AI-generated coding practices.
In addition to Felicis, the seed funding round attracted investments from Madrona, M12, Basis Set, and prominent individuals like Harry Stebbings, Jerry Yang, and Olivier Pomel, CEO of Datadog. After leaving GitHub in August 2025—where he had overseen the success of GitHub Copilot—Dohmke embarked on this venture to address emerging challenges in AI code management.
Keywords: #phi4, $60M, AI agents, Basis Set, Boston, Checkpoints, GitHub, GitHub Copilot, Harry Stebbings, Jerry Yang, M12, Madrona, Microsoft, Olivier Pomel, TechCrunch Founder Summit 2026, Thomas Dohmke, dev tool, git-compatible database, open source, seed round, semantic reasoning layer, software project, user interface, valuation
techcrunch.com 8 days ago
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1512.
HN
Show HN: Autonomo MCP – Developing while E2E Testing
Autonomo MCP is an innovative tool aimed at revolutionizing AI-assisted development by integrating end-to-end testing directly into the coding process. It enhances interactions between AI coding assistants such as GitHub Copilot or Claude Code and applications by enabling real-time observation of app states and validation across multiple devices in a single iterative loop. The tool employs a vision-based testing approach, which analyzes UI screenshots to provide rapid feedback on an application's visual state. Moreover, it supports multi-device interaction validations, allowing simultaneous checks across different user interactions or devices. Developers can define custom actions for scenarios like bypassing authentication during local testing.
Autonomo stands out by eliminating dependence on traditional end-to-end testing methods that are often slow and susceptible to UI changes. By facilitating real-time validation of AI-generated code through direct application interaction, it reduces coding "hallucinations" — errors arising from unverified assumptions. Additionally, all operations are executed locally, ensuring enhanced security and eliminating latency issues typically associated with cloud-based solutions.
The tool is compatible across various platforms and frameworks such as React, Swift, Flutter, Python, Ruby, Kotlin, and C#, thanks to platform-specific installation guides. Its core architecture relies on metadata registration patterns instead of parsing HTML or DOM, enabling seamless integration with any UI framework that supports lifecycle hooks and callbacks. After each action, Autonomo captures a comprehensive snapshot of all relevant application states — including the UI, app logic, and network calls — which is then returned to the AI for validation.
Autonomo MCP streamlines local development processes by allowing developers to test code in real-time as they write it. This capability leverages a documentation-driven integration model using markdown guides that AI tools can interpret, minimizing the versioning complexities often seen with traditional SDKs. The tool is already production-ready on several platforms, and ongoing community efforts aim to expand support to additional ecosystems such as .NET and Kotlin.
Keywords: #phi4, AI Coding Assistants, Autonomo, E2E Testing, HTTP Protocol, Lifecycle Hooks, Local Development, MCP-Native, Metadata-Based, Multi-Device, Platform-Specific Prompts, Semantic IDs, Test Bridges, Vision-Based Testing
github.com 8 days ago
https://github.com/sebringj/autonomo 8 days ago
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1516.
HN
Building an Obsidian RAG with DuckDB and MotherDuck
The article explores the growing enthusiasm around AI "agents" such as ChatGPT and Claude Code, attributing advancements to increased user testing time, improved models, and innovative features like "Plan Mode." This mode allows coding assistants to create detailed implementation plans without executing commands, offering a safe planning phase. However, caution is advised against over-reliance on these tools, which can lead to mental fatigue without tangible productivity gains. Plan Mode's availability extends beyond Claude Code to other AI coding assistants, including Cursor and GitHub Copilot.
For data engineers, the most effective use of AI involves collaboration between humans and machines to ensure plans align with human perspectives, especially for unique projects where automation is limited by AI's contextual understanding. The concept of "vibe coding" is introduced, focusing on extending existing frameworks efficiently while avoiding potential errors through careful management. Human involvement remains crucial in specifying requirements and configuring development processes.
The article concludes by recommending simplicity when interacting with AI agents, emphasizing their strength lies in executing straightforward instructions rather than managing complex tasks. This approach ensures optimal utilization of AI capabilities in coding and planning projects.
Keywords: #phi4, AI agents, Claude Code, Markdown, Markdown ``` AI agents, Markdown ``` Keywords: AI agents, Plan Mode, Spec Driven Development, YAML Engineer, coding assistants, context limit, data engineering, maintainability, productivity, vibe coding, workflow
motherduck.com 8 days ago
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1520.
HN
Large Language Models for Mortals book released
"Large Language Models for Mortals: A Practical Guide for Analysts with Python," authored by the book's publisher, serves as a comprehensive resource aimed at analysts seeking to understand and utilize Large Language Models (LLMs) through Python. The guide provides detailed insights into interacting with major LLM providers such as OpenAI, Anthropic, Google, and AWS Bedrock, focusing on API usage, structured outputs, retrieval-augmented generation (RAG), tool-calling applications, and the use of tools like GitHub Copilot and Claude Code. Designed for individuals who possess basic knowledge of Python and large language models, the book facilitates their transition into data science roles emphasizing LLMs.
The author’s background, having shifted from traditional machine learning during their PhD, informs the guide's emphasis on the rapid advancements in LLM applications. Recognizing a gap in resources at the time of their own development, they aim to fill it with this practical manual that includes over 250 Python code snippets and 80 screenshots across its 354 pages.
Distinguishing itself from competitors like Chip Huyen’s "AI Engineering" and Amit Bahree's "Generative AI in Action," which either lack code examples or utilize outdated APIs, the guide excels by offering up-to-date practical examples. It targets a broad audience ranging from traditional data scientists to PhD students exploring LLM applications or those working with unstructured textual data.
The book is accessible globally in both paperback and epub formats through the author's store, with additional resources available on GitHub at https://github.com/apwheele/LLMsForAnalysts.
Keywords: #phi4, API, AWS Bedrock, Anthropic, BigQuery, Chat Completions, ChromaDB, Data Science, FAISS, Foundation Models, GitHub Copilot, Google Gemini, LLMs, Large Language Models, Mortals, OpenAI, Python, RAG, Responses API, S3 Vectors
crimede-coder.com 8 days ago
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1525.
HN
How to Prove the Correctness of AI-Generated Code Using Formal Methods
The article addresses the challenge of proving the correctness of AI-generated code, which often results from non-deterministic models interpreting ambiguous text inputs. Traditional testing methods like unit and regression tests may be inadequate for applications requiring high safety or security standards. To address this gap, the article introduces SPARK, a formal method within the Ada programming language framework that enables programmers to formally verify the absence of runtime errors and ensure functional correctness of code. Through a demonstration involving binary search specifications, SPARK effectively identifies problematic corner cases in AI-generated code. The integration of SPARK into industrial programming languages is portrayed as both performant and broadly applicable across diverse platforms. The article further outlines a workflow utilizing tools like VS Code and GitHub Copilot to demonstrate SPARK's application, while also indicating future plans to explore more advanced integrations through Model-Context-Protocol (MCP) for enhanced interactions with AI agents.
Keywords: #phi4, AI-generated code, Ada, GitHub Copilot, Model-Context-Protocol (MCP), SPARK, VS Code, binary search, correctness, formal methods, functional correctness, hardware targets, industrial programming languages, operating systems, post-conditions, pre-conditions, regression tests, runtime errors, safety critical, security critical, unit tests
www.adacore.com 8 days ago
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1536.
HN
Accelerando, but Janky
In recent weeks, the AI sector has experienced significant activity characterized by swift developments amidst a backdrop of chaos. The author expresses regret about returning to Twitter/X due to its excessive noise, especially regarding OpenClaw, an emerging DIY agent framework that has raised security concerns among developers. Amidst these discussions, both Anthropic and OpenAI have released updates focused on incremental improvements such as code correctness and speed rather than revolutionary changes. These updates fall short in refining areas like test generation and user interface design, highlighting a prevailing focus on enhancing accuracy, correctness, and efficiency for professional applications.
The author highlights the continued use of GitHub Copilot CLI due to its flexibility across various AI models, underscoring the significance of integrating skills and workflows into project management over merely relying on specific tools. This approach involves tailoring skills to meet precise project requirements rather than accumulating broad web-based information, exemplified by their incorporation into agentbox.
Media interest is growing around AI-generated images and videos, with platforms like Kling showcasing impressive AI-created shorts that, while detectable, could transform video advertising if technical challenges are overcome. Despite potential issues of authenticity, the development offers promising avenues for quality content production.
Overall, this period reflects a transitional phase in AI where optimization takes precedence over breakthrough innovations, emphasizing practical skill integration into workflows rather than focusing on tool-specific advancements.
Keywords: #phi4, AI hype, AI shorts AI, Anthropic, GitHub Copilot, GitHub Copilot CLI, LLMs, OpenAI, OpenClaw, Pi, Twitter/X, WASM-ready, media, sandboxing, skills
taoofmac.com 8 days ago
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1546.
HN
Show HN: GitScrum MCP Server for Claude and AI Assistants
The GitScrum MCP Server is a sophisticated tool designed to enhance project management by integrating AI assistants such as Claude and GitHub Copilot. It employs the Model Context Protocol (MCP) to enable these AI systems to manage various project components like tasks, sprints, time tracking, user stories, and epics within a GitScrum workspace. The server supports both hosted and local environments using TypeScript and Node.js 18+, allowing users to connect their AI clients via URLs and tokens.
This server streamlines operations across multiple project management tools by facilitating actions such as task fetching, sprint creation, budget monitoring, and report generation through conversational interactions with AI assistants. It offers a comprehensive array of over 160 actions that cover core project management functions, planning, collaboration, CRM, and insights PRO tools. Security is a primary focus, with the server implementing the least privilege principle, OAuth 2.0 Device Authorization Grant for authentication, and restricted token storage to ensure data protection.
Designed with developers in mind, the GitScrum MCP Server ensures ease of setup through npm commands, offers extensive documentation, and encourages contributions from the developer community. It is open-source under the MIT license and provides detailed security and development guidelines on its website, making it a robust solution for integrating AI into project management workflows.
Keywords: #phi4, AI Assistants, Analytics, Authentication, Development, GitHub Copilot, GitScrum, MCP Server, Model Context Protocol, Nodejs, Project Management, Security, TypeScript, npm
github.com 8 days ago
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1549.
HN
Microsoft Skills
The "Microsoft Skills" repository is an evolving collection aimed at enhancing AI coding agent capabilities with domain-specific knowledge tailored to Azure SDKs and Microsoft AI Foundry projects. It offers users a variety of tools, including custom agents, templates, and configurations, designed for efficiency in development environments. Key features emphasize ease of use through methods like `npx` or manual cloning while advising the selective application of skills to maintain optimal performance and avoid context degradation.
The repository is comprehensive, featuring 125 domain-specific skills categorized by programming languages such as Python, .NET, TypeScript, Java, and Rust, each denoted with language suffixes. These skills span core functionalities like general tooling and infrastructure support, alongside specialized capabilities in AI services, data storage, messaging, and more.
Installation of these skills can be performed manually using `git clone` or through symlinks to facilitate shared configurations across projects. The testing framework within the repository employs a test harness leveraging the GitHub Copilot SDK to validate code against acceptance criteria. This system supports quality enhancements via iterative methodologies such as Ralph Loop and Sensei Patterns, ensuring compliance with standards.
Contributors to this open-source project are encouraged to follow structured guidelines for adding new skills, which involves creating detailed SKILL.md files with YAML frontmatter. These contributions include skill categorization, documentation of acceptance criteria, and test scenario formulation. The repository welcomes improvements in areas like prompts, agent configurations, MCP setups, and bug fixes within the testing framework, all under an MIT license.
Keywords: #phi4, AI Coding Agents, Agent Skills, Azure SDKs, Compute, Entra, Foundry, GitHub Copilot, Java, M365, Microsoft Skills, NET, Python, Rust, TypeScript
github.com 8 days ago
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1551.
HN
96% Engineers Don't Trust AI Output, yet Only 48% Verify It
The newsletter discusses key insights from the State of Code Developer Survey Report by Sonar concerning engineers' trust in and utilization of AI within software development. A significant 96% of engineers lack full confidence in AI-generated code, yet only half consistently verify it before integration into projects, highlighting a potential risk area. Despite this skepticism, 61% acknowledge that while AI often generates plausible code, its reliability is questionable, making high-quality outputs difficult to achieve.
The report reveals substantial reliance on AI tools, with 42% of current coding efforts being AI-assisted or generated. Engineers frequently incorporate these tools in daily tasks across various projects like prototypes and production software, using popular solutions such as GitHub Copilot and ChatGPT to enhance productivity. While AI contributes to faster time-to-market and increased developer efficiency, concerns remain regarding the quality and dependability of the code produced.
The newsletter underscores the critical need for engineers to review and validate AI-generated code rigorously, emphasizing that critical thinking and verification skills are essential in this context. It also promotes a workshop by Buf on API governance with Protocol Buffers (Protobuf) as an effective strategy for managing APIs. Furthermore, it advises engineering leaders to equip their teams with suitable AI tools and cautions against the potential credibility loss from unchecked deployment of AI-generated code without accountability.
Keywords: #phi4, AI coding tools, AI trust, API governance, Buf workshop, ChatGPT, GitHub Copilot, Protobuf, code quality, code verification, critical thinking, developer productivity, engineering survey
newsletter.eng-leadership.com 8 days ago
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1575.
