Andrej Karpathy

That's what I want.

Lots of Eurekas per second.

And so to me, this is a technical problem of how do we build these ramps to knowledge.

And so I always think of Eureka as almost like a, it's not like maybe that different, maybe through some of the frontier labs or some of the work that's going to be going on, because I want to figure out how to build these frontier, these ramps very efficiently so that people are never stuck.

And everything is always not too hard or not too trivial.

And you have just the right material to actually progress.

I mean, I think you always have to be calibrated to what the capability, what capability exists in the industry.

And I think a lot of people are going to pursue like, oh, just ask Chachi PT, et cetera.

But I think like right now, for example, if you go to Chachi PT and you say, oh, teach me AI, there's no way.

I mean, it's going to give you some slop, right?

Like when I, AI is never going to write nano chat right now, but nano chat is a really useful, I think, intermediate point.

So I still, I'm collaborating with AI to create all this material.

So AI is still fundamentally very helpful.

Earlier on, I built a CS231 at Stanford, which was one of the earlier, actually, sorry, I think it was the first deep learning class at Stanford, which became very popular.

And the difference in building out 231N and LLM 101N now is quite stark because I feel really empowered by the LLMs as they exist right now, but I'm very much in the loop.

So they're helping me build little materials.

I go much faster.

They're doing a lot of the boring stuff, et cetera.

So I feel like I'm developing the course much faster and those LLM infused in it, but it's not yet at a place where I can creatively create the content.

I'm still there to do that.