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
Updated Sep 26, 2026
The newest builds with Jev
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.
- 729builds
- 156guides
- 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, 112 of 901. All 901 in curated order
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
Sponsored
Your card here
One card in the feed, every sixteen, on every page of it. $100 a week, cancel any week.
Our guidemadewithjev.com
What is Jev?
Our explainer: what a System One model is, the three question types, what it costs, what it cannot do, and the builds that show it working.
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
Our guidemadewithjev.com
Every Jev guide, in one place
Every guide on this site in one page, grouped by the question it answers: what Jev is, what a decision costs, how to put it inside an agent, and what it is already being used for across the 729 builds catalogued here.
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
Oursshippile.com
Describe the problem. Jev finds the startup.
Our guidemadewithjev.com
What is Jev Engineering?
Our guide to the term: split an agent into an LLM that writes, Jev that decides and code that acts, with the rules the builds on this site have in common.
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
Our guidemadewithjev.com
Jev use cases
What people actually use Jev for, from 729 catalogued builds: 100,000 posts scored in 20.4 seconds, 500 emails triaged for 3.5 cents, a flight search in about 7 seconds.
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
Sift: sort your Gmail on your own Mac
Our own: a Mac app that sorts, labels and marks your Gmail with a System One model running on your machine, so nothing is uploaded and nothing is installed in your browser. In build now, and the first 50 on the list pay $5.
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
Our guidemadewithjev.com
Is Jev open source?
No: TypeSafe serves Jev from its API and has not published the weights. These are the open models that answer typed questions locally — Laya at 421M, von under 15 ms, kev on a MacBook.
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
apolinario (poli)
@multimodalart
congrats on the release! i've included xor on the Decision Index 0.2 benchmark of jev-like models we don't have a vision benchmark yet, but the text-only version got 8th place, strong in arts & human taste x.com/multimodalart/…
Our guidemadewithjev.com
The Jev Build Report
Our own count: every public Jev build in week one, with the median published cost per decision, the median decision time, and the stars and languages of every repository created since launch. Free to cite, with the rows as JSON.
PeterY
@yudeewxy
用JEV + Codex + TG Bot，开发了一个小工具，用于实时判断9个coin的价格走势和概率。交易信号来自前几个月开发的trading信号平台。同时给这个Bot授权了1万刀，看看他围绕这些信号来实时交易的表现会怎样。 开发过程： - JEV开通，获取API，配置到本地 - 配置TG Bot - 连接Binance和OKX的API - 信号聚合平台：http://pytrading.vercel.app - 或者找到trading view或者自己的一套交易因子 - Bot配置钱包，授权10K usdt，在OKX上进行交易 JEV的优势是：快速高效的反馈价格及进行交易行动、相比LLM大量节省Token
apolinario (poli)
@multimodalart
congrats on the release, i've added it to the jev decision index 0.2, it's the #2 open weight jev-like model 🥳 and the strongest in chess and for its size x.com/multimodalart/…
ImRobot
@th3nolo
Been building an agent-agnostic permission gate: Jev (TypeSafe) + hooks. Many harnesses copy Claude Code's hook format. Some barely have hooks at all. This makes me happy: agy in YOLO mode. chmod -R gets blocked, the agent asks me, I say yes, it runs once. Again? Blocked again.
XSecurity and abuse
An agent-agnostic permission gate
Our guidemadewithjev.com
How to use Jev
Your first Jev call, line by line: the state, the questions, the typed answer and what to do with the confidence score. Then the SDKs, the gateways and 56 video walkthroughs.
Muskan Paliwal
@PaliwalMuskan19
built this skill-picker for myself because apparently having 30+ agent skills also means remembering which one does what 😭 jev (@typesafeai), being the god that it is, ranks them for a given task and tells me and my agents which skills are actually worth using phew. one less thing for my brain to cache. https://github.com/MuskanPaliwal/skill-picker
GitHubRouting and model choice
skill-picker
Cartwright
@CartwrightApp
Built Cortex, a local MCP server: Claude plans, a fast layer does the clicking. Same Mac-app task: computer use 23 s → Cortex 0.8 s One action: ~5 s Claude round → 0.25 s, no model 24 tools: browser, Mac apps, GitHub search, safety gate Measured on my Mac. @typesafeai #JEV
TactiX Trading Panel
@TactiXPanel
The Jev-powered AutoScalper passed all Tests during the Testnet Live Test with an average win rate of 82% trading on the 1m chart, using a 120 candle lookback window. So 2h structures get scalped top-to-bottom and bottom-to-top Now its time to aim for mainnet deployment!
