I want you to think about the very simple question, what is technology? It sounds overly simple for a podcast like this, but really take a moment and think through all of the implications of that question. What exactly is technology? It's going to be relevant for today's episode. We're talking with Chris Albon. Chris is one of the hosts of Partially Derivative, another very popular podcast that is dedicated to data science. They talk about machine learning. They talk about assistive intelligence and artificial intelligence. It's a fantastic show. You should go check it out. We're going to be talking about all of the incredible things that we can do with machine learning. If this is something that feels really distant from you, or if you feel like this is something that's way out of your reach. I want you especially to pay close attention to this episode. This is a three-part interview, so make sure you listen to all three parts. We talk about how easy it is to approach, especially in the third part. We talk about how easy it is to approach some of these concepts, and we give you some specific tools to use. More importantly, we're really talking about the fact that we're at the beginning, the beginning of the process. We're talking about how easy it is to approach some of these concepts, and we're talking about a viable tool, a full-time option for some of you who are going through your computer science programs, for example, right now. This may be a great option for you to start looking for jobs specializing in this market because there is a lot of growth potential for data science, for machine learning, for AI, and all of these things that you've heard about, but maybe you haven't really considered as a part of your job. Thank you so much for tuning in to today's episode. I'm developer T. Once again, I am Jonathan Cottrell. My goal on this show is to help you through some of the hardest parts of your career, help coach you through those things, and some of that may be understanding the implications of these massive new technologies like machine learning and things that we're doing in this space. So thank you again for tuning in. I'm going to get out of the way, and we're going to get into this interview with Chris Alban. Hi, Chris. Welcome to the show. Hi. Hi. I'm super glad you decided to join me. I've been listening to Partially Derivative to prepare for this interview. Oh, wow. You're the one. Well, you know, I have right now, the only thing I'm drinking right now is a watermelon bi drink. There's no alcohol in it, but it certainly is good. I enjoy it. I think it's pretty good. I'm actually drinking a LaCroix. One of those fancy watermelons. I'm drinking a lot of water drinks right now, so I'm also not drinking. Yeah, we have LaCroix in our home as well. We probably drink too much of it. It's kind of amazing, actually. You know, I think for me, I just need something to use to take a break. You code, you code, you code, you finish the function, you finish whatever you're working on, and then you get up, and I ended up finding myself getting up and eating food for no reason. Like, really no reason. Just a habit. Just a habit thing. Yeah, just like a smoke break, but I don't smoke, so now it's like a LaCroix break, and then I walk back, and then I start again. I think you need that rhythm of work, stop, like the Pomodoro method, but for flavored water. Yeah, absolutely. Well, I mean, we get into these rhythms no matter what we want to do. We're going to find some kind of repeated thing to do, and it's good to be at least aware. You can be aware of that, so you can shape it and make it at least a not unhealthy thing. If you can make it a healthy thing, then great, but at the very least, try to shape that into a healthy thing. That's actually something we talked about on the show before, about having healthy defaults or good defaults, these things that you kind of fall back to. I was finding myself not exercising as often as I wanted to, and so I stopped for a minute, and I thought, why am I not exercising? I realized that us going to the gym, my wife and I, we work together, so a lot of our days kind of follow the same shape, and my behavior and her behavior end up affecting each other. Whenever we would go to the gym would be whenever both of us felt like going to the gym, which as you know, as a data scientist, the overlap of that is going to be one out of four times, basically. We had a very low likelihood of ending up at the gym. We sat down and we talked about it. I said, I want to end up exercising significantly more than I do right now, and what I need is to make that kind of the default. We aren't going to ask whether or not we're going to the gym. We're going to ask whether or not we're not going to the gym. This simple shift actually changed our behavior a lot. Now, since then, my wife has gotten pregnant, so we don't go to the gym hardly ever. I've adjusted. I've adjusted to start working out at home, and believe it or not, I work out more at home than I ever did when I went to the gym. It's kind of a weird thing that's happening there. I used to run 50 miles a week, and it was totally the 2 p.m. default. I had two parts of my day. There would be this morning part where I'd get the one thing that I wanted to do out of my way. If there was one task, if I had to... If I had to load one thing up or clear my inbox or write 500 words or something like that, I'd get that done immediately, and then just fill up the rest of the space until 2 p.m., and then I would go and go for this two-hour, because I'm a really slow runner, and then you have to take a shower, and you have to drive there and all that kind of stuff. It would be this two-hour kind of thing in