I’m using Jev more than Opus 5.5 or GPT-6. Here’s why. transcript

Jev, Jev, Jev. Welcome to Jev week on how I AI. We have seen a lot of new models be released in the last 5 days. We saw Opus 55. We saw GPT6 Soul, GPT6 Luna. Muse is blowing up the timeline. Everybody still loves their Grock bots. And yet there is one thing that I want to talk about in AI right now and that is this fast cheap doesn't speak system one decision model from Typesafe AI. As soon as I saw this trending on X, as soon as I saw it launched, I immediately started testing it.

Now, what I will say is more than any other model I've experienced lately, Jev has been the one that has exploded use cases, personal productivity use cases, code use cases, product use cases. Today I'm going to give you a very quick whirlwind tour of what Jeb is, what it will give you back and what it won't, and then how I have used it over the last week to do work that I think is worth hundreds of thousands if not millions of dollars.

And I have probably spent sub $10 on Jev tokens. A lot of it has been subsidized because Jeb is currently free on the AI gateway by Versel. But even at list price, it is a very inexpensive model. It is a very effective model and it is going to be in the middle of almost everything I build from here on out. [music] So, let's get to it. This episode is brought to you by Open Art Arena, the global leaderboard for creative intelligence.

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See the rankings at Open Art Arena. So if I were to explain Jev to you, I would go to this table on the type blog post announcing Jev. And it basically compares normal LLMs on the left, Jev style LLMs on the right. Both take in unstructured data as inputs. Both take in text as inputs. Jev does not take in images, but it takes in text and it takes in text descriptions of images if you really need to get there. The outputs though are very different.

With the standard LLMs that you're used to working with, you are getting strings and generated text out. So you're getting text in, text out. With Jev, you're getting text in, type safe values out. And what I mean by type safe values is these are values that are predefined that then Jev picks from and selects and returns to you. We will show you what those values are, but essentially they're like it's this or that. It's yes or no.

It's 1 through 10. Like it's pretty simple. Now it sounds simple, but it is incredibly powerful when you put it against the right problem. The other thing that you will notice about Jeff is it is cheap AF and it is fast AF. And so if you look at the current cost of input and output tokens, it can be pennies to tens or hundreds of dollars per million output tokens. Jev only charges you on input tokens because it barely outputs anything.

And it is 4 cents per million input tokens. It is like dirt freaking cheap. And because they output basically nothing, they don't even charge you for output tokens. Um whereas the standard LM are going to charge you tons for the output tokens. And then I'll go into use cases, which is how you would use like a standard LLM versus why you would use Jev. You know, standard LMS, chat bots where you want text in, you want text out.

Coding where you want, you know, code to be produced. Um, and so it's it's great for things where you need stuff generated, but Jev is really great for making decisions. That's ultimately I've been calling this it's like a decision model. So you give it a decision and it makes a decision and and it's high agency I would say. So if you need smart if statements, should I go left or right? If it's good, put it to this person.

If it's bad, send it to support. All that kind of stuff Jeff is really good at. It's really good at classifying data. That's a lot of what I'm going to talk to you about today. And it's really good at real time. So, it is fast, fast, fast. So, you can put an LLM in a real-time loop in a way that was not performant enough with these other models. I do want to explain exactly what Jev outputs. And so, I would highly recommend if you're going to use Jev, go to their docs.

The two places that I'm using the most are the primitives, so what it returns. And then I use some of the patterns in cookbooks. So what will Jev return to you? It will essentially return one of three things. It will return a choice which means you can give it text, you can give it a list of choices and it will pick a choice. So, if my question is, what should I wear on this date and my choice is a dress, jeans, workout clothes, ski gear, or nothing, it'll like maybe pick the dress, right?

And so that is choice. If I select score, it's going to say like how severe is this or on a score from one to five, what it is. This is really good for severity rankings. So say the example here is a great one which is like if you get a bug, you want to rank it like cosmetic, broken or blocking, Jev will triage that bug and give it a score. And then finally, there's a new. It's a version of a boolean. It basically tells you what the likelihood that the answer to a question is yes.

