The newest builds with Jev

Newest first, 432–464 of 1072. All 1072 in curated order

• Guide·netlify.com

  Jev on Netlify AI Gateway

  Netlify’s changelog: Jev through its AI Gateway with zero configuration.

• Live site·Search

  A forum database, searched by Jev

  Jev finds the most relevant posts in a forum database.

  Bbigmuzzy

• 

  Live site·Benchmarks and evals

  JevBench

  A reproducible benchmark for typed decision models.

  Fflorianstandhar

• Live site·Games and real time

  Jev-mice

  A mouse colony simulation with Jev deciding and a deterministic engine acting.

  Ccarsonsweet

• Guide·docs.litellm.ai

  Jev through LiteLLM

  LiteLLM’s pass-through docs for calling TypeSafe from an existing LiteLLM proxy.

• Live site·Documents and OCR

  Geo Pulse

  Classifying geopolitical news in real time with Jev.

  Ddimakrivolap

• 

  Live site·Games and real time

  Magic Jev Ball

  A magic eight ball answered by Jev.

  Ssarreph

• 

  GitHub·Coding and code review

  Jevopt

  Compiler optimisation decisions made with Jev.

  Rramneet_singh

  5
• 

  Guide·pydantic.dev

  Jev in Pydantic AI

  Pydantic AI’s docs for the native TypeSafe model, to call Jev from a Pydantic AI agent.

• 

  GitHub·Search

  Jev-powered Obsidian search

  Search an Obsidian vault with Jev deciding relevance.

  MMikeLembo

  12
• 

  GitHub·Open source

  Beating Jev with open models

  An attempt to beat Jev’s accuracy, speed and cost with open models.

  Rrob313

  8

• Live site·Open source

  Turn any LLM into a Jev-like model

  A trained KV cache bank, with a live demo.

  Ffaangguyindia

• Guide·pypi.org

  langchain-typesafe

  The LangChain package with TypeSafeClassifier, to use Jev inside a LangChain app.

• 

  GitHub·Open source

  laya-coreml

  A faster, local Laya-like model for the Mac.

  Mmkagenius

  1.6k
• 

  @kleos

  @1kleos1

  A 20-YEAR-OLD JAPANESE STUDENT BUILT A JEV POLYMARKET BOT OVER THE WEEKEND, FIRST NIGHT IT MADE $17,831 Starting capital: $148 First night: $17,831 profit Total profit so far: $276,000 he built the whole thing around one stupidly simple loop Polymarket data comes in → Jev judges the setup → confidence gate checks it → deterministic code executes Jev only gets three choices BUY YES BUY NO WAIT the bot scans dozens of markets at once looking for odds that stop matching the live data around them while a normal LLM is still explaining why something might be mispriced, Jev has already made the decision and moved to the next market position sizing, max loss, execution and exits stay in code Jev only handles the fuzzy part is this market actually wrong right now? $148 starting capital $17,831 on night one $276,000 so far and the craziest part is I have the full setup he used the Jev prompts, decision flow, confidence gates and Polymarket architecture might drop the whole thing next

  

  X·Trading and markets

  Pick

  $17,831 in the first night

  First night

  $17,831

• 

  Sodeh Abadi

  @irSodeh

  Jev vs Laya-MLX isn't really a rivalry. Jev is the original, commercial model. Laya-MLX is a local, open-source take on the same idea, running natively on your Mac. Same core concept: "System One" models. No chatting, no text generation. Just fast, typed decisions. #Jev #Laya

  

  X·Open source

  Jev vs. Laya-MLX

• 

  Guide·github.com

  Worked examples via OpenRouter

  Rajeeve Kuriakose’s runnable Jev examples, through OpenRouter, so you can start today.

• 

  WquGuru

  @wquguru

  Jev 发布才 8 天，开源替身已经一堆了，今天诺基亚也来蹭热闹了😅 我在 MacBook 上，让官方 Jev 和 3 个开源方案同跑 20 张中文工单🫱 Laya 25ms，比 Jev 快 50 倍 djev 509ms 官方 Jev 1.3 秒 AnyJev 将近 4 秒 仓库都在这： Laya（Mac 版）http://github.com/mizorewww/laya-mlx djev（Mac 版）http://github.com/jamescorbett/mlx-vlm AnyJev http://github.com/nokia-applied-research/AnyJev 但快的，不一定对 👇

  

  X·Open source

  Laya, 50× faster, on open alternatives

  Speed

  ~50× faster

• 

  Darshan Jain

  @i_darshanjain

  Jev is all over my timeline rn 👀 Went looking into it and stumbled across Laya. Open-source System 1 decision model. They even have a Jev comparison 👀 7.8× faster. 3× better calibrated. 45/51 languages. Self-hosted. And the weights are right there. 😂 Yeah… I am watching this space. https://huggingface.co/convaiinnovations/laya

  

