视频拆解 Jev：不能聊天、只做软件内决策的模型

zhod @zhodonx原推发布于 2026.09.18

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This Lab is changing everything we know about how AI is being built into software. I got a chance to look into TypeFace’s new model, Jev, and I genuinely think these guys are onto something interesting. For context: TypeSafe is a San Francisco AI lab co-founded by Diogo Almeida, one of the researchers behind the work that helped make ChatGPT possible. They’ve just emerged from two years in stealth with $40 million in seed funding and their first public model. And the interesting part about this model is you can’t even chat with it. You can’t ask it to write an essay or generate code either. Jev is built to make decisions inside software, and the way it does this is pretty interesting. Here’s all you need to know: TypeSafe calls Jev a new class of AI models, System One Models. Imagine you’re building a customer support app. Someone complains about an order, threatens to cancel, and asks for a refund. You want your app to figure out how urgent the complaint is, which department should handle it, and whether a human needs to intervene. With Jev, you provide the message, define those questions, and specify the possible answers. It evaluates them and returns structured decisions with probabilities your software can immediately act on. And here’s how it differs from conventional LLMs: ⟢ It doesn’t generate text: You define the possible answers beforehand using three primitives: Choice, Score, and Noul (yes/no). ⟢ Its structured outputs are native: Instead of generating JSON token by token like a conventional LLM, Jev produces typed decisions directly. ⟢ It evaluates questions in parallel: You can ask 20 questions about the same document and receive all the decisions in one request. ⟢ It’s trained to express uncertainty: TypeSafe developed a method called RLCD,that’s aimed at producing calibrated probabilities, so software can identify uncertain decisions and escalate them. And the numbers are pretty crazy. According to TypeSafe, Jev can be 20 to 200x faster on comparable decision making tasks. Their workflow evaluations report 193.6x faster performance and 444.6x lower costs, and It costs just $0.042 per million input tokens, with output tokens free. First of all, that’s crazy. And in independent testing, they used Jev to evaluate 777 judgments across 37 documents in under 0.7 seconds, for roughly a quarter of a cent. Of course, Jev can still make incorrect decisions, and labs might need broader independent testing. But I think there’s a pretty big opportunity here. Imagine software that can afford to make hundreds of intelligent decisions without calling an expensive reasoning model for every single one. Checking claims, routing requests, verifying agent actions, and deciding what happens next. I like to think that’s the direction TypeSafe is exploring. And if they can maintain useful accuracy at these speeds and prices, I can see why they’re betting on it. Plus they even used it to play a game. Ridiculous! There’s so much stuff we can do with Jev running behind it, and I’m trying to build something cool myself. Will lyk when I come up with something. https://x.com/MatijaSosic/status/2100190746389135772/video/1

这条是引用着另一条写的：「ChatGPT 联合发明人发布新模型 Jev」