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).

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

it being acclaimed is a poor measure of success, theres always room for improvement, how about some objective comparisons?

Objective measures like branch depth, execution speed, memory use and correctness of the results be damned.

Karpathy's implementation is explicitly for teaching purposes. It's meant to be taken in alongside his videos, which are pretty awesome.

Ironically the reason Karpathy's is better is because he livecoded it and I can be sure it's not some LLM vomit. Unfortunately, we are now indundated with newbies posting their projects/tutorials/guides in the hopes that doing so will catch the eye of a recuiter and land them a high paying AI job. That's not so bad in itself except for the fact that most of these people are completely clueless and posting AI slop.