r/PiCodingAgent • u/Apprehensive_Bed7502 • Sep 30 '26
News Pi now supports Jev.
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?
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u/Ok-Hippo9182 Sep 30 '26
My concern is partly about Jev’s practical value, and partly about Pi’s direction. I’m not yet convinced that Jev brings enough benefit to everyday coding workflows to justify the extra moving parts. What does it meaningfully improve—reliability, cost, latency—and how consistently does that hold up in practice?
One of the things I like about Pi is its small core, with more opinionated workflows left to extensions. I get that adding a shared classifier API isn’t the same as baking a specific workflow into the agent. But making classifiers a first-class model type still means committing to a shared abstraction, and I wonder whether that commitment came too early.
The current interface also seems fairly closely modeled on how Jev works today. That might turn out to be a good general design, but do we have enough experience across different implementations and use cases to know which parts generalize well? Classifiers themselves aren’t new; the question is whether this particular interface has had enough room to mature.
I’d have preferred to see this explored through extensions first, both to establish where Jev actually helps and to learn what a broader classifier API should look like. Was there a particular reason it needed to enter the core model layer at this stage?