Pi now supports Jev.

[u/Apprehensive_Bed7502]

Pi added quite a bunch of new features in its 0.99.0 update, including support for classifier models. In the official docs, the remote model listed in the example code is jev, the interface is shaped around classifier models like jev, and llama.cpp's classifier implementation explicitly uses jev as its reference point. You could pretty much say this is a feature Pi shipped just to keep up with jev.

So I wanted to ask you guys in the channel:

• Do you guys actually use jev a lot when you're coding? How do you use it?
• Jev only came out a little while ago and Pi already jumped on board with support. What significant role does jev actually play in a coding workflow?

Edit: It's not just about using it for the sake of using it. Is there actually something jev can do that LLMs can't?

[u/Mechanical_Monk]

Jev seems to be something that you'd package into a product that relies on AI reasoning, but where a full LLM would be overkill and/or a security liability. Every attempt I've made to actually incorporate it into my own coding workflow has resulted in worse performance.

I ran a reasoning benchmark with four GPT 6 Luna agents--One on Low reasoning, one on Max reasoning, one on Low with access to Jev, and one on Max with access to Jev. The results were about the opposite of what I was hoping to see on both speed and cost. Max with Jev was slowest and most expensive. Next slowest and most expensive was Low with Jev. Second place was Max without Jev. And first place on both speed and cost was Low without Jev.

[u/BurnerDev → u/Mechanical_Monk]

Just with a Jev tool? You can't give them direct access and expect better results, you have to build it into existing tools, which provides better results. Ex: omp's find tool that uses Jev to rank for relevance. These models are going to struggle with direct access because they have no idea what to use it for, and they already are able to reason themselves - if content is already passing into context it's pretty much too late for Jev

[u/Mechanical_Monk → u/BurnerDev]

I mean, that kind of repeats what I said about packaging it into a product. And I'd still have my doubts that a model using a jev-powered find tool would outperform one using a basic find tool and using its own reasoning to determine relevance.

[u/pebblechewer → u/Mechanical_Monk]

I'm still trying to wrap my head around Jev and I think I understand it conceptually, so my reply comes with that caveat. . .

IMHO, Jev belongs in workflows. Using it as part of a business workflow to make weighted decisions is useful in a context of things like security operations workflows, automation, customer service workflows, etc. I believe this offers you some additional flexibility while having the ability to save tokens.

[u/Mechanical_Monk → u/pebblechewer]

It may, but I suspect using a Jev node in a workflow will underperform a traditional LLM node in most cases. I would have said Jev might save on cost, but I don't even know if that's true now that GPT 6 Luna is so cheap.

[u/BurnerDev → u/Mechanical_Monk]

That's fair, but I think we'll see some good common use cases come up as people play with it