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Let It Run
Episode 0027: Let It Run
On March 7th, 2026, Andrej Karpathy released autoresearch — a 630-line Python repo that lets an AI agent run autonomous ML experiments overnight. The agent modifies training code, runs 5-minute experiments, keeps improvements, discards failures, and repeats. ~100 cycles while you sleep. Karpathy watched val_bpb drop from 1.0 to 0.97 without touching anything. Shopify CEO Tobi Lutke adapted it the same night and got a 19% improvement — with the smaller agent-optimized model eventually outperforming a larger manually configured one. The engineering task has shifted: you're not tuning the model anymore, you're writing the instructions that tell the agent how to tune the model.
The Repo
karpathy/autoresearch
https://github.com/karpathy/autoresearch
Three files:
- prepare.py — fixed constants, one-time data prep, runtime utilities. Do not modify.
- train.py — the full GPT model, optimizer (Muon + AdamW), and training loop. The agent modifies this.
- program.md — your instructions to the agent. The only file the human iterates on.
How It Works
The 630-line constraint is intentional: the entire codebase fits in a model's context window, so the agent can reason about the whole file at once.
Key Results
The Concept
Karpathy describes program.md as the "research org code" — you're not programming the model, you're programming the organization that does the research. The human's job shifts from running experiments to writing the instructions that tell the agent how to run experiments well.
His README opens with fictional lore: the agents are now in the 10,205th generation of the codebase, "a self-modifying binary that has grown beyond human comprehension." He's joking. The repo exists and you can clone it tonight.
Links
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By Daily Tech FeedEpisode 0027: Let It Run
On March 7th, 2026, Andrej Karpathy released autoresearch — a 630-line Python repo that lets an AI agent run autonomous ML experiments overnight. The agent modifies training code, runs 5-minute experiments, keeps improvements, discards failures, and repeats. ~100 cycles while you sleep. Karpathy watched val_bpb drop from 1.0 to 0.97 without touching anything. Shopify CEO Tobi Lutke adapted it the same night and got a 19% improvement — with the smaller agent-optimized model eventually outperforming a larger manually configured one. The engineering task has shifted: you're not tuning the model anymore, you're writing the instructions that tell the agent how to tune the model.
The Repo
karpathy/autoresearch
https://github.com/karpathy/autoresearch
Three files:
- prepare.py — fixed constants, one-time data prep, runtime utilities. Do not modify.
- train.py — the full GPT model, optimizer (Muon + AdamW), and training loop. The agent modifies this.
- program.md — your instructions to the agent. The only file the human iterates on.
How It Works
The 630-line constraint is intentional: the entire codebase fits in a model's context window, so the agent can reason about the whole file at once.
Key Results
The Concept
Karpathy describes program.md as the "research org code" — you're not programming the model, you're programming the organization that does the research. The human's job shifts from running experiments to writing the instructions that tell the agent how to run experiments well.
His README opens with fictional lore: the agents are now in the 10,205th generation of the codebase, "a self-modifying binary that has grown beyond human comprehension." He's joking. The repo exists and you can clone it tonight.
Links