Case wall
Jev AI examples, page 13
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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@ctnicholasdev shows an app where 20-plus checks run every time a cell is edited and warnings appear immediately, with no LLM. They credit Jev for making instant suggestions possible.
Jev enables instant AI suggestions 👏 In this app, 20+ intelligent checks occur every time you edit a cell, immediately providing warnings. No LLMs necessary! ht…
@eptwts used Jev to score a YouTuber’s last 100 videos for how likely each is to sell something. It finished in 12 seconds for about $0.02. They say they have not deeply checked accuracy yet.
i used Jev to classify a youtubers last 100 videos based on how likely it is to sell me something... it analyzed & assigned a sales intent score to each video w…
@anshuc says Jev can draw pictures by predicting every pixel in parallel, and links a clip. The post does not describe the image or the setup.
Jev can draw pictures by predicting every pixel in parallel https://t.co/IOwwmr07cm
Quoted @CompleteSkeptic
@mdlahfir uses a deterministic hook so Claude Code can ask Jev where to delegate before it hands work off. They say mechanical tasks go to Haiku, intelligent ones to Opus sub-agents, and long-running implementation to an external harness (Claude Code, Codex, or Opencode).
Jev solved local harness/model routing I use a combination of Claude Code, Codex and Opencode as my local agentic stack and routing to other harnesses was alway…
@Bewinxed says they made TypeSafe’s Jev generate text even though it should not, up to 20 words for $0.50, and jokes that they cannot read and it cannot write.
I made @typesafeai 's new ultra fast model, Jev, generate text, even though it shouldn't, that's fine because I can't read, and it can't write** ** up to 20 wor…
@OnerBiberkoku built a tool where you enter your X handle and someone you want to reach, and Jev finds a chain of real follow connections in under 2 seconds. They say Luke Skywalker is two people away from them.
They say you’re six connections away from anyone on Earth. So I tried Jev from @typesafeai and built this: enter your X handle and someone you’d love to reach.…
Quoted @CompleteSkeptic
@higgsfield_ai says their use case is auto-routing GenAI models: Jev picks the most cost-effective models for a task, balancing speed and quality. Generations in the demo are made with the Higgsfield API.
We found the perfect use case for Jev: auto-routing GenAI models. The idea: Jev picks the most cost-effective models for your task, balancing speed and quality.…
Quoted @higgsfield
@anshnanda posts a video comparing Jev with GPT-6 Astra, Fable 5.1, and Deepseek v4.1 Flash on four tasks: Doom, a year of Bitcoin trading, 1,500 emails at 200ms each, and 857 support tickets versus Intercom Fin. They say the Jev bill for the video was $0.49.
By now you've probably seen Jev on your timeline. Yes, it's an insanely fast & cheap model. But how does it compare against GPT-6 Astra, Fable 5.1, and Deepseek…
@iam_zachi built a SQL extension that turns plain English into a WHERE clause with TypeSafe Jev, for example WHERE jev(people, 'could work from home'). Each row is judged on its own, with no index and no embeddings.
I build a SQL extension that turns plain english into a WHERE clause with jev @typesafeai WHERE jev(people, 'could work from home') Every row gets judged indivi…
@faadilhshaik got TypeSafe’s Jev to play Super Mario Bros. They say fast inference and structured outputs make it surprisingly good for real-time use.
got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to…
Quoted @thread
@nutlope had Jev play chess against GLM 5.3. GLM won by checkmate in 29 moves. They report about 0.3s and under $0.0001 per Jev move, about 5.8s and $0.008 per GLM move, and 24 cents for the game. The takeaway they state is to use Jev for fast classification and an LLM when reasoning or lookahead is needed.
Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole ga…
@zain_hoda demos live slide selection while speaking, powered by TypeSafe Jev. They apologize for sniffling because they have a cold.
The speed and cost is going to enable use cases that haven't been possible before. Here's a quick demo of live decisions of what slide to pull up as I'm speakin…
Quoted @CompleteSkeptic
@Totzenberger built an AI-text classifier with TypeSafe Jev at jev-ai-detector.com and tested it on a Paul Graham essay. They say it is close to Pangram, about 6000x cheaper and 5–10x faster.
