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Does autoresearch work for projects that are not llm based? Eg in karpathy's example he is optimizing the nanogpt. What if I wanted to improve a Unet for image segmentation?
I wrote up some more notes on that here: https://simonwillison.net/2026/Mar/13/liquid/
Awesome breakdown! It really feels like a hyper-hyper parameter search + bug fixer. I started looking at Kaggle again and autoresearch seems to converge to many of the solution vibes there. Wild ensembles, squeezing a bit of loss out. More engineering than research IMO
Does autoresearch work for projects that are not llm based? Eg in karpathy's example he is optimizing the nanogpt. What if I wanted to improve a Unet for image segmentation?
[1] https://github.com/ykumards/eCLIP/commits/main/autoresearch
I often use LLMs to explore prior art and maybe find some alternative ways of thinking of problems. About 90% of what it tells me is useless or inapplicable to my domain due to a technicality it could not have known, but the other 10% is nice and has helped me learn some great new things. I can’t imagine letting an agent try everything that the LLM chatbot had recommended ($$$). Often coming up in recommendations are…
For all the folks spending a lot of time and energy in setting up MCP servers, AGENTS.md, etc. I think this represents more that the LLM cannot do what it is being sold as by AI boosters and needs extreme amounts of guidance to reach a desired goal, if it even can. This is not an argument that the tech has no value. It clearly can be useful in certain situations, but this is not what OpenAI/Anthropic/Perplexity are selling and I don’t think the actual use cases have a sustainable business model.
People who spend the energy to tailor the LLMs to their specific workflows and get it to be successful, amazing. Does this scale? What’s going to happen if you don’t have massive amounts of money subsidizing the training and infrastructure? What’s the actual value proposition without all this money propping it up?
The bottleneck in AI/ML/DL is always data (volume & quality) or compute.
Does/can Autoresearch help improve large-scale datasets? Is it more compute efficien than humans?
Does autoresearch work for projects that are not llm based? Eg in karpathy's example he is optimizing the nanogpt. What if I wanted to improve a Unet for image segmentation?
There are better techniques for hyper-parameter optimisation, right? I fear I have missed something important, why has Autoresearch blown up so much? The bottleneck in AI/ML/DL is always data (volume & quality) or compute. Does/can Autoresearch help improve large-scale datasets? Is it more compute efficien than humans?
There always are. You need to think about what those would be, though. Autoresearch outsources the thinking to LLMs.
Does autoresearch work for projects that are not llm based? Eg in karpathy's example he is optimizing the nanogpt. What if I wanted to improve a Unet for image segmentation?
I often use LLMs to explore prior art and maybe find some alternative ways of thinking of problems. About 90% of what it tells me is useless or inapplicable to my domain due to a technicality it could not have known, but the other 10% is nice and has helped me learn some great new things. I can’t imagine letting an agent try everything that the LLM chatbot had recommended ($$$). Often coming up in recommendations are…
I find LLMs useful in regurgitating one-liners that I can’t be bothered to remember or things where even being flat out wrong is okay and you just do it yourself. For all the folks spending a lot of time and energy in setting up MCP servers, AGENTS.md, etc. I think this represents more that the LLM cannot do what it is being sold as by AI boosters and needs extreme amounts of guidance to reach a desired goal, if it e…
I found LLMs make a fabulous frontend for git :-D