Jev AI: 10 INSANE Use Cases

Julian Goldie (0:00)

Today, I'm going to show you 10 things, 10 use cases with GEV AI, and you can watch every single one of them. This includes an inbox that sorts itself whilst you look at it, a whole website lighting up as it links itself in a website, a task board that hands its own cards to the right AI agent to get stuff done, and a context meter dropping from nearly a million tokens to 86,000 in one second. And the model doing all of it can't even write a single word. This is Jev AI.

And Jev came out this week from a company called Type Safe AI. The man behind it is Yoga Amalda, one of the people who co-invented Chat Cheapers. He spent two years quietly building this, and the claim on it is 20 to 200 times faster than a normal AI model, and 40 to 400 times cheaper. Answers come back in about a tenth of a second too. So this is what it actually does in one breath, because you need this before the builds that I show you actually make sense. So how does this work? Basically, Jeff decides, you hand it a situation, you hand it a question with a set of answers, it picks one and it tells you how sure it is, right? It won't write your emails. It won't write your article. It won't even explain itself. It just picks. So Type Safe causes a system one model. So you want to think of this as the fast part of your brain. Red light, you stop, you don't write a paragraph about the red light first, right? Every agent you run right now uses the paragraph brain for everything. Every little which folder this is going in, every is this urgent, every who handles this next goes to a big model that reads everything. Thinks, writes, audits reasoning, and hands back a one word answer three seconds later. Now, Jev itself is built for that layer, and it changes what you can put on a screen because when a decision costs almost nothing, it takes almost no time. You can make thousands of those decisions live in front of you and watch the results move. Now, there's three different question types, and you'll see all of them in the builds I'm going to show you today.

Number one is choice, so you can pick from a list, hands back the pick, a probability for every option, and a confidence score. If you rate something against levels you set yourself, you get a number back, right? No is the second type of question. This is basically where you get yes and over, probability from to 1, 0.999 is almost certainly yes. Near 0.5 means it genuinely doesn't know and that's useful on its own. And you can ask all three about the same thing, one request, you come back together. So let's get straight into the 10 builds.

Each one is something you could sit and watch or build yourself. So number one, the inbox that sorts itself. Every email comes in as the situation. The options are your folders, reply, it needs research, wait for me. Jev picks and the email moves. On screen, you see 200 emails drop into folders, one after another like cards being dealt, and a small pile of maybe 12 landing in the actually Nisi pile. We're going to know the shape works and what it costs, right? Riley Brown actually ran the same situation. So he ran 500 emails for it, sorted it in seconds, and 3.5 cents for all of them, right? The bit that makes it usable is the confidence score.

Anything Jev is sure about moves on its own. Anything it's unsure about goes in the pile for you. So you're not checking 500 emails, you're checking the 12 emails I'm sure about. Let's talk about use case number two. So this is a keyword list coloring itself in. So you could take a keyword export, for example, for SEO, that might be like 2,000 rows, or it might be 20,000, right? Every row needs an intent, informational. Is it commercial? Is it transactional, et cetera, right? And a home, either an existing page or a new one for your website, right? So let's say we're talking about which pages to create content for. By the way, these are all keywords based on my Google Search Console data, and this is actually something that we're using.

You can see a full demo of it right here. So you can run it through Jeff and watch the spreadsheet fill with color from the top down. Blue for informational, green for commercial, orange for transactional, gray for the ones. It isn't sure about which is the column you probably actually read. The cost is basically nothing. So a developer called Hassan published and pushed 1,018 documents through Jeff, 1,018 research papers. To sort them into 24 categories, 8 cents total, a quarter of a second each. Swap documents for keywords, and the job is identical. I've got a keyword agent inside my setup. The pool's ideas are weak. Now sorting, what it finds was a boring part. But now we can automatically do it with something like Jeff AI. Number three, a lead board with confidence scores.