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But seriously, for open source projects actually looking for contributors (vis-a-vis companies with a developer product they just make open source), there is no better resource.

To understand a framework, you need a mental model of it. Good documentation is helpful, but it seldom walks through why specific design choices were necessary.

> I’ve tried to find something similar for pytorch and numpy in the past and was let down.

Well I got a treat for you then

https://minitorch.github.io/

I don’t mean how torch's API could be implemented, I mean how pytorch implements it. Do you know which this is?

I think the closest thing for pytorch is the Karpathy video:

https://www.youtube.com/watch?v=VMj-3S1tku0

There's also an old book on the internals of numpy - not sure how out of date it is though.

Same! I looked into how pytest worked and got bogged down in its plugin system (which is great, but very distracting when trying to read the code). I've always been curious how the assertion rewriting works.