Andrej Karpathy shifted how he uses LLMs . Instead of burning tokens on code generation, he's spending them compiling structured knowledge bases: markdown wikis an LLM builds, links, and maintains from raw source documents. One of his research corpora has grown to roughly 100 articles and 400,000 words that he didn't write a sentence of. Two days later he published a GitHub gist designed to be copy-pasted into your LLM agent so it builds the system for you. Community implementations followed within a week. The tweet went viral for the obvious reason, which is that it works and it's cheap. But there's a less obvious reason worth paying attention to: Karpathy put a name to the architectural flaw that's been quietly breaking enterprise AI systems for three years. He called the missing piece a "compilation step," and once you see it, you can't unsee it. The part Karpathy got right Most production AI systems that consult documents use Retrieval-Augmented Generation . You index a corpus.…