1 00:00:00,080 --> 00:00:01,560 Speaker 1: Something you said to me last time we spoke, because 2 00:00:01,560 --> 00:00:03,880 Speaker 1: they're like, yeah, like, no one's typing anymore in Silicon Valley. 3 00:00:03,920 --> 00:00:06,440 Speaker 1: Everyone's like, you know, I just I just imagine people 4 00:00:06,519 --> 00:00:09,960 Speaker 1: in offices whispering into their computers with whisper flow, and like, 5 00:00:10,039 --> 00:00:12,640 Speaker 1: somehow that's the that's what's happening. But I don't know. 6 00:00:12,680 --> 00:00:13,119 Speaker 1: You tell me. 7 00:00:13,280 --> 00:00:16,160 Speaker 2: I went to his office in San Francisco and everyone 8 00:00:16,239 --> 00:00:19,840 Speaker 2: had a microphone coming from their desk, and I was 9 00:00:19,920 --> 00:00:22,360 Speaker 2: kidding around. I'm like this, so does everyone just whisper 10 00:00:22,760 --> 00:00:26,480 Speaker 2: into their laptops? And He's like, yeah, nobody's typing. Everyone's 11 00:00:26,560 --> 00:00:27,440 Speaker 2: literally whispering. 12 00:00:30,560 --> 00:00:33,120 Speaker 1: This is Andrew Young. He's my go to for all 13 00:00:33,159 --> 00:00:37,400 Speaker 1: things tech. He's an entrepreneur, an investor, startup advisor, and 14 00:00:37,640 --> 00:00:41,559 Speaker 1: a community builder. In a world that feels increasingly digital, 15 00:00:41,920 --> 00:00:46,520 Speaker 1: Andrew's superpower is offline connection. In the time I've known him, 16 00:00:46,560 --> 00:00:49,680 Speaker 1: he's built out one of the most influential tech networks 17 00:00:49,720 --> 00:00:53,320 Speaker 1: in New York City. He's thrown hundreds of events. One 18 00:00:53,360 --> 00:00:56,040 Speaker 1: news outlet actually called him the Great Gatsby of tech. 19 00:00:56,440 --> 00:01:00,360 Speaker 1: So he's completely plugged in, and every time we get together, 20 00:01:00,600 --> 00:01:03,320 Speaker 1: I ask him the same question, what are the tools 21 00:01:03,360 --> 00:01:06,920 Speaker 1: Silicon Valley CEOs are using. How are you using AI 22 00:01:06,959 --> 00:01:09,160 Speaker 1: to optimize your life? What are you hearing behind the 23 00:01:09,200 --> 00:01:11,600 Speaker 1: scenes When I want to know how to be more 24 00:01:11,600 --> 00:01:15,039 Speaker 1: productive or what AI tools people are actually using that 25 00:01:15,080 --> 00:01:19,080 Speaker 1: make life easier. Andrew is my go to. So I 26 00:01:19,120 --> 00:01:21,840 Speaker 1: want to bring Andrew to you if you're looking at 27 00:01:21,880 --> 00:01:25,280 Speaker 1: the headlines around AI with some trepidation or honestly you 28 00:01:25,360 --> 00:01:27,200 Speaker 1: just don't know where to start, but you want in 29 00:01:27,840 --> 00:01:31,440 Speaker 1: consider Andrew your guide. So much of the show is 30 00:01:31,520 --> 00:01:35,319 Speaker 1: about bridging Silicon Valley and helping us all benefit from innovation. 31 00:01:35,880 --> 00:01:40,520 Speaker 1: And Andrew embodies that I'm Laur Siegel and you're listening 32 00:01:40,560 --> 00:01:47,160 Speaker 1: to Mostly Human, a tech podcast through a human lens. Andrew, 33 00:01:47,200 --> 00:01:48,040 Speaker 1: Welcome to the PUD. 34 00:01:48,280 --> 00:01:49,040 Speaker 3: Thanks for having me. 35 00:01:49,320 --> 00:01:53,120 Speaker 1: I'm so excited. I'm excited because I was just saying 36 00:01:53,320 --> 00:01:56,640 Speaker 1: before we started rolling, we meet every couple months and 37 00:01:56,720 --> 00:02:00,440 Speaker 1: I always walk away and I'm like, oh, I know that, 38 00:02:00,600 --> 00:02:02,360 Speaker 1: and I should know that. And I feel like you 39 00:02:02,400 --> 00:02:04,480 Speaker 1: are my guy for all the things that I need 40 00:02:04,480 --> 00:02:06,440 Speaker 1: to know in tech when it comes to the future. 41 00:02:06,520 --> 00:02:08,720 Speaker 1: So I feel like there's no better way to kick 42 00:02:08,720 --> 00:02:10,160 Speaker 1: this off than to have you on. 43 00:02:10,680 --> 00:02:13,400 Speaker 2: Well, I'm excited to share the things I found recently. 44 00:02:14,000 --> 00:02:15,880 Speaker 2: It's always a shock to me when I share something 45 00:02:15,880 --> 00:02:17,520 Speaker 2: with you and you've never heard of it before, because 46 00:02:17,520 --> 00:02:20,200 Speaker 2: you're so deeply embedded into that world as well. So 47 00:02:20,240 --> 00:02:22,440 Speaker 2: hopefully this is this today's session is useful. 48 00:02:23,440 --> 00:02:26,040 Speaker 1: Well, okay, so before we get into everything, I want 49 00:02:26,080 --> 00:02:28,840 Speaker 1: to go back to March twenty twenty three. A DM 50 00:02:28,960 --> 00:02:33,840 Speaker 1: lands in my inbox on X and it's from you, 51 00:02:34,440 --> 00:02:36,639 Speaker 1: and it says would love to have you at an 52 00:02:36,720 --> 00:02:39,880 Speaker 1: upcoming event. I think you'll dig the crowd and venue. 53 00:02:40,320 --> 00:02:43,000 Speaker 1: So you literally like slid into my DMS. This is 54 00:02:43,040 --> 00:02:45,280 Speaker 1: how we met. But what's kind of interesting about that 55 00:02:45,360 --> 00:02:48,520 Speaker 1: is like you've almost like built your career reaching out 56 00:02:48,560 --> 00:02:51,320 Speaker 1: to folks building community because at the time you were 57 00:02:51,440 --> 00:02:54,120 Speaker 1: at Google and you had started building out this community 58 00:02:54,160 --> 00:02:56,120 Speaker 1: in New York. So talk to me about what was 59 00:02:56,160 --> 00:02:57,959 Speaker 1: happening March twenty twenty three when we met. 60 00:02:58,120 --> 00:02:58,360 Speaker 3: Yeah. 61 00:02:58,360 --> 00:03:01,160 Speaker 2: So I had moved to New York in August twenty 62 00:03:01,240 --> 00:03:05,520 Speaker 2: twenty and when I moved here, the city was completely empty. 63 00:03:05,639 --> 00:03:08,760 Speaker 2: I had no friends in the city, the streets were empty, 64 00:03:08,800 --> 00:03:11,600 Speaker 2: the bars are empty, and I just knew that I 65 00:03:11,639 --> 00:03:14,360 Speaker 2: had to build my own community because otherwise I would 66 00:03:14,400 --> 00:03:17,000 Speaker 2: be incredibly lonely. My family was all the way in China, 67 00:03:17,880 --> 00:03:20,680 Speaker 2: and so I started dming people on Twitter. And at 68 00:03:20,680 --> 00:03:23,399 Speaker 2: that point I didn't have a following. I had nine 69 00:03:23,440 --> 00:03:26,400 Speaker 2: hundred followers, but I started dming people on Twitter on 70 00:03:26,440 --> 00:03:29,880 Speaker 2: LinkedIn asking people to meet up with me, just because 71 00:03:30,240 --> 00:03:33,320 Speaker 2: I believe there's so many incredible people in New York City. 72 00:03:33,360 --> 00:03:36,240 Speaker 2: And over time that compounded, and those one on one 73 00:03:36,280 --> 00:03:39,920 Speaker 2: meetups turned into these smaller events and they just grew 74 00:03:39,960 --> 00:03:42,920 Speaker 2: from there, and to date, I've hosted over three hundred 75 00:03:42,960 --> 00:03:48,560 Speaker 2: events around fifty thousand people, CEOs, entrepreneurs, investors, people that 76 00:03:48,600 --> 00:03:51,520 Speaker 2: work in tech, and we've built a business out of it, 77 00:03:51,560 --> 00:03:54,160 Speaker 2: which is really incredible. But that's how it all started. 78 00:03:54,360 --> 00:03:56,040 Speaker 1: I mean, you probably, and I've said this to you, 79 00:03:56,080 --> 00:03:58,600 Speaker 1: have one of the most influential networks in New York City, 80 00:03:58,760 --> 00:04:01,480 Speaker 1: and you are so connected in the inner workings of 81 00:04:01,560 --> 00:04:05,240 Speaker 1: technology and people building the future. And because of that, 82 00:04:05,360 --> 00:04:08,200 Speaker 1: I mean, you really have this inside look what's coming 83 00:04:08,280 --> 00:04:10,800 Speaker 1: next and what and you are like practicing what you 84 00:04:10,920 --> 00:04:12,920 Speaker 1: preach and you hear like a lot of the tech 85 00:04:13,000 --> 00:04:15,560 Speaker 1: dudes in Silicon Valley talking about, like what are we 86 00:04:15,600 --> 00:04:17,360 Speaker 1: going to do with all our free time now that 87 00:04:17,400 --> 00:04:20,680 Speaker 1: AI does everything for us, but there's something about like 88 00:04:21,200 --> 00:04:23,560 Speaker 1: what you actually do that you have almost like created 89 00:04:23,600 --> 00:04:27,159 Speaker 1: this really interesting system around how you use AI to 90 00:04:27,279 --> 00:04:31,280 Speaker 1: be as productive as humanly possible. So I'd love to start, 91 00:04:31,320 --> 00:04:35,600 Speaker 1: like really specifically, what are the trends that you think 92 00:04:35,680 --> 00:04:39,360 Speaker 1: our listeners folks watching really should know about. We don't 93 00:04:39,360 --> 00:04:40,719 Speaker 1: have to do all of them, but what are like 94 00:04:40,760 --> 00:04:42,960 Speaker 1: the top trends that you think people really should know 95 00:04:43,080 --> 00:04:46,440 Speaker 1: about where we are right now with AI and society. 96 00:04:46,720 --> 00:04:48,680 Speaker 2: Well, the first thing that comes to mind, and you 97 00:04:48,760 --> 00:04:51,040 Speaker 2: let me know this is too obvious or not obvious enough. 98 00:04:52,440 --> 00:04:54,000 Speaker 3: The idea of vibe coding. 