What people are building with Jev

Kai

@hqmank

I rebuilt my job crawler with Jev. The task: start at a company's official homepage, find Careers, and identify jobs that match my profile. Before, with an LLM: ~5 minutes. After, with Jev: just over 20 seconds in my test. Every company organizes its website differently. Jev identifies the Careers entry point, chooses which links to follow, recognizes job pages, and scores each role against my profile. This is where Jev makes sense to me: automation that needs lots of small decisions, with faster responses and lower costs than calling an LLM at each step. Packaged it as a skill: jev-job-hunter. Demo below.

Kevin Wang

@mxfp4

everyone's making demos with Jev but nobody is making real products introducing lurk.so find and monitor reddit threads to get cited by AI > FREE > 4000 reddit threads scanned > email, discord, slack only possible to give for free bc of Jev & @getanyapi

Peter Wang

@the_cyw

I made a chrome extension to label all the X posts on my timeline. It tells me if each post is clean, engagement bait, promo, secondhand or filler. $0.03 for 1000 posts. Open sourced if you want to try it out.

Higgsfield AI 🧩

@higgsfield_ai

Jev is really good at content filtering and asset selection. DeepSeek + Higgsfield turn the selected assets into ad creatives.

Ira Bodnar

@irabukht

Jev dropped the price of SEO/GEO fixes by 90% Agents that audit and fix a client's SEO/GEO used to cost us ~$250 Here's where the savings come from: 1/ 30x faster reads of Search Console and PostHog/Mixpanel data 2/ 30x faster checks of what ChatGPT searches on Bing 3/ 30x faster modeling of what users ask Gemini and Claude 4/ 30x faster scans of who ChatGPT and Claude cite 5/ 30x faster analysis of the sources behind those citations 6/ 30x faster gap analysis: why they get cited and we don't 7/ 30x faster fixes across 1,000s of pages on large client sites 8/ 30x faster sorting of which page types ChatGPT cites 9/ 20x faster creation of the pages that make ChatGPT pick you Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇

Rob Hallam

@robj3d3

Jev just solved doomscrolling. You pick a niche, Jev reads 3 days of posts and asks 8 questions each. It runs in ~2s for $0.007 😅 > removes bait and hidden ads > judges the text and like/reply/repost ratios Free, no signup, go try it then touch grass ↓🌲

Pierre-Eliott Lallemant

@pierreeliottlal

JEV is insanely fast. We gave it a massive dataset based on thousands of outreach messages and asked: Which intent signals generated the most booked demos? 40 seconds later, we had the answer. Cost: less than $0.20. JEV can also rank leads, measure prospect-message fit, and uncover what actually drives campaign performance. Coming soon to @GojiberryAI + MCP.

Ackerman

@Yarilo7brigada

jev is an insanely cool product Saw a post about jev and decided to check it out This thing is straight up gold whoever built it is a genius jev is the future Scanned over 700 live ads in 40seconds flat, and it cost me just 4 cents. If you're in marketing, this kind of crazy fast data crunching is hands down the best thing out there. Doing this through Opus 5 would run around 2 to 4.5 million tokens, set you back $15 to $50, and take anywhere from 10 minutes to an hour. That’s 200 to 500 times more expensive than Jev, and way slower.

Ira Bodnar

@irabukht

Jev killed 7 more SEO/GEO workflows 👇 1/ Assess which competitor pages to copy -> It scores every competitor page on answer, depth, proof and freshness, then checks its rank in Google and ChatGPT to show which ones are worth copying 2/ Identify which page elements to change to get cited -> It reads the title, meta, H1, FAQ and schema on every page and returns keep or change for each, with a confidence score 3/ Check if your pages answer what people ask AI -> It matches real buyer questions to your best page, which shows the questions you have no page for and who AI cites instead 4/ Rate how likely each page is to get cited -> Every URL gets a citation chance and the first fix to make, like adding a compare table 5/ Sort search terms -> It asks "is this query from a buyer?" across the full Search Console export, so you write only for terms that convert 6/ Build the internal link map -> For every page it checks the 15 closest candidates and links only the ones with an honest reason 7/ Verify AI-written pages -> Each draft goes through 20 yes/no checks, and only the ones that pass reach a human Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇

Marc Köhlbrugge

@marckohlbrugge

Using Jev to filter through my @wip todos It allows me to super quickly find all the instances where I increased revenue, got stuck, switched to a different SaaS provider, etc Things a regular keyword search would never catch

jaffa

@dsqjaffa

today i'm releasing Jev for content marketing. still doomscrolling to figure out what to post on social media? that's over now... Jev watches EVERY video in your niche and judges it before it ever reaches you: 1. research: pulls every video in your niche from a database of 12.8M viral videos 2. analyze: Jev watches, studies, and judges each one, the hooks, the formats, the angles, and why they worked 3. create: turns it into a data-backed script, based on proven winners (via Claude) no more guessing on TikTok & Instagram currently available for free in @virlomain + MCP. link below ↓

Nate Herk

@nateherk

Jev is tagging X posts in real time for me. Breaking, golden nugget, or slop.

