9 things you’ll actually do with Jev transcript

Everyone is talking about Jev. >> It is so fast. Watch this. That is not sped up. That is actually real time. >> But what can you do with it and what does it do? We're going to get into it. >> 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. >> My friend, I have watched endless YouTube videos and talked to people about how they're using Jev.

I've got the best use cases that you and I can actually use. I'm so excited to show them to you. >> Presented by Zapier, the AI automation company. >> Let's get into the first one and this one is just categorizing email. >> Watch how fast Jev is going through 500 of my emails. And so it's running through all my emails. It's gone through 80, 90, 100, 110, 120, 130. And you can see here that it's actually categorizing every single email.

It's deciding when we need to respond. There's a lot of emails I need to respond to today. I'm not that great at checking my email. >> [clears throat] >> 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 can so see this tagging my email, making helping me figure out what I should be responding to, how I could respond to it and then actually at times passing it on to a smarter LLM that can actually suggest what to write.

But this is what it's great at, categorizing and speed and doing it inexpensively. Let's go into the next example that I want you to see. This is actually before we get into an example, this is how to install it, how to get it started. I hate how many people in all these videos just get going without telling me how. Turns out you just have to go to typesafe.ai and then here's how to connect it to Codex. It's very similar to connect it to everything else.

Let's take it away, Lucas. >> available to you. But in order to get this started, all we have to do is go over here to this quick start and you can just copy this agent prompt and then you can open up any agent you want. In this case, I'm going to be using Codex, but you can use Claude code, you can use Rock bot as well. And you want to open up a new workspace. My case is Jeff and we just want to paste in that prompt that we got from that website and then hit submit.

And also another thing that you have to do is you have to generate an API key. So you can just click on this over here and create a new key and then you can copy and paste that to like a simple plain text file where you store your API keys. >> One thing that you should be aware of is they went into wait list and they took off the wait list and you can sign up immediately. Now they're on the wait list again. It just goes back and forth.

Keep trying it and if you need direct access to it and it's not available, it is available through open router without having to do this whole waiting list nonsense. But keep checking the site. I think it's great and it's worthwhile. All right, next. I set an example. Let's get into an example. This is my absolute favorite one and we'll get into more practical ones, but what um Moritz did is he had it create a web browser.

Boy, did I catch him in a like weird pause here. He created a web browser that you can speak to. Here's the prompt. I'm going to 2x the speed because he's talking kind of slowly into it. >> Okay, for the first example, I want to build a voice controlled web browser. So let's write the prompt here. I want you to build an app um using the Jeff API. It should be a web app uh where I can record or I can speak into it. Automatically transcribes what I say and controls the web browser for me.

So I can say for example, open this website, click on this link, etc. and the browser just does all of that in real time. Okay, okay, this is done. So let's check it out. The dashboard control page is here. Um we can let's put it >> Okay, so on the right is the dashboard control. Now let's see the browser and how he's interacting with Wikipedia in it. >> and start trying this out so I can click here start and then should be recording my voice.

Okay, go to wikipedia.org. Okay, it's working. Okay, click on the first link. Okay, click on the a fairy syncs link. Nice. Scroll down the page a little bit. >> Okay, I'm going to pause it there a bit more. >> You get the picture. The idea is that it's acting pretty quickly by analyzing what's on the page. He did say later on in his video, and I'll have a link to his video and all the other videos below, but he did say that that at other times it was working faster, that he thought that there was something going on with his internet when he did this demo.

We've seen a few other examples of this where people have gone and breezed through it quickly. Let's understand how it works, and I pulled this clip from Moritz's video. Let's Can I hit play? >> So, think of it in in three parts that passes a note down the line many times a second. So, your voice becomes text while you're still talking. The dashboard page in Chrome listens to your microphone and turns speech into text.