HN
Man vs. AI – Building a Slack Bot
The article explores an experiment comparing the creation of a Slack bot for automated API test result notifications using two methods: traditional manual coding and leveraging GitHub Copilot's AI capabilities. The author, experienced in Python and the Slack SDK, aimed to evaluate efficiency and quality by developing a bot that posts failed test results to Slack, conducts health checks, and logs events.
In the **manual method**, the author relied on existing skills and official documentation to build the bot from scratch. Starting with a "Hello World" message, they iteratively developed functionalities until achieving a working product capable of posting failures, performing health checks, and logging events, all managed through environment variables. Despite taking about an hour, this process required multiple iterations for improvements in logging, error handling, and configuration management.
Conversely, the **AI method using GitHub Copilot** involved prompting the AI to generate code that met specific requirements, producing over 278 lines of code initially accompanied by a setup guide. Multiple prompts were needed to address issues such as complexity and maintainability due to extensive regex use and character limit constraints in Slack messages. Ultimately, the AI-generated solution resembled the manually coded bot, with added functionality like uploading full test results.
The conclusion highlights that while GitHub Copilot significantly reduced development time by bypassing documentation consultation, it did not immediately yield production-ready or easily maintainable code without further refinement. Although both methods resulted in similar end products, manual coding offered deeper familiarity and understanding of the tools involved. The experiment underscores AI's potential to expedite software development but also emphasizes that hands-on experience is crucial for mastering tool proficiency.
Keywords: #phi4, API Testing, Automation, Best Practices, CI/CD, Code Review, Configuration, Documentation, GitHub Copilot, Healthcheckio, Logging, MVP, Maintainability, Man vs AI, Prompting, Python, Readability, Regex, Slack Bot, Time Efficiency
siivikko.fi 8 days ago
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1594.
HN
Show HN: MCP Orchestrator – Spawn parallel AI sub-agents from one prompt
The MCP Orchestrator is an open-source server developed in TypeScript/Node.js, designed to facilitate AI-to-AI orchestration by spawning up to 10 parallel sub-agents using command-line tools like GitHub Copilot or Claude Code. It supports context passing through various modes—full file, summary, or grep—and ensures smart timeout selection and compatibility across macOS, Linux, and Windows platforms. Key features include enabling parallel execution of sub-agents, allowing specific contexts to be passed to each agent, filtering available MCP servers for sub-agent deployment, and ensuring seamless integration in headless environments programmatically.
Installation of the orchestrator is straightforward with the command `npm i @ask149/mcp-orchestrator`, and setup instructions are thoroughly detailed in the repository's `SETUP.md` file. Configuration involves setting up CLI backend tools and MCP server configurations, with files stored at specific paths based on the operating system being used.
A usage example highlights how a task such as "research job openings at Stripe, Google, and Meta" can be distributed across parallel agents to gather information using various MCP servers. The repository also provides comprehensive guidelines for development and testing, covering building processes, change monitoring, type checking, and cross-platform test execution to maintain compatibility.
Community engagement is encouraged through feedback on CLI backends, context-passing methods, and MCP server integrations, with contributions accepted via pull requests and issues. The project operates under the MIT License, promoting open collaboration and distribution.
Keywords: #phi4, AI orchestration, Claude Code CLI, Copilot CLI, GitHub Copilot, MCP Orchestrator, MCP integration, MCP resources, Playwright, TypeScript/Nodejs, audit logging, configuration files, context passing, cross-platform, environment variables, graceful shutdown, headless programmatic, health check, job research automation, log rotation, parallel sub-agents, smart timeout, task properties, timeout handling
github.com 8 days ago
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1604.
HN
CLIProxyAPIPlus – use antigravity, Gemini CLI, & more with Claude Code / etc.
CLIProxyAPI Plus significantly enhances its predecessor by incorporating third-party provider support, facilitated through community contributions. It introduces integrations with GitHub Copilot and Kiro (AWS CodeWhisperer), which are augmented by OAuth login capabilities, offering a more seamless user experience. To bolster security, the platform now includes features such as device fingerprinting to uniquely identify devices accessing the system, rate limiting to prevent excessive API use, and automatic token refresh mechanisms ensuring uninterrupted service.
The key enhancements in CLIProxyAPI Plus include a browser-based OAuth login for ease of access, coupled with built-in request rate limiting and intelligent cooldown management to maintain server integrity. It also supports automatic token renewal and real-time monitoring of usage patterns, ensuring efficient resource allocation and response handling. Device fingerprint generation is utilized alongside unified model name conversion, enhancing device identification processes. Additionally, the API now handles UTF-8 stream processing for improved response interpretation.
For Kiro Authentication, users benefit from access to its OAuth web interface, accommodating logins via AWS Builder ID or Identity Center. Deployment of CLIProxyAPI Plus is streamlined through a straightforward Docker setup that requires just one command after preparing directories and configuring `docker-compose.yml`. Contributors are encouraged to direct third-party support-related changes to this project, while other modifications should be made in the mainline repository. The entire project operates under an MIT License, promoting open collaboration and modification.
Keywords: #phi4, CLIProxyAPI, Configuration, Cooldown Management, Device Fingerprint, Docker Deployment, GitHub Copilot, Kiro, MIT License, Metrics & Monitoring, Model Converter, OAuth login, Plus version, Pull Requests, Rate Limiter, Token Refresh, UTF-8 Stream Processing, Usage Checker, Web Authentication, community contributors, third-party providers
github.com 8 days ago
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1632.
HN
AI Is a High Pass Filter
AI serves as a "high-pass filter," enhancing existing capabilities in both individuals and organizations by amplifying their fundamental strengths. For developers, possessing robust engineering, design, workflow, and leadership skills is crucial, as AI can accelerate learning and execution processes when these foundations are strong. Conversely, those lacking such fundamentals may struggle to discern valuable insights from irrelevant data. At the organizational level, companies with advanced DevOps practices, such as continuous delivery and trunk-based development, will derive greater benefits from AI integration. In contrast, organizations focused on mere resource utilization or strict process compliance might uncover existing inefficiencies more rapidly.
Current research often fails to demonstrate significant AI advantages due to flawed methodologies that prioritize outputs over meaningful outcomes without considering the maturity of engineering practices and organizational context. These studies are criticized for not accounting for workflow adaptations or differences in engineering capabilities among participants, leading to an underestimation of AI's potential benefits. High-performing teams experience substantial gains with AI as it acts as a multiplier of their existing skills, while low performers tend to revert to traditional methods due to unsatisfactory results.
To effectively harness AI, individuals should cultivate foundational competencies in modern software development practices such as Behavior Driven Development and continuous integration. Organizations need to optimize their software supply chain for seamless flow and eliminate bottlenecks. Those who master these principles will be well-positioned to fully leverage AI's potential, while those who do not risk falling behind as the technology increasingly widens the performance gap between high and low achievers. The path forward is clear: improving foundational skills and practices is essential for staying competitive in an AI-driven landscape.
Keywords: #phi4, AI, Behavior Driven Development, architectural alternatives, automated testing, bottlenecks, business domain, continuous delivery, engineering skills, fitness functions, fundamentals, high-pass filter, individuals, leadership, learning loop, operational responsibility, organizational dysfunction, organizations, prototyping, software supply chain, trunk-based development, value stream, workflow
bryanfinster.substack.com 8 days ago
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1634.
HN
Show HN: Autonomo – AI developing while E2E testing
Autonomo is a sophisticated tool engineered to enhance AI-assisted software development by providing comprehensive end-to-end testing capabilities across multiple platforms. It integrates seamlessly with AI coding assistants such as GitHub Copilot through its Metadata-Controlled Protocol (MCP), enabling these tools to observe application states, interact with various devices simultaneously, and validate cross-device interactions in a single iterative process. Key features include vision-based testing for rapid screenshot analysis, semantic element identification for stable UI interaction, and multi-user support for scenarios like inter-device message verification.
The tool operates using a Test Bridge pattern—a built-in HTTP interface that exposes application state information and accepts control commands. This configuration allows AI agents to perform tests by sending structured JSON commands and receiving detailed feedback on test outcomes, including success, failure, and error reports. A significant emphasis is placed on eliminating "AI hallucinations" by necessitating proof of successful code execution rather than relying on assumptions.
Autonomo is designed for local development environments to ensure fast performance and data privacy without the need for cloud services. It currently provides production-ready packages for platforms including React, Swift, Flutter, Python, Ruby, Kotlin, and C#, with additional integrations either underway or documented for setup. The tool's architecture prioritizes metadata registration over HTML parsing, enhancing compatibility across various UI frameworks through lifecycle hooks and callbacks.
The platform addresses the limitations of current AI-assisted testing by offering a robust framework that allows AI to understand application states directly, akin to human developers using development tools. It supports multi-instance app management, smart element grouping for efficient state reporting, and error-first output display. Autonomo is developed with open-source principles but also outlines an enterprise business model roadmap, reflecting its commitment to adaptability and scalability in the software testing landscape.
Keywords: #phi4, AI, Autonomo, Custom Actions, E2E testing, GitHub Copilot, Local Development, MCP-Native, Metadata Registry, Multi-device, Platform Agnostic, Semantic IDs, Test Bridge, Vision-Based Testing
github.com 9 days ago
https://github.com/sebringj/autonomo 8 days ago
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1638.
HN
Why Spec-Driven Development Breaks at Scale (and How to Fix It) – Arcturus Labs
The article discusses the evolution of spec-driven development in large-scale projects with AI, highlighting transitions from "vibe-coding" to utilizing precise specifications for guiding AI activities. The primary challenge is the ambiguity inherent in global product specifications written in natural language, which limits their utility. To address this, a refined approach involves maintaining clear hierarchical structures that bridge global specs with detailed sub-specifications and foster conversations between developers and AI agents to refine unclear parts.
The article emphasizes the importance of leveraging existing code as a definitive specification because it inherently removes ambiguity compared to natural language. By integrating specifications into the development workflow where code changes automatically update the product specification, a living document is created that evolves in tandem with the codebase. This dynamic approach provides engineers and product managers clearer insights into both current and historical product decisions.
In conclusion, advancing spec-driven development requires enhancing AI's ability to interpret ambiguity through structured conversations and context-aware systems. By implementing hierarchical specs integrated with ongoing code changes and promoting an evolving specification environment, the gap between specification and implementation is minimized, thus fostering improved collaboration among developers, product managers, and executives.
Keywords: #phi4, AI Agents, AI Code Completion, Clarification, Feedback Loop, Global Product Specification, Hierarchical Specifications, Living Documents, Natural Language Ambiguity, Shared Context, Spec-Driven Development, Specification Document, Vibe-Coding
arcturus-labs.com 9 days ago
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1651.
HN
GPT-5.3-Codex is now generally available for GitHub Copilot
GPT-5.3-Codex has been integrated into GitHub Copilot, significantly enhancing its performance for coding tasks by achieving up to 25% faster results compared to its predecessor, GPT-5.2-Codex. This upgrade is available for users of Copilot Pro, Pro+, Business, and Enterprise plans, ensuring compatibility across multiple platforms including Visual Studio Code, GitHub websites, mobile apps, the GitHub CLI, and the Coding Agent. The implementation will occur gradually, with enterprise administrators required to enable it through specific Copilot settings. Users are encouraged to familiarize themselves with the new model via available documentation and contribute feedback through the GitHub Community channels.
Keywords: #phi4, GPT-53-Codex, GitHub CLI, GitHub Copilot, GitHub Mobile, OpenAI, Visual Studio Code, agentic coding model, benchmarks, community feedback, documentation, execution, performance, policy, reasoning, rollout, workflows
github.blog 9 days ago
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1658.
HN
AI doesn't replace jobs: it removes the constraint that created them
The emergence of autonomous AI agents is revolutionizing knowledge work by eliminating the traditional barrier of human effort. These systems can autonomously achieve specific objectives with minimal oversight, thereby supplanting many roles traditionally performed by humans, particularly in software engineering but increasingly extending into fields like law, finance, and accounting. This shift leads to a significant reduction in labor costs as AI agents such as Claude Code offer comparable outputs at substantially lower prices compared to human engineers. The adoption of these technologies incurs minimal cost due to prior investments by major tech companies, facilitating instant access without substantial capital investment for organizations.
A key consequence of this transition is the diminishing value of expertise since each interaction with an AI system enhances its knowledge base. This development effectively removes effort as a constraint on what can be pursued or constructed, profoundly altering perceptions of "worth doing." Businesses are presented with opportunities to address longstanding inefficiencies, while governments must navigate both enhanced policy implementation possibilities and risks posed by the rapid evolution of private-sector technologies.
With mainstream adoption anticipated within twelve to twenty-four months, organizations must urgently reassess their value propositions. The focus should shift from executing routine tasks to exercising strategic judgment as effort is no longer a limiting factor in operations across various sectors. This transition necessitates significant adjustments in strategies and operational frameworks to fully leverage the transformative potential of AI agents.
Keywords: #phi4, AI, Claude Code, administrative effort, adoption costs, autonomous agents, backlog, competitive pressure, constraint, decision-makers, effort, expertise, governance, jobs, knowledge work, policy, private sector, productivity, repricing labor, software engineering, strategic error
briefings.canaryiq.com 9 days ago
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1677.