99 00:04:54,279 --> 00:04:57,800 Speaker 1: Not obvious enough, obvious if you're in these circles. But 100 00:04:57,960 --> 00:05:01,080 Speaker 1: like we asked around the people taping this and said, 101 00:05:01,080 --> 00:05:02,800 Speaker 1: who knows what vibe coding is? People don't know what 102 00:05:02,920 --> 00:05:06,600 Speaker 1: vibe coding is? No shame, why would you know? And 103 00:05:06,640 --> 00:05:08,080 Speaker 1: so like talk about it. 104 00:05:08,279 --> 00:05:08,599 Speaker 3: Yeah. 105 00:05:08,960 --> 00:05:14,120 Speaker 2: So one of the key Open AI executives once he 106 00:05:14,240 --> 00:05:17,200 Speaker 2: said this a few years ago, he said, English is 107 00:05:17,240 --> 00:05:20,880 Speaker 2: now the new programming language. It's not these other things 108 00:05:21,040 --> 00:05:23,880 Speaker 2: because you can now program with your voice with typing, 109 00:05:24,320 --> 00:05:27,560 Speaker 2: and the most important skill is your ability to articulate 110 00:05:27,839 --> 00:05:30,600 Speaker 2: what you want to build. And so what vibe coding is, 111 00:05:30,640 --> 00:05:33,920 Speaker 2: and it sounds ridiculous, is the idea of going into 112 00:05:33,960 --> 00:05:36,839 Speaker 2: one of these tools and literally describing the piece of 113 00:05:36,880 --> 00:05:40,440 Speaker 2: software that you wish existed. You can outline all the parameters, 114 00:05:40,480 --> 00:05:43,520 Speaker 2: you can talk about the user that you want this 115 00:05:43,600 --> 00:05:45,920 Speaker 2: to appeal to, you can even describe the esthetics of it, 116 00:05:46,480 --> 00:05:50,040 Speaker 2: and in minutes, it will create this product for you. 117 00:05:50,120 --> 00:05:53,000 Speaker 2: And it's like magic and it's fully functional, which is 118 00:05:53,040 --> 00:05:54,040 Speaker 2: like if you think. 119 00:05:53,920 --> 00:05:57,120 Speaker 1: About it, like taking a step back, that's like saying, oh, yeah, 120 00:05:57,279 --> 00:05:59,880 Speaker 1: maybe you don't need to go to computer engineering or 121 00:06:00,080 --> 00:06:02,440 Speaker 1: you know, you don't need this degree from Stanford to 122 00:06:02,480 --> 00:06:04,880 Speaker 1: be able to do this type of thing. Actually, there's 123 00:06:04,920 --> 00:06:08,479 Speaker 1: a program now that enables you to build create code 124 00:06:08,520 --> 00:06:12,080 Speaker 1: your own app within a matter of minutes, which is 125 00:06:12,120 --> 00:06:15,520 Speaker 1: pretty phenomenal. And I think that opens up this world 126 00:06:15,560 --> 00:06:18,479 Speaker 1: to people who aren't engineers, who want to build businesses, 127 00:06:18,560 --> 00:06:21,560 Speaker 1: want to build out different applications, but don't have the 128 00:06:21,640 --> 00:06:25,919 Speaker 1: money but wherewithal the education to be able to actually 129 00:06:26,000 --> 00:06:27,880 Speaker 1: learn how to code or bring in someone to do 130 00:06:27,960 --> 00:06:28,720 Speaker 1: it right. 131 00:06:28,800 --> 00:06:33,400 Speaker 2: And it almost sort of democratizes the access of entrepreneurship. 132 00:06:33,560 --> 00:06:35,839 Speaker 2: Anyone can now be an entrepreneur. Anyone can build an app, 133 00:06:36,480 --> 00:06:38,839 Speaker 2: anyone can build a tool that helps them automate parts 134 00:06:38,880 --> 00:06:42,479 Speaker 2: of their life. So, and it's very empowering in the sense, 135 00:06:42,960 --> 00:06:47,200 Speaker 2: and I'm non technical, I got into it very quickly. 136 00:06:47,320 --> 00:06:49,960 Speaker 2: The first app I ever Vibe coded was while I 137 00:06:49,960 --> 00:06:52,720 Speaker 2: was watching Netflix or to Span two hours out, just 138 00:06:53,000 --> 00:06:56,039 Speaker 2: passively describing what I wish existed, and has built this 139 00:06:56,360 --> 00:06:57,200 Speaker 2: perfect app for me. 140 00:06:57,360 --> 00:06:59,400 Speaker 1: Okay, so take me to it. You're watching Netflix. What 141 00:06:59,480 --> 00:07:00,840 Speaker 1: were you watching? You remember? 142 00:07:01,080 --> 00:07:03,960 Speaker 3: I don't remember. I was multitasking three very specifics. 143 00:07:03,960 --> 00:07:07,200 Speaker 1: Okay, you're watching Netflix and you're creating your first app, 144 00:07:07,680 --> 00:07:09,600 Speaker 1: not with code, just with voice. How does it go? 145 00:07:10,040 --> 00:07:13,120 Speaker 2: So? I wanted to create a habit tracking app because 146 00:07:13,200 --> 00:07:15,760 Speaker 2: I wanted to have a better fitness regimen. And so 147 00:07:15,800 --> 00:07:19,560 Speaker 2: I literally describe, you know, I'm in this person, I'm 148 00:07:19,640 --> 00:07:21,800 Speaker 2: in that time. I think I was in my twenties. 149 00:07:22,120 --> 00:07:23,480 Speaker 2: I want to build an app to help me do 150 00:07:23,680 --> 00:07:26,360 Speaker 2: X and Y. I wanted to look like this, you know, 151 00:07:26,400 --> 00:07:28,640 Speaker 2: for example, I think I used example of duel ingo. 152 00:07:28,720 --> 00:07:30,880 Speaker 2: I love DUELINGO. Can you make it look exactly like 153 00:07:30,960 --> 00:07:34,560 Speaker 2: dul Ingo? I also want to be hooked. I want 154 00:07:34,600 --> 00:07:37,480 Speaker 2: to keep using it. So can you research all the 155 00:07:37,560 --> 00:07:41,480 Speaker 2: product haacks that duelingo has incorporated to keep their users 156 00:07:41,480 --> 00:07:43,960 Speaker 2: hooked and put into this app and then finally make 157 00:07:44,000 --> 00:07:46,840 Speaker 2: it safe and secure so nobody else can access my data? 158 00:07:47,440 --> 00:07:48,560 Speaker 3: And it split. 159 00:07:48,600 --> 00:07:51,640 Speaker 2: The first version out, it was good, the design was 160 00:07:51,640 --> 00:07:54,560 Speaker 2: a little off so over time. The reason I was 161 00:07:54,720 --> 00:07:56,520 Speaker 2: doing it while I was watching Netflix is because I 162 00:07:56,560 --> 00:07:58,920 Speaker 2: was just passively responding, being like, can you change the 163 00:07:58,920 --> 00:08:01,760 Speaker 2: color of this button? Or can you add this font 164 00:08:01,760 --> 00:08:03,880 Speaker 2: instead of this font? And can you add this feature? 165 00:08:04,080 --> 00:08:08,160 Speaker 2: So it's a very passive exercise. And each prompt prompt 166 00:08:08,200 --> 00:08:11,000 Speaker 2: is a what you say to the tool, So each 167 00:08:11,040 --> 00:08:14,160 Speaker 2: prompt will take about two minutes to get back to 168 00:08:14,160 --> 00:08:16,880 Speaker 2: you and to ship the new feature out. Okay, so 169 00:08:16,920 --> 00:08:19,280 Speaker 2: it's a very collaborative exercise, and it's honestly a very 170 00:08:19,320 --> 00:08:20,200 Speaker 2: passive exercise. 171 00:08:20,240 --> 00:08:22,000 Speaker 3: You don't need your full attention on it at all. 172 00:08:22,240 --> 00:08:25,320 Speaker 1: And so what other types of apps have you vibe coded? 173 00:08:25,840 --> 00:08:29,760 Speaker 2: I built an app for my girlfriend. Okay, I was 174 00:08:29,800 --> 00:08:30,400 Speaker 2: on vacation. 175 00:08:30,640 --> 00:08:33,000 Speaker 1: It's just like the most tech a thing I built. 176 00:08:33,200 --> 00:08:35,000 Speaker 1: This is like your love language. Go ahead. 177 00:08:35,320 --> 00:08:38,000 Speaker 2: I thought she would appreciate it because she's a former CTO. 178 00:08:38,679 --> 00:08:41,199 Speaker 2: She's much more technical than I am, and I thought 179 00:08:41,240 --> 00:08:42,040 Speaker 2: she would find it cute. 180 00:08:42,040 --> 00:08:42,320 Speaker 3: She did. 181 00:08:42,720 --> 00:08:45,880 Speaker 2: She'd ended up using it very often, but I was 182 00:08:45,960 --> 00:08:49,120 Speaker 2: on vacation and we had a bit of a time difference, 183 00:08:49,160 --> 00:08:50,840 Speaker 2: and I was always wondering what she was up to, 184 00:08:51,320 --> 00:08:54,480 Speaker 2: like how was her day? And so literally the app 185 00:08:54,559 --> 00:08:57,120 Speaker 2: was called four pm, where at four pm every day 186 00:08:57,200 --> 00:08:59,360 Speaker 2: she would tell me I had a great day, you know, 187 00:08:59,480 --> 00:09:01,480 Speaker 2: or at a bad day, and here's why, and here's 188 00:09:01,480 --> 00:09:03,280 Speaker 2: what I need, you know, maybe I need an Uber 189 00:09:03,280 --> 00:09:06,360 Speaker 2: Eats delivery with a froyo, or I need a compliment, 190 00:09:06,480 --> 00:09:08,840 Speaker 2: or I need a bouquet of roses. And it would 191 00:09:08,840 --> 00:09:12,600 Speaker 2: also collect all that data securely and show me trends 192 00:09:12,640 --> 00:09:14,600 Speaker 2: like on you know, maybe on certain days she would 193 00:09:14,600 --> 00:09:16,520 Speaker 2: feel better and this happened that led to her feeling 194 00:09:16,520 --> 00:09:18,880 Speaker 2: like this, or maybe when I gave her roses she'd 195 00:09:18,920 --> 00:09:22,680 Speaker 2: be in better moods. So so that's what if I've coded, 196 00:09:22,720 --> 00:09:25,040 Speaker 2: and uh, I loved I loved using it. It just 197 00:09:25,080 --> 00:09:26,960 Speaker 2: gave me more. I thought it gave me more insight 198 00:09:27,200 --> 00:09:29,400 Speaker 2: into her relationship. She didn't love it as much, but 199 00:09:29,679 --> 00:09:30,560 Speaker 2: that's another story. 