Jev for ads and marketing research

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)

Zachi

@iam_zachi

I build an undetectable realtime adblocker extension with typesafe It checks every dom element and classifies as ad/non-ad and removes it if true Extremely fun to work with, expecting an incredible shift in how AI is being used in the future

Albiona Hoti

@albicodes

I built a visual reference finder with Jev One single prompt → 100 images from Cosmos, NASA, and The Met my new rabbit hole for creative work 🌻

Higgsfield AI 🧩

@higgsfield_ai

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

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.

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 ↓

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.

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

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)

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)

佐藤 裕二／さいたまのWEB広告運用者

@Ysato_su04

これができたら衝撃的。 JevがMeta広告ライブラリから長く回っている勝ちパターンを自動抽出、Google検索語句まで分析して不要クエリ除外、LPとのズレまでスコアリングしてくれる。調査から改善まで一気通貫で、人が何時間もかけていた工程を3ドル未満で済ませるのは衝撃的。 広告運用者として実務で感じるのは、クリエイティブ疲れやオーディエンス重複の見落としがROAS低下の主因になること。 このツールなら仮説立案が高速化され、PDCAが30倍速になる可能性大。

zre

@zrebroia

Jev lleva toda tu operación de marketing de anuncios por menos de 3$. ⁠→ Escanea cada anuncio activo de la competencia y lo etiqueta por hook, formato y tasa de supervivencia ⁠→ ⁠430 anuncios leídos por segundo. 0,6$ gastados.

SCRAPS

@scrapsonsolana

For marketing $SCRAPS, I’m trying something I haven’t seen anyone else do. I’ve been experimenting heavily with the latest AI tools (JEV from @typesafeai), burning through 178M+ tokens and nearly 48,000 requests testing different ideas. One of those experiments is focused entirely on marketing: finding the fastest path to high engagement and the best ROI possible. Now that SCRAPS is live on Google Play, it’s time to put it to the test. The experiment starts now.

RazeDen

@razeden0

i just merge Grok 4.7 + Jev and got the smartest thing i've ever owned grok 4.7 does the thinking. jev does the deciding. together they cost less than my coffee prompt → grok bot → jev asks 6 questions → grok 4.7 opens only the good ones → campaign jev doesn't write anything. it only answers simple questions: yes or no, pick one, or a score. it can't make things up, because it can only pick from answers you allowed i gave it 3,412 leads from x, linkedin and youtube. 6 questions each is 20,472 decisions, done in 15.7 seconds for $0.41 the same reading by hand took me 6h 12m. people say 400x faster. i did the math and it's 1,421x that's $0.00012 per lead, and grok 4.7 only reads the ones worth reading the big mistake: sending everything straight to the new model. you pay the smartest thing you own to say "skip" all day setup took me 9 minutes: key in the secure field, sdk on the agent computer, one router skill, test mode first jev decides, grok bot does the work, and i still press the button on anything i can't undo everyone is talking about the new model. the cheap one deciding what it reads is where the money is

Raph Guilhem

@raph_guilhem

Opus 5.5 + Jev = Automatic scoring of 120 competitor ads, shortlisting the 10 worth adapting, and creating versions of each. Both models came out in the recent days, so I tried them together on a marketing use case, playing to what each one is built for: → Claude Opus 5.5 built the pipeline: it found the competitors, ran the http://Pletor.ai workflow "Meta ads spyer", wrote the scoring script and built the interface. → Jev made the judgement calls on every ad The use case: I picked Grüns, the greens gummy brand, as a test case. I pulled 120 ads from 12 of its competitors on the Meta Ad Library, and had Jev score each one on 7 questions: hook, clarity, message, concept fit, tone, angle, health claim risk. Code then combined the scores and picked the 10 worth adapting, and created them, via Pletor MCP.