Context Fence
Runtime firewall for AI coding agents.
What it does
Local security proxy for AI coding agents. Sit between agent and MCP servers, check every tool call against your rules, block risky ones, log everything for audit.
Context Fence is a local MCP policy proxy for AI coding agents: schema-based checks under 10ms, secret stripping, append-only audit log, zero cloud routing.
Context Fence sits between your AI agent and its MCP tools on your own machine. Every action a schema check, not an LLM judge, so it stays under 10ms. Nothing leaves your machine. That is the point. The July 18 incident — what happened, why the usual fix doesn't fix it, and where the guard actually lives. On July 18, a coding agent read the .env file it was never asked to open, then sent what it had read across the wire. The keys left the machine inside an API payload. The usual fix — a cloud gateway and a vendor's deny-list — would have hoped for the best: every call leaves your machine first. Ours doesn't. The guard lives where the agent lives: a gate at the boundary. Nothing leaves.…from contextfence.dev
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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…
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Life & fun · 10d ago · louisabraham.github.io


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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


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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