6 minimal ML codebases worth studying, from micrograd to llm.c
cneuralnetwork · x · 2026-11-07
A curated list of minimal ML implementations worth studying, each stripping away abstraction to expose core mechanics:
- micrograd: autograd and backpropagation from scratch (scalar-valued engine)
- nanoGPT: how GPT training actually works
- minGPT: transformers without abstraction overload
- tinygrad: tensor operations and ML framework internals
- llm.c: LLM training in C/CUDA
- minimalRL: reinforcement learning algorithms in compact form
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