I love the cool tech demo! I also would have loved to see a offline sensor calibration package - or whatnot - and analysis of model precision instead of Klingon. Just trying to think up of an actual use case where you would actually truly need an LLM instead of one of the other well known data analytics methods.

But would it be as cool as a microcontroller that spits out Klingon?

Imagine a Beowulf cluster of those

Afaik there are already a few videos on YouTube.

vIparHa'bej.

The fact that by hand is emphasized so often and so often (see src/handgpt.h comments as well above backward()) makes me think it's AI.

Anyways, it looks like gradients derived by hand means they didn't use autograd. They have written out the expression for dL/dW themselves.

Author here. Fair question, and the answer is in the first line of the README: I wrote it, with AI help.

Specifically: I wrote it in Spanish first, which is my language, and then translated and condensed it into English with Claude. That's probably the blend you're picking up. The jokes, the structure and the decisions are mine; the English phrasing has fingerprints that aren't.

The code is more clear cut and it says so in the header: generated by AI under my direction. I wrote the architecture, the decisions and the validation, not most of the C.

I'd rather say that upfront than have someone find it later.

I understand that you are alluring that maximum training-state memory, not parameter count, is the key variable here. So you better start with the smallest model, and go to for highest training data quality, with the outlook of coupling systems together?