Movez

@0xMovez

I just built a Jev X Viral Post Analyser. 100,000 viral X posts. 20.4 seconds. $0.67. Claude Opus 5, same corpus, same clock, got through 214 posts and spent $0.98. per post that is ~680x cheaper the full Opus pass would have run $458. viral analysis is the perfect Jev job. • it is not writing, it is 14 yes/no calls per post: > does the hook open a loop, > is there a number in the first line, > is the proof real or claimed. classification, not prose. • what it found: 1,220 posts broke into the top 1%. baseline 1.22%. > superlative claim - 2.34% viral. 1.92x baseline > contrarian take - 1.59%. 1.31x > launch / tool drop - 1.46%. 1.19x and numbered lists, the thing everyone writes: 0.55%. below baseline. the most used hook is the least viral one. full stop. • what you are watching: left is the post under analysis, right is Jev answering 14 typed questions about it, each with a confidence score. the run stops at 20.4s because that is when Jev finished all 100k. pulled the corpus through a few X APIs, one parallel pass into Jev. should I drop it to public? Read my latest article on Jev Engineering below and turn your ideas into reality.

AI Builder Club

@aibuilderclub_

We turned Jev into a general browser skill for agents: jev-browser. Give it a website and a task. The browser opens automatically, and Jev decides every click based on what's on the screen. Here's a demo:

Paulius 🏴‍☠️

@0xPaulius

Jev brought us closer to JARVIS it instantly does things like launch agents on a canvas - without awkawardly waiting for slowGPT LLM loop @clonkapp is now the fastest agent orchestrator on the planet

Hamed Valigholizadeh

@hametgholizadeh

JEV IS INSANE. I gave it 80 real exam questions and 297 practice ones. In 80 seconds, it told me which ones are most likely to appear on the real exam and which ones aren’t. All for $0.0256. Can't stop playing with @typesafeai 😁

Yum⋆₊˚

@yuhasbeentaken

Jev classified 1,315 X posts for about $0.086 in estimated model cost 😂 seeing everyone's Jev demos made me want to build something for my own content research. i'd collected a lot of posts, but figuring out what they had in common still meant opening them one by one and taking notes. so i built a dashboard around Jev. it labels each post across 8 dimensions, including topic, hook and writing style. now i can filter by topic and hook, compare engagement, and open the original posts to see the examples behind each pattern. my archive is a lot easier to learn from now.

keno

@kenonews

JEV makes competitor research feel like a cheat code. Give it your competitors’ ads. Break them down by hook, angle, offer and format. Then turn recurring combinations into a shortlist for your next creative test. From an endless swipe file to “here’s what we should try next.” Your competitors just became your creative department.

Nick Khami

@skeptrune

you can make any open source model behave like jev with just a bit of inference engineering. it's shockingly easy. to prove it, we built a new endpoint we're calling deepseek-v4.1-flash-jev. see the demo below. here's how it's done: sglang (an inference engine) offers a scoring endpoint in addition to the normal generation one. in scoring mode, given an input & set of possible answers, it forces the model to produce probabilities for each one. example: > input: what is most common letter in abcccde? > possible answers: a, b, c > output: (c, 0.9), (b, 0.0.5), (a, 0.05) getting the above behavior instead of streamed output is as simple as using sglang's /v1/score endpoint instead of /generate. there's just one other trick required. for deepseek, you have to add a closing think tag before the response. this forces a direct answer instead of a reasoning trace. if you want reasoning, you can do that too, but imo that makes things too slow to be worth it. dsv4.1 flash is not as good as jev, but if we had enough spare compute to experiment with this same approach for a larger model then i think the decision quality would be at least as good, if not better. also, somewhat unrelated, i think decision-making models kill all prospecting & sourcing work. i would have absolutely killed to have jev or similar when i was recruiting @mintlify. absolutely incredible.

