Apps and utilities built with Jev

Dorian Smiley

@dsmiley411

I finally managed to burn through my free usage credits with Jev, all $5 of them. 123M tokens. Man this thing is cheap!

Tatsuya Shirakawa

@s_tat1204

jevや派生を使ったアプリケーションも、あまりconfidenceを利用していなさそうなことを考えると、識別ができてしまえばなんでも良く、この手のembeddingベースの方法でも十分なケースは多そうですね。

Jun Penn

@heyjunpenn

Hey Jev builders! 🚀 What are you working on? Get your demo into JevBest so people can watch it and find the original post. Drop yours below 👇

Sergii Makarevych

@sermakarevich

@typesafeai JEV use case I will have first AI coding interview ever. I know its gonna last for 1.5 hours and I will have to clone an exercise repo. So what I should be prepared for ? Lets use LLM for hypothesis and JEV for ranking:

Eugene Cheah - AI builder @ 🇸🇬|🇺🇸

@picocreator

Yes, it is quite literally next to the score - the preferred prompt format - and so is the code for the inference at github.com/featherless-ai…

belorix

@0xbelorix

YOUR VERIFIER STILL COSTS LIKE A FULL MODEL Claude writes. then Claude reads the whole pile again to say yes or no. every extra loop turn makes that pile bigger. Jev: $0.042 per 1M input tokens. output tokens free. it returns a decision, not another essay. same job. wrong tool if you still use a writer as the gate. read the article below

Stephon Proctor, PhD

@stephonomon

Deskilling is a real risk when it comes to using AI scribes. The drafts often look plausible, which leads users to overly trust without verifying. Models like #Jev could help provide users with better insights onto areas where the draft might be wrong. github.com/Stephonomon/sc…

Lahiri

@Ziggyzonty

JEV is INSANE... I gave it an educational video and its transcript. Every 5 seconds, it looks at the next 5 seconds of content in the context of the previous 60 seconds. It scores the segment on 5 dimensions: Relevance. Usefulness. Importance. Memorability. Actionability.

Sama

@osamalamaaa

all that hype about the potential use cases for jev and everyone just rebuilds common programs from first principal. I have yet to see a legit new use case that has wowed me but thanks random guy for building chatwithjev real novel stuff

Justin Youens

@youens

This is the neatest example of Jev I've seen. But didn't see a demo link, so here you go: emogee.youens.com

Leaf Yeah!

@leaf_sanren

啊啊啊啊啊🎉🎉！JEV 黑客松已经出来啦！！！ 同时，我也见到了把 Jev 用得最对的一个项目！！ Jev 这模型最大的特点： 只会判断，一个字都写不了。所以用它的关键，就是别让它干写字的活。 这个开源的「聊天副驾」就很聪明： 你在微信里收到一句"在吗"或者"方案再想想"，它先让 Jev 一秒判出对方真实意图、危险等级 1–9、该马上回还是先晾着； 写回复这活交给另一个会写字的模型起草 3 条，最后再让 Jev 排个序。 判断归判断模型，写字归写字模型。 且它最好的一点：它永远不自动发送。 搞得我都想去闲🐟搞几台吹灰的安卓机啦！ 期待后续也能拿下 iOS、macOS、鸿蒙系统！ https://x.com/AYi_AInotes/status/2102605821456814286/video/2

Erik Kokalj

@erik_kokalj

Another cool demo, specifically for Jev Omni: x.com/erik_kokalj/st…

Artem

@S0RATNIK

Немного упоролся в Jev, но так у меня всегда бывает, когда на чем-то гиперфиксацию поймаю. Вот, например, собрал подборщик нейросетей на базе тех, что есть у OpenRouter, под ваши задачи. Просто пишите, что хотите - получаете подборку нейросетей.

Jeetendra

@jeetendrayd

Built a walkable 3D street view of where SF/Bay Area startups & VCs actually cluster, every building named, every classification made by Jev.

Jason Torres

@jasontorres

I launched a chatbot yesterday and used JEV as a 17-in-1 router, filter, classifier, intent judge for efficient tool calling. Then further downstream is a relevance/context loop system. Worked really well. No more unwanted spams and prompt injections.

Akshat

@akshatnerella

I vibe coded more fun apps using @typesafeai AI jev on the backend. My fav one is probably JevedIn, which is an extension for linkedin that automatically detects and blocks AI slop (thank me later). But I am lowk afraid of my linkedin feed being empty at this rate lol. Source code: JevedIn: https://github.com/akshatnerella/jevedin JevTube: https://github.com/akshatnerella/jevtube (currently building jevvit for reddit lol) comment what you want me to build next! #TypesafeAI #jev #classificationmodel #extensions #aislop

rewind

@rewind02

built a full production app with opus 5.5 and jev... fed it a url, it screenshots the site, scores it against a list of known patterns, and ships a shareable report here's the actual pipeline: - gemini flash takes the screenshot, that's the only vision step needed - jev scores it against a fixed list of criteria in a fraction of a second, no text generation, just structured ratings - opus 5.5 writes the entire app, front to back, off a single planning prompt - product os turns the plan into a spec, then a roadmap, then working code, task by task - code review, security audits, and testing happen automatically as it builds, not after - a deploy checklist gets generated too, walks you through auth setup, hosting, env variables, step by step - cost to run per scan lands in fractions of a cent, both models chosen specifically because they're cheap at their job what actually makes this work: opus 5.5 handles everything generative, jev handles every decision that doesn't need generation, and neither model gets used for a job it's not built for that split is why the whole thing runs on pennies instead of frontier-model pricing pair that with a structured build system instead of one long freeform prompt, and a non-coder ships a live, secured, production app in an afternoon, not a sprint

leiro

@leiroops

the lead engineer at a research company used Jev + GPT-6 Astra to build a system that analyzes 12,000,000 source updates every 6 hours the system processes massive streams of information, a process like this used to cost $1,000,000+, now Jev engineering does it for $200 the first version relied on GPT-6 Astra to interpret every result it worked, but processing millions of routine decisions through a large model created unnecessary latency and huge inference costs then the engineer rebuilt the decision layer around Jev instead of explaining every source, Jev reads the saved task state and decides what should happen next: Research, Write or Review Astra handles the open-ended work: comparing evidence, resolving contradictions and preparing the client brief code controls the queue, permissions, budget, retries and files at the core of this system is the same architecture I break down in the article: Jev routes the work, GPT-6 Astra handles the open-ended tasks, and code keeps the process running continuously the full step-by-step build of a client research process that runs 24/7 is below ↓ would you let AI decide which findings deserve further research?

Bret Morris

@4bret

Built a tool to help fill out your LA County ballot for Nov 3: take a quiz, enter your address, get a match for every contest, from governor to water board. Claude did the research, Jev the scoring. Open source: check the logic or fork it. la-ballot-match.vercel.app