I see a lot of hype around Jev and "decision-models" and I genuinely don't understand why.
From what I do understand about these models, there are some specific use-cases and some value about cost and accuracy.
But I don't understand why they are so revolutionary and why so many people seem (to me) to shoehorn it into anything they can.
I hope someone can explain to me what is so game-changing about it, considering I do understand most basic concepts but not too much of it regarding how the models actually work inside the box.
You mention a basic understanding but forgive me if I rehash things you already know:
Jev is good for simple fast decisions, in their own words - system one thinking. Anything that can be broken down into yes/no, a confidence %, or a set list of answers provided prior to the question. As you say, lots of specific use cases sure, but what's the big deal?
Well, right now most other models are purely system two thinking, big slow thoughts. Put an open ended question in, get an open ended answer out (plus hallucinations when confidence is low!).
When you ask ChatGPT or Claude a simple question, that system two thinking burns tokens and takes time to answer you.
Jev is a proposition to the whole ecosystem to make our current AI systems more efficient.
Going further than just the simple idea of "Get Jev to answer the yes/no questions, reserve Claude for bigger questions" is the concept that LLMs are making tonnes of their own questions as they go during their thought processes and agentic runs that Jev could be inserted as a tool call to speed it up.
You ask for research on a topic, Claude fans out and builds a list of possible resources, Jev selects the ones to pursue, Claude needs to determine if the user prompt was asking for something specific or broad, a joke or a serious query, deserving a long or short response, etc etc, it just gives Jev those options and asks it to pick. So no more having a big bulky frontier model making small decisions, Jev does it for a fraction of the cost and time and Claude gets on with the system two bits.
You can extend that logic moreso to coding applications where Jev could be essentially auto-completing basic work.
All the demos you are seeing now are people mucking about with the concept before we see OpenAI or Anthropic release an update.
Hell, maybe Opus 5.5 is doing some Jev-style stuff to be 40% more efficient.
1. Jev is generally useful as a classifier, out of the box, in many different situations. No training data, no training runs, it "just works" and makes it easy to integrate at points we always wanted, but never found the time to.
2. The hype machine hasn't had much since OpenClaw, I expect things to wane similarly with time.
But I don't understand why they are so revolutionary and why so many people seem (to me) to shoehorn it into anything they can.
I hope someone can explain to me what is so game-changing about it, considering I do understand most basic concepts but not too much of it regarding how the models actually work inside the box.
You mention a basic understanding but forgive me if I rehash things you already know:
Jev is good for simple fast decisions, in their own words - system one thinking. Anything that can be broken down into yes/no, a confidence %, or a set list of answers provided prior to the question. As you say, lots of specific use cases sure, but what's the big deal?
Well, right now most other models are purely system two thinking, big slow thoughts. Put an open ended question in, get an open ended answer out (plus hallucinations when confidence is low!).
When you ask ChatGPT or Claude a simple question, that system two thinking burns tokens and takes time to answer you.
Jev is a proposition to the whole ecosystem to make our current AI systems more efficient.
Going further than just the simple idea of "Get Jev to answer the yes/no questions, reserve Claude for bigger questions" is the concept that LLMs are making tonnes of their own questions as they go during their thought processes and agentic runs that Jev could be inserted as a tool call to speed it up.
You ask for research on a topic, Claude fans out and builds a list of possible resources, Jev selects the ones to pursue, Claude needs to determine if the user prompt was asking for something specific or broad, a joke or a serious query, deserving a long or short response, etc etc, it just gives Jev those options and asks it to pick. So no more having a big bulky frontier model making small decisions, Jev does it for a fraction of the cost and time and Claude gets on with the system two bits.
You can extend that logic moreso to coding applications where Jev could be essentially auto-completing basic work.
All the demos you are seeing now are people mucking about with the concept before we see OpenAI or Anthropic release an update.
Hell, maybe Opus 5.5 is doing some Jev-style stuff to be 40% more efficient.
My understanding is that
1. Jev is generally useful as a classifier, out of the box, in many different situations. No training data, no training runs, it "just works" and makes it easy to integrate at points we always wanted, but never found the time to.
2. The hype machine hasn't had much since OpenClaw, I expect things to wane similarly with time.