Proposed KDE LLM guidelines (#187) · Issues · Plasma / Plasma Workspace · GitLab
invent.kde.org
This is a continuation of this mailing list thread about "fully LLM-generated merge requests, preserved at https://mail.kde.org/pipermail/kde-devel/2026-September/004496.html There, I proposed...
  • Victor@lemmy.worldEnglish
    61·
    24 hours ago

    You say Luddite as if that’s a negative thing.

    They stood up for not being replaced by machines.

    The reality of using LLMs to do the actual work (which is the real lazy part), is that yes, you can get more shit done in a shorter time, especially when you know what you’re doing.

    But the reality is also that employers don’t look at this and go “oh, nice, we can get 10x more things done in the same time now”. They go “oh, we can get the same stuff done in the same time but for LESS MONEY,” meaning layoffs.

    That’s one of the many ethical arguments I have against using AI to code. 👍👍

    • 9tr6gyp3@lemmy.worldEnglish
      1·
      23 hours ago

      Whats your viewpoint on open weight models that run on local hardware only? Such as a gemma4 or qwen3.6 models that can run on pretty much any gaming PC?

      • Victor@lemmy.worldEnglish
        5·
        21 hours ago

        🤷‍♂️ You do whatever you want, but I would want to ask myself:

        • what/whose is the training data?
        • will this be in production or is it a PoC or other learning experiment?
        • will this code be published? If so, which license?
        • am I getting worse at what I do by using it? Will I be able to create the same thing without the LLM with unlimited time? Am I learning or getting dumber/stagnating?

        Things like that plague me, and dissuade me from using AI in the first place. I don’t want to become dependent.

        • 9tr6gyp3@lemmy.worldEnglish
          11·
          9 hours ago

          Those are good things to ask prior to using it, but thats just the beginning.

          • If the training data matches what you would look up on the internet anyway, is it a deal breaker?
          • If it cannt be used for production, what about for organizational purposes? Documentation? Resource tracking? Upstream changes? Merging considerations? Security auditing?
          • It sounds like you would rather do the coding part, correct me if I’m wrong. This way you have full control of the license. For someone who isn’t a coder, they would probably have to consider that the code it generates could closely resemble an existing licensed work since the models are trained on open-source data.
          • I think the getting dumber argument is going to depend on the user, and its not an LLM issue specifically. You could definitely bypass the thinking process, but you could also make it explain things step by step, which could also accelerate learning stuff.

          But you’re right, to each their own.