1 00:00:01,111 --> 00:00:02,411 All right, how's it going, everyone? 2 00:00:02,411 --> 00:00:08,871 Welcome to another episode of Forward Thinking Founders, where we talk to founders about their companies, their visions for the future, and how the two collide. 3 00:00:09,091 --> 00:00:13,771 Today, I'm very excited to be talking to Beau Schaefer, who's the founder, CEO of Playswise. 4 00:00:13,771 --> 00:00:15,491 Welcome to the show, how's it going? 5 00:00:15,544 --> 00:00:17,158 I'm doing well, how are you? 6 00:00:17,783 --> 00:00:18,503 I'm good. 7 00:00:18,503 --> 00:00:26,423 I am, you know, just here doing a bunch of calls, but we have this podcast, which I always look forward to whenever I have podcasts scheduled in my day, because I get to meet 8 00:00:26,423 --> 00:00:29,617 someone new, interesting, working on working on cool stuff. 9 00:00:29,617 --> 00:00:33,991 um So let's kind of just dive right into what you are working on. 10 00:00:33,991 --> 00:00:35,543 What is Playswise? 11 00:00:35,822 --> 00:00:44,042 Yeah, so placewise is using computer vision to get in-depth basketball analytics from broadcast video. 12 00:00:44,722 --> 00:00:48,862 So NBA has a ton of cameras in their facilities to get some of these insights. 13 00:00:48,862 --> 00:00:59,402 And right now, we're working on a way to use computer vision, AI, machine learning, that type of stuff to get the same analytics just from broadcast video. 14 00:01:00,535 --> 00:01:06,102 So let's say, once this is built, why is first, is this already built? 15 00:01:06,102 --> 00:01:06,763 Are you building it? 16 00:01:06,763 --> 00:01:11,358 And two, once you're able to get this data, uh who would be interested in it? 17 00:01:11,358 --> 00:01:13,815 Is me as a spectator, am I interested? 18 00:01:13,815 --> 00:01:18,637 Is a coach interested in evaluating talent to maybe get someone from another team? 19 00:01:18,637 --> 00:01:20,329 Who cares about this data? 20 00:01:20,642 --> 00:01:26,684 Yeah, so I think starting off, I played basketball at Duke, so this is what triggered my curiosity in it. 21 00:01:26,684 --> 00:01:31,785 And I saw AI being used in other applications and thought, I wonder if that's happening. 22 00:01:31,785 --> 00:01:34,006 Or I knew it wasn't happening in the basketball world. 23 00:01:34,006 --> 00:01:43,288 um But essentially, my thought was, can you use something like ChatGBT and ask it for stats or questions and be able to get an output that's actually accurate and good? 24 00:01:43,288 --> 00:01:48,870 um And current answer is I'd say no, especially to the depth we want it now. 25 00:01:48,870 --> 00:01:51,110 um 26 00:01:51,110 --> 00:02:00,224 I think right now, uh since I'm on the side of basketball, I'd say as like a player, my thought is like, how can coaches use it or people on staff? 27 00:02:00,224 --> 00:02:03,016 So I think that'll be like the initial target. 28 00:02:03,016 --> 00:02:13,090 However, as a player, like there are definitely times where I think I would want, you know, to record myself practicing shooting or games and be able to get just data from it, 29 00:02:13,090 --> 00:02:20,944 like even just how many shots I made, uh how many ah like dribbles I took or what 30 00:02:20,944 --> 00:02:23,311 kind of tendencies I had and stuff like that. 31 00:02:24,983 --> 00:02:26,164 Yeah, that's awesome. 32 00:02:26,164 --> 00:02:32,807 I am curious, know, were you in, I guess, what came first, your interest in basketball or your interest in tech? 33 00:02:32,807 --> 00:02:35,008 And if it was basketball, then tech. 34 00:02:35,008 --> 00:02:39,243 ah How did you, how did you kind of discover the tech world? 35 00:02:39,243 --> 00:02:41,940 And if it's the opposite, like, how'd you first get into tech? 36 00:02:42,476 --> 00:02:43,627 Yeah, that's a good question. 37 00:02:43,627 --> 00:02:47,448 think I really stumbled upon this kind of accidentally. 38 00:02:47,448 --> 00:02:52,561 I think I always knew from for a while, I knew I wanted to get into sports somehow. 