Okay, that makes sense. And then the next step is Jeff answers a quiz about that text. Okay. Um each time a new fragment arrives, the node server looks at the controlled browser window. So, it looks at what's in the browser, makes a short list of what's on the page. Okay, like up to 100 things like there's a link which says new, there's a button which says search search. And it sends that list plus your words to Jeff, okay, with a fixed set of multiple choice and yes and no questions.

Like, what does the user want? Like, do I want to navigate? Do I want to search, click, type, and so on. Which element on the page do they mean? Which website are they talking about? Is the sentence finished yet? Um is the user even talking to the browser or to someone in the room? Okay, interesting. Um and which is action buy, delete, or send something? Okay, cool. And after that then plain code decides and acts. So, the server has a handful of if statements with thresholds.

If is this is a command is below 0.5 ignore it. So, these are the the probability that are basically returned. If it's under 0.5 ignore it and if is the sentence finished is below 0.6 wait for more words and so on. Okay, so it has like a bunch of rules and based on the probabilities that it's it's getting back it's making those decisions and like acting on it basically. And all of that is happening in real time. And so, the thing here is that if an LLM were to make this decision it would take a lot longer to actually arrive at that decision, right?

Because it needs to reason through the steps. It would need to like look at everything that's there and then kind of reason through it. Whereas Jeff is just it gets all of that data and then it can make very fast decisions on what probability is it that this button should be clicked and then it just goes and and clicks that button. Okay. >> to I'm going to oversimplify it and say that Jev turns everything into a multiple-choice test where most things like Opus turns it into an essay where you're almost scored based on how much information you know and how much information you share.

And the fact that it's just making decisions like that means that it's going quicker. Obviously oversimplified. Let me know in the comments if if you've got a better analogy here. Let's tell you about Zapier. Listen to this. I know a lot of you are skipping through some of my Zapier ads. I want you to know why this is the one to never skip because Zapier now has Jev integration in it and here's why this is important.

Jev makes decisions like it could look at your calendar and say is this another nudnik who's now posting that you have to like do you ever get those people who are spamming your calendar with you've got a voicemail or you've got to show up to this webinar or whatever? Zapier can send each one of your calendar entries over to Jev which will then decide is this spam or not quickly. And because Jev is inexpensive and Zapier is pulling of this is inexpensive, you've got a very inexpensive decision maker.

And that's just one way to use it. This is so live that the CTO of Zapier actually rushed it out and said to the whole team, "We got to get this up." They did get it up, but it's not even in Google yet. So, when I Googled for the link, I couldn't find it. I got it directly from Zapier, my sponsor, and I'll put it for you in the description so you can go directly in and try it with Zapier. Zapier makes you happier. Let's move on to the next.

Here's the next example. You know, everyone tells you send some things over to the expensive model, some things over to the inexpensive faster model. How do you do it? It's always been a pain in the, you know. Let's get into what Jason >> really a quick and cost-effective way for us to automate model routing up until Jev. And so, to set this up, you can just use this prompt for you to get started. And just to give you a visual demo of the test that I set up.

Essentially, what I asked Claude to do is to do a comparison of around 12 prompts with Jev and another one where it's running with 5.1 every time. And you can see here that because of Jev and the fact that it's actually routing to the right model depending on the task, it actually resulted to 70% savings because nine out of those 12 tasks never needed the top model anyway. So, that is quite useful, but I'll >> Okay, he's got the prompt in his video.

Of course, I'll link to it. The other thing that he said in his video is create a skill for yourself that will allow you to turn on this um this Jev integration and turn it off. So, it could be like a {slash} Jev. When you turn it on, it will decide what to send where and sometimes you just turn it off and you can focus on sticking with Fable because that's what you want. All right, really good video. And by the way, if you like this kind of analysis where I find you the best use cases out of what, hundreds of videos?

I've got uh well, I've got more coming up. So, subscribe. I'm working so hard on these. Next. This is email tree Excuse me. Yeah, this is customer support triage um where you know how when email comes in to your customer support, somebody has to decide, is this something that we can just ignore? Is this something we can send one of our automated responses to, which you can see here on the bottom, or do we need a human being to respond to it?

