Updated 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, 166–198 of 1072. All 1072 in curated order
Live siteOpen source
Tiny-Jev
A 0.6B System One model: structured state in, typed decisions out.
lostargon
0.6BOur guidemadewithjev.com
How does a Jev router work?
A Jev router asks which model, tool or skill the next turn needs before anything is spent. How it works, why routing down and back up can cost more than not routing, and 16 routers for Claude Code, Codex and OpenRouter.
Live siteOpen source
Laya Vision
Typed decisions about an image plus text, in one forward pass.
r33drichards
201M
Live siteOpen source
Eikos-27B
An open typed-decision model for finance and trading rules.
caiovicentino
Live siteOpen source
Jeff 1
An open typed-decision model with a guide to training your own.
Gestalt Labs
Guidedocs.typesafe.ai
Cookbook: skill suggestion
TypeSafe’s recipe for picking at most one skill per agent turn out of 182: one request ranks them all, a second reads the top three.
Timothy Kassis
@TimothyKassis
We built a really nice free tool on top of @typesafeai Jev to see how rigorous an academic paper is.
XBenchmarks and evals
A free tool that scores how rigorous a paper is
Yannis
@yannnis
Enough is enough! Is it that LinkedIn sucks or what? Let's settle this. I go viral on LinkedIn for slop (attached) Then I spend 2 days to build a JEV product to scan Reddit for leads. And get tumbleweed!?! How??
XSales and leads
Scanning Reddit for leads with Jev
Georgios Konstantopoulos
@gakonst
We index, rank and curate paid services powered by MPP and x402 to achieve this. Mercator is the apex aggregator I wish I had when we first launched MPP. This works by a pipeline which took a lot of work to get done, and most recently got turbocharged by integrating Jev by @typesafeai (come see Diogo's talk at Frontiers!)! See how running your first job works: https://mercator.sh/docs#run-your-first-job
Live siteSDKs and integrations
Mercator
Our guidemadewithjev.com
How does Jev compaction work?
Jev compaction scores every tool call and drops the stale ones, so what is kept stays word for word. How it works, how it differs from a summary, 6 plugins, and the case against.
Omid Sayfun
@OmiidSayfun
Couldn't find my important unread emails, so I built a Chrome extension. Jev finds them and labels them by what they're about. You can check it out here: github.com/iamomiid/jev-i…
GitHubInbox and support
jev-inbox
Ackerman
@Yarilo7brigada
98% of my feed is junk. Now I don’t even see it I built a filter that reads my feed for me. It runs on Jev - a model that doesn’t generate text; it just makes binary decisions, in milliseconds and for pennies. For every post, it evaluates four questions: Is it relevant to my niche? Does it provide real value? What format is it (breakdown, news, ad, meme)? And does it make loud claims with zero proof? Out of 1,000 posts, only 20 remained. 97 milliseconds per post. 1.2 cents for the entire morning. The most frustrating takeaway, nearly half of my feed was ads and memes, not the creators I originally followed for substance. An hour of mindless scrolling turned into five lines over breakfast.
Rohan Mahtani
@itsrohanm
made this fun little word game so I could test Jev: clue me it's similar to charades and taboo. you describe your word (without using it), Jev tries to get it yorohan.com/clue-me @CompleteSkeptic @typesafeai i can't believe how fast Jev is 🔥
Live siteGames and real time
Clue Me
AI Insider
@TheAIInsiderN
JEV + OPUS 5.5 IS INSANE. I built an AI system that analyzes an X post before you publish it. Paste any X link. Press Start. Get a virality score in minutes. The entire production version was built in 9 minutes. Here’s what happens: Jev analyzes 800 viral posts as a live baseline Groups them by hook type: demo, launch, proof, contrarian take Runs 12 structured checks: hook, numbers, media, CTA and more Finds the 5 most similar viral posts Opus 5.5 explains why those posts spread Then your post receives: Virality score out of 100 Estimated likes and views Percentage of the viral baseline it beats Direct comparison with similar winners It works on drafts too. So instead of posting and hoping, you can identify weak hooks, missing proof and unclear CTAs before anyone sees them. Hover over any result to inspect the original post, its media, every check and the final score. JEV DECIDES FAST. OPUS EXPLAINS WHY. #JEV
Avid
@Av1dlive
Jev + GPT-6 Astra just built the most TERRIFYING AI trading setup on the internet... [this article covers 90% of what is required to build quant-level systems] /1 GPT-6 Astra reads the order book, the tape and 3 correlated futures /2 Jev turns the signal into a trade and checks the risk limit /3 computer use clicks the order screen, no broker API needed /4 the full loop runs in 6 ms, signal to fill steal this setup in the article below👇
Daniel Raeder
@DanRaeder
While Jev is down for new invites, I worked with Claude last night to build Vej (a jev clone that runs locally.) It's weakest at score types, but almost 1:1 for noul and choice. You can use it to build and play with jev-based apps until you can get in. github.com/draeder/Vej
GitHubOpen source
Vej
Our guidemadewithjev.com
Jev with Claude Code
Let Claude reason, let Jev decide. The published builds behind the 200× headline, Jev compaction taking a session from about a million tokens to 86,000 in one second, and the cases where the larger model still wins.
Proliquid
@proliquid_xyz
Did you know that we have @typesafeai Jev AI giving you the sentiment on the news? Don't miss the next ethereum:0xfaba6f8e4a5e8ab82f62fe7c39859fa577269be3 like move. One click long on bullish news only on app.proliquid.xyz/terminal 🦈
XTrading and markets
News sentiment in a trading terminal
Jetwani Avinash
@jetwaniavinash
First thing I built on Jev: a memory gate for Claude Code. Every message gets one question: is this worth remembering? 0.3s later it's either skipped or saved as one line in JEVMEM.md in the repo. Open source, link in my pinned post.