So if the answer to a question is yes or no, is Claire a podcaster? it's going to give you like a 99% nule because there's a 99% chance that I am a podcaster. Okay, so this is how you know you would think about the three things it can return. And again, very simple, but if you've been a software engineer, this is like 90% of software engineering is like doing these things, returning a choice, scoring something, routing, saying yes or no.

And so it is just so so so powerful. And the number one thing I have been using it for is classification of a vast sets of unstructured data that would have been annoying to classify but is very high value. So I'm going to show you those use cases and hopefully this will inspire you about how you can use Jev. Okay. So I'm going to pull up Codeex and show you like two or three examples that are super simple to run, incredibly cheap, and incredibly powerful.

This is one CTO's, VPs of product, V, you know, chief product officers, listen up. This is the one that three years ago I would have paid truly $100,000 for. So what I had Jev do is look at thousands of PRs. I got PRs. I have connection to to GitHub. Pull every single PR. And then what I had it do, which would have been so impractical in the past, is I said categorize all the PRs. And so use Jev to do pair-wise relations.

And so this is something that I found Jev is really good at is compare like P to PRB and say, are you the same? Are you working on the same thematic area or not? Again, Jev's not going to tell you what the thematic area is unless you give it choices. is it's going to say like yes these two are related or no they're not and I've been doing this a lot where I'm taking vast amounts of data and I'm saying like yes related not related yes related not related and it's doing clustering which I find very very useful so I create these clusters of data sets and that's what Jev did and then I used a really cheap model I use Gemini flash light to then take those clusters and categorize them and I first ran it on our marketing site where we only have like 112 PRs.

It cost me 1.1 cent. I guess they wouldn't round down that.1 penny. And you can see here, it lets me see how much my work is related to maintenance, content, tools, site, and conversion services or docs. Super useful. Again, it ran very, very, very fast and very cheap. But then I ran it on my chat PRD app, which has about 2,000 PRs on it to date in this calendar year. because it has a lot more PRs. It cost me a lot more money.

And by a lot more money, I mean nine whole cents. It cost me 9 cents to do this. It took probably about 2 minutes. What it did is it analyzed 1,700 PRs. It did 17 found 17,000 pairs in those PRs of things that could be matched. And then the themes were labeled by Gemini Flash Light. And it pulled all of the data out. I mean, again, CTO's, CEOs, like I know you're asked this by the board and by your team and by your boss all the time, like what percentage of work is going to what initiatives?

Like, tell me the percentage to tech debt. Tell me the percentage to this product or that product. And you can see here it got down. It excluded our docs PRs because those are completely separate. almost 30% of the effort the PRs that we do at Chat Pured are around platform security and infrastructure. on being a good citizen and investing in security performance and infrastructure and then of course because we're a chatbot a conversational AI reliability and then some product areas that we invest in are like data and integrations document editing and prototyping which is new and so this was so cheap so fast it is accurate like I can just tell you yes this is where we're investing our time and you can even see as we invest more in different things over the last couple months it goes up.

And so this is just an example of how powerful, cheap, and good Jev can be on large data sets. And then I want to give you all one that even if you don't have like a big repo with PRs everywhere that you can run, which is you can run this on your local cloud code and codec sessions. So all of your cloud code and codec sessions are stored locally. And so you can actually run this analysis on everything stored on your local machine.

So I did that while Codex kicked off a thread to do that. And you can see depending on what you count as like effort how much I'm spending on different things. So I do a lot of product engineering. And so for grouping by user turns in January I was almost exclusively doing engineering tasks in cloud code and codecs. And then as you kind of like come into this new world now in September less than 40% it looks like of my tasks are actually engineering tasks.

I'm doing a lot more work with agents which makes a lot of sense. And then I'm doing a lot more like publishing media. I'm doing a lot of our videos through. And then you can see I have a new business. So client delivery, family and personal stuff. And then where it couldn't categorize or it was unclear. If I take it to session days, you can see again most of my sessions were coding. And now it's like more equally split across different use cases.

And so if you think about this, it is just super useful to do meta analysis on all this data that's sitting on your desktop that LMS can totally parse. that will cost you basically no money and give you a lot of insight that I think previously would have been hard to get. So these are two use cases I think everybody who is coding should do. The third one and I won't show it because it's a lot of my private information is I did run this on my personal Gmail.

