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My chrome extension for youtube built with jev now reminds you what you came for. For all of us, who open YouTube for one tutorial and, ten unrelated videos later, still haven't resolved the problem they came for. Thanks @The1Broom for suggesting this. OnPurpose now nudges you after 10 minutes off topic viewing. 10 minutes is by default, you can change the timer as per your comfort. Link to extension in comments

Live https://t.co/6DhHClRm9u filter made using Jev @typesafeai and @OpenRouter Add category scroll and filter out on the fly https://t.co/rrTngMFugZ

Here is a useful tip for anyone using JEV. Once you extract probability estimates using JEV, you need to perform calibration. But what should be the criteria for this calibration, and how accurate are JEV's predicted probabilities in reality? More importantly, how should we adjust our business logic based on the values generated by JEV? JEval is an open-source tool that helps you discover metrics, recommends them, and displays them across various viewers. It also provides a metric-extraction library and a CLI interface. I've even converted it into a skill so it can be fed into AI agents. JE

The bookkeeping services industry is dead. This weekend, I built a better solution using Jev from @typesafeai. I fed it 34 months of work a firm charged $20,000+ for. Jev did a better job in 20 seconds, for just $0.32 🤯 https://t.co/8lA9h32eOo

Soon as I made a project using Jev, it got outdated. The tool launched barely a week ago!!

Introducing Jevussy 💦 (Jev + Debussy) A real-time piano player using Jev. This is INSANE it actually sounds like Claire de Lune 😂 Check it out here: https://t.co/cvU0t6dCCy https://t.co/7A4USP0cay

Karpo Discover just got an upgrade ✨ We’re using Jev from @typesafeai to rank places and plans by both what you’re browsing and your taste. We also simplified the pipeline: average homepage response time is down 30%, and category pages are down 55%. Less waiting. More “oh, that’s my kind of thing.” Fun to put Jev to work here. Thanks @CompleteSkeptic and @hackgoofer!

Man this project has been addictive. New addition - fast semantic search of k8s logs using Jev. ✨ Looking for something specific? Search for things like "Database issues" or "Requests from ip https://t.co/pWCVdIuXYd" Supported in the terminal and local dashboard. #jevops https://t.co/sSRT5m4v6n

I am getting better and better results in Querypanel, in my AI Analytics tool with @typesafeai Jev. I have a test script that i use for measuring whether the changes i introduced are improved my system indeed, and with my new planning system using Jev, my app reached 20% better results quicker and cheaper.

had a lot of fun making Aesthetic Spiral using Jev by @typesafeai, try it: https://t.co/BILWgJc6iA describe the aesthetic you're looking for, Jev finds the best matches from CARI/Aesthetics wiki and shows them on the canvas. optional https://t.co/bNSvjeHygL search button https://t.co/PtKJVHVATk

I built an AI editor using Jev. And you can download it for free. Read through to learn why Jev is better at this than LLMs and how this pattern applies to other use cases. https://t.co/SjvHlwrqkK

3 days ago I launched made with jev. 205 people are on it right now. I still cannot believe the visitors and the LLM mentions. Live analytics and the site, below 🤯 https://t.co/XyUS3NcjvO

Fast natural-language search for Azerbaijani names, built with Jev. 💬 "a Turkish girl's name, flower-related, not too popular" → ranked results in half a second 11,000 names, 93 semantic traits. Unlike an LLM, Jev doesn't write answers - it makes decisions. ~0.5s. $0.00025 a query. #jev #typesafe https://t.co/DaHVDrdeQ1

I built a thing using Jev. Have you ever been annoyed by not finding an old Claude Code or Codex chat? => Now you can just describe what it was about and Jev finds it in milliseconds. Open Source: https://t.co/RbAgyiZWeO https://t.co/ltuW7VNKEk

Been experimenting with using jev as an attention layer over code retrieval. On 356 held-out SWE-Explore tasks, adding Jev reranking to the same 100 zvec candidates improved core recall@1k lines from 11.96% → 22.95%. More here: https://t.co/ZNp3AnD8Bn #jev #TypeScript

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

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