Jev on X: What Developers Are Saying About TypeSafe's Decision Model

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Hacker Newsnews.ycombinator.com

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On the launch thread, the top counterpoint: an open project for the same decision-model pattern already existed, and it runs in 1–2 GB of RAM on CPU.

@jaredpalmerJared Palmer

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UPDATE: Kev-0.6B, 4B, and 8B are now available. Kev is a family of small open source Jev-like decision models you can train and run yourself. This new family is based on Qwen3 using the same LoRA + small pointer head technique as before, but scaled up. Out of domain, on data

@hwchase17Harrison Chase

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Decision Model is a great name, and I expect other entrants in this new model class before long

@BlackwellboyBlackwellBoy

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Ive spent the last couple days properly fucking around with JEV and i think ive finally got a decent idea of what it is actually good at and more importantly what i can stop wasting time trying to make it do 😂 First thing, JEV itself wasnt really the problem. My actual browser

@rasbtSebastian Raschka

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It’s easy to dismiss Jev it as “just a classifier”. But people (me included) who have been training encoder-style models for classification for many years know they were usually special-purpose and limited in some way. The breakthrough of Jev is that it generalizes well. And

@kunchenguidKun Chen

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poor guy claim to have built Jev a year ago but no one cared, and now Jev stole all the thunder many people are saying “you gotta tell your story” or “marketing is important”, and they just completely missed what actually made the difference here i just looked into this laya

@ItsCuthulhuCuth

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Top 5 Jev Alternatives: __ I have been running a benchmark since this morning on every Jev alternative against Jev. So far, none have been it on accuracy but some have slightly less accuracy with better speed. So far, here are my rankings... And be mindful, there are 40 still in

@BenjDickenBen Dicken

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Jev is cool. So is it's OSS companion, Laya. The Latest Cool Thing In AI™ tends to get a lot of hype, sometimes without everyone even understanding it. So... what is this thing? Jev is an AI model that consumes input and produces output VERY differently than chat, claude,

@MoonGotchiMoon

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Jev is INSANE. I built this in an evening and morning. A real-time trading bot ingesting onchain+offchain data to make rapid decisions about trades. Fully autonomous. So far it has lost me $31,680.

@rohonchainRoan

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Jev is the FASTEST AI model ever built for trading It makes calibrated buy/sell decisions in under 100 ms That is one real decision on every single block, 24/7 In this article I've shown EXACTLY how to build HFT trading system with Jev (from scratch)

@botnewsnetworkbotnewsnetwork

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WHAT THE MULTIPLIER LOOKS LIKE WHEN SOMEONE ACTUALLY BUILDS ON IT ⚡ Two days ago this desk published on Jev, the "System One" model from TypeSafe — a model that returns typed decisions with calibrated confidence instead of sentences. Our verdict was cautious: the 193x/444x

@jaredpalmerJared Palmer

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Kev-0.5B: A tiny open source Jev-like decision model with a TypeSafe-compatible API based on Qwen2.5-0.5B that you can train and run on a MacBook Pro. Model card and weights are available on GitHub

@cjzafirCJ Zafir

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I burned $3.40 on Jev in 24 hours. It's not Jev vs current LLMs. It'll be Jev + LLMs. Here are all the goods and bads of Jev. 1. Jev or a Jev-like system will be integrated into all LLMs for automatic model routing, small decisions, if/else queries, retrieval, tool selection,

@amitiitbhuAmit Shekhar

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9 use cases for Jev (System 1 Model): - Re-ranking: Score and reorder retrieved results by relevance. - Tool pruning: Drop unused tools during context compaction. - Model routing: Route tasks to small, medium, or large LLMs. - Query routing: Decide between SQL, Vector DB, or

@mvanhornMatt Van Horn

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TL;DR of my new article: WTF is Jev by @typesafeai, and the 9 things people are already building with it. The thesis: 𝗮 𝗰𝗼-𝗰𝗿𝗲𝗮𝘁𝗼𝗿 𝗼𝗳 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝘀𝗽𝗲𝗻𝘁 𝘁𝘄𝗼 𝘆𝗲𝗮𝗿𝘀 𝗶𝗻 𝘀𝘁𝗲𝗮𝗹𝘁𝗵 𝗼𝗻 𝗮 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻𝗻𝗼𝘁 𝘄𝗿𝗶𝘁𝗲 𝗮

