This project implements autonomous research loops on Apple Silicon using MLX instead of PyTorch. The system runs fixed 5-minute training experiments, evaluates model quality via validation bits-per-byte (val_bpb), and relies on an AI coding agent to iteratively edit train.py based on results. It uses git to keep winning changes and revert losing ones, enabling systematic hyperparameter exploration without manual intervention.