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Using Jev to evaluate fault in machines The idea is simple: I wanted to try out Jev and evaluate its performance compared to LLM or rule-based decision-making. So I created a compressor operating manual (since I couldn’t use a real one due to copyright issues, I replicated the scenario) and applied machine error evaluation with Jev, based on real-time data and the confidence level of the error scenario. It replays real compressor telemetry through an emulated device, spots suspicious behaviour with plain code, and asks a decision model which fault from the machine manual explains it. The project is open source, and you can find all the technical details in the repo. What do you think?

Jev is insanely fast fr and I still obsessed with it to detect my gestures to control actions on my computer in real time. Like when Stark use Jarvis. See it in the video how I use Claude Opus 5.5 to build Jev to control computer by detecting my hand gestures and movement in milliseconds. Domo from Workser Computer btw Official waitlist live now on See u next week

MuJoCo xArm7 study where Jev chooses movement directions and gripper actions from physical feedback.

EmbodiedJev: MuJoCo robot decision workbench with MiniCPM5-2B, Jev and compatible model APIs

@typesafeai we are tested Jev as the decision maker for a Unitree G1 humanoid in Automata Room. The task: carry 4 crates to a storage rack, one at a time, then return to the entrance. Fewer, shorter trips score better. Full run below 👇 https://t.co/xpliAUN5fR

I put TypeSafe’s JEV in control of G1 humanoid in my virtual kitchen. The mission: reach the stove with obstacles on the way. A VLM and depth information helped map the kitchen. A path planner produced possible ways around the obstacles, and JEV chose which move to make next. A pretrained walking policy handled locomotion, with calibrated controls for turns, forward movement, and curves. The robot reached the stove in about 64 seconds of simulation time. This video shows the highlights. Still a prototype, but pretty cool watching perception, decisions, and movement come together.

This weekend we took part in the @PromptQL "Rebuild With Jev" hackathon and won! Thanks @tanmaigo and @rajoshighosh for a great event. In 90 minutes we asked one question: "Can Jev, introduced by @CompleteSkeptic from @typesafeai supervise a robot in real time?" Full write-up, with the @rerundotio recordings: P.S. From idea to experiments to the blog post we never left the @PromptQL chat window. The VLM and Jev calls, the @rerundotio viewer, @huggingface datasets, the writing, the video and the review agents all ran inside one bot's VM. That's the robotics harness we want for end to end agenti

LIBERO robot-control environment where Jev chooses fine-grained actions using task state and physics previews.

Jev the Band: I made a jam band using jev. (🎧on) I taught jev how to read and write music Guitar, Bass, Drums, and Keys controlled by jev lights controlled by Jev soundboard, yup, it's Jev I've been obsessed with recording Jev's jams this weekend and now I'm sharing them with you

Built a System 1 + System 2 robot brain using @typesafeai's Jev in PyBullet. 🧠 Claude: Generates the high-level plan to stack cubes. ⚡ Jev: Acts as the split-second reflex layer, evaluating sim states in <100ms to adapt. Fast, schema-safe neurosymbolic robotics in action. https://t.co/WVNB1aqo7n

Tried to build a Visual Arena for robotics demos. I uploaded a @RewardAI_ demo and prototyped a real-time visual evaluation layer with Jev. Instead of asking only “Did it succeed?”, the system tracks completion, stability, safety, recovery, and uncertainty over time. Could this become a transparent Visual Arena for robotics demos?

i've always wanted to build an autonomous robot, but I was always missing the last and most important piece A REAL fast decision making AI, which LLMs could't solve yet And Jev came to solve the problem, please take a look at my baby's first steps: https://t.co/1xEu2iArGJ

LE HABLO A UN BRAZO ROBÓT IGUAL QUE A UNA PERSONA 🤖🚀 Usando @typesafeai Jev el robot ahora puede entender frases como Abri la mano Anda al techo Estirate lo máximo que puedas Demo arranca en 1:25 Venimos a pleno preparándonos para el @ArgentinaSpace https://t.co/qOTkch12gN

Jev is INSANE. I connected it to a bio-inspired robot running off a simulated nervous system. Neural activity gets fed into Jev, Jev makes a rapid decision, and that decision gets translated into physical movement. https://t.co/EH6CmHezqm

Can Jev drive a car despite not being able to see? I gave Jev access to an unreleased world model used to train self-driving cars and a local segmentation model to see if it could drive autonomously. TLDR: Kinda? It's a little Crazy Taxi - but kinda amazing this works at all. https://t.co/NKoe7RqFIe

I builded Jev Reflex which can control your mac with hand gestures and voice. It is build with Jev 1.13 from @typesafeai . I builded it with @OpenAI Codex using Astra and Sol. It is using Jev credentials from @OpenRouter and for all this just used $0.01. As you can see in the video, it recognizes hand gestures and voice and can do various task on mack, like mazimize or minimize anything you point you index finger. And then pinch gesture to complete it. It took me 2 hours to build it on a monday morning, which included code, enabling controls on mac and calibrating it first. Project availbl

