Jev is HERE. How to use it transcript

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 be like 83% buy uh 17% no buy type of thing. And obviously the more the the answer that is the stronger percentage would win and that would get returned to you.

But it's not a 100% decisive action type of thing. >> Okay. So instead of asking chat GPT claude whatever read this email and explain what I should do. You're you're the new mental model is use ask Jeb I mean you ask Jev like how you know read this email and choose a set of actions. So like reply or escalate and then you get like a choice from Jev and that gives you some sort of confidence score. Is that the way to think about it? >> It's kind of so we've been the the LLMs that we know nowadays have like corrupted our minds so much because there was an interesting point you said.

You said ask Jev. You don't really ask Jev because Jev isn't a text model. What's really interesting, if you look at the actual spec of Jeb, it doesn't generate any text at all, which you're like, okay, that's kind of weird. Um, it obviously generated text because how did you get the data for this, right? That was defined in the schema. So, let me let me see if I can uh pull up a little little whiteboard here, a little whiteboard action.

Not too good at this. So, we've got our schema, right? We'll call it uh I don't know. Um we'll have our email, right? And this will be our email input. And and then we'll we'll do a circle for Jev. Jev Jev seems like a circle guy. I would say Jev, >> there's the entertainment you promised. There we go. >> Yeah, exactly. Jev seems like a circle guy. That's that's just the type of guy that Jev seems like. Okay.

Maybe a tiny circle. There we go. Tiny circle because he's fast, you know? It's fast and cheap. Okay. So, we've got our email and that goes in to Jev. It doesn't get asked to Jev. It doesn't You're not asking Jev, hey, what should I do with this email? It's just an input like an a like a standard API and you define a schema up here and we'll have a we'll have like a a simple little schema and be like is spam and that can be a what they call a new which is a true false but it's a scale.

So it could be a one to zero. Let me format this. Yeah, I told you I wasn't good at whiteboards. I don't I don't know about this. So it could be a 1 to 0 which means that it could be 0.31 or it could be I don't know like 90 and that's that percentage. So it's if it were to return is spam uh 0.90 that would be a 90% chance that it is spam type of thing. So it doesn't give those definitive answers but you can infer definitive answers from that sort of choice.

And then let me get rid of this. Why are we doing Jason? Um, and then we could have a choice like uh let's see category and that would be like uh marketing uh it could be finance. It could be uh spam and it doesn't generate the categories itself. it looks at the categories that you've passed into it as like a model because like you pass all of these this like essentially this output schema in and you say here's the email here's the output schema I need you to generate the answer for me and >> by the way a schema is just a fancy word for how a database is organized right >> it's just how the database is organized but not even the database it's just how the output is organized it's just a fancy way which is why the develop why all the developers love it because they're like oh my gosh It's actually type- safe, which is a whole another video on everything like that.

But it just means that you can take the output that this Jev model gives you and instantly use it in code because like this new that it returns is a number object. It's not like text that is a number or something weird that you would have to do some additional data processing on. It just basically gives you this object which is the structure of the data. And um so like let's say we pass in this email and we have these two uh classification categories.

So then the model would just evaluate okay is this spam and what's the category and it would just return the percentage and the category. So it's not exactly like generating text like in a traditional like LLM like chat GBT. It's not saying hm well I think this is a spam email from Kickstarter so I should probably rate it. Nope. It just says category spam is spam 90%. type of thing. There's no internal reasoning or anything like that.

Which is why people are like well I don't know if I can trust it because the whole recent development with AI as you've probably seen is the models are reasoning which is basically just saying the models are speaking out loud to identify possible issues in their sort of thought progression. And Jev doesn't do that at all. Or it might do that, but it might just do it like really fast on the server. We don't really know.

But from our point of view, it doesn't reason. It doesn't have any other text output. It just gives you the output. So just shoots it back. >> It just gives you a decision. >> Exactly. >> That's the way to think about it. That's the way I'm starting to think about it. >> It's it's a decision model. And that's what I uh I pointed it out. Um uh like right here, like all of these are just decisions.