200 00:09:30,800 --> 00:09:32,800 Speaker 1: It's just like the most techie thing that I have heard. 201 00:09:32,840 --> 00:09:35,079 Speaker 1: It's like, like, why not just check in with her? 202 00:09:35,480 --> 00:09:37,120 Speaker 2: I should That's what I should have done. I did 203 00:09:37,120 --> 00:09:38,640 Speaker 2: a bit of both. I did a bit of both. 204 00:09:38,679 --> 00:09:40,640 Speaker 1: But I love that this was like your love language. 205 00:09:40,679 --> 00:09:42,080 Speaker 1: And you said before, this is like kind of like 206 00:09:42,080 --> 00:09:45,160 Speaker 1: the modern lovely love letter for your for your girlfriend. 207 00:09:45,440 --> 00:09:49,600 Speaker 2: I love writing letters, so I love I'll write her emails. 208 00:09:49,760 --> 00:09:52,559 Speaker 2: Uh huh and they're they're you know, email letters, and 209 00:09:52,640 --> 00:09:54,680 Speaker 2: she loved them. And I was like, I was thinking 210 00:09:54,679 --> 00:09:57,959 Speaker 2: about the idea of how today we send each other content. 211 00:09:58,640 --> 00:10:01,360 Speaker 2: I'll send you a meme as a form of love language, 212 00:10:01,520 --> 00:10:02,719 Speaker 2: or at least in the tech world. I don't know 213 00:10:02,720 --> 00:10:05,360 Speaker 2: if I'm in a bubble here in the future of 214 00:10:05,360 --> 00:10:07,400 Speaker 2: that is like you send each other code. If I'm 215 00:10:07,440 --> 00:10:09,199 Speaker 2: thinking about you, instead of sending you a meme, I'll 216 00:10:09,200 --> 00:10:10,599 Speaker 2: send you this app that it built for you in 217 00:10:11,200 --> 00:10:14,520 Speaker 2: minutes based on a recent conversation. So I wanted to 218 00:10:14,559 --> 00:10:22,719 Speaker 2: test that theory. 219 00:10:26,800 --> 00:10:30,320 Speaker 1: Okay, so we're vibe coding modern love letters for our 220 00:10:30,360 --> 00:10:35,760 Speaker 1: significant others and creating applications with no computer engineering degrees. Amazing. 221 00:10:36,200 --> 00:10:38,320 Speaker 1: What are other types of things that you're using that 222 00:10:38,400 --> 00:10:40,560 Speaker 1: you think that the trends that really kind of speak 223 00:10:40,600 --> 00:10:42,440 Speaker 1: to the trends that are that are happening right now. 224 00:10:42,840 --> 00:10:45,200 Speaker 3: AI assistants are really big right now. 225 00:10:45,679 --> 00:10:50,760 Speaker 2: There's this tool called Poke that is basically my AI 226 00:10:50,800 --> 00:10:54,440 Speaker 2: assistant that lives in I Message. You plug Poke into 227 00:10:54,440 --> 00:10:57,720 Speaker 2: your Gmail and into your calendar, so Poke knows everything 228 00:10:57,760 --> 00:11:00,040 Speaker 2: about you based on your email history. 229 00:11:00,000 --> 00:11:01,079 Speaker 1: And you're not worried about that at all. 230 00:11:01,280 --> 00:11:01,920 Speaker 3: I'm not worried. 231 00:11:02,000 --> 00:11:06,240 Speaker 2: I'm also pretty risk seeking when it comes to like 232 00:11:06,280 --> 00:11:10,120 Speaker 2: trying these new AI tools. I've read the security policies. 233 00:11:11,280 --> 00:11:13,000 Speaker 2: Feels it's at my level of risk. 234 00:11:13,400 --> 00:11:13,760 Speaker 1: Yeah. 235 00:11:13,880 --> 00:11:14,679 Speaker 3: Yeah. 236 00:11:14,760 --> 00:11:17,040 Speaker 2: It plugs into my calendar and so it knows everything 237 00:11:17,080 --> 00:11:21,160 Speaker 2: about my personal and professional life. And so, for example, 238 00:11:21,160 --> 00:11:24,200 Speaker 2: the best use case is if someone sent me an 239 00:11:24,200 --> 00:11:29,480 Speaker 2: email eight years ago, it's impossible to find it in Gmail, 240 00:11:29,960 --> 00:11:32,599 Speaker 2: and especially with what I call natural language search, like 241 00:11:32,640 --> 00:11:35,400 Speaker 2: if you just use everyday language, but POPE can find it. 242 00:11:35,480 --> 00:11:39,079 Speaker 2: So for example, you know, like my health insurance files, 243 00:11:39,640 --> 00:11:42,240 Speaker 2: or if I had a conversation with someone that I 244 00:11:42,280 --> 00:11:44,400 Speaker 2: met a few years ago and I don't remember their name, 245 00:11:44,400 --> 00:11:46,640 Speaker 2: but I kind of remember their context, pokell diga up 246 00:11:46,679 --> 00:11:49,600 Speaker 2: for me. There's also the more obvious things where It 247 00:11:49,679 --> 00:11:53,920 Speaker 2: reminds me of doctor appointments, basketball games. It can book 248 00:11:54,120 --> 00:11:56,800 Speaker 2: you know, it can book sort of a meetings for you. 249 00:11:56,760 --> 00:11:57,520 Speaker 3: And your friends. 250 00:11:58,120 --> 00:12:00,560 Speaker 2: It's basically anything an assistant can do, PO can do, 251 00:12:00,840 --> 00:12:03,480 Speaker 2: but Poke has a lot more context because it knows 252 00:12:03,559 --> 00:12:05,040 Speaker 2: it's read all your emails. 253 00:12:05,200 --> 00:12:08,400 Speaker 1: Hmm, and it just texts you like like a friend 254 00:12:08,440 --> 00:12:11,959 Speaker 1: wood like a frame would. There's a real tension between 255 00:12:12,000 --> 00:12:15,240 Speaker 1: like do we just let these ais just like run 256 00:12:15,520 --> 00:12:17,679 Speaker 1: and know everything about us and make our lives more 257 00:12:17,679 --> 00:12:20,720 Speaker 1: efficient and or are we worried about privacy? And so 258 00:12:20,760 --> 00:12:22,320 Speaker 1: many of the folks I know in tech are like, 259 00:12:22,720 --> 00:12:25,679 Speaker 1: you know, this is actually it's not positive. It's going 260 00:12:25,720 --> 00:12:28,480 Speaker 1: to know all of these things I'm saving, Like how 261 00:12:28,480 --> 00:12:30,840 Speaker 1: many hours a day do you think you save with Poke? 262 00:12:30,920 --> 00:12:33,480 Speaker 1: And with these other applications we're talking about hours? 263 00:12:33,960 --> 00:12:36,080 Speaker 3: Yeah, right, five six hours. 264 00:12:35,800 --> 00:12:37,640 Speaker 1: Five or six hours a day. And so I think 265 00:12:37,679 --> 00:12:40,640 Speaker 1: a lot of people and kind of the inside baseball 266 00:12:40,640 --> 00:12:42,800 Speaker 1: tech world say, hey, this is worth it. Where's a 267 00:12:42,840 --> 00:12:45,920 Speaker 1: lot of people over here are on the other side, 268 00:12:45,920 --> 00:12:49,160 Speaker 1: are like, I'm really worried about letting this kind of 269 00:12:49,320 --> 00:12:51,719 Speaker 1: just run amok on all my data, because like I'm 270 00:12:51,840 --> 00:12:55,680 Speaker 1: terrified of it and Truthfully, I'm somewhere in between right Like, 271 00:12:55,880 --> 00:12:58,280 Speaker 1: I'm kind of like, Wow, this is going to happen, 272 00:12:58,320 --> 00:13:00,400 Speaker 1: and this is an inevitability, and these people are kind 273 00:13:00,400 --> 00:13:03,720 Speaker 1: of having a leg up, and I'm pretty nervous about 274 00:13:03,800 --> 00:13:06,760 Speaker 1: what happens when we realize that there's different scams with 275 00:13:06,800 --> 00:13:10,000 Speaker 1: prompt injections and all sorts of stuff. So I do 276 00:13:10,080 --> 00:13:14,320 Speaker 1: think it's really interesting. Okay, so Poke to really help you. 277 00:13:15,040 --> 00:13:16,680 Speaker 1: And by the way, I really need that. I feel 278 00:13:16,720 --> 00:13:19,320 Speaker 1: like I meet I forget full people that I've met 279 00:13:19,320 --> 00:13:21,719 Speaker 1: like a week ago, I mean truly, So I think 280 00:13:21,760 --> 00:13:24,000 Speaker 1: that would be really helpful for me. What else? 281 00:13:24,720 --> 00:13:26,400 Speaker 2: Uh, one last thing in Poke, because I think you 282 00:13:26,480 --> 00:13:30,080 Speaker 2: really enjoy this. Pok is quirky, and so you have 283 00:13:30,160 --> 00:13:33,720 Speaker 2: to pay a monthly fee to use Poke. But Poke 284 00:13:33,840 --> 00:13:37,000 Speaker 2: knows and knows it probably knows what income bracket you're 285 00:13:37,040 --> 00:13:38,880 Speaker 2: in just based on your emails. It knows what you 286 00:13:38,920 --> 00:13:41,120 Speaker 2: do for work, and so you have to negotiate with 287 00:13:41,200 --> 00:13:44,000 Speaker 2: Poke about how much you paid it every month. And 288 00:13:44,040 --> 00:13:46,840 Speaker 2: so I pay fifteen dollars a month. I negotiated that 289 00:13:47,000 --> 00:13:50,160 Speaker 2: down from one thousand dollars. What but I have a friend. 290 00:13:50,080 --> 00:13:52,000 Speaker 1: And you negotiated with an AI just with an. 291 00:13:51,960 --> 00:13:53,839 Speaker 2: AI and it's back and forth, and it's Poke is 292 00:13:53,920 --> 00:13:58,360 Speaker 2: challenging you. It's very very quirky, very provocative. I have 293 00:13:58,400 --> 00:14:00,800 Speaker 2: a friend who a mutual friend of mine, who sold 294 00:14:00,800 --> 00:14:03,680 Speaker 2: this company for three hundred million dollars. He pays one 295 00:14:03,760 --> 00:14:05,360 Speaker 2: hundred and fifty dollars a month for Poke for the 296 00:14:05,360 --> 00:14:06,280 Speaker 2: same exact product. 297 00:14:06,520 --> 00:14:07,800 Speaker 3: But it just knows his. 298 00:14:07,800 --> 00:14:09,480 Speaker 1: Profile because it like knows his finances. 299 00:14:09,520 --> 00:14:11,720 Speaker 2: It knows his finances well, not like I don't think 300 00:14:11,720 --> 00:14:14,840 Speaker 2: there's banking information links, but it can deduct based on 301 00:14:15,160 --> 00:14:16,359 Speaker 2: you know, research online. 