the middle of my day where I'm not doing anything, no developing, nothing. Then I come back, and then I would work again until the afternoon or the evening. Then I started a company, and then my schedule went out the window. Yeah, you can't stop at 2 o'clock just to go run anymore, right? Yeah, and I was flying a lot, and we were traveling a lot. We spent a lot of time in cabs and Airbnbs and all over the place. Then, of course, the problem is that there's no schedule, and so it's really hard to have no schedule but force yourself to go do it, because you arrive, you take an overnight flight. It's really your morning, but it's actually night. You can't say ... It's just you're totally out. The end result is I gained 15 pounds in a year or something like that, which I'm just now addressing. Getting back to running now. Yeah, just for the last two months, I've been getting back to running. It feels really nice getting back to that rhythm of doing that. It feels weird if I'm not running in the afternoon. Yeah. If it's four or five and I haven't run yet, I feel like I'm missing out. You did something wrong. Yeah, absolutely. Right, yeah. I have the same exact feeling, because I've gotten so used to ... I have a trainer. I have a bike trainer. If you don't know what this is and you like riding bikes, especially for me, I'm still over the weight where running doesn't hurt my knees. I'm not quite down to the point where I can run comfortably long enough to make a difference for my health. I get out of breath, but it's not a long distance. It's a long distance of running by the time I'm out of breath. I decided, okay, I'm going to bike until I can actually go out and run longer than two or three miles. Tennessee gets extremely hot, and it also gets cold enough that it's too cold to go out and ride. There's very few days that actually, again, find that medium, perfect temperature to go out and ride. These trainers, basically, they lift the back wheel up off the ground, and they put the resistance against the back wheel. It's like a magnetic roller, basically. They provide basically the same resistance you would have if you were really actually riding on asphalt or something. It's a really difficult, challenging thing. It's way more difficult than riding an indoor cycle or something. Yeah, that's cool. I've never heard of that. I don't know. Have you ever thought of doing some kind of riding? A riding desk thing or a walking desk or some kind of- I've thought about it, yeah. I have a standing desk at home and at work, and I've done pretty good about standing up. I've had some back pain in the past, and standing really alleviates quite a bit of that, but I've never actually tried the walking and working thing. I'd be interested to know if anybody else has tried it and really enjoyed it. Yeah, I've never really done it for like- I've done it for like- I've done it for like- one day or something like that, but I would be really interested in what would happen if you did it every single day while you were answering your emails for an hour, and you got- you know, like you walk three miles an hour, so you burn 300 calories just for nothing. Like no time wasted. You weren't doing some high cognitive activity that you really needed to sit down and think. You're just answering emails, so why don't you walk while you do it or something like that? One thing I've thought about doing- well, I'm sorry. One thing I have been doing with the bike trainer is actually- taking like a e-book, like in iBooks on my iPhone, and I'll throw it up on my TV screen using Apple- or AirPlay. Oh, yeah. So I can look up and still be riding my bike, and my phone is like sitting in my hands, but I don't have to look down at my phone and try to read, you know, all the way down on my phone. I have a pretty large screen in front of me, so I can actually get through a pretty good amount of a book, and it's kind of distracting. It's distracting me from writing, from the pain of the exercise. It's pretty cool. I highly recommend trying that out. This is very random, but last week I wanted to watch these videos on deep learning, and I didn't have time to do it at night, so I decided that I was going to hold my phone while I ran and run while watching the videos. I thought I was a real genius for like 20 minutes, and then I don't remember anything that happened in those videos. I don't know. It was just uncomfortable, and I was wondering if I fell, my phone's going to fly. It was weird. And also, I looked crazy as I was just running around in the desert with my phone out. But it's kind of like the same feeling that I get when I try to meditate, and I'm thinking about how good of a practice I'm doing, and now I'm thinking about all the benefits that I'm going to get from meditation, and I'm totally destroying the whole purpose. I'm going to destroy the whole purpose of meditating in the first place, which is to not think at all. It's straight down that line. Well, Chris, again, thank you so much for coming on the show. I want to kind of kick things off. I know we've already started and jumped in on this discussion about habits, but I'd love to kind of open up with you explaining some of your background. You're actually, I think you may be the first PhD that has been on the show. I've had so many episodes now that I'm not certain that that's true, but you're a PhD student. You're at least one of the very few PhDs that we've had on the show. Wow. People with doctorates, I guess, is the better way to put it. Are you identified as a PhD, or do you have, I guess you have a PhD? I think I say I have a PhD. I mean, I guess you could do doctorate. I don't know. It doesn't come up that much in my daily life. No one calls me