🆕@typesafeai dropped Jev this week, a new frontier model optimized for decisions. We put it to work on an incident triage workflow in Box. Jev doesn't generate text. You send it state plus typed questions and it returns booleans with probabilities, enums, and scores in a single pass, so there's no output to parse and no schema to keep in line. The demo uses Jev to classify and route incidents. It pulls the incident report from Box, builds the state, asks whether it's customer-facing and how severe it is, moves the file into Escalate, Monitor, or Review folders, and sets a metadata template instance with the result. The answers comes back with a confidence estimate, so anything below threshold goes to Review for a human instead of routing itself. Check it out.👇

Jev 1.14 from @typesafeai is now available through AI Gateway. 𝚝𝚢𝚙𝚎𝚜𝚊𝚏𝚎-𝚊𝚒/𝚓𝚎𝚟-𝟷.𝟷𝟺.𝟶 • Ask typed questions about images • Enable experimental long context • Both features are supported in Jev 1.13

What’s remarkable about @typesafeai’s Jev isn’t just how obvious it feels in hindsight, it’s that it didn’t come from a major lab. It’s an inspiring glimpse of AI’s future: an explosion of frontier innovation from teams across the industry, not just a handful of labs.

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 Fable + Claude Code. - Changes to an ios app run on one of my macs

Jev from @typesafeai is on AI Gateway. Build agents that decide, route, score, and stop in milliseconds: 𝚊𝚠𝚊𝚒𝚝 𝚎𝚟𝚊𝚕𝚞𝚊𝚝𝚎({ 𝚖𝚘𝚍𝚎𝚕: '𝚝𝚢𝚙𝚎𝚜𝚊𝚏𝚎-𝚊𝚒/𝚓𝚎𝚟', 𝚜𝚝𝚊𝚝𝚎, 𝚚𝚞𝚎𝚜𝚝𝚒𝚘𝚗𝚜, });