302 00:14:16,480 --> 00:14:20,200 Speaker 1: I mean, what could go wrong, but also what could 303 00:14:20,200 --> 00:14:23,080 Speaker 1: go right. It's super interesting. I just have in my 304 00:14:23,120 --> 00:14:26,720 Speaker 1: head like you are creating apps for love for your 305 00:14:26,760 --> 00:14:29,880 Speaker 1: girlfriend and negotiating with Poke, and like it does feel 306 00:14:29,920 --> 00:14:31,760 Speaker 1: a bit like an episode of Silicon Valley. And it 307 00:14:31,800 --> 00:14:34,480 Speaker 1: also feels like the future in a very real way. 308 00:14:34,600 --> 00:14:37,160 Speaker 1: So that's fascinating. Okay, what else. 309 00:14:37,560 --> 00:14:39,920 Speaker 2: There's another tool that I've been using for the last 310 00:14:39,960 --> 00:14:43,840 Speaker 2: few years called Granola. And so my first job at 311 00:14:43,840 --> 00:14:47,240 Speaker 2: a school was working for this big telco company, and 312 00:14:47,280 --> 00:14:49,760 Speaker 2: as a junior, I spent most of my time taking 313 00:14:49,760 --> 00:14:52,720 Speaker 2: notes and meetings, and that was most of my job. 314 00:14:52,800 --> 00:14:56,120 Speaker 2: Take notes, come up with action items, help my boss 315 00:14:56,160 --> 00:14:57,760 Speaker 2: draft an email that he would send to the full 316 00:14:57,800 --> 00:15:01,760 Speaker 2: group after and then like track progress both digital meetings 317 00:15:01,800 --> 00:15:03,360 Speaker 2: and in person meetings, so it could be doing it 318 00:15:03,440 --> 00:15:06,200 Speaker 2: right now. Gives you the key takeaways and then turns 319 00:15:06,240 --> 00:15:10,240 Speaker 2: them into followup emails. And I remember after, I think 320 00:15:10,680 --> 00:15:12,400 Speaker 2: after the first time we met, the first time we 321 00:15:12,440 --> 00:15:14,840 Speaker 2: had coffee, you sent me a follow up email in 322 00:15:14,880 --> 00:15:18,000 Speaker 2: seven minutes from your phone and I was, I wish, 323 00:15:18,040 --> 00:15:20,280 Speaker 2: I've never seen that before, and I was shocked. 324 00:15:20,280 --> 00:15:22,120 Speaker 1: Am I granola? Am I AI? 325 00:15:23,440 --> 00:15:26,360 Speaker 2: Okay, but now everyone's going to be doing that because 326 00:15:26,360 --> 00:15:27,240 Speaker 2: they have granola. 327 00:15:27,400 --> 00:15:29,640 Speaker 1: So what differentiates what kind of gets through? 328 00:15:30,280 --> 00:15:32,400 Speaker 3: You know, that's a that's the question. 329 00:15:32,600 --> 00:15:36,760 Speaker 2: Yeah everyone, Well you very clearly see there's there's been 330 00:15:36,800 --> 00:15:39,440 Speaker 2: a divergence of people who use AI tools and people 331 00:15:39,440 --> 00:15:42,560 Speaker 2: who don't, and one group is faster, but they also 332 00:15:42,720 --> 00:15:47,720 Speaker 2: sound very similar, very generic sounding emails, generic follow up processes. 333 00:15:49,120 --> 00:15:51,400 Speaker 2: But there's I'm definitely seeing a bit of a split happen. 334 00:15:51,600 --> 00:15:53,600 Speaker 1: Yeah, I could really see that of like, we are 335 00:15:53,600 --> 00:15:57,200 Speaker 1: going to enter into this really efficient society, but people 336 00:15:57,240 --> 00:15:59,680 Speaker 1: are going to start sounding like robots, like you know, 337 00:15:59,720 --> 00:16:03,000 Speaker 1: when you know Gemini right when it like every time 338 00:16:03,200 --> 00:16:05,120 Speaker 1: it shows me what it would write and a follow 339 00:16:05,160 --> 00:16:07,200 Speaker 1: up email, I'm offended by it. I'm like, that is 340 00:16:07,240 --> 00:16:09,240 Speaker 1: like I just don't think that's it, right, And so 341 00:16:09,280 --> 00:16:11,000 Speaker 1: I think there's got to be somewhere in between. And 342 00:16:11,040 --> 00:16:14,160 Speaker 1: I think this tension is good, right. This tension is 343 00:16:14,240 --> 00:16:17,440 Speaker 1: good of like efficiency and humanity kind of coming together. 344 00:16:17,520 --> 00:16:19,440 Speaker 1: And you know, I feel like that email I sent 345 00:16:19,480 --> 00:16:22,440 Speaker 1: in seven minutes probably sounded pretty human. Would it have 346 00:16:22,480 --> 00:16:24,440 Speaker 1: had the same impact? Would you have remembered it if 347 00:16:24,440 --> 00:16:26,360 Speaker 1: it sounded like AI wrote it? And we're going to 348 00:16:26,440 --> 00:16:28,840 Speaker 1: have to kind of prove human a little bit more 349 00:16:28,880 --> 00:16:32,680 Speaker 1: than we used to anything else before we kind of 350 00:16:32,720 --> 00:16:34,760 Speaker 1: do a little bit of a demo because I definitely 351 00:16:34,840 --> 00:16:37,280 Speaker 1: want to actually take the time to do some vibe 352 00:16:37,280 --> 00:16:40,360 Speaker 1: coding and understand like what actually it's it's easier to 353 00:16:40,400 --> 00:16:44,120 Speaker 1: do than just talk about but anything else you want 354 00:16:44,120 --> 00:16:47,320 Speaker 1: to bring up. On our first productivity hack session. 355 00:16:47,280 --> 00:16:50,000 Speaker 2: We talked a bit about voice dictation, but I'll we'll 356 00:16:50,000 --> 00:16:51,920 Speaker 2: do it in the demo where we can use voice 357 00:16:52,120 --> 00:16:55,640 Speaker 2: like literally tell the AI what we want to build 358 00:16:55,680 --> 00:16:56,480 Speaker 2: through vibe coding. 359 00:16:56,760 --> 00:16:58,120 Speaker 3: But we could do that in demo. 360 00:16:58,040 --> 00:17:01,160 Speaker 2: And you talk about whisper right, whisper Yeah, whisper Flow 361 00:17:01,600 --> 00:17:04,800 Speaker 2: the first time. So all the tools I'm mentioning right now, 362 00:17:05,760 --> 00:17:07,760 Speaker 2: I like so much because the first time I use it, 363 00:17:07,760 --> 00:17:10,000 Speaker 2: it feels like magic. And I think Steve Jobs talks 364 00:17:10,000 --> 00:17:12,320 Speaker 2: about the iPhone feeling like magic the first time you 365 00:17:12,400 --> 00:17:14,680 Speaker 2: use it. A whisper Flow felt like magic the first 366 00:17:14,680 --> 00:17:18,119 Speaker 2: time I used it. And I can't imagine life without 367 00:17:18,119 --> 00:17:19,560 Speaker 2: whisper Flow. 368 00:17:19,800 --> 00:17:21,960 Speaker 1: What is whisper Flow. I feel like you've just like 369 00:17:22,160 --> 00:17:24,520 Speaker 1: come in here and said whisper flow and like, now 370 00:17:24,520 --> 00:17:26,280 Speaker 1: there's like a beam of light around you. 371 00:17:26,359 --> 00:17:27,199 Speaker 3: What is this? 372 00:17:27,320 --> 00:17:28,920 Speaker 1: And are you getting paid by them. 373 00:17:28,720 --> 00:17:31,720 Speaker 3: To say that? I'm not I'm not. I know the founder, 374 00:17:32,400 --> 00:17:34,359 Speaker 3: but I'm not getting paid. You know. 375 00:17:34,400 --> 00:17:37,080 Speaker 2: It's it's a really good voice dictation tool. It's the 376 00:17:37,119 --> 00:17:40,080 Speaker 2: best voice dictation tool in the market where it takes 377 00:17:40,640 --> 00:17:43,040 Speaker 2: a split second for it to convert everything you're saying 378 00:17:43,040 --> 00:17:48,359 Speaker 2: into perfectly formatted, perfect punctuation English unwritten text. 379 00:17:48,640 --> 00:17:50,840 Speaker 3: And that's really it's so simple, but it's so good. 380 00:17:51,080 --> 00:17:52,560 Speaker 1: Something you said to me last time we spoke is 381 00:17:52,560 --> 00:17:54,920 Speaker 1: you're like, yeah, like no one's typing anymore in Silicon Valley. 382 00:17:54,920 --> 00:17:57,520 Speaker 1: Everyone's like you know, I just I just imagine people 383 00:17:57,520 --> 00:18:01,000 Speaker 1: in offices whispering into their computers with whisper flow, and like, 384 00:18:01,040 --> 00:18:04,119 Speaker 1: somehow that's what's happening, But I don't know. You tell me. 385 00:18:04,320 --> 00:18:07,200 Speaker 2: I went to his office in San Francisco and everyone 386 00:18:07,240 --> 00:18:10,879 Speaker 2: had a microphone coming from their desk, and I was 387 00:18:10,960 --> 00:18:13,400 Speaker 2: kidding around. I'm like this, so does everyone just whisper 388 00:18:13,760 --> 00:18:17,480 Speaker 2: into their laptops? And he's like, yeah, nobody's typing. Everyone's 389 00:18:17,560 --> 00:18:20,200 Speaker 2: literally whispering. And he did a demo for me where 390 00:18:20,240 --> 00:18:22,800 Speaker 2: he was like, the reason it's called whisper flow is 391 00:18:22,880 --> 00:18:25,600 Speaker 2: because and then he like whispers to the point where 392 00:18:25,600 --> 00:18:27,960 Speaker 2: we're sitting this far away from each other, three foot apart. 393 00:18:28,200 --> 00:18:30,560 Speaker 2: I could not hear him, but he was able to 394 00:18:30,640 --> 00:18:33,879 Speaker 2: dictate a full sentence or even a full paragraph. So 395 00:18:33,920 --> 00:18:36,560 Speaker 2: you can literally whisper without anyone else hearing you. And 396 00:18:36,560 --> 00:18:38,040 Speaker 2: you can use it in the subway, you can use 397 00:18:38,080 --> 00:18:39,359 Speaker 2: it out in public. 