  X·Open source

  Laya compared to Jev

• 

  Saksham Malhotra

  @SakshamMalhot27

  Launching JevRelevanceRetriever just $0.73 across 6,460 calls Open src Jev LangChain BaseRetriever for rel scoring Install and use it, no chain changes ;) pip install jev-relevance Repo-https://github.com/saksham-malhotra-27/jev-relevance Open for issues and PRs, feel free to contribute

  

  X·Search

  Pick

  JevRelevanceRetriever

• 

  Matt Van Horn

  @mvanhorn

  WTF is Jev by @typesafeai? Here’s the tl;dr ELI5: Think AI multiple choice, not AI essay writing. It doesn’t chat. It makes decisions your software can act on: “Spam or not?” “Which tool should this agent use?” “Does this need a human?” The exciting part: roughly 200x faster and 400x cheaper than frontier LLMs in TypeSafe’s own workflow benchmarks, with responses in a fraction of a second. Why that’s powerful: imagine an app or agent making hundreds of little judgment calls without hundreds of expensive, slow conversations with an LLM. Keep the big model for the hard thinking and writing. Use Jev for the rapid-fire decisions in between. Excited to dig in.

  Guide·x.com

  Jev, explained like you’re five

  Matt Van Horn: think AI multiple choice, not AI essay writing. Keep the big model for the hard thinking.

• 

  Koimiao🐈

  @jaunatis_q

  We built a Stanford Town powered by Jev. It isn’t a game demo, there is no player. The residents decide where to go, who to meet, and when to talk. Humans can only watch their pixel world unfold. Demo + code: github.com/NevaMind-AI/je… #Jev #AIAgents

  

  X·Games and real time

  Pick

  Stanford Town, run by Jev

• 

  Nitin.nn

  @NitinthisSide_

  After seeing Jev everywhere on the feed lately, we had to try this direction ourselves. We’re bringing Laya to Insight O Mate. A fast, open-source, local decision model that fits perfectly with our privacy-first approach to querying NoSQL databases. Your data stays local. Your decisions stay local. Insight O Mate + Laya is coming. Try Insight O Mate at https://insightomate.lunamic.co

  

  X·Open source

  Bringing Laya to Insight O Mate

• 

  Choroshin Alex

  @choroshin

  "לקוח יקר, זוהתה פעילות חריגה בחשבון הבנק שלך. החשבון יוקפא תוך 24 שעות. לאימות פרטים לחץ כאן." רציתי לשחק עם Jev, המודל החדש של TypeSafe, וגם לבנות איתו משהו שיש בו תועלת אמיתית. אז בניתי ב-Skills IL כלי שבודק הודעות כאלה: מדביקים את ההודעה, מ-SMS, מוואטסאפ או ממייל, ומקבלים

  

  X·Security and abuse

  A phishing message detector

• 

  Isaac Flath

  @isaac_flath

  I've been using Jev by @typesafeai Here's the six things i've tried and am confident I'll still use Jev for 60 days from now. There's many more experiments, ideas, and things I think I will use it for. It's a big deal (more on why in next post). But I am only sharing things that I am 99% sure will lead to stuff I will still be using Jev for in 60 days. That means I started with small, boring, but useful, stuff. - Fact-checking my scripts - Ranking my news feed - Finding the right text in PDFs - Checking citations - Grouping my review notes - Figuring out why agents fail (eval over traces) https://isaacflath.com/writing/six-things-i-tried-with-jev

  Guide·x.com

  Six things I will still use Jev for

  Isaac Flath’s shortlist: fact-checking scripts, ranking a news feed, finding text in PDFs, checking citations, grouping notes, and evals over agent traces.

• 

  Saksham Sharma

  @code_saksham

  And just when I thought I have a crazy idea with JEV that i will open source then this happened. Please @typesafeai fix it ASAP, the curiosity inside me to build that thing is insanely high

  

  X·Benchmarks and evals

  A Jev idea that hit a wall

• 

  Siim Haugas

  @siimh

  scoreboar v8: 128 MB instead of 597, about 30 ms a post instead of a second. and more accurate. guessing which of two X posts did better for its account size: 61.4%. grok 4.7: 55.3%. jev: 52.5%. free, in your browser. github.com/Siim/scoreboar…

  