I was also looking into how we can integrate Jev into a real hardware and I built a custom firmware for my keyboard that predicts and highljghts the next key using Jev. Mostly real time. This is not a simple dummy 2kb model that lives in the board. In my project Jev gets context of the current active app on a screen so it can suggest actual vim commands completion! For real. And a real word endings or even phrases. lmao this experiments are getting out of hand this quite cool

使用TypeSafe的jev的决策能力，用文字指令控制机械臂执行任务，速度杠杠的。PS：这运动学控制算法和建模，是用gpt6 astra帮我生成的，太强了。 https://t.co/Ge4Z5B0PZx

Ok I got Jev + SAM 3 working on a simulated mobile robot! SAM 3 + camera depth provide 3D observations, alongside joint state and contact feedback. Code samples numerical movement, grasp and placement poses; parallel Jev calls rank them, then a final Choice picks the action. Noul and Score add supporting judgments, while deterministic controllers handle IK and motor control. It’s almost through th

I TURNED JEV’S TYPED DECISIONS INTO A GHOST. Built JEV BOARD, an Ouija-style board powered by TypeSafe’s JEV. The use case was simple: give JEV a structured set of possible next words, let it choose the strongest path with probabilities, then make the planchette physically spell that choice across the board. Every reply is built through real decisions inside the app. The board gives those decision

A small reproducible MuJoCo pilot comparing Jev, Claude Haiku, and reactive rules for pick-and-place.

I tortured Jev into being a RISC-V CPU.

This demo uses Jev from TypeSafe AI to autonomously fly a drone in a random city from point A to point B, avoiding obstacles along the way. A trip costs $0.01.

Zero-shot English goals on a sim Franka. Jev chains hardcoded primitives.

Camera-only autonomous drone in MuJoCo with a small judgment model (TypeSafe Jev) in the loop at 2.5Hz.

Fast structured Android control loops with TypeSafe Jev and Mobile MCP.

Home Assistant Assist conversation agent powered by TypeSafe's Jev (System One) model.

I gave @TypeSafeAI's Jev a robot and one rule: keep the flower alive. 🔥 Fire? Put it out. ❄️ Frost? Warm it up. 🐝 Bee? Let it through. https://t.co/hX0N7SvXkU

Now that we're all jiving with jev, I plugged jev in to replace claude in an older robotclaw experiment. You put an object on the board, and after every movement, the model tries to figure the route you should take to reach the goal https://t.co/3S51ITC6cM

have not seen anyone actually using Jev for robotics outside of a simulation, so here is Jev running with no policy, zero-shot controlling the robotic arm. @typesafeai @CompleteSkeptic https://t.co/a8CUe9PBdo

I wanted to test Jev's spatial ability. In a loop I gave it the: - Goal - Current geometry and contacts - controls and their predicted effects - Previous action outcome I think it's pretty remarkable how it zero shots the task with no vision capability https://t.co/BL4TCSAJRO

Got @typesafeai Jev driving our @makermodsai Metal arm in MuJoCo. Images are of the sim camera is not passed in, sim hands Jev the state of the environment in JSON and it picks the next bounded action (hover, descend, grasp, lift, place) as a typed choice with a confidence score. Jev makes the decisions for example "Put the red apple in the blue bin", and it runs in 9 decisions at ~150 ms each, an

DiffusionGemma-as-Jev (aka djev) running near-real-time vision detection from a mobile phone using its native vision tower. Please don't fall down the stairs! https://t.co/C9kBCD6irx

tested Jev as a real-time robotics policy in MuJoCo it struggled at first, so i split each update into two calls: decide what to do next, then decide how to move the arm and gripper Jev doesn’t accept images, it gets simplified geometry and contacts as text here https://t.co/iKK7jcrrpO

Applying @typesafeai 's Jev to robotics & swarms. JevDuck (https://t.co/suJgqvf7pv) connects Jev's typed decisions to the official Microduck simulator. MuJoCo WebAssembly computes the robot dynamics in the browser and the original ONNX policies control the articulated joints. Try it here https://t.co/r8DToTfmcg

I've built a drone autopilot using Jev @typesafeai. It flies from point A to point B, avoiding obstacles along the way. One trip costs $0.01. Uses @vercel @aisdk. Code is on GitHub: https://t.co/gNQZIbRP7E https://t.co/1uZuiEOLhn

I gave Jev a robot arm. Goal: red cube in the green box. Catch: the cube was out of reach. It grabbed a hook, dragged the cube closer, put the hook down, and finished the job. Tool use was never programmed, the model just decided. Fast enough to run a robot 👀 @CompleteSkeptic @typesafeai @dotpem

Gf approved the demo 🥳 Zero-shot robot tasks with the @typesafeai api Give it a goal in plain English. Jev chains a set of hardcoded primitives. The arm does the thing. No training. https://t.co/cTSJcVzJDf

Jev PoC on an iOS device remotely. @typesafeai great work, OCR is the bottleneck in speed. https://t.co/hwH4nTyJZu https://t.co/fGwQKh4SFq

Some more experiments with Jev. It can (sorta) control a robot body https://t.co/OHaYU2rCqG