Thread

1. amluto · 1790881961 · focus · HN ↗

   I’ll go out on a limb and suggest that I don’t think a Jev-like model is particularly useful unless you can fine tune it. The Jev API has zero ability to pass in a prior [0], and, if you can neither pass in a prior nor fine tune for your system, you will get an output that may be almost meaningless.

   I’d love to see someone build a model of this sort that can actually accept priors and do something intelligent with them.

   [0] You can feed Jev a prior as text. I’ve tried it. It works poorly.

   1. okpatil · 1790882206 · focus · HN ↗

      We were able to completely automate 20,100 token prompts with At0m[<a href="https:&#x2F;&#x2F;at0m.pienomial.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;at0m.pienomial.com&#x2F;].

      We believe entire compliance workflows (even multilingual) could be automated.

      Would you like to get a demo ?

      1. sheepscreek · 1790883118 · focus · HN ↗

         You’re coming on a bit strongly - a couple of comments with a link is sufficient. Before trying to sell, try to genuinely further the conversation, provide some useful knowledge in return for the reader’s attention.

         1. okpatil · 1790884510 · focus · HN ↗

            Point taken. Let me explain if you allow me.

            It is possible with deterministic decision models, such as At0m, to gauge the probabilities at every decision. This behavior in addition to hard coded logic, it is possible to completely replicate a prompt&#x27;s logic.

            Using Fable 5.1, it is a matter of minutes.

            I believe that most of the compliance check documents will be a solved problem, 3-6 months in future.

            None of the LLMs can do it.

            Hence I asked to the comment poster if he would want to demo, so that I can show it to him, how to do it step by step. By bad, if it came out too strongly.

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