Because it's an acclaimed, often cited course by a preeminent AI Researcher (and founding member of OAI) rather than four undocumented python files.

Cleaner, more straightforward, more compact code, and considered complete in its scope (i.e. implement backpropagation with a PyTorch-y API and train a neural network with it). MyTorch appears to be an author's self-experiment without concrete vision/plan. This is better for author but worse for outsiders/readers.

P.S. Course goes far beyond micrograd, to makemore (transfomers), minbpe (tokenization), and nanoGPT (LLM training/loading).

I'm very sorry, I should have phrased my original post in a kinder, less dismissive way, and kudos to you for not reacting badly to my rudeness. It is a cool repo and a great accomplishment. Implementing autograd is great as a learning exercise, but my opinion is that you're not going to get the performance or functionality of one of the large, mainstream autograd libraries. Karpathy, for example, throws away micrograd after implementing it and uses pytorch in his later exercises. So it's great that you did this, but for others to learn how autograd works, Karpathy is usually a better route, because the concepts are built up one by one and explained thoroughly.