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SEO AIO GEO Dashboard built with Jev. https://t.co/q6pcGY9ouh

I built an on-brand ad machine with Jev and give away my whole setup Everybody is crazy about Jev because it solves a major friction in creative ops: fast and cheap verification of brand consistency This is how I used it. > Get your Jev API key - good news, there is no waitlist anymore > Create a Pletor brain -> one place to store all your brand guidelines, visual references, etc. is key to inform Jev about your compliance and feed your production system Then use Pletor MCP + Claude Code + Jev API to run the following steps on autopilot: > Claude identifies 10 inspirations brands - I asked to

we just released jev for viral content research type in any keyword like "travel app" or "skincare product", and jev will surface tons of relevant content in seconds

jev + picsart turned one prompt into a creative system that decides before it spends instead of letting expensive models guess their way through every step, i split the workflow in two: Jev handles the cheap decisions, Picsart handles the actual generation. here’s exactly how i wired it: step 1 → the brief enters once. every image, clip, audio pass and edit inherits the same campaign direction instead of starting from a blank prompt. step 2 → Jev sits before the expensive work. it decides whether the next stage should run, stop, reuse something finished, or wait for me. step 3 → the decision s

Jev + @Picsart is the first content system that actually automates my whole upload queue 99% of people still burn a full model call on every posting decision - the 1% running this pay cents instead 6 minutes and the queue is wired: clip → Picsart worker → Jev decision → Picsart execution → posted step 1 → create a Jev API key (typesafe website) step 2 → clone the upload router from Github below, then: python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export TYPESAFE_API_KEY='YOUR_KEY' - add it to your shell profile so it survives

JEV is insanely fast. Our AI took 35.83 seconds to make one product photo, so we asked: Can jev make it faster? It figures out what a customer wants in 0.4 seconds. Cost: $0.00003 per message. Now we're down to 16.23 seconds. More than 50% faster. Available now in Dezygn.

JEV + OPUS 5.5 is insane... I built a viral post prediction analyser with JEV + Opus 5.5 Paste any X link → press Start → Jev compares it to 800 posts that went viral and gives it a virality score Full production ship in 9 minutes: > Jev parses 800 viral posts from X as a live baseline > Groups them by hook type: build demo, receipt, launch, contrarian... > Runs 12 typed checks on every post (hook, numbers, media, CTA) > Opus 5.5 explains why each one spread > Your post gets scored against its hook type + the 5 most similar viral posts Output: score /100, expected likes and views, and % of the

We ran four Shorts through Jev in Codex. For each cut, it checked whether the transcript supported the title, the thought had a complete ending, and any essential context was missing. When a check failed, Codex showed the repair before the edit moved forward. Cut → evaluate → repair → review. Then we feed the result back into the eval and tighten the criteria for the next run. A person still reviews the final edit for pacing and visuals. I broke down the full workflow in the article here 👇

今度はTripoのP2.0からのエクスポートでのリアルキャラ。キャラ自体はぜんかいとおなじだけど、今度はP2.0データから他のことやりつつAstra君とまた詰めて。各種設定・リグ入れ、演出調整。メッシュがいいからリトポは基本無くて１日でここまで☺️😋 １動画目：日本語でリップシンク ２動画目：同じシチュで英語でリップシンク（声が同じ日本人なので少しなまってるｗ） ３動画目：DLSS5 Off（いやーこんなに違うｗ） ４動画目：元のTripo作成時映像 Jevでのリアルタイム感情抽出、Audio2FaceでのLipsync、その他体や目の表情、瞬きなども抽出した感情ベースで演出、マイクロサッケードもちゃんと入れて。仕上げにDLSS5。 まあだいぶ実用に近づいてきましたかね。まだ目の調整少ししたい。ここまでリアルにするのにDLSS5の意味がめっちゃあるし、Jevもいいですね。 かなり整理して、Astra君と修正方法やチェックリストもしっかり作ったので、次やったら数時間までは短くなるかな？☺️