It's not >> cuz everyone has started to uh assimilate AI with LLMs which is like that next token prediction where it's a conversational agent. This isn't that at all. This is still AI because it's like machine learning but it's a decision model strictly. You can't ask it to be like hey how are you doing today or can you? So, I I like to I like to think around with these ideas a little bit. And I was like, okay, it's a decision model, right?

Well, I'm a decision model. When I'm typing on my keyboard, I'm making the decision to type each letter. So, like if I were to type hello, I'm making the decision to type H, E, L, L, O, which is technically text, but I'm also making the decision for each key. So I'm like, what if I can apply that same principle to Jev? So if we go back to our Excalibur whiteboard here, let's say instead of this category, we just have all of the letters A through Z, right?

And each one of those letters is a new. So the model can basically predict each letter and say, okay, what's the percentage? What's the decision of this letter based on previous letters? So, if it types H E L L, it's like, okay, my next best decision is to type O to complete the word hello. And I didn't know how it would work, but uh this is how it worked. So, this is me asking it the prompt, what is bigger, a cat or an elephant?

H, >> and this is all real time, by the way. So, it's very fast, but obviously it's not as trained in these sort of next uh letter uh completion stuff, but it's still fun to see because it's just like this is cool, but it also shows this isn't a traditional type of LLM where it like you can talk to it and it's a conversation. It's a decision-based LLM, which we've kind of learned. >> So, I guess that I mean that begs the question around what should I use Jeb for?

Especially the person listening to this is someone who wants to build a business, who wants to invest in themselves, who, you know, could be a sidetime job, a side hustle or their own thing, and they see this and they're like, I I notice that this is really interesting. I see like I I I believe Ryan when I when I when I hear him talk, and I could see that this is a glimpse into the future, but I don't know how to use it. >> Right?

And there's something really interesting about this because this is the first model that's been that can cater to a lot of different applications, which I'll say in a second, but it's also really fast and really cheap. So, it's you don't have this high barrier to entry that we've seen with other AIs where it's like, okay, I've got to dedicate like $1,000 a month to this. you could dedicate like $5. Like when we got uh when our open code team got set up on this account, we had like a $5 like I guess like intro uh credit I guess on the account.

We were able to use that for 2 days without hitting it. And we were using it like a ton. Like all of my demos and everything like that, we were using it. So it's extremely cheap. So you could probably like load 10 bucks on it and be good for like maybe 3 months. But some cool things that you could probably use with this is um I already got my girlfriend working on it because she runs a graphic design agency and she gets a lot of inbound and she needs to know if this inbound is high quality or just like if it's just maybe like solicit solicitation spam because she has a contact form on her website.

So she's using Jev to essentially say is this a good lead and uh it basically does that same sort of category where it's like is good lead and it ranks that on a percentage. So it's like is good lead and it ranks it from one or 0ero to one. So if it's like if you get a 98% lead that's a pretty high lead and you're probably going to want to reply to that. But then if you get someone who's like hm yeah I think I might want graphic design but I'm not too sure. they probably don't know what they want and that would probably require more effort from you as a business owner or her as the graphic designer to sort of feel out that client.

So, you can use Jev to make a lot of the decisions in your business that you might have to do yourself. So, like going through I I love the email example just because it's such an easy fix. That way, you can go through all your emails and all your historical emails and be like, "Are there any leads I missed? Are there any high-v value clients that I could maybe attack again to see if I can extract more value for them and me?

And basically, you can kind of think through your workflow and anything where you're looking at some data. It can be any type of data. If you're looking at some data and thinking, hm, I have to make a decision on this, you should probably think about adding Jev at that layer. Obviously, not for like 100% of interactions and stuff like that. should be a very heavy advisory role, but Jeb is really good because it can make those split-second interactions.

