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Jev AI Apps cases, page 6
213 public posts filed under Apps. English summaries on this page, with a link to each case.
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@oragnes describes a trading bot a Monad engineer built with Jev: it reads the MON/USDC price, Jev only chooses buy or sell plus confidence, and orders go to Kuru’s on-chain book. They note Monad’s 300ms blocks as the reason a non-writing model fits.
卧槽，JEV 的出现很可能就是自动智能交易的起点。 昨天JEV在AI 圈刷屏，今天 Monad 工程师就用JEV做了一个自动交易机器人。 实时读取 MON/USDC 价格，JEV 不负责写分析报告，也不跟你解释一堆逻辑，它只做最核心的事，判断 Buy 还是 Sell，再给出置信度，然后直接把交易发到 Kuru 的链上订…
Quoted @oragnes
@akira_papa_IT built a chat app by making Jev judge the next character. They say it is very fast, input is $0.042 per 1M tokens, and output is free without a cap.
Jevで次の文章予測判断させるロジック作って、 判断特化AIのJevで、AIチャットできるアプリ作ったんだけど、まず爆速だし、入力は$0.042 / 1Mトークンだけど、出力が無制限で無料でした。 出力無料のAIチャットってなんだこれw 「え？これ作れちゃっていいん？」ってなってる。。。 #Jev https://t.…
@coolish compared playing Slay the Spire 2: GPT-6 Astra was capable but slow, while Jev acted in 0.7 seconds, fast enough that they say they could not follow the screen.
昨天出的 https://t.co/6eiMFaJTrj Jev 模型 适合高频决策，那岂不又是适合打游戏？ 之前用 GPT-6 Astra 代打杀戮尖塔2 能力强但速度慢。 我刚实测用 Jev 打，行动思考只需 0.7秒， 画面我都没看清它就操作完了。 这超人类游戏速度， 着实又让我震惊瘫坐了😅 语言很难描述这感觉，…
Quoted @CompleteSkeptic
@nikunj says Jev scored 3,000 kid snacks on nosugarforkids.com against multiple criteria in 28 seconds for $0.11.
Jev scoring 3000 kid snacks with multiple criteria on https://t.co/KSiodPdZHT in 28 seconds for $0.11 🤯 https://t.co/c9O6uTyiUK
Quoted @nikunj
@jpschroeder says they rebuilt Tesla Full Self-Driving with Jev in under an hour. The post states that claim and links a clip; it does not describe the sensors, stack, or what the demo actually drove.
I rebuilt Tesla Full Self Driving with Jev in less than an hour. This model is a total unlock. https://t.co/mUrvaFrlQ7
Quoted @thread
@tsuyoshi_osiire prototyped a sensor that watches a conversation in real time with Jev, updating explore versus converge, resolved versus open, and disagreed versus agreed after each utterance, for uses such as live user-interview analysis.
Jevで、会話をリアルタイムに観測するセンサーを試作しました。 発言のたびに ・探索 ↔ 収束 ・解消 ↔ 未決 ・未合意 ↔ 合意 を更新し、会議の状況を可視化します ユーザーインタビューのリアルタイム解析などにも使えそう。 https://t.co/cO21f4AK99
@reczko_konrad combines TypeGPU, ruNNtime, and TypeSafe Jev: local inference and rendering share the GPU with zero copy, three neural nets plus rendering run in real time, and Jev adds the semantic step from camera and mic (Moonshine, YOLO26, DepthART) to lights, shadows, and bloom.
TypeGPU + ruNNtime + Jev @typesafeai is a very fun combo :D ruNNtime gives me efficient local inference, TypeGPU lets inference and rendering share GPU resource…
@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
@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
@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 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…
@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…
@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
@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
@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
@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
@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…
@uttkarsh_42 gave Jev full control of a Falcon-9-class rocket in MuJoCo, with no autopilot or scripted trajectory. After 12 runs it landed the booster using 245 calls and $0.04. Controls included launch or hold, engine count, when to burn, cutoff, coast, entry, legs, and touchdown. They say decisions run at about 1Hz and return in about 0.4s while the vehicle goes unstable every 0.8s.
so I gave jev full control of the falcon-9 class rocket full launch to landing no autopilot, no scripted trajectories, no safety veto in the code every decision…
@waynesutton launched askjev.ai: you can ask Jev anything and it will judge rather than answer. They invite people to try for 1 million questions and say TypeSafe and Convex work together.
Ask Jev anything. Give it a try at https://t.co/P9QMfdDc0r It won't answer. It will judge. Let's see if we can get to 1 million questions. @typesafeai 🤝 @convex…
@miiura shows function calling that does not wait for Enter. TypeSafe Jev reads intent and arguments as you type and executes once everything is in place, and only when an answer is needed.
Function calling without waiting for Enter. Answers only when needed. @typesafeai Jev understands intent and arguments as you type, then executes ahead of time…
@michaeltefula hacked a demo with Astra where Jev turns natural-language requests into UI plans in about 1 second or less. They say it is 6x faster than Gemini 3.8 Flash and GPT 5.6 Luna, and cheaper.
You can use Jev to assemble interfaces on the fly. Here’s an example I hacked together with Astra. In this demo, Jev turns natural language requests into UI pla…
@marcusyul describes labeling 1,891 competitor ads with Jev in 19 seconds for $0.12, by funnel step and style, plus an account-level read of spend, repetition, and gaps. They say it is coming to the Maxfusion MCP.
UN TÍO ACABA DE LEERSE LOS 1.891 ANUNCIOS DE SU COMPETENCIA POR 12 CÉNTIMOS Bueno, leérselos no. Se lo pidió a JEV, el modelo nuevo de TypeSafe → 1.891 anuncios…
Quoted @OriSilver