So I basically said like given a subject line and a snippet categorize whether or not I can like delete this email like score whether or not I can delete this email. It did it very fast and then it gave me very clean tags that I could go through and then have another model work through categorized emails. And so this is where I would say like Jev alone is okay. Jev with an LLM buddy is super powerful. So what I like to do with Jab is I like to take a big corpus of information, tag it, categorize it, cluster it, filter it, and then apply really precise AI actions to the right clusters.

And so that could be take your bugs, take your high severity ones and really triage them deeply. It could be take your emails, group them into like you can definitely delete them and ignore them, delete them, and then work your other emails with an agent. It's just all those things that you can do where a very fast but accurate filter can be helpful. So, I think this is so powerful. I hope it unlocks your minds on what Jev can do.

I want to show you like the biggest version of this that I've done just to like kind of give you numbers and ideas of what I'm working on and then I'll show a couple like fun little apps that you can build with Jev that I think would have been hard to build before. So the example I want to give is you all have heard me talk about this product. So chat PD I'm trying to build like a product insights graph. Basically, I'm like trying to suck in everybody's data and tell you really interesting insights about it.

And I have used every frontier model there is to try to figure out. And it's just really hard. There's just too much nuance in all the data to kind of like gro all this, get it in structured format, do the right like structured, unstructured. It's super expensive. I've spent thousands of dollars if not tens of thousands of dollars trying to prototype this. It's just hard. And I think I cracked this baby with Jev. I kind of want to show you what that means, which is it's not that I like Jev has done it all at all.

It is actually I have figured out where classification, clustering, and mapping are important and I put Jeff there. And then I figure out where like the big brains like analysis, strategy, insights, extraction, and I put like Astra there. And then like where generation is important, I use like a soul or a Luna. And so if you look at this map and it's like probably not that interesting to you all because it's very internal to chat pd but you can see like I extract things, I extract labels, I then quickly assign everything to those labels with Jev, then I pair those clusters in those groups with Astra and I say like what the heck's going on across all of this.

And then it extracts more insights and all of a sudden I have this really cool product. And just to give you a sense of like the scope of data here, I'm probably pulling about a,00and um, 1100 individual signals into this. So these are PRs, these are support tickets, these are granola conversations, these are um, linear tickets. They're like kind of like all the signals in our business about what people are worried about and what is going on.

And then I'm getting now like this gap between what are customers telling us they want and what are we actually working on and I can get those trends over time I can get them categorized I can get details about them so it's just really super fascinating very helpful has made this feature I would say probably more margin accretive to my business if you all know what that means because before I was really like trying to brute force with these like brainy models, what I needed to work on and how I could do this data.

And now I can like combine these like very cheap decision models like Jev with a really brainy model and get this like interesting completely hard to generate set of data context like auto wiki. It's like really unlocked this product and it has taken about,00 raw sources of data and done over like 200,000 classification and pair-wise groupings and it probably cost me four bucks on the Jeff side. It's cost me a lot more on the Astra side.

But I just think like thinking through where classification like super smart classification clustering decisions could unlock really complex products and how you might use as I'm showing here Jev alongside some smarter models. It's like really blowing my mind right now and I think is it should be interesting for those out there building interesting data products. So, so far I've told you like what Jeb is, how you can use it on PR review or data analysis, how you can use it on your local sessions, how you could even use it on your email, but I'm going to show you two things that I built with it in an afternoon that I think are really cool.

And this is my attempt to like go against the like eyepopping demos that you're seeing on X. I think people have shown interesting things, but they actually haven't shown how you have to build it and how it works to get that real-time effect. And so, I'm going to show you these apps live because I think it's important to kind of like understand where the latency comes from and what the real experience is as opposed to like kind of a 30-se secondond demo that goes goes viral.

But I do think it shows some like pretty cool stuff. So the first one I want to do is how I AI audience signals. So you wonderful people give us comments on the how I AI podcast and I go through them. I read them every day and I reply to them when I can, but I've never done analysis on them. And there's about 4,500 comments and I just wanted to know like are they good, are they bad, are they happy, are they sad? And I also wanted to know if you all had any episode ideas for me where I could extract them and come up with episodes that I could do like this Jeff one.