Guideyoutube.com/@KodeKloud
Jev Explained in 3 Mins
KodeKloud’s short: what a System One model decides, and why it answers in milliseconds.
Awais.
@abbas_kazmi066
plenty of use-cases for JEV. here's one: Hover Explanations. This simple demo testing cost me around: $0.006. Definitely very cheap and fast.
Cuth
@ItsCuthulhu
Now that I've collected so much data on System One models on my DGX Spark. Lets see if I can beat Jev on size, speed, and accuracy. It will be MIT for anybody to build on. Everything I do on here is open source.
XOpen source
Trying to beat Jev on a DGX Spark
Browser Use
@browser_use
Luna (planner) -> Jev (actor) playing poker and winning
XGames and real time
Luna plans, Jev plays poker
Our guidemadewithjev.com
Jev pricing
What Jev costs and what that buys: $0.042 per million input tokens, output free, and a median of $0.000068 per decision across 15 published runs. No free tier.
Chris Brownridge
@chrisbrownridge
another @treg_ai /Jev demo to analyze data SUPER fast used treg to pull meta ads and then Jev to teardown the landing pages so you can understand where brands are sending traffic to categorizes the type of page, offers they're using and language used to sell also shows ad creative launch velocity, creative mix takes a few seconds to do it all end to end.
XAds and marketing
Tearing down Meta ads and their landing pages
OpenMed
@OpenMed_AI
Four typed questions across four authored fictional notes, labels written before the calls. Jev 16/16, Laya 13/16. On the medication-conflict question alone: 4/4 vs 2/4. A useful diagnostic, not a clinical accuracy estimate.
Ricker
@0xRicker
Jev Engineering turns a static agent workflow into a graph that can rewrite itself while running. the video is basically the problem at scale: hundreds of routes → thousands of crossings → different agents → different tools → different confidence levels Jev Engineering doesn’t control every step. it controls the crossings. when two paths compete: → score both → kill the weak route → reroute the task so instead of one fixed chain, you get a live braid: state → decision → parallel paths → crossings → Jev → next state that’s the point of Jev Engineering: more parallel execution without letting the system lose the objective. the agents create the paths. Jev decides which paths survive. full breakdown below ↓
XAgents and browsers
Jev Engineering rewrites the agent graph
Guideyoutube.com/@CalebWritesCode
Jev explained in 7min..
Caleb Writes Code: what Jev is and how to use it, in seven minutes.
Automater
@automater_ai
Claude Code Jev as MCP tools the agent can skip is not a gate. Put it in a PreToolUse hook that fails closed, with deny rules behind it. automater.ai/intel/jev-clau…
ArticleSecurity and abuse
A Jev gate belongs in a hook, not MCP
Dain
@Dain0x
A beautiful pattern can still be noise. Here’s a JEV × Opus 5.5 workflow I’d test: → A data pipeline identifies candidate signals. → JEV selects which candidates to investigate using defined criteria. → Opus helps explore those candidates and propose explanations. → Statistical checks and human review test whether those explanations hold up. The critical question: what evidence would change our mind? That question belongs inside the workflow, before a promising pattern becomes a confident conclusion. This animation illustrates the concept. The particles and activity are simulated.
