Jev AI Just Dropped, And.…

0:00 Jev just dropped and it is the world's fastest model and 400 times cheaper than GPT6 Astro. It is a fundamentally different type of AI that you need to understand and we're going to cover

0:10 exactly what it is and five incredibly powerful use cases that you can use today to get light years ahead even if you're a complete beginner and also when you definitely shouldn't use it. Now,

0:20 first thing to understand is that it is significantly cheaper than the Frontier models. I'll show you what that looks like in a second throughout this video. Now, six reasons why we actually care

0:29 about this. Number one, it gives you unbelievably fast decisions. Of course, guys, grab the coffee. Very, very, very cheap. Super important to bear in mind. Its

0:38 outputs are 100% free. Yeah, exactly. 100% free. It can do parallel questions, so everything at the same time. It can actually also measure certainty as well as give you a very specific output. And

0:50 also, it looks at typed answers. Now to really understand how Jev actually works, it was created by the f one of the co-founders of track GPT and they've been building in silence with the doors

1:02 locked and the windows closed for a couple of years and then Jeff popped out and a company called type safe. It's a fundamentally different way of thinking about AI. Obviously we only care about

1:12 what does it actually mean and therefore what changes. Now essentially it can give you an output in three different ways. It can give you a very simple yes do it, no don't do it. It can give you a

1:22 choice. So if you preload it with different things that it can choose from and it can also give you a score. So a rank from one to 100 and many different ways. So these are the three ways that

1:31 it can decide what to do. But the cool thing here is that when we combine it with GPT6 Astra with Claude Fable 5.1 and Jeff by using some rules and a free skill I'm going down below you can grab

1:43 completely for free. It'll be the second link in the description. We can effectively supercharge get results way faster that are going to be significantly cheaper. Now with this Jev

1:51 can do some crazy stuff. It can play games. It can play anything cuz all it's doing frame by frame is deciding from these different buttons WD which is where to move and also jump. What is the

2:01 most optimum decision for me to make? So you can use Jev to do anything like this like play games in real time. You can for example use something like this where it's lightning fast where it can

2:10 actually based on many different classifying criteria decide what the best image would be or what the best message would be. Now on top of that what else can you do? This is an

2:19 internal link dealer and look at how fast it's actually filtering it through. But guys, I'm not here just to show you video games. I'm here to show you real life use cases that are going to blow

2:28 your mind. Now, let's kick off with five levels. We're going to go from inbox, community, slop. We're going to do loads of really interesting stuff. But let's begin with level one. So, I pulled

2:37 together a specific test. And what we're going to do here is we're going to have YouTube comments and we're going to have a series of different tests. I'm going to compare this with Astra to show you

2:45 how this works. So, this is a spam and scam filter. So effectively what we do is connect Jev to let's say it could be anything right it could be for example our agentic operating system here I can

2:56 ask questions power up and see everything I want to or I could just literally connect it to chat GPT or claude if I want to now what I'm going to do here I've given it 20 different uh

3:05 emails and some are scams and some are spam and you're going to see how quick they are identifying that going to run both models we can see the time here look how fast Jev is going that was

3:14 complete in 3.41 41 seconds. Astra, it took well, it's still going to be fair. And I put the scores here so we can identify it. 9.93 seconds. So, it was three times faster. And if we look at

3:25 the cost for Jev, uh, that was about 1 cent. Okay, so it finished three times faster. And for Astra, that was significantly more expensive. Oh, and I should say by 1 cent if you were to do

3:36 this over a,000 emails. And over a,000 emails for Astra, you're looking at just under $5. Let's take a look at this one. This is ownership. Again, let's run both models and see how it does that. This

3:45 one is essentially saying go through my emails and identify who should earn what. Again, Jev is just significantly faster and it has once again a perfect score. It would have cost you 2 cents

3:55 with Jev and $75 with Astra. You get the idea. They've got ones for urgency. Got ones kind of identify good buying signal. And look at how fast that is, guys. 2.57 seconds over $1,000 emails

