Karpathy's Autoresearch utilizes a constrained optimization loop with a large language model (LLM) agent. The author applied Autoresearch to legacy code from eCLIP while managing household tasks.

Claude Code was given access to 16 GPUs on a Kubernetes cluster and submitted approximately 910 experiments over 8 hours. It determined that scaling model width was more significant than any single hyperparameter and achieved a 2.87% improvement in validation performance, reducing val_bpb from 1.003 to 0.974.