39 00:02:52,561 --> 00:02:54,161 I didn't know when or how that would be. 40 00:02:54,161 --> 00:03:05,396 And then I think I've been always pretty interested in, uh you know, AI tech and it's been booming, uh obviously, with chat, tpt and LLMs and BLMs even. 41 00:03:05,477 --> 00:03:12,440 And I kind of just saw a lot of it being developed in like a broad focus for users. 42 00:03:12,470 --> 00:03:15,522 like your everyday person for random things or and whatnot. 43 00:03:15,522 --> 00:03:23,077 And then it kind of started to dabble into other verticals like legal stuff or uh medicine, stuff like that. 44 00:03:23,077 --> 00:03:26,880 And then I just kind of thought of, you know, this would be really cool on the basketball side. 45 00:03:26,880 --> 00:03:38,197 think uh having a lot of friends or like scouts, coaches, analysts, that type of thing, hearing their daily workflow and me thinking, like you guys have to do all this manually, 46 00:03:38,197 --> 00:03:42,320 like seems seems kind of crazy to me, considering all the advancements 47 00:03:42,320 --> 00:03:45,960 with technology right now and so I think that's probably what kind of sparked it. 48 00:03:45,960 --> 00:03:55,620 So I don't know if there's probably I guess my initial interest was basketball but I didn't mean for it to be basketball when I found the tech side of it. 49 00:03:55,691 --> 00:03:56,372 No, totally. 50 00:03:56,372 --> 00:03:57,672 That makes sense. 51 00:03:57,813 --> 00:04:03,677 And, you know, we were talking, you know, earlier before we were recording about about Twitter, that's how we met, right? 52 00:04:03,677 --> 00:04:04,508 We met on Twitter. 53 00:04:04,508 --> 00:04:11,624 And, you know, as I was saying, I probably interview 80 to 90 % of the guests who come on the podcast, I meet on the internet. 54 00:04:11,944 --> 00:04:21,643 How has like the internet been useful for you as you've been, you know, you know, building this out, you know, getting into tech, you know, using Twitter, are there other sources or 55 00:04:21,643 --> 00:04:22,644 websites that you use? 56 00:04:22,644 --> 00:04:24,234 Like, how's the internet helped you? 57 00:04:24,245 --> 00:04:29,461 Yeah, uh I think the tech world's very open. 58 00:04:29,461 --> 00:04:31,894 They open source a lot, which is cool. 59 00:04:31,894 --> 00:04:37,139 So you can kind of go on there, test things, see if it could work for your application, or see how someone else is using. 60 00:04:37,139 --> 00:04:39,073 uh 61 00:04:39,073 --> 00:04:47,100 their LLM or VLM or whatever it is or their models and seeing how they're using it and seeing if you can kind of apply it to yours. 62 00:04:47,100 --> 00:04:50,222 So it's definitely been uh inspiring in that sense. 63 00:04:50,222 --> 00:05:00,390 And I honestly think if you aren't on Twitter or really keeping up with podcasts or using something like Perplexity to get updates, think you're definitely falling behind. 64 00:05:00,390 --> 00:05:03,332 um I think that is a battle, though. 65 00:05:04,466 --> 00:05:10,132 Every time I go on Twitter, I feel like there's an update in some sort of new model or new this, new that, new capabilities. 66 00:05:10,132 --> 00:05:13,899 um But yeah, I think, yeah. 67 00:05:13,899 --> 00:05:15,099 Yeah, sorry. 68 00:05:15,099 --> 00:05:25,719 It's just, it's interesting because the standard advice is focus on your customers, you know, turn all that stuff off and just, you know, talk to your users, build product, 69 00:05:25,719 --> 00:05:26,459 right? 70 00:05:26,459 --> 00:05:30,739 But in this age of AI and you know, look, there's been AI for decades, right? 71 00:05:30,739 --> 00:05:32,019 But we're just in a moment. 72 00:05:32,019 --> 00:05:36,019 We're in a moment where it's progressing at a very rapid rate. 73 00:05:36,259 --> 00:05:37,719 I think you do have to be online. 74 00:05:37,719 --> 00:05:42,219 Like I think you do have to be on Twitter, know what's dropping, just so you're in the know and 75 00:05:42,219 --> 00:05:48,165 Maybe if you're using a model today or using a tech technology today, in a year it could be completely irrelevant. 