Um, if you run a business that has a a contact form or like for issue triage, um, let's say you get a lot of support uh, inquiries and someone comes in and asks you and they're like, "Hey, I need help with XYZ product." Jev can do instant classification and say, "Okay, let's make the decision. What product team does this need to get routed to? Let me route it over here. Let me route it over here." And there's so many different things where if you say, "Hm, this is a decision.

Maybe I can use Jev here." I guarantee you will have good results and it will be super cheap and fast because it takes around 200 milliseconds per query um to Jev no matter like what the input output structure is. So that is something really shocking too because AI can take like up to like 30 seconds for some things and you normally have to do like streaming where then you wait for the response to be done and then you've got to like have a listener and it's all this complex stuff but with Jev you can just do like a boom like quick API call and it just works. >> So Jev is basically this you know AI traffic cop.

So there's information that needs to come in and then Jev is going to decide, you know, where it should go and what should happen next. So Jev is basically going to pump out, you know, what what is this information, how important is it, what should happen next, and it's either going to go to a human being in the case of, you know, your girlfriend's agency where it's like, oh my god, this is a lead that she needs to act on like right now.

This is like Coca-Cola. This is CMO of Coca-Cola. Um, but if it was, you know, the confidence score was lower, but also like, you know, a local business in Orlando, maybe it's you automate it or use an LLM to to do something, draft something up or or send something or >> right, >> the confidence is so low that you just ignore it. So, am I getting that right? Yeah, that's like spot on where you basically think of anything that you would have to make a decision that would need to be quick and fast and maybe like provide feedback to a user and you can do it with that. >> So, where my brain goes with you don't know me too well, but like I'm all about like startup ideas.

That's what this podcast is about. >> Oh, me too. My brain is always always thinking the the next way to to do something like this. >> So, I'm kind of like, "Oh, wow. So, Jev now exists. How do I find a business with an expensive queue of incoming information and then just put Jev at the front of that queue? What I mean by that it like what do I mean by a queue? I mean like >> you got like a lot of inbound coming in where people need stuff from you and you need to get them routed to the correct person. >> Exactly.

There's something that immediately >> comes to mind which would require a little bit of architecture but let's say you run a services aggregation business like a like a SAS level on top of a local a lot of local services stuff in your area and you type in and you say hey I need my uh my driveway powerwashed right and Jev could take in that information and then it could take in a lot of the input stuff of like all of the other businesses in the area and it could like return um percentages of which one would probably be the best fit for you.

So you type in a form and then you get an instant match with a company that's like near you. Could require it's obviously a little bit more complicated under that, but the Jev could do stuff like that where whenever you uh you know the forms that you always see when you're trying to sign up for a website and it's like get an instant quote and it's never instant and it always is like we'll email you by end of day. then a lot of that stuff could be put into like a classifier and it could genuinely be an instant quote that they could get to say, "Hey, this is a good match.

Hey, this isn't a good match." And Jev could be uh used to do that. And that's why the speed of Jev is nice because then that client could see you're not wasting the client's time, which if that client does become your client in the future, that's an insane insanely good uh virtue signal to say, "Hey, we're not trying to waste your time. We're not trying to waste our time. Let's get this done and work on it together." So, >> what other Jev use cases do you want to show? >> Let me see.

I was I was messing around with this um and it doesn't seem to be doing well, but I wanted to see if I could hook Jev up to a Bitcoin signal. So, basically every minute it would run and it would have this decision mix right here where it would tell me to buy, hold, or sell. And it does not seem to be doing well, which shows that this model is great, but it does have some regressions. I would not put this model in front of like your stock portfolio or Bitcoin or anything like that.

This is just for like routing or other sort of decisions like that where it doesn't need insane and model intelligence. Like I did a test with this with GPT6 Astra, the OpenAI's latest frontier model, and it did a little bit better than this because it cross referenced some news information and everything like that. But that's it's it's completely uh it's not apples to apples comparison. it's apples to oranges because it's just a different type of model.

