SEO and marketing work is full of judgment calls made at volume. Is this page clear? Does this ad lead with a hook? Which story should a brand react to? A person can answer each one. A person cannot answer thousands. A decision model answers them as typed questions, a score or a choice with a confidence value, so a spreadsheet or a script can sort the results. The examples in this hub run on Jev and other decision models. Many are demos of one job done over a pile of ads or posts, such as the breakdown of live ads by hook, format and offer. Others are tools, such as AkashPriyadarshii/jev-seo, a CLI and MCP server for SEO and GEO (generative engine optimization, meaning being cited in AI answers), and kitze/pagegrade, which grades the sections of a web page. Before you copy one, check what the score is based on, whether the questions are published, and whether anyone checked the scores against real results.
21 entries
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Open source skills that read a morning's news feed with Jev and tell a list of brands which stories to act on. In a demo run it read 384 headlines and matched them to 15 brands in 24.9 seconds for $0.19, against 4 of 384 for $0.77 on Claude Opus 5 over the same feed.
Elvis (@elvissun)Opennewsjack on newsjack.sh851 starsCost $0.19vs Opus/headline ~390x cheaper
Generative engine optimization platform that tracks brand mentions across ChatGPT, Claude, Gemini and Perplexity. Jev runs its chat router, GEO judge and feedback classifier by default, with LLM fallbacks; the NOTRA_JEV_CLASSIFIERS setting turns it off.
An agent-first SEO and GEO CLI and MCP server written in Rust, combining DuckDuckGo search with Jev for the judgment calls.
Grades the sections of a web page for clarity, writing and on-page SEO with Jev, returning a score per section. Built as a browser extension with WXT.
Tests whether Jev can pick the winner of real headline A/B tests across 10,984 Upworthy randomized experiments. Reported 64.5% overall and 74.7% on decisive pairs.
Gaurav-GosainOpenjev-headline-bench on github.com
A demo breaking down 724 live ads from 37 brands in 40 seconds for 9 cents, covering hook, format, offer, CTA, funnel stage and landing-page mismatch for each ad.
Matthew Berman (@TheMattBerman)OpenBreaking down 724 ads from 37 brands with Jev (X) on x.com1.1M viewsSpeed 724 ads/40sCost 9 cents
A video demo has Jev read 586 pages of a site in 45.1 seconds and rebuild the internal link map, placing 584 links and refusing 139 pages where nothing fit, for $0.21 total. In the same time, Claude Opus 5 processed only 21 of the same pages for $1.43.
borja (@borjafat)OpenAn internal-link SEO audit (X) on x.com827k viewsPages read 586 in 45.1sCost $0.21
Describes an SEO/GEO agency workflow, auditing Search Console, PostHog/Mixpanel and AI citation data, that used to cost about $250 per client; each step now runs roughly 30x faster with Jev, cutting the total cost by 90%.
Ira Bodnar (@irabukht)OpenAn SEO/GEO agency workflow that cut costs 90% with Jev (X) on x.com366k viewsCost cut 90%Prior cost/client ~$250
Free SuperX tool that scores a draft X post from 0 to 100 against a normal post from an account your size, with lists of what helped and what hurt and a simulated feed preview. Rob Hallam's launch post says each draft gets 61 Jev questions in about a second, fitted on 9,481 posts from 207 creators.
Rob Hallam (@robj3d3)OpenTweet Tester: Preview & Score a Post Before You Send It on superx.so238k viewsPer post ~1s, $0.0004Accuracy 2 in 3
A video demo classifies 1,891 ads from a competitor's ad library in 19 seconds for $0.12, tagging each by customer journey step and ad style.
Stav Zilbershtein (@mightyking)OpenMaxfusion competitor-ad research (X) on x.com171k views1,891 ads 19s, $0.12
A follow-up demo where Jev scrolls 723 ads as 30 simulated buyer personalities, producing 21,690 stop-or-scroll decisions for 22 cents.
