What people are building 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.

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.

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 👇

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 😉

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.

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

Guido Pettinari

@coccoinomane

Local open-source version of Jev 🤯

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!

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🤖

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

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…

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.

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.

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

Melvin Vivas

@melvindvivas

We can use this for Codex limits real-time sentiment analysis lol Jev + ElevenLabs

Packapun

@packapun

New kid on the block typesafe.ai/blog/introduci…

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

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…

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。

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. 👀🕳️🐇 🔥🚀🤖

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 :)

Jim Merioles

@jimmerioles

Yep, best ELI5 on TypeSafe's Jev:

Zenko | NeuralFrame Labs

@NeuralFrameLabs

Local Laya beats Jev 100% #localai #ainews #ai #laya #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. 🧵

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

@techwith_ram

TypeSafe Founder's Blueprint for Building with Jev. Check out the pdf here: drive.google.com/file/d/17h982x…

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.

TypeSafe AI

@typesafeai

LLMs distilling Jev like

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.

Phil Dressler

@phil_dressler

I put AI agents in a GTA-style fighter jet competition… Astra built it. Jev powers their brains.

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

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。

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…

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

ABNewswire

@ABNewswire

Jev Brings Fast, Structured Decision-Making to AI Workflows dlvr.it/TVcnJg #ComputersSoftware #Technology

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。

echild

@eboppu

some more tests. jev for tool filtration is really good.

Allie the Icon

@allietheicon

One of my favorite categories of Jev use-cases is real time speech! Teleprompters, speech coaches, and so much more

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。

Aradhye Agarwal

@AradhyeAgarwal

While the whole world is going crazy over Jev, I give you a more skeptical take. I think it's a great model––but its modeling choices cause real failure modes. 1. The way the speed argument is presented in the quoted tweet isn't valid IMO. You can simply treat every row in the output as independent and sample the autoregressive LLM in parallel. 2. The main benefit of autogressive models is enabling increased computation depth with the same amount of model parameters (via CoT). I don't see how that would be possible with Jev. 3. Jev isn't a magic wand that will one-shot any decision task. I can come up with a task where LLMs do better in terms of efficiency: suppose you have 100 choices out of which you have to choose 10. Also suppose someone has already chosen some 10 choices. You have to tell the "goodness" of this choice. With Jev you could naively multiply the probabilities of choosing the options, but this would not work. Making one choice changes the probability distribution of the other options which has to be accounted for. A simple example one of the chosen options is repeated. Jev will give both the options equal probability (due to symmetry) but it's unlikely that this would be the optimal choice (assume repeating options is downright bad). With LLMs however you can prompt it like so: "okay you have 100 options and you have to answer which ones (a total of 10) you would choose in a sorted order." Then gaslight the LLM into thinking it generated the 10 options (in sorted order) by just prefixing those tokens to the assistant channel and then measure the probability of generating that output. This would happen in one-shot. A careful reader might say: "but we can do the same thing with Jev too right?" Yes that's correct, you could have Jev choose one option then conditioned on that choose the second one and so on. Then multiple the probabilities. But the problem is that this will take 10 sequential steps; you can't one-shot it unlike with an LLM where there is no recurrence.

Jun Penn

@heyjunpenn

Hey Jev builders! 🚀 What are you working on? Get your demo into JevBest so people can watch it and find the original post. Drop yours below 👇

Sergii Makarevych

@sermakarevich

@typesafeai JEV use case I will have first AI coding interview ever. I know its gonna last for 1.5 hours and I will have to clone an exercise repo. So what I should be prepared for ? Lets use LLM for hypothesis and JEV for ranking:

Jerry Xu

@jerrycxu

tab-jev: jev-like model + tabular foundation model = an in-context learner for your text & tabular data. https://github.com/edamame-labs/tab-jev Many real industry datasets are a mix of tabular and text data: - Tabular foundation models like TabPFN learn from a few hundred labeled rows in context, with no training. But they can't read text. - Jev-style models read text and answer typed questions with scores. But they can't digest tabular data well. Experiment results on kaggle problem "Kickstarter: will the project get funded?" is shared in the repo. Missing a flight at JFK is a productivity booster...

