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Introducing System One Models and Jev

1989 points · 520 comments · albelfio

  1. jacobgold · · focus · HN ↗
    First, congrats to the team on launching something genuinely interesting and new.

    Seems like a more accurate title would be "Jev: Trading general purpose generation for fast typed inference" or something like that.

    This is interesting, but the speed comparison seems misleading? A generative model that can output code in a Turing-complete language can do anything a computer can do.

    Jev can only generate structured output, right? This is probably super useful for classification/routing/scoring, but it's nothing like the code generating models we're all using today for code and automation.

    Also "can't hallucinate" seems wrong? Sure, it can't emit an invalid type, but it can still emit a completely wrong valid value. You can enforce structured output from an LLM too, with an appropriate harness, etc.

    Assuming there's no funny business, the Doom demo is cool.

    1. dbbk · · focus · HN ↗
      When they say "can't hallucinate" they mean they produce a confidence value for every result, so you could see for example it has 0.1 confidence, and you can disregard the result - that'd be different from hallucinating where it believes it's correct
      1. bradly · · focus · HN ↗
        What about the LLM calls though that are done midchain? In the Home Assistant video the multi-intent prompt gets split using what looks like a traditional llm model, which I'm assuming is vulnerable to classical hallucinations.
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