4:09 that will cost you a cent. What can you buy with this ads? And again, Astra comes in at 10 seconds and 31. So the point here is that we can actually now just connect and give Astra hands. So

4:20 essentially with the skill that I built, we just give it to Astra and effectively when that task could be done by Jev, we can do it significantly faster and at a way cheaper rate, which takes us nicely

4:30 onto level two. So could it for example in a customer environment could it identify I don't know from a member post exactly how risky they are at potentially churning based on what

4:40 they're saying. So say for example you have a CRM with your client and you want to give it loads of data loads of different data and insights. It could actually calculate a score about how

4:49 likely it is that individual is going to leave that employee is going to hand a resignation anything that you want to. So this here is a churn risk. I've given it loads of stuff like hey I got my

4:58 first automation ship. This is a message and it's just going to quickly identify how likely it is to do it. This is all fictitious stuff and again it was done in 2.82 seconds and significantly

5:07 cheaper. You can do the same thing with member intakes with support routting anything that you want to. You can see effectively we're getting the right ticket to the right queue. So we have

5:16 people dropping messages asking questions and let's just say that we need to triage this and say hey great uh this is a billing question this is an access question. This is a tech

5:24 question. We can use Jev and Astra together to go ahead and solve that. Same with intake forms, right? It's cool. I want to build something useful. What's their goal? Are they vague? They

5:33 build it. They want clients. And again, Jev can do that for you straight away. And again, the big time save here, guys, is speed and cost. That's basically it. Speed and cost. And the reason why it

5:43 works this way is because Astro and other models like it, like anything else, like Claude, have been designed to give you words. Now, Jeff doesn't give you language like that. It's not been

5:53 optimized in that way. Hence why it can do things in a completely different way. Now level three is a completely different one. This is going to be the ability for it to identify AI slop. And

6:02 by the way, if you're wondering, Jev is accessible via open routter. So all we're going to do is just ask to do that. You give it your open routter key and it can call Jab effectively whenever

6:10 you want to. That practically is how you're going to use it. Unless you're doing something like this where you have say an agentic operating system and you're asking questions. I can come down

6:18 here have a conversation. And if you just come down to this free link down below, just give this over to Astra or Claude and effectively it tells you when you should bring in Jev when not and

6:26 just give it your API key and you'll be fully connected. So let's actually test its ability to identify slop. Now this is one of my favorite use cases. So check this one out for example. This guy

6:34 built a real time slop detector. So you know like LinkedIn's been overtaken by slop a little bit, right? Well, this will scroll through Twitter and you can actually say hey great if you identify

6:43 anything that sounds like it was written by Claude Fable 5.1 just add slop and remove it from my feed. You could do this for ads. And the reason why this was so revolutionary is cuz it can do it

6:52 at a rate that no other model can in terms of its speed. And it's so cheap to do that. Now, when you make something very cheap and very fast, you genuinely unlock new use cases like this. So, you

7:01 could just say, "Hey, has this been written by a person? If so, keep it. If not, get rid of it at the same time." So, for example, what we can do here is identify if something is slop or not.

7:10 Uh, you can do this to identify AI text and everything. So, I'm using the slot monster, which is Get Ripper that I built. It's free. I'll put link down below. It's like built off all the best

7:18 slop detectors with some additional bells and whistles that make it really cool. So, what we're going to do here is let's look at illustrative cost. Now, for Astra to do this, you're looking at

7:26 $15. Jev is 4 cents, which is crazy. We're looking at words, phrases, punctuation, rhythm, and proof. So, let's see if it can identify slop. Okay, I've given it a same text here. Let's

7:36 run this comparison real quick and see. Look at this. Reads as slop. And it it correctly identified the punctuation cadence and it came down using my slopology framework. Okay. And it did it

7:47 at lightning fast speed. Let's give it another example. Okay. So here's another one for example. Let's run this comparison. See again reads a slop. Then we come down and test whether or not

7:55 it's written by a human or was it written by an AI. Let's go ahead and see if this one works there. And again we can try this one more time. Now as you can see it's likely AI assisted. Astra

8:04 itself can't tell you. So this is another really cool use case. Let's say that you have a website. I'm just going to throw this in here to my Aentic OS for a second. Right. This website here

8:12 we built with GPT6 Astra. Now, let's say that you're doing this for a client. Maybe you want to go ahead and do different things. You can edit it. Whatever you want to do here is fine.