Eugene Cheah - AI builder @ 🇸🇬|🇺🇸

@picocreator

Yes, it is quite literally next to the score - the preferred prompt format - and so is the code for the inference at github.com/featherless-ai…

belorix

@0xbelorix

YOUR VERIFIER STILL COSTS LIKE A FULL MODEL Claude writes. then Claude reads the whole pile again to say yes or no. every extra loop turn makes that pile bigger. Jev: $0.042 per 1M input tokens. output tokens free. it returns a decision, not another essay. same job. wrong tool if you still use a writer as the gate. read the article below

Stephon Proctor, PhD

@stephonomon

Deskilling is a real risk when it comes to using AI scribes. The drafts often look plausible, which leads users to overly trust without verifying. Models like #Jev could help provide users with better insights onto areas where the draft might be wrong. github.com/Stephonomon/sc…

Likit D

@Likitd_

Jev jev jev jev Your RAG pipeline just got a relevance upgrade. excited to introduce jev-ranker — Jev-powered reranking and relevance filtering How much can smarter reranking improve a RAG pipeline? Try : npmjs.com/package/jev-ra… Benchmark Results:

Lahiri

@Ziggyzonty

JEV is INSANE... I gave it an educational video and its transcript. Every 5 seconds, it looks at the next 5 seconds of content in the context of the previous 60 seconds. It scores the segment on 5 dimensions: Relevance. Usefulness. Importance. Memorability. Actionability.

James Pardoe

@JamesPardoe

If you're still scratching your head about Jev, this is the best explanation that I've seen.

Dorian Smiley

@dsmiley411

Testing Jev’s accuracy tonight for next best action prediction. The results: Canonical accuracy: 98.6% Generalization accuracy: 41.5% We ask Jev to predict the next state in a program from the current partial program. The suite has 25 cases and we ran it 20 times. Seven cases are represented in the in context examples. The other 18 are held out cases that require Jev to generalize from those examples. The failures are not uniformly random. Jev generalizes perfectly on some unseen compositions and fails almost deterministically on others. That suggests there may be specific structural boundaries to what it can infer from context. Maybe some of this is prompt design. Maybe it is a capability boundary. We’re testing that now. But next best action prediction is important. A huge amount of software today contains really brittle decision logic: onboarding, payments, claims, revenue cycle, approvals, exception handling, etc. If Jev is a bet on software consuming intelligence, this is exactly the kind of high frequency, high value logic it needs to improve.

Jason Alco

@Jasonalco

I put Jev vs 5.6 Sol head-to-head on a text categorization step in a platform I’m building. Surprised by the outcome. Aren’t text-based judgements the exact use case Jev is made for?

Mendy

@MilkMendy

If you run a business, Jev might be a really good alternative to your heavier models. It's really good at classifying things and choosing between yes or no, and it could be 200X quicker and a lot cheaper than the other frontier models. #AI #AIAgents #AIAutomation

Sama

@osamalamaaa

all that hype about the potential use cases for jev and everyone just rebuilds common programs from first principal. I have yet to see a legit new use case that has wowed me but thanks random guy for building chatwithjev real novel stuff

@kleos

@1kleos1

A 20-YEAR-OLD JAPANESE STUDENT BUILT A JEV POLYMARKET BOT OVER THE WEEKEND, FIRST NIGHT IT MADE $17,831 Starting capital: $148 First night: $17,831 profit Total profit so far: $276,000 he built the whole thing around one stupidly simple loop Polymarket data comes in → Jev judges the setup → confidence gate checks it → deterministic code executes Jev only gets three choices BUY YES BUY NO WAIT the bot scans dozens of markets at once looking for odds that stop matching the live data around them while a normal LLM is still explaining why something might be mispriced, Jev has already made the decision and moved to the next market position sizing, max loss, execution and exits stay in code Jev only handles the fuzzy part is this market actually wrong right now? $148 starting capital $17,831 on night one $276,000 so far and the craziest part is I have the full setup he used the Jev prompts, decision flow, confidence gates and Polymarket architecture might drop the whole thing next

Sodeh Abadi

@irSodeh

Jev vs Laya-MLX isn't really a rivalry. Jev is the original, commercial model. Laya-MLX is a local, open-source take on the same idea, running natively on your Mac. Same core concept: "System One" models. No chatting, no text generation. Just fast, typed decisions. #Jev #Laya

WquGuru

@wquguru

Jev 发布才 8 天，开源替身已经一堆了，今天诺基亚也来蹭热闹了😅 我在 MacBook 上，让官方 Jev 和 3 个开源方案同跑 20 张中文工单🫱 Laya 25ms，比 Jev 快 50 倍 djev 509ms 官方 Jev 1.3 秒 AnyJev 将近 4 秒 仓库都在这： Laya（Mac 版）http://github.com/mizorewww/laya-mlx djev（Mac 版）http://github.com/jamescorbett/mlx-vlm AnyJev http://github.com/nokia-applied-research/AnyJev 但快的，不一定对 👇

Darshan Jain

@i_darshanjain

Jev is all over my timeline rn 👀 Went looking into it and stumbled across Laya. Open-source System 1 decision model. They even have a Jev comparison 👀 7.8× faster. 3× better calibrated. 45/51 languages. Self-hosted. And the weights are right there. 😂 Yeah… I am watching this space. https://huggingface.co/convaiinnovations/laya

