A (5:51)

Jordan, what's up, man? Thanks for having me.

B (5:53)

All right, I'm excited for this one live stream audience. Thank you for tuning in, everyone. Samuel from YouTube, Michael, Brian, Marie, everyone get your questions in. But before we dive into this concept of frontline workers, Al, can you tell us a little bit about Levy, what it is y' all do?

A (6:10)

Yeah, definitely. So at Levy, we're using vision and voice AI to reduce the cost of operating a hotel. We're building AI agents that will integrate with the hotel system to train new employees, inspect rooms for the hotel, even help submit maintenance tickets. All just do vision, voice AI that is meant for the frontline workers to use. The housekeepers, the housemen which help the housekeepers. Everyone who's kind of back office, who, you know, you really don't think about when you think of AI and really just like software in general.

B (6:40)

So I said. I said, this guy can pitch and there's. There's a reason for this. So first of all, thank you for everyone that put this event together. But as we talked about in the newsletter, I was a judge at a recent event from the Gen AI Collective, the Chicago chapter. They had the demo night at mhub, and Al and his company Levy were the winners of the competition. And one of the prizes was you get to come on the Everyday AI show. So, Al, talk a little bit about that experience. Because you know what, I see a trend here because every. Every event that I'm asked to, like, speak at or judge, it seems like you win, like, talk. Talk a little bit about that experience.

A (7:19)

Yeah, it was awesome. You know, I think a lot of really, really cool companies, and I think it's probably like one of the most impressive group of companies that I've had a chance to pitch against. What was cool about it was, I think half the companies I was texting my co founder who's in the audience, I was like, hey, we need to use this. Or like, hey, we got to get this tool. And then we go up and we're the only ones who are going with this frontline approach to AI and the way we're building, which is cool. I think it's also very special that we're here in Chicago. I said it to someone yesterday, but I think, like, Chicago is one of the only places where blue collar AI tools would win an AI pitch competition versus some other parts of the country. So I think it like, speaks to the city, speaks to the community that we have here. And it's pretty awesome.

B (8:02)

Yeah, absolutely. Like, I'm so hardcore Chicago, you know. Yeah, it's like. But you bring up a really good point, right? Because, yeah, if something's in, you know, Silicon Valley or if something's, you know, New York, East, east coast, like, I think it's so much different. But I do think that, you know, the blue collar, you know, frontline workers and how you were able to highlight how generative AI can, can be a part of that was great. So, yeah, shout out to everyone at the Gen AI Collective. Brennan Woodruff from Go Charlie. We got Jonathan there. Uh, you know, that helped, uh, put the event on. So thanks to everyone. But, uh, let's, let's get back to Levy real quick here. Al, how, how did you come up with this idea? It's very clever. I think it's something that can scale, which we're going to talk about. But where did you even get the idea for Levy?

A (8:51)

Yeah, so actually, funny story. I'll tell you the short version of it here. Friend came over to my place one time and goes, hey, your place is really clean. Right away I'm like, oh, isn't your place clean? Or isn't everyone's place clean? So my co founder and I had an idea of building a smart cleaning bottle. So this was like before Dyson even came out with the smart vacuum thing and everything. But we built a really cool product. Really, really bad company. This smart bottle had IME sensors and was really cool. Map of surfaces he cleaned. Ended up trying to figure out who I would sell it to. I get connected with a director at DoubleTree and in the very first call that I have with him, he goes, yeah, I want this. What are next steps? In the back of my head I was like, no, you don't. This is like a really crap. He was like, this is a really crappy product actually. Why do you want this? That's exactly what I said to him after he said that. And he started explaining me the issues going on with hotels, the challenges. And right away we started looking into why they were having those challenges. What we saw was that the tools that they were trying to arm the hotel workers with weren't meant for them. They were meant and built very much from what I like to think of a white collar perspective or someone who's used to working with technology. Not so much someone who is going and making beds all day or scrubbing toilets all day. It's like they just want to do a good job and go home. How do we help them do that and then help the hotel get the data, the info that they want? We decided to tackle it and figure out how to approach it. But I was born from one idea that led to another, that led to another, that led to another. And I'm sure there'll be a few more iterations here as we go.