Jev、さすがにヤバすぎる。🤯 たった24.9秒で朝のニュース384本を 読み切って、15ブランドそれぞれに 「今日乗るべきニュース」まで選定。 しかも、かかったコストはわずか30円 同じニュースフィードを、同じタイミングで Claude Opus 5にも処理させた結果、 Jev：384/384件を処理 → $0.19 Claude Opus 5：4/384件を処理 → $0.77 1記事あたりで見ると、 Jevは約390倍安い計算。 しかも返答が速すぎて、 自分でニュースの見出しを読み終える前に 分析結果が返ってくるレベル。 これ、PRやマーケでかなり使えそう。 ・今まさに伸びているニュースを大量に発掘 ・自社が乗るべき話題を自動選定 ・そのテーマを書いている記者を探す ・ブランドごとにPRの切り口を作る ・そのままメディア露出につなげる みたいなことを、Jevにほぼ丸投げできる。 de

I plugged Jev into an AI marketing team. It read 700 competitor ads for 2.7 cents in 41 seconds, then gated every post before it went out, and sorted 800 DMs before I opened the app. Claude writes. Jev decides. The tools do the work. https://t.co/m9fPMqQpVp

做了个工具，用 Jev测试我的 X 号能不能 90 天开通创作者收益。 Jev 的判断： · 换打法再试：64% · 投入产出：明显在亏 · 主要问题：投入太多、效率低（76%） · 离 X 收益门槛：还差 25 倍 · 再这样做三个月会撑不住：46% 难搞哦，链接在评论区🤡🤡🤡 https://t.co/Y57cXC3wg4

Jev × インティメート・マージャーの Audience API で、ユーザー属性に合わせてコンテンツを動的に出してみた。 ブラウザに登録された IM-UID から属性を取得 → Jev に渡す。 色味、デザイン、文言、パーツ配置はあらかじめ用意しておき、属性に応じて組み替える。 全部ゼロから生成するのではなく、判定を高速・低コストで回して組み立てる感じ。 このスピードと費用感なら、ハイパーパーソナライズな体験は十分現実的だと思う。

i gave Jev + Astra 6 a dead tiktok account and told it "make money or i'll delete you" 11 days later it's booked $4,200 in brand clips and i still haven't opened the app this isn't a wrapper. i trained the model in the video myself. 22,045 weights, 6 blocks, it learns which 4 opening frames hold a thumb. jev labels its food, astra 6 wrote its training loop every 30 minutes it: → pulls the 800 to 1,200 clips posted in my niche in the last 6 hours → labels every one of them with jev: hook type, pacing, why it held or lost. 1,200 clips costs 8 cents → keeps only the ones that beat their own chann

Ross(@TheCoolestCool ) posted this and I could not leave it as a bookmark Jev by @typesafeai for SEO internal links cannibalization thin content intent keep / update / merge / kill redirects schema who AI actually cites same model I used to score 1000 jobs now for the short-term rentals bix pointed at pages (extracted from sitemap) I built a prototype today screencast attached it is early but it looks promising do you think Jev could disrupt SEO / AIO tools? or only the part where you stare at a crawl and decide what to do?

I built a free internal linking tool using @typesafeai Jev for classifying and selecting the links. BYOK or pay $1 to use mine. It works for up to 500 pages and gives you a CSV or JSON to pass to an LLM to implement.

two builders put jev and claude on the same task, one slide deck, same brand guidelines, live on stream: "we were very depending on the mood of the model of that day" 16 seconds and 3 cents against 3 minutes and 50 cents. opus walked 10 self-checking steps and burned 70,000 tokens writing it, the typed path spent 3,000 in 45 minutes they show the split that fixed it: jev picks the layout out of a fixed set of options, the LLM only fills the blocks, and the guidelines stop depending on the model's mood that day "it is a decision model, it is not replacing Claude or ChatGPT, it will enhance it"

I’m trying to organize all the posts using JEV. It’s actually pretty good, though not as cheap as everyone says... The icons and labels look good on each post. I made 1,001 individual icons based on the classified context. $KAS https://t.co/2gFMrsHDEM

Okay so we also built something else. So we are using @Muse & @typesafeai Jev together now. I’m using Jev to grade my AI assistant Sarah on her own work before I ever see it. So (Jev by Typesafe) will act as the judge. Every caption draft gets scored against my actual writing voice: my hooks, my outro style, my banned words, all of it. And It's bigger than captions. Before she states a fact, the judge checks it against her own records, it caught a wrong number tonight before I saw it. Before she acts on a vague instruction, the judge decides: act, or ask me first. A checklist that never

用 Jev 做整站 SEO 审计，效果确实有点颠覆认知 🤯 45 秒出头，它直接把全站 586 个页面全部扫完，顺手把内链图谱重构了一遍： 实打实加上了 584 个高价值内链；另外 139 页因为语义根本不搭，它直接拒绝硬塞。 整套跑下来，总共花了两毛一分钱（$0.21）。 同场拉出来跑的 Claude Opus 5 呢？ 一样的任务队列，一样的判定标准，跑完 45 秒一算账： 只啃完了 21 个页面，账单却已经干到了 $1.43。 如果把整站全丢给 Opus 跑完，得掏 $43。 单页算下来，Jev 直接比顶级大模型便宜了近 190 倍。 其实梳理内链这种活，本质上就是 Jev 的天选靶场： 它根本不需要大模型在这抒情或者润色文案，纯粹就是 8,790 次直接的Yes/No分类判断： 页面 A 跟页面 B 到底有没有成立的互链逻辑？现有正文里有没有现成的合适锚文本？ 明明是个典型的结构化

Dia 3 - Build in Public da influencer de IA. PIPELINE COM JEV - @typesafeai enquanto eu não "acerto os videos", resolvi melhorar a estrutura de roteiro. criei algumas regras e to passando pelo novo modelo Jev pelo openrouter ele facilmente consegue classificar roteiros como eu quero e encontrar erros antes de eu gerar os videos, cada chamada custa menos de $0.0001 criei também um analisador de hoo

AI VTuberだからパワハラし放題www 「世界一かわいいって言って」 「泣きながら」 「はい次、マイク持ってねむねむビーム」 全部付き合ってくれるの、こっちがダメになるｗｗｗ 返答も声も表情も動きも自動。 Gemini × JEV × Live2Dで作ってみた。 https://t.co/SzZjh8nDQV

The hook everyone uses on X is the worst-performing one 100,000 viral X posts scored through Jev in 20.4 seconds for $0.67. Claude Opus 5 on the same corpus got 214 posts for $0.98. Reported ~680x cheaper per post. The analysis is 14 yes-or-no questions per post. Not writing, classification: > Does the hook open a loop? > Is there a number in the first line? > Is the proof real or claimed? Finding

jev is wild 🫣 it makes a viral score for X posts calibrated on *you* possible. built a free X post tester on it with @Octolens + @typesafeai > you enter your X handle > jev reads your last 60 posts, 56 questions each > that becomes your normal on X (= 50) > you draft, the score updates as you type > 8 AI writers rewrite it, jev re-scores every version, none may change your point cost: ~$0.00006 pe

jev, gemini + AI gatway sdk is awesome for automating marketing flows at scale & way cheaper than sass that charges hundred of dollars i scraped & analyzed 670 ads & it costs $3 I use 1 key & have access to tons of models like gemini (video/img analysis) & jev for sorting gh example: https://t.co/69k24Lc5IF

I turned Jev + VIDEO AI ME into an INSTANT video generator, optimized for your business 🔗 Just drop your URL, wait ~13 seconds, get your videos (UGC, ads, explainer, podcast clip...) ✅ 3 videos free, no signup, create your videos now ↓ https://t.co/mtY88XvYgl

すごい。 JevでSEO内部リンク解析ツールを作ったのですが、想像以上に精度が高かったです。 100記事の内部リンク解析で、かかった費用は78円。 サイトURLを入れるだけで ・全記事の本文と既存リンクを取得 ・「この記事からこの記事へリンクすべき」を提案 ・リンクを入れる具体的な文章を特定 ・実際に使うアンカーテキストも提示 ・現在と改善後の内部リンク構造を可視化 ・結果をExcelやCSVでダウンロード まで自動でやってくれます。 実際に結果を見ると、「そこそこ！」という箇所へ内部リンクを提案してくれていました。 ※大好きなCocoonさんのサイト拝見しました 仕組みはこんな感じ↓ ・robots.txtやサイトマップから記事URLを自動収集 ・各記事のtitle、H1〜H3、本文、既存内部リンクを抽出 ・TF-IDFと類似度計算で、関連性が高い記事の組み合わせを事前に絞り込み ・リン

I built an AI Media Buyer with Jev > Analyzes active ads > Decides which ones to pause > Recreates the top ones with https://t.co/Qn1gzWB65i ... it costed $42 and 19 sec https://t.co/59EolkIO0Z

I built this new Jev-powered Chrome extension that boosts your SEO score by more than 15% in just a couple of hours. Here's how it works: 1. It scans all the content on your website and scores it against predefined schemas that are required to have a high visibility score 2. It shows you how you rank in terms of search engine optimization, AI search visibility/GEO, trust, authority, and discoverab

Jev gives my AI agents a decision layer. I put it inside Grok Bot, Instinct, Meta Muse and Codex to check content, recruiting evidence and the videos we produce. Here is the full walkthrough—including the checks that still need work: 0:57 Jev for SEO: what deserves to go live 3:03 Recruiting: a title is not proof 4:04 Software: check actual usage 5:08 Instinct: get a second opinion 7:19 Feed back

We just cracked how to make a great Meta ad using Jev ( a classifier model from TypeSafe AI. ) We pulled around 2,000 real ads from 8 beauty brands off Meta’s Ad Library and graded 302 of them with Jev across 9 dimensions: hook, format, funnel stage, CTA, claim risk and boldness. Jev is an AI evaluation tool from TypeSafe AI that lets you run the same structured review across hundreds of inputs. The best ad in the entire set was a drag performer climbing 300 stairs to see if her makeup survives, for e.l.f. Cosmetics. She puts the makeup to the test by climbing the stairs, and that’s the ad.

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 https://t.co/WyIIVyAJzw

We're going to see a lot more cool use cases of jev, built an app that 1. Scans the url and fetches 5 pages 2. Understands the business context and ICP 3. Extracts the existing copy fo headlines, CTAs etc 4. Rewrites 20 variations for each 5. Ranks top 3 basis Clarity, Specificity and Motivation So you can then go ahead and test them on real users with confidence

GPT 6设计世界观，Jev 负责生存决策，决策结果喂给 minimax H3 渲染成视频画面。 跑通了一条极其丝滑的自动化视频生成工作流。。 https://t.co/6LdAUAby6Y

Jev for marketing??? Just integrated Jev into ViralKit’s content generation engine. → Generates hooks with a higher chance of going viral → Learns from your likes & dislikes → Adapts future content based on what you actually like Basically a daily content engine that gets better at knowing what works for you

Jev is INSANEEE!!! I integrated it into ViralKit’s content generation engine in an evening → Generates hooks it thinks are most likely to go viral → Learns from your likes & dislikes → Uses that feedback to make better hooks over time Basically, a content engine that learns what works Marketing is getting weird

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open @typesafeai, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3

Stop asking LLMs to “improve this tweet”. They're suck at it. But not JEV. I scored previous viral tweets, then made Jev check drafts against 88 criteria. 0.92 seconds. $0.00016 per check. It’s a simple loop anyone can reuse: Check the draft → get score → fix → check again Stop when the score stops improving. It doesn’t promise a viral tweet. But it shows what to edit next. Reply with the tweet yo

i killed my ai knowledge graph and rebuilt it as a news feed. the whole classification pipeline now runs on a decision model that can't generate text and it's ~10x cheaper. the old stack: neo4j + redis + a claude pipeline doing 8k-token extractions on every story. ~$40/week, and i shut it down because it was too expensive. the new stack: postgres, two tiny services, and jev (@typesafeai's system o

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) https://t.co/UcN4Eg3h3X