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Building a Harness with Jev Learn all about Jev, a new System One model from TypeSafe AI, and how you can use it in your agent harness. Jev is up to 200x ...

Jev Explained for Beginners with Demo Try Jev free with KodeKey: https://kode.wiki/4rtyLz5 Jev by TypeSafe is a System One AI model that answers your code with ...

Jev: The AI Model That's Breaking The Internet (Full Tutorial) Github Repo - https://github.com/mayank953/Jev Jev is a new AI model from TypeSafe AI that cannot write a sentence, but makes ...

Fake Jev demos are taking over the internet Try Jev: https://typesafe.ai/ Try Agent-Native: https://github.com/BuilderIO/agent-native/ Try Agent-Native Mail: ...

Jev explained with Python examples (hands on) AGENTIC CODING CLUB [ ⚡ become an AI expert ] ▻ https://www.skool.com/zazencodes-agentic-coding-club-7823 ⚡ Weekly ...

🚨🚨 TRYING JEV : The new STYLE of AI!!! 🚨🚨 https://twitch.tv/ThePrimeagen - I Stream on Twitch https://twitter.com/terminaldotshop - Want to order coffee over SSH? ssh ...

Jev: The Ultimate Evolution of AI? Non-Autoregressive System-1 Model llm #rag #aiagent #ai #jev 00:00 - What JEV Solved 01:59 - What makes JEV unique? 03:16 - What about JEV hallucinations?

Jev Explained: Demos and Use Cases In this video CJ explains what Jev is, how it works and goes through several use cases and demos. View the code for jev chat ...

How to Build Things with Jev & OpenJevs In this video, we build a model router using both the API-based original Jev and also using Semif. ‍ Github: code will be up in ...

Open Jev Models Are Here!! The open versions of Jev are here . In this video we go through 7 of the open Jev style models to see how good they are and what ...

Jev - The Ultimate Classification Model? Jev by Typesafe AI is an interesting new model that focuses more on System 1 thinking with extremely high-speed results to be ...

Jev + Claude is Insane... Let's Run it Base44: https://bit.ly/3TBxolz My Community: https://bit.ly/4kol0y5 Glaido: https://bit.ly/4eGoI3R (1 FREE month) * Howdy* ...

10 Wild Things Developers Are Building With Jev What happens when an AI model completely gives up writing sentences and only makes instant, typed decisions? Within six days ...

Jev Fully Explained: 10 Practical Ways to Use It Build, Learn, Earn with AI: https://www.skool.com/earlyaidopters/about Get the Free Jev Starter Kit: ...

Jev is INSANE! How to Use it & CRAZY Use Cases (Jev Fully Explained) 551 use cases to build with Jev: https://www.shipwithjev.com/ ZapConnect 2026 Keynote (see what's possible with Next Gen Zaps ...

I Tested Jev: Here's What You Can Build TypeSafe just dropped Jev — a System One model that makes fast, cheap decisions instead of writing text. In this video I walk ...

Jev Full Tutorial for Beginners: How to Use & Set up Jev (Typesafe) Claim your FREE $499 Masterclass: Build & Sell Apps, AI Agents & Websites with AI https://mikeyno-code.com/Skool-base44 ...

It's Not an LLM, So What Does It Do? 3 Real Apps I built with Jev "Okay, you told me it's not an LLM. So what does it do?" That is the question I keep getting since the last video, and it is a fair one.

Jev: The New AI Model That's Breaking The Internet (Full Tutorial) Master AI tools and systems: https://www.skool.com/promptwarrior/ All FREE resources: ...

How To Build A Harness With Jev | A LangChain x TypeSafe Conversation Hear from Sydney Runkle, Product Manager, OSS at LangChain, Allie Laabs, Founding Developer Advocate at TypeSafe AI, and ...

Build Your Own Jev With Claude Opus 5.5 Build, Learn, Earn with AI: https://www.skool.com/earlyaidopters/about Get the Free Jev Build Prompt + Guide: ...

JEV: How It Works and What You Can Build Typesafe's Jev: The Ultra-Fast Decision Model That Can't Write (But Can Route, Classify & Filter Everything) 00:00 Intro 02:08 ...

Jev is HERE. How to use it In this episode, I talk with Ryan Vogel about Jev, a new type of AI built for classification. Ryan shows how Jev takes an input plus ...

Build Anything with Jev, Here’s How Get your own Hostinger VPS: https://hostinger.com/davidondrej10 Join my discord: https://discord.gg/gY3ebZnq2w Follow me on ...

Open visual decision model that encodes an image once and answers multiple structured questions.

Solar Mini–based local model for Jev-style typed decisions.

