All builds

Built a Ticket Triage Copilot using Jev (@typesafeai) instead of a general LLM for support ticket classification. 🔗 GitHub: https://t.co/zQ9oB7t7Eq

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

No, 'caliente, caliente no más! We shipped JarvisCore 1.12 and it comes with Jev by @typesafeai ! Come and learn about Jev next week on Wednesday. We're bringing @askmuyukani founder at @prescottdata for a live discussion on how JarvisCore is using Jev to build faster and cheaper agents, and what opportunities exist to extend Jev in agent harnesses.

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

I was also looking into how we can integrate Jev into a real hardware and I built a custom firmware for my keyboard that predicts and highljghts the next key using Jev. Mostly real time. This is not a simple dummy 2kb model that lives in the board. In my project Jev gets context of the current active app on a screen so it can suggest actual vim commands completion! For real. And a real word endings or even phrases. lmao this experiments are getting out of hand this quite cool

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

Holy shit. Using Jev in my JARVIS just made it like actual super intelligence. what the fuck. Will release the update asap. https://t.co/LJZA4z7fSf

I think Jev really flourishes inside your code more than in the actual product. Right now, I’m using Jev to classify the thinking mode of our AI agent, so it can select a high, medium, or low mode depending on the prompt.

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

Humble beginnings using Jev. Trying to integrate it into Codex and help with speeding up browser use. Been working on it all day and running hundreds of trails. So far I've spent... $0.00725 https://t.co/yX3mPB41pL

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

As promised… I just open-sourced the Jev X (twitter) Market Sentiment Analysis terminal powered by @typesafeai - Ingests 50 - 1,000 live tweets per crypto asset (e.g. BTC, SOL, ETH, etc.) - Scores each tweet for bullishness vs. bearishness using JEV - Combines social sentiment with live funding rates, RSI & volume - Detects short squeeze risks & outputs structured entry/stop/target cards - Built-in SQLite deduplication so you don't burn API credits on repeated searches Powered by Python, CCXT & TypeSafe AI's Jev model. Fork it, tweak it, build on it, or sell it ...but don't forget to enjoy

Built an AI dispatcher for Zammad tickets using Jev (TypeSafe AI). New ticket comes in, Jev decides which team owns it with a confidence score attached. Not a chatbot, a structured decision. Bridge is a tiny Ruby/Sinatra service. Open source, MIT. https://t.co/bXdJpGWHuw #jev

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

Built an SLO-aware inference router using Jev It uses Jev as a typed decision model to select the optimal LLM for each request based on predicted quality, latency, cost, and live backend load Releasing full walkthrough video soon https://t.co/XUjKj9Ryyn

Built jevmetrics — an OTel Collector component using Jev inference to score metric relevance, redundancy, and keep/reduce/drop decisions. Fail-open, with a deterministic policy still in control. https://t.co/1zLhyNjoEp #OpenTelemetry #Observability #Jev #Typesafe

I just had to explore using Jev with my existing Japanese print metadata extraction pipeline (where I use gpt-5.6 luna). Turns out that in some cases I could replace luna completely and save a bunch of money - in others I could augment what I had for higher quality! https://t.co/iSFOGPJjf8

.@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 made an AI agent using Jev to reply to my wife We’re getting a divorce now and she wants to know who Jason is

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.

Using Jev to catch Codex workers ignoring AGENTS.md. Combine live Codex output, Git changes, and AGENTS.md into one observation Use Jev to score the probability that the worker is drifting from AGENTS.md Steer the active Codex turn when drift crosses a threshold, stopping it if the drift continues Added to Foreman, which automatically watches and steers workers on the software factory floor. https://t.co/g9prp3tsy8

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

everyone's using jev to route between models. we put it on inbound sales replies the question isn't which model handles this turn, it's whether "sure" means yes. ours used to log that as a rejection and bin the lead [N] real replies through ai gateway. [x]ms p50, whole batch cost [z] papercut on the way: experimental_evaluate doesn't exist in ai v6, evaluation modality is v7 only, and nothing on the gateway model page says so. @vercel worth a line

i just shipped a small repo that will help you understand it perfectly.its a Real-time support triage + response bot using Jev and cerebras https://t.co/l4gFvr0GdW PS if you like it,approve my entry to the hackerhouse. I applied with rewantgoenka87076@gmail.com

exactly!! just shipped a real-time support triage + response bot using Jev and cerebras. https://t.co/l4gFvr0GdW

youre right. For example in a support ticket jev can classify way faster that which agent should get the ticket. I built a Real-time support triage + response bot using Jev and cerebras https://t.co/l4gFvr0GdW

I made a little experiment of using Jev to autonomously move around rooms of my 3d game. Headless operating based on textual position information. Rendering a screenshot at every step to be made into a gif. https://t.co/bCv0aoNxa9

just shipped a Real-time support triage + response bot using Jev and cerebras: https://t.co/l4gFvr0GdW this should help you get started!