And so I hooked up the YouTube V3 API. You just have to enable it in Google console. And then I said pull all the comments and categorize them into positive, negative or neutral. So that would be a choice, right? Or a score probably. And then I said also use Jev to identify whether or not includes a idea for a future episode and then analyze all the data and give me a dashboard to look at the data. And so you can see here about half are positive.

There are 58 comments in here with episode ideas and then there's like quality praise which is praise for our production team which is like how nice the podcast looks. And so then it made me this dashboard which is telling me is it positive, neutral, negative or mixed. And then it gave me an audience request board. So you all want like openw weight models, you want instinct, you really want a comparison of Grock and Muse for personal use.

That's coming soon. And then do I use Grock or Brockbot or like what am I using? And then you can also see by episode the sentiment which is Ryan's very popular three-step AI coding workflow. 61% positive. We had an awesome cloud code for product managers episode. 80% positive comments. And then we can actually go through the comments themselves. And I using Jev built live search against these comments. So, if I wanted to find comments about screen share, it would search and find very quickly something that says screen share.

If I wanted to say, "What are the comments about slop?" I could do slop and then very quickly it scanned all 4,400 and found all the comments that are related to slop. So, again, this is very performant, very fast. just for like behind the scenes, you have to like batch the results and then score them and then push the high scores up to get that kind of performance. So, there is some architecture here. I don't want to pretend like Jev you just like slap Jev in the middle and evaluate all 4,400 that quickly, but you can imagine this is very useful.

And so, one, thanks for all the comments. And two, this was really, really cool to build. And then finally, I have to give Astra props. Really nailed the Howa AI podcast styling. So, good job on the front end, but I want to show one last one before I get us out of here. And then I said this was Jev week. So, like and subscribe for a second follow-up Jev episode with one of our How Ia guests that's going to come later this week.

But I wanted to show like kind of a fun realtime use case for Jev. So if you have been seeing any of these like real time app applications of Jev like playing a video game or you know playing playing Tetris doing live search all these things you can do a lot of real time stuff where making a very quick decision or returning a set of choices can be very powerful. Okay, so I built a real time app that takes in voice and then uses Jev to determine what color is like my emotion and then returns a quote in reflection of my emotion.

It uses OpenAI's real time voice API. It uses Jev and then it uses like API ninja quote API. There's apparently an API for quotes. And so we're going to see if this works and how fast it is. I'm feeling so tired today. I'm feeling very happy today. Okay, the quotes. Oh, there we see the quote worked. Okay, hold on. Oh, it's excited. I'm in love today. I can't wait for the weekend. Doing this podcast is the best job ever.

Look at that. So, we got all these Okay, I'm going to turn it off because it's going to like keep listening to me. Look at all these quotes where like immediately it picked the right color. It picked the right quote. You can tell the quote API has a little latency in it, but you can imagine how fun it would be to be able to build these real time experiences. And again, when it's like pick the right color to match the sentence, it can do that really well.

Just for you all to know kind of how it works, I gave it a list of hex values. So, a list of colors. Every sentence or like phrase it ingests from the real-time API, it asks what color or it scores the colors. It gives the top ranking score color and then it also picks from a set list of filters for the quote API. So it filters the quotes and then it scores them against my sentiment and then it shows it on the screen.

So again like running locally not the fastest. I'm sure I could optimize it by caching a bunch of this stuff and you know doing all sorts of things but again like a little like magic use case that I think is really indicative of the kinds of things you can build with Jeb. So that is my Jev 101. How to use it on your own data how to use it with other models and how to build some fun realtime experiences in your app intro.

Again, as I said, this is Jev episode number one this week. So, we're going to have another one midweek with one of our most popular guests. So, if you are excited about that episode, subscribe, comment, let me know what you want us to talk about, what questions I can answer about Jeb. And until then, it has been so nice showing you my favorite new model. Thanks for joining How I AI. Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube, or even better, leave us a comment with your thoughts.

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