ctxlint
Linter for AI agent context files (AGENTS.md, CLAUDE.md)
What it does
AI agents break when AGENTS.md has stale commands or wrong ecosystem references. ctxlint catches these before your agent does, wrong build tools (cargo in a Node.js repo), missing files, bad paths, ecosystem mismatches. Just run: npx @ctxlint/ctxlint check Tested on 8 real repos including LangChain and Next.js. Works with any AI agent that reads context files.
Does the same job
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Recently several AI labs have published experiments where they tried to get AI coding agents to complete large software projects. - Cursor attempted to make a browser from scratch: https://cursor.com/blog/scaling-agents - Anthropic attempted to make a C Compiler: https://www.anthropic.com/engineering/building-c-compiler I have been wondering if there are software packages that can be easily reproduced by taking the available test suites and tasking agents to work on projects until the existing test suites pass. After playing with this concept by having…
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Hi HN, Zidan here. I’ve been experimenting with AI-assisted debugging and noticed a recurring gap: most tools optimize for agent-led exploration (ex: giving claude code a browser to click around and try to reproduce an issue). But in many cases, I've already found the bug myself. What I actually want is a way to hand the agent the exact context I just saw - without retyping steps, copying logs, or hoping it can reproduce the behavior. So we built FlowLens, an open-source MCP server + Chrome extension that captures browser context and lets coding agents inspect it as structured, queryable…
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npx agentseed init AGENTS.md (https://agents.md) is a standard file used by AI coding agents to understand a repo (stack, commands, conventions). Agentseed generates it directly from the codebase using static analysis. Optional LLM augmentation is supported by bringing your own API key. Extracts languages, frameworks, dependencies, build/test commands, directory structure, and monorepo boundaries.
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, April 2026
the whole month →
- AG
Thought the resources for GPU arch were lacking, so here we are
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Built a ~9M param LLM from scratch to understand how they actually work. Vanilla transformer, 60K synthetic conversations, ~130 lines of PyTorch. Trains in 5 min on a free Colab T4. The fish thinks the meaning of life is food. Fork it and swap the personality for your own character.
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Life & fun · Apr 2026 · sam-burns.com
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With social media and now AI, its important to keep the indie web alive. There are many people who write frequently. Blogosphere tries to highlight them by fetching the recent posts from personal blogs across many categories. There are two versions: Minimal (HN-inspired, fast, static): https://text.blogosphere.app/ Non-minimal: https://blogosphere.app/ If you don't find your blog (or your favorite ones), please add them. I will review and approve it.
AI · Apr 2026 · text.blogosphere.app