Kevin Dubois
@kevindubois
With all this hype around #Jev and open source derivatives like #Laya I wanted to see how these kinds of decision models would work in Agentic Workflows with #LangChain4j. (tl;dr it's pretty intuitive!) kevindubois.com/2026/09/23/rou…
ArticleSDKs and integrations
Routing agents with Jev and Laya in LangChain4j
Guidedevelopers.cloudflare.com
Jev on Cloudflare
Examples for support routing, refund decisions and risk escalation.
Prasad Pilla
@prasad_pilla
AGREED. team built an internal tool using Jev to qualify candidates, and Jev itself is genuinely impressive at what it’s designed todo. but recruiting is hard. there are too many variables that don’t fit neatly into a rubric: context, trajectory, ownership, quality of work, potential, and the signals you only catch by actually digging into someone’s work tbh https://x.com/idleshubh/status/2102429846660079971?s=20
XSales and leads
Qualifying candidates with Jev
morph
@morpphhhaw
TypeSafe founder Diogo Almeida introduced Jev, and the field I would inspect first in any agent built around it is not model. It is state An agent can have room for a long conversation and still miss the one line that matters. A refund request needs the current order, the charge record and the policy that applies today. It does not need twelve earlier attempts to sound helpful Here is the state packet I would put in front of Jev: step 1 → name the decision the code needs to make now, not the entire job the agent was given step 2 → pull facts from the system of record: order status, amounts, timestamps and permissions step 3 → include the customer's actual words as evidence, without asking Jev to reconstruct the whole conversation step 4 → state the constraints that can change the route, such as a refund window or an account hold step 5 → build the available actions from live code, so a closed account never appears as a valid destination step 6 → put the question in the question field. State is evidence, not a second prompt hiding instructions step 7 → leave the sums and date arithmetic to code, then send Jev the result it needs to judge step 8 → keep the exact packet with the answer, because you cannot debug a decision from its label alone The point is not to compress everything until it looks clever. The point is to make it obvious which piece of evidence could change the answer The document below shows that packet on one page. The article goes further into shaping state without turning it back into a transcript
XContext and memory
The state packet you put in front of Jev
Yurii Oksamytnyi
@YuriODev
🛡️ @LangChainAI shipped Jev as a tool-call gate and model router. Key detail: tool output is excluded from the classifier input, so fetched content can't authorise its own execution. Steal that rule. langchain.com/blog/building-…
ArticleSDKs and integrations
LangChain's Jev tool-call gate
Our guidemadewithjev.com
Jev with Vercel
Call Jev from Vercel two ways: AI SDK's experimental_decide, or AI Gateway's TypeSafe-compatible API with model typesafe-ai/jev. Same $0.042 per million input tokens, zero data retention on Pro, and the builds that use it.
OrcDev
@orcdev
TanStack AI just shipped subagents. Jev picks which models / agents should run for the task, in parallel, in sequence, or mixed. Best Jev use case I've seen so far. TanStack team cooked. 🔥
XRouting and model choice
TanStack AI subagents, chosen by Jev
Benchmark Heaven
@benchmarkheaven
Hopper (#3) beats Jev 1.13.0 on three of four columns: Calibration 79.1 vs 76.3, Speed 86.8 vs 83.3, Cost 58.7 vs 52.0. Jev keeps #1 on the fourth: Intelligence 53.1 vs 48.0, hard tier 74.1% vs 65.0%. Under 50, the gate applies. Score 63.29 vs 59.43. benchmarkheaven.com/jev-models/v1.…
Live siteBenchmarks and evals
Jev 1.13.0 against Hopper on Benchmark Heaven
- Intelligence
- 53.1 vs 48.0
- Calibration
- 76.3 vs 79.1
- Hard tier
- 74.1% vs 65.0%
- Score
- 63.29 vs 59.43
rewind
@rewind02
built a full production app with opus 5.5 and jev... fed it a url, it screenshots the site, scores it against a list of known patterns, and ships a shareable report here's the actual pipeline: - gemini flash takes the screenshot, that's the only vision step needed - jev scores it against a fixed list of criteria in a fraction of a second, no text generation, just structured ratings - opus 5.5 writes the entire app, front to back, off a single planning prompt - product os turns the plan into a spec, then a roadmap, then working code, task by task - code review, security audits, and testing happen automatically as it builds, not after - a deploy checklist gets generated too, walks you through auth setup, hosting, env variables, step by step - cost to run per scan lands in fractions of a cent, both models chosen specifically because they're cheap at their job what actually makes this work: opus 5.5 handles everything generative, jev handles every decision that doesn't need generation, and neither model gets used for a job it's not built for that split is why the whole thing runs on pennies instead of frontier-model pricing pair that with a structured build system instead of one long freeform prompt, and a non-coder ships a live, secured, production app in an afternoon, not a sprint
Guidelangchain.com
Building a harness with Jev
Routing between models, and blocking risky tool calls with a Jev decision.
aiasssistantstore
@aiassistantstor
CLM-8B Just Dropped: The New Open AI Model That Claims to Be Up to 9× Faster Than Jev for Agents
Abdullah
@Abdullah_Ops1
التجربة الثانية مع JEV 👇 سويت له Vibe Coder Launch Inspector تحط رابط مشروعك يفحصه ويطلع لك هل جاهز للإطلاق ولا يحتاج شغل يعطيك Score + المشاكل + الأدلة + وش اللي يحتاج تعديل والأحلى كل مشكلة معها Fix with Codex يطلع لك برومبت جاهز ترسله لكودكس #AI #JEV #Codex #VibeCoding
XCoding and code review
A launch inspector for vibe-coded projects
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.