Applied research from ruby.im: a small open-source intelligence
toolkit built in Ruby, and a connected series on how the best builders, teams and labs extract high leverage —
all from public data, with the method shown and the limits stated.
Every leverage pattern, tier-rankedThe map: 23 distinct ways to multiply output, ranked S to C by how much they
multiply, how permissionless they are, and how long they last. Start here.
How high-leverage builders actually workThe patterns in people: Simon Willison, antirez, Mitchell Hashimoto, Linear, 37signals and
Anthropic's Claude Code team — their real tools and setups, compared and contrasted.
The high-leverage teamsThe patterns in organisations: about a hundred teams grouped by how they extract leverage,
plus the refined lessons from running the toolkit to assemble it.
Analysis & tradecraft
Intelligence tradecraft, amplified by AIThe deep dive: the highest-leverage analytic techniques intelligence agencies use to
understand a complicated world — competing hypotheses, key-assumptions checks, premortems, calibrated
forecasting — and what AI does to each.
The research system
Building an intelligence research stackThe whole build: a token-cheap collection toolkit over free structured sources, Katana and
GPT Researcher, an honest access policy, and three worked case studies. The method behind the series.
Retrieval-augmented generationThe foundation, on ruby.im: a full how-to for RAG that cites page numbers, with real chunking
math, retrieval metrics and an honest benchmark against naive RAG.
Meta
ChangelogEvery change to this subsite, newest first.
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