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Linus Torvalds shares his thoughts on Anti-AI coding rules (europe.pub)
submitted 6 days ago by to c/linux@programming.dev
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[–] 10 points 5 days ago (4 children)

And the person you replied to is saying it is not just another tool because tools are deterministic.

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  • [–] -2 points 5 days ago (3 children)

    Except they're not? Very few tools are deterministic. Only Mathematics is, and that's only a small subset of tools.

    You're free to argue that non-deterministic tools lack value, but that's clearly false: OCR only exists because of lossy training machine learning algorithms. Or, going even more basic, Monte Carlo simulations are by far the most efficient method of testing entire categories of problems that are intractable deterministically.

    LLMs can be shitty, as a business practice, due to their unpredictability, but (hopefully obviously) that depends on how it's used.

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  • [–] 5 points 5 days ago

    I'd argue that Monte Carlo is a bad example here, and is an example of a deterministic tool.

    You use a tool to create an outcome, you're looking for an answer or a solution that you can trust.

    If you want to examine behavior of a system that has input variables across some known range, you could brute force it, try every single possible combination and wait a millennia. Or you use a monte carlo strategy, let randomness enter the simulation to build out a basic characterization. You want more detail? More confidence? Increase the number of runs. I can trust that if I increase the number of runs I gain higher fidelity, if the tool/strategy was non deterministic I couldn't do that

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  • [–] 2 points 4 days ago

    The point is a tool is something you wield. Your skill is needed for and improved by the use of a tool. An LLM is an automation. You don't wield it, you give directives. Your skill is unnecessary for its use and is left untrained. The worst part is that, unlike a good, deterministic automation (like cruise control), where you understand the limitations and can take over to avoid them, you can't do that with LLMs. The best you can do is carefully review the slop output.

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