A toy archipelago air-traffic demo where every in-flight judgment - divert or continue, declare an emergency, give way, and who lands first - is made live by Jev from each plane's local state in about 150 ms.
Every ~1.6 seconds each aircraft asks four questions in one batched request: route, broadcast, response, and clearance.
With terse option labels Jev split 0.46 give-way versus 0.41 maintain; operationally-worded criteria for the same state moved it to 0.70 versus 0.05.
Sending comparable time estimates - fuel, fuelMinutesRemaining, destinationMinutesRemaining, and minutesToReach - made the fuel-leak drama findable.
Safety invariants stay in code: runway occupancy is enforced deterministically, with zero simultaneous-occupancy violations across 4x-speed stress runs.
A Cessna's emergency question returned 52/48 at confidence 0.04 while the adjacent divert question sat at a decisive 0.99.
Quickstart
bash
cd demo
export TYPESAFE_API_KEY=YOUR_API_KEY
node server.mjs # http://localhost:8765; the server proxies POST /jev to api.typesafe.ai
Watch out
MIT-licensed and an independent community project not affiliated with TypeSafe AI; needs Node for the local server, which proxies POST /jev to api.typesafe.ai with a server-side key, and the findings are small experiments rather than benchmarks.
One-pass option scoring with a local Gemma 3 4B on Apple silicon via MLX: it prefills the context once, expands that KV cache across the option batch, and scores every option in a single padded forward pass with no decoding.
An independent CUDA and vLLM take on parallel typed decisions: it scores verified single-token labels with the model's own head and assembles JSON in code.
CLI that fits a per-question confidence threshold to a target accuracy on your own labeled data, verifies it on a held-out split, estimates how much traffic still needs an LLM, and re-checks locked thresholds in CI. It publishes no Jev results of its own.
Jev 发布没几天,开源社区已经开始疯狂复刻了🔥
最值得推荐的五个模型:
1、Laya 421M:原生决策模型,支持 Mac
2、Decider-2B:最像 Jev,基于 Qwen3.5
3、NanoJev 0.6B:专门的 Decision Head
4、Reflex:Qwen3.5 + Direct Logits
5、System-One 4B:专门做概率校准Show more
小墨同学
@xiaomovps
Jev 刚发布没几天,开源社区就出现了同款🔥
Decider-2B模型,是基于 Qwen3.5-2B 做了特殊调整
它和 Jev 模型是一样的 只做选择 评分和判断 不是文本类的 LLM 模型
但两者还是有几个明显区别:
1、模型
Jev:闭源 System One Model
Decider:Qwen3.5-2B,约 1.9B 参数,Apache 2.0 开源
2、价格
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400xShow more
I built a trading bot with Jev!
Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades.
It uses Monad to place the orders on Kuru's on-chain order book in every 300ms block.
Demo link → jev-trader.vercel.app
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x
we built blazing fast computer/browser use with Jev + @Stagehanddev.
this task cost $0.001 and executed at near instant speed (in a remote browser btw)
the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then StagehandShow more
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x
Jev has spoken.
It picked which model is AGI.
20–200x faster. 40–400x cheaper. This could make things like LLM-as-a-judge insanely fast and nearly free.
(I tried a bunch of prompts and still didn’t burn through $0.10.)
Diogo Almeida
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x