8:20 But let's say, for example, you have this design lab, right? And we have a brief and we wanted to identify. Okay, I fed this guys over 300 design systems and I'm basically assessing their

8:31 ability to identify what the best one would be. Well, let's run the comparison check. And we can pick any of the astro reasoning. I'm going with medium just to keep it fair. Um, I'm not even going

8:39 extra high. Bam. It's found one immediately off the different references. It's gone through over 300 pages. That would only cost you $1.94 if you did a,000. Whereas Astra, for

8:50 example, $500 to do the same thing, which is crazy. Let me give you another brief example here. If I had a studio portfolio, okay, and I run the comparison. Let's see what

9:00 Jeff does. Jeff found it. And let's see what Astra does. Jeff found it in a second. Ridiculous. Astra is still going. And look, it found the exact same thing. And if you look at the

9:09 difference, um, $1.95 per $1,500. You get the idea. We could use Jev itself to classify anything you want. But if this all sounds guys like I'm speaking design Spanish, I'm going to

9:20 put a link down below for you to my full uh cla code and Astro Masterass. You get full access to my entire Aentic operating system as well as my entire courses on learning this technology,

9:30 building direct system, building beautiful websites, how to use it to get light years ahead of everybody else. I'll put a link down below before your competitors actually grab it. So, for

9:39 example, if it just needs simple and straightforward tasks, it goes to a light model. If it's general tasks with a little more context, we send it to a balanced model like an Opus 5.1,

9:48 something like that. But then, if it needs complex reasoning, we can send it to Frontier. Well, what we can do is add Dr. Jeff in the middle of it. When did he get his doctor? Somewhere between the

9:57 beginning and this part of the video. Now, check this out. What I've done here, for example, is I've given a a query. We're going to run it and we're going to see basically which models it

10:06 would rank it and push it through to. As you can see, it's identified. And if you look guys, the exact things. So, what I did with this is I basically added a bit of a description. I did all the models

10:15 Dan Hazy, GBT Astro 6, uh, Gemini 3.8, Flash, Kimmy K3, and all of them had a kind of a brief like rooted this model under these circumstances. And you'll notice that it did that exact thing for

10:26 us in the exact same order perfectly. It's it's really crazy. Let's give another example. a 650k video research pack. I run it again. Let's see how fast Jev does. Again, under half a second.

10:36 Ash was going all the way through. Now, the idea here isn't to have Jev solve anything. What I'm demonstrating with this is that Jev itself is the hand. It's a tool that we're going to use with

10:46 Astra. And when you give it prompt, you'll be able to set that up. Now, in terms of Jev's intelligence, and this is really important to understand, it's about as smart as like a Sonnet 5. So,

10:54 it's like smart. It's quite smart. It's not frontier level, but we're not using it for that. So the times where we do want to use it is whenever and this is the key distinction guys. It is one of

11:04 these three things. Whenever there is a yes no whenever it can pick an option and whenever there's a score. Now you could use any of the models you want to for this. What we're saying here is it's

11:12 significantly faster and significantly cheaper. It's not something that is a chat model. This is a model that's optimized for making incredibly fast micro decisions and people are blowing

11:23 up with their creativity on this. As you can see people are generating UIs very quickly. Effectively, you're only really limited by imagination. But what I would say to you is really simply this. If you

11:33 can actually reduce anything down to a series of decisions and you can quantify those decisions, you can accomplish some pretty spectacular things. But it does bring us onto an interesting question

11:42 and that's that speed and price are one thing, but capability is something completely different. Which is why the next thing we need to do is learn how to 10x your capability by watching this

11:51 video here