Florian Darroman

@floriandarroman

Jev is INSANE. I asked 100 Indie Hackers to build a post scheduler with: Jev vs Fable 5.1. The results are unexpected 🤯 (You can clearly see Jev is faster at doing stuff)

t0t0

@t0t0_build

Un JEV me está monitoreando más de 25 grupos de whatsapp con clientes en tiempo real y evaluando si tengo que saber algo por urgencia problemas, pedidos irresueltos etc. Si tengo algo que saber, le avisa a una LLM que me escriba. 🔥

ares. 🎧

@aresotik

ESTA HERRAMIENTA ACABA DE ROMPER TODO EL MERCADO DEL AD SPY Maxfusion ha cogido JEV, el modelo nuevo de TypeSafe, y le ha metido la ad library entera de una marca → 1.891 anuncios clasificados → 19 segundos → 0,12 $ Y no es un resumen: cada anuncio etiquetado por etapa del funnel y estilo creativo, más la radiografía completa de la cuenta Llega pronto al MCP de maxfusion

Hiroyuki Ota (ほた)

@hota911

Jev にぷよぷよをさせてみた。 - 実行エンジンは https://github.com/puyoai/puyoai 。ゲームはリアルタイムで進行 - 盤面とそれぞれのポジションにおいたあとの結果を与え、①今どのフェーズか ②フェーズごとに、戦略からどの結果がベストか の1+3つの質問をして、選択肢を選ぶ 画像は Random に勝つ様子。決断が早いぶん Random も強い。 最初は盤面だけ渡して、単にどこにどの向きで落とすか判断させていたが弱すぎたので、配置後の結果を選ばせるようにしたり、戦略を渡したり、判断を多段化した結果なんとか3連鎖もでき Random に勝てるようになった。

Anusha

@acharyaagamya

I made a Magic Jev Ball for code reviews 🎱 Click it on any GitHub PR and ask: "should I approve this?" It checks CI, diff size, and reviews, then lets @typesafeai Jev decide your fate in ~200 ms No more thinking. Just shaking.

Jiaqi Gu

@droidqw

github.com/droid-Q/jev-sk… ，基于 jev 的 skills 路由

Chris Adcock MD 🍊💊

@ChrisAdcockMD

Took Gregor’s Ultrafast idea and wired it into Grok Bot. @bot @OpenRouter @typesafeai @gregpr07 Your bots can now use Jev to drive the real Chrome on the machine instead of slow look-and-click. Drop in the API key you already have (OpenRouter or TypeSafe), and it gets going. It also walks your existing bot workflows and flags which decisions Jev can take over — the quick yes/no and “pick one of these options” calls — so you’re not guessing where it helps. Share link if you want to try it: https://x.ai/bot/sM_Xi4OF09cGU8KGyLvlC

Geek Lite

@QingQ77

复刻 Jev 的并行决策思路，让 0.6B 小模型一次前向直接输出完整概率分布，不做输出 token 解码。 github.com/TianyuCodings/…

Trinay Hari

@hari_trinay

Built a construction plan-set classifier with Jev. Proq turns civil and building plan sets into bills of materials using an LLM pipeline we built on GPT-4.1. Jev classified an entire 26-sheet plan set in 2.9 seconds for $0.0052. It matched GPT-4.1 and GPT-6 Astra on 100% of sheet-level classifications while running 17–21x cheaper and 5x faster than our production pipeline.

Sim Audience

@SimAudience

Jev is WILD I gave it two launch tweets and fed it over 4000 demographic profiles of real survey participants Twelve seconds later, a simulated A/B test tied to actual personas voting on the best tweet you can just do things i made it 100% free (link below)

Izzuddin

@Izzuddin_Shafi

1/8 Saw Jev from @typesafeai on my feed, so I made it play Pokemon Showdown. Codex built the harness. It was damn fast. Its choices were a mixed bag. Full match, video 1/2. This is a saved replay with decision data, latency and added reading pauses.

Saeed

@stringsaeed

built a highlighter on jev paste any language → my code tokenizes → jev names the lang, colours every word, then says which of 9 lint rules fire and where nine rules in code. jev just answers. near instant lab.saeed.sh/highlight

appcypher

@theappcypher

okay Jev is an INSANE unlock, I just gave Mario a multiverse. built "Mario Never Dies" with @typesafeai's Jev + microsandbox Jev picks every move and every time Mario dies, we fork the entire VM into 4 timelines and try again. whichever Mario survives becomes canon. it is like the others never happened.

