Jev for marketing
Marketing is full of judgments that are cheap once and unaffordable at scale: is this hook good, is this lead worth the email, does this page read like a machine wrote it. 54 builds in this directory hand those to Jev at a median of $0.000065 a decision. Here they are, grouped by the five jobs they fall into.
Updated · by Jon Kraayenbrink
In short
- Jev grades, ranks and filters. It never writes the copy — that stays with a writing model.
- The unlock is volume: 100,000 posts on 14 questions each in 20.4 seconds for $0.67.
- A rubric can be 61 questions deep and still return in about a second, because questions in one call run in parallel.
- Two verticals have pages of their own: SEO and ads.
The five jobs, with a published figure each
Every figure below was published by the person who ran it, and each links to where they published it. Nothing here is measured by this site.
| The job | A published result |
|---|---|
| Research the market’s own material | 1,891 ads labelled in 19 seconds, $0.12 |
| Test before you spend | 21,690 persona judgments over 723 ads, $0.22 |
| Score the draft while it is a draft | 61 questions about one post in ~1 s, $0.0004 |
| Rank the people, not just the copy | 700 leads and their messages scored in 40 s, $0.09 |
| Label the archive and read the pattern | 100,000 posts, 14 questions each, 20.4 s, $0.67 |
Research: read everything the market already published
Competitor ads, niche video, feeds and threads, read in full instead of sampled. This is where a marketing team gets the most out of a cost per decision measured in hundredths of a cent.
Matthew Berman
@TheMattBerman
jev is INSANE. in 40 seconds it broke down 724 live ads from 37 brands. every hook. every format. offer. cta. awareness stage. landing page mismatch. used 9 cents of tokens. (will be avail in @stealads + mcp)
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
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 ↓
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
Testing: get a verdict before you spend
Personas, focus groups and A/B tests run as decisions rather than as generated opinions. Two launch tweets against 4,000 profiles takes about twelve seconds.
Matthew Berman
@TheMattBerman
jev KILLED the focus group. it scrolled 723 ads as 30 buyer personalities 21,690 stop or scroll decisions. 22 cents. (will be avail in @StealAds + mcp)
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)
leo
@leojrr
rebuilt the X algorithm with Jev - uses real weights - simulates virality of your post - has a global feed (you see everyone) it's insanely accurate
XSocial feeds
The X algorithm, rebuilt with Jev
Publishing: score the draft while it is still a draft
A long rubric costs about the same as one question, so the check can be 61 questions deep and still return before the writer has stopped typing.
Rob Hallam
@robj3d3
Jev + SuperX = virality solved ✅ Every post gets 61 questions in ~1s for $0.0004 🤯 > fitted on 9,481 real posts from 207 creators > picks the viral post 2 in 3 times > never rewards reply bait So: write, score, rewrite, stop when it peaks. Free, no signup. try it below ↓
Riley Brown
@rileybrown
Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential. Going to try and actually make this good, will need to scrape a lot of twitter data... Notice how it also categorizes the tweet live... I could have it surface similar tweets on the right side for inspiration... idk just experimenting.
XSocial feeds
Live viral post analyzer
GitHubAds and marketing
hookmeter-jev
Millisecond viral hook telemetry, in a Chrome extension.
ehui1226
22
Steve Krouse
@stevekrouse
typesafe's jev is fun! live demo you can play with: typesafe-demo.val.run
Jon Kraayenbrink
@kraayenJon
jev is INSANE. in 243 ms it checked a website for 35 tells of ai slop. purple gradients. emoji headers. "seamlessly". fake testimonials. bento grids. the works. used $0.00015 of tokens. paste any url, get a slop score. free: madewithjev.com/free-tools/ai-…
Demand: score the people, not just the copy
Lead scoring, outreach fit and call review. 700 leads and their personalised messages came to $0.09; 400 companies matched to one candidate came to $0.0005.
Romàn
@romanbuildsaas
JEV is INSANE. We gave it 700 high-intent leads and personalised outreach messages. In 40 seconds, it predicted how each message would perform, assigned a confidence score and detected lead-message mismatches. All for just $0.09. JEV can also score leads, analyse buying signals, match each prospect with the best message and identify the campaigns most likely to perform based on data. Coming soon to @GojiberryAI+ MCP. Comment “JEV” for early access.
GitHubSales and leads
clay-jev-people-ranker
Lead scoring in Clay, ranked by Jev instead of a prompt.
PromptGTM
3
Sarvagya Kulshreshtha
@sarvagya_kul
JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score companies, analyse your experience, match you with the right roles and identify the opportunities you’re most likely to get based on your profile. Coming soon to @textbackdoor Comment “JEV” for early access.
Analysis: label an archive and read the pattern
The analysis nobody ran before, because the per-item cost made it silly. 100,000 posts on fourteen questions each came back in 20.4 seconds for 67 cents, with the answer sitting in the percentages.
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.
Ian Nuttall
@iannuttall
I gave Jev 3,282 of my X posts across 100M views and asked it to find what actually works for growth. 4,252,330 tokens $0.1282 for the full 8m 34s run! Each post got 8 questions about the topic, hook, tone, whether it teaches something, etc. How-to posts got 150 median likes vs the average median of 44. AI and coding was a 1.9x multiplier topic compared and SEO, despite recent posts, was right at base median 1.0x - surprisingly. The recommended topic + angle + voice formula was: AI coding + teach something + provocative
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.