  X·Social feeds

  Scoreboar v8

• 

  Chase

  @0xChaseTM

  Jev Engineers just released Jev, and the architecture is simple: state goes in, auditable decisions come out I mapped the decision layer into a field blueprint: what Jev reads, what it returns, and how an agent uses those answers in a real loop: step 1 → give each layer a job. The LLM plans and writes. Jev chooses between bounded options. Code carries out the action. step 2 → build the state before asking for a decision. Include the goal, evidence, available actions, previous actions, and what the environment looks like now. step 3 → define the questions in code. Choice selects an option. Score rates it. Noul estimates whether a condition is true. The answers are typed, so the runtime can use them directly. step 4 → ask independent questions together. One state snapshot can support routing, risk, and relevance decisions in the same pass. step 5 → use the answers to route work. A lookup, a complex reasoning task, and a tool call don’t need the same model or execution path. step 6 → add a gate before the tool runs: allow, ask, or deny. A high-confidence answer still has to pass the action policy. step 7 → execute the selected action and check the outcome. Record what the tool actually did, including failures. step 8 → update the state with that result. Otherwise, the agent’s next decision is based on a world that no longer exists. step 9 → apply the pattern where small decisions repeat: model routing, tool guardrails, inbox triage, research classification, browser actions, and context compaction. step 10 → judge the whole task. Track whether the agent reached its goal alongside cost, latency, approvals, and failed actions. the result: you can trace an action from the evidence Jev received to the decision it returned and the outcome your tools produced. Save the Jev Decision Infrastructure blueprint for your next agent build ↓

  

  X·Benchmarks and evals

  State in, auditable decisions out

• 

  Kostas

  @Kostastsale

  This made me rethink where AI actually fits into security engineering. For purely engineering work, forget about ChatGPT or Claude. TypeSafe AI just released Jev, and I think it’s going to change how we build AI into security workflows. Instead of asking an LLM to “investigate this,” you define the questions and possible outputs, then get structured probabilities and decisions your code can actually use. For security, the possibilities are huge. Think of the below use cases 🤯: Threat Hunting: ➡️ Rank broad hunt results by relevance ➡️ Score users, hosts, processes, or sessions based on how suspicious their surrounding activity looks ➡️ Classify noisy activity at scale. Think thousands of rundll32.exe executions automatically grouped into expected admin activity, software execution, suspicious usage, or unknown Detection Engineering: → Classify historical alerts for FP analysis → Add context-aware scoring on top of deterministic detections → Validate whether an alert actually supports the behavior the rule claims to detect Incident Response: → Reduce massive timelines down to the events most relevant to the intrusion → Continuously score hosts/users for possible compromise → Help prioritize scope expansion, triage, and response decisions This feels much closer to how AI should be integrated into security engineering. I'm currently working through most of the above, mostly focusing on instant response, but at the same time doing some of the threat hunting use cases that I mentioned. Typesafe AI can be basically a decision engine sitting inside the workflow while being x200 fast and cheaper. Don’t sleep on this... This is huge! 👉 https://typesafe.ai/

  Guide·x.com

  Jev for security engineering

  Kostas on threat hunting with Jev: define the questions and outputs, then rank, score and classify activity at scale.

• 

  YZ

  @robot_yz

  使用 #Jev 做了一个填写外链信息的演示。 眨眼的功夫就找到入口，并准确的把已知信息填写完了。 准备把之前的Submit Agent改成使用JEV模式。 关注github，在评论区

  

  X·Agents and browsers

  Auto-filling external link info

• 

  Harsh Singh

  @haaarshsingh

  i created a 404 page that fixes your typos for you, and redirects you to the correct page built with @typesafeai jev try it out: kobra.systems/cmr-table

  

  X·Routing and model choice

  A smart 404 page

• 

  Justin Youens

  @youens

  This is the neatest example of Jev I've seen. But didn't see a demo link, so here you go: emogee.youens.com

  X·Tools and apps

  Emogee

• 

  Jason Zhu

  @GoSailGlobal

  拿 Jev 做搜索重排，我先泼一盆冷水：单独用，它没打赢向量检索 TypeSafe 的 Jev 这阵子很火，一堆项目拿它做重排。我们在 Agent Skills Hub 的 33,047 条目录上认真测了一次，164 条中英文真实查询，9,831 对分级标注，整套只花了 2.6 美元 三个结论 01｜单独重排，约等于没赢 Jev 重排 bge-m3 的前 30 条，NDCG@10 只多了 0.012，置信区间跨过零。MRR 和前三命中率倒是涨得明显，它很会把最强的那一条顶到第一，后面几条基本是重新洗牌 02｜裁判偏差，被我们量出来了 Jev 自己也参与了打标注，这就是循环 只用 Jev 当裁判，它领先 0.053 两个裁判合并，领先 0.012 只用 Haiku 当裁判，反而落后 0.028 同一组比较，换个裁判结论直接翻面。所有涉及 Jev 的结论，我们只认 Haiku 那一列 03｜真正稳赢的是融合 把 Jev 和 bge-m3 的排序做 RRF 融合，NDCG@10 到 0.864，比纯向量高 0.064 到 0.116，三种裁判下都成立。代价是每次查询多一次 API 调用，约 0.0002 美元 顺便暴露了我们自己的问题：Hub 线上 CLI 用的关键词排序只有 0.609，短板是召回。相关结果有一半压根没进候选，后面怎么重排都救不回来 数据、标注、每条查询的得分全部开源，不用 API key 就能复现打分

  

  Guide·x.com

  Jev as a reranker: an honest negative

  Jason Zhu tested Jev reranking on 164 real queries. Alone it did not clearly beat vector search; fused with it, it did. In Chinese.