B (10:24)

It's always funny to hear founders and I've had CEOs of Fortune 500 companies on the show before and I think great products, great ideas, great services start from something that were kind of, you know, maybe initially not that great. So it's cool to hear the, the origin story there, Al, but let's, let's go ahead and do this. So live stream audience, you're gonna get a little bit better of an idea, but maybe also, Al, if you can walk our podcast audience through this as well. So what we have here, a little demo video and I'll set it up. So essentially I believe this is, you know, someone who is cleaning a hotel room. They have the, the Levy app in hand and then they're going to get started. So maybe Al, just kind of walk us through as I play this quick one minute video and, you know, try to describe for our audience that isn't watching along exactly what's happening here.

A (11:18)

Yeah. So what most people don't know is after a hotel room is cleaned, has to be inspected before it can be checked back out to guests. What we've developed is a way that a housekeeper, after they clean the room, they can inspect it themselves and figure out what's wrong with it. All within AI native interface that has one button on the app and everything else is done again through Vision Voice. As they go through the room, all they do is take a 30 second video that's automatically verifying everything in the room, that everything's done correctly from an inventory perspective, from a brand standards perspective for the hotel, if it's not done correctly, they receive feedback right away on how to correct it. That way, if a hotel room is cleaned at 10 in the morning, by 10:01, it's inspected by 10:02, you can check in. So it's that I like to say that we're going to kill the 3pm Check in. It's like people are going to be able to check in as soon as they, as soon as the hotel room is ready. Yeah.

B (12:08)

And so like as an example, so this, in this demo, right, you have the Levy app, it's going around Showing a live video feed of the hotel room. And then here, you know, where I paused it, there's two water. Right. It looks like there's, it's kind of, kind of hard to see, but it looks like there's two taller water bott or I don't know what that is. There's two shorter ones. There's two glasses. Right. Can you talk about the importance of kind of these different checkpoints in a room and what this means in this example of a frontline worker, someone cleaning a hotel room? Like, help put into perspective why, you know, some of these details are important. Yeah.

A (12:44)

So, you know, big brands like Marriott, Hilton, they spend a lot of money making sure that they know exactly how to deliver the best experience for guests. And, you know, they develop what they call brand standards. So everything in the room is optimized and should be done a certain way. If it's not, it costs the money. It costs them brand equity, which again, they spend millions of dollars, billions of dollars building up. So things like having two water bottles in the exact same spot every time, people love that. You know, people pay money for that. There's a reason people are loyal to Marriott, loyal to, you know, the Ritz Carlton, which, you know, I aspire to be loyal to one day when the pocket, when the wallet's there. But yeah, you know, people pay a lot of money for that because they, they like the experience. And there's all these little things that go into that. Like, for example, you just said, Jordan, the two water bottles that are these nice glass water bottles are heavy. Just, you know, it's a big sign of quality. So, yeah, a lot of things like that.

B (13:37)

All right, so let's, let's continue playing, playing the little video here. So continuing to look around and check the hotel room, not just for cleanliness. Right. But to make sure everything's up to standard. So what, what happens here? I've seen this before, so I know what happens.

A (13:51)

But walk, for example, here, this is not placed correctly in the room. So before the housekeeper can continue moving through the room, they have to correct any errors or any placement errors that, that, that they might have completed. What I would say is all these rooms kind of look the same. And the reason we're so focused on helping these frontline teams is because they're already pushed to the max of your have to clean 16 rooms for the day and now you have to clean 20 rooms. You're going to start forgetting what the little nuances between each room are. So really, what we're trying to do is augment their ability, help them remember, help them get these rooms up to, you know, the standard that Marriott wants in a way that's easy for them. So in the example here, we see how this. It's actually this really nice, like, little lavender perfume that's supposed to be placed behind this outlet like that. It wasn't. So the mobile app that we have for the housekeepers prompts them, hey, this is what it should look like. They correct it, and then it verifies it for them, and they can continue, you know, about. Continue going about the scan.

B (14:50)

So I think. I think, you know, everyone kind of has an idea now of. Of how this can be useful. But I'm sure there's people out there thinking, al, like, this also, like, it sounds great, but at the same time, it seems like it might be terrible, right? Like, oh, it seems like Big Brother. Now I have to do extra work, right? To, like, check my homework. Like, are there downsides? Or, like, you know, what would you say to people that might say, like, oh, this is too much technology, too much AI?

