@rohit4verse: Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated mon…

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autonomous-research-agent machine-learning open-source experiment-automation gpu-training git-memory andrei-karpathy

Summary

Andrej Karpathy open-sourced an autonomous research agent that runs its own ML experiments overnight using a single GPU, automatically iterating on improvements by editing code and keeping changes that lower validation loss.

Every night you're not running an autonomous research agent, you're hand-running experiments someone else automated months ago. Most people are still hunting for the "right" setup. Frameworks, orchestration, glue code. You don't need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python. Here's the exact setup (takes 2 minutes): 1. Clone it: (repo link in comments) 2. uv sync, then uv run prepare[.]py 3. uv run train[.]py once to confirm the baseline runs 4. Point your coding agent at program.md and walk away The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn't. Git is the memory. The metric is the judge. You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.
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Cached at: 05/25/26, 12:53 PM

Every night you’re not running an autonomous research agent, you’re hand-running experiments someone else automated months ago.

Most people are still hunting for the “right” setup. Frameworks, orchestration, glue code.

You don’t need any of it. Andrej Karpathy open-sourced his own version that runs its own ML research. One GPU. ~100 experiments overnight. You never touch the Python.

Here’s the exact setup (takes 2 minutes):

  1. Clone it: (repo link in comments)
  2. uv sync, then uv run prepare[.]py
  3. uv run train[.]py once to confirm the baseline runs
  4. Point your coding agent at program.md and walk away

The agent edits one file, trains 5 minutes, keeps the change if val_bpb drops, reverts it if it doesn’t. Git is the memory. The metric is the judge.

You wake up to a staircase of validated improvements, not a backlog of ideas you never tested.

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@sitinme: Saw Karpathy open-sourced a very interesting project autoresearch, which gives a real but small-scale LLM training task to an AI Agent, letting it do research, modify code, run experiments, look at results, and then decide whether to keep or discard the changes. The project is based on a single NVIDIA…

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Karpathy open-sourced an experimental project, autoresearch, that lets an AI Agent automatically complete the research loop for small-scale LLM training: modify code, run experiments, evaluate results, and iterate. Humans only need to write the research plan and constraints.