398 00:18:39,720 --> 00:18:42,800 Speaker 1: And it gets me thinking, first of all, efficiency, efficiency, efficiency, 399 00:18:42,800 --> 00:18:45,040 Speaker 1: that is astounding. And then it gets me thinking about 400 00:18:45,040 --> 00:18:47,040 Speaker 1: when I learned, Okay, this is really going to age 401 00:18:47,080 --> 00:18:49,399 Speaker 1: me because I am literally a decade older than you, 402 00:18:49,520 --> 00:18:52,520 Speaker 1: So just here we go. But when I learned like cursive, 403 00:18:52,680 --> 00:18:54,960 Speaker 1: and I learned how to write when I was little, right, 404 00:18:55,080 --> 00:18:57,159 Speaker 1: and it's like and then we learned how to spell, 405 00:18:57,680 --> 00:18:59,520 Speaker 1: and then all of a sudden you have autocorrect, and 406 00:18:59,760 --> 00:19:02,359 Speaker 1: I I do I always say, because I kind of 407 00:19:02,359 --> 00:19:04,840 Speaker 1: live in between these worlds, I do worry that in 408 00:19:04,920 --> 00:19:07,280 Speaker 1: a world where AI is kind of writing everything, where 409 00:19:07,280 --> 00:19:10,040 Speaker 1: we're whispering instead of you know, instead of ever having 410 00:19:10,040 --> 00:19:12,359 Speaker 1: to type or write or think or sit or have 411 00:19:12,480 --> 00:19:14,800 Speaker 1: any of the friction that comes along with like having 412 00:19:14,840 --> 00:19:19,840 Speaker 1: to do something, that we might lose that ability to type, 413 00:19:20,000 --> 00:19:22,240 Speaker 1: like to even like think in that way for those 414 00:19:22,280 --> 00:19:25,879 Speaker 1: thought process and processes to go and and so I 415 00:19:25,920 --> 00:19:28,159 Speaker 1: think there's definitely a little bit of tension there, at 416 00:19:28,240 --> 00:19:30,760 Speaker 1: least when I think about it, of like how efficient 417 00:19:30,840 --> 00:19:34,080 Speaker 1: do you want to be? And what does efficiency backfire? Right? 418 00:19:34,240 --> 00:19:37,719 Speaker 2: Yeah, that's a good point about friction. There's this article 419 00:19:37,840 --> 00:19:41,199 Speaker 2: floating around about taste, like what is good taste and 420 00:19:41,240 --> 00:19:43,040 Speaker 2: good you know, I think the point they're trying to 421 00:19:43,040 --> 00:19:45,720 Speaker 2: make is good taste comes from the friction of doing things. 422 00:19:46,080 --> 00:19:48,080 Speaker 2: Like to be a good designer, you can't just vive 423 00:19:48,160 --> 00:19:50,000 Speaker 2: code it. You have to have the experience of doing 424 00:19:50,000 --> 00:19:51,560 Speaker 2: it multiple times, over and over again. 425 00:19:51,760 --> 00:19:53,960 Speaker 1: Okay, let's do it. Let's do it. Enough of enough 426 00:19:54,000 --> 00:19:55,879 Speaker 1: of whisper flow. I want to I want to I 427 00:19:55,880 --> 00:19:58,120 Speaker 1: want to code an app, or not code an app. 428 00:19:58,160 --> 00:19:59,879 Speaker 1: I want to talk to a computer that will co 429 00:20:00,000 --> 00:20:00,679 Speaker 1: it up for me. 430 00:20:01,119 --> 00:20:03,600 Speaker 2: We'll have you codd I'll tell you what to say, 431 00:20:03,880 --> 00:20:06,439 Speaker 2: and then you'll talk to the computer and the computer 432 00:20:06,520 --> 00:20:09,080 Speaker 2: that do all the work. Okay, So I guess, first 433 00:20:09,080 --> 00:20:10,440 Speaker 2: of all, what do you want to create? 434 00:20:11,400 --> 00:20:11,919 Speaker 1: My god? 435 00:20:12,080 --> 00:20:15,159 Speaker 2: Any ideas not I can give you some, but anything 436 00:20:15,200 --> 00:20:16,320 Speaker 2: you've always wanted to build. 437 00:20:16,640 --> 00:20:19,240 Speaker 1: I think a lot about So. I have an eleven 438 00:20:19,280 --> 00:20:25,120 Speaker 1: month old and he's eating, but like has all these 439 00:20:25,160 --> 00:20:29,360 Speaker 1: opinions now, and maybe there's like something there of like 440 00:20:29,640 --> 00:20:33,680 Speaker 1: food management and meals or something with my husband and 441 00:20:33,720 --> 00:20:35,960 Speaker 1: our schedule. I don't know. I feel like it's something 442 00:20:35,960 --> 00:20:38,040 Speaker 1: in the parenting department that I haven't figured out what. 443 00:20:38,920 --> 00:20:42,680 Speaker 1: But maybe you, as Andrew, could help me figure that out. 444 00:20:42,760 --> 00:20:44,360 Speaker 1: Or this could. 445 00:20:44,400 --> 00:20:47,480 Speaker 2: Maybe some sort of tracking app to track his meals. 446 00:20:47,800 --> 00:20:50,240 Speaker 1: Yeah, we could all kind of be in his sleep 447 00:20:50,280 --> 00:20:51,520 Speaker 1: and his meals. And I know there are things that 448 00:20:51,560 --> 00:20:53,359 Speaker 1: are out there like this, but I wonder if it's 449 00:20:53,359 --> 00:20:55,840 Speaker 1: a way to do this in like a personalized way. 450 00:20:56,359 --> 00:20:59,000 Speaker 1: Also that includes like my husband and his schedule and 451 00:20:59,080 --> 00:20:59,800 Speaker 1: all that stuff. 452 00:21:00,119 --> 00:21:02,520 Speaker 3: Okay, So why don't we do this? 453 00:21:02,560 --> 00:21:04,800 Speaker 2: We can even describe the outcome you want okay, and 454 00:21:04,800 --> 00:21:07,960 Speaker 2: then tell AI to like use their technical genius to 455 00:21:08,000 --> 00:21:09,320 Speaker 2: think of a product idea for us. 456 00:21:09,359 --> 00:21:13,000 Speaker 3: Great love that, Okay, So I'm going to put you 457 00:21:13,040 --> 00:21:13,480 Speaker 3: on the spot. 458 00:21:13,720 --> 00:21:16,040 Speaker 2: Oh no, I'm going to hit what the function key, 459 00:21:16,119 --> 00:21:19,000 Speaker 2: which means it's going to start recording. Okay, but start 460 00:21:19,000 --> 00:21:22,679 Speaker 2: by describing what you want the app to accomplish for you. 461 00:21:22,960 --> 00:21:25,200 Speaker 1: Okay. Do I need to like address the app by 462 00:21:25,280 --> 00:21:29,960 Speaker 1: name at all in case the AI come takes over. 463 00:21:30,520 --> 00:21:31,960 Speaker 3: I've heard that does make a difference. 464 00:21:32,320 --> 00:21:35,200 Speaker 1: No, no, No, honestly, I think like you giving full access 465 00:21:35,200 --> 00:21:36,879 Speaker 1: to your whole life to the AI might make it 466 00:21:36,920 --> 00:21:39,560 Speaker 1: easier when AI just takes over the world. I am 467 00:21:39,600 --> 00:21:41,960 Speaker 1: so nice to my AI, even when I'm pushing back 468 00:21:41,960 --> 00:21:42,280 Speaker 1: on it. 469 00:21:42,720 --> 00:21:45,879 Speaker 3: So well, we can we can start with please okay, Okay. 470 00:21:46,000 --> 00:21:47,600 Speaker 3: So I'm going to hit the function key. 471 00:21:47,680 --> 00:21:54,680 Speaker 1: Okay, Okay, So okay, when you hit that here, okay, 472 00:21:55,440 --> 00:21:58,720 Speaker 1: all right, So I don't know why I'm nervous to 473 00:21:58,880 --> 00:22:01,399 Speaker 1: like vibe code. This is the lead this is like 474 00:22:02,200 --> 00:22:05,240 Speaker 1: the least credible thing I have done as a technology place. Okay, 475 00:22:05,320 --> 00:22:08,320 Speaker 1: here we go. Okay, so okay, So vibe coding, it's 476 00:22:08,359 --> 00:22:10,240 Speaker 1: not like you just like type in vibe code, Like 477 00:22:10,280 --> 00:22:13,240 Speaker 1: where do you go? What am I looking at right now? 478 00:22:13,600 --> 00:22:16,440 Speaker 2: There's a few tools in a market. One of them 479 00:22:16,760 --> 00:22:20,960 Speaker 2: is Lovable. I use Lovable because it's the most beginner friendly. Now, 480 00:22:21,000 --> 00:22:24,199 Speaker 2: there are other more advanced versions that come with more features, 481 00:22:24,720 --> 00:22:28,280 Speaker 2: especially if you want to build something for that's enterprise grade, 482 00:22:28,400 --> 00:22:30,520 Speaker 2: i e. A product you want to raise money for 483 00:22:30,600 --> 00:22:31,960 Speaker 2: and ship out to millions of people. 484 00:22:32,280 --> 00:22:35,040 Speaker 3: But this is just for us, so Lovable is perfect. 485 00:22:35,480 --> 00:22:38,600 Speaker 1: Great, Okay, So I'm looking at Lovable and basically all 486 00:22:38,600 --> 00:22:40,280 Speaker 1: I have to do is press function here and then 487 00:22:40,280 --> 00:22:43,800 Speaker 1: I'm going to speak to the to this and it's. 488 00:22:44,000 --> 00:22:47,600 Speaker 2: The combination is a Whisper plus Lovable. That's the tech 489 00:22:47,680 --> 00:22:50,920 Speaker 2: stack we're using here. It's the combination of voice and 490 00:22:51,040 --> 00:22:51,639 Speaker 2: vibe coding. 491 00:22:51,760 --> 00:22:54,640 Speaker 1: And by the way, all these things you mentioned like Lovable, 492 00:22:54,840 --> 00:22:57,480 Speaker 1: whisper Flow, like Poke, we're going to have in our 493 00:22:57,480 --> 00:22:59,879 Speaker 1: show notes of like your tech stack for people. It's 494 00:22:59,920 --> 00:23:02,280 Speaker 1: like basically your productivity stack. It's all the things that 495 00:23:02,320 --> 00:23:04,040 Speaker 1: you use to be the most productive. So now I'm 496 00:23:04,040 --> 00:23:06,080 Speaker 1: not just looking at a website that's going to help 497 00:23:06,119 --> 00:23:08,240 Speaker 1: me code or that's going to create an app for me. 498 00:23:08,480 --> 00:23:11,800 Speaker 1: I'm using whisper which you've talked about whisper flow, so 499 00:23:11,880 --> 00:23:13,119 Speaker 1: I don't actually have to type. 500 00:23:13,280 --> 00:23:14,520 Speaker 3: You don't have to type. You could do it on 501 00:23:14,520 --> 00:23:15,040 Speaker 3: the treadmill. 