I am working on a game https://imperiaquiz.com/en that needs over 20,000 questions. I use jev to classify 'has statement' and 'can be a question' paragraphs/chunks taken from cli script to reduce token usage. Meaning, an LLM only starts work once I've chunked text and marked it as 'to be reviewed' instead of parsing the full content. This reduces tokens usage at least 10x on average

I see Jev as a major step towards commoditizing current LLM paradigm. One thing would be to further optimize this particular route to work purely on CPU. This will grant an option to embed this feature into any application, from MS Office to games. The other is integrating this into agent workflow to vastly minimize token consumption.

Not in a serious manner but I created a testing harness for a Nintendo 3DS game I'm making that uses the OpenAI Decisions API. The main advantage is the speed (~2-300ms per input) which I really need for this purpose.

Not with Jev, but you can use classifiers for a lot of use cases, here’s a few: https://playground.jeffyclassify.com/

Also like to see a "layman harness" like LM Studio integrate an open decision model in its workflow.

use it to classify a bunch of research papers (by feeding it section by section, or summaries of sections if too long)

works like a charm

(not a product, so not much to share)

I use Jev in a Claude code hooks to detect dangerous commands.