What people are building with Jev

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。