There are libraries like tensorflow and PyTorch that allow the user to define their neural net in simple, readable Python code, and they internally "compile" and optimize your neural net to run on GPUs and such.

Tinygrad is like a very, very lean PyTorch with a different philosophy -- it intends to keep the codebase and API surface very very small and focus most of its energy on optimizing the way the output neural net runs on physical hardware.

The author, George Hotz, has observed in the last few years that neural net performance is hindered by lack of optimization here, particularly around memory accesses.