Awesome AutoResearch

A curated list of AutoResearch use cases and open source implementations.

What is AutoResearch?

AutoResearch is, at its core, a prompt. Karpathy released it as a single markdown file - program.md, that instructs a coding agent (Claude Code, Codex, or similar) to follow an optimization workflow. The agent edits one file (train.py, that trains a language model), runs for a fixed 5 minutes on a GPU, checks whether the metric improved, and either commits the change or reverts it. Then it loops forever.

The specific program.md that ships with AutoResearch is written for one task: training a GPT model. But the structure - iteratively optimizing a file against an evaluation metric, with a discard/keep loop - turns out to be portable. In the weeks since release, the community has adapted it to GPU kernel optimization, template engine optimization, tabular ML engineering, and more. The program.md for each of these looks different, but the loop is the same.

Use Cases

| Use Case | Description | Author | Links | Traces | |----------|-------------|--------|-------|--------| | LLM training optimization | The original - optimize nanoGPT training code. 20 improvements found overnight on hand-tuned code | Andrej Karpathy | GitHub · Tweet | progress chart | | Speed up Shopify's template engine | 53% faster parse+render, 61% fewer allocations from 93 automated commits on Shopify's Liquid engine | Tobi Lutke (Shopify CEO) | GitHub · Tweet | PR with traces | | GPU kernel optimization | Autoresearch applied to CUDA kernel optimization (18 → 187 TFLOPS) | RightNow AI | · | | | Voice agent prompt engineering | Optimize voice agent prompts with automated evaluation (score 0.728 → 0.969) | Archie Sengupta | · | | | Predict baseball pitch speed | Build predictive model for pitch velocity from biomechanics data (R² 0.44 → 0.78) | Kyle Boddy (Driveline Baseball) | | | | XGBoost for tennis match prediction | Predict ATP/WTA match outcomes - encountered and documented reward hacking | Nick Oak | [Blog](https://ni...