What people are shipping with Jev.

Has anyone tried using Jev inside a speculative decoding pipeline — e.g. generate many candidate continuations cheaply, use Jev to rank them in parallel, and only have the large LL

Just solved one of the biggest legal questions of the past 30 years using Jev. Turns out the Juice didn't do it after all https://t.co/tyAYKjso5W https://t.co/v0pRD35L7F

Muse can go through all of Facebook and Instagram to find potential customers for your products. That’s a ton of data so I’m experimenting using Jev to see how good it is at quickl

agentjido/req_llm · issue · open · 1 comments | Feature Type New provider and possible core library enhancement. Provider TypeSafe AI (Jev). Goal Plan Jev support in ReqLLM. This i

I am going to build for companies who have lot of candidates to evaluate based on resume I am using the coolest model JEV by @typesafeai to analyse all of them and find the rig

What about using Jev to attach the only tool schema necessary for the task based on prompt classification? might this solve the MCP overhead problem?

My custom harness is shaping up, off loaded most/all decision to @typesafeai Jev, let these local models focus on work, consult frontier when needed. It's a complete 360 , suddenl

we're using Jev in our robotics system AMA

Typesafe built an AI that can’t write you a single sentence but is also one of the most intelligent, cheapest, and fastest models ever. In Thinking, Fast and Slow, @kahneman_danie

I think @typesafeai Jev has solved a much bigger AI problem than it first appears: LLMs shouldn’t have to think about everything. A huge amount of “reasoning” is actually classif

@mormonnegro @typesafeai que crack! construi un catalogo con recursos de Jev y subi tu caso de uso ;)

@chaturvedikun @typesafeai bfs for shortest path then jev on spatial metrics is a clean handoff. decision models belong on the branch not the path search same split i used for offl

@typesafeai Post 4/7 The biggest gain came from a traffic-policy incident: 0/5 → 3/5. Baseline agents chased plausible telemetry noise. Jev rejected a weak causal explanation and h

@rodrigovmedia @antonioleivag Yo me armé esta tool para que todo el plan viva en el disco, con soporte multiagente, resume, switch, etc, debería agregarle algún soporte para Jev, p

Just solved one of the biggest legal questions of the past 30 years using Jev. Turns out the Juice didn't do it after all https://t.co/B7m8evBqmj https://t.co/v0pRD35L7F

@kyamilass @typesafeai Jev showing up on these lists this fast is wild 👀

@ethereaglehq @o_kwasniewski @typesafeai both are valid, different layers. Jev is System One: Choice/Score/Noul over state, not a browser driver. so "slot in as pass/fail (or sever

@K_aditya25 @typesafeai the latency win is less "Jev does browser" and more "stop paying an LLM to plan in prose". kyle's loop is the pattern: observe a11y as state, Choice over ca

As I dive deeper into Jev from @typesafeai , it's looking more and more likely that this is the missing piece for hyper-personalization of digital experience with Snowplow Signals

@typesafeai @CompleteSkeptic System One models are emerging: no chat, just decisions, returning structured values with probabilities. If these mature, agent workflows will re-layer