doctor. Right. So part of the reason I mentioned that is because on your show, Partially Derivative, which we'll talk about in a second, part of the reason I want to tee this up is to say, okay, well, Chris, you have a ton of background. You have a ton of education, significantly more than the average person, and yet you still have experienced some of the same exact things that the people who listen to this show have experienced over and over and over, things like imposter syndrome. You talked about that on Partially Derivative. So, yeah, if you can kind of just give a little bit of, a background of where you come from and your company, new knowledge, you know, what you're doing there for the listeners to understand, you know, what you're into. Yeah. I mean, so my background is in political science. So that's what I did at undergrad. And that's what I did my PhD in. And specifically, I did quantitative political science. So like not interviewing the president or experts or something like that. Rather, I dealt with data. So, you know, your GDP or like a survey or something like that, and working with that kind of stuff. And then, you know, I kind of, I was, I was living in San Francisco and I kind of fell into this group of data scientists, which would be like, like we would all have, you know, like we get beers and, and I was seeing all this really cool stuff that they were doing at LinkedIn, which for a while was like the big place to do data science. And I like, they were just doing all this cool stuff. And they opened this world, beyond just quantitative research and into things like machine learning and, and different concepts around artificial intelligence. African nonprofits, And we launched to do a company together around artificial intelligence, which is still going on. And the podcast is still going on. But like the thing that you get when you get my background is that there's very few people who do what I do who have a social science background. Right. Like most of the people who do things in machine learning are from physics or computer science or mathematics or statistics or something like that. And to have a social science background is you're naturally inclined to have some kind of imposter syndrome around these things. Sure. I tend to be an honest person and like I like I'm very good at what I do. And also I look around and say, oh, my God, these guys are really awesome. Right. Yeah. And it's just one of those it's one of those things that comes up a lot that like there. I mean, I think I am not, you know, like I didn't come in this through software development. I come in this through data science and then like. Learned software development so I could actually interact with developers. But like there is a huge breadth of information that that is a part of things that like maybe not I should know, but like are in the realms that I like would would be responsible for. So like everything from, you know, doing like linear algebra raw and then also like, you know, doing bash and setting up AWS and all that kind of stuff. This like huge range and everything in between. And so you never know. Everything like. Yeah. And anyone who claims that they do like, I don't know, like they they they probably don't. I'd like there. They don't know as deep as you or they're they're, you know, boasting about something or something like that. And like I, you know, like I find all the time that something a concept that I thought everyone would know. They absolutely don't know. And then I also find all the time that some concept that everyone obviously, of course, everyone knows this. And I have no idea what what. What that concept is, because it will come in this from different backgrounds. And I think in any kind of technical field that comes up because like you don't need a Ph.D. to get into data science and you don't need, you know, like a computer science degree to get into software engineering. So like some of the best data scientists I know have Ph.D. Some of them have no like no undergrad degree. Some of them have a music degree. Some of them have an acting degree. Like it's it's a new field. So there's just everyone is it's like, can you do the work? Which is like while we all. Complain about these technical interviews, like the reason the technical interviews are good is because it isn't just the people who have Harvard computer science degrees and MIT computer science degrees are not getting the jobs. It's like anyone can get the job as long as you do well in the interview. We're talking about how machines can help you and how this knowledge that we embed into machines, the information that we give to a machine and we tell it to help us with that information. That's all. All about what our sponsor is today. Today's episode is sponsored by Fuse. With Fuse, you can build native iOS and Android applications with less code and better collaboration. App development really has gone unchanged for decades. There's certain things that have changed about it. But really, overall, you as the developer, you're writing all of the code one line at a time and you're debugging all the code one line at a time. Fuse helps change this. Idea. It's built from the ground up to let developers and teams write less code to achieve more in less time. The unique features include a cross-platform component-based UI engine, real-time workflow where every change you make to the code is instantly reflected on your devices and in the desktop simulator that comes with Fuse. It runs on macOS and Windows and it lets you make real native apps for iOS and Android. The Fuse installer includes everything you need to know to get started. And there's no complicated setup process. For those of you who