JEV: How It Works and What You Can Build

We have huge news in the world of AI. A company called Type-safe just released a new type of model that's very different from the other models I talk about regularly like GPT-6 Astra and Fable 5.1. This new model is called Jev and

it's not a chatbot. It can't even write a sentence. It simply makes smart decisions way faster and way cheaper than any of the other models. In this video, I'm going to show you three examples of tools I built with this new

model Jev and I'm also going to explain the 10 things you need to know about this model before you start using it. So, I just had Claude create this app for me that uses Jev. Watch how fast Jev is going through 500 of my emails. And

is going through 500 of my emails. And so, it is running through all my emails. It's gone through 80, 90, 100, 110, 120, 130. And you can see here that it's categorizing every single email. It's deciding when we need to respond. There's a lot of emails I need

respond. There's a lot of emails I need to respond to today. I'm not that great at checking my email. And you can see here it's going through all of these and it is done. It has gone through 500 out of 500 emails and then

it creates this pie chart of all of the different types of emails that I've received. And you might be thinking, "Okay, Riley. That seems pretty cool. But like, what the hell is it?" And don't worry. We are going to be talking about the 10 things that you need to know about Jev in order to start using it. The first thing I want to talk

it. The first thing I want to talk about, however, is the launch video. So, a few days ago, Diogo Almeida, he said, "After co-inventing ChatGPT, I kept asking myself, why have superhuman chat models not led to AGI? I've spent the

last 2 years in stealth building a new way to train models, RLCD, and a new type of frontier AI model that we are releasing today, Jev. It is 20 to 200 times faster, 40 to 400 times cheaper,

optimized for decisions. And we'll get into what that means in just a second. Right before I filmed this video, I created an AI agent that has a model router that uses Jev. So, based on my input, it will use Jev to immediately

decide which model it should use. And I created this in a single prompt using Claude. I'll show you the prompt in just a second, but let me show you how this works. Hey, I'm Riley. Now, this is a very simple request. It should use a very cheap model for this. There's no need to use a really powerful model. Watch what happens. I'm going to hit

need to use a really powerful model. Watch what happens. I'm going to hit send. And Jev immediately selected Nano, which is the smallest model. Let's go up the stack a little bit. So, I'm going to refresh. I'm going to say, "Hey,

I'm Riley. I want to build an app that uses API wrapper. Tell me the best way to do it." Now, I don't think this will be like frontier. I think this might be one level up. It might use a model

one level up. It might use a model slightly smarter than Nano. And look at that. It selected using Claude Sonnet 5, which is this balanced, right? There's tiny or nano, fast, balanced, and frontier. And I created this

created this agent that can search the web, it can generate files, it can write code. I created this in a single prompt, and it's pretty cool. And so, here it says, "Please" I'm going to say, "Please, can you generate all of the code for this. Make sure it's

perfect." Now, I don't think it's going to do the frontier. I haven't gotten it to select frontier. But as you could see here, it is 95% sure that it should be this balanced model. And what Jev is made for is to make really fast, high-quality decisions

for incredibly cheap. Okay, the first thing that I realized about Jev when I was talking to my friends about it, they said it's really good for a classification. So, what does classification even mean? Classification is a way of sorting an input into a few

boxes that you've named ahead of time. Right? If we were to have this set up on our email, it sorts them into categories. It's like a customer question, a sponsorship, or it's spam. This model is really good at accurately and quick quickly sorting it into a

predefined category. So, what is Type-Safe AI? And so, Type-Safe AI is the name of the company, Jev is the model. The guy who worked on ChatGPT left to start a company called Type-Safe, and then they just released their model called Jev. So, is Jev an

LLM? And so, they say, "No, it is not an LLM. It reads language like an LLM, but it never writes any." Right? That's That's why there's no output token cost because there is no output tokens. It just spits out a verdict and how sure it

is. The LLM predicts the next word one word at a time or one token at a time. Jev will just respond with, "Yes, it is 91% confident in yes." Or it is 90%

confident it falls into this category. Okay, so this is the most important thing that you need to understand about Jev when you're using it for the first time. And that is, how does it respond and what are the different types of responses? Let's go over all three types of responses.

So, this model will respond in three ways. It's either a choice, a score, or a null. A choice is like multiple choice, right? It fits into one of your predefined choices. That's how it does categories. Now, there's also a score.

So, you can create a scale, and you get to decide what the scale is, and I'll show you how that's created. And then there's also a null. How likely is this true? To illustrate the different types of outputs that Jev can have, I made some changes to my email app, which

gives us full customizability over the inputs and the outputs. So, here we can see that we can add a choice, we can add a score, or a null. Let's start with the simplest one, which is a null, right? Where the answer is just a probability of yes. So, remember, we're analyzing