76 00:05:48,165 --> 00:05:51,188 And you need to know that and be on the ball. 77 00:05:51,188 --> 00:05:53,270 But as you said, it's a balance. 78 00:05:53,270 --> 00:06:00,378 You don't wanna switch models every five seconds or you don't wanna switch technologies every day or every week, because that's just gonna be a pain in the butt. 79 00:06:00,378 --> 00:06:02,941 ah But it is the battle. 80 00:06:02,941 --> 00:06:04,030 How do you think about that? 81 00:06:04,030 --> 00:06:05,813 Like you're building in this space. 82 00:06:05,944 --> 00:06:11,033 How do you get, do you get distracted or do you let yourself get distracted by these new models? 83 00:06:11,033 --> 00:06:12,529 Like how do you handle that? 84 00:06:12,529 --> 00:06:13,510 that's a good question. 85 00:06:13,510 --> 00:06:23,054 think I tend to, if I get distracted, it's like, okay, I look at the post, I might go to the link or whatever and check it out for a second, but I don't immediately switch. 86 00:06:23,054 --> 00:06:27,936 uh I'd have to have a lot of conviction that this model's better, this is why we're gonna use it. 87 00:06:27,936 --> 00:06:35,780 ah But right now, I'm working on fine tuning our current models and more customizing them to our needs. 88 00:06:35,780 --> 00:06:41,883 A lot of the stuff on Twitter, from what I've found, is if they're open sourcing, it's pretty, 89 00:06:41,903 --> 00:06:43,924 It's too like a broader demographic, I'd say. 90 00:06:43,924 --> 00:06:53,730 um But the biggest thing is, uh if I do see something new, I'll, I have a couple advisors I talk to that are experts in like computer vision or machine learning that I trust, and I 91 00:06:53,730 --> 00:06:59,534 know they've done a lot of good work, so I'll kind of consult with them and get some advice if I do. 92 00:06:59,534 --> 00:07:02,185 Or I'll send it to them and say, did you see this? 93 00:07:02,185 --> 00:07:04,657 Check it out, and they'll kind of give me some feedback. 94 00:07:06,103 --> 00:07:07,043 Yeah, that makes sense. 95 00:07:07,043 --> 00:07:10,343 It does leave me to wonder, like, how are you spending your time right now? 96 00:07:10,343 --> 00:07:11,903 It sounds like you're fine tuning your models. 97 00:07:11,903 --> 00:07:13,763 Do you just be taking a lot of shots? 98 00:07:13,763 --> 00:07:14,383 Are you dribbling? 99 00:07:14,383 --> 00:07:16,143 Are you like giving a data to train? 100 00:07:16,143 --> 00:07:21,643 And if it's not that specifically like how are you training without giving away your special sauce? 101 00:07:21,965 --> 00:07:29,408 ah I think yeah, right now a lot of my time is coding slash getting these models better and more accurate. 102 00:07:29,408 --> 00:07:39,722 um It's funny like on one hand, I think it's like the chicken versus the egg battle where, okay, I need to make these models better and more accurate for teams, coaches to use and 103 00:07:39,722 --> 00:07:41,393 trust, but. 104 00:07:42,094 --> 00:07:46,634 the computational power it takes to get these models accurate. 105 00:07:46,954 --> 00:07:51,474 For example, like, let's say I have a model that's 90 % accurate what it wants to do. 106 00:07:51,474 --> 00:08:08,074 To get it from 90 to, or I should say, let me say 95 % to 96 % versus getting it 80 to 90 % is exponentially harder because I just like the amount you have to train it to get 107 00:08:08,074 --> 00:08:09,054 better. 108 00:08:10,094 --> 00:08:12,047 So yeah, right now that's why I'm kind of in the race 109 00:08:12,047 --> 00:08:15,107 money pre-seed. 110 00:08:16,447 --> 00:08:19,867 But yeah, sorry I don't remember the initial question. 111 00:08:19,867 --> 00:08:20,107 fine. 112 00:08:20,107 --> 00:08:20,807 It's fine. 113 00:08:20,807 --> 00:08:26,087 I it's like, when you're as you're training or as you're like, you're building the tech, I have no idea. 114 00:08:26,087 --> 00:08:27,147 I'm not I'm not technical. 115 00:08:27,147 --> 00:08:29,267 I'm like, you know, I'm a BC guy. 116 00:08:29,267 --> 00:08:30,287 like a podcast guy. 117 00:08:30,287 --> 00:08:31,607 I'm not like a tech guy. 118 00:08:31,607 --> 00:08:32,367 Right. 