Birbla

Join the discussion

Write your take first — we'll ask for email only when you're ready to publish.

Sign in
  • Hacker News
  • If you rather run your decision model on your CPU, check gutsy [0]

    [0] - https://news.ycombinator.com/item?id=49976996

    by mrkn1
  • Different APIs for different things reminds me of the early auto-complete vs instruction apis.

    Will this get folded into models / post training pipelines at some point and make them better at calibrated outputs?

    by jasonjmcghee
  • Jev really shook up the industry. This seems obvious in hindsight
    by sidcool
  • Just going to drop this here: https://jeffyclassify.com/

    Open source classifier models you can run and train locally on CPU

    by nico
  • Ran my decisions evals (still rudimentary, less than 600 calls (UI component selection, chat charting, tag selection, PKM stuff)) on this via OpenRouter against Jev and Mercury Decide. Jev because it has replaced my mt0 efforts by sheer force of affordability (more importantly, the limits running on a MacBook Neo bring even after vocab pruning and quant insanity) and Mercury Decide because I do like dLLM efforts (and I'd like to use fewer model providers if possible).

    Preliminary of course, but seems to be slower than Jev and similar to Mercury Decides latency, though not in growing linearly with the amount of input (346ms p50 and 860ms p95, (Mercury Decide also had some extremes up to 1,3s that were around 800ms today, likely preview related, it scaled far more consistently with size)), less "confidence" concerning my ambiguous UI component and response shape specific tasks (have very specific use cases for these models which Luna often fails to meet at 0.6 and lower), lead to a few failed calls which neither competitor had (4 vs 0 for both) and measured more expensive than Jev to boot by a factor of 3,1 times on average (Mercury Decide pricing I think is still unknown so no numbers there).

    Basically slower, more expensive and less capable than Jev, roughly on par with Mercury Decide (provided, in my insane set of use cases and requirements that are a PKM focused Firefox fork with multiple infinite canvas using decision models to improve information synthesis from multiple sources).

    Seems a bit undercooked overall and I'd rather frontier-labs don't jump on bandwagons until they can offer something competitive in price, performance or both. In fairness, though, I have yet to test image input, maybe that makes all the difference. Also, again, mine is unlikely to reflect everyones use case, so interested in seeing others results.

    Didn't comment at the time, but having read up on Devday after the fact, there seems to have been a lot of that going around. Notion and GDocs, Jev, Muse, most seems to have been cloned from existing competitors (and despite infinite, ultrafast, ultra code tokens with unsandboxed Mega Astra not that amazing to boot).

    Prefer less announcements, but focused and at a higher quality. Considering ChatGPT Atlas (their Chromium based browser) and its insanely fast death, I'd be skeptical to put much into any of these even if they were in some way an improvement over what is out there. Maybe focus on a fresh pre-train and some sandboxing improvements.

    by Topfi
  • If we compare this with using the older solution of writing a prompt to find out the answer of the classification

    - Cost : It is the same for both scenarios $0.10 per 1M tokens

    - Speed : decisions is 10x faster than responses API

    - Quality : I guess if we compare with luna which is a pretty good model it itself, both will be at par

    So essentially it has to do more with speed vs any other factor.

    by ashu1461
  • The response to Jev should be the nail in the coffin over whether or not the AI business is a commodity market.

    Out of no where Jev appeared as the next round of the price wars. Jev showed the value of System One models. A fast yes/no/confidence score not only is cheaper but also often all people want. Open source versions flood hugging face and now the big players are giving up a potentially big driver of output tokens to keep customers and race to the bottom price wise.

    If I were OpenAI or Anthropic I’d be racing to make their products as sticky as possible bc ppl will flock to what’s cheapest otherwise.

    by TSiege

  •   curl https://api.openai.com/v1/decisions \
    -H "Authorization: Bearer $(llm keys get openai)" \
    -H "Content-Type: application/json" \
    --data '
  {
    "model": "gpt-6-luna",
    "input": [{
      "role": "user",
      "content": [
        {"type": "input_text", "text": "I am angry about the new product feature"}
      ]
    }],
    "questions": [{
      "type": "predicate",
      "name": "complaint",
      "instructions": "Is this a complaint?"
    }, {
      "type": "predicate",
      "name": "compliment",
      "instructions": "Is this a compliment?"
    }]
  }'
    Returned:

      {
    "model": "gpt-6-luna",
    "answers": [
      {
        "type": "predicate",
        "name": "complaint",
        "probability": 0.91
      },
      {
        "type": "predicate",
        "name": "compliment",
        "probability": 0.06
      }
    ],
    "usage": {
      "input_tokens": 310,
      "input_tokens_details": {
        "cached_tokens": 0,
        "cache_write_tokens": 0
      },
      "output_tokens": 0,
      "output_tokens_details": {
        "reasoning_tokens": 0
      },
      "total_tokens": 310
    }
  }
    That https://api.openai.com/v1/decisions endpoint is notable because usually when OpenAI define an endpoint like that it ends up as a defecto standard for other providers.

    (I turned this all into a new llm plugin: https://github.com/simonw/llm-openai-decisions)

    by simonw

Explore Birbla archives