502 00:23:15,080 --> 00:23:18,240 Speaker 1: You could do it while you're watching a movie watching Netflix. 503 00:23:18,400 --> 00:23:20,440 Speaker 1: All right, here we go. So now I'm just hold 504 00:23:20,480 --> 00:23:20,960 Speaker 1: it down and I. 505 00:23:20,880 --> 00:23:22,120 Speaker 3: Speak, hold it down when you speak. 506 00:23:22,240 --> 00:23:25,960 Speaker 1: Yeah, okay, hi there, I'm very excited for you to 507 00:23:26,000 --> 00:23:29,280 Speaker 1: be helping build something today. I'm looking to build an 508 00:23:29,320 --> 00:23:34,040 Speaker 1: app that will help me track my child, Charlie, his 509 00:23:34,320 --> 00:23:37,199 Speaker 1: eating times, what he's eating, what he's not eating, and 510 00:23:37,400 --> 00:23:40,199 Speaker 1: also something that my nanny can plug into, that my 511 00:23:40,280 --> 00:23:43,199 Speaker 1: husband can plug into that we can also track his 512 00:23:43,320 --> 00:23:46,360 Speaker 1: sleep in each other's schedules, and really try to come 513 00:23:46,400 --> 00:23:49,800 Speaker 1: together to think about how we can give him the 514 00:23:49,840 --> 00:23:54,199 Speaker 1: best experience. That sounds weird to say, but do you 515 00:23:54,320 --> 00:24:00,800 Speaker 1: understand that was really fast? Okay, so I. 516 00:24:00,800 --> 00:24:03,320 Speaker 2: Think this is good to start with. The last thing 517 00:24:03,359 --> 00:24:06,879 Speaker 2: we'll do is like, let's let's figure out what it 518 00:24:06,920 --> 00:24:09,240 Speaker 2: should look like. Oh, now, is there an app that 519 00:24:09,280 --> 00:24:11,120 Speaker 2: you really like the design of, because we'll just tell 520 00:24:11,119 --> 00:24:11,880 Speaker 2: it to copy that. 521 00:24:12,160 --> 00:24:15,879 Speaker 1: You know, I've recently been looking at ASH because I'm 522 00:24:15,880 --> 00:24:18,040 Speaker 1: interviewing the founder. I think that has really good design. 523 00:24:18,280 --> 00:24:22,920 Speaker 1: ASH the Therapy Chips therapy app. Yeah, but you could 524 00:24:22,920 --> 00:24:25,520 Speaker 1: also say do it in the style of Vanity fair, right, 525 00:24:25,600 --> 00:24:28,840 Speaker 1: like do it yeah, or just any anything, just to 526 00:24:28,920 --> 00:24:31,639 Speaker 1: kind of like reiterate, like you don't need to do that. 527 00:24:31,720 --> 00:24:33,520 Speaker 1: I just think like that's kind of cool. You don't 528 00:24:33,520 --> 00:24:35,160 Speaker 1: even have to look at other apps. You could say, 529 00:24:35,600 --> 00:24:37,280 Speaker 1: I want this to look like a New York Time 530 00:24:37,359 --> 00:24:38,920 Speaker 1: like New York Times app or something. 531 00:24:39,160 --> 00:24:40,480 Speaker 3: Do you want to use that example instead? 532 00:24:40,680 --> 00:24:41,199 Speaker 1: Sure? Why not? 533 00:24:41,720 --> 00:24:44,080 Speaker 2: I want this to look exactly like the New York 534 00:24:44,160 --> 00:24:49,680 Speaker 2: Times app with similar typeface, branding and esthetics. 535 00:24:50,040 --> 00:24:53,359 Speaker 3: Okay, now I'm gonna click enter. Okay, so it's gonna ship. 536 00:24:53,560 --> 00:24:54,200 Speaker 3: I'm nervous. 537 00:24:54,359 --> 00:24:55,160 Speaker 1: I'm nervous too. 538 00:24:55,720 --> 00:24:57,200 Speaker 3: Oh so it's. 539 00:24:57,080 --> 00:25:00,960 Speaker 2: Loading, it's coding. So here's his thought. It's thought process. 540 00:25:02,400 --> 00:25:04,280 Speaker 2: It replied. First of all, it replied to you. It 541 00:25:04,359 --> 00:25:06,040 Speaker 2: says I love this. 542 00:25:06,240 --> 00:25:07,720 Speaker 1: I mean, do you love this? Or are you just 543 00:25:07,760 --> 00:25:10,880 Speaker 1: being like affirmative because you're AI. But I'll take it 544 00:25:11,400 --> 00:25:11,879 Speaker 1: that's true. 545 00:25:12,200 --> 00:25:14,080 Speaker 3: They're very agreeable. Uh huh. 546 00:25:14,200 --> 00:25:17,120 Speaker 2: A family coordination app for Charlie with New York Times 547 00:25:17,280 --> 00:25:20,639 Speaker 2: editorial esthetics. So it describes what it believes that is 548 00:25:20,800 --> 00:25:25,399 Speaker 2: so bold serf headlines, black white palette, and then it 549 00:25:25,480 --> 00:25:29,480 Speaker 2: describes the features of the V one. Okay, it's just 550 00:25:29,520 --> 00:25:33,480 Speaker 2: talking through its design decisions and what is building on 551 00:25:33,520 --> 00:25:34,400 Speaker 2: the back end as well. 552 00:25:34,760 --> 00:25:36,679 Speaker 1: It's almost as if you're just sitting there talking to 553 00:25:36,760 --> 00:25:39,160 Speaker 1: a bunch of like engineers, being like, hey, I want 554 00:25:39,200 --> 00:25:40,919 Speaker 1: you to build out this app, and they're asking you 555 00:25:40,960 --> 00:25:42,879 Speaker 1: some questions or they're just telling you what they're doing, 556 00:25:43,320 --> 00:25:45,800 Speaker 1: and they're just doing it. But this is just automated 557 00:25:46,000 --> 00:25:48,240 Speaker 1: and you can do it in a short time without 558 00:25:48,840 --> 00:25:51,280 Speaker 1: I mean, I would be very worried if I were 559 00:25:51,280 --> 00:25:52,840 Speaker 1: an engineer in Silicon Valley right now. 560 00:25:53,000 --> 00:25:53,440 Speaker 3: Yeah. 561 00:25:53,600 --> 00:25:56,880 Speaker 2: Yeah, And so it's been a few minutes so far. 562 00:25:57,480 --> 00:26:00,320 Speaker 2: I think given another two minutes, i'll give you a 563 00:26:00,480 --> 00:26:03,000 Speaker 2: V one version of it. But so far, so good. 564 00:26:03,240 --> 00:26:05,920 Speaker 2: The main features it's suggesting based on what you said, 565 00:26:06,359 --> 00:26:09,679 Speaker 2: is sleep tracker, meal tracker, a dashboard where you can 566 00:26:09,720 --> 00:26:14,400 Speaker 2: see everything that's going on, and then a caregiver selector, 567 00:26:14,960 --> 00:26:18,960 Speaker 2: which is sort of what you mentioned, so it's live. 568 00:26:19,520 --> 00:26:24,200 Speaker 2: Oh see what it looks like. It's done starting live preview. 569 00:26:26,160 --> 00:26:30,080 Speaker 3: Oh, ok so this is a this is what it 570 00:26:30,119 --> 00:26:30,480 Speaker 3: looks like. 571 00:26:31,640 --> 00:26:34,160 Speaker 1: Okay, So just to jump in for all my listeners. 572 00:26:34,440 --> 00:26:36,879 Speaker 1: What I'm looking at on Andrew's screen is a clean 573 00:26:36,960 --> 00:26:39,800 Speaker 1: and simple black and white layout. There are two columns. 574 00:26:39,800 --> 00:26:41,600 Speaker 1: The left column is where I can log my kid's 575 00:26:41,640 --> 00:26:43,920 Speaker 1: food and take the right column is where I can 576 00:26:43,960 --> 00:26:47,000 Speaker 1: log his sleep, and below that there's today's log that 577 00:26:47,080 --> 00:26:50,959 Speaker 1: summarizes that information. And then at the top it's like 578 00:26:51,119 --> 00:26:53,639 Speaker 1: a by the numbers four box grid that allows me 579 00:26:53,680 --> 00:26:56,520 Speaker 1: to have a quick takeaway at any point. So two 580 00:26:56,600 --> 00:26:59,560 Speaker 1: meals had so far, four out of six foods eaten, 581 00:27:00,119 --> 00:27:03,160 Speaker 1: one refused, and a ninety minute sleep so far. 582 00:27:04,480 --> 00:27:08,320 Speaker 2: It's got this dashboard here with sleep, meals refused. 583 00:27:08,359 --> 00:27:09,960 Speaker 3: That's that's funny, meals refuge. 584 00:27:09,960 --> 00:27:12,840 Speaker 1: Actually, no, that's good because he keeps refusing everything. We 585 00:27:12,960 --> 00:27:15,359 Speaker 1: might I was saying before the start of like, my 586 00:27:15,440 --> 00:27:16,879 Speaker 1: husband and I thought we were parents of the year 587 00:27:16,920 --> 00:27:19,680 Speaker 1: because he was eating avocado and salmon and all these things. 588 00:27:19,680 --> 00:27:21,920 Speaker 1: And now he literally is refusing everything, and we don't 589 00:27:21,920 --> 00:27:24,399 Speaker 1: know what to do. I wonder if there's kind of 590 00:27:24,440 --> 00:27:27,360 Speaker 1: like built in advice on what to do if he's 591 00:27:27,520 --> 00:27:28,720 Speaker 1: you know, if he's refusing. 592 00:27:29,080 --> 00:27:30,439 Speaker 3: You could build that in if you want to. 593 00:27:30,600 --> 00:27:32,520 Speaker 1: I want to build that in for sure. And where's 594 00:27:32,520 --> 00:27:34,520 Speaker 1: that advice coming from Reddit? 595 00:27:34,800 --> 00:27:39,720 Speaker 2: Because that's not good wherever chat ept or Claude gets 596 00:27:39,720 --> 00:27:41,600 Speaker 2: its stuff right, so probably read it. 597 00:27:41,720 --> 00:27:45,760 Speaker 1: So just flagging the play here like yeah, yeah, okay. 598 00:27:46,280 --> 00:27:48,080 Speaker 2: So, I mean this is what it looks like so far, 599 00:27:48,160 --> 00:27:52,360 Speaker 2: it's it doesn't look like it's super functional. Aside from 600 00:27:52,359 --> 00:27:54,719 Speaker 2: these two features, you can log meal and see what happens. 601 00:27:55,000 --> 00:27:57,879 Speaker 2: You could put you could log each meal breakfast, lunch, dinner, snack, 602 00:27:58,280 --> 00:28:01,080 Speaker 2: who fed mom, Dad, nanny? 