I don't understand how this is different from oai "structured output" (and whatever the similar paradigm was on Sonnet ~3.7 back then) which everyone moved on from. On their gh they say:

"Jev is TypeSafe's closed service for runtime-defined semantic decisions. This project reproduces that interface pattern with open models; it does not reproduce Jev's undisclosed model or training"

As someone else pointed out it isn't actually Jev... can someone enlighten me

Jev is, as far as I understand, essentially very optimised for zero shot classification [0]. Something like BERT could be and has been tuned to provide similar "decision making" at a similar latency and cost advantage quite some time back. Advantage over full on LLMs is mainly the efficiency and of something like Jev over e.g. the encoder/decoder based classifier I had in front of an LLM to route to different prompts depending on the users likely needs, that Jev does perform at a more consistent level, allegedly roughly akin to GPT-5.6 Terra, but at the lower cost and latency. Currently testing that, but seems promising, if Jev classifies at or above Terra level, I see no reason not to leverage it.

Can add that I tried using a heavily pruned mt0 based model for structured classification along with structured output for local tagging and simple renaming suggestions. While it does work, the balance is hard to get right for the machine I was targeting as a minimum spec (Macbook Neo), so that's on ice. Focusing on one of the tasks easily goes below 100mb with solid latency across all EU Latin script languages, but the second you add a few, it's simply not in the quality budget, so while LLMs can do anything Jev and similarly focused models can, it comes at a literal cost. Could maybe accomplish the goal with multiple models (BERT+mt0+...), but that get messy.

In general just happy to see a bit of the millions flooding into the industry being used to improve on less flashy but immensely useful solutions. It's amazing that you can technically use LLMs for most tasks, but not every org has a near infinite budget and there is still a lot to gain from applying more recent learnings to old solutions along with just updating their training data to the current year. Also makes business sense, competition on frontier or mid-tier LLMs is vicious, focusing on an underserved niche with clear application is clever.

[0] https://huggingface.co/tasks/zero-shot-classification

It's a non-instruction-tuned classifier model trained on a confidence-aware RL variety that generates its own schema and follows it, with a confidence score output. Think BERT on crack, smart enough to be used as a decision maker (conceptually). They call it "not an LLM" because it's non-generative but of course it's a language model in the same way all non-instruction-tuned classifiers are.

In Jev you pass options in the input and its output just gives some probability for each. Oai structured output just follows a schema. The exact output is still generated and there is no probability

in short: it's faster, cheaper, smart structured output.

each "question" is answered in parallel instead of a sequential (like an LLM). so if you have an input like:

    {"is_it_hotdog": noul, "is_it_apple", noul}

I'm in the middle of moving my app to openAI structured output.

Could you please explain what you mean by "which everyone moved on from"?

No idea at all what this particular project called openjev is doing, but https://github.com/TheoLeeCJ/openjev and https://github.com/ekzhang/openjev-sglang (neither of which I have any relation to) generate a single token, rather than JSON structured output. I wrote up this technique here: https://sgnt.ai/p/jev/