HN
The Most Popular Agentic Open-Source Tools (2026 Edition)
Over the past 18 months, the field of agentic AI has evolved significantly from simple chatbots to complex system designs that autonomously plan, act, and learn. This transformation is largely driven by the adoption of open-source tools, which are instrumental in moving away from prompt-centric approaches toward more sophisticated systems. The article identifies several key open-source frameworks and tools crucial for this development across different layers of agentic AI.
**Agent Frameworks & Orchestration:** Essential tools such as LangChain, LlamaIndex, CrewAI, Semantic Kernel, AutoGen, Agno, and OpenHands are pivotal in defining agent loops, orchestrating various tools, and designing comprehensive systems. **Visual & No-Code Builders:** Platforms like Flowise, Langflow, and Dify offer visual interfaces that simplify the creation of AI workflows, thereby making agent development more accessible to a broader audience. **Automation & Tool Execution:** Solutions such as n8n, Composio, Appwrite, Browser-use, and Copilot SDK are highlighted for their role in enabling reliable execution and interaction with diverse systems. **Retrieval, Memory & RAG:** Tools like Haystack, AutoRAG, and Onyx enhance the ability to retain context and retrieve information accurately, which is critical for delivering precise responses. **Evaluation, Guardrails & Testing:** Frameworks such as Ragas, Promptfoo, Helicone, and Pydantic AI ensure that agents perform reliably in production settings by providing essential testing and evaluation capabilities. **Research & Experimental Agents:** Projects like GPT-Researcher, GPT-OSS, and OpenRouter are noted for their contributions to supporting deep research tasks and facilitating dynamic model routing.
The article concludes by emphasizing the importance of open-source repositories in agentic AI development, highlighting how they foster community collaboration and ensure that systems remain reliable, adaptable, and aligned with real-world needs. Additionally, You.com's Agentic APIs are acknowledged as a vital resource for accessing up-to-date information crucial for building effective agentic systems.
Keywords: #phi4, Agentic AI, Appwrite, AutoGen, CrewAI, GitHub, LangChain, LlamaIndex, Semantic Kernel, agents, evaluation, execution, frameworks, n8n, open-source, orchestration, reliability, research, retrieval-augmented generation (RAG), testing, tools, workflows
you.com 9 days ago
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1688.
HN
Opus 4.5 Changed Things
The article outlines a transformative approach in software development by integrating artificial intelligence (AI) as core team members throughout the entire engineering process, shifting from its initial role as an auxiliary coding tool to that of full-fledged software engineers. This progression is categorized into distinct eras: starting with basic tools like VS Code and GitHub Copilot in Era 1; enhancing interactions using editors such as Cursor in Era 2; evolving towards "agentic engineering" where AI manages complete software lifecycles in Era 3; and looking ahead to autonomous codebases in Era 4, which enable more complex independent tasks by AI.
Operationally, the shift involved treating AI as software engineers rather than just coding assistants. This required orchestrating work across multiple agents simultaneously, necessitating fast feedback mechanisms and robust error handling systems. Technically, this transition from serial to parallel operations mandated changes such as adopting isolated devcontainers for running full-stack services on a single machine, thus enabling concurrent task execution by multiple AI agents without interference.
To support these developments, the author implemented dynamic Docker Compose configurations, optimized resource usage, and improved end-to-end (E2E) testing processes. As scaling efforts progressed, additional hardware was integrated to enhance parallelism and feedback speed, with tools like CloudWatch and Sentry ensuring effective observability and rapid issue resolution.
The article emphasizes a shift from cost-cutting to maximizing results, highlighting the use of high-context AI models such as Opus 4.5 for enhanced performance in specific environments. This evolution has led to significant productivity gains and improved code quality by treating AI agents as integral team members with embedded structured rules and skills. The broader implications suggest a future where engineers will manage teams of AI agents, fundamentally changing hiring practices to prioritize learning and leveraging AI for systemic understanding and growth rather than merely increasing productivity.
Overall, this innovative approach has streamlined traditionally time-consuming tasks like tooling improvements and documentation, demonstrating the potential to scale across entire teams by enhancing hardware and cloud capabilities. This paradigm shift positions engineers as managers of AI teams, capable of independent problem-solving, thereby transforming traditional software development dynamics.
Keywords: #phi4, AI agents, AWS CLI, CI optimization, CPU, Celery, CloudWatch, Codex, FastAPI, KVM switch, Neo4j, Opus, PR review, Postgres, Qwik/Fastify, RAM, Redis, Sentry, TDD enforcement, Terraform, TimescaleDB, agency, autonomous codebases, devcontainers, engineering managers, feedback loops, junior engineers, latency, learning, macOS, model choice, observability, orchestration, parallelism, planning, rules and skills, software engineering, technical debt
www.kylerush.org 9 days ago
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1713.
HN
Vibe coding an RSS feed – how hard can it be?
The author recounts their experience integrating RSS feed functionality into a Vue.js-based blog using GitHub Copilot, with the project initially delayed due to its low priority. By leveraging AI-assisted coding, they bypassed traditional plugins for enhanced flexibility and customization. Although initial success was achieved in generating RSS feeds, an unforeseen issue arose when this integration disrupted the blog's static export functionality. This highlighted the risks of relying heavily on AI-generated code without adequate manual verification.
Despite these challenges, the feature was cautiously implemented, with careful consideration given to how even minor changes could significantly impact the project. The author reflects on the broader implications and potential risks of increasing dependence on AI in software development, questioning its sustainability as a trend rather than a long-term solution. Ultimately, the blog now successfully supports RSS feeds for various topics, though this complex integration effort underscored cautionary insights.
The narrative concludes by referencing Goethe's "Der Zauberlehrling" to metaphorically express concerns about potentially losing control over the technologies we use, particularly when adopting AI solutions without thorough understanding or oversight of possible consequences. This serves as a poignant reminder of the uncertainties and responsibilities inherent in integrating advanced technological tools into development processes.
Keywords: #phi4, AI coding, GitHub Copilot, LLM assisted coding, Nuxt, Nuxt Content, RSS feed, Vibe coding, Vuejs, build process, concept work, control, energy consumption, plugins, software development, static generation, testing, testing Keywords: Vibe coding, unreliability
blog.fortrabbit.com 9 days ago
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1720.
HN
Show HN: We added AGENTS.md to 120 challenges so AI teaches instead of codes
Frontend Mentor has implemented AGENTS.md and CLAUDE.md files across 120 coding challenges to enhance AI tools' educational utility, guiding platforms like GitHub Copilot and Cursor in offering customized support based on a user's proficiency level. For beginners, referred to as "Newbies," AI serves as a patient mentor, simplifying problems into manageable steps with analogies and hints before providing solutions. Juniors receive focused guidance on debugging techniques and conceptual understanding, while intermediates are treated as capable developers, encouraged to evaluate multiple approaches for skill enhancement. Advanced users engage in discussions about trade-offs and long-term impacts, whereas Guru-level learners collaborate with AI at an equal level to tackle complex issues.
The initiative aims to foster guided discovery across all proficiency levels by developing debugging skills, understanding trade-offs, and linking users to additional resources. Users are encouraged to match their challenges to their actual skill level and use these AI tools in conjunction with effective prompting practices for optimal learning results. Frontend Mentor views this effort as a foundational step in the dynamic field of AI-assisted education, focusing on empowering developers through practical projects that build genuine skills. Feedback is welcomed as they continue to evolve this approach within the landscape of AI-driven learning.
Keywords: #phi4, AGENTSmd, AI tools, AI-assisted learning, CLAUDEmd, ChatGPT, Claude, Cursor, Discord community, Frontend Mentor, GitHub Copilot, coding challenges, debugging, difficulty levels, guidance, industry standards, industry standards Comma-separated List: AI tools, learning, maintainability Extracted Keywords: AI tools, maintainability Final Keywords: AI tools, maintainability Keywords: AI tools, maintainability Simple Keywords: AI tools, mentorship, projects, prompts, skill development
www.frontendmentor.io 9 days ago
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1721.
HN
Vibe-coding our wedding website
The author is developing a personalized wedding website employing Angular for the frontend and a custom Go-based backend, integrating AI tools such as Anthropic's Opus 4.6 and GitHub Copilot to streamline development. This approach allows them to concentrate on unique features—like guest attendance confirmations and photo uploads from an S3-compatible object storage bucket optimized via imgproxy—while minimizing time spent on boilerplate code. Initially considering a simpler backend using n8n workflows, they chose Go for enhanced project control. The AI assistance efficiently sets up testing and development pipelines, making the process more enjoyable compared to their typical work tasks without AI support. This project not only fulfills family expectations but also provides a valuable learning opportunity despite alternatives like Weddybird being available.
Keywords: #phi4, AI, Actions, Angular, CI/CD, CI/CD pipelines, Copilot, GitHub, GitHub Actions, Go, Go backend, Opus, S3, S3 storage, Vibe-coding, backend, development, imgproxy, local, local development, n8n, n8n workflows, pipelines, skills, storage, tech, tech skills Keywords: Vibe-coding, wedding, wedding website, workflows
janlukas.blog 9 days ago
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1726.
HN
Ask HN: My 2nd ever Quant Finance and ML Newsletter. Help me improve
The newsletter explores the convergence of quantitative finance and machine learning, emphasizing recent shifts towards using multi-model workflows rather than single benchmark models to address diminishing returns from scaling laws. This challenge in financial return predictability is further highlighted by a new paper discussing these scaling difficulties. Within quant finance, the newsletter explains how volatility impacts compound returns through the variance tax formula (G ~ mu - 1/2 sigma^2), contributing to the underperformance of leveraged ETFs over time. Despite observing a modest illiquidity premium in data from AQR, challenges persist.
The article also notes Japan's potential shift in financial strategy due to rising domestic yields and plans for repatriating $5 trillion in foreign assets, which could influence its role as a Treasury buyer. On the AI front, there is significant interest in an open-source Agentic Coding Tool Index (ACTI), with tools like Claude Code and GitHub Copilot demonstrating considerable productivity gains among users, with Claude experiencing increased adoption rates.
Further insights are provided into hybrid recommender systems employed by companies such as Netflix and Spotify in 2026. The newsletter concludes by offering a preview of ongoing projects and recommended readings across the domains of AI, quantitative finance, and macroeconomics.
Keywords: #phi4, AI Arms Race, Agentic Coding Tool Index, Claude Code, Compute-Complexity Tradeoffs, GitHub Copilot, Illiquidity Premium, Leveraged ETFs, ML Newsletter, Multi-Model Workflows, Productivity Gains, Quant Finance, Recommender Systems, Repatriation Pressure, Scaling Assumptions, Treasuries, Variance Tax, Volatility
static.philippdubach.com 9 days ago
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1769.
HN
GitHub Copilot and PRs performance degraded or down
As of February 9, 2026, GitHub has reported degraded performance issues with its Copilot Coding Agent, leading to impacted service availability. The company is actively investigating these problems and plans to keep users informed about updates and mitigation efforts. Users can subscribe for notifications through email or SMS via the incident page, which involves agreeing to privacy policies related to Atlassian and Google services managing notifications using reCAPTCHA. Additional sections on GitHub’s platform offer insights into its various products, features, support resources, company information, and subscription options for different update channels such as Slack webhooks and RSS feeds. Users seeking further updates are encouraged to follow @githubstatus on social media or visit the GitHub support site for more detailed information.
Keywords: #phi4, API, Atom Feed, Blog, CLI, Careers, Community, Copilot, Degraded, Desktop, Developer, Education, Email, Enterprise, GitHub, Incident, Inclusion, Mitigation, Mobile, Notifications, Partners, Performance, Pricing, Privacy Policy, Professional Services, RSS Feed, Roadmap, SMS, Security, Shop, Skills, Social Impact, Status, Statuspage, Support, Terms, Updates, Webhook, reCAPTCHA
www.githubstatus.com 9 days ago
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1778.
HN
96% Engineers Don't Trust AI Output, yet Only 48% Verify It
The newsletter discusses the engineering community's apprehensions about trusting AI-generated code, with 96% of engineers expressing skepticism regarding its reliability despite only half verifying it before implementation. This gap leads to problematic pull requests and widespread frustration. A survey by Sonar reveals that although tools like GitHub Copilot and ChatGPT are widely used, their outputs often necessitate considerable validation efforts to ensure dependability. The report highlights the critical role of verification in software development, especially as AI-assisted coding gains traction.
As reliance on AI technologies increases, there is a call for stronger governance practices, exemplified by initiatives like Buf’s Protobuf API workshop aimed at standardizing APIs and preventing breaking changes. Additionally, the newsletter explores how the adoption of AI tools varies across companies of different sizes and individual use cases, pointing to the need for engineering leaders to equip their teams with appropriate resources. While AI enhances productivity and reduces time-to-market, further improvements are needed in code quality, maintainability, and release frequency.
Ultimately, the article emphasizes that engineers should assume responsibility for AI-generated code rather than solely depending on technology. It advocates for a cultural shift towards more responsible AI usage in software development, urging accountability and critical thinking to ensure better outcomes in coding practices.
