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You can map tokens to CPU instructions and train a model on that, that's what they do for input images I think.

I think the main limitation on the current models is not that cpu instructions aren't cpu instructions (even though they can be with .asm), it's that they are causal, the cpu would need to generate a binary entirely from start to finish sequentially.

If we learned something over the last 50 years of programming is that that's hard and that's why we invented programming languages? Why would it be simpler to just generate the machine code, sure maybe an LLM to application can exist, but my money is in that there will be a whole toolchain in the middle, and it will probably be the same old toolchain that we are using currently, an OS, probably Linux.

Isn't it more common that stuff builds on the existing infra instead of a super duper revolution that doesn't use the previous tech stack? It's much easier to add onto rather than start from scratch.

by mikestorrent2 hours ago|

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Those CPU instructions still need to be making calls out to things, though. Hallucinated source code will reveal its flaws through linters, compiler errors, test suites. A hallucinated binary will not reveal its flaws until it segfaults.

by small_model1 hours ago|

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Programs that pass linters, compile and test suites can still segfault. A good test harness that test the binary comprehensively can limit this. The model could be trained to have patterns of efficient assembly it uses rather than source code.