GrapheneOS (@[email protected])
grapheneos.social
@[email protected] Linux itself is heavily using AI models. Therefore, every Linux distribution moving to recent kernel versions is heavily built on top of using AI models. Creating a Linux distribution not heavily using it would require a hard fork of the Linux kernel and many other projects. It's unclear what would be accomplished by banning AI for a tiny portion of the code while continuing to use Linux, AOSP, Chromium and hundreds of other projects heavily using it. We'd still be benefiting from it.

GrapheneOS is currently defending its use of AI coding tools on Mastodon against complaints by various accounts claiming to be users.

We do not understand where you’re coming from or why you’re so incredibly angry with us. It’s not justified and does not make sense.

  • Spice Hoarder@lemmy.zipEnglish
    0·
    23 hours ago

    LLM is just fine for next text prediction, in the same way my phone keyboard can sometimes guess what I’m about to say. But the notion of building this huge mass is where I draw the line, it’ll never be finished. Does overfitment mean nothing to anyone anymore?

    • droopy4096@lemmy.caEnglish
      0·
      21 hours ago

      your points support my statement though: how vs what is the real question here. LLM is fine performing repetitive mundane tasks that we find boring and uninteresting. Like refactoring small chunks of code out or “search and replace” when context matters more than matching string etc. Also, let’s assume someone had enough bitcoin mining gear laying about to repurpose it into LLM training infra and trains his/her small model on stuff they care for, suddenly we lose all the other arguments and use of LLM becomes more acceptable. So blanket ban at this point is not going to achieve any of the goals, really. Consider that majority of developers were mandated to use LLMs for work, consequently their skill shape changed, for project to draw from that pool of developers you need to offer comparable tools.

      Context matters too: Graphene has to deal with AOSP, which lost it’s development transparency a while ago and Graphene developers likely have to contend with big (likely LLM-generated) code dumps within mainline AOSP with each new release, so to keep up with that onslaught they either need to expand developer base (and we just said that pool is shrinking) or leverage LLM to cut through that mess. Again “how” tool is used matters a great deal.