everyone's talking about @karpathy autoresearch and most of you have no idea what it actually does.

there's a training script (train(dot)py) that trains a small language model, basically a baby GPT. and there's an instruction file (program(dot)md) that tells an AI agent what to do.
you press go. the agent tweaks the training script, trains for 5 min, checks the score. better? keep. worse? revert. repeat 100 times overnight while you sleep.
that's literally it.
what it's actually optimizing: the MODEL ARCHITECTURE. not predictions. not trades. not your portfolio.
stuff like:
→ 4 layers or 8?
→ best learning rate?
→ AdamW or Muon optimizer?
→ what batch size works best on THIS specific GPU?
optimal architecture depends on your hardware. an H100 wants a completely different model than your MacBook. autoresearch finds the best config for your machine automatically.
what you CAN do with it:
> build a tiny LLM that writes code, autoresearch finds the best architecture, you train on your dataset
> create a lightweight chatbot that runs offline on your phone
> train a model on your own writing so it sounds like you
> test "does RoPE beat ALiBi for small models?" 100 variations in one night instead of 3 weeks of PhD work
> optimize a model for a Raspberry Pi or edge device
what you CANNOT do:
> predict stock prices
> find trading edges
> analyze spreadsheets
> predict sports outcomes
autoresearch is a tool for people who want to BUILD language models, not USE them. Karpathy built an autonomous loop where AI improves AI. genuinely brilliant. but it solves a very specific problem.
and that problem is probably not yours. which is fine, just stop pretending it's something it isn't.