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.
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.
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.
politelemon | 2 hours ago
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/...
ayhanfuat | an hour ago
butterisgood | an hour ago
re | an hour ago
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.
jsheard | 57 minutes ago
In 3 days they've covered machine learning, geometry, cryptography, file formats and directory services.
[OP] growingswe | 56 minutes ago
windowshopping | an hour ago
For a long time, it seemed the answer was it doesn't. But now, using Claude code daily, it seems it does.
malnourish | 32 minutes ago