Another brilliant launch for developers: and its 20-200x faster than LLMs because it skips token-by-token gene

@DeRonin_

4 things to do when you get access to Jev: 1. don't replace your model, put Jev in front of it Jev makes the small choice first, and your expensive model only runs when it's really needed one email tool swapped 2 AI calls per email for 1 Jev call, and kept all their normal safety checks on a 120 ticket test: 42 seconds with Jev in front → 22 minutes without it cost: $0.0003 → $0.059 the person who ran that test said his slow version was slowed down even more by retry errors, so the real gap is smaller how to do it: - look through your code for AI calls that only choose something and never write text - write the list of possible answers in your own code, don't let the model invent them - send that list to Jev, and keep your old call as a backup - put it behind an on/off switch so you can undo it in one line - run both for a week and compare them before you delete the old one you're not rebuilding your product, you're replacing one call 2. ask 5 small questions instead of 1 big one someone tested Jev on 2,000 phishing emails ask it one big question and it gets 89.4% a simple 2 line text rule gets 91.8% Claude Haiku 4.5 asked the same question gets 94.2% so when it has to give the final answer alone, Jev loses even to a text rule then he asked 5 small questions instead, and added the answers up in his own code 95.0%, the best score in the whole test SAME MODEL, SAME EMAILS how to do it: - take the big question you were about to ask - write down the 5 things a pe

@aigclink

前OpenAI研究员Diogo Almeida发布了一款新型模型：Jev，一种“放弃生成文字只输出类型化概率决策”的模型 称速度快40–200倍，输入成本便宜5–238倍，输出接近免费 就是说把大模型从会聊天会写长文的生成器，改成软件可以直接调用的概率决策函数 用并行概率决策替代逐token字符串生成，主打结构化、可校准、低延迟、低成本 创始人Diogo Almeida称曾在OpenAI参与指令跟随/对话方法相关研究，他的问题是：模型聊天能力早已超过人，但真正可嵌入代码的自动化仍缺关键一环 Jev背后是一套全新栈：新架构、并行采样器以及新训练方法RLCD 像前沿智能函数调用，输入非结构化状态/程序状态，输出预先定义好的类型化概率决策。放弃自由字符串生成，换来无类型错误、schema内不生成非法/幻觉式输出，并自带置信度 它抢的应该是海量的微决策场景，每一次请求背后几十上百个小判断的基础设施层 可塞进业务系统里做判断器，作为分类、路由、打分、抽取，实时应用里的快速判断，工作流里的“智能if”等 如果真能把每次判断压到毫秒级、接近零输出成本、附带置信度，那这个还是有价值的 #Jev #新型模型

@teach_fireworks

我快速看了Jev的一些公开帖子和官方文档说明以及例子，终于有人AI模型关心速度，幻觉和成本，而且Jev不是一个大语言模型，这是它能做到这么极致的原因！ 官方也给出了好几个例子，比如模型路由和LLM安全护栏。 所以在大语言模型狂奔3年后，AI模型的新的分叉出现了： 不是大尺寸，不是语言模型，极低的幻觉，成本，极快的速度和准确率，没有太多的推理。 几乎每一条都是和LLM反着来的。 不得不多，这个方向真的非常对，希望早点体验到它。 下面这个视频来自官方demo一个并行化的例子，真的快如闪电，成本极低！ 这几篇来自官方的文章一定要看看： https://t.co/bm5dYnjZ9O https://t.co/6ZXGhuISMh https://t.co/iFBnlbp1XI https://t.co/HId6Dkb5se https://t.co/M5lBmItSON

@0xPascual

Jev shipped from stealth last week and nobody's talking about it. Setup: One of the original ChatGPT co-authors spent two years building an AI that cannot generate text. Surface Story: It is not a cheaper chatbot. It is a decision engine. You ask it a natural-language question - 'does this ticket sound fraudulent?' - and instead of writing a paragraph it returns a number. 0.8 means 80 percent yes. You can define your own categories too: annoyed, irritated, outraged, and it returns a probability per label. Think of it as a smart if-statement for your business logic. Twist: The interesting part is the architecture. It does not generate token-by-token, so it answers in milliseconds. TypeSafe claims 20 to 200x faster and 40 to 400x cheaper than an LLM call. Pricing is $0.042 per million input tokens, output is free, and there are official Python and JavaScript TypeScript SDKs. Parallel questions run simultaneously. You stop treating an LLM as a data extractor and start treating it as a classifier with calibrated confidence. Hidden Reality: The real use case is agent guardrails. Before your agent fires a tool call or spends money, Jev returns a probability that the action is wrong. That is the bet - code plus AI, where the model scores options and your code decides how to proceed. Not another chat wrapper that rambles and breaks your pipeline. AI Reveal: The math is the story. $0.042 per million input tokens with free output against frontier LLM pricing means a million classi