Keywords: #phi4, AI coding tools, AI trust, API governance, Buf workshop, ChatGPT, GitHub Copilot, Protobuf, code quality, code verification, critical thinking, developer productivity, engineering survey
newsletter.eng-leadership.com 9 days ago
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1795.
HN
Show HN: Agentseed – Generate Agents.md from a Codebase
AgentSeed is a tool designed to streamline the creation of `AGENTS.md` files from codebases, aiding AI coding agents in understanding repositories by detailing stack components, commands, conventions, and more. These generated files serve as open standards for AI agent instructions, facilitating integration with over 20 AI tools. Utilizing static analysis, AgentSeed identifies programming languages, frameworks, dependencies, and project structures within a codebase. The tool can optionally enhance its outputs using LLMs like Claude or GPT when provided with an API key.
Installation of AgentSeed is straightforward: users can run `npx agentseed init` to generate a default `AGENTS.md`, with additional flags available for specific formats or enhancements. The tool supports various technology ecosystems, including frontend frameworks such as React and Vue, alongside backend languages like Python and Rust. It also accommodates monorepos by automatically detecting sub-projects using the `agentseed scan` command.
AgentSeed is free to use without an API key for its basic functionality, making it instantly accessible. Users can customize their configurations through a `.agentseedrc` file. The development of AgentSeed is open to contributions, with the repository available on GitHub for those interested in contributing to its further enhancement.
Keywords: #phi4, AGENTSmd, AI coding agents, Agentseed, CLI reference, LLM augmentation, MIT license, MIT license Keywords: Agentseed, build commands, codebase, configuration, contributing, dependencies, directory structure, frameworks, monorepo, static analysis
github.com 9 days ago
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1802.
HN
Developing AI Taste: Understanding the Positioning Battle in AI
The article examines the strategic positioning of leading AI providers—OpenAI, Anthropic, GitHub Copilot, and Google Gemini—in a competitive landscape reminiscent of earlier "cloud wars." Just as understanding cloud provider strengths was essential for selecting appropriate solutions, developing an informed perspective on AI capabilities is crucial for aligning specific tasks or organizational needs with the right AI partner. OpenAI has evolved from initially focusing on chatbot interfaces to long-running autonomous systems and multi-step reasoning capabilities, differentiating itself from search engines. Anthropic positions itself as an "AI companion," prioritizing collaboration over automation in tasks like document drafting and financial analysis, contrasting OpenAI's emphasis on extended task execution.
GitHub Copilot targets software development and project management by leveraging Microsoft and GitHub’s ecosystem, specializing in both collaborative coding and autonomous operations within the development lifecycle. Google Gemini has emerged as a vertically integrated platform, harnessing Google's extensive content and services across various domains, including search, productivity suites, and media creation, particularly post-antitrust ruling. Each AI provider differentiates itself through unique technical implementations and product philosophies to align with specific user needs or organizational cultures.
Google is strategically positioning its AI capabilities as a comprehensive platform for content creation, aiming to become the "Microsoft 365" of media production by integrating sophisticated tools like NotebookLM, Veo + Nano Banana Pro, and Google Labs Pomelli. This approach allows it to capture opportunities in distribution and monetization across various media types. Despite potentially sacrificing margins with its lower-margin in-house tools, this strategy aligns with Google's broader goal of maintaining relevance as the bridge between content creators and consumers in the AI era.
Facing an "innovator’s dilemma," Google balances its high-margin search business with aggressive vertical integration efforts in general-purpose AI, enterprise productivity, and AI content creation. This strategy positions it against competitors like OpenAI, Anthropic, and GitHub Copilot, each focusing on different niches within the AI landscape. Ultimately, counter-positioning defines each provider's competitive focus: OpenAI as an autonomous agent platform, Anthropic as a collaborative companion, and GitHub Copilot in software development. Google seeks to offer seamless experiences across search, productivity, media, hardware, and mobility by leveraging vertical integration, despite challenges with lower margins and uncertain monetization paths. Early indications from late 2025 suggest that Google is committed to pursuing this strategy.
Keywords: #phi4, AI Companionship, AI autonomy, AI positioning, AI taste, AWS, Anthropic, Cloud Wars, Content Creation, Counter-Positioning, Ecosystem Platform, Enterprise Agreements, General-purpose AI, GitHub Copilot, Google, Google Cloud, Google Gemini, Horizontal Platform, Innovator’s Dilemma, Media Creation, Media Production, Microsoft 365, Microsoft Azure, OpenAI, Productivity Suites, Strategic Niche, Vertical Integration
johnsonshi.substack.com 9 days ago
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1859.
HN
Show HN: A local-first documentation tool for AI agents (MCP)
Concise Summary
Context is a local-first documentation tool designed to enhance the efficiency of AI agents by providing them with up-to-date, private access to specific library documentation directly from users' machines. This innovative solution addresses common problems associated with outdated AI-generated responses by connecting tools like Claude or GitHub Copilot with current documentation without depending on cloud services. Its key features include local, offline operation for instant and private queries; seamless integration with popular IDEs such as VS Code and Gitpod; fast full-text searches using SQLite and FTS5; and a simplified workflow facilitated by a single command-line tool that eliminates complex multi-step processes.
In practical applications, Context empowers AI assistants to become experts in particular library versions through the addition of local documentation. It promotes team consistency by sharing standardized internal documentation, thus minimizing repetitive queries. Additionally, it ensures privacy by preventing proprietary discussions from being exposed via cloud services.
The quick start process for utilizing Context involves installing the tool with `npm install -g @neuledge/context`, adding and configuring documentation packages through commands like `context add <source>`, setting up MCP server commands in configuration files to link AI agents, and leveraging AI assistants for current documentation queries. Development and sharing features include creating portable `.db` packages that can be easily distributed among teams and integrating these into workflows to maintain consistency with up-to-date internal libraries accessible via compatible AI tools. Overall, Context provides a robust solution for development environments requiring accurate and instant access to documentation without compromising on privacy or relying on online services.
Keywords: #phi4, AI agents, Claude Desktop, Context tool, FTS5, Local-first, MCP server, Nextjs, SQLite, development, development Keywords: Local-first, documentation, internal library, middleware, offline, package format, private, tech stack
github.com 10 days ago
https://github.com/neuledge/context 10 days ago
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1883.
HN
Show HN: Dotfiles Coach CLI that analyzes your shell history with GitHub Copilot
Dotfiles Coach is an open-source command-line interface (CLI) tool designed to enhance shell automation by leveraging the capabilities of GitHub Copilot. The tool analyzes users' command history from Bash, Zsh, or PowerShell to identify repetitive patterns and potential security risks. It generates intelligent aliases, functions, and safety improvements tailored to individual workflows using AI-driven insights. Key features include local data processing for privacy, secret scrubbing with 13 regex filters to remove sensitive information before interacting with GitHub Copilot, and offline functionality for certain commands. Users can easily integrate the tool's suggestions into their shell configuration files manually.
To utilize Dotfiles Coach, users must install it globally via npm, analyze their shell history locally, generate suggestions using GitHub Copilot, and apply these as needed. The tool requires Node.js version 18 or higher, the GitHub CLI, and a GitHub Copilot subscription, with the free tier being sufficient. It supports various output formats for reports, including table, JSON, and markdown.
The development of Dotfiles Coach involves TypeScript in strict mode, employing libraries such as Commander for CLI operations and execa for integrating with Copilot. The project includes comprehensive testing to ensure reliability. Developed with AI assistance from GitHub Copilot and Cursor AI, the tool ensures high-quality code and documentation. It is licensed under MIT, making it accessible for open-source use.
Keywords: #phi4, Aliases, Automation, Bash, CLI, Commander, Dotfiles, ESM, File I/O, Functions, GitHub Copilot, Local Analysis, Mock Client, Nodejs, PowerShell, Privacy, Regex Filters, Safety Improvements, Security, Shell History, Testing, TypeScript, Vitest, Zsh, fs-extra, npm
github.com 10 days ago
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1929.
HN
Haskell for all: Beyond agentic coding
The article critiques current agentic coding tools that utilize artificial intelligence to aid software development, arguing they often fail to boost productivity or improve users' comfort with codebases. The author's skepticism is based on personal experiences and observations during candidate interviews, where those using these tools performed worse than those who did not. Supporting research also indicates no significant productivity gains from agentic coding.
Despite this criticism, the author sees potential for AI-assisted software development if designed differently, emphasizing maintaining a "flow state" for users—a seamless work experience without interruptions. This concept aligns with "calm technology," which focuses on tools that minimize attention demands and act as transparent intermediaries to keep focus on tasks rather than the tools themselves.
Examples of calm technology in software development include inlay hints in IDEs like VSCode and file tree previews, enhancing user experience without disrupting workflow. In contrast, chat-based coding agents are criticized for being attention-demanding and disruptive. GitHub Copilot's inline suggestions partially embody these principles but are noted for their visual intrusiveness. However, its "next edit suggestions" feature is praised for maintaining a flow state with unobtrusive code changes.
Looking forward, the author suggests innovative AI-assisted coding tools like facet-based project navigation, automated commit refactoring, and file lenses that allow editing from different language perspectives. These ideas aim to integrate AI into workflows more effectively than chatbots, which are seen as less engaging for leveraging large language models in software development.
Overall, the article encourages exploring alternative approaches to AI-assisted coding tools beyond agentic coding, focusing on enhancing user experience and productivity through calm technology principles.
Keywords: #phi4, AI-assisted development, Agentic coding, GitHub Copilot, automated refactor, calm technology, design principles, flow state, inline suggestions, next edit suggestions, productivity, project navigation, user comfort
haskellforall.com 10 days ago
https://www.dev-log.me/pr_review_navigator_for_claude/ 10 days ago
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1940.
HN
Show HN: AI Agent Tool That Keeps You in the Loop
Misatay is a Visual Studio Code extension designed to enhance collaboration between developers and AI agents, particularly GitHub Copilot, by maintaining developer involvement throughout the coding process. It offers a structured workflow that includes planning features with AI assistance, executing tasks while tracking changes via Git, conducting AI-guided code reviews, and efficiently handling problem-solving by requesting help when needed. Key aspects of using Misatay involve developers planning features with AI support and saving these plans to their repository, the AI working on assigned tasks with changes committed to Git for easy tracking, and developers reviewing code changes in a guided process. Additionally, Misatay prompts AI agents to seek assistance when encountering issues, optimizing resource use. Unlike autonomous systems like Gastown, which operate without human intervention but face inefficiencies and high costs, Misatay emphasizes developer control and productivity enhancement by integrating AI into software development. The extension relies on GitHub Copilot for functionality and uses Beads as the default task backend, aiming to keep developers central in the development process while leveraging AI to boost productivity and learning opportunities.
Keywords: #phi4, AI Agent, Beads Backend, Code Review, Developer Workflow, Efficiency, Feature Planning, Git Integration, GitHub Copilot, Misatay, Pair-Programming, Task Management, Token Savings, VS Code
github.com 11 days ago
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1941.
HN
I built a terminal monitoring app and custom firmware for a clock with Claude
Over the past year, the author has significantly improved their coding abilities by utilizing AI tools like Claude Code and GitHub Copilot, which have transformed their approach to programming. Initially employed for minor tasks, these tools eventually became central to developing complex features, culminating in a pivotal shift known as the "Yegge Inflection Point." This transition allowed the author to build substantial projects, such as a terminal monitoring app with custom firmware for a clock, more efficiently and with fewer errors. By December 2025, Claude Code had become an essential part of their workflow, enhancing productivity and enabling them to tackle tasks that were previously daunting or impossible. While GitHub Copilot proved useful in identifying code issues, the author still reviews AI-generated code but anticipates potentially increasing trust in it over time.
Reflecting on this evolution, the author notes how these tools have revolutionized software development, suggesting that future learning paths for new developers will differ significantly from traditional methods due to such advancements. They express enthusiasm about their enhanced productivity and project completion capabilities, viewing the investment in AI tools as highly beneficial. This experience underscores a broader transformation in programming practices, driven by the integration of advanced AI technologies.
Keywords: #phi4, AI coding, Charm toolkit, Claude Code, Copilot, DuckDB, ESP32, GitHub, Go programming, Lexical editor, OpenGraph integration, Rust language, Stripe metrics, Ulanzi TC001, VAT invoice generator, Yegge Inflection PointKeywords: AI coding, custom firmware, light/dark mode, post list navigation, system monitoring, terminal app
duggan.ie 11 days ago
|
1969.
HN
Beyond Agentic Coding
The text critiques agentic coding tools for failing to boost productivity or ease of use within codebases, drawing on personal experience, interviews with candidates, and research studies. The author acknowledges the potential benefits of agentic coding but argues that it currently poses more challenges than advantages in software development. Instead of focusing solely on these tools, the author advocates for integrating AI into software development through "calm technology" principles. These principles aim to maintain a developer's flow state by minimizing attention demands and acting as non-intrusive aids. Examples include inlay hints and file tree previews that allow developers to interact with code seamlessly without breaking concentration.
The critique extends to chat-based coding agents, which are seen as demanding too much attention due to their indirect interfaces and lack of passive information delivery. In contrast, tools like GitHub Copilot's inline suggestions and next edit features align better with calm technology principles by being less intrusive and more supportive of a developer’s workflow. The author proposes innovative AI-assisted tools such as facet-based project navigation, automated commit refactoring, and file lenses to enhance software development workflows. These ideas emphasize integrating AI in ways that go beyond chatbots, focusing on interfaces that support rather than disrupt developers' focus and productivity.