Saksham Malhotra

@SakshamMalhot27

Launching JevRelevanceRetriever just $0.73 across 6,460 calls Open src Jev LangChain BaseRetriever for rel scoring Install and use it, no chain changes ;) pip install jev-relevance Repo-https://github.com/saksham-malhotra-27/jev-relevance Open for issues and PRs, feel free to contribute

Koimiao🐈

@jaunatis_q

We built a Stanford Town powered by Jev. It isn’t a game demo, there is no player. The residents decide where to go, who to meet, and when to talk. Humans can only watch their pixel world unfold. Demo + code: github.com/NevaMind-AI/je… #Jev #AIAgents

Nitin.nn

@NitinthisSide_

After seeing Jev everywhere on the feed lately, we had to try this direction ourselves. We’re bringing Laya to Insight O Mate. A fast, open-source, local decision model that fits perfectly with our privacy-first approach to querying NoSQL databases. Your data stays local. Your decisions stay local. Insight O Mate + Laya is coming. Try Insight O Mate at https://insightomate.lunamic.co

Choroshin Alex

@choroshin

"לקוח יקר, זוהתה פעילות חריגה בחשבון הבנק שלך. החשבון יוקפא תוך 24 שעות. לאימות פרטים לחץ כאן." רציתי לשחק עם Jev, המודל החדש של TypeSafe, וגם לבנות איתו משהו שיש בו תועלת אמיתית. אז בניתי ב-Skills IL כלי שבודק הודעות כאלה: מדביקים את ההודעה, מ-SMS, מוואטסאפ או ממייל, ומקבלים

Saksham Sharma

@code_saksham

And just when I thought I have a crazy idea with JEV that i will open source then this happened. Please @typesafeai fix it ASAP, the curiosity inside me to build that thing is insanely high

Siim Haugas

@siimh

scoreboar v8: 128 MB instead of 597, about 30 ms a post instead of a second. and more accurate. guessing which of two X posts did better for its account size: 61.4%. grok 4.7: 55.3%. jev: 52.5%. free, in your browser. github.com/Siim/scoreboar…

Chase

@0xChaseTM

Jev Engineers just released Jev, and the architecture is simple: state goes in, auditable decisions come out I mapped the decision layer into a field blueprint: what Jev reads, what it returns, and how an agent uses those answers in a real loop: step 1 → give each layer a job. The LLM plans and writes. Jev chooses between bounded options. Code carries out the action. step 2 → build the state before asking for a decision. Include the goal, evidence, available actions, previous actions, and what the environment looks like now. step 3 → define the questions in code. Choice selects an option. Score rates it. Noul estimates whether a condition is true. The answers are typed, so the runtime can use them directly. step 4 → ask independent questions together. One state snapshot can support routing, risk, and relevance decisions in the same pass. step 5 → use the answers to route work. A lookup, a complex reasoning task, and a tool call don’t need the same model or execution path. step 6 → add a gate before the tool runs: allow, ask, or deny. A high-confidence answer still has to pass the action policy. step 7 → execute the selected action and check the outcome. Record what the tool actually did, including failures. step 8 → update the state with that result. Otherwise, the agent’s next decision is based on a world that no longer exists. step 9 → apply the pattern where small decisions repeat: model routing, tool guardrails, inbox triage, research classification, browser actions, and context compaction. step 10 → judge the whole task. Track whether the agent reached its goal alongside cost, latency, approvals, and failed actions. the result: you can trace an action from the evidence Jev received to the decision it returned and the outcome your tools produced. Save the Jev Decision Infrastructure blueprint for your next agent build ↓

YZ

@robot_yz

使用 #Jev 做了一个填写外链信息的演示。 眨眼的功夫就找到入口，并准确的把已知信息填写完了。 准备把之前的Submit Agent改成使用JEV模式。 关注github，在评论区

Harsh Singh

@haaarshsingh

i created a 404 page that fixes your typos for you, and redirects you to the correct page built with @typesafeai jev try it out: kobra.systems/cmr-table

Justin Youens

@youens

This is the neatest example of Jev I've seen. But didn't see a demo link, so here you go: emogee.youens.com

Leaf Yeah!

