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> The entire point of LLMs is that you don't have to spend money training them for each specific case.

I don’t agree. I would say the entire point of LLMs is to be able to solve a certain class of non-deterministic problems that cannot be solved with deterministic procedural code. LLMs don’t need to be generally useful in order to be useful for specific business use cases. I as a programmer would be very happy to have a local coding agent like Claude Code that can do nothing but write code in my chosen programming language or framework, instead of using a general model like Opus, if it could be hyper-specialized and optimized for that one task, so that it is small enough to run on my MacBook. I don’t need the other general reasoning capabilities of Opus.

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Why would you think a system that can reason well in one domain could not reason well in other domains? Intelligence is a generic, on-the-fly programmable quality. And perhaps your coding is different from mine, but it includes a great deal of general reasoning, going from formal statements to informal understandings and back until I get a formalization that will solve the actual real world problem as constrained.

> I don’t agree. I would say the entire point of LLMs is to be able to solve a certain class of non-deterministic problems that cannot be solved with deterministic procedural code

You are confusing LLMs with more general machine learning here. We've been solving those non-deterministic problems with machine learning for decades (for example, tasks like image recognition). LLMs are specifically about scaling that up and generalising it to solve any problem.




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