Jev：低成本高并发AI分类器原理与邮件处理实战

Jev is here and it's a big deal. It was created by Dooo Almeida. Yes, that's the same guy whose research built chatbt. 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 Open Code 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 Vogle, welcome to the pod. By the end of the episode, what are people going to learn?

We're going to learn about a new type of AI, a an type of AI that we haven't really seen before, but I think it's good. It's Jev. And uh 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 uh 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.

Cool. And I So I just have a few things I need from you because I haven't used Jev. I want you to give me the simplest possible explanation to Jev. I want you to ex give me like, you know, three or four insane use cases so that people can walk away from this episode with like productivity, making money, just like, you know, even boring use cases that could become, you know, $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 Vogle?

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 uh been working with email. If you know me at all, you know that I love email because it seems unsolved. I mean, like, Greg, how many spam emails do you get every day? Like, there's too many, right?

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 like take like traditional AI where it's like they're having like a GPT 5.6 Luna like kind of read every email and then score it, but that takes time and it's not like instant and it's just like h 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, uh I'm going to 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 uh you have this iPhone as an input, right? And that's the input and then the output is a schema.

So we could uh have the schema be what color is the iPhone is the question almost. and it has uh blue, orange, red, green, yellow as the output options for that question. And the classifier Jev then looks at this phone in a text uh format and says, "hm, what uh is this orange? Is it 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 the snazzy email jazz. And what the input is is that just entire email object. There's no sugar coding 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. It's it's a it's a range, if you will. So, it's like some emails are more spammy, like this uh Kickstarter one. It's obviously trying to sell me a bunch of stuff and junk.

I don't really care about that. I signed up for that Kickstarter thing like two years ago. Still haven't been able to unsubscribe from the list since. And then we've got some uh some like Mercury things. Okay, this is just like a payment thing. It's like, okay, Exxon Enterprise received $22 from Stripe. That doesn't seem spammy. That seems just like it's infor uh informative uh and it's just informing me that uh something happened.

And then we've got the reply percentage. This is how much does this warrant your reply. So, if we go back here, and I'm not going to click on this because this is a real email, but 90% account violation possibility. This is a user saying, "Hey, my account seems to be violated somehow." Jev identified, hey, this user seems to be having some trouble. We should probably warrant a response on this. Now, I've already got these all uh categorized, and there are uh 1,700 of these emails.

And and and this is where we come back where it's it's so sad because it just takes so much time to run all of these and it's probably going to take like 10 hours to do and then and then I'm going to have to go through and probably pick out some of the data and oh my god the price is going to be so expensive and oh it it's done. Oh it didn't cost 18 cents

1,700 emails.

That is the power of Jev. I can't explain it any better than that. We had 4.2 million input tokens and 500,000 output tokens. The entire cost was 18 cents for each one of those emails. All categorized, all I mean, you can see here they're all categorized. They're all ranked. They're all given that score.

So, if you were to imagine like let's say

Ryan, what I'm here's what I'm hearing. I just want to make sure I I I have a good mental model for what Jeb is and correct me you know where I'm wrong. Okay.

So Jev is basically like an AI decision maker.

Yes.

So you you give it some information. In this case you're giving it you know the contents of the email and like a set of possible choices like is it spam or not? Jev's going to go ahead and look at that information and choose an answer. So, for example, like is this email spam or urgent or no normal? Um, but you can also have it do things like, you know, is this customer likely to buy or unlikely to buy,

right? Exactly. You're almost there. That's like 90% correct. It makes a it makes a probability of a decision.

Okay. So the difference between it making a decision because a decision would be you uh like you uh submit an API or something like that and it tells you buy or not to buy. Technically what happens on the underside is that percentage. So it would