Jev is HERE. How to use it

Greg Isenberg (0:00)

Jev is here, and it's a big deal. It was created by Diogo Almeida. Yes, that's the same guy whose research built ChatGBT. Now it's such a big deal because it's a whole new way to do AI. So I brought on my friend Ryan, who's on the founding team of OpenCode, to just come on and clearly explain what Jev is, what are some insane use cases, and break down some startup ideas that are now unlocked. As of publishing this, Jev is invite-only, but good news, by the end of the episode, you're going to see how you can get access today.

So you're going to want to like, comment, and subscribe right now, so your algorithm knows to bring you content like this, to get your creative juices flowing in the future. Happy Jev Day, and I'll see you at the end of the episode.

Ryan Vogel, welcome to the pod by the end of the episode. What are people going to learn?

Ryan Vogel (0:58)

We're going to learn about a new type of AI, a type of AI that we haven't really seen before, but I think it's good. It's Jev, and people are ready for this new type of classifier AI, because we've been so used to just learning and using these LLMs, which are slow, they stream, and I think, as we'll cover today, this AI is fundamentally different in so many different ways with quality, speed, and price, that there are so many different usage applications for it that the possibilities are truly endless, and it just becomes on the humans again about how creative you can be.

Greg Isenberg (1:40)

Cool, and so I just have a few things I need from you, which is I haven't used Jev.

I want you to give me the simplest possible explanation of Jev. I want you to give me three or four insane use cases so that people can walk away from this episode with productivity, making money, even boring use cases that could become $10 million businesses, $100 million businesses. And I just want you to put it all together, wrap it in a bow that people understand, you know, if they stick around to the end, that they'll be able to understand why should they care about it. Can you commit to that, Ryan Vogel?

Ryan Vogel (2:16)

I can. I can. And I'll add one better. I'll make it entertaining so that way you can actually get excited about it, because first up, I'm just going to start out with a demo. This is my email. I'm not afraid to share it. I've been working with email. If you know me at all, you know that I love email because it seems unsolved. I mean, Greg, how many spam emails do you get every day?

There's too many. There's too many. You can't reply to all of them, and it's just so frustrating. And some of the email algorithms that exist are good, but it's not the best. But then some people are trying to take traditional AI, where it's like they're having a GPT 5.6 Luna, kind of read every email and then score it. But that takes time and it's not instant. And it's just like, oh, I wish we could just have something that could like instantly categorize all the emails. So this is that, this is using Jev. And before I run it, I'm gonna break down Jev in a super simple example. Jev is a classifier at its truest being that's what it is. I won't get into the architecture and stuff like that, because honestly, I don't even understand it that well. But essentially you define an input, let's say you have this iPhone as an input, right? And that's the input, and then the output is a schema. So we could have the schema be, what color is the iPhone is the question almost? And it has blue, orange, red, green, yellow as the output options for that question. And the classifier Jev then looks at this phone in a text format and says, hmm, what is this orange?

Is it red? It could be red. But then it says, okay, this is about, I'm pretty confident it's 80% orange, but it could be 10% red, or it could be 10% blue, which adds up to 100 And it's the probabilities of those choices. So it's not just going to be a 100% affirmative, this is orange, this is blue, this is red. It's a, hey, I'm 80% confident that this is orange or this is red. And the best way to illustrate that is with this email example. So each one of these rows that you see on the table is a full email object. It's got a subject, it's got a description, it's got a body, it's got a sender, all the snazzy email jazz. And what the input is, is that just entire email object. There's no sugar coating or any special treatment, it's just the email object. And we have four outputs. We've got a category, which is an option, where basically it can say, is this shopping, work, marketing, finance, security, yada yada yada. Then we've got a priority, which it can allow to select from, I think five different options, where it's like low priority, medium, high, important or urgent, which is like, oh no, you have a missed credit card payment or something like that. That's obviously urgent. You want to be able to nail that right on the head as soon as that comes in. And then we have a spam score. This is what I was talking about with those percentages. Obviously, not every email is going to be a true or false, when it comes to spam. It's going to be a percentage.