Projects built with Jev

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Chrome Dino with live decision scores@typesafeai jev playing chrome dino! the model is incredibly fast! - avg response time: ~280ms - spent 116M tokens - cost? just $4.65. - the input here is a snapshot of the chrome runner - jev decides whether to jump, run or duck with a confidence score repo link if you wantParth

A Three.js driving simulationExperimenting typesafe's Jev api in a three.js driving sim.𝕕𝕖𝕧 / 𝕒𝕔𝕔 | Vlad Rez

Dragon Ball Sparking Zero with Jev@typesafeai’s Jev playing Dragon Ball Sparking Zero. Astra and Fable each write an initial policy for Jev (system 1) to make the real-time decisions. Astra and Fable (system 2) updates the policy as the game progresses.Kariminal

Blitz chess against frontier modelsJev VS Fable 5.1 VS GPT-6 Astra: chess typesafe's new Jev V13 isn't an LLM. it doesn't chat, doesn't explain, doesn't write code — it only makes decisions. so we made it play blitz against frontier LLMs. the test: 5+0 blitz. every move is one API call. Jev V13 vs Fable 5.1:AI/ML API

Tetris with real-time Jev decisionsI got early access to @typesafeai's new Jev model and built a demo of it playing tetris The generation speed is so fast that it's pushing blocks down This feels like another "this changes everything" momentMarcus Lowe

Five hundred agents in a 3D environmentJev is actually insane. We benchmarked it, and the results completely change the game for us: > 500 real-time agents > running in parallel > in a 3D environment The preliminary results are crazy: > 500ms average latency > 35 API calls/s > all with a naive implementation WeCris Lenta

A Rubik's Cube that solves itselfI got a rubik's cube to solve itself with @typesafeai 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM, it just answers one question in ~250ms with a probability. so I put the beginner method in code (the one you'd learn onPaolo Rosson