Knowl
One shared brain. Every AI. Any session. Always current.
What it does
Your agents work something out, the session ends, and it's gone. The next one relearns it. Knowl is an MCP memory server for Claude Code, Cursor, Codex... What an agent stores keeps its reasoning and its evidence - and when a fact changes, the new one supersedes the old instead of sitting beside it. So what your agents read back is the current answer, not the loudest. Knowl Cloud shares that memory with your team, and connects to claude.ai and chatgpt.com so it follows you off the terminal.
Knowl is an MCP memory server for Claude Code, Cursor and Codex. What your agents work out survives the session, and a change retires what it replaced — so the answer they read back is the current one.
Because it gets trusted. Knowl gives every fact your agents store a record of where it came from , a review trail , and a successor when it changes — so the answer they hand back is the current one, and you can see who settled it. How this was built — the approaches that failed first, and the measurements that ruled each one out. $ npm install -g @dat999zx/knowl added 1 package $ knowl --version 5.21.0 Node 20 or newer. No API key, no vector database, no service to run alongside it. In March an agent works out your API rate limit. 100 a minute. Every fact carries its reasoning, its evidence and its provenance — observed, stated by a person, or inferred. A claim you can check without asking…from knowl.cloud
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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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