A (15:20)

You know, the biggest thing that we've gotten, the biggest piece of kind of feedback or the best thing that we've seen is the adoption numbers go kind of through the roof with the teams that we've rolled it out to at these hotels. You know, the reality is everyone. No one wakes up going like, oh, I want to do a really bad job today at work. I think everyone wants to do a good job. And again, they want to do a good job. They want to do it in an easy way. So the first time we rolled this out, we trained a group of five housekeepers on how to use it. Awesome. They learned how to use it in about four minutes, which was unheard of. I thought that we had done something wrong because they learned it so fast. I was like, wait, hold on. What did we miss? But quite literally, the very first piece of feedback we got from one of them was, does this mean I'm not going to get yelled at anymore? And what they meant by that was, you know, when they make a mistake, their manager comes over to them. And now you talk about, like, Big Brother. The manager comes over to them and says, hey, you forgot the towel in this room. You forgot this. You got to go correct these mistakes. Like, the reality, again, is people want to do a good job the first time people want to do good work. How do we make it easy for them to do good work? How do we make it easy for them to not have to worry about, oh, this Room is a king junior suite and the other one was a king master suite. So the king master suite has an extra garbage can and two more towels. They all kind of look the same, you know, so it's like making it easy for them to do a good job. And again, I think at the end of the day, like everyone wants to do a good job. How do we make it easy for everyone to just do a good job and enjoy life?

B (16:53)

So, you know, I do want to get away from the hotel and talk about what this means beyond that. But a good question here from Michael joining us on YouTube. So he's saying, I'm just wondering what happens in different rooms or suites. Right. So he's saying he used to work in a 600 room hotel with like a hundred different unique setups. So yeah, how does that work for maybe some of these more complex hotel scenarios where there might be so many different, like, you know, elements of a brand standard, different layouts. Right. How does it work?

A (17:22)

Yes, I'm like trying to think through, like, I know all these hotels now, so I'm like, all right, 600 rooms, 100 unique layouts. Like, what brand was that? What's nice is that we can take what we learned from one room and apply it to the next. So for example, you know, we looked at like the water bottles in. I think that was like a common, common queen room that they'll call it. So let's say that in that room there is 34 point inspection and then you go to the next room and that room might have like a 40 point inspection, but half of those are the same as the other one. We can take what we learned from the first room and apply to the next. The nice thing is also that, you know, kind of like what Michael is saying here is all these setups might be a little unique. The layout isn't as important in terms of the room layout, I guess what would you call it? The square footage doesn't matter as much. What we got to learn is these unique setups, these unique brand standards. And a lot of it becomes applicable as we learn more from one or the other. And we can also learn quickly since hotels are higher turnover. Every time they do one of these scans. We're learning what should, what each room should look like and what we need to kind of keep an eye out for the next time. So that's one area where we're able to learn pretty quickly and adopt again what we learned from one room to the next.

B (18:35)

Yeah, I think this is a great visual use case. Right. Especially for those joining us live, that you can see how large language model powered computer vision and bringing that real time accessibility can actually make things a little bit easier. But Al, like let's now zoom out a little bit because I'm guessing the long term goal is probably beyond just hotels. How can this type of technology really help frontline workers? And maybe if you can. Right. Maybe just even describe, right. Like what are some of these common across industries? What are some of these common problems frontline workers are facing that generative AI can actually help tackle?

A (19:19)