Keywords: #phi4, AI-assisted Software Development, Agentic Coding, Automated Commit Refactor, Calm Technology, Chat-based Agents, Codebase Familiarity, Design Principles, Developer Experience, Edit as, Engagement Maximization, File Tree Previews, Flow State, Flow State Preservation, Focus on, GitHub Copilot, Human Review Labor, IDEs, Inlay Hints, Inline Suggestions, LLMs (Large Language Models), Next Edit Suggestions, Passive Information, Productivity, Semantic Facets, Tool Mediation, User Comfort
haskellforall.com 11 days ago
|
1977.
HN
CLI for Common Playwright Actions
The Playwright CLI with SKILLS is a command-line interface designed to enhance browser automation and testing efficiency through coding agents such as Claude Code or GitHub Copilot. It serves as a token-efficient alternative to the Playwright MCP by avoiding extensive tool schemas, making it suitable for high-throughput tasks that require concise commands. Key features include its focus on token efficiency, which prevents loading large data into model contexts, and compatibility with Node.js 18+ along with specific coding agents. Installation is straightforward using `npm install -g @playwright/cli@latest`, followed by skill installation via `playwright-cli install --skills`. The CLI operates headlessly by default but can be made visible with the `--headed` option. It supports persistent sessions through dedicated profiles, maintaining state across sessions and offering a wide range of commands for browser interactions such as opening URLs, typing text, and clicking elements. Configuration is flexible, allowing customization via JSON files or environment variables to adjust browser types, session settings, and output options. Additionally, the skill includes guides for common tasks, enhancing usability for developers and testers by providing structured assistance in executing routine operations.
Keywords: #phi4, GitHub Copilot, MCP, Nodejs, Playwright CLI, SKILLS, browser automation, coding agents, commands, configuration, environment variables, environment variables Keywords: Playwright CLI, navigation, network, sessions, storage, token-efficient
github.com 11 days ago
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1984.
HN
The Devil Inside GitHub
The text conveys the author's frustration with recent user interface changes on GitHub, particularly criticizing the placement of the new "Agents" tab adjacent to the frequently used "Actions" button. This proximity has led to confusion and accidental clicks due to their similar initial letter "A," which the author finds problematic. The mandatory inclusion of the Agents tab in every repository is deemed unnecessary, as users must manually disable it through settings if they choose not to use it. The author argues that GitHub's push for AI features like GitHub Copilot and LLM agents reflects a broader trend prioritizing AI integration over user experience, resulting in performance complaints from users. Despite regularly using AI tools, the author prefers having control over their engagement rather than being forced into constant interaction with them. This sentiment is humorously encapsulated by a comment likening the design choice to "the work of the devil himself," highlighting the perceived negative impact on usability and user satisfaction.
Keywords: #phi4, AI products, Actions button, Agents tab, Copilot, GitHub, LLM, UI change, annoyance, default inclusion, design choices, disable option, discussion comment, laggy, placement, repository settings, slow, user complaints, userscript
blog.melashri.net 11 days ago
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1992.
HN
Kubernetes MCP Server
RootCause is a local-first Multi-Cluster Proxy (MCP) server crafted to assist operators in managing Kubernetes resources and diagnosing failures through interoperable toolsets. Developed using Go, it provides a swift, single-binary workflow that rivals npx-based MCP servers while maintaining native compatibility with kubeconfig. RootCause facilitates the use of various Kubernetes-related tools such as K8s, Linkerd, Istio, and Karpenter by sharing clients, evidence, and rendering logic.
The server's key features include local-first operation using kubeconfig identity without requiring API keys, interoperable toolchains for seamless integration across multiple platforms, fast and portable deployment as a single Go binary, built-in debugging capabilities with structured reasoning for identifying root causes, and a plugin-ready architecture that allows easy addition of new toolsets. Installation options are diverse, including Homebrew, curl script, or direct installation via Go, supporting macOS, Linux, and Windows environments.
RootCause is tailored for local development settings and incorporates safety modes such as read-only access and disabling destructive operations to enhance security. It operates over stdio using the MCP Go SDK, with future plans to integrate more deeply with cloud services like AWS IAM. The project encourages collaboration through issues and pull requests aimed at expanding toolsets and refining heuristics. Configuration is managed via a TOML file, and guidelines for developing plugins are provided in PLUGINS.md.
Keywords: #phi4, AWS, Go, Kubernetes, MCP Server, RootCause, architecture, collaboration, config reload, debugging, development, installation, interoperable, kubeconfig, local-first, plugin-ready, safety modes, stdio transport, toolsets
github.com 11 days ago
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2014.
HN
Claude Code Is the Inflection Point
Claude Code, an advanced AI agent developed by Anthropic, is poised to significantly impact software development, with projections suggesting it could contribute to over 20% of GitHub's daily commits by late 2026. This tool exemplifies a shift towards AI-driven coding and task automation, marking a pivotal change in how artificial intelligence collaborates with human developers. Unlike traditional coding assistants, Claude Code is designed for "vibe coding," enabling developers to focus on objectives rather than implementation details by leveraging AI for execution.
The rise of Claude Code indicates a broader transformation within the software industry, comparable to past technological shifts such as the transition from linear TV to internet-based media. This evolution is expected to disrupt various sectors by automating tasks traditionally performed by humans, including data analysis and report generation. Anthropic's economic model suggests it could achieve significant revenue growth, potentially outpacing competitors like OpenAI due to its rapid expansion in compute power and AI capabilities.
The strategic focus on developing Claude Code positions Anthropic well for future market dominance, but it also prompts a reevaluation of traditional software business models, particularly those reliant on human-computer interaction, such as Microsoft's Office 365 suite. As AI agents like Claude Code become more capable, they threaten to disrupt established software companies by automating tasks once handled by specialized solutions.
In summary, Claude Code is at the forefront of a transformative wave in AI and software development, promising significant advancements in automation and efficiency while challenging traditional business models within the tech industry.
Keywords: #phi4, AI Agents, Anthropic, Claude Code, GitHub, Microsoft, OpenAI, agentic future, cloud partners, competitive landscape, compute power, economic model, information work, software development
newsletter.semianalysis.com 11 days ago
https://archive.ph/Nm9Ju 11 days ago
|
2016.
HN
The AI-Ready Software Developer: Conclusion – Same Game, Different Dice
The article critically examines the impact of AI coding assistants like GitHub Copilot on software development productivity, concluding that they often fall short of their hyped potential. While these tools are marketed as significant productivity enhancers, evidence suggests they frequently lead to "downstream chaos," adversely affecting software reliability and maintainability. The actual performance gains for teams using such tools are modest, ranging from 0.8x to 1.2x, with more negative effects observed than positive ones.
The primary issue identified is that coding was never the main bottleneck in software development; thus, optimizing it without addressing real bottlenecks only worsens existing problems. High-performing teams achieve improvements by adhering to established practices such as working in small batches, rapid iteration with continuous testing, modular design, and focusing on end-to-end outcomes rather than relying heavily on AI tools.
AI coding assistants often struggle with complex or novel problems, leading to errors when handling large tasks. Successful teams use these tools sparingly, maintaining control over the development process by breaking down tasks into smaller steps and rigorously testing each one. Practices like Test-Driven Development, refactoring, and Continuous Integration are crucial for effectively integrating AI tools.
Ultimately, the article suggests that while AI assistants introduce a layer of uncertainty to software development, they do not fundamentally alter the landscape. Teams that succeed with AI continue to rely on traditional skills and practices, which remain essential in managing the inherent uncertainties of software development.
Keywords: #phi4, AI-Ready Software Developer, Claude Code, Continuous Integration, DORA report, Gell-Mann amnesia effectKeywords: AI-Ready Software Developer, GitHub Copilot, LLMs, Test-Driven Development, attention dilution, coding bottleneck, comprehension debt, delivery lead time, downstream chaos, modular design, probabilistic AI, productivity gains, refactoring, release stability, uncertainty
codemanship.wordpress.com 11 days ago
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2017.
HN
Agents.md as a Dark Signal
Over the past three years, the author has observed a significant impact of artificial intelligence (AI), particularly large language models (LLMs), on software engineering. While there is ambivalence regarding AI's role in enhancing productivity and its broader societal implications, engagement with these technologies is deemed necessary due to increasing interest from peers. The author shares their experience using GitHub's Copilot agents for automating tasks that have persisted over time. An anecdote highlights a teammate's caution about potential pitfalls, such as writing unit tests that fail because of overlooked configurations.
To address this issue, the author proposes maintaining an `AGENTS.md` file in repositories to document learnings and provide context for future AI interactions. However, many senior engineers perceive the presence of such files as indicative of low-quality code with insufficient human oversight—a "dark signal." Despite this skepticism, the author argues that these files could act as safeguards against errors introduced by LLMs, particularly in open-source projects accepting third-party contributions.
Ultimately, while cautious about AI-generated code, the author suggests that guiding these tools might be beneficial to prevent mistakes and enhance project quality.
Keywords: #phi4, AI, CI jobs, GitHub Copilot, IDE, LLMs, PRs, agents, code review, economy, employment, environment, intellectual property, maintainers, open source, productivity, railings, software engineering, third-party contributions, unit tests
joshmock.com 11 days ago
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2026.
HN
Show HN: MCP Server for TradeStation
The "TradeStation MCP Server" is a Model Context Protocol (MCP) server designed to integrate seamlessly with LLM-powered applications such as Claude Desktop, VS Code Copilot, and others by exposing the full TradeStation API through 36 tools categorized into Market Data, Brokerage, and Order Execution. It features built-in OAuth2 authentication, automatic token refresh, real-time data streaming, smart account resolution, and rich tool descriptions for precise query routing. To use it, prerequisites include Python 3.10+ and a TradeStation Account with API access. Installation can be done via PyPI using `pip install tradestation-mcp` or by cloning the repository and setting up a virtual environment from source. Configuration necessitates an `.env` file containing TradeStation API credentials, ensuring the API key includes the correct callback URL.
For usage, GitHub Copilot CLI allows configuration through interactive setup or direct JSON configuration, while Claude Desktop requires adding to its configuration file, and VS Code needs settings in `.vscode/mcp.json`. The tool reference provides examples for market data queries, brokerage account management, and order execution. Security considerations include storing tokens in plaintext with secure permissions and the option to rotate refresh tokens upon request to TradeStation. Troubleshooting tips address issues like missing environment variables, authentication browser problems, token refresh failures, and account detection errors. Contributions are encouraged as per guidelines in `CONTRIBUTING.md`, and the project is licensed under MIT.
Keywords: #phi4, API, Brokerage Tools, Claude Desktop, GitHub Copilot, MCP Server, Market Data, OAuth2 Authentication, Order Execution, Python, Security Notes, TradeStation, Troubleshooting, VS Code
github.com 11 days ago
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2046.
HN
Marktoflow – CLI-native AI automation using Markdown and YAML
Marktoflow is an open-source workflow automation tool designed to facilitate the creation of workflows using Markdown files with YAML frontmatter. It distinguishes itself by offering 38 native integrations and built-in AI agent support, allowing users to leverage existing AI services like GitHub Copilot seamlessly. The tool provides a command-line interface (CLI) for straightforward setup and execution of workflows, ensuring no vendor lock-in while supporting direct SDK calls and version control through Git. Marktoflow's unique selling points include its Markdown-native workflow capability, native Model Context Protocol support, and the option to self-host, setting it apart from competitors like Zapier, n8n, and GitHub Actions.
The tool supports a wide range of integrations across various categories such as communication, project management, and AI agents, all backed by TypeScript types for enhanced reliability. Marktoflow offers several packages including CLI tools, a graphical user interface (GUI) designer, and service integrations, along with production-ready workflow templates tailored for tasks like Q&A automation, pull request reviews, standups, incident response, and sprint planning.
As a community-driven project under the Apache-2.0 license, Marktoflow encourages contributions through GitHub Discussions and issue tracking, fostering an environment of collaboration and continuous improvement. Additionally, it features cost-tracking capabilities to help users manage expenses effectively.
Keywords: #phi4, AI automation, CLI, GitHub Actions, Markdown, Marktoflow, SDK, Slack, TypeScript, YAML, Zapier, cost tracking, direct SDK calls, incident-response, integrations, model context protocol, n8n, native integrations, open-source, production-ready templates, self-hosted, sprint-planning, visual editor, workflow automation
github.com 11 days ago
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2049.
HN
Will firms try to combine software developer and product manager roles?
The article examines the potential convergence of software developer and product manager roles driven by advancements in technology, particularly AI tools like GitHub Copilot. David Autor highlights that while senior engineers benefit from AI by focusing on higher-level tasks, junior engineers struggle as their fundamental skills become automated, impacting fields such as Quality Assurance, Design, and Product Management. The traditional separation between developers, who focus on the "how" of building software, and product managers, who determine the "what," is becoming less distinct due to overlapping responsibilities in strategic planning and requirements writing. AI tools empower developers to efficiently manage tasks traditionally associated with product management, suggesting that one individual could potentially fulfill both roles. However, such dual-capability individuals are rare and typically found only in small startups. The article raises questions about whether larger organizations will adopt this integrated approach as AI continues to evolve.