So that's where it's like this is something that a classifier and decision maker could be used to do but it's not the best in all of the situations and everything. And >> I also let me see if I can find it. Yeah, right here. >> I want to test out. So, I made a little I made a YouTube video here where everyone who makes content is aware of this issue where you make content and you make a like a longer form YouTube video or something like that, but you want clips.

And the cool part about this is so this right here, I'm dragging and dropping in a video file. And what this process is going to do, and I'll explain it really quick, is it's going to transcribe the video and get a like word level transcript of it. And then it's going to pass that entire thing into Jev with some different classifier decisions to find the best clips. And we'll get to see how quickly it works. Paste it in.

It prepares audio and scores 17 moments and around like 3 seconds. And each of these moments are like one of the interesting parts of the video. They're not like the filler text where I'm like, "So, um, I'm going to set this up." It's like, "Let's go ahead and watch that. It's flying. It's absolutely fine. We've got 1.1 million tokens." yada yada yada. So, it allowed me in this uh demo to be able to find the best clips that I could publish on short form content.

So, honestly, and I worked on this for maybe 10 minutes. So, if you worked on this and iterated on this to create your own startup with this type of idea, you could probably get pretty far, especially if you combined it with other different AI agent types. So, that way you could have a really good clipping sort of uh feel on it. But there's so many different uh ideas that you could come up with this. And honestly, the best way that I've thought about it is if you just think about it for like a night in the morning, you'll be buzzing with ideas like, "Oh, I could do this.

I could do this." Um, I don't know if I already showed this one, but the browser use uh for browser control with Jev is pretty insane, too. I'm going to play this clip right here. This is in real time uh done by the browser control guys or the browser use guys where this is Jev controlling this browser to pick a flight from Zurich to London in 7.1 seconds. Let's watch it. This is all real time by the way. So selecting the dates and it found a flight in 7.1 seconds.

If you asked any other sort of like browser use AI agent, this probably would have taken a minute, two minutes, even three minutes in the same type of regard. >> Yeah, that's a big deal. That's a really big deal. Um, if people want to get set up with Jev, how do they do it? >> So, so Jev right now is on a wait list, but by the time this video drops, it might be out in general accessibility. But if you want instant access to it, you can go to the Verscell gateway and they have Jev available on it right away.

So you can just instantly start testing it out. They've added some stuff into their AI package so you can start messing around with it. But honestly, if you ask your AI agent and drop it this link and the type safe AI to say, "Hey, how can I start experimenting with Jev, you can probably get started right away." And that's a great way to get started, too. any type of AI agent, you could talk to it about your business and with Jev and say, "Hey, what sort of workflows do I do on the daily basis that could benefit from a decision maker like Jev?" That's a that's a huge tip.

I appreciate that. Um, I'll include link uh in the show notes in the description for where you can go and and and play around with this. I'll also include links for where you can follow Ryan. He's got a criminally underfollowed YouTube channel. I think it's like a thousand subs. >> I know. >> Um, it's crazy. So, uh, I'll include that as well. Ryan, thank you so much for coming on. You know what I'm doing after this?

I'm I'm going to this versel link. I'm going to play with Jev. I'm going to start classifying some stuff. I Let me caution you though. It is dangerously It is dangerously addictive. The amount that once you see the speed and once you see the price, you will just be like, "Holy cow." And to all of you guys watching at home or listening, please just try it out. It's so cheap, you won't even notice. It will be like like 1/ one,000 of a cent type of thing to test it out.

It is so cheap. Please test it out. This is a new type of AI. If you've ever done any sort of classification or if you just want to build something for your own email or other system, try it out. It's so fun to use and the experience with it. It's just going to be mind-blowing because I don't think we've seen AI this fast in a long time. >> All right. Can't wait to play with it. Thanks everyone for your time. Ryan, you're a legend.

Uh, and I'll see you next time. See you.