Andrej Karpathy attributes agents’ strong performance on common internet tasks to the abundance of examples of those tasks in model training data.

“it and that's the agents um i do feel like the agents work in very specific settings and i would use them in specific settings but again these are all tools available to you and you have to like learn what they what they're good at and what they're not good at and when to use them so the agents are actually pretty good for example if you're doing boilerplate stuff boilerplate code that's like just cop you know just copy paste stuff they're very good at that they're very good at stuff that occurs very often in the internet”

“um because there's lots of examples of it in the training sets of these models um so so there's like features of things that where the models will do very well i would say nanochat is not an example of this because it's a fairly unique repository there's not that much code i think in the way that i've structured it and um and it's not boilerplate code it's like actually like intellectually intense code almost and everything has to be very precisely arranged and the models are always trying to”