1 00:00:00,080 --> 00:00:03,840 In this 350 2nd episode of data driven, Frank 2 00:00:03,840 --> 00:00:07,520 and Andy speak with Blake Reichenbach. Blake is a 3 00:00:07,520 --> 00:00:11,275 product manager at HubSpot, focusing on the Content AI platform, 4 00:00:11,415 --> 00:00:14,875 and is the owner of Howdy Curiosity, an online nonfiction 5 00:00:15,014 --> 00:00:18,680 bookstore and learning community. Stay tuned for a 6 00:00:18,680 --> 00:00:22,040 delightful conversation on data, AI, and the love of 7 00:00:22,040 --> 00:00:22,540 books. 8 00:00:28,575 --> 00:00:32,335 Hello, and welcome to Data Driven. The podcast where we explore the emergent fields 9 00:00:32,335 --> 00:00:35,715 of artificial intelligence, data science, and, of course, data engineering, 10 00:00:36,060 --> 00:00:39,820 Which is really the underpinning that makes it all possible. And to that 11 00:00:39,820 --> 00:00:43,200 end, I have my very favoritest, data engineer in the world, 12 00:00:43,579 --> 00:00:47,425 Andy Leonard. How are you doing, Andy? I 13 00:00:47,425 --> 00:00:50,805 am unlike both of you, I have not yet had COVID, 14 00:00:51,345 --> 00:00:54,870 but I and I'm doing well. But it's in it's It's in our 15 00:00:54,870 --> 00:00:58,550 house. We have a a home member here who has tested 16 00:00:58,550 --> 00:01:02,330 positive. So we're all walking on eggshells, 17 00:01:02,870 --> 00:01:06,315 over here. And, but I am doing well. I love 18 00:01:06,315 --> 00:01:10,075 the, you know, the data engineering part. I really love Frank's 19 00:01:10,075 --> 00:01:13,610 article that it was written way back last year. It's So 20 00:01:13,610 --> 00:01:17,369 2023 about roadies about roadies versus the 21 00:01:17,369 --> 00:01:20,909 rock stars, and he calls weed data engineers the, roadies. 22 00:01:21,575 --> 00:01:25,175 And, yeah, doing well. Frank, I got to present last 23 00:01:25,175 --> 00:01:28,875 night in person In person. Richmond Richmond, Virginia, 24 00:01:29,740 --> 00:01:33,580 Not not Kentucky. Which is a good segue to our guest It is. Who 25 00:01:33,580 --> 00:01:37,020 is in Richmond. Andy and I met in Richmond. We organized the Richmond code camp, 26 00:01:37,020 --> 00:01:40,424 although that was Richmond, Virginia. We are here today with Blake 27 00:01:40,424 --> 00:01:44,024 Reichenbach, who is a project manager 28 00:01:44,344 --> 00:01:47,869 product manager, sorry, at HubSpot Focusing on the Content 29 00:01:47,869 --> 00:01:51,710 AI platform, and I love to know more about HubSpot in general. One 30 00:01:51,710 --> 00:01:55,149 of the podcasters that I follow and and admire is John Lee Dumas, and I 31 00:01:55,149 --> 00:01:58,555 know he has with HubSpot. But welcome to the show, 32 00:01:58,854 --> 00:02:02,695 Blake. As we were talking in the virtual green room, you're recovering from 33 00:02:02,695 --> 00:02:06,410 COVID. I had COVID flu strain a, Sinus infection then 34 00:02:06,410 --> 00:02:10,250 followed up with this week. You're such an overachiever, Frank. I have to do 35 00:02:10,250 --> 00:02:12,810 it all day. I just have to do it all day. I know. He's putting 36 00:02:12,810 --> 00:02:16,125 he's putting my COVID to shame. I Should've got out and gotten back into something 37 00:02:16,125 --> 00:02:19,965 else before joining so that I could keep up. I have 3 kids, also 38 00:02:19,965 --> 00:02:21,990 known as bioweapon incubators. So 39 00:02:23,730 --> 00:02:27,490 Fair. Fair. My my only kid is, you know, won't 40 00:02:27,490 --> 00:02:31,335 be visible on the podcast, Yes, of course. But he's the, large bulldog sitting 41 00:02:31,335 --> 00:02:34,695 behind me, and thankfully, he doesn't tend to break too many germs into the 42 00:02:34,695 --> 00:02:37,835 house. That's awesome. That is That's awesome. 43 00:02:38,450 --> 00:02:41,910 So so tell us about HubSpot and and what it is you do there. 44 00:02:42,770 --> 00:02:45,650 What is HubSpot exactly? It's one of those things that's on I have a board 45 00:02:45,650 --> 00:02:49,415 of things I'm supposed to look at. And HubSpot is is is on the list, 46 00:02:49,415 --> 00:02:52,555 but with everything going on, I haven't had a chance to. 47 00:02:53,415 --> 00:02:57,150 Yeah. Well, if that's like your Kanban board of Software to dig into. I would 48 00:02:57,150 --> 00:03:00,849 definitely recommend moving that up in your backlog to dig in HubSpot. 