> By the end of training, the model produces names like "kamon", "karai", "anna", and "anton". None of them are copies from the dataset.
Hey, I am able to see kamon, karai, anna, and anton in the dataset, it'd be worth using some other names: https://raw.githubusercontent.com/karpathy/makemore/988aa59/...
You are absolutely right. The whole post reads like AI generated.
The rate they are posting new articles on random subjects is also a pretty indicative of a content mill.
In 3 days they've covered machine learning, geometry, cryptography, file formats and directory services.
I didn't get that sense from the prose; it didn't have the usual LLM hallmarks to me, though I'm not enough of an expert in the space to pick up on inaccuracies/hallucinations.
The "TRAINING" visualization does seem synthetic though, the graph is a bit too "perfect" and it's odd that the generated names don't update for every step.
ISWYDT
Thanks, will fix
I read through this entire article. There was some value in it, but I found it to be very "draw the rest of the owl". It read like introductions to conceptual elements or even proper segues had been edited out. That said, I appreciated the interactive components.
The part that eludes me is how you get from this to the capability to debug arbitrary coding problems. How does statistical inference become reasoning?
For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.