119 00:08:32,427 --> 00:08:37,827 So do you do know, feed your model like videos of like, like I'm a Phoenix Suns fan. 120 00:08:37,827 --> 00:08:41,327 If you could you feed it a Phoenix Suns game and it gets better? 121 00:08:41,327 --> 00:08:46,347 Is you actually feed it like live images or a video of you of you working is actually none of that. 122 00:08:46,347 --> 00:08:47,881 And you're just going off of like a 123 00:08:47,881 --> 00:08:50,581 it already knows how the motions work because it's AI. 124 00:08:50,581 --> 00:08:52,379 It's like, how do you know how to train it? 125 00:08:52,379 --> 00:08:53,377 I don't know. 126 00:08:53,377 --> 00:09:00,310 I think, I guess know how to train it based on, let's say there's a model, let's say there's, yeah, there's a model of like object detection. 127 00:09:00,310 --> 00:09:11,726 So just being able to detect like player hoop ref ball, for example, and you'll, put in a video through an API, get the results back to see what it labels. 128 00:09:11,726 --> 00:09:19,489 You'll go through and see, okay, it's misreading like people court side as players or it's misreading refs as players. 129 00:09:19,489 --> 00:09:23,041 So that's where you would go in basically uh redo the 130 00:09:23,041 --> 00:09:24,592 and then train it again. 131 00:09:24,592 --> 00:09:27,605 uh So yeah, a lot of my time is spent doing that. 132 00:09:27,605 --> 00:09:36,504 I think a lot of it's going to a bunch of basketball events and games and things like that and connecting with coaches, ah users, things like that. 133 00:09:36,504 --> 00:09:41,068 uh That's pretty much it right now. 134 00:09:41,697 --> 00:09:43,450 Yeah, that makes sense. 135 00:09:43,450 --> 00:09:50,601 bet when coaches or folks in the industry or in like, I guess the sport, hear about what you're doing, they must be jazzed. 136 00:09:50,601 --> 00:09:54,096 Like there's probably not that much out there that's quite like this, right? 137 00:09:54,096 --> 00:09:55,096 Is that right? 138 00:09:55,096 --> 00:10:02,637 Yeah, so there's a couple things that are out there that are going or do something similar to what I want it to do. 139 00:10:02,637 --> 00:10:08,224 uh Like the base product of being able to... 140 00:10:09,197 --> 00:10:13,238 uh get possessions clipped up, right, from a full game. 141 00:10:13,279 --> 00:10:16,310 But that's done manually, uh for the most part right now. 142 00:10:16,310 --> 00:10:20,462 I know of a couple other companies trying to do some computer vision stuff. 143 00:10:20,462 --> 00:10:24,603 I don't think like they have all the features that I'm thinking of. 144 00:10:24,603 --> 00:10:26,444 It's my knowledge anyways. 145 00:10:26,444 --> 00:10:34,987 uh yeah, like everybody I've talked to in the basketball world and even in general have been super supportive, like think it's a great idea, like it would be sick. 146 00:10:34,987 --> 00:10:37,619 ah They tell me I'm the person to do it. 147 00:10:37,619 --> 00:10:38,409 So. 148 00:10:39,145 --> 00:10:42,254 Now I'm just trying to have that come into fruition. 149 00:10:42,645 --> 00:10:43,203 no, 100%. 150 00:10:43,203 --> 00:10:44,297 That's great. 151 00:10:44,571 --> 00:10:54,301 Do you spend any time thinking about other parts of tech or like is there any is there any developments in the startup world or tech tech world that are interesting to you that may 152 00:10:54,301 --> 00:10:57,545 not be directly related to what you're currently building? 153 00:10:57,729 --> 00:11:06,253 Yeah, I think something interesting, it's kind of tied into what I'm building, but just like the whole AI agent thing, like how much you can really automate. 154 00:11:06,253 --> 00:11:13,436 uh And I honestly think AI, I don't know if it'll replace entire jobs. 155 00:11:13,436 --> 00:11:17,598 It probably is already starting to a little bit, but like you're higher, more skilled jobs. 156 00:11:17,598 --> 00:11:21,880 I think if you don't know how to use AI, like you're definitely gonna fall behind. 