It's been really tough to do that in the past and expensive. What Jeff does is handles it quickly. This video from uh Mayank, I thought is really good, but because of his accent, too many people are missing it. Let me show you a little bit of the video. >> Subject, third time asking. This is the third time asking about my broken export. >> Yeah, you know, that you feel that, right? As a human being, you see that and you go, "Third time asking about my broken export." You understand this cannot have an automated message. >> This is unacceptable if it is unfixed this week.

So, let's triage the ticket. Jeff will quickly send it to human queue. Queue, basically it should be sent to human, okay? And that is what happened. >> Fraction of a fraction of a penny is what it cost to do that. And let's take a look at a race between Jeff and Kimmi. Kimmi, of course, being not equally inexpensive, but also inexpensive. Let's take a look at that. >> And if I triage the ticket, pay attention. So, Jeff got it very nicely.

Kimmi is still taking time. So, it is still taking this much time in real part to get you the answer. So, pay attention. Jeff speed, 7.2x, 1.16 versus 8.38. Jeff cost advantage, 16x. And >> Yeah. And one of the things that he said was, "Use Jeff for the routing part because it can't write messages." And then in his case, I think he used Kimmi to draft the replies when they were necessary. So, it's it's not a do-all, as you heard at the beginning of the video.

Boy, I'm talking fast, right? Like if you see a model, talk fast, you almost want to talk faster. Okay. All right. This one is actually going to take a little bit of time for you to understand, but I think it's such a good use case. What he said is that he wanted to create a Typeform competitor, but it's more than that. What David is doing is he's saying, "I have people who are coming in and filling out a qualification form to see whether they should be working directly with me.

I don't want to have them qualified later on and then say to the people who are the right fit, send them a Calendly link. To the people who are not, add them to my email newsletter drip, or whatever it is. He needs to make the decision instantly. And that was expensive and slow before Jeff. And so he's going into his um uh he's going in and at and asking what was he using? I think Claude or Opus uh to do to build this. >> Let's listen in.

We build a Typeform competitor. Basically, an intelligent form that ranks the candidates, the applicants in real time. Basically, it's solving my own problem when you know, I have a waitlist or have I'm hiring for a position. You really cannot have a LM reason over it and take 10, 15, 20 seconds for a candidate until you decide whether to show them a Calendly or not. But 100 milliseconds does nothing, right? >> Let's see it.

Let's see it. >> Help me build a basically a competitor to Typeform where we ask a couple of questions and the app is kind of split in like a 60/40 view where on the left is the 60% you have the form and we can ask like three or four questions. And then on the right we actually see like the admin view of what's happening with this new AI model from Type Safe AI. Okay, it's also misspelled here. Boom. So obviously I'm doing >> You see that what he's doing is dictating his prompt, right? >> dictation to be faster.

We can nicely visually see the probabilities it's assigning for each user whether he's like highly qualified, qualified or like a mediocre or disqualified, right? >> Okay. All right. Now, let's see it in action. What you're going to see is on the left is the the form and on the right is the analysis. >> uh me as a potential lead. So obviously I didn't know it was for a business automation service. So let's uh let's do a founder executive.

Continue. Let's see how that changes things. Okay, slightly more qualified. Let's do 20,000 plus. Okay, qualified now. Not more qualified. Uh would you like to make it happen as soon as possible? So now we should be even Yeah, so we went from qualified to highly qualified. Nice. So, obviously my first response wasn't the the best because I didn't give Astra the criteria. It just kind of figured that out. But, yeah, this is how Jeff works. >> All right, it's really good at making decisions like that quickly.

Let me ask you something, and this is not one of these BS ask you a question so you just comment. Um, but I really want to know. You know I had to adjust this table so that you get the view of behind me and the whole thing looks good. Meanwhile, David over here has got nothing but a white background. He's got his Breathe Right nasal strip still on his nose. Am I putting too much effort into my background, and should I just be getting right into it?

