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I was thinking about this the other day. If we did a plot of 'model ability' vs 'computational resources' what kind of relationship would we see? Is the improvement due to algorithmic improvements or just more and more hardware?

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i don't think adding more hardware does anything except increase performance scaling. I think most improvement gains are made through specialized training (RL) after the base training is done. I suppose more GPU RAM means a larger model is feasible, so in that case more hardware could mean a better model. I get the feeling all the datacenters being proposed are there to either serve the API or create and train various specialized models from a base general one.

I think the harnesses are responsible for a lot of recent gains.

Not really. A 100 loc "harness" that is basically a llm in a loop with just a "bash" tool is way better today than the best agentic harness of last year.

Check out mini-swe-agent.

Everyone is currently discovering independently that “Ralph Wigguming” is a thing

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