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An experimental framework where AI agents autonomously modify and iterate on LLM training code, running experiments in 5-minute cycles to optimize model performance. Agents edit train.py, evaluate results, and decide whether to keep or discard changes—enabling hands-off research overnight. Built on simplified nanochat training with a markdown-based instruction system for directing agent behavior.