Yup, I’m posting another this week. Sorry.

This week I’m hoping we can wrangle a solution around AI and our selfhosted community. There are plenty of strong opinions (both pro and con), but one thing is for certain - there needs to be better disclosure in promo posts. Two options (that aren’t mutually exclusive):

  • Any posts of an AI focused, AI Developed, etc software gets an [AI] tag. No, a [Not-AI] tag is not needed to accomplish this, thats kind of a “non-golfer” sort of tag.
  • Comment requiring an AI disclosure response to every promo post, if its not detailed in the post itself. Specifics (generating docs for commands, translation, whole-boat vibe-coded this app, etc) would be requested.

I will say that having disclosure and/or tagging would mean that comments that just say “slop” or “fuck ai” or whatever would be off topic at that point, that information is already provided, so its just noise (and sometimes pretty uncivil - I’ve been light on that for now due to the need for a rule on this).

The tag [AI] would make it easy to filter out (or search for, if that’s your thing), but there is a wildly different degree of AI use out there, and from the posts with a positive score, its usually due to responsible AI use (translations, a snippet they had to do something obscure with, available to use with AI but doesn’t require it, whatever), which is why I think the disclosure has a place as a benefit to everyone.

Please provide any input or alternative options on this, and I can then put it to a vote like the last one. Comments seem to be the best approach without involving something off-site, but if you have a better idea/option, please share.

  • Scrubbles@poptalk.scrubbles.techEnglish
    121·
    1 day ago

    I like the AI tag idea. I’m someone who has what I’d call a noderate approach to AI, not an AI bro but any means but I’m also okay with some things built with AI if they’re done with care. If others don’t want to see it, fine, then that’s what a tag could be useful with. However the fuck AI/slop comments on something that admits to being AI is annoying to me. (We know it’s AI, they literally said it is).

    If it becomes too much content, then yes would be okay with bi-forcating the community, buy only after it becomes a problem.

    • brucethemoose@lemmy.worldEnglish
      10·
      1 day ago

      I’m not consistent about it yet, but because of exactly this, I’m trying to differentiate the two when I talk.

      Responsible automation? I use ML or machine learning.

      The grift consuming the world? A Tech Bro? “AI”

      I think one of the saddest things is the conflation between the two, like you can’t even talk about one without invoking the other. Or it opening up that whole ethical debate, when you’re just talking about, like, a 100M transcription model trained by one research in some university on a potato.

      • Scrubbles@poptalk.scrubbles.techEnglish
        2·
        12 hours ago

        Yeah it’s heresy on Lemmy, but I do find it genuinely useful. My only regret is that I have to use Claude/Anthropic more than I’d like, which is why I have a vested interest in selfhosting myself. I’d rather figure out how to run the larger models myself and cut them off completely, but you even begin to mention that here and you’ll get downvoted to hell.

        • brucethemoose@lemmy.worldEnglish
          3·
          6 hours ago

          You don’t even need Claude anymore. GLM 5.2 API is good enough for 95% of the same things and vastly cheaper.

          MiMo 2.5 Pro and Kimi are also very good. And then there’s Cerebras API if you just want simple things done quick.

          The thing with self hosting, while awesome, is that it requires a lot of hardware and considerable time investment for what’s essentially a “base tier model,” or at best one step down for what’s still a very cheap API. I still love it, especially the privacy and control aspect, but you aren’t running Claude at home unless you’ve got a threadripper or server hardware collecting dust.

          …Hence I can understand why people don’t pursue it. Especially since a cursory Google search will lead you to trying the Deepseek distillation on Ollama (which is awful).

          • SuspiciousCarrot78@aussie.zoneEnglish
            2·
            43 minutes ago

            What Ollama did what that distill is shameful.

            For those not in the know: they took a small, 8B model with Deepseek fine tune (Qwen3-8B iirc) and claimed it was the 400+B param Deepseek.

            They essentially tricked folks into thinking they were running a near-peer SOTA model at home when in fact they were running a small language model (SLM) with crippled settings (again, iirc, ctx -4096 by default).

            Lying via obfuscation is still lying.

          • Scrubbles@poptalk.scrubbles.techEnglish
            1·
            4 hours ago

            That’s where I am okay with hardware, but can’t seem to fit the models on my 3090. I have dreams of something like an A100 someday, but not until there’s a ton of used ones that hit the market. What do you use for your hardware?

            • brucethemoose@lemmy.worldEnglish
              1·
              2 hours ago

              I have a single 3090!

              That’s the dream GPU, these days.

              And I have 128GB CPU RAM. So the best model I can run is MiMo 2.5 (a 300B model) at around 10 tokens/sec, using hybrid CPU inference.

              …But that’s the worst-case scenario, for speed. It’s an IQ3_KT quant (a high quality “trellis” quantization type, but very slow on CPU), with a gigantic model that barely fits in my RAM+VRAM combined, with no DFlash or any kind of speculative decoding turned on. I could tune it to be much faster, but I mostly just want “max quality, fast enough to read as it streams, barely fits in memory” for this model.

              For speed, or prompts with lots of thinking or context (like agenic use), I just run Qwen 3.6 27B now. That would fit in your 3090 no matter how much CPU RAM you have, but you have to be smart about the backend and quantization you pick. If you just use Ollama, it’s gonna tell you it won’t fit, or use some horrible default that spits out garbage.

              …This is what I meant to emphasize.

              It’s not just the hardware. You kinda have to be part developer, part enthusiast to even follow this stuff, it up optimally, and keep it up-to-date. If you just try to Google “best LLM for 3090,” you will get absolute garbage.