Matthew Berman (@TheMattBerman)OpenScoring 723 ads against 30 simulated buyers with Jev (X) on x.com131k viewsDecisions 21,690Total cost 22 cents
Jev filters and selects creative assets, which DeepSeek and Higgsfield then turn into ad creatives, shown as a video demo from Higgsfield AI.
Higgsfield AI 🧩 (@higgsfield_ai)123k viewsOpenFiltering creative assets for ad generation with Jev (X) on x.com
Describes 7 SEO/GEO workflows built on Jev: scoring competitor pages, flagging on-page changes for citation, matching buyer questions to pages, rating citation likelihood, sorting search terms by buyer intent, mapping internal links and verifying AI-written pages before a human sees them.
Jev scored 3,282 of the poster's X posts, covering 100M views, on 8 questions each about topic, hook and tone, for $0.1282 and 8m34s total. How-to posts got a 150 median likes versus 44 overall.
Ian Nuttall (@iannuttall)OpenJev scored 3,282 X posts for what drives growth (X) on x.com88k viewsTotal cost $0.1282Run time 8m 34s
Ran 14 yes/no questions per post, such as whether the hook opens a loop or whether a claim is proven, over 100,000 X posts in 20.4 seconds for 67 cents, then compared which hook types actually beat the viral baseline.
Movez (@0xMovez)OpenA viral post analyzer for 100,000 X posts (X) on x.com82k viewsFull run 20.4s, $0.67vs Opus 5 per post ~680x cheaper
A live viral-post analyzer built with Jev: it scores viral potential and categorizes a tweet as the user stops typing, shown here as an early, in-progress demo.
A tool where Jev watches videos in a chosen content niche, drawn from a database of 12.8M viral videos, and judges hooks, formats and angles before turning the analysis into a script via Claude; free in Virlo plus MCP.
jaffa (@dsqjaffa)12k viewsOpenA content marketing tool that judges viral videos with Jev (X) on x.com
See how AI ranks your brand across ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews. Mentions, citations, a $49 scan, or weekly reports.
Jev breaks down competitors' ads by hook, angle, offer and format, then turns the recurring combinations into a shortlist for the next creative test.
The author scanned more than 700 live ads with Jev in about 40 seconds for roughly 4 cents, and compares the cost to running the same job through a general-purpose model.
Ackerman (@Yarilo7brigada)5.4k viewsOpenScanning 700 live ads with Jev in under a minute (X) on x.comLive demo that tests a post against a crowd of 10,000 AI readers with their own jobs, tastes and memories. Jev answers whether each one likes, dislikes, reposts, follows or blocks, and every reaction comes from an AI reader, not a real person.
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The loop is: collect a pile of items, ask the same typed questions about each, then aggregate. Ian Nuttall's post scores his own X posts on questions about topic, hook and tone, then compares groups. The competitor-ad demos tag each ad by hook, format and offer, then count the common combinations to build a shortlist. Treat the output as a table, not a verdict.
Score ranks and choice tags. Noul checks one trait at a time. Gaurav-Gosain/jev-headline-bench tests whether a model can pick the winner of real headline A/B tests, which is a direct check of the whole idea. Its numbers are the author's own, so read the method first. A check like that is rare, because most entries only show scores and leave the real-world outcome unmeasured.
The limit is that a score is a proxy. A model that rates a hook as strong has not seen your audience. Crowdcheck simulates a crowd of readers with personas, which adds variety but is still a model of people. Use the output to shortlist, then test with real traffic. Entries that show the full set of questions let you judge for yourself what a score means.
Entries such as AkashPriyadarshii/jev-seo and kitze/pagegrade use Jev for judgment calls like grading a page section or scoring a competitor page. The search data and crawling come from other tools. Jev supplies the typed scores.
Yes. Several demos tag competitor ads by hook, format, offer and funnel stage and then summarize the patterns. The tags are a starting point for a human creative review.
Generative engine optimization means making your content likely to be cited in AI answers. usenotra/notra and AI SEO Tracker are examples here that track brand mentions across AI assistants.
