A companion to the PKM Phase 1 post. The foundation was built by two AIs — Claude.ai for design, Claude Code for implementation — with a spec as the contract between them. Ten SPEC revisions in four days, and what the rules caught that 'pragmatic' would have missed.
Eleven million words of personal documents, four canonical questions none of Khoj, Paperless-ngx, Obsidian, or Karpathy's LLM Wiki can answer, and a content-addressed extraction foundation that takes content-addressing seriously. Phase 1 of a multi-phase build.
A Bayesian agent plays text adventures with four information sources and a VOI gate on every query. The LLM recommends actions. The mathematics decides whether to listen.
Most LLM agents use tools like a tourist uses a phrasebook. A few lines of probability theory can fix it.
I built a Bayesian agent and set it against LangChain on a tool-use benchmark. LangChain got more answers right and still lost — by 120 points.
Part 2 of the Bayesian agent series. We confront the question of what should be designed versus what should be allowed to emerge, and discover that it's agents all the way up and all the way down.
A visual demonstration of Bayesian inference and the exploration-exploitation tradeoff. An agent navigates a grid world, learning which foods are safe through exact conjugate updates.
How I built a Raspberry Pi dashboard to capture and visualize body composition data from a GoodPharm TY5108 Bluetooth scale
How I built a Getting Things Done Telegram bot with natural language parsing using Claude Haiku, Python, and PostgreSQL - all in under an hour with Claude Code.