157 00:11:21,880 --> 00:11:27,693 uh And so if you know how to, know, prompt LLMs, build agents, things like 158 00:11:27,693 --> 00:11:29,284 that can make you more efficient. 159 00:11:29,284 --> 00:11:32,146 think that's where AI is the most helpful. 160 00:11:32,146 --> 00:11:37,300 Like my thing even, I don't want this to replace video coordinators or coaches or analysts. 161 00:11:37,300 --> 00:11:47,936 I think it'll make their job much more efficient because they'll be able to go through more film, at more data, things like that, and just have more informed decision making. 162 00:11:48,555 --> 00:11:54,395 The thing with agents though is I think that no one really 163 00:11:54,965 --> 00:12:03,599 has a defined definition of what an agent is and everyone just slaps agent on their product and they're like, hey, we have agents and then us as a excited industry is like, 164 00:12:03,599 --> 00:12:07,741 oh, know, XYZ has an agent and we get excited. 165 00:12:08,641 --> 00:12:13,029 have you seen uh an agent that you think is the future? 166 00:12:13,029 --> 00:12:16,025 Like, guess, let me actually, let me ask a different question. 167 00:12:16,585 --> 00:12:24,259 Have you seen technologies that you think will be around in 15 years that like power agents or an agent that you think in its current form will exist in 15 years or are we 168 00:12:24,259 --> 00:12:25,389 just really early? 169 00:12:25,612 --> 00:12:29,367 in that development of like a technology in your opinion. 170 00:12:29,367 --> 00:12:32,110 yeah, I think a lot of it is really early. 171 00:12:32,110 --> 00:12:37,075 think a lot of people are kind of throwing and then we're gonna see as time progresses what sticks. 172 00:12:37,075 --> 00:12:42,861 uh I think as far as AI agents, I think things that are good are being able to... 173 00:12:43,246 --> 00:12:45,086 use it as almost like an external brain. 174 00:12:45,086 --> 00:12:55,146 So if you can connect it to your email, Otter, Otter bots or whatever, note taking apps, things like that, and it's able to recall information for you that you might have 175 00:12:55,146 --> 00:12:58,906 forgotten, I think that's where it could be really cool or helpful. 176 00:12:59,006 --> 00:13:01,846 And I kind of have some stuff set up for that. 177 00:13:02,906 --> 00:13:07,146 But as far as like an agent doing something completely for me, 178 00:13:08,982 --> 00:13:10,248 You can do it. 179 00:13:10,248 --> 00:13:14,001 I don't know if I trust it yet, but you can obviously do it. 180 00:13:14,805 --> 00:13:16,119 Yeah, I-I- 181 00:13:16,119 --> 00:13:24,399 I haven't leaned out on it, but I'm kind of waiting for just the industry or at least some folks to be like, oh, this, this is the thing to try. 182 00:13:24,399 --> 00:13:32,198 Cause I feel like I've been not burned, but kind of disappointed in being an early adopter through a lot of this tech specifically in the agent world. 183 00:13:32,198 --> 00:13:35,959 It's just never quite, at least from my experience, never quite as marketed. 184 00:13:35,959 --> 00:13:42,919 So I'm like, I'm expecting to do blanks and then we can get you maybe 80 % of the way there, but you can't get the full, the full hundred. 185 00:13:42,919 --> 00:13:46,025 And it's kind of a letdown cause the whole idea of a gut. 186 00:13:46,025 --> 00:13:47,745 an agent is it can work a ton. 187 00:13:47,745 --> 00:13:52,345 You tell it what to do and it does it, then it tells you once it's done it, right? 188 00:13:52,345 --> 00:13:53,665 Or you just know. 189 00:13:53,825 --> 00:14:00,825 So I think it's probably out there, just like the phrase, what the future is here, but it's not evenly distributed, like something like that, right? 190 00:14:00,825 --> 00:14:02,045 It's probably similar. 191 00:14:02,857 --> 00:14:06,410 So I have a couple questions around your time in college. 192 00:14:06,410 --> 00:14:11,534 did you, like you play basketball, you you play basketball. 