공개 사흘째 Jev, 타임라인을 삼켰다 — "온통 jev jev jev"
글을 못 쓰고 판단만 하는 모델 Jev가 공개 사흘째에도 X와 Threads를 뒤덮었다. "타임라인이 전부 jev jev jev"라는 글이 나올 정도다. 그저께의 데모 자랑을 넘어 실측 비교가 쏟아졌다. 한 사용자는 아침 뉴스 384건을 24.9초에 읽어 15개 브랜드에 붙일 기사를 골라내는 데 0.19달러가 들었고, 같은 일을 Claude Opus 5로 돌리니 같은 시간에 4건 처리에 0.77달러였다고 밝혔다 — 헤드라인당 약 390배 저렴. 586쪽 사이트의 내부 링크맵을 45초에 다시 짠 SEO 감사(0.21달러, Opus 대비 약 190배), 채용 매칭, 세금 문서 분류 사례가 이어졌다. 배포처도 붙었다. OpenRouter가 베타로 올렸고, Vercel AI Gateway·Cline 플러그인·전용 프로그래밍 언어 "Probably"까지 나왔다.
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열광만 있었던 건 아니다. 가장 많이 퍼진 반박은 Jev로 컨텍스트를 압축하자는 유행을 겨눴다. theo는 "압축은 필터가 아니다. 히스토리를 정리해 에이전트를 집중시키는 일인데, Jev는 자기가 무엇을 지우는지조차 모른다"고 정면으로 반박했다. 값이 싸다는 데는 이견이 없지만, 판단만 하는 모델을 아무 데나 끼우는 것과 제대로 쓰는 것은 다르다는 신중론이다.
출처 8건 보기· @peer_rich, @elvissun, @borjafat 외 5
- @peer_richngl entire timeline is jev jev jevX ♥637
- @elvissunJev is INSANE. 🤯 in 24.9 seconds it read 384 news from this morning and told 15 brands which stories to hop onto today, for $0.19. Claude Opus 5, running on the same feed at the same time, got through 4/384 and cost $0.77. per headline that is ~390x cheaper, and the answer comes back before you finish reading the headline yourself. there's going to be so many ways for JEV to help you find trending stories, find journalists covering it, and get press coverage. everything in the video open-sX ♥2.2천
- @borjafatJev 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 thaX ♥755
- @OpenRouterJev by @typesafeai is now on OpenRouter, in beta. Jev is a System One model. Instead of generating text, it takes your app's state plus a typed question and returns a typed decision with a probability attached. There is no JSON prompting, parsing layer, and nothing to validate against.X ♥3.3천
- @theoThis is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work. Seems like a lot of people are confused so let's break this down. 1. Compaction isn't a filter The role of compaction is to clean up history to keep the agent focused, not just deleting noise. It should be used sparingly when context gets too long, not constantly to keep context small. 2. Jev doesn't even know what it's deciding on! Models use the context of the thread to dX ♥2.3천
- @southpolesteveI've seen people describe Jev as an "AI if statement". But what if it actually WAS an if statement? Introducing Probably: a programming language powered by Jev: https://t.co/6OqaNPRK6b Jev baked into the language. “feels” asks a question. “match” routes between descriptions. “while” keeps going until something stops feeling true. This is obviously a toy, but it's fun to think about what something like Jev unlocks. Jev makes the decisions, an LLM does the writing, and a little program ties it X ♥3.5천
- @clineWe built a plugin that gives Jev a browser in Cline, and have been blown away by the results. 1. Install it in our new desktop app: Customize > Marketplace > Plugins > search 'jev-browser' 2. Create a Vercel AI Gateway API key, then save it to ~/.cline/plugins/cline-jev-browser.config.json as {"gateway": {"apiKey": "..."}} and restart Cline. 3. Ask any browser task and it will launch Chrome in the background to complete it.X ♥183
- @nedwizeBuilt a tax document classifier with Jev. We ingest thousands of tax documents using an LLM pipeline I built last tax season. I read multiple articles as late as April this year claiming AI fails at tax document classification. Wasn't the case back then, and it's proved wrong again now. Jev classifies 100% of our tax document corpus at $0.001 per page. 34x cheaper and 6x faster than the LLM setup. Open sourcing it:X ♥1.5천