Let me know in the comments what you think. On to the next. Here is Moritz again. This time what he's showing you is how he organizes all the files that his AI keeps on what he's doing so that he has a good internal memory system, and how using Jeff he can get at his data faster and cheaper. Here's how his files are organized. >> A bit more context. My the way my Claudia OS works is, you know, it's essentially a folder which has a couple of subfolders, and one of those subfolders is this memory folder which has a bunch of daily memory files where whenever I chat with my AI from inside of this folder, it kind of saves what I've been chatting about.

And so, this kind of builds this like knowledge base. And all of these other folders here are are things that I've built over time, so it contains a lot of memory. >> And this is the kind of stuff that we were all told just uh what was it 2 months ago when everyone was talking about second brain, add more data, markdown file this, markdown file that. The problem with that is now your LLM in order your agent in order to get you an answer to something has to search through all of it, which was really expensive.

Let's take a look at what Jeff does with that. What's the current offer positioning? >> He just asked him a question. >> It's kind of waiting and asking Jeff. Okay, so now it did the recall, and it returned what you can see here. So, um it says 2,756 tokens versus 13,000. So, this request took almost 80% fewer tokens because we're using Jeff. And it says here the cost of this recall was $0.00297, okay? And um it answers the question.

So, it was able to answer the question. Okay. >> What I really like about Morris's video was he he didn't fully understand this, but he did give us an explanation of how everything worked by basically going back into the AI that he was using to build this. I forget which one he was using. And then saying, "Now, tell me why did this work? How did it work?" And here's the answer that he got. >> I asked for a quick explanation here.

So, the way it was before, Claudia's memory is a folder of markdown files. And the problem is that it's very inefficient the way the LLM is doing it. So, it's basically when I ask a question, it's like trying to guess which file the answer is in based on the title of the file. So, it's you know, doing a search for like keywords and file names, basically. And then it's reading the entire file to see if the answer is actually inside of that file.

And then, yeah, it's basically hoping that it's right, which is, you know, pretty bad. And if it's writing memory, it's actually even worse. So, when the agent learns something new, it should append something, but it will never really know which file it should actually append it to. And usually it will just, I guess, append it to the daily memory file. Yes. >> So, these are the problems. Okay, so how work with the new system that uh now includes Jeff.

So, it doesn't write text. You give it some is the files, a list of sections, a new bullet, and a batch of small typed questions. Yes or no, pick one, score. Okay, so these are like kind of the criteria. And it answers all of them in one request in a few hundred milliseconds with probabilities. Okay, so it's a judge and not a judge and the code does the counting, chunking, and and that's all makes a small semantic calls.

So, the way I understand is this new system that using Jeff is just able to much more easily find the right place where the memory is and it's kind of like doing this search like in parallel and get getting like all of these data points and then it's able to pinpoint the right file where the memory is located much more efficiently and much >> One of the things that I love about him is that he keeps calling it Jeff. I I know he's trying to say Jeff.

He's seen it. Um and I said earlier I didn't know what he was using. He's using cursor and he's using Fable 5.1 in there um to build it and to analyze. Let's go on to the next video. Here is Greg Eisenberg doing an interview with someone who's using it for video. >> 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 Jeff with some different classifier decisions to find the best clips and we'll get to see how quickly it works. >> I wonder if I should be using it to find my clips. >> Paste it in, it prepares audio and scores 17 moments in around like 3 seconds.

And each of these moments are like one of the interesting parts of the video. They're not >> The frustration that I have with this video is that he didn't show the clips. I've seen people for years talk about how great Opus is at using AI to find the right clips in your long-form content. Meanwhile, I've tried it so many times and it's always junk. They the people who created, I believe, do not sit and watch it. I don't know if this is any good.

I would have loved for him to say, "This is what it does. Now, let me hit play and show you." Um okay. But, it's worth a shot for me to try it, too. Oh, you know what? Let's take a look at this quick hit here. >> That's pretty slick. Now, the last example I have here is with my smart home. So, I hooked this up to the Home Assistant MCP, and I actually have a light back there that is uh controlled by Home Assistant. So, I can ask it to turn it on and off.

