Wiring Jev into a driving simulator to run the decision loop
Someone used Jev in a simulated car for steering, lane changes and merging decisions, building the loop in under an hour; the model returns actions with probabilities and a confidence score rather than text.
Someone already tried wiring Jev into a driving simulator, and it's a genuinely interesting stress test Jev, TypeSafe's decision-only model, is showing up in early experiments with autonomous driving. According to the person running it, a full decision loop for a simulated car went together in under an hour on the new model: turns, lane changes, intersections, merging onto a highway. For every action, the model doesn't return text, it returns typed probabilities across the possible decisions along with a confidence score. That's the actual difference from a regular chat model here. You feed in system state, and you get back a set of allowed actions with probabilities attached, not a paragraph you have to parse into a decision. Worth being clear about the limits before this gets overhyped: the demos circulating so far, including a similar real-time Doom control clip, feed the model clean structured text state, positions, distances, angles, not raw camera input. Jev has no vision component. Real self-driving needs on-device decisions in the 10-20ms range with actual sensor fusion, and Jev's own benchmarks put it at 70-500ms for a single decision. A simulated environment handing over perfect JSON state is a much easier problem than reading the road. Still, if this kind of typed-decision approach scales past toy simulations, it could meaningfully lower the barrier to prototyping decision layers for autonomous systems, even if the perception stack stays somebody else's problem entirely.
This post quotes another post: x.com/grokkedd/status/2100151292404302270