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I taught SQL to ask questions using jev: SELECT * FROM tickets WHERE jev_bool(body, 'Is this customer angry?') No embeddings. No keyword list. No LLM writing SQL. jevsql: natural-language predicates, a typed control plane that never lets an LLM touch your data... @typesafeai https://t.co/JtqOsiYoIl

made a color palette generator using jev, because why not? given the input, jev returns probabilities over ten hue families, scores for warmth / light / energy, and a font pick. code maps that onto oklch and paints the page. https://t.co/lAe0TlYKdz

Every company writes copy rules. Nobody enforces them after launch week. So I made the reviewer that never gets tired. It reads your marketing site on every PR and names each sentence that makes a claim with no evidence beside it. It's using Jev from @typesafeai It doesn't know your facts. It knows what a claim without proof looks like. https://t.co/bHrzAWVicC @MParakhin don't hate me

built using jev @typesafeai https://t.co/CaKowqwRqW

Proof of concept using Jev by @typesafeai to analyze image metadata and estimate how likely an image is to be AI-generated. Built with @laravel, @inertiajs, and @vuejs. Hosted on Laravel Cloud. Thinking of using it to screen photo competition entries. https://t.co/HskTF6u3to https://t.co/3qfC2SC7BB

I started using Jev as a tool call for our users on supercenter. And oh boy are they gonna love this. https://t.co/JXnhrZXogy

i built a website that ranks all 1,025 Pokémon by literally any question you type, using jev it thinks mimikyu is most performative https://t.co/F0qca4H84o

tenet using Jev is up to 275x cheaper and 104x faster than Claude agents 🤯 Same 2,000-line diff, same rules. tenet: 1.5 s, $0.0036. One Claude Fable 5.1 call: 157 s, $0.99. Your agent fixes its own review findings before you ever see the diff. https://t.co/Rr1BpU53ak

I built a real-time chat where Jev judges every message in before the room sees it. ❌ No keyword lists ✅ Rules are plain English ✅ Unsure? Held for a human Built with Jev (@typesafeai) + @convex + @vercel Try to break it 👇 https://t.co/P5HYDS6aJX https://t.co/gGWn6XmPaA

Built jevscan - a Chrome extension to detect malicious addresses and transactions on @etherscan in real-time using Jev from @typesafeai. https://t.co/gGgITyJgjT

Created a one-off Chrome extension to remove low quality posts (filtered using Jev) from Twitter, and posts with videos. Feed has become so much better.

Spent a day building tenet, a review gate for code that agents write. It judges each commit, commit message and PR against rules written in plain language, using Jev by @typesafeai. The rules come from @poteto's unslop skill, @hvpandya's stop-slop, @dillon_mulroy's anti-slop and @mattpocockuk's new /pr skill. It also generates rules from your AGENTS.md or CLAUDE.md, and you can write your own rules and presets. Here an agent trips four of them, gets blocked in 1.07 s for $0.0003, and fixes its own findings.

My first real experiment with Jev: project scorer. It takes the specs, source code, and graph (Graphify output) from a codebase, then analyzes them using Jev, Lizard, and Networkx to create project "quality metrics" based on a configurable rubric. https://t.co/lCryvd749u

i made a smart calculator notebook using jev it can calculate ANYTHING!! 😅 https://t.co/0kxyblZVjN

Controlling Figma with voice using Jev from @typesafeai Total cost from first line of code to recording this video: $0.01 🤯 https://t.co/HTGcrBYpTb

I made a Telegram plugin using Jev that warns if you're about to message an ex after a long time of not speaking. https://t.co/eH328cRCky

Using Jev to detect and classify a statement against 50 logical fallacies only takes a few hundred milliseconds and costs $0.000128 per 3k input token request. The response times make the UX feel instant! https://t.co/pwh2b7crGV

Guys I made an extension for LinkedIn using Jev from @typesafeai to remove anything it classified as ai-slop and this is what it left me - did it in under 200ms btw and only cost me a fraction of a penny. https://t.co/0falPlXy0N

I've been developing https://t.co/Wiou7lLysx to keep our minds running using jev https://t.co/i8IemSaIJe

I got up to 50% less context usage on Claude Code and Codex using Jev from @typesafeai a proxy asks Jev which parts of the history are still needed and drops the rest before the request goes out. 8/8 exact answers on the real APIs, numbers is in the last tweet bellow 🔥🔥🔥 https://t.co/WnxBl3I380

I built a simple reranker using Jev that performs on par with Voyage, and outperforms Cohere and Mixed Bread https://t.co/r4Xdfuk6BJ https://t.co/4StSjjKoRw https://t.co/DlJBc3A88Y

I vibe-coded this simple, open-source and local Telegram content analyzer using Jev as a classifier. Jev Classifier • https://t.co/Ux7mYLwStF Export your channel data as JSON and it will analyze each post's intent, quality, sentiment, and reaction tone and if it's a DM/Group chat it will analyze topic, intention, and emotion, plus a per-speaker tone summary. I made it very extensible so you can use Vercel AI Gateway API Key or @typesafeai's, you can also goto the settings and configure Jev so the questions and choices can be customized to your needs. https://t.co/sWz5x38mGl

AI coding agents just got caught lacking! Built Greenwash, a GitHub app that catches AI coding agents pass the CI checks silently. This tool reviews the PR lightning fast! ⚡️ Built with Jev, the model from @typesafeai. Huge thanks to @notkevinzhang and @justKDeng for getting me off the waitlist. Link in the Thread…🧵

Built a text -> emoji sentiment analysis demo using Jev Try it here: https://t.co/MNprxC9KIr

remade an AI filter composer I was working with, using Jev instead of Luna Medium, numbers are good, can probably iterate a bit to make it cheaper and faster https://t.co/pFGsRa456G

🚨 I gave the Trump vs Kamala debate a live BS meter using Jev every sentence, both candidates, 5 yes/no questions each 1,191 Jev calls / 1.18M tokens / 415 ms median total cost : $0.0497 same questions for both, clips picked by one fixed rule, not a fact-check https://t.co/m8u4ALl6Me

using jev and the sick new markdown renderer in @pidotdev , i made a pi-extension that lets you toggle how much detail you want in the agent output. this was motivated by my inability to understand what opus 5 was getting at https://t.co/PE4HQhc84T

these are results of using jev on one of our security pipelines. we already have to use smaller (and dumber) models to make it economic and this model does it over 5x cheaper, faster and while maintaining much higher accuracy. i don't normally hype over new model releases but that's the first one that actually impressed me. good job @typesafeai