Construct a minimal deep learning framework from scratch, layering a lazy numpy-backed buffer, a reverse-mode automatic differentiation engine, a tensor API, and neural network primitives. By the end you train a small MLP on a toy digit dataset, mirroring how real autograd libraries like tinygrad work under the hood. - View it on GitHub