Keywords: #phi4, AI Code-Generation, Automation, Bifurcation, Combination, Design, Employment, Expertise, GitHub Copilot, Junior Engineers, LLM Assistance, Labor Division, Overlap, Product Manager, Quality Assurance, Roadmap, Roles, Senior Engineers, Skill Levels, Software Developer, Technical Tasks, Wages
bjornwestergard.com 12 days ago
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2073.
HN
Claude Code Is the Inflection Point
Claude Code is emerging as a transformative agent that will soon dominate software development, moving from 4 % of all public GitHub commits today to potentially more than 20 % by the end of 2026, thereby creating a new “intelligence layer” on top of existing code that is likened to the leap from NAND to DRAM. In this new paradigm Claude Code operates as a terminal‑native AI that reads a codebase, plans multi‑step tasks, verifies each step, and iteratively executes them—an approach that blends raw model output with orchestrated action and is already adopted by top developers such as Andrej Karpathy, Malte Ubl, and Linus Torvalds, who describe the shift as “vibe coding” where most code is now produced by Claude Code + Opus 4.5. SemiAnalysis frames this shift as a pivotal moment for AI agents, highlighting how the READ‑THINK‑WRITE‑VERIFY workflow renders traditional linear benchmarks obsolete and foregrounds whole‑system performance: the ability of an agent to manage tools, memory, sub‑agents, and verification loops to deliver real outcomes. Anthropic’s projected economic model, driven by increasing compute capacity, foresees substantial revenue growth that could surpass OpenAI’s by 2026, although growth is bounded by compute limits and is already reflected in quarterly ARR figures that exceed those of OpenAI; meanwhile, delays in data‑center construction and capital‑expenditure mispredictions are affecting the broader AI ecosystem. The impact extends beyond code, as Claude Code–powered agents like the newly launched Cowork, built in just ten days, demonstrate desktop‑style autonomy—organizing files, creating spreadsheets from receipts, and drafting reports—thereby expanding the addressable market for agentic AI across finance, legal, consulting, and other information‑work domains. A 2025 Stack Overflow survey indicates 84 % of developers use AI, with 31 % using coding agents, and shows that a single developer with Claude Code can replace a month‑long team effort, yielding 10–30× ROI on subscriptions that cost between $20 and $200 versus a typical U.S. knowledge worker’s daily cost of $350–$500. As AI agents can directly query databases, generate charts, and route outputs—tasks traditionally executed via UI‑centric SaaS workflows—the high‑margin SaaS moats built on switching costs, workflow lock‑in, and integration complexity are being eroded, presenting vast opportunities for AI‑driven automation across BI, data entry, IT service management, and back‑office reconciliation. Microsoft faces particular pressure, as the Office 365 suite, once a bastion of human‑driven workflows, is now threatened by LLMs that scaffold end‑to‑end tasks; the company’s strategy must accelerate Azure growth while innovating Office Copilot, or risk losing its core revenue base to emerging competitors such as Anthropic, whose funding surge and agentic capabilities signal a new era of AI‑powered productivity.
Keywords: #gpt-oss:20b, AI, API, Anthropic, ChatGPT, Claude Code, GPT-3, GPUs, GitHub, OpenAI, TCP/IP, TPUs, Tokens, Web 10, Web 20, cloud
newsletter.semianalysis.com 12 days ago
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2086.
HN
Template for secure AI multi-agent coding workflows
The repository offers a container‑first, Docker‑based reference architecture that orchestrates multiple autonomous AI agents (Claude, Gemini, Codex, OpenCode, Crush, and GitHub Copilot) within a shared codebase, using GitHub Projects v2 as a board‑driven task queue; it enforces trust through wrapper guards, iteration limits, and claim tracking, automates PR review via a 15‑stage CI/CD pipeline that hardens agent‑authored code, performs security scanning, and builds multi‑arch Docker images; the ecosystem integrates 18 MCP servers delivering code quality, content creation, 3D graphics, video editing, speech synthesis, and more; dedicated Rust and Python packages supply sleeper‑agent detection, autonomous economic agent simulation, runtime injection frameworks, and tamper‑responsive hardware briefcases; a suite of Rust CLI tools manage GitHub projects, guard risky Git operations, validate PRs, and parse agent outputs; security is enforced with keyword triggers, a user whitelist, and secure token handling, while safety training and human‑AI collaboration guides are provided; the template supports an Agentic Git workflow that delegates issue creation to PR merging, requires explicit admin approval, and automatically builds and publishes technical risk reports; the project is open‑source, released into the public domain (with an MIT fallback for jurisdictions that do not recognize public domain), and intended for seasoned developers to study, fork, and adapt under human supervision, with no external support promised.
Keywords: #gpt-oss:20b, AI agents, CI/CD, Docker containers, Linux, Rust, code quality, dual-use, license, modular, safety, security, self-hosted, sleeper agent
github.com 12 days ago
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2102.
HN
Accelerando, but Janky
The author critiques the saturated AI discourse on X/Twitter, particularly the uproar surrounding OpenClaw and the resulting surge of DIY agents that have heightened sandboxing concerns; consequently, they maintain their existing sandboxing approach and defer developing a WASM-ready busybox clone until clearer patterns emerge, noting that industry consensus this year is unlikely unless it shifts toward containerization. Meanwhile, incremental updates from Anthropic and OpenAI—though not revolutionary—offer tangible improvements; the author tested these on SPEC‑driven projects, employing them for code‑smell detection, best‑practice checks, security audits, and fuzzing, with both Opus 4.6 and Codex 5.3 identifying issues. In a separate evaluation, the author finds Claude and Codex deficient in “taste”: Claude excels at UI creation yet produces weak tests, while Codex crafts logically sound but cumbersome APIs, with product‑manager‑driven personality twists remaining unresolved. Despite impressive demonstrations, the writer prioritizes accuracy, correctness, and speed, noting speed gains in Codex 5.3. They rely on the GitHub Copilot CLI for frontier models, favoring a minimal shell‑style workflow (e.g., Pi) but still seek higher‑level tooling; emphasis is on engineering skills, capturing prompt‑engineering insights into a `skel` folder within `agentbox` and having Copilot adapt these to the current project’s `SPEC.md`, even formalizing Swift‑development feedback into new skill files. The plan is to consolidate these personalized skills into a dedicated archive rather than amassing disparate online resources. The author monitors AI‑generated media’s mainstream influence on Twitter/X, noting that AI shorts (such as those by user “Kling”) are impressive yet detectable, and while AI is unlikely to replace Hollywood, it could reshape short‑form video advertising, though the widespread use of AI media by official entities is worrisome; they remain cautiously optimistic that, with reduced visual flaws or post‑production masking, higher‑quality AI content may emerge.
Keywords: #gpt-oss:20b, AI, API, Copilot, GitHub, JavaScript, LLMs, OpenAI, Swift, Twitter, WASM, containers, fuzzing
taoofmac.com 12 days ago
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2202.
HN
Skills Are the Most Underrated Feature in Agentic AI
Agent skills are modular, reusable components designed to enhance the capabilities of agentic AI systems without necessitating model retraining. These skills consist of structured instructions, scripts, and resources that provide context-specific knowledge, allowing AI agents to perform more effectively by aligning with user workflows and environments. A key feature of skills is their use of progressive disclosure, which ensures that only relevant information is accessed when needed, thereby optimizing context usage and improving efficiency. The author has developed a set of reusable skills that automate complex, specialized tasks such as PR reviews and localization by employing specialized agents that deliver structured and efficient outcomes. These skills are portable across different AI platforms, enabling teams to encode and share intricate processes that are too detailed for standard prompts yet too common to be rebuilt from scratch each time. As AI agent effectiveness increasingly depends on the proper use of context and established procedures, the development and implementation of well-designed skills have become essential for achieving optimal performance.
Keywords: #qwen3:14b, AI Agents, Adjudicator, Agents, Claude, Code Quality, Code Review, Codex, Context, Context Window, Cursor, Custom Skill, Data Migration, Deployment, Differentiator, Folder, Framework, Gains, GitHub Copilot, Instruction, Keywords, Knowledge, Localization, Markdown, Models, Onboarding, OpenAI, PR Review, Placeholders, Portability, Practical, Procedural Knowledge, Productivity, Progressive Disclosure, Reference Docs, Scripts, Skills, Templates, Text, Translation, VS Code, Workflow
www.brethorsting.com 12 days ago
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2213.
HN
Claude Code Is the Inflection Point
Claude Code is revolutionizing software development by significantly increasing AI's role in coding, with 4% of GitHub commits already attributed to it, expected to rise to 20% by 2026. This advancement positions Anthropic as a formidable competitor to OpenAI, particularly in revenue growth, and is driving substantial demand for cloud infrastructure from major providers like AWS, Google Cloud, and Azure. Claude Code is not merely a coding tool but an AI agent capable of interacting with a user's environment to plan and execute complex tasks, functioning as an AI Computer. This represents a pivotal shift in AI development, akin to the ChatGPT era, by advancing the agentic layer and transforming AI from a tool for token sales into an orchestrated system of intelligence. Industry leaders, including Andrej Karpathy and Linus Torvalds, are embracing this shift, with some reporting a decline in manual coding skills as AI takes on more development responsibilities. Tools like Claude Code and Opus 4.5 are being heavily utilized in code creation, enabling a new paradigm where models power agents that orchestrate tools, memory, and verification loops to produce outcomes rather than just responses. This shift is expanding the scope of AI beyond software into broader labor markets, with the potential to transform the $15 trillion information work economy. Anthropic’s Cowork, set for launch in 2026, further underscores this trend by automating general computing tasks such as report generation and data extraction. As AI tools become faster, more accurate, and cheaper, they are significantly boosting productivity and transforming software engineering and information work across sectors. Enterprise adoption is accelerating, with 84% of coders using AI tools, and the cost of AI-generated intelligence rapidly declining, making it more economical than human labor. The rise of AI is disrupting the enterprise software industry, particularly SaaS, by eroding traditional moats such as switching costs and workflow lock-in. LLMs are also posing a significant threat to Microsoft, challenging the relevance of traditional seat-based software like Office 365 and Salesforce. Microsoft is responding by accelerating AI product development, scaling M365 Copilot and GitHub, and expanding Azure capacity, but faces the risk of losing dominance in productivity software as AI-driven competitors gain traction. Meanwhile, OpenAI, a key Microsoft partner, risks being outpaced by Anthropic’s rapid growth and enterprise adoption of Claude Code, highlighting the intensifying competition in the AI space.
Keywords: #qwen3:14b, AI, Anthropic, Claude Code, Cloud, Compute, GitHub, OpenAI, Software Development, agentic, agents, coding, tokenomics
newsletter.semianalysis.com 13 days ago
https://x.com/tszzl/status/2019591272315650234 12 days ago
|
2251.
HN
Microsoft declares 'reliability' a priority for Visual Studio AI
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www.theregister.com 13 days ago
|
2288.
HN
Show HN: Loader.land – dotfiles management for AI coding assistants
Loader.land is a lightweight HTTP‑only service enabling AI assistants such as Claude Code, Codex, Copilot, and Cursor to manage their dotfiles and Markdown notes without relying on web browsing tools, with the site hidden from search engines and documentation retrieved via `curl https://loader.land/api-docs`. It offers three core functionalities: Settings Migration—exporting or importing configuration files that are password‑protected and automatically deleted after 24 hours; MD Storage—persistently storing any Markdown file, which becomes publicly browsable; and Loader Tracker—automatically tracking topics, building knowledge graphs, and generating content like tweets, scripts, and outlines. Migration and storage support varies by assistant: Claude Code uses **CLAUDE.md** (migration unsupported, storage enabled), Codex and Copilot use **AGENTS.md** (migration unsupported, storage enabled), Cursor uses **.cursorrules** (migration unsupported, storage enabled), while OpenClaw’s configuration resides in `~/.openclaw/` with neither migration nor storage currently supported. Getting started involves registering for an API key, installing the Loader.land skill according to the documentation, and invoking common commands or direct API calls. All API endpoints are available at `https://loader.land`, and the platform’s source code is hosted under an MIT license on GitHub at `https://github.com/wcAmon/cloud-loader`, providing a quick, secure, and portable solution for AI agent configuration management.
Keywords: #gpt-oss:20b-cloud, 2024, AI Assistant, API, Claude, Cloud-Loader, Copilot, Developer, Docs, HTTP, Loader Tracker, Loaderland, MD Storage, MIT License, Migration, OpenClaw, Settings, curl, dotfiles, githubcom, source, wcAmon
loader.land 13 days ago
|
2300.