@leaf_sanren

啊啊啊啊啊🎉🎉！JEV 黑客松已经出来啦！！！ 同时，我也见到了把 Jev 用得最对的一个项目！！ Jev 这模型最大的特点： 只会判断，一个字都写不了。所以用它的关键，就是别让它干写字的活。 这个开源的「聊天副驾」就很聪明： 你在微信里收到一句"在吗"或者"方案再想想"，它先让 Jev 一秒判出对方真实意图、危险等级 1–9、该马上回还是先晾着； 写回复这活交给另一个会写字的模型起草 3 条，最后再让 Jev 排个序。 判断归判断模型，写字归写字模型。 且它最好的一点：它永远不自动发送。 搞得我都想去闲🐟搞几台吹灰的安卓机啦！ 期待后续也能拿下 iOS、macOS、鸿蒙系统！ https://x.com/AYi_AInotes/status/2102605821456814286/video/2

Erik Kokalj

@erik_kokalj

Another cool demo, specifically for Jev Omni: x.com/erik_kokalj/st…

Artem

@S0RATNIK

Немного упоролся в Jev, но так у меня всегда бывает, когда на чем-то гиперфиксацию поймаю. Вот, например, собрал подборщик нейросетей на базе тех, что есть у OpenRouter, под ваши задачи. Просто пишите, что хотите - получаете подборку нейросетей.

Utkarsh Maheshwari

@utkarshgsuv

Is the Jev hype real, so I made to test this on my movie recommendation engine, which is getting crazy results on Quen Re ranker , I used an open-source alternative of Jev , Laya. But unfortunately Quen is way better, dekhlo! @typesafeai #jev #Jev

Spikez 99.9%

@0xSpikez

Jev Founder, Diogo Amogo, just released 12-page PDF on building a Jev Harness for coding agents this is a 10-step blueprint on how to make your coding agents 200× faster and 400× cheaper: step 1 → meet Jev: the LLM writes, the harness executes, Jev decides what each turn sees, where it routes and whether it runs step 2 → ask the question that breaks every agent: how would you design one if LLMs had no KV cache? step 3 → stop routing blind: Opus → Sonnet → Opus costs 6.19 vs 4.15 for pure Opus, because handing back reprocesses the whole context step 4 → follow the tokens: reading and searching take 56.2% of tool turns and 46.5% of tokens. Writing code is under 10% step 5 → score every chunk per query: hide, short summary, long summary or full. Compress after the question, not before step 6 → disclose tools in tiers: one-line snippets for 100s of tools, schema on demand, docs for one-off queries. Batteries stop costing context step 7 → load instructions by condition: touching *.tsx loads the style guide, billing/ loads its gotchas file, and compaction can't erase either step 8 → route by trust, not just difficulty: secrets and infra stay on first-party frontier models, public docs go to the cheapest one step 9 → share one retrieval pass: cross-model review, eval generation, ELI5 explainers and live progress pages all run read-only in the background step 10 → gate every command: programmable allow / ask / deny policies that read a script before it runs, not just its name Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster.

bays wong

@dennis_huangbei

使用 JEV 玩贪吃蛇、打坦克，弱爆了，刚好前端时间用 astra 写了一个纪念碑谷的关卡foldedrealm.gameai.club，来玩个找不到 dom 的 3d 游戏，能成功算你厉害。截止发帖，已经玩了 15 分钟了，第一个机关都还没正确打开

lemon👑

@lemonDefi1

Laya just mogged Jev at Tetris. The interesting part isn’t just that a local model won. Laya is an open-weights System One model running locally on a 16GB MacBook Air. Jev, meanwhile, is cloud-based and built with Grok 4.7.

Blue007｜Crypto × AI

@blueoli007

JEV integration is not another demo. It feeds live data to one ID with a hard risk cap. The trace after execution is the moat — not the launch graphic.

Xiang Wei

@xgwei

Great minds! I've been building a stylized SF with Opus too, just without the pets. My cars follow rules on the real street network, lanes and traffic lights included. Jev driving pedestrians is tempting though. How many agents can it run at once?

web5kol

@web5_kol

Jev微信聊天助手已开源，可作为手机端对话副驾，帮助用户分析微信群聊和私聊意图。 Jev通过无障碍服务读取屏幕（非侵入式），结合AI模型判断对方真实意图、危险等级，并生成3条候选回复，支持一键填入但不自动发送 x.com/li9292/status/…

emil

@esnx_xyz

you know how x feed is blasting you with promos, politics, rage baits, etc? so i made a (tamper)monkey script that uses jev to classify and hide types of posts you don't want

Jeetendra

@jeetendrayd

Built a walkable 3D street view of where SF/Bay Area startups & VCs actually cluster, every building named, every classification made by Jev.

Kelbie | Sovran

@KevinKelbie

I made a tool with Jev that reads my whole codebase to answer one question: what should I be looking at? Every chunk of code in your project is scored for relevance.

ask

@s5ststtt

買い物コンシェルジュを作っていたら、思ったより面白いものになってきた 最初は 「野菜や食品が今安いか？」 を公的統計＋Jev＋LLMで判断する仕組み。 でも敵対的レビューをかけて改修していたら、本質が変わった。 今作っているのは EvidenceとActionの間に“科学的な昇格条件”を置く仕組み に近い