Diogo Almeida explains why TypeSafe built Jev for machine-to-machine decisions rather than prose, and describes its probability outputs and training approach.
Original by The TWIML AI Podcast with Sam CharringtonAgent workflowsOverview89 min 40 secPublished
Almeida describes Jev as a model for programmatic decisions and automation rather than generating text for people.
He says the model deliberately sacrifices string-generation performance to focus on general classification.
He proposes setting action thresholds from probability outputs. Teams still need to test whether those probabilities match outcomes on their own data.
Worth knowing
First-party show notes and dates were freshly opened, with original YouTube metadata retained from October 8, 2026. Technical claims are interviewee explanations, not reproduced benchmarks. The full interview transcript was not reviewed, and capabilities are attributed to the presenter's claims without independent verification.