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Comment by smusamashah | Hacker News Reader

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smusamashah·

With this speed, you can keep looping and generating code until it passes all tests. If you have tests.

Generate lots of solutions and mix and match. This allows a new way to look at LLMs.

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Not just looping, you could do a parallel graph search of the solution-space until you hit one that works.

Infinite Monkey Theory just reached its peak

You could also parse prompts into an AST, run inference, run evals, then optimise the prompts with something like a genetic algorithm.

Agreed, this is exciting, and has me thinking about completely different orchestrator patterns. You could begin to approach the solution space much more like a traditional optimization strategy such as CMA-ES. Rather than expect the first answer to be correct, you diverge wildly before converging.

And then it's slow again to finally find a correct answer...

It won't find the correct answer. Garbage in, garbage out.

How about if you run this loop (one year from now) on this kind of hardware but with something like Claude/Kimi K2. How about that? Because that's where it'll go.

This is what people already do with “ralph” loops using the top coding models. It’s slow relative to this, but still very fast compared to hand-coding.

This doesn't work. The model outputs the most probable tokens. Running it again and asking for less probable tokens just results in the same but with more errors.

Do you not have experience with agents solving problems? They already successfully do this. They try different things until they get a solution.

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