Jev AI examples, page 11

@rinte0321 demos slides that appear automatically from what is being said, so the speaker does not touch the PC or a pointer. They say the speed and price are what make that possible.

jevデモ② 発話内容に応じたスライドの自動表示。 喋った内容にマッチしたスライドを自動で表示してくれます。PCやポインターを触らずにページを表示できるので、これで誰でもジョブズになれるかも？？ 速い・安いだけでできることがこれだけ広がるのかと感動している。 https://t.co/3UO6DQcbJ2

@MatijaSosic posts a 45-second TL;DR of Jev. They say the core idea is simple, but another video made it hard to understand.

Here's a 45-second TL;DR on Jev. I find the core idea beautifully simple, but the video made it really hard to understand. Hope you find it helpful. https://t.c…

Quoted @CompleteSkeptic

@hi_im_isaac_ says Jev cannot generate text, then gave it a few hundred common English words plus punctuation to choose from so it appears to say something.

"jev can't generate text" But jev has a lot to say! I gave him a couple hundred common english words to choose from + punctuation https://t.co/WmDkTyiG9u

@sydneyrunkle says Jev from TypeSafe returns structured output instead of generated text, which suits classification such as model routing, tool selection, and guardrails, and that it is fast and cheap compared with LLMs on those tasks.

instead of generating text, jev from @typesafeai generates structured output this makes it great for classification tasks like model routing, tool selection/sea…

@ytiskw asked 150 fictional personas 12 questions each about whether they would adopt a product. They report about 1.8 yen and about 5 seconds, including Japan–US latency.

話題のJevで架空のペルソナ150人に製品の導入意向を聞く仕組みを作ってみた。150人ごとに12個の質問をAPIで投げる。 実際にかかった費用は、1.8円でかかった時間は約5秒。十分爆速だけど日本↔︎アメリカのレイテンシが含まれているので本来はもっと早いはず https://t.co/6lbFXY9jjH

@nutlope classified 100 emails (50 legit, 50 fraudulent) with Jev in 1.42 seconds, then sent the 31 predictions under 95% confidence to Kimi K3. The combined pipeline was 96/100 correct in 16 seconds for about $0.07, of which about $0.003 was Jev.

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 c…

@aimlapi had Jev V13 play 5+0 blitz chess, one API call per move, against Fable 5.1 and GPT-6 Astra. They report Fable was up a queen and +16 material by move 29 but lost on time because it spent 6–15 seconds per move while Jev answered in about 2.6 seconds; Astra checkmated Jev in 18 moves (Qe1#) with 2:27 left.

Jev VS Fable 5.1 VS GPT-6 Astra: chess typesafe's new Jev V13 isn't an LLM. it doesn't chat, doesn't explain, doesn't write code — it only makes decisions. so w…

Quoted @CompleteSkeptic

@savboj posts that Jev plus computer use is 100x faster than any LLM, and that if you blink you might miss it. The text does not describe the task.

If you blink you might miss it Jev + Computer use = 100x faster than any LLM @typesafeai https://t.co/JG09Wx7UCo

Quoted @CompleteSkeptic

@nutlope classified 1,018 AI papers: DeepSeek V4 Flash summarized them for $3.99, then Jev classified title plus summary into 24 topics for $0.08 total, with 256ms median end-to-end latency per paper. The site 1kpapers.com is live, and they say evals are still running before the Jev labels replace the current ones.

I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each…

• 1kpapers.com

@JonPTaylor shows Jamcat, a voice UI where Pipecat hands off between a Jev command agent that drives the UI and PhoneLLM for generations and discussion. They are in London calling us-west and still call it snappy.

Wow, @typesafeai Jev is a game changer for voice-driven UI. Responsiveness really matters for an experience to 'feel' right, and this certainly does the job! La…

@AlanDaitch says Claude wired Jev to Playwright to search used listings. It reads about 26 listings a minute, decides in 406ms, discards poor fits, bids on matches, and messages sellers when a field is missing. They say the search cost $0.00085, or about 26,000 listings per dollar.

Claude me integró Jev con Playwright para buscar usados. Lee unos 26 artículos por minuto y decide qué hacer con cada uno en 406 milisegundos Descartó los que n…

@hot_town posts an explainer covering how Jev differs from an LLM, how it works, real-life examples, and when to use it.