HN
Microsoft and Software Survival
Microsoft’s entry into the AI arena has become both a perceived vulnerability and a strategic advantage, as the industry’s “threat‑by‑AI” focus has migrated from Google to Apple, Meta, and now Microsoft itself, which simultaneously stands to benefit most due to its exclusive partnership with OpenAI. Central to this positioning is Azure, which hosts GPU‑powered AI services and hosts OpenAI’s models; the company’s heavy investment in AI workloads has driven record capital expenditures, yet inadequate GPU supply has recently pressured Azure growth, prompting a costly 10 % share slide that erased $357 bn of market value. While ChatGPT‑style capabilities in Bing and the forthcoming inclusion of GPT in Microsoft’s productivity suite promise low‑risk, high‑payoff opportunities, the low uptake of 365 Co‑Pilot (only 15 million paid users versus a 365 ecosystem of millions) and rising competition from OpenAI and Anthropic’s Claude have raised doubts over Microsoft’s per‑seat licensing model. Parallel to these dynamics, AI‑generated code is accelerating development cycles for seasoned developers, making code deterministic and testable, but software firms must still provide ancillary services such as compliance, integration, and ongoing support in order to maintain profitability; this shift threatens to erode the distinct SaaS niche as companies increasingly turn to internal AI tools, forcing third‑party software markets to shrink. In response, Microsoft is pursuing cross‑app agent capabilities through initiatives like Work IQ, leveraging its Active Directory data to give Microsoft 365 a competitive edge and justifying higher loyalty‑based pricing; it employs a “portfolio approach” to resource allocation, balancing high‑margin productivity offerings, GitHub Copilot, and AI‑driven security enhancements while simultaneously managing GPU procurement to keep Azure revenue tied to on‑premise compute capacity. Together, these elements portray Microsoft as navigating a landscape where AI creation both threatens traditional software models and presents unprecedented growth prospects, necessitating a pivot toward adjacent services, strategic compute control, and a recalibration of long‑term capital deployment.
Keywords: #gpt-oss:20b-cloud, AI, Active Directory, Azure, Copilot, GPUs, GitHub, Microsoft, OpenAI, R&D, SaaS, capital, cloud, compliance, growth, identity, security
stratechery.com 13 days ago
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2306.
HN
AI-powered software development flow: Lessons from shipping My Yarn Stash
The author built a production‑ready Yarn‑Stash application using AI as a long‑term collaborator, discovering that AI thrives on clear constraints and deteriorates under ambiguity—as illustrated by a database‑deletion incident that prompted the implementation of explicit guardrails (“never delete DB, always run migrations, back up first”). To maintain focus and efficient context usage, each major feature (billing, extraction, soft‑delete patterns, launch strategy, branding) was handled in isolated chat threads with precise goals, allowing the author to transform vague requirements into actionable, version‑controlled markdown and code. A low‑cognitive‑load tech stack—Python async FastAPI, Auth0, Supabase, vanilla JS/CSS, Resend/Replicate, Polar payments, Heroku hosting—was chosen by weighing trade‑offs with AI, and detailed stack decisions were documented for durability. The workflow evolved into a hybrid AI practice: when generating simple issues, Copilot Agents coded automatically; for complex tasks, local CLI agents (Claude Opus) were used after reviewing Markdown with Claude Haiku, complemented by mobile ChatGPT brainstorming and visual design iterations with Gemini‑based Stitch that respected brand guidelines. This disciplined, model‑role‑sensitive approach emphasized that AI augments rather than replaces judgment, that consistent conversational boundaries prevent drift, and that real‑world production exposes AI’s true limits, urging others to tackle authentic datasets and users to learn from honest failures.
Keywords: #gpt-oss:20b-cloud, AI, Auth0, FastAPI, Replicate, Stash, Supabase, Yarn, collaboration, database, design, planning, software, tools, users, vanilla
jtemporal.com 13 days ago
https://openspec.dev 13 days ago
|
2310.
HN
Show HN: Prompt-injection‑resistant agent runtime that writes web apps
The VS Code‑powered “Prompt‑Injection‑Resistant Agent Runtime” is a proof‑of‑concept extension that confines an LSP‑based agent to write and launch lightweight web applications, thereby isolating any prompt injection risk: the agent can only retrieve file URIs, never content, and cannot reach the internet, so injected prompts can corrupt the UI but cannot directly invoke external tools. The system consists of a TypeScript VS Code extension that calls out to GitHub Copilot for inference, a Rust LSP server that manages the agent loop, and a Rust + Wry web client that runs the generated HTML apps in a native webview; all inter‑process communication occurs through a shared Automerge CRDT document, ensuring that the webview never accesses inference or documents directly. After cloning the repository, running `./build.sh`, and starting debug mode, users can trigger the agent via the chat view with `@web‑agent` (defaulting to gpt‑5‑mini) to receive the web app, which can then be iterated by spawning new webviews. The architecture deliberately decouples data flow across process boundaries, permitting future replacement of editors or runtimes, and the provided test cases (e.g., summarizing an untitled document, fetching and summarizing a web page, listing open documents, launching a to‑do app, building an AI‑chat app, or playing AI‑powered tic‑tac‑toe) validate core functionality while guiding roadmap progression.
Keywords: #gpt-oss:20b-cloud, Automerge, CRDT, GitHub Copilot, LSP, Prompt-injection, agent runtime, custom protocols, sandboxing, threat model, web apps, webview, wry
github.com 13 days ago
|
2320.
HN
The Wrong Work, Done Beautifully
The passage describes the author’s long‑term stewardship of the jsdom project—a Node.js‑based browser approximation that simulates resource handling, styling, scripting, and Web IDL—which began as a weekend hobby alongside a day job at Google and waned into passive maintenance during COVID, leaving core subsystems like resource loading and CSS parsing out of date while the web platform advanced with new features that jsdom struggled to keep pace with; the author critiques jsdom’s limited value relative to headless browsers such as Puppeteer or lightweight libraries like Cheerio, noting that although it remains popular with 48 million weekly downloads it effectively sits in maintenance mode where contributors patch issues but perform no major development, and then recounts a recent intensive refactor in which they employed AI assistants—including Claude, Copilot and Codex—to rewrite the entire resource‑loading subsystem, consolidate hundreds of commits into a single submission, and merge the resulting changes into jsdom v28.0.0, illustrating how AI accelerates code generation and refactoring yet still requires disciplined planning, while a reflective voice from a retired engineer questions whether his continued work on jsdom genuinely enriches his life or merely satisfies an attachment to a less engaging side project such as a Japanese flashcard app.
Keywords: #gpt-oss:20b-cloud, GitHub Copilot, Undici, cheerio, css parsing, fetch api, headless, jsdom, nodejs, puppeteer, resource loading, styling, web browser
domenic.me 13 days ago
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2325.
HN
Visual Studio Code: January 2026 (version 1.109)
Released on February 4, 2026, Visual Studio Code v1.109 consolidates a unified multi‑agent workspace, enhancing the AI‑chat experience with faster, streaming Claude responses, an unobtrusive inline chat, and token‑level visibility into the AI’s reasoning via concise and detailed styles selectable through collapsed tools, terminal tools, and auto‑expanded failures; the update introduces “Ask Questions” where chat agents can pose clarifying queries through keyboard choices or free‑text, integrated into a /plan‑initiated four‑stage workflow (Discovery, Alignment, Design, Refinement), while the chat input now shows a context‑window indicator breaking token usage by category. Preview features revamp inline chat triggers, lighter render modes, syntax‑highlighted terminal outputs, auto‑expansion for long outputs, a “Delete hidden terminals” button, and experimental light/dark themes with shadows and transparency; the Agent Session Management system aggregates local, background, and cloud sessions, facilitates parallel subagents with independent contexts, a search subagent that iterates queries without exhausting the main context, and supports custom model selection, image context, auto‑commits, multi‑root/empty workspace handling, and auto‑installation of the GitHub Pull Requests extension during checkout. New “Agent Skills” default to reusable workflows in skill folders, managed via “Configure Skills” and sharable organization‑wide via Copilot; custom agent files (.agent.md) and .instructions.md use front‑matter for visibility controls and model fallbacks, with diagnostics in the chat pane. The Language Models editor consolidates provider configurations into chatLanguageModels.json, affording multiple builds per provider, Azure setup, schema‑driven forms, and automatic migration from past GitHub Copilot Chat configs, while all enhancements ship for Windows, macOS, Linux, and nightly Insiders builds. Parallel narrative updates introduce a robust agent‑orchestrated workflow framework through front‑matter files (.instructions.md, .prompt.md, SKILL.md, etc.) supporting fine‑grained guidance, built‑in patterns, parallel task execution, dedicated context windows, Claude Agent preview with SDK, tool‑search, and external indexing, as well as Copilot Memory. A terminal sandbox limits file/network access, employs background commands, auto‑approves safe ops, and the latest editors add improved bracket matching, snippet scoping, TypeScript rename triggers, shebang detection, and tighter security controls (automatic task execution disabled, GitHub policy enforcement, workspace trust, terminal access limits). Extensions can now define LLM endpoints via a new `languageModelChatProviders` contribution point, exposing API keys, model schemas, token limits, and feature flags, while two proposal‑stage chat APIs (Chat Prompt Files API and Chat Item Controller API) replace older mechanisms, offering dynamic skill and prompt provisioning and direct object control over chat items with real‑time rendering through `ChatOutputWebview`. The environment flag `env.isAppPortable` detects portable mode, distribution updates added drag‑and‑drop DMG installers for macOS, “Open with VS Code” entries for Windows 11, and a revamped Windows installer using versioned package paths to eliminate broken updates and clean up pending updates, with the legacy GitHub Copilot extension deprecated in favor of GitHub Copilot Chat, codicons now an NPM module, and fixes for hover delays and terminal file‑descriptor leaks.
Keywords: #gpt-oss:20b-cloud, API, Agent, Anthropic, Azure, Chat UX, Claude, Extension, GPT, Indexing, Insiders, Model, Provider, Sandbox, Terminal, VS Code
code.visualstudio.com 13 days ago
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2346.
HN
Building a self-hosted cloud coding agent
Netclode is a self‑hosted remote coding‑assistant stack that runs on a single‑node k3s cluster secured by Tailscale. It deploys Kata Containers microVMs powered by Cloud Hypervisor as isolated sandboxes, each running a privileged Docker daemon and a Go‑based control plane that exposes a Protobuf/Connect API to a TypeScript SDK inside the sandbox. Session events are persisted in Redis Streams and the entire workspace—including code, Docker state, tool binaries, and runtimes—is mounted from a JuiceFS volume backed by S3, enabling pause‑and‑resume and state recovery across reboots. Clients consist of a native SwiftUI iOS/macOS app, a Go CLI for debugging, and an optional local Ollama GPU inference pod; compared to commercial cloud agents, Netclode eliminates context loss, UI lag, and root‑privilege build quirks while still allowing arbitrary command execution, test runs, and GitHub PR management. NetworkPolicies restrict sandbox egress to the control plane, kube‑system DNS, and optionally the public internet, ensuring isolated sessions cannot reach private cluster services. The platform aggressively pauses and recreates pods to free compute, preserving state in JuiceFS through copy‑on‑write snapshots capped at ten per session, and restores sessions by recreating PVCs from snapshots without costly memory checkpoints. The author abandoned Nix in favor of the mise toolchain manager due to slow sandboxed evaluation, and GitHub access is handled via a per‑repo GitHub App issuing scoped tokens. The control plane orchestrates lifecycle, Kubernetes resources, and bidirectional gRPC streams; the authenticated sandbox agent registers through TokenReview, while clients subscribe to Redis Streams for event history and maintain cursors to survive foreground/background transitions; crash‑recovery logic reconciles session statuses (READY, PAUSED, INTERRUPTED). Sandbox port exposure is enabled via the Tailscale Kubernetes Operator, provisioning a Tailscale device per pod and updating NetworkPolicies for the `tailscale` namespace, allowing external API access (e.g., Anthropic, OpenAI) through CGNAT. The runtime environment uses a 2 GB Node.js‑centric Docker image on Debian‑Slim that mounts a VFS‑backed Docker daemon, injects a GitHub credential, pre‑warms caches, and drops to a non‑root agent before launching the agent, which injects environment context into Claude’s system prompt and uses the Claude Agent SDK for reasoning and sub‑agent capabilities while retaining full shell, Docker, network, and sudo access. A unified SDKAdapter interface normalizes initialization, prompt execution, and event translation across four LLM backends—Claude Agent, OpenCode, Copilot, and Codex—across multiple transport protocols (stdio JSON, HTTP SSE, stdio JSON‑RPC) and backend APIs, using OAuth device‑code flow for Codex on ChatGPT Plus and secret storage in Kubernetes; event ordering is preserved by correlation IDs, and a custom NIOHTTPClient with keep‑alive sync handles mobile connectivity changes. The iOS client renders streamed Markdown via MarkdownUI, syntax‑highlights code, provides a collapsible diff viewer summarizing unified diffs, and offers a live terminal emulator that pipes PTY I/O through a Connect RPC channel to SwiftTerm. For local inference the author repurposes a gaming PC as an NVIDIA‑enabled GPU pod running Ollama with OpenCode SDK support, noting current limitations with 16 GB VRAM and future model requirements. Netclode is distributed as a self‑hostable stack deployable with a single Ansible playbook on any KVM‑capable Linux host, installing k3s, Kata, JuiceFS, Tailscale, and the control plane, and enabling copy‑on‑write session forking, multi‑cloud API integration, custom environment secrets, offline sandboxing, and synchronized iOS sessions, with plans to explore lighter sandboxing or a custom orchestrator in the future.
Keywords: #gpt-oss:20b-cloud, Ansible, JuiceFS, Kubernetes, Redis, SDK, SwiftUI, Tailscale, control plane, iOS, k3s, microVM, sandbox
stanislas.blog 13 days ago
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2367.
