That's the push factor. The pull is, say there's a giant repo like windows or Adobe. LLMs produce spaghetti code because the repo doesnt fit in the context and it wasn't in the training set. If Adobe fine tuned it the LLM would be much more useful.

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jacquesm 4 hours ago | parent [-]

I'm running a max of 1M context locally. I think the next real move will be continuous learning/infinite context. That will be another game changer.

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throwaway85825 3 hours ago | parent [-]

Infinite context requires Infinite memory.

	▲	jacquesm 3 hours ago \| parent [-]
		It wasn't a theoretical thing, it was a practical thing. Just like when we talk about 'infinite tape' when looking at Turing machines. As opposed to 'clear boundaries to how much context you can have because that is baked into the model'.