#knowledge

A model read 45,047 emails from eight Enron mailboxes and built a 214-page wiki of the firms, regulators and banks around the company, with no human edits. Each of its 49,401 facts links to the message it came from, and a timeline slider shows only what the mail knew by a given date, so facts appear and lapse as the story unfolds. Don't skip the methodology: the model kept slipping in what it already knew about Enron, and they show how they caught it and what the run cost.

Made by this+that. Jeff Reynar, a co-founder, posted it to Hacker News: news.ycombinator.com/item?id=50207620

What did Enron know? The Enron Brain

Point it at a folder of Markdown (an Obsidian vault, meeting notes, an agent's memory) and it builds a local index. Rather than summarising, it drafts questions from your recent notes and lets each one pull in related passages from across the years, so an agent can trace a thread through thousands of notes. A short .enzyme program sets what it should notice. It ships as one binary under 30 MB with its embedding model inside, open source under Apache 2.0.

Made by Joshua Pham. Hacker News thread: news.ycombinator.com/item?id=50221066

GitHub - byenzyme/enzyme: Local-first compile step for knowledge bases. Save 350x cost, 1000x speed vs. frontier models