Interesting project. Do you think manual memory management help understand computational graph lifecycle better, or does it distract from backprop itself? btw,

Interesting project. Do you think manual memory management help understand computational graph lifecycle better, or does it distract from backprop itself? btw, I went down the micrograd path with numpy-primitives all the way to building a PyTorch clone that can pre-train and post-train LLMs (https://github.com/workofart/ml-by-hand https://github.com/workofart/ml-by-hand). My learning focus was on the math/calculus <-> high-level APIs, instead of efficiency. I'm glad to see more people tackling this problem from different angles.