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
TypeSafe AI debuts model for machines that plays Doom
The Register's launch report on TypeSafe AI's Jev: the $40 million raise, typed probabilistic decisions, the Doom demo, the 70-500 ms latency and $0.042/MTok pricing claims, and the structured-output caveat.
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- 2026-09-22
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Highlights
- Reports a $40 million raise and Jev as a frontier model that returns typed probabilistic decisions instead of natural language.
- Example output routes a support query as {billing: 0.08, technical: 0.85, sales: 0.07} with a confidence of 0.82.
- Cites TypeSafe's 70-500 ms response range, 40x-200x faster than LLMs, and a Doom demo at 0.114 s versus 8.566 s for GPT-5.6 Terra.
- Pricing is given as $0.042/MTok input with free output, against $2.00 input and $12 output per MTok for GPT-5.6 Terra.
- Notes the hallucination-free claim is not a fair comparison since output is structured, which still does not preclude incorrect answers.
Watch out
A press account based on vendor statements and a company demo; the latency, cost, and Doom numbers are TypeSafe's own, and the Doom run consumes structured game state rather than pixels.
More like this
Wikipedia's article on Jev: TypeSafe AI, the September 15, 2026 early-access release of jev-1.13.0, the Choice, Score, and Noul primitives, RLCD training, and the unpublished weights and technical paper.
Sites & GuidesArticle#community#article#models
Community
Tom's Hardware's launch report on Jev, covering how the API takes program state and typed statements and returns yes/no answers, choices, or probability distributions with confidence values.
Sites & GuidesArticle#community#article#api
Community
Latent Space's AINews roundup of launch day: the Jev thread atop Hacker News, the RLCD decision-model framing, and community readings that compare it to DSPy-style typed signatures for routing and scoring.
Sites & GuidesArticle#community#article#models
Community
From the community
Posts from builders shipping with Jev right now.
Trading bot, one decision per block
Classifying 1,500 real emails
this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away
@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
Fast browser use with Stagehand
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 Stagehand Show more
@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
LLM-as-a-judge, sped up
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.)
@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
Instant compaction with Jev
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant
A Claude session from 1M to 86K tokens
This is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be Show more
found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant