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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

Can Jev pick the winner of a real headline A/B test? 64.5% across 10,984 Upworthy randomized experiments, 74.7% when the difference was decisive.

Experimental: live X draft viral scorer powered by TypeSafe Jev.

Browser extension that grades page sections for clarity, writing quality, and on-page SEO with Jev.

Agent-first SEO and GEO CLI suite and MCP server using DuckDuckGo evidence and Jev scoring.

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

兄弟们，我用 Jev 测了一轮 Computer Use + 滑雪视频混剪。 随机抽取 16 条滑雪素材，基于抽帧描述让 Jev 评分、选择开场和收尾，再通过 Computer Use 操作 CapCut，完成分割、裁剪、变速，输出 16 秒 Phonk 混剪。 我的判断标准：动作够不够刺激、切换是否流畅、配乐有没有力量，能不能按我的要求调整。 个人感觉效果不错。尤其提出“更重的运动 Phonk”后，配乐和节奏都更接近我想要的效果。 这一轮基本不到 5 分钟就剪好了，还保留了可编辑工程。 消耗也不大：20× 额度下使用 GPT‑6 Astra中，占用不到 1%——这里说的是额度变化，不是精确 token 统计。 Jev 负责判断，Computer Use 负责把判断落实到剪辑软件里。 以后批量混剪，视频切片分发这个行业可能不需要找外包了。全程jev写好判断，直接程序后台就能跑。 我这里演示

I asked Jev to find trending art on Instagram. Jev routes the request. socai reads the real posts. Took 23 seconds, extermely fast. Code below ↓ https://t.co/iRYRBAhJKe

Generating a unique page for each visitor, on the fly with JEV. Same components, never the same page twice. https://t.co/5qJtS61rOP

I used Jev to create a platform for A/B testing tweets, LinkedIn posts, and YouTube hooks across hyper-specific demographics. The best part is that it simulates responses from real people rather than imagined personas. It turns Jev into a testing ground for how specific audiences might respond before you publish. Link below.

話題のJevを使いたすぎて、Shortsの「どのフックが伸びるか」を投稿前に判定するアプリ作ってみた 自分のYouTube Analyticsをもとに、チャンネルの視聴者像を再現した「仮想視聴者」を大量生成 ↓ 候補のフックを1人ずつに見せる ↓ 「この冒頭なら見るか、スワイプするか」をJevで判定 ↓ 投稿前に“擬似スワイプ率”を算出 フックのABテストを、投稿前に仮想空間で回すイメージ なんかまぁ、うん。可能性は感じた。

I used Jev, TypeSafe's new AI that doesn't write a single word It works best paired with Claude Opus. I tried that on my own article Opus prepared all the prompts and questions, and Jev scored the article on 17 key questions at once. All 17 answers came back in under half a second, and six checks cost me a fraction of a cent. Jev is a good analyst, it found the weak spots in my article and shows a

i built a Jev tool that reverse-engineered what actually goes viral in ai twitter. 18,000 posts. 31 seconds. 71 cents. the same run on opus 5 crawled through a few hundred and cost me ~$400. per post that is hundreds of times cheaper. 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 real number in the first line > is

Jev classified 1,004 TikToks & Reels for about $0.07 in estimated model cost 😂 i'd saved hundreds of videos that went viral or converted well. figuring out why was still a coin toss. so i built a dashboard around Jev. it labels every video across 8 dimensions: hook type, format, script structure, CTA placement, creator persona, and more. i can filter by hook and format, compare views and saves, an

I JUST BUILT A JEV-POWERED X VIRAL POST ANALYZER. The results are almost ridiculous: 100,000 viral X posts analyzed. 20.4 seconds. $0.67 total cost. For comparison, Claude Opus 5 analyzed the same corpus under the same time limit. It processed just 214 posts and spent $0.98. That makes Jev approximately: 680× CHEAPER PER POST. A complete Opus run would have cost around $458. Jev finished the entir

inspired by jev, i created something real fun: crowd check! test your post/tweet on a crowd of 10,000 AI readers before the real internet sees it. they have their own jobs, personalities, tastes and memories. post anything and watch what happens: - do they like it? - hate it? - repost it? - follow you? - block you? - or nobody cares? then see exactly how your post performed across the crowd 10,000 ai readers. millions of tiny decisions https://t.co/KCQimIi1n7 using jev via vercel ai gateway @CompleteSkeptic @rauchg