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Jev AI examples, page 12
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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@hr98w demos a local visual stand-in for Jev: a 4-bit multimodal Qwen-3.5-0.8B maps candidates to fixed labels, samples after prefill, and limits output tokens. It runs on a 16GB M4 MacBook and sorts three-color goods in about 200ms. They say this copies the form of Jev at inference time, not its real method.
视觉版 Jev 来了 https://t.co/x0kzeE3hdx 仿照着社区的思路，把候选结果映射成固定标签，prefill 完直接采样，限制输出 token，采用多模态的 qwen-3.5-0.8B 4bit 量化部署在本地，16g m4 mbp 顺利运行 叠甲：这只是在 infer 层小小的复现一下 jev 的…
@picocreator introduces simplejev.ai, an open-source library that takes a Hugging Face model and exposes a Jev-like API, including vision, which they say hosted Jev lacks. They say it is on GitHub and in production at FeatherlessAI.
love jev, but upset it - isn't open source? - it lack vision capability? We fixed all of that, introducing https://t.co/DlNeKo2k5e A fully open source library w…
@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…
@awlevin built computer use with TypeSafe and says it is 155x cheaper than Opus 5, about 20x faster, and generalizes across operating systems. How it works is in the video and thread.
i built computer use using @typesafeai ! it is 155x cheaper than opus 5, ~20x faster, and generalizes across OS's more on how it works in the vid & thread b…
Quoted @CompleteSkeptic
@Neriousy posts a short demo of testing apps with TypeSafe Jev and OpenCode, and says that combination will be fast. The post does not name the app under test.
Testing apps with @typesafeai jev & @opencode will be soooo fast small demo: https://t.co/WLSkpe9OZn
@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…
@KinasRemek argues System-1 models like Jev are a universal classifier, an intelligent if, and says current LLMs simulate System-2. They mention implementing self-play in their Pac-Man, where the query schema stays fixed and only state changes.
Takie modele jak JEV (System-1) zmienią moim zdaniem bardzo dużo. Obecne modele LLM symulują System-2. Dla mnie implementacja jev to jedna z najświeższych konce…
@moritzkremb says a quick test used Jev to improve a custom memory system, with 94% fewer tokens and 2–3x faster memory retrieval.
This is cool. I used Jev to improve my custom memory system. Results: → 94% fewer tokens used → 2–3× faster memory retrieval This was just a quick test, but I b…
@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…
@sawyerhood says Jev picks the agent, model, computer, and folder instead of filling those prompt-box fields. A major rewrite uses Fable plus Claude Code, and iOS app changes run on one of their Macs.
thanks to @typesafeai jev I no longer have fill out all of those fields on prompt boxes. It picks the agent / model / computer / folder for me. - For a major re…
@jpschroeder posts “Silicon Valley predicted Jev a decade ago” and a clip. The text does not explain the scene.
Silicon Valley predicted Jev a decade ago. https://t.co/ZfSjPYWsTn
@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
@yoheinakajima tried graph extraction with Jev: score every word for semantic significance from 1 to 5, tag relevant words with an ID, then build a unique list and a graph of the high-scoring words.
had to try something with jev so i tried what you'd expect from me: graph extraction - score every word with semantic significance (1-5) - also tag relevant wor…
@dsqjaffa says Jev searched a library of 11 million-plus TikTok and Instagram videos for viral Bloom Nutrition content and, in 20 seconds for $0.09, returned more than 384 videos with hook, format, and angle. They say it is in @virlomain and MCP.
jev is INSANE. we gave it a library of 11 million+ videos on tiktok & ig and asked it to find viral "bloom nutrition" content. in 20 seconds, it gave us over 38…
@hqmank rebuilt a job crawler with Jev: from a company homepage it finds Careers and scores roles against their profile. They report about 5 minutes with an LLM versus just over 20 seconds with Jev in their test, packaged as the skill jev-job-hunter.
I rebuilt my job crawler with Jev. The task: start at a company's official homepage, find Careers, and identify jobs that match my profile. Before, with an LLM:…
@KinasRemek demos Jev in two acts: an X-ray that asks many questions in one request (67 judgments in about 1s for $0.0002 on a short press note; up to 145 in 1.5s for $0.0005 on a longer report), then a firehose that filters the public Wikimedia SSE stream and classifies real English Wikipedia diffs.
jav @typesafeai - jestem pod wrażeniem 🤩 Cool. Napisałem kolejne demo. Myślę, że teraz to widać potęgę modelu ... i o co chodzi i widać prawdziwą wartość biznes…
@Cloudflare announces @CompleteSkeptic, CEO of TypeSafe, as a speaker at Cloudflare Connect and links registration.
📣 Speaker announcement: @CompleteSkeptic CEO, @typesafeai at Cloudflare Connect Register today: https://t.co/YCwgnPlLDj https://t.co/TwT5kMw5bT
@MisbahSy describes a Doc-OCR router using TypeSafe Jev. It looks at a PDF page by page, decides which pages need OCR, and extracts the rest locally, to spend OCR only where it is needed.
Doc-OCR router using Jev @typesafeai A Jev-powered router that looks at a PDF page by page, decides which pages actually need OCR, extracts the rest locally. Re…
@yongfook shipped field mapping on Bannerbear: when template fields and a source such as Airtable use slightly different names, Jev maps them in one click. Examples given are photo/avatar, name/full_name, and company_name/business.
First Jev use case published live on Bannerbear! Instant field mapping between template and source, when names are slightly different eg template - photo - name…
@crislenta reports a benchmark of 500 real-time agents in parallel in a 3D environment: about 500ms average latency and 35 API calls per second, with what they call a naive implementation and zero optimizations.
Jev is actually insane. We benchmarked it, and the results completely change the game for us: > 500 real-time agents > running in parallel > in a 3D environment…
Quoted @CompleteSkeptic
@johnyeo_ says their Slack agent got 2x faster by using TypeSafe’s model to classify the best skill, tool, and params from the prompt before handing work to the agent, instead of the agent reading skills and choosing tools itself.
Jev made our Slack agent 2x faster ⚡️ Our agent can be quite slow because it needs to read skills and figure out which tools to call. We used @typesafeai's new…
@BhosalePratim swapped the LLM that decides tool calls for Jev. Their note is that an agent could act before the user finishes a sentence, and that voice agents might decide on partial utterances instead of waiting for the end of the turn.
Played around with @typesafeai Jev today, mostly to understand what it does for tool calling. Instead of an LLM deciding what to do, I substituted that part wit…
@niazmorshed_ built jev-review, an experimental local-first MCP plugin. Coding agents call TypeSafe Jev while they work, get scored on quality metrics, improve, and repeat.
built `jev-review` @typesafeai it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics. age…
Quoted @CompleteSkeptic