Yeah. Then when we look at frontline industries as a whole, the labor shortage that we constantly hear about is very much a blue collar labor shortage. So I think when we look at where can we start building tools, where can we help people? Who needs help, what jobs need help? We look at frontline blue collar jobs, so it's like, okay, how do we help them? How do we build software? And I think that the pandemic kind of really opened a lot of people's eyes to how important frontline workers are in every industry. So when we look at how do we help them, how do we bring software to them? I think the most important thing is from a workflow perspective, how do you make it easy for someone who doesn't sit at a desk all day to use software in a way that works for them? You know, we talk about, I think someone I saw a comment about, like robotics and AI, we can't use it yet with a lot of these jobs because we don't have that foundational data. So it's like, how do we start gathering the data to even understand what's going on with a blue collar job? You know, like, what's going, where do people need help? Where are the challenges? And then from then from there, once we have this, you know, foundational data, we can start using these generative AI tools to run reports. Like, for example, what we're doing right now is as we use the vision, the computer vision, and we just added voice features. As we gather those notes, we can use LLMs to transcribe them, we can use LLMs to understand them and then create a maintenance ticket for something that might be broken all in just an automated workflow. So I think that's when we look at frontline workers, how do we help them? It's like, okay, let's gather the data, let's understand what's going on and then, you know, start again using these tools that are out there to provide support. They're the ones who need the Help right now more than any other industry.

B (21:03)

So one thing, and you know, we have to go there, I think in this conversation is, you know, and started out with some of the AI news today, right? Like all of these new innovations when it comes to robotics, when it comes to computer vision, when it comes to these world models, right? So you know, Sasika here from YouTube saying, you know, frontline workers could soon be replaced by humanoid robots, in my honest opinion, right. And there's obviously that fear, right, that a lot of these maybe blue collar jobs when it comes to robotics, when it comes to advanced computer visions right now these, you know, these humanoids have, you know, the world's most powerful large language models on board. But it seems like al, maybe there is this missing piece that you're trying to tap into where it's like, hey, maybe what's actually needed here is just better tools for frontline workers that maybe normally don't get access to them. Do you see that as the case?

A (22:02)

100%. Yeah, I think, yeah, the like there's a couple companies or not a couple. I think there's like some people who are like trying to roll out some of these robots to housekeeping teams. And I think if you were to see what these housekeeping teams do, you'd be like, oh my God, this is like insane. Like robots can't do this yet. It's a lot from like a dexterity perspective. I'm struggling to remember it. A friend of mine who has a different company mentioned it. But essentially the more mindless a task is, the harder or not mindless, the less you think about how you complete a task, the harder it is to actually replicate. With robots and AI, it's something paradox, someone might know it who's watching. But yeah, I think that's what's interesting. There is like number one, again, we don't have the data to really even understand how to approach these jobs. Number two, from a speed dexterity standpoint, the robots aren't there yet. At some point they will be, just not today. And then lastly, I think that people don't understand just how big the worker shortage is. I spoke to a Hilton in Hawaii recently and said 80% of their current workforce will age out in the next four years. And right now they have no way of replacing them. And I was like, in my head, I was like, oh crap, we can help with some of that. So I think from a robot perspective, in some situations that even be welcomed when they do come, we're just not there yet. It's okay. How do we provide relief today? And then at the same time they'll start building that data repository for the robots for the different AI tools that roll out? Because yeah, again, it's really from a perspective of how do we help teams, Dave, for that future? And one of the, I was kind of thinking through this question, I thought it might come up, but like 10 years ago, the idea of someone hosting a podcast for a living, people would have been like, what are you talking about? New technology creates these new opportunities or we're able to do this today. So I think as new technology rolls out in these frontline jobs, they'll create new jobs at the same time, which will be exciting.

B (24:05)

Al, it's great insights there, but also, you assume people like myself are making a living from this. But you know, a couple, a couple of good questions from the audience that I'll get to here in a second. So if you are tuning in live, please get your question in now. But what are the potential, you know, because I think we can see the, the benefit, right, of Levy and also just see the benefit of, of how and, and why generative AI can actually help, you know, frontline workers. But, you know, what are the potential downsides? What are the challenges and how are you tackling those? Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Genai. Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for ChatGPT training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to your everydayai.com partner to get in contact with our team or you can just click on the partner section of our website will help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on gen AI.