Another useful use-case with Jev from @typesafeai - AI text classification. Very close to @pangram, 6000x cheaper, 5-10x faster Tested here on a @paulg essay! h…
Quoted @thread
@nickvasiles says Jev, from a ChatGPT co-inventor, is playing Minecraft on its own computer in the cloud on Orgo, using computer tools in real time. The rest of the post imagines many such Jevs; that part is speculation, not a reported result.
Jev is a new model from the co-inventor of ChatGPT that can use computer tools in realtime right now it's playing Minecraft on its own computer in the cloud on…
@RomanSlack1 says Jev works well for a drone app they made in 15 minutes for 10 cents, and links a repo. The post does not describe the drone’s controls.
Jev by @typesafeai works quite well for drone applications. Made this in 15 minutes and only cost 10 cents. Repo: https://t.co/b2ytFvehTV https://t.co/rC36lr4iq…
Quoted @thread
@SUOHA_AI compares Jev and DeepSeek on matching 15 brands to PR hooks in a live news stream and tagging intent. They say Jev finished all 428 items in 28 seconds while DeepSeek V4.1-flash had done 6 of 428. They attribute the gap to Jev returning a decision in one pass instead of writing text, and point to an Elvis Sun GitHub demo.
完全睡不着了....这个视频让你完全看懂JEV的恐怖能力 我实际演示了一下，用 JEV 和 DeepSeek 做同一个任务：从当天的海量实时新闻流里，为 15 个品牌快速匹配有没有适合借势的公关热点，并打上结构化意图分类标签 结果是：28 秒内，Jev 狂刷完了整整 428 条数据，并完成了全部的目标分解与分类；而同一…
Quoted @SUOHA_AI
@marcus_lowe built a Tetris demo with early access to TypeSafe’s Jev. They say generation is fast enough that it is pushing the blocks down.
I got early access to @typesafeai's new Jev model and built a demo of it playing tetris The generation speed is so fast that it's pushing blocks down This feels…
@sengpt shows four Jevs playing the Turkish tile game 101 against each other and says the video is not sped up. They treat “make a model play 101” as a personal benchmark because the model must follow the rules and decide from the table state and opponents’ moves.
arkadaşlar jev'e early access aldım ve test etmeye başladım ve gerçekten çok impressed olduğumu söylemem lazım sadece şu kaydettiğim demoyu bi izleyin lütfen. k…
Quoted @CompleteSkeptic
@mikegee controls Figma by voice with TypeSafe Jev. They say the total cost from the first line of code to recording the video was $0.01.
Controlling Figma with voice using Jev from @typesafeai Total cost from first line of code to recording this video: $0.01 🤯 https://t.co/HTGcrBYpTb
@RaghavPunnam posts an explainer for people trying to understand how Jev differs from LLMs and how to use it.
For those trying to understand the difference between Jev and LLMs + how to use it https://t.co/55o5WEq3Va
Quoted @CompleteSkeptic
@stevekrouse says TypeSafe’s Jev is fun and links a live demo at typesafe-demo.val.run. The post does not describe what the demo asks Jev to decide.
typesafe's jev is fun! live demo you can play with: https://t.co/ctnPF0w70r https://t.co/i6kMGkkWOV
@mdlahfir posts “Jev solved delegation” and a link. The text does not say what was delegated or how.
Jev solved delegation https://t.co/1TznArCaSY
Quoted @mdlahfir
@higgsfield_ai shows Jev plus Higgsfield routing generation models: Jev reads a prompt and picks models for video and image generation on the Higgsfield API.
Jev + Higgsfield = solved auto-routing for genAI models. In this demo, @typesafeai’s Jev evaluates prompt and picks the most fit models for video and image gene…
Quoted @higgsfield
@libukai compared Jev on OpenRouter with Gemini Flash Lite on classifying whether People’s Daily articles mention Hubei, using 500 positive and 500 negative examples. They report about 0.35s per article versus 3s, disagreement on about 15% mostly from person names, and about $1 for roughly 5,000 items and 30M tokens.
这两天关于 Jev 这个模型的热度非常之高，甚至可以说是近半年我重点关注了英推以来，看到的原创内容密度最高的一次。 正好我过去一年一直在跑一个小项目，其中有一个环节就是要对每天出版的《人民日报》上的新闻做一个分类，判断每一篇文章中是否包含有湖北相关的元素。 一年跑下来，积累了接近 2 万 4 千条数据，其中判定包含的有…