603 00:28:01,240 --> 00:28:01,520 Speaker 1: Okay? 604 00:28:01,600 --> 00:28:03,920 Speaker 2: And add food items. So let's try pancakes. I don't 605 00:28:03,920 --> 00:28:04,720 Speaker 2: know Charlie's pain. 606 00:28:04,920 --> 00:28:08,920 Speaker 1: This morning he had he had scrambled eggs with spinach 607 00:28:09,160 --> 00:28:13,720 Speaker 1: and cheese. She's really good. I mean, Charlie has a 608 00:28:14,520 --> 00:28:15,200 Speaker 1: really lifist. 609 00:28:16,160 --> 00:28:21,440 Speaker 3: So save meal eight, eate some or refuse the whole Thing's. 610 00:28:21,040 --> 00:28:24,400 Speaker 2: The whole thing, save meal boom, so it's logged, okay, 611 00:28:24,560 --> 00:28:28,080 Speaker 2: And then you can log sleep too, so nap or night. 612 00:28:28,040 --> 00:28:31,280 Speaker 1: Sleep yep, he's had one nap today for two hours. 613 00:28:31,520 --> 00:28:33,000 Speaker 1: I mean, I guess I would have to look at 614 00:28:33,040 --> 00:28:34,600 Speaker 1: my see if he's still asleep. 615 00:28:34,600 --> 00:28:36,480 Speaker 3: But yes, and then you can put in the time 616 00:28:37,040 --> 00:28:38,280 Speaker 3: that's nine. 617 00:28:38,120 --> 00:28:41,840 Speaker 2: Am, nine to eleven, nineteen eleven, So then you have 618 00:28:41,880 --> 00:28:43,920 Speaker 2: a log of the sleep as well. 619 00:28:44,160 --> 00:28:47,000 Speaker 3: And then so and so far, so good. It's still 620 00:28:47,040 --> 00:28:47,840 Speaker 3: it's very basic. 621 00:28:48,160 --> 00:28:49,959 Speaker 1: Yeah, And I think the question is like, okay, so 622 00:28:50,040 --> 00:28:51,960 Speaker 1: what so we're able to get this all in one place, 623 00:28:51,960 --> 00:28:53,719 Speaker 1: So how do we kind of like you you were 624 00:28:53,720 --> 00:28:56,360 Speaker 1: talking earlier about insights of like when your girlfriend might 625 00:28:56,400 --> 00:28:58,960 Speaker 1: feel sad, Like, what are insights that I could gain 626 00:28:59,040 --> 00:29:01,120 Speaker 1: from this? And what is the you know, maybe we 627 00:29:01,160 --> 00:29:03,120 Speaker 1: ask the app to help us with that. 628 00:29:04,720 --> 00:29:05,760 Speaker 3: Let's go back to the chat. 629 00:29:06,120 --> 00:29:09,560 Speaker 2: I'll pass it back to you, okay, okay, so you 630 00:29:09,560 --> 00:29:11,960 Speaker 2: can do a brain dump of anything that comes to mind, 631 00:29:11,960 --> 00:29:13,960 Speaker 2: any features you want to add, if you want to 632 00:29:13,960 --> 00:29:14,680 Speaker 2: ask for advice. 633 00:29:15,120 --> 00:29:18,720 Speaker 1: Okay, all right, So I just and I just press. 634 00:29:18,560 --> 00:29:21,880 Speaker 2: Press, press the speak and then let go when you're done. 635 00:29:22,280 --> 00:29:24,440 Speaker 1: This is a nice start. I would also love to 636 00:29:24,480 --> 00:29:28,120 Speaker 1: take this to the next level, so I understand his sleep, 637 00:29:28,160 --> 00:29:29,720 Speaker 1: his meals, and we can have this all in one 638 00:29:29,880 --> 00:29:32,120 Speaker 1: one place. But could you help come up with ideas 639 00:29:32,560 --> 00:29:36,880 Speaker 1: to help make his eating more efficient or sleeping more efficient, 640 00:29:37,040 --> 00:29:39,080 Speaker 1: or be able to in real time, say if we 641 00:29:39,120 --> 00:29:42,800 Speaker 1: should have naps that are that are shorter or longer. Also, 642 00:29:42,880 --> 00:29:46,320 Speaker 1: we need to add his bottle intake because he's still 643 00:29:46,520 --> 00:29:50,000 Speaker 1: on the bottle, and just anything to make this go 644 00:29:50,160 --> 00:29:53,400 Speaker 1: from just data gathering to something that actually will help 645 00:29:53,480 --> 00:29:59,320 Speaker 1: all of us with a better program. Okay, all right, 646 00:30:00,440 --> 00:30:02,520 Speaker 1: so now that was our version one. 647 00:30:03,040 --> 00:30:05,400 Speaker 2: So you brought up a good point where the easiest 648 00:30:05,440 --> 00:30:07,920 Speaker 2: kind of apps to build are data gathering apps, like 649 00:30:07,960 --> 00:30:10,160 Speaker 2: if it's track, like data input. Where it gets a 650 00:30:10,200 --> 00:30:13,360 Speaker 2: little harder is like when you need advice or when 651 00:30:13,360 --> 00:30:15,840 Speaker 2: you need it to go to chatchypt to pull out 652 00:30:15,880 --> 00:30:19,680 Speaker 2: insight and like use the api. API is like the 653 00:30:19,720 --> 00:30:22,480 Speaker 2: way I know, you know, just everyone, the way it 654 00:30:22,480 --> 00:30:25,640 Speaker 2: talks to other products and tools. So let's see what 655 00:30:25,640 --> 00:30:29,800 Speaker 2: it's saying. So new features bottle tracker, smart insights panel 656 00:30:30,080 --> 00:30:32,800 Speaker 2: to analyze patterns and give you recommendations and then daily 657 00:30:32,840 --> 00:30:37,400 Speaker 2: targets and progress. It's turning this from a logbook to 658 00:30:37,440 --> 00:30:38,280 Speaker 2: a smart assistant. 659 00:30:38,760 --> 00:30:41,880 Speaker 1: You know what's interesting about this is it is really 660 00:30:41,880 --> 00:30:43,320 Speaker 1: cool to be able to do this. It brings up 661 00:30:43,360 --> 00:30:46,560 Speaker 1: all sorts of you know, would parents, I mean, I'm 662 00:30:46,600 --> 00:30:48,800 Speaker 1: looking at this from a parent point of view, especially 663 00:30:48,920 --> 00:30:51,680 Speaker 1: knowing that it's going to be pulling insights from like 664 00:30:51,840 --> 00:30:54,320 Speaker 1: let's say it's using open Ai, it's using chat GPT. 665 00:30:55,160 --> 00:30:58,280 Speaker 1: I've gone, you know, I've used chat gipt before for 666 00:30:58,400 --> 00:31:00,720 Speaker 1: like sleep training, and then I've actually talked to a 667 00:31:00,800 --> 00:31:04,280 Speaker 1: human sleep trainer, and like there are some you know, 668 00:31:04,320 --> 00:31:06,720 Speaker 1: there are some real differences and how they both you know, 669 00:31:06,760 --> 00:31:08,440 Speaker 1: So I think it's actually kind of an important point 670 00:31:08,480 --> 00:31:11,320 Speaker 1: to say, you can create an app, but like, how 671 00:31:11,320 --> 00:31:13,680 Speaker 1: do you make sure that you're getting the best information 672 00:31:13,760 --> 00:31:17,560 Speaker 1: and the best insights? And so the barrier to entry 673 00:31:17,760 --> 00:31:20,600 Speaker 1: is lower, but is the standard and as high? 674 00:31:20,880 --> 00:31:23,680 Speaker 2: Right, that reminds me of something right online, which is 675 00:31:24,040 --> 00:31:26,920 Speaker 2: information is so abundant now because chat ept will give 676 00:31:26,920 --> 00:31:29,280 Speaker 2: you everything, but it's up to you to figure out 677 00:31:29,400 --> 00:31:32,360 Speaker 2: what's the most valuable for you. So judgment is more 678 00:31:32,400 --> 00:31:34,080 Speaker 2: important than ever, right. 679 00:31:33,920 --> 00:31:35,560 Speaker 1: And it's like, okay, who are people I trust? So 680 00:31:35,640 --> 00:31:38,880 Speaker 1: say like Emily Oster, like who has a book called 681 00:31:38,880 --> 00:31:43,200 Speaker 1: Expecting Better. She looks at all these statistical studies around 682 00:31:43,600 --> 00:31:46,280 Speaker 1: children and she's analyzed all of them. So I could 683 00:31:46,360 --> 00:31:49,560 Speaker 1: actually probably program this app to say, could you use 684 00:31:49,600 --> 00:31:52,760 Speaker 1: this based off of insights from this woman or from 685 00:31:52,800 --> 00:31:54,880 Speaker 1: this source, right, And I think that's the kind of 686 00:31:54,880 --> 00:31:57,200 Speaker 1: stuff just to think about as we're just like, oh, 687 00:31:57,360 --> 00:31:59,240 Speaker 1: create an app, but it'll do anything, it'll do whatever, 688 00:31:59,280 --> 00:32:03,400 Speaker 1: but like really looking at it in a critical thinking way, 689 00:32:03,560 --> 00:32:05,160 Speaker 1: like knowing that if we're going to have the access 690 00:32:05,240 --> 00:32:06,760 Speaker 1: to do this, we have to have the access to 691 00:32:06,880 --> 00:32:09,239 Speaker 1: understand information flow in that in a better way. So 692 00:32:09,320 --> 00:32:11,160 Speaker 1: not to get so inside baseball, but I think that's 693 00:32:11,160 --> 00:32:12,240 Speaker 1: like an important point here. 694 00:32:23,360 --> 00:32:24,520 Speaker 3: So it looks like it's done. 695 00:32:25,000 --> 00:32:29,400 Speaker 2: It added the three features you asked for, okay, and 696 00:32:29,480 --> 00:32:32,160 Speaker 2: so what do we have here now? Oh, so it's 697 00:32:32,240 --> 00:32:37,720 Speaker 2: got smart insights, more nap time needed again to your point, 698 00:32:37,760 --> 00:32:40,040 Speaker 2: based on whose recommendation, I'm not sure. Yeah, because we 699 00:32:40,080 --> 00:32:43,400 Speaker 2: didn't program that in yet. H But then the length 700 00:32:43,400 --> 00:32:45,200 Speaker 2: of the nap looks good. Ninety minutes is in a 701 00:32:45,200 --> 00:32:45,760 Speaker 2: sweet spot? 702 00:32:46,000 --> 00:32:49,400 Speaker 1: Yeah, Okay, Just to paint a picture of the final product, 703 00:32:49,840 --> 00:32:53,120 Speaker 1: same black and white, simple design, same by the numbers 704 00:32:53,160 --> 00:32:55,960 Speaker 1: at the top. But now we have a line graph 705 00:32:55,960 --> 00:32:59,800 Speaker 1: that charts eating and sleeping progress and the smart insights 706 00:33:00,320 --> 00:33:03,040 Speaker 1: that pulls an advice and then the same meal and 707 00:33:03,080 --> 00:33:06,360 Speaker 1: sleep logs plus bottle intake push to the bottom. 