Speaker 1Jev is here, and it's a big deal. It was created by Diogo Almeida. Yes, that's the same guy whose research built ChatGPT. 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?
Speaker 2We'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, and it's going to be a lot of fun. 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.
Speaker 1Cool, and 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 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, just like 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. 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?
Speaker 2I 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, 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 take traditional AI where it's like they're having 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, 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 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 you have this iPhone as an input, 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 sugarcoating 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 a range. If you will. So it's like some emails are more spammy, like this 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, 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 informative. And it's just informing me that 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, cause this is a real email, but 90% account violation possibility. This is a user saying, Hey, my account seems to be violated somehow. Jeff 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 categorized and there are 1,700 of these emails. 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 gonna take like 10 hours to do and then and then I'm gonna have to go through and probably pick out some of the data and oh my god the price is gonna be so expensive and oh it it's done oh it didn't cost 18 cents or 1,700 emails that is the power of Jeff. I can't explain it any better. Then 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 me. You can see here. They're all categorized. They're all ranked. They're all given that score.
Speaker 1So 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 enter. I have a good mental model for what Jevin. I have a good mental model for what Jevin. And correct me you know where I'm wrong. OK. So Jeff is basically like an A.I. decision maker. Yes. 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. Jeff is 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. But you can also have it do things like. You know is this customer likely to buy or unlikely to buy.
Speaker 2Exactly right. You're almost there. That's like 90 percent correct. It makes it. It makes a probability of a decision. OK. So the difference between it making a decision because a decision would be you like you 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 percent by 17 percent. No buy type of thing. And obviously the the the answer that is the stronger percentage would win and that would get returned to you. But it's not a 100 percent decisive action type of thing.
Speaker 1OK. 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 Jeff. I mean you ask Jeff like you know read this email and choose a set of actions. So like reply or escalate and then you get like a choice from Jeff and that gives you some sort of confidence score. Is that the way to think about it.
Speaker 2It's kind of so we've been the the LLMs that we know nowadays have like corrupted our minds so much because there's an interesting point you said you said ask Jeff. You don't really ask Jeff because Jeff isn't a text model. What's really interesting if you look at the actual spec of Jeff it doesn't generate any text at all which you're like OK. That's kind of weird. 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 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. I don't know. We'll have our email. All right. And this will be our email input. And then we'll we'll do a circle for Jeff. Jeff Jeff seems like a circle guy. I would say Jeff.
Speaker 1There's the entertainment. You promised. There we go.
Speaker 2Jeff seems like a circle. That's. that's just the type of guy that jeff seems like okay maybe a tiny circle there we go tiny circle because it's fast you know it's fast and cheap okay so we've got our email and that goes in to jeff it doesn't get asked to jeff it doesn't you're not asking jeff hey what should i do with this email it's just an input like an eight like a standard api and you define a schema up here and we'll have a we'll have like a simple little schema and be like is spam and that can be a what they call a newel 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 know about this so it could be a one to zero which means that it could be 0.31 or it could be i don't know i don't know like nine zero 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 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
Speaker 1a fancy word for how a database is organized right it's just how the database is organized
Speaker 2but 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 nother 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 newel 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 it's just a fancy word for how a database is organized 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 hmm 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 jeff 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
Speaker 1exactly that's the way to think about it that's the way i'm starting to think it's a decision
Speaker 2model 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 because 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 computer i'm typing on my computer 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 jeff so if we go back to our 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 null 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 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 can 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's like you can talk to it and it's a conversation it's a decision-based llm which we've kind of learned
Speaker 1so i guess that i mean that begs the question around what should i use jev 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 side time 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
Speaker 2right 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 a thousand dollars a month to this you could dedicate like five dollars like when we got uh when our open code team got set up on this account we had like a five dollar like i guess like intro uh credit i guess on the account we were able to use that for two 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 three 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 solicitation solicitation spam because she has a contact form on her website so she's using jev to 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 zero 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 reply to that but then if you get someone who's like 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 love the 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 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 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 jev 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 ask 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 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 is and what the output is and what the output is 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
Speaker 1this you know ai traffic cop so there's information that needs to come in and then jev is going to 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 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 Coca-Cola. It's the CMO of Coca-Cola. But if it was, you know, the confidence score is lower, but also like, you know, local business in Orlando, maybe it's you automate it or use an LLM to do something, draft something up or send something. Or the confidence is so low that you just ignore it. So am I getting that right?
Speaker 2Yeah, 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.
Speaker 1So 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.
Speaker 2Oh, me too. My brain is always, always thinking the next way to do it. Do something like this.
Speaker 1So 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, like, what do I mean by a queue?
Speaker 2I mean, like you've got like a lot of inbound coming in. Exactly. People need stuff from you and you need to get them routed to the correct person. Exactly. 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 power washed. 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. It 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 can be put into like a classifier and it could genuinely be an instant quote that they could get to say, Hey, this is what I want. 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 signals 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.
Speaker 1So what other Jev use cases do you want to show?
Speaker 2Let 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 GPT six Astra, the open AI'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 comparisons, 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. 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 three 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 Jeff 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 Jeff controlling this browser to pick a flight from Zurich to London and 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.
Speaker 1Yeah, that's a big deal. That's a really big deal. Um, if people want to get set up with Jeff, how do they do it?
Speaker 2So, so Jeff 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 Vercel gateway and they have Jeff 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 Jeff? You can probably get started right away. And that's a great way to get started to any type of AI agent. You can probably get started right away. You could talk to it about your business and with Jeff and say, Hey, what sort of workflows do I do on the daily basis that could benefit from a decision maker like Jeff?
Speaker 1That's it. That's a huge tip. I appreciate that. Um, I'll include link, uh, in the show notes, in the description where you can go in and, and, and, and play around with this. Also include links for where you can follow Ryan. He's got a criminally under followed YouTube channel. I think it's like a thousand subs.
Speaker 2I know.
Speaker 1Um, 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 for selling. I'm going to play with Jeff. I'm going to start classifying some stuff.
Speaker 2I, 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. We like, like one, one thousandth of a cent type of thing to test it out. It is. It's 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.
Speaker 1All 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.
Speaker 2See ya.
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