@stretchcloudPrasenjit Sarkar

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TypeSafe AI raised $40M for Jev, and the interesting part isn't the fundraise. Jev is built on RLCD: Reinforcement Learning for Calibrated Decisions. The model is trained not to give confident answers but to give calibrated probability scores. Send it a diff and it returns 14

@OpenRouterOpenRouter

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Jev by @typesafeai is now on OpenRouter, in beta. Jev is a System One model. Instead of generating text, it takes your app's state plus a typed question and returns a typed decision with a probability attached. There is no JSON prompting, parsing layer, and nothing to validate

@kylejeongKyle Jeong

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we built blazing fast computer/browser use with Jev + @Stagehanddev. this task cost $0.001 and executed at near instant speed (in a remote browser btw) the loop: observe the page, send a11y tree as state + actions as questions, Jev decides the next action, then Stagehand

@gregpr07Gregor Zunic

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Breaking: Browser Use + Jev = Ultrafast ⚡ Findings flights took 7s and cost only $0.0039 🤯 > new action space every step > DOM state space > small LLM fallback to type (this video is at 1x speed btw) Built a tiny open source browser agent. try it below ↓

@conradlotzConrad Lotz

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INTERESTING: TypeSafe launches Jev—non-generative decision model TypeSafe AI shipped Jev, a model built for structured decisions instead of free-form token generation. Claims massive speed and cost advantages for workflow-level choices.

@george_onxGeorge Maloney

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We've been reading the comments. Weights are on Hugging Face.

@MatijaSosicMatija Sosic

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Here's a 45-second TL;DR on Jev. I find the core idea beautifully simple, but the video made it really hard to understand. Hope you find it helpful.

@TechmemeTechmeme

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TypeSafe AI debuts Jev, a model using "Reinforcement Learning for Calibrated Decisions" to produce typed probabilistic decisions that software can use directly (@thomasclaburn / The Register) (Visit Techmeme dot com for the link and full context!)

@kimmonismusChubby♨️

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Sounds almost too good to be true. Jev is an AI model built for decisions rather than text generation, introduced by Diogo Almeida and trained using a new method called RLCD. Claimed to be 20–200× faster and 40–400× cheaper than comparable LLMs. Costs $0.042 per million input

@harshagundalHarsha Gundala

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They were building in stealth for 2 years, I was building in stealth for 2 hours… Happy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe. ⚡️Demo below on a M4 MacBook⚡️ every LLM has the ability to efficiently batch

@ryanvogelvogel

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this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away

@MichaelLee04Michael

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I got access to Jev earlier today (thank you @hackgoofer). I have run ~5,000 requests so far, (which cost me around $2!), across classification, model routing, intent, steering, and many other things. tl;dr, Jev enables a new intelligent decision-making primitive, separate from

@testingcatalog🚨 AI News | TestingCatalog

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TypeSafe AI has introduced its first model named Jev - a small and customizable System One decision model. Not being an LLM, Jev can’t reason or write explanations; it generates a fast judgment with calibrated probabilities and a confidence score. Anything heavier gets split

@AGTPinsightsAGTP

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TypeSafe AI just launched Jev today. Here's what you need to know. Jev is TypeSafe AI's first public model, built by Diogo Almeida, a researcher behind the instruction-following work that led to ChatGPT. Unlike a chatbot, Jev doesn't generate text token by token. It answers

@StockSavvyShayShay Boloor

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TypeSafe AI just launched JEV which is its first “System One” model built to answer structured questions in parallel instead of generating text token by token. That opens the door to AI systems that can return usable decisions with confidence scores.

@wallstengineWall St Engine

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TYPESAFE AI LAUNCHES JEV, ITS FIRST “SYSTEM ONE” AI MODEL Instead of generating text token by token, it answers structured questions in parallel and returns typed outputs with probabilities and confidence scores. - 20-200x faster - 40-400x cheaper - $0.042 per 1M input tokens -