A (25:49)

Yeah. Oh man, good question. You know, I think what we focused on was making it easy and focused on hardware that was more ubiquitous than anything. And I think that's one of the things where we might face some challenges is originally going back to how we got to this idea, we had the smart cleaning bottle and then we pivoted for a two week period to wearables and we thought like, hey, let's get someone could wear some smart glasses and we'll build our software to work with these smart glasses. And right away they were like, absolutely not. We're not putting smart glasses on our teams. They're going to quit, they're going to go next door. There's a labor shortage, we don't need people quitting. So I was like, okay, bad idea, bad idea. How do we again bring the tech to someone in a way that's easy for them? And I think that's kind of a potential challenge that we could face is like, do people push back on using the phone or like the hardware? I think what's interesting also is it's not just with what we're doing, but frontline software and frontline tech in general. Today if you go start a job and they were to tell you to bring your own laptop, you'd be like, absolutely not. It's kind of like the standard that they give you a laptop right now one of the questions that we often get asked is, hey, are you supplying the phones? Are we giving them phones? Companies don't have that line item for frontline worker hardware yet because it's so new and it's an opportunity and a challenge at the same time. We're working with some partners now to get some bulk pricing on like $50 Samsung or not Samsung, like $50 Android phones. That'll be something interesting. Where can we navigate that successfully to where we start bringing hardware against the frontline workers where these businesses don't have a budget for yet? Because right now a lot of it is them using their own phone. And what's interesting also at times they would rather use their own phone than use two devices. So I think that's a potential challenger is how quickly will companies adapt and adopt the fact that they're going to need to supply their frontline workers with, with hardware in a sense.

B (28:06)

Yeah. And you know, a couple good questions here like Corey from YouTube saying, you know, talking about the glasses and even speaking of that. Right. Very timely. Poor Google glass was like 10 years too early. Right? But, but now we have, you know, Meta's Orion, we have Google's Project astra, we have OpenAI's live video API. Right. Like this really shaking up even in the last couple of days. Could, could, could you see that as, you know, even like an onboarding device? Right. Because Samuel was saying Here, like AI could certainly be useful for getting employees up to speed quickly when switching careers. How do you see this and do you see this, that the future form factor maybe just being these AR XR glasses for onboarding employees?

A (28:52)

Yeah, 100%. So that's part of what we, you know, right now we're focused on kind of this core job functionality. But the hotels that we're working with are already using it as a way to train new employees. And we don't have the full case study yet, it's actually underway. But we were able to get someone who was their first day on the job, they went into a hotel room, they cleaned it, they were trained on it, cleaned it within four rooms, they were able to be as accurate as someone would have been there six plus months. To give you an idea to train someone to get to that level, it usually takes about two weeks. So it takes about two weeks of training to get them there. Four rooms was before lunchtime on the first day. So they were able to be as good as someone who'd been there six months before lunch. The AR VR play is 100%. It's funny, when we were first raising money way back when that was actually in our pitch deck and I think people thought I was crazy. So I think there's like some people who are a little crazy like being the chat also. But that's exactly it. I think when we look at that as like, how do we again start collecting the data today? So these AR VR plays become a possibility in the near future. And I think that's what we're seeing already in like some industries that are using it. But I 100 agree that that's a thing of direction we had and we're already seeing the benefits from that sped up training.

B (30:14)

Yeah, great, great question here from Jonathan. Jonathan, shout out. Great, great job at the kind of emceeing the event there. But saying, you know, Levy as a use case is bigger than any single industry. When specialists find their job integrated with AI, they're often resistant. So al question from Jonathan here. How do you see Levy's philosophy applying more broadly to increasing AI adoption?

A (30:41)

Good question.

B (30:42)

We don't make it easy, right? Like 7:30am live. But yeah, how do you see that playing out?

A (30:53)

I think, you know, when I think like for me I was the reason I, like I'm obsessed with making people's jobs easier, like doing a good job and being able to go home is I remember first using gong. I don't know if you're familiar with gong, but As I say it, like I was a career salesperson. I remember I was very resistant to it because I was like, oh, why does this thing need to listen to my calls? Why is it going to take notes for me? Then I realized I no longer had to go and have a sales call and then go tell Salesforce that I had a sales call and then like actually do my follow ups. Like I streamlined everything so I was able to do a better job. And right now we're getting ready to raise money for levy in Q1. And when I tell part of our, what we're raising money for is for the education portion is like how do I educate the people? And it's like marketing education spend but like how do I educate the people that are, we're trying to get to use our product? How do we educate the buyers, you know, on the benefits, on how easy it is to use, how easy it is to adopt and how they can leverage it. I think that's, you know, part of it is like for me, resistance standpoint. I think it's a big education piece. And I mean that's kind of like the fun thing about being also a startup, being an innovative company is that there's no playbook for it. You kind of gotta try stuff and see what works, what doesn't work. But yeah, I think education and ultimately just getting people to figuring out how to be creative and getting people to use it. Like even we've toyed around with the idea of like gamification with some of this stuff and giving people scores they can see having them close their rings and getting that dopamine hit of closing your rings. So I think education and then guns, incentives that you know, tie into the, to the gamification part potentially are how you get people to do stuff like that.

