graphify-labs--graphify/worked/karpathy-repos

Karpathy Repos Benchmark

This is the corpus that produced the 71.5x token reduction benchmark.

Corpus (52 files)

Code — clone these 3 repos

git clone https://github.com/karpathy/nanoGPT
git clone https://github.com/karpathy/minGPT
git clone https://github.com/karpathy/micrograd

Papers — download these 5 PDFs

• Attention Is All You Need — https://arxiv.org/abs/1706.03762
• FlashAttention: Fast and Memory-Efficient Exact Attention — https://arxiv.org/abs/2205.14135
• FlashAttention-2 — https://arxiv.org/abs/2307.08691
• Neural Attention Residuals — https://arxiv.org/abs/2505.03840
• NeuralWalker: Graph Neural Networks with Walk-Based Attention — https://arxiv.org/abs/2502.02593

Images — save these 4

• gpt2_124M_loss.png — nanoGPT training loss curve (in the nanoGPT repo)
• gout.svg — micrograd computation graph (in the micrograd repo)
• moon_mlp.png — MLP decision boundary (in the micrograd repo)
• Any screenshot or diagram from the Attention Is All You Need paper

How to run

Put all files into a single folder called raw/:

raw/
├── nanoGPT/
├── minGPT/
├── micrograd/
├── attention.pdf
├── flashattention.pdf
├── flashattention2.pdf
├── attn_residuals.pdf
├── neuralwalker.pdf
├── gpt2_124M_loss.png
├── gout.svg
└── moon_mlp.png

Install and set up the skill for your platform:

pip install graphifyy

graphify install                        # Claude Code
graphify install --platform codex       # Codex
graphify install --platform opencode    # OpenCode
graphify install --platform claw        # OpenClaw

Then open your AI coding assistant in this directory and type:

/graphify ./raw

What to expect

• ~285 nodes, ~340 edges, ~17 meaningful communities
• God nodes: Value (micrograd), GPT (nanoGPT), Training Script, Layer
• Surprising connections: nanoGPT Block and minGPT Block linked across repos, FlashAttention paper bridging into CausalSelfAttention in both repos
• Token reduction: 71.5x vs reading all 52 files directly

Actual output is in this folder: GRAPH_REPORT.md and graph.json. Full eval with scores: review.md.