Okf

This is the post I’ve been building toward across three earlier ones. It pulls the whole system together — the toolkit, the agent, the benchmarks — and ends on the experiment that reframed the entire project for me: I handed the same knowledge graph to a tiny local model and to a frontier model (Claude Sonnet), graded both with the same judge, and the frontier model didn’t win. That result is the thesis. When you put a domain behind a good retrieval layer, the expertise lives in the graph, not the model reading it. Which means you can build something I’ve started calling a micro-scale domain expert: a small, portable, inspectable knowledge artifact that any agent — a 4-bit model on your laptop or a frontier model in the cloud — can pick up and answer from. This post is about the toolkit that makes those, and the evidence that the cheap part (the model) is interchangeable while the valuable part (the graph) is where the work goes. ...