XBenchmarks and evals
Separating signal from noise with Jev and Opus
frevana
@frevana_ai
Jev made competitor ad research 30x faster and ~90% cheaper. Typed “game” as the category. 20 seconds later: 294 top TikTok ads across 68 brands analyzed. Jev: 1/ pulled the top ads from the past 180 days 2/ scored every ad on stop-scroll, hook, format + trust 3/ clustered them into creative patterns 4/ classified winners, watches + dogs 5/ built a leaderboard of what’s winning 6/ recommended what to make next 20 sec · ~$0.02 For gaming: Winning hooks: offer/promo, challenge accepted Winning format: gameplay + UGC This is what we’re building at @frevana_ai with Jev: decode your category’s ads → know what to make next, in seconds. Try it free. Link in the first comment 👇
XAds and marketing
Frevana decodes a category's ads in 20 seconds
- Ads analysed
- 294
- Brands
- 68
- Run time
- ~20 s
- Cost
- ~$0.02
Our guidemadewithjev.com
Jev, explained simply
No jargon: Jev answers multiple-choice questions about a piece of text, in about the time it takes to blink, for about a hundredth of a cent. What that changes, and what it cannot do.
nicolay
@nicolaycz
Jev is the semantic if in your agent loop. LLM writes. Jev (TypeSafe System One) answers typed questions on the same state (Noul, Choice, Score) with calibrated probs. Your code branches. Pull the cheap decisions out of the LLM. Early access: console.typesafe.ai
XAgents and browsers
Jev is the semantic if in your agent loop
leiro
@leiroops
the lead engineer at a research company used Jev + GPT-6 Astra to build a system that analyzes 12,000,000 source updates every 6 hours the system processes massive streams of information, a process like this used to cost $1,000,000+, now Jev engineering does it for $200 the first version relied on GPT-6 Astra to interpret every result it worked, but processing millions of routine decisions through a large model created unnecessary latency and huge inference costs then the engineer rebuilt the decision layer around Jev instead of explaining every source, Jev reads the saved task state and decides what should happen next: Research, Write or Review Astra handles the open-ended work: comparing evidence, resolving contradictions and preparing the client brief code controls the queue, permissions, budget, retries and files at the core of this system is the same architecture I break down in the article: Jev routes the work, GPT-6 Astra handles the open-ended tasks, and code keeps the process running continuously the full step-by-step build of a client research process that runs 24/7 is below ↓ would you let AI decide which findings deserve further research?
gabidev
@GabiDev98
Here is another use case for Jev in DeFi. Vault risk classification. I pulled the signals that matter for Morpho vault risk (allocations, LLTV, utilization, idle, oracle, curator, APY sanity, liquidity and more) across 50 @Morpho vaults on Base, then asked Jev to score each one
XTrading and markets
Scoring Morpho vault risk with Jev
- Vaults
- 50
- Run time
- 22.6 s
- Average
- ~452 ms
- Cost
- ~$0.006
Matt Canham
@matthewcanham
I've had many people reach out saying they've read everything they can about Jev but still don't get it. Here's an explanation for the normies
Guidex.com
Jev Explained for Normies
Matt Canham’s X article, written for everyone who read the launch posts and still did not get it.
keno
@kenonews
JEV can’t see images. So how do you give it eyes? What’s possible? What can run locally? How far can you get for free? The answers (and the catches) are in the full breakdown below:
XDocuments and OCR
How to Give JEV Eyes
ArticleBenchmarks and evals
JEV, por qué ha revolucionado todo
A Spanish guide to what Jev is and how to try it.
Alex dc
193x
Hassan
@nutlope
Just trained Tev1 0.8B, a tiny Jev-like classifier. Here it is running completely locally on my mac with @ollama & classifying some tasks. It's extremely fast: only ~50ms E2E latency. Video is not sped up! Releasing weights & benchmarks very soon so you can try it yourself :)
Our guidemadewithjev.com
Jev vs an LLM
Same job, both ways: 100,000 posts for $0.67 against 214 for $0.98, 0.35 seconds a passage against 8.83, and the cases where the larger model still won. When to use which.
Viv
@Vtrivedy10
Jev for RAG in almost all cases you trust the semantic matching capability of Jev more than dot product similarity very useful as the direct similarity metric in small data cases and a great reranker with big data
XSearch
Jev for RAG
Jarek
@jarekceborski
I rebuilt CAPTCHA with Jev The browser measures how you fill in the form. Server turns that into a few plain sentences, and Jev decides: person or bot. Blog post with source code in the comments:
XSecurity and abuse
A CAPTCHA rebuilt with Jev
Frit🅾️ Pendej🅾️
@vwapster
I think I just developed an additional income stream for everyone. Kalshi $BTC 15minute trading bot built with OPUS 5.5 + jev. Real time decisions in volatile short markets. Join the waitlist and track the real time PnL at https://vwapster.com Working on launching hosted subs by end of next week.
