GitHubOpen source
lejudge-jev-jepa
Natural-language constraints for JEPA planning, judged by a decision model.
AbdelStark
10Updated Nov 10, 2026
The newest builds with Jev
855 projects, posts and guides about Jev, the System One model from TypeSafe AI. Each entry links to its source and shows the cost and speed its author reported.
- 855builds
- 181guides
- 21use cases
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 cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution
Newest first, 399–431 of 1072. All 1072 in curated order
Guideyoutube.com/@TechBrewRideHome
What is Jev? (Tech Brew Ride Home)
A podcast segment on TypeSafe AI, its $40 million raise, and why the name is a nod to Jevons paradox.
GitHubOpen source
laya-candle
A Jev-like Rust port using Laya, fully local and with no Python.
Trystan-SA
10
Guideyoutube.com/@GaryExplains
Jev: fast and cheap, but there is a caveat
Gary Explains on the catch: Jev understands natural language but it is not an LLM. It answers with structured values and a confidence level.
GitHubOpen source
A WeChat reply assistant with Jev and DeepSeek
Windows and Apple Silicon builds, with local OCR and human control.
Aimark-dai
16
Guideyoutube.com/@samwitteveenai
Jev: the ultimate classification model?
Sam Witteveen on System 1 thinking, then demos of Choice, Score and Noul, a practical example, and chained actions.
GitHubDocuments and OCR
JEV-Paper-Radar
Jev reads every new arXiv paper each morning and keeps the few you need.
Eliot5566
31
GitHubBenchmarks and evals
awesome-jev-robustness
Tests, calibration audits and failure-mode studies of Jev.
Yifan-Lan
7
Tonino Catapano (tonnoz)
@tonnoz
You still don't understand the use cases Jev unlocks. I've been waiting for something like this since early ChatGPT models. prediction: we will see the fastest-growing SaaS by MRR in history within the next month or two
Guidex.com
The use cases Jev unlocks
Tonino Catapano’s demo, and his prediction: the fastest-growing SaaS by MRR within a month or two.
GitHubAgents and browsers
Learn Jev end to end
A free course: 13 agent use cases with a fast brain and a slow brain.
harshithsunku
17
GitHubOpen source
laya-fast
An Apple Silicon runtime for Laya: Neural Engine plus GPU router.
DJLougen
6
GitHubOpen source
awesome-jev-survey
An evidence survey of Jev and Jev-like typed decision models.
Eurekaleo
7
Ian Nuttall
@iannuttall
Unsure how to get started with Jev? Install the skill: npx skills add typesafe-ai/skills --skill typesafe-ai Then prompt in your project: use /typesafe-ai to see how Jev can be used to replace slow, expensive LLM usage and find possible new features it would enable for users.
Guidex.com
Getting started: install the skill
Ian Nuttall: add the official skill, then run /typesafe-ai in your project to find where Jev can replace slow, expensive LLM calls.
GitHubAgents and browsers
macos-computer-use-kit
AX-first computer use on macOS with optional Jev semantic guards.
Sur-Cai
5
OpenRouter
@OpenRouter
Jev by @typesafeai is now on OpenRouter, in beta. Jev is a System One model. Instead of generating text, it takes your app's state plus a typed question and returns a typed decision with a probability attached. There is no JSON prompting, parsing layer, and nothing to validate against.
Guidex.com
Jev on OpenRouter
Jev is live on OpenRouter in beta: app state and a typed question in, a typed decision with a probability out.
GitHubTrading and markets
jev-as-quant
Typed System One decisions as the judgment layer of a quant stack.
jiayylu
7
rari🈀
@0xwhrrari
Jev + Laya + any AI agent is an absolutely insane stack Jev became one of the most talked-about AI releases almost overnight, but most people still have no idea how to use it beyond isolated demos - especially together with Laya, Grok Bot, Claude Code and Codex So I mapped the complete architecture into a 12-page guide explaining how to combine all of them into one decision-and-execution system - and the result is genuinely ridiculous: step 1 → stop asking one LLM to do five different jobs: state, judgment, generation, execution and authority should be separate layers step 2 → use Jev as the escalation layer: it handles difficult routing, evidence verification, completion checks and high-consequence decisions through typed answers step 3 → use Laya as the local fast path: it handles frequent classification, scoring, filtering and worker selection without sending every decision to a cloud model step 4 → put both behind one decision gateway: Laya and Jev can accept similar choice, score and yes-or-no questions, but their confidence values need separate thresholds step 5 → keep project state outside the agents: goals, artifacts, evidence, gaps, risks, budgets and approvals become the shared source of truth step 6 → build the worker menu before asking a model to choose: only include agents that exist, are available, have the required tools and are allowed to access the data step 7 → give every executor a different job: Grok Bot handles apps, browsers and routines; Claude Code handles deep repository work; Codex handles implementation, testing and review step 8 → turn every assignment into a bounded packet: one owner, one objective, approved inputs, expected artifacts, forbidden actions and an exact definition of done step 9 → run the hybrid decision loop: Laya answers locally first, Jev receives uncertain or high-risk cases, and a human reviews consequential disagreements step 10 → never average confidence blindly: if Laya and Jev disagree, preserve both distributions, inspect the evidence and route the case through an explicit review policy step 11 → gate effects instead of tool names: reading, searching, testing and drafting can run automatically; publishing, deploying, spending, deleting and exposing secrets still require approval step 12 → test the whole system before giving it autonomy: run shadow mode, measure disagreements, inject failures, test rollback and automate only the safest branches first the result: Laya filters routine decisions, Jev judges difficult boundaries, Grok Bot, Claude Code and Codex execute the work, deterministic code controls the system and you approve the consequences Save this, then send the complete 12-page Jev + Laya + AI Agents guide to your Claude Code or Codex instance and build the stack ↓
Guidex.com
Jev + Laya + AI agents, in 12 steps
rari’s guide to one decision-and-execution stack: Laya answers routine decisions locally, Jev takes the uncertain and high-risk ones, and Claude Code, Codex and Grok Bot do the work behind approval gates.
GitHubContext and memory
cairn-jev-lab
Test what your AI should remember, with Jev deciding admission.
Cairn-ink
5
Cloudflare Developers
@CloudflareDev
Jev from @typesafeai is now live on @CloudflareDev AI Gateway. Try the first System One model — send state and typed questions; get structured answers your code can use directly. developers.cloudflare.com/ai/models/type…
Guidex.com
Jev on Cloudflare AI Gateway
Cloudflare’s announcement: send state and typed questions, get structured answers your code can use directly.
arminn
The newest Jev builds, by the day they were added
Same directory, different order: the entries added most recently, rather than the curated order the home page opens in. The date on a card is the day it was added here, not the day its author published it — this directory is newer than most of the work in it.