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.
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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.
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ESTO ES PURO ORO
le metes la ad library de tu competencia a JEV y en 19 segundos te clasifica 1.891 anuncios
cada uno etiquetado por:
→ fase del funnel
→ estilo creativo
→ ángulo de copy
más la radiografía completa de la cuenta: en qué invierte, qué repite y dónde tiene huecos
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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)
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Jev is WILD for SEO audit 🤯
in 45.1 seconds it read all 586 pages on my site and rebuilt the internal link map. 584 links placed, 139 pages it refused to link because nothing honestly fit. total cost $0.21.
Claude Opus 5, same 586 pages, same clock, got through 21 of them and spent $1.43.
per page that is ~190x cheaper. the full Opus pass would have run $43.
internal linking is the perfect Jev job. it is not writing, it is 8,790 yes/no calls: does this page have a real reason to link to that one, and is there anchor text already sitting in the copy. that is a classification problem, and we have been paying frontier prices to do it one page at a time.
what you are watching: left column is Jev, right is Opus, same queue, same rubric. the run stops the moment Jev finishes so Opus stops burning tokens.
coming soon to @distribb_io + connector + gpt plugin
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話題AIモデル「Jev」をSEO業務で使ってみた
まずは検索クエリ分類
・BigQueryからクエリ抽出
・Jev判定
・未判定はGPT-5.6 Lunaへ流す
これで検索流入データを
・情報収集系
・比較検討
・購入/申込
・ナビゲーショナル
とかで集計し、推移を追える
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Jev is TypeSafe AI's first System One Model.
It is 100x faster and cheaper than frontier LLMs.
That opens up a lot of use cases people usually skip because the big models are too slow or too expensive for them.
Here are 9 use cases where we think Jev could be used instead of an LLM.
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Breaking: Browser Use + Jev = Ultrafast ⚡
Findings flights took 7s and cost only $0.0039 🤯
> new action space every step
> DOM state space
> small LLM fallback to type
(this video is at 1x speed btw)
Built a tiny open source browser agent. try it below ↓
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jev is insane 🫣
it makes realtime virtual try-on hauls possible.
built this experiment for Drape with @typesafeai
> i talk
> jev reads transcript + what i'm wearing
> picks from my closet
> changes my outfit in realtime
cost: $0.0011 per decision
time: ~620ms per decision
imagine getting ready like this:
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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 👇
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Jevmaxxing for marketers
Jev can speed up most marketing workflows 30x and do it for < $3:
1/ Scan the whole Meta Ad Library
-> It reads every live ad in your category and tags each one by hook, format, offer and days running
2/ Find the ad patterns that survive
-> It compares formats by how many ads are still live after 60 days, so you know what lasts before you test it
3/ Score briefs before you shoot
-> Your LLM writes the briefs, Jev scores each on hook, brand fit and survival odds, and only the top ones get made
4/ Sort search terms
-> It asks "is this query from a buyer?" across the full Google Ads report, so negatives land the same night
5/ Catch fatigue early
-> For every ad with frequency up and CTR down, it picks replace, refresh or leave
6/ Check ad to landing page match
-> It scores whether the page delivers what the ad promised, the cheapest CVR fix in most accounts
7/ Score every lead
-> It rates each form fill 0 to 100 against your ideal customer within seconds, so Google and Meta learn to find more of the good ones
Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇
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Wallsy
wallsy.live
