A single function Jev-like wrapper for LLMs, including vision models

Now this is how[0] we get some of the most magical Star Trek technology that eludes us to this day, such as sliding doors. Because if you notice, they work much, much better than real-life ones, because they seem to be doing something like this:

  if(within 10 meters of door then) {
    if(Jev(
       [A] Intends to go through the door
       [B] Passing by, loiters, or otherwise
       [C] Other
    ) == most definitely A) {
      // open doors, +/- identity/security check
    } else {
      // ignore
    }
  }

Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer. The computer seems to be aware of the user and surrounding, and actively infers intent from context, to DWIM ("do what I mean") and when they mean it, instead of doing dumb things[1] on simple triggers.

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[0] - The direction, not final implementation - surely we can work out how to do it more efficiently than wrapping around final stage of LLM. But the point is, multimodal.

[1] - Obviously it's a fictional show, but in this, both Watsonian and Doylist perspectives align: this is/portrays advanced technology, that Just Works and doesn't do stupid shit. Same intent recognition algorithm is there - fictionally in the computer, in reality in the minds of set operators.

Nice! I would love to use it for images as well. Then again is using Grammar-Based Decoding with a json response not the same? Is Jev just that with nice caching? Because then I have been using that already…

Ah sweet it’s like Jev but several order of magnitude more expensive, and slower too.

Likely works even better with fireworks ai since they have proper grammar support

[A] Hotdog

[B] Not a hotdog

Of course it works, Jev is nothing but an API breakthrough

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