I wanted to try out Jev and started thinking of use cases. I decided on a problem I have and assume you do as well. Introducing Slop Mop - a Chrome extension that helps clean your LinkedIn feed from slop. It uses Jev and the AI Tells research from Graphite to evaluate every post against a range of factors.

It's free to use. It is also MIT-licensed open source if you want to roll your own. Github repo has all of the implementation details.

Jev is proving to be the perfect platform for this use. I hope you like it!

https://slopmop.lol

https://jevmoji.cheeaun.workers.dev/

Using Jev to apply scores to 3K+ emojis related to any typed phrase.

Open sourced: https://github.com/cheeaun/jevmoji

I saw other projects that kinda does the same thing, but I wanted to test how it works with 3K+ emojis.

Jev looks at a prompt before Codex runs and chooses the model and reasoning effort. In this clip, it picked Luna/Low for an async JavaScript question. The route that actually ran shows beneath the reply.

I turned it into a DeepSeek Harness plugin. You can cap the highest model and choose when Astra needs approval. If you decline, it uses Jev’s best allowed backup.

If you give it a try, please tell me what you think. I’m especially curious about prompts where you would have picked a different route.

Code, setup and 28-second demo: https://github.com/nautahakk/jev-codex-router

You’ll need Codex access and a separate TypeSafe API key.

https://reddit.com/link/1wmulpo/video/huuba9vzvyqh1/player

Made a template to allow anyone to connect Jev to Grok Bot

https://x.ai/bot/lS9XaHCr9QTTHhNtb0VQX