Open-weight Jev alternative that returns typed decisions with probabilities in one forward pass.

Open-model semantic-if engine for local Jev-style decisions.

Go SDK and CLI for TypeSafe Jev typed yes/no, Choice, and Score decisions.

Swift client for TypeSafe Jev typed judgments instead of generated text.

npm install is the scariest command in your terminal. One typo → lookalike package → malicious postinstall → secrets gone. jev-sec-audit flags it in milliseconds using Jev, a System 1 model built for fast decisions, not chat. One step in GitHub Actions. Open source. https://t.co/PhC7TLRwCt

Research notes and an interactive Cloudflare Worker demo for Jev, TypeSafe AI's structured decision model

Jev-kit: all the Jev stuff I've wired into Claude Code, now in one repo (guard hook, sub-agent sizing, file search, browser agent) I've been using TypeSafe's Jev with Claude Code for a couple of days and kept adding more use cases/ideas. So I've cleaned it up and put it in one repo. For anyone who doesn't know Jev; Jev is a small fast model that answers pick-one and yes/no questions (and more) in about 0.3s, which makes it cheap so you can put it inside your agents loop and make calls Claude would otherwise spend a turn on (eating away our already limited weekly usage). What's in it: A PreToolUse guard. Plain code checks let about 93% of tool calls straight through in ~33ms, the grey ones get one question to Jev. Catches stuff like cat on an env file or a find across the whole disk Sub-ag

Built a tool that searches Google Icons using JEV (Laya-MLX) Should I publish it? 👀 https://t.co/97qw7oIkOt

best way to close my day with a webinar on @typesafeai Jev in JarvisCore! i covered the 4 places we are using Jev in our agent runtime: RAG classification and ranking, model routing, subagent routing, and directly in agent decisions this is not the end, i believe there is much more to come in our memory assembly, tool recovery, peer delegation, and human-in-the-loop. Every agent harness, runtime, loop needs some good Jevglue! also check out this https://t.co/nrzsIzpxM8 by @CompleteSkeptic

I built an app that uses Jev to classify your entire query workload and tell you which queries to EXPLAIN ANALYZE first I posted here last month about RDST, the free desktop app that runs the slow query to EXPLAIN to index recommendation loop. The biggest change since then is what happens before EXPLAIN. When a database has thousands of distinct query shapes, you can't EXPLAIN ANALYZE all of them, and sorting queries by their performance doesn't always tell you which ones are actually badly written. RDST now answers that with Jev, a small classification model from TypeSafe that answers fixed multiple-choice questions about a query in a fraction of a second. Every query shape it discovers gets classified from its SQL and the schema, without executing anything, on five structural questions:

jev-browser: give it a task and a URL, Jev picks one action per step Fast and cheap browser use: Jev chooses one action per step from the clickable, typeable and selectable elements of the page, and scores how likely the goal is met or the run is stuck. Budgets, retries and stopping stay in code. Ships as an MCP server, CLI or library. Around 178 stars.

Tried using Jev to check what an agent should remember We've been trying Jev for agent memory, mostly to catch cases where the saved note says more than the original conversation did. Think "we might use Postgres" turning into "we chose Postgres." Once that gets saved, the next conversation starts from a decision nobody actually made. We gave Jev the original text and a proposed memory, then used its judgments to decide whether to save it, skip it, or leave it undecided. We ran the same setup with Luna as a comparison. On 100 synthetic cases, using a .40 cutoff, Jev kept 39 of the 50 memories labeled worth keeping. Luna kept 41. Neither saved a candidate labeled skip or defer. Median gate latency was 250 ms for Jev and 1,593 ms for Luna, including network time. Then we checked what happen

Built an AI slop finder for @X using Jev @typesafeai . • Tweet → Content script extracts text • Text → Backend API → Jev • Jev → AI-slop score • Score → Badge displayed on the tweet #jev #aislop #newmodels https://t.co/VMzJA1Tyde

I used Jev to make Claude actually follow my CLAUDE.md, checked on every turn in ~350ms CLAUDE.md is context, not a constraint. It holds for a few turns, then a handler shows up with raw SQL in it and a failing test quietly becomes it.skip. Nothing checks the output against the rules you wrote. So I added the check. A Stop hook takes the final reply, a PostToolUse hook takes each edit, and every rule in your CLAUDE.md becomes one typed yes/no question in a single Jev request. All rules answered in parallel, anything over the threshold goes back to Claude with the rule quoted, and it fixes it in the same turn. Benchmark (59 labeled examples, 19 rules): 93.3% precision, 93.3% recall, 348ms p50, ~$0.045 per 1,000 checks. What it's bad at is in the repo too: exact characters, dataflow rules l