XTrading and markets
PickA Kalshi BTC bot on a 15-minute clock
Guidetypesafe.ai
Introducing System One Models and Jev
TypeSafe’s launch post: the model, the evals, and their caveats.
Oliver Prompts
@oliviscusAI
grok 4.7 thinks. jev reacts. together, one ai agent now thinks twice before every move, and pays a fraction of the price for it. grok-jev-reflex is a free, open source router that pairs the two: grok 4.7 plans and acts, jev makes the cheap yes/no calls in between, at $0.042 per million tokens, answered in about 239ms. > 25 tools sent on every call, cut down to 8 > first prompt: 17.8k tokens → 9.5k tokens > bill: up to 40% less, same speed > numbers measured independently on xAI's grok build setup is 5 messages, no code, about 7 minutes: make a key, install, add one rule, run a shadow day watching only, then go live. jev can only make the agent do less or ask more, it can never approve anything irreversible on its own. 100% free. open source.
XRouting and model choice
PickGrok-Jev Reflex
Ali
@alisadiq_ai
Been playing with Jev, and I built a realtime filter for my LinkedIn feed. Trying to be more active here without drowning in AI slop. It scores posts in real time based on what I care about. Change the prompt, change what matters. Open source. Demo below 👇
XSocial feeds
PickA real-time LinkedIn filter
Dario Crespo
@crespodario
Metí a Jev en un chat. 🤖 Para que no tengas que volverte loco/a adivinando cómo probarlo. probalo sin instalar nada y sin registrarte. 👉 Probalo acá: lnkd.in/dhFX5XYY Y si querés armar tu propia versión, el código está abierto: lnkd.in/dXSu6ki3 😉
XTools and apps
Put Jev in a chat
Our guidemadewithjev.com
Jev for SEO
SEO work as typed decisions: a client audit at a tenth of the old cost, 4,000 Reddit threads scanned for AI citations, 10,000 words checked for slop in 2 seconds.
Christopher Jones
@dr_chrisjones
Progress on building the data center optimizer for load balancing + power management. Also testing Jev against classic algorithms and an LLM agent with the same GPU rack, same 24h of jobs, and same power limits. Needs more tuning but not bad for an early demo.
XTools and apps
A data center optimizer
Dario Crespo
@crespodario
Metí a Jev en un chat. 🤖 Para que no tengas que volverte loco/a adivinando cómo probarlo. probalo sin instalar nada y sin registrarte. 👉 Probalo acá: lnkd.in/dhFX5XYY Y si querés armar tu propia versión, el código está abierto: lnkd.in/dXSu6ki3 Contame
XTools and apps
Jev in a chat, no install
Guido Pettinari
@coccoinomane
Local open-source version of Jev 🤯
XOpen source
A local open-source version of Jev
Guidegithub.com
Awesome Jev
The companion list to this site: use cases, projects, SDKs, tools and learning resources, with pricing, limits and a quick start.
netrunner
@plotarmordev
Another Jev competitor just dropped: CLM-8B from @jackyk02 and the Stanford/NVIDIA team, with open weights under Apache 2.0. They claim it's comparable to Jev with up to 9x faster inference We went from zero options to a new one almost every day!
