> what are the use cases for this kind of model? Could it be used in the context of coding agents

Yeah, it could. The most obvious usage would be to have local fast cheap "feedback" / "control" over a slower more expensive agent (i.e. cc / codex / opencode). Things like "goals" could now be split from a long prompt into "actions" and "verifiers". Where for each action you also produce a verifier. Then after each action you run the verifier w/ this kind of "universal classifier" and decide if the step was done correctly, if it needs follow-up and so on.

Example: implement auth in this repo -> llm_plan() -> for item in plan generate_verifier() -> for item in plan implement() ; verify() ; accept() / followup().

Verifiers could be something like this. take a plan item as input, generate classification questions that might verify the task "is this following project conventions?" | "is this touching files from other tasks?", etc.

You can do that with LLMs, but some things might become cheaper / faster. And you can pretty much use it to check against an ever growing list of conventions. Yours or project specific.

Consider every situation where you "force" an LLM to output only a choice / category, or a set of them. If you have workflows like that, you're now being promised significant cost- and latency reduction.

For coding agents it'd only be useful in a subset of situations. E.g. you could imagine using one to classify bash tool calls into safe and unsafe for example.

To develop a smart ai system for my 2d roguelike platformer? game has way too many moving system for classic state-machine ai + i cant spare the time to develop it. its low latency entices me.

You should call Jev-like models when you give it a JSON-like structure to produce, it is useful when you need _some_ intelligence in your code.

Edit: I want to add that you can see Jev like a smart if-statement.

Yeah same. Got access to their API and then realised I don’t really have an immediate use case