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Ollaya – Ollama for open-source, Jev-style decision models

618 points · 145 comments · Ardakilic

  1. pradn · · focus · HN ↗
    I'm not sure what this means for AI startups if their innovations can be copied by OSS so quickly (what, like 2 weeks?). There's "consumer surplus" for everyone, to borrow an economic concept. But we do ideally want some of the surplus to flow to the innovator, too. I know there were precursors, but that's fine - it's hard to have a totally novel idea in such a popular field. I don't know what the end game is for TypeSafe - they'd need to demonstrate perpetually better results, or compete in another axis: UX, support, custom solutions, etc. So much of the time, someone proving a concept, or it simply getting enough publicity, is enough for a "Cambrian explosion" of follow-ups and copies. Famously, that was true for "Attention is All You Need", and the general idea of "next-token prediction" being so powerful.

    We've stumbled into general differentiable models..

    1. redox99 · · focus · HN ↗
      Because what they did is kinda trivial. Its basically like the Dropbox comment really[0], except here you don't need petabytes of storage and infinite VC pockets.

      After chatgpt everything in AI mostly became LLMs and building wrappers around them. It's like people forgot how to do ML.

      To those of us who actually trained models back in the day, its kind of cute to see people wowed by a classifier. Yes, this is 0 shot and doesn't need training (most people wanting this would've used structured output, this is cool because it's cheaper and faster). But anyone with basic ML knowledge could've built this in a few hours.

      The question is mostly why wasn't this productized. And it's interesting indeed that it took this long to become a finished product.

      [0] <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=9224">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=9224

      1. eadwu · · focus · HN ↗
        The answer for why it wasn&#x27;t productized might just be pretty straightforward.

        LLMs still are better than Jev at the task, just across the board slower.

        Anyone who had a reason to try this already tried it (ads&#x2F;recommendations) - back in 2023&#x2F;2024 during the first fine tuning wave and it was accurately determined that it was not worth the effort, the results were more bogus than just using CoT, so frankly parallelism meant nothing if bogus * parallel = bogus.

        So thrown into the dumpster and nobody really cared to revisit because it was already tried.

        Pretty much sometime between then and now it somehow became the state where the tradeoff makes sense now.

        1. wongarsu · · focus · HN ↗
          However in the last 2-3 years LLMs became a lot more efficient. Small models without CoT are still bad, but leagues ahead of where they used to be

          Maybe it&#x27;s just a case of the idea just now crossing the threshold into working just good enough to be worth it

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