193 00:14:11,534 --> 00:14:12,865 Were you, are you art? 194 00:14:12,865 --> 00:14:15,227 Well, I'm gonna ask, are you still in college or have you graduated? 195 00:14:15,227 --> 00:14:18,139 Cool. 196 00:14:18,139 --> 00:14:23,383 So when you were in college, were you, were a lot of your peers? 197 00:14:23,383 --> 00:14:27,592 uh You said you wanted to do, was there a lot of tech like in college? 198 00:14:27,654 --> 00:14:32,645 Were you influenced by the scene at your college or not necessarily? 199 00:14:32,786 --> 00:14:37,808 Um, I think when I was in college, like part of my college years was COVID. 200 00:14:37,808 --> 00:14:39,749 Um, so that's when we were all on zoom. 201 00:14:39,749 --> 00:14:41,270 So that's, guess, tech, right? 202 00:14:41,270 --> 00:14:49,824 As far as AI, like no one was really worried when I was there worried about people using chat to BT for essays or anything like that, because it wasn't that good. 203 00:14:49,824 --> 00:14:54,816 Like, I don't think it was until this past year, maybe where it became really good and you can give it context. 204 00:14:54,816 --> 00:15:02,920 can give it like templates for it to kind of follow, um, an instruction, but, um, I wasn't using any, any of like chat to BT. 205 00:15:02,920 --> 00:15:08,323 or entropic or Gemini or any of that when I was in college. 206 00:15:08,443 --> 00:15:11,665 But I think we definitely knew it was kind of on the come up. 207 00:15:11,665 --> 00:15:17,468 um And it was more innovative than real life. 208 00:15:17,528 --> 00:15:23,972 I think we still thought it was, as far as I know and what I use, speaking personally, I think we thought it was more innovative. 209 00:15:23,972 --> 00:15:27,733 This will be in the near future, but it wasn't implement now. 210 00:15:28,587 --> 00:15:29,567 Totally. 211 00:15:29,567 --> 00:15:34,432 I honestly can't imagine having access to this tech while I was in school. 212 00:15:34,432 --> 00:15:38,107 I graduated Arizona State 2016 before all of this. 213 00:15:38,107 --> 00:15:40,417 Just like a year after opening, I was founded, I guess. 214 00:15:40,417 --> 00:15:50,396 And it's just kind of crazy to think that, yes, students just today, any age, not just college age, but high school, middle school, they've access to this tech. 215 00:15:50,396 --> 00:15:52,107 And then some schools are gonna... 216 00:15:52,115 --> 00:16:00,913 lean in and they're gonna help the students use it to the best of their ability, kind of like a calculator, and some are gonna say no AI for anything and it'll be interesting to 217 00:16:00,913 --> 00:16:04,746 see how things just, know, things go in the future. 218 00:16:05,167 --> 00:16:09,021 So what do things look like for you, you know, over the next couple of years? 219 00:16:09,021 --> 00:16:11,443 Like what's the focus on for plays-wise? 220 00:16:11,443 --> 00:16:13,715 Like you said, you're raising a pre-seed. 221 00:16:13,715 --> 00:16:17,418 What's like some of your goals that you'd like to have accomplished within a couple of years? 222 00:16:17,710 --> 00:16:29,230 I think a couple years, I mean, I'm kind of delusional, but like if I could say every like Power 4 school was using it, I think that would be the goal in two to three, four years. 223 00:16:29,510 --> 00:16:31,470 Like I want it to be kind of league wide. 224 00:16:31,470 --> 00:16:36,150 want it to be accessible for teams and players. 225 00:16:36,150 --> 00:16:38,830 I want as many people and users as possible. 226 00:16:38,830 --> 00:16:41,630 I think that's with any given software, right? 227 00:16:41,790 --> 00:16:47,730 But I think something to be able to say, oh, every Power 4 school, 228 00:16:47,734 --> 00:16:52,447 college uses this software, um I think would be incredibly cool. 229 00:16:52,687 --> 00:17:01,463 And I think just being able to like right now the industry or like the the products that I'm I guess competing with have completely monopolized the industry and kind of everybody 230 00:17:01,463 --> 00:17:10,139 in in basketball has told me that like, huddle and synergy are their main video platforms to get game footage uh kind of reviewed and stuff. 