Work

• Evals for a model cascade — knowing when a prompt change makes things worse

  nutri.'s AI extraction runs on a multi-model fallback chain. This is the golden-set eval harness that turns 'the prompt feels better' into a scored regression gate — including what it exposed about the weakest model in the chain.

• nutri. — running a production app inside an agentic stack

  A nutrition-tracking PWA I design, build and operate entirely inside Claude Code. The interesting part isn't the app — it's the operating system that keeps an AI agent effective and safe on a real codebase with no second reviewer.

• A remote MCP server where row-level security is the only authorization layer

  nutri. exposes its own MCP server: OAuth 2.1 with dynamic client registration on Supabase Auth, eight tools that reuse the app's domain validators, and no privileged key anywhere in the stack. Verified against production with a zero-net-alteration battery.

• RAG that only cites what it retrieved — semantic search and a question-answering food log

  Two retrieval features built on one rule: the model never produces a number. A pgvector hybrid search over the food catalog, and a three-step structured RAG over the user's log — verified against the database to the third decimal.