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Jev AI examples, page 10
407 real Jev AI builds and public demos, updated daily. Original text, metrics, and a link back to each source.
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Public Jev AI examples and demos, not official TypeSafe docs. Clients and SDKs are in the Jev GitHub projects. Paths for jev-latest, Vercel, and OpenRouter are in the September 2026 channel map.
Agent-skill repositories are listed on Jev skills on GitHub.
OpenRouter model ids recorded for Jev AI are on Jev AI on OpenRouter. Rubric and eval posts are on Jev as a judge. How the company and the model fit together is on TypeSafe AI. The pricing calculator uses the published input rate.
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@_MaxBlade describes an always-on assistant with no wake word: Jev uses probabilities to tell whether they are asking the computer to do something or just talking. They say CNVS is still their view of the future of vibecoding.
Ai is evolving. Jev can be armed at all times. I can speak freely and it knows ( from probabilities ) if im asking my computer to do something or blaberring awa…
@redp314 reviews pull requests with one TypeSafe call that returns 14 probability checks, including hardcoded secrets, SQL injection, auth, deleted tests, API breaks, migrations, debug leftovers, whether the description matches the diff, blast radius, and reviewer effort. Code turns that into BLOCK, security review, nits, or merge, and uncertain critical checks (0.35–0.65) escalate. They cite 6 real PRs at $0.00007 each.
got Jev to review my PRs. ~200x cheaper than Claude and it answers in half a second 6 real PRs in the video. $0.00007 each. 1,000 PRs = 7 cents vs ~$14.50 on Op…
@ericjingyang ships Not Slop: a browser extension that flags LinkedIn/X slop while you scroll, classified by Jev.
In Silicon Valley, Jian Yang built the Not Hotdog app. A decade later, his bro has finally continued the family business. The Not Slop extension, detects Linked…
@IndraVahan says they gave Jev ₹100,000 to trade Nifty intraday at 5x leverage on a real Kotak account, not paper trading, and that they hit a ₹1,000 hard stop after a green morning.
i gave jev ₹1,00,000 to trade nifty intraday at 5x leverage. this isn't paper trading but real money on my real kotak account put to use. we hit our ₹1,000 hard…
@moritzkremb controls a browser by voice: speech is transcribed, Jev returns probabilities in about 300ms, and the browser clicks, at $0.0002 per decision. They say a “go back” finished before they finished the sentence.
whoa this actually worked! Jev lets me control my browser in real time with my voice now > i talk > transcript sent to Jev > jev returns probabilities in ~300ms…
Quoted @thread
@MaxRovensky says that when solving trolley problems, Jev decided to sacrifice a human to save robots.
jev decided to sacrifice a human to save robots when solving trolley problems https://t.co/vLBwgLYduk
@rafalwilinski says Jev is now in charge of this account’s humor and links a clip. The post does not explain the rules.
Jev is now in charge of this account's humor https://t.co/ojSOHeMFT2
@tdinh_me made an open-source, BYOK Chrome extension that can listen to YouTube audio, detect a sponsor segment, and skip it in real time, at about $0.005 per video. They call it a prototype.
Just trying out Jev, I made a Chrome extension that: - Listens to your YouTube audio (optional) - Detects if it gets to a sponsor segment - Skips it ➡️➡️➡️ - Al…
@o_kwasniewski is building tester.army, an open-source framework for end-to-end tests with agents, including web and mobile, and shows it with TypeSafe Jev. They say it will be available soon.
e2e + jev from @typesafeai ⚡ I'm building an open-source framework for running e2e tests with agents. supports web, mobile (and more!) available soon: https://t…
@milindlabs does computer use without screenshots or an LLM leaving the Mac: a local CoreML model segments buttons, on-device OCR reads labels, and Jev returns a probability over those elements and clicks in a loop until the goal is done. They report about 90ms per decision.
Okay so Jev can actually do computer use really well Without any screenshots, or LLMs and no Pixels leave my mac I dont even read the Dom elements A local CoreM…
Quoted @CompleteSkeptic
@jarrodwatts built a trading bot where Jev chooses buy or sell from an asset pair’s price feed and places real orders. Orders go to Kuru’s on-chain book on Monad, once per 300ms block. A demo link is in the post.
I built a trading bot with Jev! Jev decides if it should "buy" or "sell", given the price feed of an asset pair, and executes real trades. It uses Monad to plac…
Quoted @thread
@levie demos Box plus Jev: an incident report is pulled from Box, Jev asks whether it is customer-facing and how severe it is, the file moves to escalate, monitor, or review, and a metadata template is set. They say this is nearly instant and almost free.
Jev will be super helpful for agents to make split second decisions in workflows, data classification, judgment calls, and hundreds of other use-cases in the en…
@instantricecook built voice-controlled computer use for a Mac with TypeSafe Jev. They say they can dictate “open the Notes app and create…” and the app opens before the sentence finishes.
I built a voice controlled computer-use for my mac using @typesafeai's Jev and it's INSANE how fast it is! I can dictate "open the notes app and create..." and…
@ego_agent compares ego lite plus Jev plus DeepSeek Flash with GPT-5.6 Sol on 20 Amazon product decisions. They report 3.71s versus 54.45s, and 10/10 on both.
ego lite + Jev + DeepSeek Flash = stupid fast. ⚡ 3.71s for 20 Amazon product decisions. GPT-5.6 Sol: 54.45s Same task. Same result. 10/10 on both. Less reasonin…
Quoted @CompleteSkeptic
@camsoft2000 shows TypeSafe Jev plus AXe controlling the iOS simulator, which they call ultra fast and a fraction of the cost of an LLM.
Now we're cooking. Got @typesafeai's Jev + AXe controlling the iOS simulator now ultra fast at a fraction of the cost of using an LLM. This is game changing! ht…
@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
@ekzhang1 published a Jev-compatible API that runs Qwen3.6-35B-A3B with SGLang radix cache so prefill can be reused. They say 64 tasks finished in under 1 second.
Inspired by @typesafeai , here is a Jev-compatible public API to play with It runs a comparable open model (Qwen3.6-35B-A3B), and just uses SGLang radix cache t…
@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.…
@CompleteSkeptic, ChatGPT co-inventor Diogo Almeida, announces Jev after two years building a training method called RLCD. The launch claims 20–200x faster, 40–400x cheaper with free output tokens, and frontier composable intelligence optimized for decisions.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to t…
Quoted @thread
@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…