So, I'll say, "Turn off the top bulb." Instant. >> [laughter] >> And so, there's no there's no movie magic here. This is the other cool thing about how fast this is. Like, if I were to ask an LLM this, it might respond with a tool call and then have to call the tool call. This took 300 milliseconds. >> Now, most people will not think this is amazing. A few people who have Alexa-run devices, like lights in their homes, will know exactly why this is amazing.

How many times do I say, "Alexa, turn on the outdoor lights." and it [clears throat] just waits. And I'm like, "Did I get it right?" Meanwhile, I have my guests over and do that Will the light go on? Will it go off? And do I look like a fool? And for a few seconds, I do look like a fool. And then, even when it works, all they remember is Andrew does this foolish thing instead of just hitting the switch, which works instantly.

This is fast, and I should say, I don't think this is the right use for Jev, though. I actually interviewed the founder of Needle, which is similar to how Jev works, but it's so small that you can put it on device that I would love to see Alec love to see the A-word put it into its own device and make it work. And if they won't over at Amazon, you could into the software and the hardware that you build. But, essentially, what we're looking at here is a much better way to interact with devices like this, because it's a yes or no.

Light on, yes or no. Let's go on. Final one um from Eric Seu here. No, no, this is the penultimate, the one before. What I like about Eric here is he is not running it in Claude. He is is running it in a regular tool. What you're seeing up on the screen is um uh Grokbot. On the left is the chat, on the right is the setup for Grokbot where you give it a name, where you give it a description of what it does, and so on.

Um and what he's using it for is an understanding of content ideas that will help him with search engine optimization and answer uh agent answer age engine optimization. Okay, here goes. >> So, I've added a massive upgrade to my AEO SEO bot inside of Grokbot. So, you can see what this does is it looks for ideas to mine from basically all the content that I've been putting out or maybe internal calls that we've been having or customer calls, sales calls, things like that.

It looks for content spikes, right? And it gives me a set of ideas to look >> Okay, so it can do that. >> And so, it gave 40 ideas here. And what Jev did was it evaluated. It said, "Hey, do we already have similar content?" So, here's 40 ideas. It shortlisted 10. Um so, it eliminated 30 off the off off the the the bat. And it said, "Hey, um you know, is this type of content already live on your website already?" Okay, right?

And so, I can kind of fix the evals here, but this is its first pass at it and I didn't really give it any guidance. So, so it saying, "Hey, this is already live." It gives the justification for it. And then from here, I'm just like, "Okay, I can I just quickly look at it." And I can say, "Okay, well, um let's just go for these over here." Also, but here's a compare table. And we can just see that um now it's it's basically drafting it and it's putting it into uh here the the the drafts.

I can just review it afterwards. I'm so >> his video. I didn't know that I could add in codex into my Grok bot or Opus and all the all the Anthropic models until I complained that my bill was crazy last week and then a bunch of people said, you can do it and none of them told me how. They just said, go ask your agent and they were right, too. Any explanation they would have given me would have been a a waste of time.

I just asked my agent, you do the same thing with this. It'll take it on and it'll add it. All you need to know from here is it is possible and it can actually help you with your content ideas for search engine optimization or other uses. Finally, let's look at what it doesn't do well. I like this fail. I love all fails, they teach a lot. Here we are. >> I wanted to see if I could hook Jeff 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 >> Okay, fair enough. I think that he could have done a little bit better with this, but still I like the the I mean I I don't think that him tweaking it what was it a day after the thing launched was enough for him to understand whether it could do it or not, but I do think that the big lesson here is don't just give it your stock portfolio and expect it to do to do magic on it.

It's a decision-making machine based on your criteria that let's face it, not that you give it, but that your agent gives it. All right, if you like this, I have another video for you right on the screen. I'm looking forward to you subscribing and seeing more of these reaction videos that I do and I'll see you in the next video that's on the screen.