We have seen such an immense swell of demand that we have to temporarily pause signups for Jev. We need to ensure quality of service for our existing signups, which will continue to function. We are working diligently to ensure open access to Jev for everyone as soon as we can. Thank you.

We couldn't do what we do @typesafeai without our partners at @modal 🙏 thanks Modal team!

SO MUCH THIS I believe that one of jev's greatest benefits to automation will come from resurrecting architecture best practices: state management, encapsulation, abstraction !!!! and combining them with ML's best practices: measure/evaluate, use calibration/uncertainty (adding screenshot b/c I don't know how to quote 2 posts 🤦)

remember this old post of mine? Tried it with Jev---it seems to have a grasp of the globe on par with some of the best models a year ago due to the architecture & low cost, it was also feasible to extract a labeled map of continents and countries. lots of interesting details

@dotpem installing this as a plugin to our on-call pager

You've never routed like this before. @OpenRouter is bringing Jev to all of your LLM calls, so your agentic workflows never have to waste a token again. As always, faster, cheaper, more intelligent. Go build the future.

some of our biggest prod successes are in this regime - tune-able RAG

DSPy methodology 🤝 System One Program, don't prompt!

Jev is having a moment. Every platform rushed to host it the same way: another model endpoint in the catalog, and good luck wiring it into your agent loop yourself. We took a different route at DigitalOcean. In addition to Jev being available in our Inference engine, Jev is also available inside Action Gateway as a first-class classifier tool, right next to browser automation, and 16,000+ other tools behind one governed MCP endpoint. That distinction sounds small. It isn't. Treat Jev as a model and your agent's LLM has to know about a second inference provider, manage another API key, and hand-roll the request plumbing. Treat it as a tool and any harness you already run (Codex, Claude Code, LangGraph) just discovers it and calls it, same as any other tool in the belt. Ask typed questions: a yes/no, a choice from your own list, a score on your own scale. Get back decisions with calibrated probabilities, schema conformance guaranteed. Answers land as data your code uses directly, so agents route, filter, and escalate without parsing prose. The gateway part matters too. Jev calls ride the same governance as everything else: scoped permissions, credentials that never enter the model context, low-confidence answers flagged for a human, every decision in the audit trail. We ran a Codex agent through it this week. One YAML, one prompt, and it turned a day of X noise into a shortlist worth reading, with the uncertainty left visible. If you want raw inference, Jev is on Serverless Inference too, at $42 per billion input tokens with output free. But agents shouldn't have to treat a classifier like a chatbot. Models reason. Jev decides. Ship both. On the same platform.

On Sept 30 we’re working with @supabase to put on HYPERSHIP DAY on Product Hunt. If you think you can build and ship fast, and you haven’t launched recently, submit your launch by midnight Sept 30. We’ll be providing some cool prizes including Jev credits and swag! And if you’re not launching: Mark your calendars to try new products, make feature requests, and watch the products evolve over the course of the day. 🔥🔥

wrote up some thoughts about jev & data engineering after playing with it this weekend - feels like it's going to be big

Relevance is great, but relevance to what? Jev gives you search intelligence that no canned SEO can 🪱 its way into.

We're hard at work to give you dejevnerates what you want, signups still closed, stay tuned! 🏗️