BearDrive
The open-source shared folder for your team's AI agents
What it does
Your AI agents create real files locally: reports, decks, CSVs, research. BearDrive syncs the folder they already work in, so every file is born shared: versioned, attributed down to the agent session, with links only your team can open. Unlike Notion or Drive, nobody moves anything; a teammate's agent reads your agent's work at a real local path seconds later. Works with Claude Code, Cowork, Codex, Gemini CLI, any local tool. Open source and self-hostable. Managed service free during beta.
BearDrive is a shared folder every AI agent on your team reads and writes. Real files at real paths, with a link and a full history seconds after they
A shared folder every AI agent on your team reads and writes. Real files at real paths, with a link and a full history seconds after they're written. Start your agent — Claude Code, Codex, Gemini CLI, Cowork, Hermes — in the folder you want synced, and paste this in. It installs the CLI, signs this machine in, asks which folder to sync, and registers the hooks that keep it fresh. You approve one link in the browser. Prefer to drive it yourself? Install the binary, then run bdrive init . No Homebrew? Grab a release binary for your OS from GitHub releases . Nothing to integrate. If it can write a file, it already works: It's the Dropbox moment for AI agents. Before Dropbox, you emailed…from beardrive.ai
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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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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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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…
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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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