An AI Agent Found 20 ML Improvements Karpathy Had Missed in 20 Years

Andrej Karpathy released autoresearch on GitHub last week, and the results are worth understanding carefully. Not because of the hype, but because of how the architecture actually works. The framework is 630 lines of Python. It runs an AI agent in a loop: read a training script, form a hypothesis, modify the code, run a short training job (five minutes), evaluate results against a scalar metric, repeat. On Karpathy's own ML training setup, the agent ran 700 experiments over two days on a single GPU and found an 11% training speedup through 20 optimizations he says he hadn't discovered in 20 years of working on the same codebase. Then Shopify's CEO ran the same approach on internal data. 37 overnight experiments. 19% performance gain. Applied to their Liquid templating engine: 53% faster rendering, 61% fewer memory allocations, 93 automated commits, all 974 unit tests passing. The repo hit 42,000 GitHub stars in its first week. The Architecture Is the Lesson The design is deliberately m