Jev is here. - how is different from an LLM? - how does it work under-the-hood? - what are some real life examples? - when should I use it? I answer all that be…

@wuyang_zhou posts what happens if you ask Jev to play Minecraft together with GPT-6 Astra.

btw this is what you get if you ask Jev to play Minecraft with GPT-6 Astra. I still can’t believe it https://t.co/ERDnsVWPLZ

Quoted @CompleteSkeptic

@SUOHA_AI posted a 50-second video explaining Jev and its architecture: a System 1 model from Diogo Almeida after two years, one parallel pass instead of token-by-token generation, RL for calibrated decisions, and typed outputs. They say the video also shows an 8.5-second autoregressive delay next to a real-time minigame, referencing a @MatijaSosic demo.

很多人没看懂爆火的JEV是什么？ 所以我做了一个 50 秒的演示视频，带大家把海外刷屏的 JEV 以及它背后的底层架构看透👇（见下方视频） JEV 是前 OpenAI、参与 ChatGPT / RLHF 的 @CompleteSkeptic（Diogo Almeida）创办 @typesafeai 后的第一款模型 他在…

Quoted @SUOHA_AI

@marcelpociot asks whether TypeSafe Jev will turn $10,000 into profit and shows paper trades whose decisions run through Alpaca.

Will Jev @typesafeai turn $10.000 into profit? Let's find out! Running decisions for paper trades via https://t.co/28WVX50sv3 https://t.co/2XglDmGTNM

• alpaca.markets

@Neel490 says the implications of Jev for self-driving could be huge and links a clip. The text does not describe a system.

I think the implications of jev on self-driving could be huge https://t.co/advoRDSOz7

Quoted @CompleteSkeptic

@atomic_chat_hq had Jev v1.13 pick a safe tile every 330ms while rockets fell. It survived 25 of 26 for under a cent. They link running Jev via the atomic.chat API.

Jev v1.13 dodges rockets with probability calculation 🚀 @typesafeai's new non-LLM model returns decisions instead of text so we had it calculate a safe tile eve…

Quoted @CompleteSkeptic

• atomic.chat

@ephraimduncan built a model router with TypeSafe Jev: Jev decides which model fits the request, and the request is sent to that model.

Built a model router with Jev by @typesafeai. Jev decides what model fits your request best and the request is sent to that model. https://t.co/HHlmjOE66u

@chetaslua put a live BS meter on the Trump versus Kamala debate with Jev: every sentence, both candidates, five yes/no questions each. They report 1,191 calls, 1.18M tokens, 415ms median, and $0.0497. Clips were picked by one fixed rule, and they say it is not a fact-check.

🚨 I gave the Trump vs Kamala debate a live BS meter using Jev every sentence, both candidates, 5 yes/no questions each 1,191 Jev calls / 1.18M tokens / 415 ms m…

Quoted @CompleteSkeptic

@_trou3 shows Jev reading dozens of structured trading signals and then showing decisions, as an example of what TypeSafe Jev can do.

For those who still didn't understand what Jev from @typesafeai can do, here is the example. Jev can read dozens of structured trading signals and then show int…

Quoted @CompleteSkeptic

@redp314 solved a Rubik’s cube with TypeSafe Jev using the beginner method in code. Jev only picks which case the cube is in, in about 250ms, and code checks every pick. They report 94 moves, not a 22-move optimal solve, and about 4 seconds of model time. The video is slowed down.

I got a rubik's cube to solve itself with @typesafeai 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM,…

Quoted @CompleteSkeptic

@alin_zone says Jev fits Codex computer use: use Jev as the judge that tells Codex how to operate the computer quickly while saving tokens. They recorded a video of how to apply for access on the official site.

🔥 卧槽了，jev 简直太适合 codex 用户了，强烈建议 codex 用户去尝试下 jev 因为 jev 非常适合需要快速进行复杂决策和评估的场景，所以和 codex 的 computer use 功能简直是绝配。 直接把 jev 用作判官，指导 codex 快速操作你的电脑，既准确又节省 token。 要使用的话…

@marcelpociot built a browser extension with TypeSafe Jev that hides or collapses X posts from a natural-language rule. They say it is fast enough not to notice and cheap enough to be the future of ad blockers and content firewalls.

I built a browser extension with Jev @typesafeai that can hide/collapse posts on X based on natural language. It's so fast that it's not noticeable and insanely…

@typesafeai posts a behind-the-scenes clip of the launch video, with chief of staff @justKDeng keeping CEO @CompleteSkeptic on track.

Behind the scenes of our launch video… chief of staff @justKDeng keeping our CEO @CompleteSkeptic on track https://t.co/81HHtsj4eK