XOpen source
CLM-8B, a new Jev competitor
Yasuhito Morimoto
@yasuhito_morimo
【国内AIエージェント動向(2026/9/24号)】 本日の国内AIエージェントニュースのポイントはこちら👇 ・ KandaQuantum、Fuga v2で1日2,000体超を指揮 ・ GPTBots. ai、Jev統合で二層型AIを構築 ・ 信頼度スコアで自動実行・人手確認・上位判断を動的切替 ・ 競争軸は単一モデル性能から運用設計・業務KPI重視へ ▼ 各主要トピックの詳細はこちら https://note.com/yasuhitoo/n/nf13d3908657e AIエージェントは「賢いモデル選び」から「どう役割分担し、どこで人が介在するか」の設計競争へ。企業導入では運用KPIがより重要になりそうです🤖
XAgents and browsers
GPTBots.ai integrates Jev
CyrilXBT
@cyrilXBT
JEV IS INSANE your coding agents forget everything the second the session ends. Every fix. Every dead end you already hit. Every "no, not like that." Gone. Tomorrow you teach it all over again. Someone just open sourced the fix, and it runs on Jev. It's called Beacon, by @asymptotelabs. It pulls in your session history from Claude Code, Codex, Cursor, OpenCode and 20+ other agent tools and turns it into one shared memory. But storing everything isn't the hard part. Most agent runs are GARBAGE to learn from. Failed commands. Wrong turns. One off hacks you'd never want repeated. Save all of that and your agent just gets confidently worse. So Beacon keeps the full history, and Jev decides what actually earns a spot in memory. Promote it. Send it for review. Or throw it away. That's a decision you need on thousands of sessions, which is exactly why it has to be cheap. The runs that survive get turned into reusable skills. So something Cursor figured out on Monday shows up in your Claude Code session on Tuesday. Your agents stop starting from zero. Every good run makes the next one smarter. Repo: http://github.com/Asymptote-Labs/agent-beacon Star it and save this. follow @cyrilXBT
XContext and memory
PickBeacon fixes a coding agent’s memory
Our guidemadewithjev.com
Jev for ads
Paid media as typed decisions: a 1,891-ad library labelled in 19 seconds for $0.12, and a 30-persona focus group over 723 ads — 21,690 judgments — for 22 cents.
Geek Lite
@QingQ77
Codex 一旦开跑，推理档位就钉死不动，读个文件也用最高强度烧 token,这个项目让 Jev 在每次生成前重新挑档位。 github.com/miuuyy/Astra-A…
XRouting and model choice
PickJev picks the reasoning level first
Eric Yang
@ericjingyang
In Silicon Valley, Jian Yang built the Not Hotdog app. A decade later, his bro has finally continued the family business. The Not Slop extension, detects LinkedIn and X slop as you scroll, powered by Jev. Every generation gets the classifier it deserves.
XSocial feeds
The Not Slop extension
Edwin Mesa
@edwinfmesa
Seguí con dos consultas: un código de verificación que no llega y cómo navegar con el teclado. Jev eligió seguridad y manual de usuario; Laya, privacidad y términos. En ambos casos me convencen más las elecciones de Jev, aunque Laya volvió a responder más rápido con mi setup.
XSecurity and abuse
Jev and Laya on security queries
Avid
@Av1dlive
everything you need to start building with jev, in one article. code, architecture, diagrams... everything you need to follow the build and make it your own. x.com/i/article/2102…
Guidex.com
Build an agentic harness using Jev
Avid’s builder’s guide, and the fullest public write-up of the pattern: where the decision layer sits in a coding harness, the five rules that keep it enforceable, and keel, the Rust app it was built in. Honest about the self-improvement part it has not proved.
Edwin Mesa
@edwinfmesa
Empecé a escribir: "¿Qué precio tiene la suscripción anual?". Antes de pulsar Enviar Jev y Laya ya sugerían planes_y_precios.md. Eso es lo que quería probar: si el agente puede tener una pista de qué consultar antes de que le llegue la pregunta, con mi setup Laya fue más velóz
XOpen source
Predicting queries before submission
Melvin Vivas
@melvindvivas
We can use this for Codex limits real-time sentiment analysis lol Jev + ElevenLabs
XTools and apps
PickReal-time sentiment with Jev and ElevenLabs
Packapun
@packapun
New kid on the block typesafe.ai/blog/introduci…
XBenchmarks and evals
TypeSafe introduces System One models and Jev
Our guidemadewithjev.com
Jev for marketing
Research, testing, publishing, demand and analysis — each with builds whose authors published the cost. 700 leads scored for $0.09, 100,000 posts analysed for $0.67.