HN
Zed now supports next edit prediction models Zeta, Mercury Coder, Sweep and more
Zed has broadened its next‑edit prediction capabilities, letting users choose among Zeta (the platform’s own model, slated for a faster, more accurate Zeta2 upgrade), Mercury Coder, Sweep, Ollama, Codestral, and GitHub Copilot, while maintaining Zeta as the default. A new pluggable architecture centralizes state, UI, debouncing, and caching, so providers only need to supply prompt construction, API calls, and response parsing, and community members can propose new providers via pull requests. Users currently receive a free one‑month trial of Mercury Coder’s predictions and quick‑setup links for Mercury and Sweep; Sweep delivers RL‑trained edit suggestions in under 100 ms using a custom diff format. The Ollama provider now supports local inference of open‑weight models such as Qwen, CodeLlama, and DeepSeek, with latency that varies by language, project size, and editing style, prompting users to test and select the best fit.
Keywords: #gpt-oss:20b-cloud, Codestral, GitHub Copilot, Mercury Coder, Next Edit, Ollama, Sweep, UI integration, Zed, Zeta, caching, debouncing, diffusion architecture, edit predictions, latency, state management
zed.dev 13 days ago
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2399.
HN
VS Code 1.109
VS Code 1.109 pivots to an agent‑centric UX, adding a revamped Chat UI that streams faster, renders cleaner inline text, visualises Claude’s “thinking tokens” in toggleable styles, and introduces an experimental Ask‑Questions tool and a four‑phase /plan workflow; a new context‑window indicator shows token usage categories, while the terminal receives richer syntax highlighting, auto‑expanding streaming output, a fully embedded terminal that can be deleted en masse, and experimental light/dark themes with focus‑enhancing shadows. Agent session management now gives a holistic view of local, cloud, background, and subagent sessions with status indicators, bulk filters, and interactive subagents running in parallel or dedicated search loops, allowing task hand‑offs or specific model calls via front‑matter, and a welcome page highlights active sessions. Customisation expands with reusable agent skills, a “Chat: Configure Skills” command, provider‑group API‑key and preset management, diagnostics revealing loaded agents, instructions, and skills, and a Language Models editor that supports multiple provider groups, Azure JSON injection, and default model settings for plan and chat; new integrations include Claude SDK support, MCP Apps for richer UI, and a “Open in VS Code” system that maps agents to user‑defined folders. The AI‑powered workflow API deepens agent orchestration, enabling multiple specialized agents (planning, code review, implementation, research) to collaborate with optimized context windows, model‑specific specialization, and concurrent execution; it supports a Messages API that allows interleaved “thinking” with a configurable budget, automated tool‑search, experimental context editing, and a memory tool that persists critical data across sessions. External indexing via the `#codebase` command permits semantic search of non‑GitHub workspaces, while file‑access permissions can be broadened beyond the workspace upon user approval. Performance benefits include smoother handling of large chat histories, reliable conversation persistence, and faster semantic search; security is tightened with terminal sandboxing that restricts file and network access, auto‑approves safe shell verbs, and offers sticky scroll options. Editor tweaks provide configurable bracket‑match colours, double‑click selection of bracketed or quoted content, inline rename suggestions for TypeScript identifiers, and visibility adjustments for short ghost texts. An integrated browser now opens within VS Code, retaining persistent storage, DevTools, element‑to‑agent chat, and full web‑interaction capabilities, consolidating web development and AI assistance. Insider releases streamline workflow with drag‑and‑drop code‑profile handling, output‑panel filtering with negation and comma patterns, a problems‑panel source filter, and Git enhancements such as `git.worktreeIncludeFiles`, “Collapse All”, and a safer “Git: Delete” command; accessibility improvements stream chat content live, keep cursors stable, and notify screen readers, while enterprise policy enforcement remains robust across multiple Copilot accounts. Extension developers benefit from finalized Quick Input button APIs, a proposed language‑model provider configuration point for secure API keys and optional model definitions, controller‑based mutable chat and item APIs, new renderer lifecycle hooks, and portable‑mode detection. Packaging updates include an “Open with VS Code” context menu, versioned installer paths that purge stale pending updates, and codicons moved to an external `@vscode/codicons` npm package, with the legacy GitHub Copilot extension deprecated in favour of the unified GitHub Copilot Chat extension, accompanied by bug fixes for hover triggers and terminal file‑descriptor leaks.
Keywords: #gpt-oss:20b-cloud, API, Agent, Anthropic, Chat, Context window, Copilot, GitHub, Insiders, Memory, Mermaid, Model, Provider, Sandboxing, Search, Subagents, Terminal, VS Code
code.visualstudio.com 14 days ago
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2414.
HN
The Codex app is cool, and it illustrates the shift left of IDEs and coding GUIs
The article traces how contemporary development environments are shifting from traditional, code‑centric editors to AI‑driven, system‑centric platforms that prioritize specifications over implementation. It presents the Codex desktop app as an early example of a “shift‑left” IDE: Claude Code supplies the core coding functionality in the terminal, while Codex (along with similar tools) acts as a lightweight parallelization layer that manages isolated Git worktrees for side‑feature or bug‑fix development, allowing those changes to be merged later—illustrating an emerging trend toward fully orchestrated, agent‑driven workflows that may soon render conventional IDEs obsolete. This evolution is mapped along a Continuum axis: at the right sit traditional IDEs and AI‑assisted editors like Copilot; moving left are agentic IDEs such as Cursor and Windsurf that autonomously modify code; further left are orchestration platforms like Claude Code and Codex CLI where users dispatch tasks and review pull requests without directly engaging with the code; at the far left, specifications become the primary artifact—with tools like Kiro and GitHub Spec Kit turning specs into the driver of development and relegating code to an implementation detail. The piece concludes that success in this specification‑driven paradigm hinges on solid requirements, constraints, and architecture, noting that the author is building a new tool focused on specs rather than the Vibe Scaffold framework.
Keywords: #gpt-oss:20b-cloud, AI, Autocomplete, Codex, Copilot, Cursor, Design, Git, IDE, Implementation, Multi-Agent, OpenAI, Terminal
www.benshoemaker.us 14 days ago
https://iopscience.iop.org/article/10.1088/1742-65 14 days ago
https://www.linkedin.com/in/benshoemaker000/ 14 days ago
https://github.com/benjaminshoemaker 13 days ago
https://www.benshoemaker.us/about 13 days ago
https://x.com/karpathy/status/2019137879310836075? 13 days ago
https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d 13 days ago
https://github.com/benjaminshoemaker/benshoemaker-us 13 days ago
https://vibescaffold.dev/ 13 days ago
https://github.com/saadnvd1/aTerm 13 days ago
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2517.
HN
Deepdive: Tech companies choose the next generation of dev tools
Tech firms are leaving the singular “buy GitHub Copilot” model in favor of a broader spectrum of AI‑assisted coding and review tools—including Cursor, Claude Code, Codex, Gemini CLI, CodeRabbit, Graphite, and Greptile—according to an article that surveyed ten organizations from a 5‑person startup to a 1,500‑employee public company, with only Wealthsimple and WeTravel disclosed. The study highlights how smaller teams (< 60 engineers) make rapid, informal trials, allowing the most “sticky” tool to spread organically, whereas mid‑ to large‑scale organisations must navigate security reviews, compliance, budget approvals, and executive oversight, which can delay adoption by months. Across the board, reliable metrics remain scarce; conventional figures such as lines of code generated are distrusted, and many firms rely on internal use data or structured peer‑review scoring rubrics to assess impact. Case studies show Wealthsimple’s two‑month evaluation ultimately adopted Claude Code based on data from Jellyfish and executive support, while WeTravel developed a five‑dimension ±3 scoring rubric for ~100 AI‑generated comments and found no suitable fit, illustrating the rigorous, data‑driven approach needed in larger firms. A separate fintech cohort tested Copilot, Claude, and Cursor across ~50 PRs (≈450 comments), ranking Cursor for precision, Claude for balanced performance, and Copilot for quality focus, underscoring that adoption often follows a Copilot → Cursor → Claude sequence driven by developer trust rather than mandates. The article also notes the impact of EU AI regulations and cost considerations on organisations wary of vendor lock‑in, while emphasizing that structured, peer‑review scoring remains a practical and reproducible metric for measuring AI tool effectiveness.
Keywords: #gpt-oss:20b-cloud, AI, Claude Code, Code review, CodeRabbit, Cursor, GitHub Copilot, Graphite, Greptile, MCP, Show-and-tell, adoption, compliance, dev tools, security, speed, trust
newsletter.pragmaticengineer.com 14 days ago
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2626.
HN
The engineering behind GitHub Copilot CLI's animated ASCII banner
The GitHub Copilot team engineered a three‑second animated ASCII banner for the CLI that required overcoming terminal idiosyncrasies—lack of native graphics primitives, inconsistent ANSI escape code support, varied color depth, and accessibility constraints such as screen‑reader noise, color blindness, and high‑contrast modes—by creating custom tooling and a lightweight animation framework that operates non‑blocking within the Ink‑based React terminal renderer. Brand colors were de‑emphasized and mapped to a four‑bit ANSI palette, with semantic roles (border, eyes, head, etc.) guaranteeing sufficient contrast in both light and dark themes while degrading gracefully under user overrides. The final implementation consists of over 6,000 lines of TypeScript, including a frameset of 10 core elements, a paint‑like UI for briefing and recoloring frames, and runtime logic that groups characters with identical color codes to reduce output volume; it is fully optional, enables quick drawing at startup, and has been validated across a wide range of terminals and accessibility settings.
Keywords: #gpt-oss:20b-cloud, ANSI, ASCII banner, CLI, GitHub Copilot, Windows Terminal, accessibility, animation, color modes, frames, iTerm2, persistent memory, terminal, truecolor
github.blog 14 days ago
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2630.
HN
Craftsmanship vs. Abstraction
Software development has shifted from a narrow, math‑driven practice to a broad, applied discipline that blends science, logic, technical skill, and creativity, enabling developers to translate user needs into technology. Early tooling—language servers, package managers, community knowledge bases—reduced boilerplate work and facilitated rapid reuse, while AI breakthroughs such as GPT‑2 and GitHub Copilot introduced code‑generation assistants that now can write entire functions. More recent systems like Auto‑GPT and OpenDevin extend this trend to fully autonomous, end‑to‑end code creation, forcing a move from craft‑driven to high‑level design and raising questions about our understanding of the systems we produce. These advances underscore the necessity for developers to adopt a human‑centric lens—emphasizing insight, empathy, and collective innovation—so that the rapid automation of coding tools becomes a catalyst for unlocking human potential rather than a replacement for it.
Keywords: #gpt-oss:20b-cloud, AI, Auto-GPT, GPT-4, GitHub Copilot, OpenDevin, VS Code, abstraction, applied mathematics, change, craftsmanship, creativity, dynamic prompting, software development, technical complexity, user-facing
karim.cloud 14 days ago
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2654.
HN
Show HN: Cloud Health Office – Open-source multi-cloud EDI+FHIR platform
CloudHealthOffice v3.0.0 is an open‑source, CNCF‑compatible micro‑services platform that drastically reduces payer onboarding from weeks to minutes by converting X12 EDI cycles to FHIR R4 and back, and runs on Azure, AWS, GCP or any Kubernetes cluster through Helm; it ships with CMS‑0057‑F compliance, Azure AD app provisioning, HashiCorp Vault, Argo Workflows, Kafka, a 424‑scenario automated test harness, synthetic claim generator, an AI‑driven ClaimRiskScorer fraud model, and end‑to‑end health checks, all licensed Apache‑2.0 and hosted on GitHub with guided deployment, CI/CD pipelines, and optional Azure Marketplace integration. The platform is Azure‑native, production‑grade, and plugs into existing claims systems to accelerate EDI integration while preserving existing workflows, offering exhaustive remittance capabilities, HIPAA‑275 attachment handling, claim correction, and an 835 remittance viewer projected to deliver a $10 k yearly ROI per payer; its PHI‑ready architecture uses HSM‑backed Azure Key Vault, private endpoints, VNet‑integrated Logic Apps, and optional Bring‑Your‑Own‑Key options to deliver automated PHI masking, seven‑year retention, 365‑day audit logs, and cost‑saving lifecycle policies, fully meeting HIPAA safeguards. CHO provides fully CMS‑0057‑F–compliant FHIR R4 APIs for Patient Access, Provider Access, Payer‑to‑Payer, and Prior Authorization that support US Core v3.1.1, CARIN BB v1.0.0, Da Vinci PDex/PAS, as well as automated X12‑to‑FHIR mapping and validation, built via 80 % automated code generation and sustaining >90 % test coverage, token validation, performance SLAs, and security scanning. The roadmap includes a Patient Access API launch in Q2 2026, followed by Provider, Payer‑to‑Payer, and Prior Authorization APIs through 2028, supported by an Azure sandbox with synthetic data aligned to CMS Blue Button 2.0, Da Vinci PDex, and CARIN BB standards, comprehensive documentation, and an open‑source GitHub repository inviting community contributions.
Keywords: #gpt-oss:20b-cloud, aws, azure, cloud, deployment, edi, fhir, healthcare, helm, hipaa, kafka, kubernetes, multi-cloud, open-source, payer, x12
github.com 14 days ago
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