231 00:17:10,139 --> 00:17:11,890 And I I kind of want to disrupt that. 232 00:17:11,890 --> 00:17:17,584 Because I think I think they're lacking with some things and I don't think they've been uh 233 00:17:17,584 --> 00:17:20,398 um as present for their users. 234 00:17:21,599 --> 00:17:27,007 I kind of wonder if they have a disadvantage in that they were built well before this AI boom. 235 00:17:27,007 --> 00:17:33,177 So they're not AI native versus you're building this now you're able to build kind an AI native company from the ground up. 236 00:17:33,177 --> 00:17:34,979 It probably gives you an advantage. 237 00:17:35,080 --> 00:17:35,531 Mm-hmm. 238 00:17:35,531 --> 00:17:37,162 Yeah, would think so. 239 00:17:37,162 --> 00:17:44,549 I think I can be more efficient at things um than somebody else could, you know, manually doing a lot and they're still doing stuff manually. 240 00:17:44,549 --> 00:17:48,243 Built, you know, 20, 30, 40 years ago. 241 00:17:48,243 --> 00:17:54,529 Like, you can even tell by the UI of HODL and Synergy, like, it looks like it's from the 1990s or whatever. 242 00:17:54,529 --> 00:17:55,755 um 243 00:17:55,755 --> 00:17:57,376 That's crazy. 244 00:17:57,376 --> 00:17:58,337 I'm familiar with Huddle. 245 00:17:58,337 --> 00:18:01,689 There was a period I had a company a few years ago called Seed Scout. 246 00:18:01,689 --> 00:18:05,152 We were helping connect at that time founders, investors and job seekers together. 247 00:18:05,152 --> 00:18:09,065 But then we started to expand for a period of time to athletes. 248 00:18:09,065 --> 00:18:13,888 So we had an ambition to connect athletes to coaches, to schools, to brands. 249 00:18:13,888 --> 00:18:15,990 And we did it for six months or something. 250 00:18:15,990 --> 00:18:18,832 But I found Huddle around this time and it's fine. 251 00:18:18,832 --> 00:18:23,035 It does the job, but it's definitely not a magical experience by any means. 252 00:18:23,498 --> 00:18:24,278 yeah, I would agree. 253 00:18:24,278 --> 00:18:36,378 And then I don't know what I guess package you had, but seeing some of the stuff on what they give to teams on like how they label plays or actions like half that like there was 254 00:18:36,378 --> 00:18:42,098 one time I was on it and it labeled the complete wrong player like not even close. 255 00:18:42,098 --> 00:18:44,238 So it's just like things like that. 256 00:18:44,558 --> 00:18:48,268 Like when we're talking like winning games and 257 00:18:48,268 --> 00:18:52,278 the threshold to what's gonna push you over to win a game. 258 00:18:52,278 --> 00:18:54,690 We just can't have silly mistakes like that. 259 00:18:54,690 --> 00:18:59,925 And that's why we obviously have coaches, analysts, a lot of people on staff to do it manually. 260 00:18:59,925 --> 00:19:07,730 But when you're paying a large subscription, I think it should be more accurate or better product, better user interface, stuff like that. 261 00:19:08,119 --> 00:19:09,939 100 % no it makes sense. 262 00:19:09,939 --> 00:19:18,299 Well if someone wanted to kind of learn more about what you're doing online, connect with you maybe on Twitter or X, I still call it Twitter, I probably always will. 263 00:19:18,899 --> 00:19:24,219 Where can someone learn more about Playswise, you know, your online presence, where can someone connect? 264 00:19:24,524 --> 00:19:26,095 Yep, mean Twitter's good. 265 00:19:26,095 --> 00:19:37,125 check my Twitter and Instagram DMs pretty regularly, LinkedIn, all the basic social media platforms and then uh can email me at bo at playswise.com or check out the website and 266 00:19:37,125 --> 00:19:38,365 connect with me there. 267 00:19:38,977 --> 00:19:39,348 Cool. 268 00:19:39,348 --> 00:19:41,080 Well, I appreciate you coming on the podcast. 269 00:19:41,080 --> 00:19:44,285 It's really interesting space that you're building in and I'll be following along. 270 00:19:44,285 --> 00:19:45,497 Thanks for coming on. 271 00:19:45,497 --> 00:19:46,472 Thanks, Matt.