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Semantic code search CLI and MCP server: Jev scores authorized repository excerpts and returns exact file paths and line numbers to coding agents.

Rails-native Jev wrapper that turns typed, calibrated decisions into application control flow.

Open local System One implementation for NVIDIA DGX Spark, with a Qwen3-based model, Jev-compatible API, architecture notes, and evaluation tooling.

Community Go client for the TypeSafe System One API with typed question builders, retries, examples, and jev-latest as the default model.

i've built an extension for twitter that ranks every tweet by relevance based on your interests using @typesafeai 's Jev, highlights very recent tweets to maximize engagement and assesses your tweets based on your goal (also using Jev) should I open source it? https://t.co/GgQjTT2NWy

I wanted to post a preview of our Jev benchmark dropping tomorrow. We are using Jev to generate symbolic programs (state machines) through an iterative process. The results so far have been amazing! The old process took ~2–10 seconds. With Jev, it’s ~0.3–2 seconds. The costs have also dropped by an order of magnitude. Jev is also used in control flow, removing the need for brittle heuristics.

I built a token compression engine using Jev. It evaluates document (huge text) chunks in parallel, in sub-100ms passes, stripping 85% of boilerplate so we only pay Claude or GPT-4 for high-signal answers. Here is a demo https://t.co/3nVjtG8zZY

3 days. First AI product I've ever built, with Jev doing some of the heavy lifting inside it. Here's the landing page. No Figma at all, just Cursor ai and the design skill I built. Launching soon https://t.co/VBhWBCVEMf

Use @typesafeai's Jev model with Pinecone to rerank results with natural language criteria! Usually with rerankers, it's hard to cleanly specify what should and shouldn't be returned in results. Jev resolves this by refactoring the problem into evaluating against distinct binary criteria, which pairs great with Pinecone retrieval! In this demo, we compare using Jev and Claude to rerank 200 returned candidates from Pinecone. Jev returns a reranked list in about a second — 830 to 1,300 ms across eight test queries. Claude Opus 5, doing the same job in one long-context call, takes 4.2 to 6.8

simple real-time bug checker made with jev in case you code by hand sometimes also works with english (underlines inaccurate sentences) https://t.co/hJt7xzRLpB https://t.co/XsitOtjnnh

This weekend I've built a tool using Jev, was a really fun thing to learn! It classifies your followers in seconds, so you can analyze your audience, check comments⬇️ https://t.co/4YmKUVVlBq

Your browser has 100 tabs. You're using 6. Meet Tab Bouncer 🚪 a web extension I built that checks every tab at the door and shows the freeloaders out. Built with Jev by @typesafeai: ⚡ 100 tabs judged in one call, ~1s 💸 a hundredth of a cent per sweep https://t.co/H4eMmMTM4S https://t.co/st0zISjA81

I built Jev Dark Pattern X-Ray — a small experiment using Jev from @typesafeai to detect manipulaive UI patterns on mainly ecommerce sites. It also have a demo site to check how it works, but you can paste a real url and it will check for fake urgency, scarcity pressure and other tactics used my ecommerce sites. Code: https://t.co/5nqnd8TFrb

Demo of using Jev to analyze spoken text in real time. Try it out at https://t.co/R4t6r110wI Tested in Chrome and Safari on Mac and iOS. Source code is at https://t.co/F5UaD8AcDz Thanks @typesafeai @CompleteSkeptic @notkevinzhang https://t.co/Hfa0JrWQMJ

Evaluation framework for detecting AI hallucinations and instruction-following failures with Jev.

Connector for integrating TypeSafe Jev decisions into application workflows.

Local Paseo plugin that exposes Jev evaluations through MCP.

Adds jev(), jev_prob, jev_choice, and jev_score to vanilla PostgreSQL queries through a psql-shaped CLI and Go, TypeScript, and Python SDKs.

JSON-in, typed-decisions-out CLI for the TypeSafe System One API.

Introducing Cursor for writing. Who says Jev can't generate text? Is Jev fast enough to finally solve auto-complete? Code is a lot easier to predict, so how does it work? It keeps a history of your previous 1000 sentences to predict the next word faster and better as you keep typing with binary trees to speed it up. As you type more, it gets better at predicting your next word until it actually accelerates writing. Built with Jev from @typesafeai

I'm building a real voice-controlled remote for Mac. And I'm using JEV for almost everything. 🧠 Decision making — auto allow / deny actions 🖥️ Computer Use — interact with the Mac 🌐 Browser Use — navigate and control websites, much faster 🧩 Generative UI — dynamically generate forms and controls 🎙️ Voice Control — control the entire workflow remotely The problem is macOS TCC. When an agent triggers certain macOS permissions, you still need a real physical interaction. A Computer Use Agent can't simply click the approval button. That's intentional. Great security. Terrible for unatt