ラプター | 高専 ロボコン ビジコン

@Raptor_zip

型付き意思決定AIモデル「Jev」で双腕ロボを動かした結果まとめてみた🦾 3層制御のうち意思決定（2層）をJevに任せ、IKや物理計算はコード側に分離する構造を作りました。500msの応答と0.5円/試行で早くて激安。

Ranjan

@manofsteel3129

built askjev on typesafe jev for all-site navigation with claude you talk to claude in plain english and askjev runs your real browser on any site. jev decides every next click — open pages, switch tabs, scroll feeds, fill forms, run multi-step goals without you babysitting the DOM. mcp server + chrome/brave extension. auto-connect once, then stay in chat while the browser moves. claude handles the conversation. jev handles the decision on each step. askjev is the hands on the web. install: load the extension → paste your typesafe key → auto-connect → restart claude → talk example: use askjev, open http://x.com and scroll my feed and find the best posts http://github.com/ranjan2829/AskJev npx -y askjev-mcp

Tamir

@TamirSPIRITT

introducing JevForm, a form that dynamically branches and chooses what to ask next usinng @typesafeai’s Jev in my life i’ve made hundreds of forms with crazy if/then logic. Jev solves it. built with @vercel json-render (by @ctatedev), so theoretically it can support any generative form UI, and @DavidKPiano’s xstate for the actual state Play with it here: https://jevform.spiritt.app/

aniol

@0xaniol

today i built talkr, a speech analyzer using @typesafeai > talkr gives you a topic > you talk about it for 30s > jev analyzes your speech: pauses, filler words, repetitions, confidence, clarity > you get a score and feedback to improve can’t wait to 10x my speaking skills

Jason Lu

@jasonlu_ai

JEV is changes the world of E2E testing! Same eBay test flow, completed-run medians: Jev: 47s / $0.0067 GPT-5.6 Luna: 62s / $0.0277 Claude Sonnet 5: 79s / $0.4062 Try jev-e2e. github.com/perixtar/jev-e…

Chandramouly Kandachar

@chandamamz

@typesafeai's Jev controls the 2 hands and each finger to play the piano in real-time. Jev only "sees" what we see and plays this from the "note waterfall". It uses @browser_use's jev-ultrafast and some decision scheduling to make this happen in real-time. Sound on 🔈🔉🔊

Ateeq

@TPateeq

I used Jev to solve a problem every agent eventually runs into: reading logs. 22.8M lines, and running an LLM on every one would've cost $1,120. Tocsin groups them into 11,812 repeating patterns, then asks Jev about each pattern once. 6 minutes, 64 cents, 123 patterns that actually needed to be looked at. http://github.com/TPAteeq/tocsin The paging policy is just a prompt. You tell it what should wake someone up at 3 am and what's just another log line.

Everton Carneiro

@everton_dev

I built a tool that finds App Store keywords by reading the competition, and uses Jev to judge them. What it actually does: 1. Turns the app's own listing into a handful of search queries, with Jev filtering out the ones nobody would type. 2. Runs those searches on the App Store. Whatever ranks is the candidate pool. 3. Jev judges each candidate: is this really an alternative to the app, or does it just share a word? The lookalikes get dropped. 4. The strongest survivors become the competitor set it mines for keywords. No competitor list to maintain, nothing hallucinated: competitors are whoever Apple already ranks, minus the ones Jev rules out.

Aditya Singh

@xyz04274951

I made this for fun. Wired a mic over Fusion 360: click, speak, Fusion runs the feature. @typesafeai ‘s Jev only decides if the utterance is a command.

Kun Chen

@kunchenguid

almost every day i hear people ask "when should i /compact my session" there's no easy answer because it depends on how likely your future action will need detailed context in the existing window but we have Jev now! introducing compact-adviser - an agent plugin you can use in claude and pi today to help determine whether you're likely at a task boundary that's safe to compact https://github.com/kunchenguid/compact-adviser i built a private eval set from 40 real sessions and manually labeled all the safe vs unsafe checkpoints to evaluate this, and hillclimbed the Jev prompt till it performed quite well i also made it so that the classifier will - optimize for precision (not triggering a compaction prematurely) when context window is small - and gradually shift to optimize for recall (not missing an opportunity to compact) when context window fills up, because the cost of not compacting becomes higher, and at the end the agent will be forced to compact anyway it supports a "hint" mode (just give you a hint and it's up to you to run /compact) vs "auto" mode which runs compaction whenever Jev says it's safe to do so if you have Jev and want to put your compaction on autopilot, try this out and let me know how it goes! support for more harness is coming soon as well

Milind S

@milindlabs

Aaaaaand this is now open-source here: A tiny AI pointer companion for your Mac driven by JEV or Gemini Live - Bring your own keys - Local OmniParser running on CoreML - Jev drives the pointer - Ctrl + K to type a task Voice mode next if people want it! https://github.com/milind-soni/tiptour-macos