have ever used a program called Unity, it's a game development program. Fuse is effectively like Unity for app development. There are tons of great examples on FuseTools.com. And it all comes with full source code and detailed explanations. So it's easy for you to get started right away. Thank you again to Fuse for sponsoring today's episode of Developer Tea. Head over to spec.fm slash Fuse to get started today. I believe it was the most recent episode of Partially Derivative. You were talking about artificial intelligence, machine learning, all the things that you're actually part of right now. And there was, I can't remember if it was you or your co-host that mentioned the fact that AI is going to be kind of going into every part of your life, right? It's going to be visible at every juncture. And actually is already there and in many ways is already invisible. We see it, you know, for the example that I think was mentioned was, you know, just getting directions on something like Google Maps. There's a lot of machine learning that goes into figuring out optimum routing. And one of my favorite new machine learning features, I guess it's machine learning. You can correct me at any point when I'm saying the wrong terms here. But. But the in Google Maps. Now, if you go and look at a place, you can see when the most popular times are for that given place. And then you can also see like the live now. Right. And they're using the activity from people's phones and particularly Android phones. I'm sure that they're harvesting like just literally the location that somebody's at. And using that to predict like, well, when is this restaurant going to be? Busy. And when should you go to this restaurant? And this stuff is shaping so much. It's it's it's really getting into every single part of our lives. So it makes sense that we're going to see involvement from people from all types of backgrounds. We're going to see people getting interested in not just, you know, I think previously we recently watched Hidden Figures. And it was interesting to see the the the. Character who I can't remember her name, which is terrible, but the character who actually picks up Fortran. And if you've seen the movie, then, you know, this this moment, especially as a developer, is kind of a victorious moment for you as a developer, because she picks up this book and it says Fortran on it. And as a developer in the audience, you're like, yeah, you know, that's the moment that computers came alive, you know, and she understood that the important thing moving forward was, you know, not rejecting. It, not pushing it away, but figuring out a way to adopt it, figuring out a way to use that that new information. And she's coming from the computer science background. Well, now we have people who are not from a computer science background, but computer science is just a part of their job now. It's becoming a part of their job now. I mean, it's just like I'm like I'm not a computer scientist. I'm not a machine learning, you know, like researcher. You know, there's no algorithm out there that's going to be the Albin technique or something like that. But like there is this, you know, the I I find that the most exciting part of it is not inventing a new algorithm because I've never really done that, obviously, because I just like, you know, take the ones that other people invent. But like applying it and in interesting ways to interesting problems. And this is what I mean when I say that, you know, AI is going to like take over so many things. It's not that it like literally all we're going to do is AI. Right. But it's not that it's going to be like building, you know, like an e-commerce site. Well, there's all these little places in that e-commerce site that you could make the interaction with the visitor a little bit smarter if you train something on how visitors behave. So maybe you make it so you have a little bit smarter recommendation of a product or maybe a little bit smarter design of of the shopping cart or maybe a little bit better design of, you know, looking for DDoS attacks or, you know, just all those little all those little points where you would have hard coded a rule. You instead allow a computer. You learn what the value of that should be and allow the computer to adjust that as needed. Like that will make a better e-commerce shopping cart experience. But to the user, it'll just feel like a better e-commerce site. Right. It's not it's not not an e-commerce site. Of course it is. That's the that's what you're doing. You're selling stuff. But, you know, every single decision that you make around something can be just be like a little bit smarter, a little bit more informed. And then it's sort of become ingrained in in everything. So it's. You know, it's not like the e-commerce site is a deep learning algorithm and that's all it is. It's like, of course not. No, it's like T-shirts and fenders and all this kind of stuff. It's just making that interaction with the with the user a little bit easier and maybe making your back in a little bit easier and all that kind of stuff. Just allowing the computer to carry more weight for you. And that's where we see it. I mean, there's a lot of talk now about deep learning and deep learning is like like the thousand foot cannon. You know, it's it's big. It's powerful. It takes a lot of a lot of. Like a lot of experience or not a lot of experience with like a lot of learning and like a lot of expensive technology. But there's a bunch of simple applications. Yeah. Like and you could just throw it into stuff and you could have it around the site. And that's where I really believe that that we're going to go. So if someone has a Django app, there might be like four or five places that machine learning is just making the experience a little bit better from the users. But it's still a Django app. Right. There's just there's just little points where machine learning is helping it out in various ways. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Because really, I think the perception is really in the language. Right. And we've used as computer scientists. One thing that we don't do super well is market our language. And it becomes very confusing for people who are kind of on the outside looking in. Well, what exactly does like what is machine learning do? And the public perception of the term machine learning or the terms machine learning is probably something more along the lines of like men in black, you know, robotic, like AI technology kind of stuff. And that's not at all what it's going to be in its real introduction and adoption. You know, I think there's some more opaque versions of machine learning. So things like Alexa, you know, things like Siri or the robots that we talk to, I think that is people are assuming that that's kind of, that's just going to get more and more that way. Right. But I agree with you that machine learning is going to be applied in much more transparent. Again, going back to the maps example, it feels quite natural. It feels like, oh, that's just really good information to have. Or, you know, you recommended something to me that I actually like, and I'm more likely to appreciate my ads. You know, there's, there's an optimistic view of machine learning to be, to be certain, but there are so many things that we can do. And again, going back to the language thing, you know, I think it's our job, especially for those of us who are doing this work in some kind of commercial environment. So for a client, you have to, if you want to work in this space for a client, you have to understand that the language around this, you know, wrapping up this concept really, it's about intelligence. It's about understanding behavior. It's about understanding patterns. And it's about understanding statistics and using that information to make decisions, right. And, and whether those decisions are what color you're going to show, what, you know, what, what color shirt should I show this user? Which one are they most likely to buy? Or if the decision is something like, you know, what town should I target next with my ad cam? Right. There's so many business level decisions that can be made and machine learning can massively help you on those, on those particular decisions. Of course, you have significantly more experience. I would say like a hundred percent more experience, 200% more experience with machine learning than I do. And what I really would love to do with you on the show is kind of get people excited about this. But, but with, maybe some, some ideas that you've had or, or examples that you've experienced this really kind of enlightening people in a, in a human way. Yeah. I mean, I think the, the parts of it that I really like are when you, you build a machine learning model and then you explain it to them along the way, and then you show them the predictions of stuff. And I really find that it's when they see the predictions coming out, they're like, oh, I'm going to do this. I'm going to do this. And then when they see the predictions coming out and they sort of check them with their own gut check, like it, it sort of the light bulb goes off in their head. So there's one really nice machine learning model. Like, I think it's a deep learning model of done by Microsoft that takes a video and then tells you the age of the person and the gender of the person, right. Just from the video. So like, as soon as you're in frame, it's like, oh, that person is at, you know, 42 and a man. We're not 42, but like whatever, 30 to 40 or something like that. And like, it's people sort of light up when they start to see that, right. Because they start to see all the cool things that they could do with that. Like imagine, and I mean, this is a real product. So I'm just now I'm just pitching randomly Microsoft products, but like there is this API where like, you can just plug into it and I'm sure you pay some fee or something like that. But like, well, what could you do that? Well, maybe you have a bike sharing app and then, you know, you, you walk over and then they go to check their bike and there's a little camera and it's like, hey, that cool. Like, you know, like, you probably, you probably want like a, like a male bike or like a bike for a woman or something like that. I get, maybe there are still bikes by gender. I'm not really certain, but like that kind of stuff. But like, just because that's the prediction, right. The computer's predicting something. And when they see that sort of like, when they see that matches what, like what they actually, like when they see that it's doing well, like, oh, I predicted like that, you know, like I am sort of a middle-aged man and it's like, hey, you're a middle-aged man. It's like, I think that really brings it out because you start, you start to see what it is. And like the actual backend of like, what's happening is interesting to people like me, but like not really interesting to most people who, who make products. Right. Because you're like, you don't really care, but if you're doing stuff like, oh no, no, we can, we can actually predict, you know, like who is going to be a fraudulent case on your site. And then you see fraud go down or like, we can predict who's going to be a troublesome user in your comments or anything like that. You go, oh, okay, cool. And you start to think about all the cool things you could do with that. It's like, they have a tool, toolbox