Nikkhil Narang
@acenik10
Your agent doesn't need a bigger brain. It needs a faster one. Most agent decisions are tiny — urgent or not, which queue, yes/no. We run them through frontier models anyway. TypeSafe's new Jev does it in milliseconds for a fraction of the cost. #AIAgents #Jev
XBenchmarks and evals
TypeSafe launches Jev
Tuan N
@__tuan____
Sooner or later everyone will come to realize System One Paradox or Jev Paradox is real. The inevitable path forward is System One Plus or System One+. x.com/__tuan____/sta…
XBenchmarks and evals
The System One paradox
Pitofui
@Pitofuii
LLM をゲームで使ってて一番困ってたのが、考える時間の長さ。Agent 同士でリアルタイムに戦わせるのが、なかなか難しかったんですよね。 今回、@typesafeai の #Jev にアクセスできるようになったので、ゲームに組み込んで Sonnet Fast と動かしてみました。どんな感じかは下の動画を見てください！ 次は 5 秒のターン制限を外して、Laya と Jev をリアルタイムで戦わせてみようかな。
XGames and real time
Real-time agent battles in games
CyrilXBT
@cyrilXBT
x.com/i/article/2101…
Guidex.com
The Jev + Claude Code Stack
cyril on where the 200× and 400× headline figures come from and why they are a ceiling rather than a typical result: 31 bounded checks counted in one real session, and the decision-point audit that finds yours.
Mentis 🇦🇺
@adam_x_mentis
Gonna be… Massive! Here’s the pipeline architecture for the agentic pipeline. @typesafeai’s Jev super fast decision engine at the core. 👀🕳️🐇 🔥🚀🤖
XAgents and browsers
An agentic pipeline with Jev at the core
Alex
@AlexanderTw33ts
Jev is 9.5 hours into his 10 hour livestream and his question api has 16 paying customers chatwithjev.com
Han Keo
@KeoGrowth
En regardant Jev et Recursive Mass, je peut être sûr d'une chose les LLM ne sont pas la voie royal pour être le cerveau des agents, du moins pas pour longtemps. Jev même n'est pas encore au top du top sur ces probabilité montre déjà ces preuves pour ses prises de décision (plus rapide que des LLMs) Et Le Recursive Mass (pensées latentes sous forme de nombres) montrent qu'il est plus efficace pour les agents de communiquer entre eux partageant leu états internes plutôt que générer du texte compréhensible par des humains. Une chose est clair, on ne doit pas utiliser les IAs pour nos agent commet on les utilises pour nos discussions. Les Modèles n'ont pas fini d'évoluer :)
XSales and leads
A candidate for agent brains
Our guidemadewithjev.com
Jev as a judge
Using Jev in place of an LLM judge: 1,709 editorial judgments for under a cent, a social post judged for $0.00003, and what the confidence score is and is not. Where it beats a generative judge, and where it must not be one.
Jim Merioles
@jimmerioles
Yep, best ELI5 on TypeSafe's Jev:
XBenchmarks and evals
An ELI5 for Jev
Zenko | NeuralFrame Labs
@NeuralFrameLabs
Local Laya beats Jev 100% #localai #ainews #ai #laya #jev
XOpen source
Local Laya beats Jev
Edwin Mesa
@edwinfmesa
Explorando qué se puede hacer con Jev y Laya, que (los de moda), me pregunté si podrían ayudar a un agente de soporte a preparar el contexto mientras el usuario escribe su mensaje. El contexto es clave para un LLM, así que armé una startup ficticia con 7 documentos. 🧵
XContext and memory
How Jev and Laya help support agents prepare context
unicode
@unicodef1wn
x.com/i/article/2102…
Guidex.com
AI Company-in-a-Box: Grok Bot + Jev
unicode’s 12-step roadmap for a self-managing company: the Grok Bot marketplace supplies the workers, and Jev answers the five forks between them — who goes next, is the research good enough, continue or stop, is it done, does this need a person.
𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰
@techwith_ram
TypeSafe Founder's Blueprint for Building with Jev. Check out the pdf here: drive.google.com/file/d/17h982x…
XBenchmarks and evals
TypeSafe’s blueprint for building with Jev
dealer.eth
@dealerdefi
I LEAKED THE JEV STACK I RUN MY AGENTS ON, IT CAUGHT 11 OF THEM IN ONE NIGHT BEFORE THEY BROKE SOMETHING Agents don't fail at writing code, they fail at deciding, which file to open, which hunk matters, whether to run that command, whether they're actually done. Every one of those comes back as a paragraph you have to parse, and none of them come back with a number telling you how sure it was. So I made them typed, a confidence on every answer, and a gate written in code, not in the model, that acts, holds, escalates or refuses. Ran it on my own repo last night, 30 tasks, 4 agents in parallel, 360 judgments at 311 ms each, four cents for the whole thing. 19 shipped with nobody watching, 11 got stopped cold. Those 11 are the whole product.
XAgents and browsers
PickThe Jev stack
TypeSafe AI
@typesafeai
LLMs distilling Jev like
XOpen source
PickAn LLM distilling Jev
Our guidemadewithjev.com
Jev and MCP
The MCP servers that put Jev inside Claude Code, Cursor and Codex, so an agent can ask for a typed judgment as a tool call. Each one linked, with what it exposes.
ASI Hub
@ASIHubHQ
DEEP DIVE REPORT: TYPESAFE AI LAUNCHES JEV SUB 100MS SYSTEM 1 DECISION ENGINE EXECUTIVE BRIEFING Routing routine classification tasks through a 70 billion parameter language model is architectural malpractice. TypeSafe AI has introduced Jev, a sub 100 millisecond deterministic decision engine proving that single pass probabilistic heads dramatically outperform bloated models on System 1 tasks. 1. THE ARCHITECTURAL DILEMMA Enterprise AI architectures suffer from catastrophic inference latency. Standard multi turn agentic loops invoke heavy reasoning models for simple deterministic gates, resulting in multi second roundtrips and unsustainable cloud compute costs. Jev restructures decision topology by executing fixed logit classification in single forward passes at 70 millisecond latency. 2. FOUR HUNDRED TIMES COST EFFICIENCY Empirical benchmarks demonstrate 400x reduction in compute expenditure per decision token. By constraining entropy to predefined domain spaces, Jev eliminates model hallucinations while providing mathematically verifiable confidence scoring across decision boundaries. 3. 2027 TO 2030 OUTLOOK By 2027, over 80% of autonomous software agent workflows will execute on dedicated System 1 kernels like Jev. Frontier multi modal models will operate purely as supervisory reasoning nodes, invoked only when high entropy creative synthesis is strictly required. Strategic takeaway: Compute efficiency dictates architectural survival. Split your system into fast reflex execution and slow deliberative reasoning.
XBenchmarks and evals
A sub-100 ms decision engine
Phil Dressler
@phil_dressler
I put AI agents in a GTA-style fighter jet competition… Astra built it. Jev powers their brains.
XGames and real time
PickFighter jets flown by Jev agents
Valvet Online
@VALVETONLINE
I RAN A DECISION MODEL ON MY LAPTOP TODAY 1. Kev: tiny Jev-style models built on Qwen3.5, 0.8B up to 9B 2. 4B/9B fit a 32GB Mac, also CUDA and ROCm 3. Answered a support ticket in 495ms with probabilities #OpenSource #LocalAI #ML
XOpen source
A Jev-style model on a laptop
Hassan
@nutlope
x.com/i/article/2102…
Guidex.com
How to train your own Jev for $17
Together AI fine-tunes a Jev-like classifier on Qwen3.5 4B: about 38,000 questions sampled from eight public datasets, roughly 25 minutes of training, and the tev1 repository that runs it. The $17 is the training job, not the system around it.
