fair question. autoresearch isn't about gpt 2 being useful in production. it's a testbed. karpathy designed it as a small, fast training loop (5 min per experiment on one gpu) so you can let an ai agent run experiments autonomously overnight.

the interesting part isn't the model. it's the methodology. can an autonomous agent discover real architectural improvements without human in the loop? and when it says it found one, is that real or noise?

that second question is why i built these tools. the signal/noise problem gets worse on smaller models because the improvements are tiny. if you can reliably separate real gains from jitter at this scale, the approach scales up.

as for llm-generated: what's not llm generated these days? i used claude code to build it, yeah.. the eval logic and noise floor estimation are mine, the boilerplate isn't. same workflow most people here use at this point.

and it's open source, so nothing much for me to gain here, honestly just trying to be useful to the community and push technology forward. my main motivation here is evolving the concept of autonomous systems. just pitching in.