Built for my mum, who has MND / ALS , using Jev @CompleteSkeptic @typesafeai thank you! AAC kit is exhausting - forcing a hunt through grids and having no context of what the other person says. I had Jev make a conversationally and context aware decision engine to pick… https://t.co/ESUAJcZ8r9

let's start simple. using jev for paragraph-ization: give a block of text and it will add paragraph breaks. I demo it using a text-only youtube client named jevtube. no videos, no distraction, just gets the transcript and adds paragraph breaks. https://t.co/rWuXnxBPUx

started using jev as a judge in @_moodshelf_ and the rankings in semantic search results and items classified by moods, have significantly improved, that too at much lower latency and cost

🔮 Built a real-time click prediction right over the DOM using JEV that feels like a crystal ball. Powered by jev-latest to predict the next clickable element and intent in 345ms per check, highlighting candidate elements on screen. It's extremely cheap to use and helps predict user intention, hitting 83% accuracy in testing. Usages that come to my mind: - Web performance optimization via predictive resource load - UX Research and testing - Progressive UI loading based on intention - Accessibility for reduced motor precision (highlight based on intention allowing to click, for example) - An

.@jonptaylor recorded a detailed walkthough of Jev vs GPT-5.6 Luna as the "operator" element of a Pipecat speech interface pipeline. GPT-5.6 Luna: - 81.3% command accuracy - 1,008 ms median latency Jev - 92.6% command accuracy - 296 ms median latency A few notes here ... 1) We expected to see a big latency benefit. But the higher accuracy is maybe more interesting. Jev (with a bunch of code wrapped around it) is much better at turning messy transcriptions from a noisy speech environment into correct command structs. The very hand-wavy explanation here is something like: LLM too

Can we estimate a book’s literary quality and how enjoyable it is to read by using Jev to perform a multicriteria classification of its prose? I wanted to investigate that, so I built Salomón, a tool designed to do exactly this. I analyzed 32 books blind using Jev, and this is the map I got. Infographics, details and links in the thread.

Opencode sends every MCP tool schema to the model on every step. With 18 common MCP servers, that's ~90,000 tokens before the model even reads your request. I built a plugin using Jev that cuts it to ~7,300. (-92%) 🧵

Built a chrome extension that covers distracting youTube videos using Jev https://t.co/5TVn6cnLjD

I have a script running that's going through all of my magi-code sessions, extracting every bash command and then categorizing them by similarity using Jev. It's going to end up being about ~$0.60-$0.70 to do this for 4200 bash commands and take ~10 minutes with a very inefficient python script My goal here is to identify instances where the model keeps repeatedly writing python or bash scripts to perform very similar actions - and provide the agent with a small set of scripts that do those things for it so it doesn't keep wasting output tokens on writing repeated code.

I swear this is the last Jev demo I'm doing... 🙏🏻 I'm using Jev by @typesafeai to get a third person opinion on my vibe coded projects... 👀 Checkout FirstScreen 🔗 https://t.co/nZnEsgxl1n As usual, it's again open source so feel free to star the repo if you want : ) For the past three days, I've had access to Jev, and I'm having so much fun playing around with it 🔥 I built Diffjury and JevArena in the past 2 days and shared with you guys on here, and today I built FirstScreen, where Jev basically takes a look at the website and quickly gives a verdict whether it's ready to ship or need

Jev can tell if your resume is good enough for a job. I added a simple resume scorer using Jev by @typesafeai 1. You give your resume (Doesn't get saved) 2. PDF to Markdown conversion 3. Jev, does the analysis, across on 5 Dimensions 4. Your resume score Jev is quite fast btw. but he is very reasonable, so if the score is low, it is what it is.

#jev #typesafe #flutter #mobile #test Using Jev from https://t.co/wVCGdOU10g, I created a flutter package that allows you to write integration tests for Flutter apps using natural language. https://t.co/hG0mtW2FGp https://t.co/OkXWEkObbJ

We did a test using Jev 1.13.0 on official LoCoMo data benchmark. Jev makes AtMem better at putting the right memory first, but it does not yet help AtMem find memories it missed entirely. The significant increase is on how often the correct memory was the very first result. Improved from 33.99% to 54.23% AtMem used about 1,593,522 token and costed about $0.0583 Jev remains as option in the next releases since AtMem Position is local first. We continue to explore more use cases for Jev

Agent-ergonomic CLI for TypeSafe's Jev: fast calibrated judgments (pick, rate, check, rank, triage, guard) from the shell.

Context-pruning proxy for Claude Code and Codex: Jev judges which history is still needed, measured not claimed. POC here now, heading soon into Compozy.

MCP server that puts TypeSafe Jev on the coding loop in Cursor, Codex, and any MCP client.

Proof of concept MCP for Typesafe's new Jev AI model.

Local-first MCP plugin for continuous software-quality review by AI coding agents, powered by Jev.

A staged code-review workflow and local dashboard built with TypeSafe Jev.