markupR
Open-source AI bug reports. Free forever, BYOK/local.
What it does
AI coding agents are blind. When you find a bug, you context-switch into writing mode — describing the layout, manually screenshotting, hoping the agent understands. markupr records your screen while you narrate what's wrong, then runs an intelligent pipeline that correlates your voice with the video to produce structured Markdown with screenshots placed exactly where they belong. Ships as a desktop app, CLI, MCP server for Claude Code/Cursor/Windsurf, and a GitHub Action. MIT licensed.
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FunBlocks AI Markdown Editor Oct 2025 · ▲382Experience the joy and speed of pure text, with AI copilot
- PGProofShot – Give AI coding agents eyes to verify the UI they buildMar 2026 · github.com · ▲161
I use AI agents to build UI features daily. The thing that kept annoying me: the agent writes code but never sees what it actually looks like in the browser. It can’t tell if the layout is broken or if the console is throwing errors. So I built a CLI that lets the agent open a browser, interact with the page, record what happens, and collect any errors. Then it bundles everything — video, screenshots, logs — into a self-contained HTML file I can review in seconds. proofshot start --run "npm run dev" --port 3000 # agent navigates, clicks, takes screenshots proofshot stop It works with…
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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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AI · 19d ago · company-app.joinastute.com
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
