You could use this approach with DeepSeek as well. The innovation here is that you can generate a bunch of solutions, use a small model to pick promising candidates and then test them. Then you feed errors back to the generator model and iterate. In a way, it's sort of like a genetic algorithm that converges on a solution.

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eru an hour ago | parent | next [-]

Why do you need a small model to pick promising candidates? Why not a bigger one?

(And ideally you'd probably test first, or at least try to feed compiler errors back etc?)

Overall, I mostly agree.

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hu3 6 hours ago | parent | prev [-]

Indeed but:

1) That is relatively very slow.

2) Can also be done, simpler even, with SoTA models over API.

	▲	yogthos 6 hours ago \| parent [-]
		Right, this works with any models. To me, the most interesting part is that you can use a smaller model that you could run locally to get results comparable to SoTA models. Ultimately, I'd far prefer running local, even if slower, for the simple reason of having sovereignty over my data. Being reliant on a service means you have to share whatever you're working on with the service, and the service provider decides what you can do, and make changes to their terms of service on a whim. If locally running models can get to the point where they can be used as a daily driver, that solves the problem.