B (32:39)

Yeah, no, I, I love that. I should also like update my like settings because I feel half the time I'm like sitting all day and then my rings like you closed your activity level. I'm like, I haven't moved. Right. Maybe I should have higher standards for myself. So, so al, I mean we, we, we've talked about a lot. We've talked about, you know, how Levy is, is currently using this in hotels, how that might translate outside to other frontline workers. And you know, I think it's important, right, because I, I think you said that, you know, 80% of the global workforce right now is considered frontline workers. So as, as we wrap up today's show, what's maybe the one most important takeaway that you Want people to understand when it comes to how gener and could continue to impact frontline workers.

A (33:28)

Yeah, I think the big thing that I would say takeaway here is like as we try and bring Gen to these frontline jobs is we think of like we sometimes, I think over complicate Gen AI a little bit where it's like it doesn't have to generate, you know, this crazy image like mid journey or anything like that. It's like can I just understand the data that you have? And like, you know, there's people who just like dump excel spreadsheets into ChatGPT and say give me some insights from here. But it's like on the frontline side, how do we get the data so we can start extrapolating insights from it, learn what's going on? I think that's where we look at use cases, look at the potential for Genai in these frontline industries. There's just so much we don't know and it's like going to these hotels sometime, I got to get them to buy into the crazy vision of like, hey, we're going to learn stuff that we have no idea. We don't even know what these opportunities are yet. We just got to have the data to begin to understand them. And my favorite one I said at the pitch night last night is we were able to figure out when Marriott was running out of inventory for those big water bottles before they were. And this is just my understanding the data and then seeing, okay, the reports are coming in from these room inspections. We see the blue water bottles which are part of the brand standard and oh wait, hold on. All of a sudden we don't see the blue water bottles and now we don't see blue water bottles again. We don't see the blue water bottles again. So now we have these data points of okay, all of a sudden we stop seeing them. What can we interpret using Genai, oh, we're out of these water bottles. Can we use an LLM to create an order form and submit it to our inventory for procurement? So it's like a lot of these things where from a genai perspective it's like we don't need to do. I think what is funny to say is I don't think we need to do anything crazy with Genai but it's all kind of crazy when we, I don't think we need to do anything crazy to have a major impact. Just like how do we get out of people's way who are working, make it easy for them to just do their job and go on to the next thing. And then we get the data, we handle the reporting, we do all these maintenance, ticketing things that right now take a long time for them. Especially when we look at these workforces that might not be the most used to using tech on the job or might not be like us, who are delivered on our phones or computers 10, 12 hours a day. How do we make it easy for them to use technology, see the benefits without really getting in the way?

B (35:50)

I think, I think today's conversation, Al, was an important one because, yeah, generative AI isn't just for, you know, you and me and knowledge workers, you know, trying to use chat, GBT to make sense of spreadsheets. It's for everyone and it's coming. And I think Levy is doing a great job at helping the transition, you know, from, from behind the computer to frontline workers. So, Al, thank you so much for joining the Everyday AI Show. We really appreciate your time and insights.

A (36:20)

Appreciate it. Thanks for having me. All right.

B (36:22)

And hey, as a reminder, y', all, that was a lot. Great conversation. I think this is somehow after 400 plus episodes. We haven't even covered this. And I, I, I think what we talked about today, whether you know it or not, even if you are not a frontline worker, I think it is going to really impact your daily interactions with the outside world. Right. So very important that I think you go check out your everydayai.com sign up for the free daily newsletter. If you want to know more about Levy, we're going to be including a link to their website in there as well. So thank you for joining us. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'. All.

A (37:03)

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic.

B (37:15)

Visit youreverydayai.com and sign up to our.

A (37:18)

Daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.