It's used extensively in my own agentic products. basically as a filter for various purposes, one example, to reduce the main agent's cognitive load.

there was a dynamic context loader (mixer) that combined results from embedding top k, regex filters etc and then there was a sub agent as a ranker to filter out the useless ones not matching the queries (wrong results from regex, and useless ones from top k etc). it was the job of a 5.6 luna(which worked very well ngl) but then replaced by jev.

Saved 5-10s in each round, no precision loss, and saved the bill a bit more. net win for me

Made a Jev-based daily guessing game that's like a crossover between Wavelength and Family Feud (or Family Fortunes if you're from the UK or Ireland).

It's called Vaguely Right (https://vaguelyright.com).

Each round the you get a spectrum and a target on that spectrum, (e.g. Calm ----X-- Stressful). You then have to name something that Jev will place on that target on the spectrum (a job interview, a walk in the park, a pelican riding a unicycle?). The closer you are, the higher your score.

I actually posted about it on Show HN earlier today: https://news.ycombinator.com/item?id=50071000

My one experiment with these models so far hasn't worked. I was hoping I could use them to find my "best" photos - most aesthetically pleasing, best composed, etc.

I tried using GPT-6 Astra Decisions since they accepts multiple image attachments, but it's not clear to me that the order of attachments is available to the model. I couldn't find a prompt for "which of these photos is best" that wasn't obviously affected by the order in which I attached the images.

[dead]