Greywall
Local agent sandbox with real-time network control dashboard
What it does
AI agents run on your machine, with your credentials, making network requests you never see. Greywall puts a deny-by-default layer in front of any CLI agent. Run greywall -- claude and it restricts file access, blocks outbound connections, and shows you a live dashboard of what the agent is trying to reach. You decide what to allow. Nothing is containerized. Your workflow stays the same but safer. It runs on Linux and macOS and costs nothing.
Does the same job
all alternatives →- STSkill that lets Claude Code/Codex spin up VMs and GPUsFeb 2026 · cloudrouter.dev · ▲138
I've been working on CloudRouter, a skill + CLI that gives coding agents like Claude Code and Codex the ability to start cloud VMs and GPUs. When an agent writes code, it usually needs to start a dev server, run tests, open a browser to verify its work. Today that all happens on your local machine. This works fine for a single task, but the agent is sharing your computer: your ports, RAM, screen. If you run multiple agents in parallel, it gets a bit chaotic. Docker helps with isolation, but it still uses your machine's resources, and doesn't give the agent a browser, a desktop, or a GPU to…
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RunwallJul 2026 · runwall.vercel.app · ▲9A Runtime security layer that lets AI agents execute safely
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We just open-sourced CargoWall - a lightweight eBPF firewall for GitHub Actions. We originally built it to stop LLM agents from connecting to untrusted domains. After recent GitHub Actions supply chain compromises like the Trivy attack, we realized it'd work well for blocking untrusted connections from CI runners too. It uses iptables DNAT to redirect all outbound port 53 traffic to a local DNS proxy, which checks each query against a hostname allowlist before forwarding. Resolved IPs from allowed responses are inserted into eBPF LPM trie maps, and a TC egress classifier attached to the…
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.
AI · 17d ago · simedw.com
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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, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