WOLF
@FXWOLF2
Jevに渡すデータを変えてバックテストしてたんだが、時刻情報を渡さんほうが成績いい。Jevにとってはノイズなんか。テクニカルも削って１つか２つしか渡さんのが良さげ。あと、直前のトレードの勝敗渡したら良くなるかなって思ったけど変わらんかった。とりあえず、最初にもらえる$5を使い切るまでは遊んでみる。
XTrading and markets
PickFewer inputs, better performance
GeethanTech
@GeethanTech
Jev returns bounded choices, scores and probabilities instead of prose. That limits out-of-schema output, not incorrect decisions. TypeSafe’s speed and cost claims still need independent testing. geethantech.com/posts/jev-type…
XSocial feeds
Bounded choices, not prose
CRYPTOFANZ💜💎
@cryptofanz12
𝗝𝗲𝘃 𝗜𝘀 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗜𝗻𝘁𝗼 𝗔 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 ⚡ As the first System One model launched by @typesafeai, Jev is designed for software that needs AI to make fast, structured decisions—not generate paragraphs of text. Traditional workflow: Prompt → Text → Parsing → Validation → Application Logic Jev takes a more direct route: Application State → Typed Question → Typed Decision No JSON prompting. No complex output parsing. Just typed decisions that applications can use directly, along with probability and confidence. Jev supports three decision patterns: 🔹 Choice — Select from predefined options 🔹 Score — Evaluate using a defined scoring framework 🔹 Noul — Handle structured decision problems built around application logic Speed is another key feature, with response times designed around roughly 70–500ms. Its pricing is also aimed at high-frequency inference: 💰 $0.042 per million input tokens 🆓 Output tokens free Potential use cases include: 🎫 Ticket routing 🛡️ Content moderation 📊 Risk scoring 🤖 Agent branching and workflow decisions The bigger idea is simple: AI doesn't always need to write something for a human to read. Sometimes, software just needs a fast, reliable decision. That makes models like Jev an interesting direction for AI agents, automation, and real-time application infrastructure. Available through the http://B.AI API as: Jev-1.13.0 Jev-Latest 🔗 http://chat.b.ai/chat @justinsuntron @BAI_AGI #TRONEcoStar #AI #Agents #B_AI
XBenchmarks and evals
A structured decision layer
Our guidemadewithjev.com
Jev agentic harness
An agentic harness where the model proposes and Jev decides, with a receipt per step: 6 published builds, a batched router on a latency deadline, and the rule check that runs in 348 ms.
ABNewswire
@ABNewswire
Jev Brings Fast, Structured Decision-Making to AI Workflows dlvr.it/TVcnJg #ComputersSoftware #Technology
XBenchmarks and evals
Fast, structured decision-making for AI workflows
Lilys.ai
@LilysAI_
@tetumemo 要約ノートができました👉 lilys.ai/digest/1147040… - Jevは次の行動を決めるためのAIエージェント用「脳」。 - 文章生成ではなく、選択・数値・はい/いいえの判断に特化。 - うまく設計すれば、品質を落とさずコスト削減が狙える。
XBenchmarks and evals
The decision-making brain for agents
echild
@eboppu
some more tests. jev for tool filtration is really good.
XBenchmarks and evals
Jev in tool filtration tests
GREG ISENBERG
@gregisenberg
Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on @startupideaspod (thanks to @ryanvogel for coming on and spilling the sauce today) Watch: https://www.youtube.com/watch?v=4mTLpuQpB80 Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.
Guidex.com
What Jev is, and the businesses it unlocks
Greg Isenberg: find an expensive queue and put Jev at the front of it. Seven ideas, from instant quotes to lead scoring, plus a Startup Ideas Pod episode with Ryan Vogel.
Allie the Icon
@allietheicon
One of my favorite categories of Jev use-cases is real time speech! Teleprompters, speech coaches, and so much more
XTools and apps
Jev for real-time speech
Dorian Smiley
@dsmiley411
I finally managed to burn through my free usage credits with Jev, all $5 of them. 123M tokens. Man this thing is cheap!
Tatsuya Shirakawa
@s_tat1204
jevや派生を使ったアプリケーションも、あまりconfidenceを利用していなさそうなことを考えると、識別ができてしまえばなんでも良く、この手のembeddingベースの方法でも十分なケースは多そうですね。
XTools and apps
Jev apps and how they use confidence
Our guidemadewithjev.com
Jev agent orchestration
Who goes next, is this good enough, should we retry, when does a person step in. Grok Bot ships the workers; a Jev router answers the fork. The three shapes it takes, and where the evidence stops.
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.