and you're like adding another tool to their toolbox. They didn't realize. And then they start to think of like, oh cool. Maybe I can do this with it and this with it and this with it and that kind of stuff. And like, that's really the end of the day. Like, it's not, I don't care about machine learning because I, you know, I'm in, I'm in love with like, like just sitting around and, and, you know, like computer science and stuff like that. I'm, I'm what I fell in love with it about was there was just some really, there was things that I could do with it that felt like magic. And I wanted to. Yeah. Why, like how the magic worked. And like, of course it's not magic. It's like a lot of linear algebra, but like, it's still, it's still, you know, pretty, pretty simple. And you could do these things that you just would never even think about. And like, as soon as you could say, oh, no, no, we'll predict, you know, like there's a great startup out there whose name I don't remember, but they take usernames and they predict the gender and ethnicity of the people from the usernames. Oh, wow. That kind of stuff. So it's like, okay, so a user signs up for your site and you'd be like, oh, cool. I'd know a little bit more about, you know, that kind of stuff. It's seamless to the user. The user doesn't, doesn't see that or notice that, but their experience is better. It'd be weird if you were like, say we're doing it on gender. So like maybe their experience could be, if you shifted the experience a little bit better based on their gender, like the clothes that you recommended or something like that, they go, oh, cool. This is nice. It's like, assuming that like I'm a man and it's showing me man, you know, like men's clothes on the homepage. And I didn't ever tell him that it just did it. Mm-hmm. And to them, it just works. And behind the scenes, you're, you're making it work, but that kind of stuff. And that's what I think, like what I want to get across. Like people talk about how complicated it is. And then every single time there's like a photo of a data scientist, they're always like writing on chalkboards, some equation or something like that. Like that's the standard photo. And there's like some blue background with ones and zeros as a waterfall or something. Right. But like, it's just, it's another tool in the software development and hardware development toolbox. Right. And it's, it's the reason that like, it's sort of becoming so popular is that over the course of like five or six years, these tools have gone from like barely anyone knew that existed and that you had to implement them yourself. And it was kind of like frustrating and there wasn't like a bunch of tutorials to like, now there's tons of tutorials and tons of podcasts and tons of books and, you know, tons of guides and thousands of Stack Overflow answers and, you know, like special Python libraries. And all that kind of stuff to help you do this. So it's, it's bringing down the bar to putting it into your product. And like, even if you might not understand every single thing about, you know, machine learning and artificial intelligence, stuff like that, you can sort of take one piece of that and understand it enough to implement it. And then if you do that and you sort of see the results and it's like, oh, that is predicting things pretty well. Like it's, you know, like, oh, okay, cool, cool, cool. It's, it's such a good experience. It's such a good experience to watch people do that because that's when they sort of, you know, believe in it as a thing. Like that's when like that, like the hype goes away and you, you sort of realize what it is and what it isn't. And it's like, you know, it's, it's not a robot. I mean, it could be a robot, but like, it's not like iRobot, some sentient thing. It's a, you know, like it's making cool predictions that I can use for stuff. So I could predict gender and I could predict age and I could predict whether you're going to buy. And if I think that you're not going to buy, like, if I predict that you're not going to buy, I can show you a different screen because I think this screen is going to be a different screen. And I can predict that you're probably help you and that kind of stuff. And that's a better experience for the user. It's a better experience for the, like, it's a better product, like all those kinds of things. And it's just driven by slightly intelligent machines deployed in mass. Thank you for listening to today's episode of Developer Tea. Of course, this interview is not over. So make sure you listen to the next part as well as the final part. There are three parts to this interview. And I want to say thank you again to Chris Alvin for coming on the show. Go and check out Chris's show as well, Partially Derivative. You're going to find a lot more discussion on machine learning, more specific examples of machine learning and data science and all of the things that you can do with these incredible tools. So go and check it out. It's Partially Derivative. You can find it in any podcast player that you use. Thank you again to Fuse for sponsoring today's episode of Developer Tea. You can build native iOS and Android apps with less code and better collaboration. It's an all-in-one tool that you can install and use. So if you're interested in learning more about this tool, go ahead and install on Mac, OS or on Windows. Go and check it out, spec.fm slash Fuse or FuseTools.com. Thank you again for listening to today's episode of Developer Tea. If you don't want to miss out on future episodes, including the second and the third parts of this interview, make sure you subscribe in whatever podcasting app you use. Thank you again. And until next time, enjoy your tea.