Dan Shipper
@danshipper
we almost never test new foundation models but we've been testing this for ~a week @every and it's pretty wild. the kind of things that will be obviously indispensible in 6-12 months it doesn't produce words as output, it produces probabilities. so it can efficiently act as a judge in cases where you'd need a Fable-level model—but in our testing was 25x faster and 600x lower priced excellent vibe check by @hammer_mt on @every: https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds?utm_cta_source=home_main_a_3
ArticleBenchmarks and evals
PickEvery’s editorial vibe check
- Judgments
- 1,709
- Total cost
- <$0.01
- Median per passage
- 0.35 s
Content research: judge the window before a person looks in
The newest and most contested corner of marketing Jev is the one in front of the content pipeline rather than inside it. The job a creator loses hours to is not writing — it is scrolling a niche and judging whether each video belongs before deciding whether it is worth copying. That is two bounded questions per video, and it is the reason the decision layer belongs here: filter the pile, label what passes, and hand a person only the videos that cleared the bar.
Virlo published the clearest description of the split, and it is worth keeping the parts apart. Virlo pulls every video in a niche from a corpus of 12.8 million short-form videos, and Jev decides which ones earn a place: does this actually match the niche, judged off the caption, hashtags and transcript rather than the tags alone, and are the hashtags lying. Only what clears runs through Virlo’s own eighty-signal panel. Jev decides what is worth tagging; Virlo tags. Confusing the two is the most common mistake in the threads.
Two published findings from Virlo’s live head-to-head, labelled Calibration while it runs:
| Test | Jev | The judge it may replace |
|---|---|---|
| 44 human-labelled videos, 6 intents | 33 correct | 30 correct |
| Tightening the brief so the topic is the requirement and everything else a preference | 33 of 44 | Same set scored 16 of 44 before the change |
The second row is the transferable lesson, and it is the same one that governs every schema on this site: the biggest accuracy gain did not come from a bigger model or a longer prompt. It came from one sentence telling Jev that the topic was the requirement and the format, tone and audience were only preferences. Same test set, roughly doubled.
Read the whole thing as a test in progress, not a finished result. The sample is small, and the second check is a disagreement set that is only partly graded. The other builds below are independent of Virlo and publish their own numbers: 100,000 viral posts on fourteen questions each, a 1,315-post archive labelled on eight dimensions for about nine cents, and 4,000 Reddit threads scanned for citation, which is a research filter rather than a content one.
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 ↓
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.
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.
Riley Brown
@rileybrown
Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential. Going to try and actually make this good, will need to scrape a lot of twitter data... Notice how it also categorizes the tweet live... I could have it surface similar tweets on the right side for inspiration... idk just experimenting.
XSocial feeds
Live viral post analyzer
GitHubAds and marketing
hookmeter-jev
Millisecond viral hook telemetry, in a Chrome extension.
ehui1226
22Kevin 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
Matthew Berman
@TheMattBerman
jev is INSANE. in 40 seconds it broke down 724 live ads from 37 brands. every hook. every format. offer. cta. awareness stage. landing page mismatch. used 9 cents of tokens. (will be avail in @stealads + mcp)
The pattern worth copying
Look at the analysis group closely, because it is the one that changes how a team works rather than how fast it works. Movez asked fourteen yes-or-no questions of 100,000 viral posts — does the hook open a loop, is there a number in the first line, is the proof real or claimed — and then read the percentages. Superlative claims went viral at 2.34% against a 1.22% baseline. Numbered lists came in below baseline at 0.55%.
That is not a faster version of something a marketer already did. It is a study nobody ran, because at eighteen cents a post the same pass would have cost roughly what a small campaign does. The whole argument for Jev in marketing is in that gap, and Jev vs an LLM has the side-by-side: the same corpus on Claude Opus 5 got through 214 posts for $0.98 in the same twenty seconds.
Where to go from here
Deeper on one vertical: Jev for SEO covers audits, GEO and content quality; Jev for ads covers ad libraries and pretesting. For the mechanics of a call, read how to use Jev; for what it costs, the pricing page; and for every build in the directory grouped by the decision rather than the department, the use cases.
Common questions
- What can marketers use Jev for?
- Five jobs, in the order a campaign runs: reading the market's existing material, testing an idea before spending on it, scoring a draft before publishing, ranking the people you are about to contact, and labelling an archive afterwards to see the pattern. It decides; a writing model still writes.
- Does Jev write marketing copy?
- No. It answers closed questions about copy that already exists — is the hook strong, does this open a loop, is the proof real or claimed, does the landing page match the ad. Most teams here keep an LLM for the writing and give Jev the grading.
- How much does this actually cost?
- The median across 15 published runs in this directory is $0.000065 per decision. In marketing terms: 700 leads scored for $0.09, 100,000 posts analysed on fourteen questions each for $0.67, 1,891 ads labelled for $0.12.
- Can Jev do content research, like finding what is working in my niche?
- Yes, and it is a good fit, because the job is filtering before it is writing: judge every video in a niche against whether it belongs, then label the ones that pass by hook, format and angle. The tags still come from your own signals; Jev decides what is worth tagging and what reaches a person. Virlo runs this over a corpus of 12.8 million videos and reports the head-to-head in Calibration on this page.
- Is it worth it for a small team?
- Only where the same judgment repeats. One landing page is not worth moving. A weekly pass over every competitor ad, every draft and every inbound lead is, because the per-item cost is what made those passes not worth running before.
- What should I try first?
- A competitor ad or content teardown. It is the cheapest to build, it has the most published examples to copy, and the output is immediately legible to everyone else on the team.
Made with Jev is independent and not affiliated with TypeSafe AI. Every figure on this page is the one its author published, linked to where it can be checked.