What people are building with Jev

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