What people are shipping with Jev.

Fast browser agent on langchain-typesafe

Played around with @typesafeai Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part with Jev. My learni

@Antoniocoppe @o_kwasniewski @typesafeai Jev never invents the click, only ranks the a11y list you built. on escalate, does a human get that same list, or does a free-form agent ta

Tried JEV from @typesafeai and vibe-coded a little voice-controlled browser agent.

Jev made our Slack agent 2x faster ⚡️ Our agent can be quite slow because it needs to read skills and figure out which tools to call.

@johnyeo_ @typesafeai damn thats a nifty agent I wonder who added Jev to it

Jev from @typesafeai plays @BalatroGame.

@o_kwasniewski @typesafeai another builder picking jev as the agent layer for e2e. the dom snapshot approach keeps beating screenshot loops on reliability. what handles the mobile

Drive-thru ordering at Big Kahuna burger powered by @typesafeai Jev The model is really good/fast/cheap at Natural Language -> Menu -> Selection https://t.co/Q2b0DRH9pV https

Can a small and fast decision model make SRE agents more reliable? 🤔 We gave an agent access to @typesafeai’s Jev through the Codex harness and tested it on 10 SREGym-Lite problem

@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

thanks to @typesafeai jev I no longer have fill out all of those fields on prompt boxes. It picks the agent / model / computer / folder for me. - For a major rewrite it uses Fab

@BhosalePratim @typesafeai @GradiumAI acting on a prefix only works if the wrong tool is cheap to undo. for voice, do you wait for a Jev confidence cutoff, or fire as soon as one t

made this in under an hour to play around with jev.

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

Jupyter Notebook | 17 stars | Typesafe.ai System One Model Jev navigating a Neo4j graph by using a classifier over neighbouring relationships

16 hours ago ... Jev by TypeSafe returns typed decisions with confidence, not text, at up to 200x LLM speed. Why a non-hallucinating decision model fits AI agents.

1 day ago ... be testing Jev, TypeSafe's System One model designed for fast, inexpensive, and structured AI decisions ... demo shows how it can support fast browser ...

9 hours ago ... It seems that the automated bot player is using Jev to determine where to shoot and when to shoot, based on calculating probabilities and then acting on those 

16 hours ago ... Typesafe pitches Jev as a “system one model,” a different architecture built ... A Minecraft bot built by a developer using Jev reportedly ran for two ...