708 00:33:06,840 --> 00:33:09,880 Speaker 2: So the crazy thing about this is that three years ago, 709 00:33:10,000 --> 00:33:13,240 Speaker 2: this would have taken weeks and for the average person 710 00:33:13,480 --> 00:33:15,680 Speaker 2: tens of thousands of dollars to hire a developer to 711 00:33:15,720 --> 00:33:18,880 Speaker 2: do it. Now it's we did this in five ten minutes, 712 00:33:19,560 --> 00:33:21,320 Speaker 2: and it's it's a good V one. 713 00:33:21,880 --> 00:33:24,200 Speaker 1: Yeah, I think. And okay, so now let's say I 714 00:33:24,200 --> 00:33:25,400 Speaker 1: want to ship this. What do I do? 715 00:33:25,960 --> 00:33:28,400 Speaker 2: So if you want to ship it, you hit publish. 716 00:33:28,600 --> 00:33:31,160 Speaker 2: Uh huh, and let's actually publish it. 717 00:33:31,480 --> 00:33:34,040 Speaker 1: Okay. I can't wait to just randomly send this with 718 00:33:34,160 --> 00:33:35,480 Speaker 1: no context to my husband. 719 00:33:36,360 --> 00:33:39,720 Speaker 2: It's called Charlie nanny Hub. It's so it's live. It's 720 00:33:39,720 --> 00:33:43,600 Speaker 2: on the internet. Wow, obviously, like anyone can access it, 721 00:33:43,600 --> 00:33:45,720 Speaker 2: but nobody knows the URL, so you're safe. 722 00:33:46,000 --> 00:33:51,760 Speaker 1: Interesting, Wow, amazing we created an app and how many 723 00:33:51,800 --> 00:33:53,560 Speaker 1: minutes and like ten minutes, in less than ten minutes 724 00:33:53,600 --> 00:33:54,960 Speaker 1: and less than ten minutes, and it's. 725 00:33:54,880 --> 00:33:56,920 Speaker 3: Usable and maybe a little useful. 726 00:33:57,200 --> 00:33:59,000 Speaker 1: Yeah no, no, I think this is I think that 727 00:33:59,520 --> 00:34:01,040 Speaker 1: you know, there would be some work to do, but 728 00:34:01,040 --> 00:34:03,440 Speaker 1: I think it's really interesting. How would you see this 729 00:34:04,040 --> 00:34:07,400 Speaker 1: impacting like not folks just in tech, but like, yeah, 730 00:34:07,440 --> 00:34:09,520 Speaker 1: I'm a parent, Like what about someone who's a small 731 00:34:09,520 --> 00:34:12,440 Speaker 1: business owner. How do you see this kind of trend 732 00:34:12,520 --> 00:34:16,240 Speaker 1: impacting small business owners or folks who just aren't tech. 733 00:34:16,800 --> 00:34:19,759 Speaker 2: Yes, well, I believe the everyday person is going to 734 00:34:19,800 --> 00:34:23,719 Speaker 2: stop buying subscriptions to these apps out there, whether they 735 00:34:23,840 --> 00:34:27,080 Speaker 2: are consumer apps or you know software apps, think you 736 00:34:27,120 --> 00:34:30,600 Speaker 2: know payroll software, and they'll just build their own based 737 00:34:30,640 --> 00:34:32,440 Speaker 2: on their own needs. And so I think there's a 738 00:34:32,440 --> 00:34:36,240 Speaker 2: world where I don't need anything from what Silicon Value shipping. 739 00:34:36,280 --> 00:34:39,000 Speaker 2: I'll just build my own apps. Wow, I don't need 740 00:34:39,040 --> 00:34:41,080 Speaker 2: du lingo. I'll just build my own language learning app. 741 00:34:41,320 --> 00:34:45,520 Speaker 1: Wow. Amazing. I feel like we have vibe coded. We 742 00:34:45,600 --> 00:34:48,360 Speaker 1: have talked about all of these different types of programs 743 00:34:48,360 --> 00:34:51,839 Speaker 1: to make our lives more efficient, and so I know 744 00:34:51,880 --> 00:34:54,000 Speaker 1: we're kind of nearing the end. I want to go 745 00:34:54,080 --> 00:34:57,640 Speaker 1: back to something that I saw that you see. We 746 00:34:57,640 --> 00:35:01,120 Speaker 1: were talking about your dad and he you're close with 747 00:35:01,160 --> 00:35:03,080 Speaker 1: your dad, and he was always saying, build something for 748 00:35:03,120 --> 00:35:06,799 Speaker 1: yourself and always think bigger. Right, that seems to be 749 00:35:07,080 --> 00:35:10,920 Speaker 1: kind of a theme in your life with advances in AI. 750 00:35:11,400 --> 00:35:12,600 Speaker 1: How big do you think we can think? 751 00:35:13,200 --> 00:35:15,719 Speaker 2: I think AI is really empowering. I think it's going 752 00:35:15,760 --> 00:35:18,800 Speaker 2: to empower a lot of people to build their own businesses. 753 00:35:19,200 --> 00:35:21,920 Speaker 2: And when people think about building businesses, they typically think 754 00:35:21,920 --> 00:35:25,440 Speaker 2: about the Silicon Valley mantra of building these venture backed, 755 00:35:25,640 --> 00:35:28,279 Speaker 2: you know, massive companies with the goal of hitting a 756 00:35:28,320 --> 00:35:30,920 Speaker 2: billion dollar valuation. But I think there's a bunch of 757 00:35:30,920 --> 00:35:33,040 Speaker 2: other types of businesses that exist, including the one I've 758 00:35:33,040 --> 00:35:37,720 Speaker 2: built for myself, which is a lifestyle business as incredibly 759 00:35:37,760 --> 00:35:41,120 Speaker 2: fulfilling and flexible and gives me the ability to not 760 00:35:41,239 --> 00:35:45,560 Speaker 2: work for an employer. And I think there's going to 761 00:35:45,840 --> 00:35:49,160 Speaker 2: be a whole wave of solopreneurs where people can build 762 00:35:49,200 --> 00:35:53,120 Speaker 2: multiple income streams with these AI tools, whereas before they 763 00:35:53,160 --> 00:35:55,759 Speaker 2: would not have been able to. It makes me think 764 00:35:55,800 --> 00:35:59,680 Speaker 2: back to the Shopify era when Shopify came out, I 765 00:35:59,680 --> 00:36:04,440 Speaker 2: think around early twenty tens maybe, where everyone was building 766 00:36:05,320 --> 00:36:08,120 Speaker 2: these direct and consumer businesses because it was so easy. 767 00:36:08,280 --> 00:36:09,680 Speaker 2: So you could build a T shirt business, you could 768 00:36:09,680 --> 00:36:11,600 Speaker 2: build a shoe business because they had just built the 769 00:36:11,640 --> 00:36:15,040 Speaker 2: physical infrastructure to make it so easy. Now we're seeing 770 00:36:15,040 --> 00:36:18,920 Speaker 2: that with Vibe coded software, where anyone can build software 771 00:36:19,200 --> 00:36:21,200 Speaker 2: to solve your own problems, but also to turn them 772 00:36:21,239 --> 00:36:25,560 Speaker 2: into these small businesses, and so hopefully that's empowering to people, 773 00:36:25,719 --> 00:36:30,040 Speaker 2: and I really believe in entrepreneurship as a viable career 774 00:36:30,080 --> 00:36:31,200 Speaker 2: path for more and more people. 775 00:36:31,640 --> 00:36:35,000 Speaker 1: Wonderful to hear what is the one thing every listener 776 00:36:35,040 --> 00:36:36,880 Speaker 1: needs to do today to get a bit of an edge? 777 00:36:37,280 --> 00:36:40,320 Speaker 2: Going lovable, describe the lovable what you want to build 778 00:36:40,800 --> 00:36:42,000 Speaker 2: and see magic happen. 779 00:36:42,360 --> 00:36:43,920 Speaker 1: Great, and so we're going to have you back on 780 00:36:43,920 --> 00:36:45,759 Speaker 1: the pod. We're going to I think that you are 781 00:36:45,840 --> 00:36:48,520 Speaker 1: kind of like my cheat sheet for the future. So 782 00:36:48,520 --> 00:36:52,200 Speaker 1: if there are questions for Andrew anything AI related, productivity, hacks, hustle, 783 00:36:52,680 --> 00:36:56,040 Speaker 1: anything you're thinking about, email Melariat, Mostly human dot Com 784 00:36:56,160 --> 00:37:00,279 Speaker 1: or dm me Mostly Human Media on Instagram and then 785 00:37:00,400 --> 00:37:02,600 Speaker 1: tell me Andrew, where can people get in touch with you? 786 00:37:03,360 --> 00:37:04,920 Speaker 1: And we'll have to put this out to your newsletter 787 00:37:04,920 --> 00:37:06,760 Speaker 1: in your community to see what they want to hear about, 788 00:37:06,800 --> 00:37:09,480 Speaker 1: because we got to get you back and giving us 789 00:37:09,520 --> 00:37:10,440 Speaker 1: more of the cheat sheet. 790 00:37:10,520 --> 00:37:13,279 Speaker 2: Yeah, I'm most active on LinkedIn. So Andrew Young Young 791 00:37:13,360 --> 00:37:16,800 Speaker 2: is y eu n G. I'm the I says, I 792 00:37:17,160 --> 00:37:20,440 Speaker 2: throw parties. That's my tagline, so just follow me there. 793 00:37:20,440 --> 00:37:24,920 Speaker 1: Great Awesome. Mostly Human is a production of iHeart Podcasts 794 00:37:24,920 --> 00:37:27,840 Speaker 1: and Mostly Human Media. It's produced and edited by Lori Siegel, 795 00:37:27,960 --> 00:37:31,520 Speaker 1: Lauren Hanson, and Nicole Bouchet. Sound design and mixing by 796 00:37:31,560 --> 00:37:35,360 Speaker 1: Derek Clements. Additional production health from Abooz of Var Special 797 00:37:35,360 --> 00:37:38,359 Speaker 1: thanks to Mark Weinhaus. Find us on all socials at 798 00:37:38,360 --> 00:37:41,040 Speaker 1: mostly human Media. You can also watch mostly Human on 799 00:37:41,080 --> 00:37:43,000 Speaker 1: our YouTube page. If you want to get in touch, 800 00:37:43,080 --> 00:37:46,520 Speaker 1: email us at hello at mostlyhuman dot com. And if 801 00:37:46,520 --> 00:37:48,560 Speaker 1: you like what you're hear, please rate and review the 802 00:37:48,600 --> 00:37:50,760 Speaker 1: show and share it with your friends. See you next week.