Over the past few years, the evolution of AI-driven tools like GitHub’s Copilot and other large language models (LLMs) has promised to revolutionise programming. By leveraging deep learning, these tools can generate code, suggest solutions, and even troubleshoot issues in real-time, saving developers hours of work. While these tools have obvious benefits in terms of productivity, there’s a growing concern that they may also have unintended consequences on the quality and skillset of programmers.

  • beeng@discuss.tchncs.de
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    2 months ago

    You write machine code?

    No, you only describe what you want the compiler to write in machine code.

    With copilot it’s still a description.

    • Snarwin@fedia.io
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      2 months ago

      If the compiler produces a program that doesn’t match your description, you can debug the compiler. Can you debug an LLM?

      • beeng@discuss.tchncs.de
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        2 months ago

        Why wouldn’t a compiled program match your description (code)? The compiler is broken?? Compiled programs alwsys match their description(code).

        So more likely your translation from idea to function is wrong.

        Re-read your description, step through it slowly, what did you assume, that was wrong, or where did you add a mistake or typo? Sounds like I can do this in natural language or in Rust.

        You can say that llms are not deterministic of what they produce, but that’s got nothing to do with making a programmer worse at their job.

        If you can’t translate your idea into function and test its output to be what you want, then you are a bad programmer.

    • Ethan@programming.dev
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      2 months ago

      Copilot frequently produces results that need to be fixed. Compilers don’t do that. Anyone who uses copilot to generate code without understanding how that code works is a shit developer. The same is true of anyone who copies from stack overflow/etc without understanding what they’re copying.

      • beeng@discuss.tchncs.de
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        2 months ago

        You’re missing the point. If the program doesn’t do what it’s meant to its YOU that didn’t use the tools between you and metal, correctly. LLM involved or not, it’s how you’ve described it, in whatever ‘language’ you chose (natural or Rust)

        • Ethan@programming.dev
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          2 months ago

          The key difference is that compilers don’t fuck up, outside of the very rare compiler bug. LLMs do fuck up, quite often.