Jev for beginners: how to use it and what to build
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What makes Jev different from every other AI model?
Jev, Jev, Jev. Welcome to Jev Week on How I AI. We have seen a lot of new models be released in the last five days. We saw Opus 5.5. We saw
GPT-6 Sol, GPT-6 Luna. Muse is blowing up the timeline. Everybody still loves their Grokbots. And yet there is one thing that I want to talk about in AI right now, and that is This fast, cheap, doesn't speak system one decision model from TypeSafe AI. As soon as I saw this trending on
X, as soon as I saw it launched, I immediately started testing it. Now, what I will say is more than any other model I've experienced lately, Jev has been the one that has exploded use cases, personal productivity use cases, code use cases, product use cases. Today, I'm going to give you a very quick whirlwind tour of what Jev is, what it will give you back and what it won't, and then how I have used it over the last week to do work that I think is worth hundreds of thousands, if not millions of dollars. And I have probably spent sub $10 on Jev tokens. A lot of it has been subsidized because Jev is currently free on the AI Gateway by Vercel. But even at list price, It is a very inexpensive model. It is a very effective model, and it is going to be in the middle of almost everything I build from here on out.
So let's get to it. This episode is brought to you by Open Art Arena, the global leaderboard for creative intelligence. Every week, new AI models launch, and everyone claims to be the best. But best at what? Open Art Arena is built to answer the question that actually matters. Which model is best for your specific job? Instead of one overall winner, Open Art Arena ranks models across real creative tasks. From advertising and film to animation, product, graphic design, editing, and lip sync, covering both image and video. And these rankings aren't based on hype. They're judged by professionals, industry leaders, and working creators through blind evaluations. So judges never know which model produced which output.
That means you can see how models actually perform when it comes to the creative work you're doing. So stop guessing which model to use. Explore rankings based on real creative work and find the right model for your project and save time and cost. See the rankings at Open Art Arena. So if I were to explain Jev to you, I would go to this table on the TypeSafe blog post announcing Jev, and it basically compares normal LLMs on the left, Jev style LLMs on the right. Both take in unstructured data as inputs. Both take in text as inputs. Jev does not take in images, but it takes in text and it takes in text descriptions of images if you really need to get there. The outputs, though, are very different.
How do type‑safe values work and why are they important?
With the standard LLMs that you're used to working with, you are getting strings and generated text out. So you're getting Text in, text out. With Jev, you're getting text in, type safe values out. What I mean by type safe values is these are values that are predefined that then Jev picks from and selects and returns to you. We will show you what those values are, but essentially they're like, it's this or that. It's yes or no. It's one through ten. It's pretty simple. Now, it sounds simple, but it is incredibly powerful when you put it against the right problem. The other thing that you will notice about Jeff is it is cheap AF and it is fast AF. And so if you look at the current cost of input and output tokens, it can be pennies to tens or hundreds of dollars per million output tokens.
Jev only charges you on input tokens because it barely outputs anything. And it is four cents per million input tokens. It is like dirt freaking cheap. And because they output basically nothing, they don't even charge you. for output tokens. Whereas the standard LLMs are going to charge you tons for the output tokens. And then I'll go into use cases, which is how you would use like a standard LLM versus why you would use Jev. You know, standard LLMs, chatbots, where you want text in, you want text out. Coding, where you want, you know, code to be produced. And so it's great for things where you need stuff generated.
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Chapters
8 chapters
1
What makes Jev different from every other AI model?
0:00–3:12
2
How do type‑safe values work and why are they important?
3:12–5:45
3
What can Jev output (choices, scores, probabilities) and how should I interpret them?
5:45–9:04
4
How can I use Jev to categorize and cluster thousands of PRs cheaply?
9:04–12:36
5
How can I run a personal meta‑analysis on my local Claude and Codex sessions with Jev?
12:36–15:47
6
How does Jev help triage Gmail messages and prioritize email handling?
15:47–19:26
7
How did I turn 4,500 YouTube comments into a searchable audience‑insight dashboard?
19:26–22:19
8
What real‑time apps can I build with Jev (voice‑to‑color emotion mapping and quote generation)?
22:19–26:22
Speakers
1 identifiedMore from How I AI
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