49 00:03:01,629 --> 00:03:05,455 I'm clearly biased as a HubSpot employee, but It is a really cool company and 50 00:03:05,455 --> 00:03:09,135 a really cool product. So we're a leading customer relationship 51 00:03:09,135 --> 00:03:12,435 management platform or CRM platform for scaling companies. 52 00:03:13,280 --> 00:03:17,120 Our platform includes a bit of everything that a business 53 00:03:17,120 --> 00:03:20,735 needs for their front office. So we have marketing, we have sales, we have 54 00:03:20,895 --> 00:03:24,735 Service, we have data operations, and we have the part of the 55 00:03:24,735 --> 00:03:28,260 platform that I live in, which is our CMS. And 56 00:03:28,420 --> 00:03:32,200 All of these different hubs as we call them or or product lines, 57 00:03:32,739 --> 00:03:36,385 exist around that central CRM. We try and make 58 00:03:36,385 --> 00:03:40,144 everything as, you know, fittingly for this this podcast, as 59 00:03:40,144 --> 00:03:43,670 data driven for our customers as possible so that they have these 60 00:03:43,910 --> 00:03:47,670 Integrated systems across their different business pillars, so that 61 00:03:47,670 --> 00:03:51,190 things can stay in sync and aligned, and, you 62 00:03:51,190 --> 00:03:54,735 know, staying true to The data that they 63 00:03:54,735 --> 00:03:58,515 have about their customers to make the most informed decisions possible. 64 00:03:59,375 --> 00:04:03,180 As far as what I do at HubSpot, I've been with 65 00:04:03,180 --> 00:04:06,700 the company for, I'm going into my 7th year 66 00:04:06,700 --> 00:04:10,379 now, which I guess by, like, SaaS industry standards makes 67 00:04:10,379 --> 00:04:13,825 me a grandfather. But 68 00:04:14,444 --> 00:04:18,125 I for the last about, two and a 69 00:04:18,125 --> 00:04:21,810 half years or so, I've been a product manager. I 70 00:04:21,810 --> 00:04:25,569 started out in our product security organization. My focus is 71 00:04:25,569 --> 00:04:29,409 really on, you know, maturing our content abuse 72 00:04:29,409 --> 00:04:32,905 and fraud detection systems. And then I moved over more 73 00:04:32,905 --> 00:04:36,445 recently into our content AI platform. So 74 00:04:36,585 --> 00:04:40,400 thinking about this really exciting emerging world of 75 00:04:40,639 --> 00:04:44,100 AI and generative AI and how that's reshaping 76 00:04:44,240 --> 00:04:47,919 or informing the way that content marketers work and figuring 77 00:04:47,919 --> 00:04:51,595 out what solutions are going to have a 78 00:04:51,595 --> 00:04:54,095 meaningful impact for content marketers. 79 00:04:55,435 --> 00:04:59,169 Interesting. I would imagine AI and generative 80 00:04:59,229 --> 00:05:01,409 AI is probably very much on your radar. 81 00:05:03,069 --> 00:05:06,575 Oh, yeah. Has that been I don't think I've Sorry. Go 82 00:05:06,575 --> 00:05:09,935 ahead. I was gonna say I don't think I've had a conversation in the last 83 00:05:09,935 --> 00:05:13,315 6 months that has not included something about generative 84 00:05:13,375 --> 00:05:16,930 AI Or, you know, machine learning or chat 85 00:05:16,930 --> 00:05:20,450 GPT or Sam Altman. It's very, very 86 00:05:20,450 --> 00:05:23,750 central to, you know, what I'm working on and where my focus is. 87 00:05:24,595 --> 00:05:28,194 Interesting. So so how disruptive has it been for your 88 00:05:28,194 --> 00:05:31,794 your business? I mean, so is it I guess it's fair to say that that 89 00:05:31,794 --> 00:05:35,570 that HubSpot is a CRM, right? And 90 00:05:37,390 --> 00:05:40,990 how do people use AI? Like, how is 91 00:05:40,990 --> 00:05:44,715 AI integrated into your platform? Yeah. 92 00:05:44,715 --> 00:05:48,495 So we have, quite a bit 93 00:05:48,555 --> 00:05:52,139 of of new AI features that we've rolled out Over the 94 00:05:52,139 --> 00:05:55,580 last year or so, you know, I 95 00:05:55,580 --> 00:05:59,295 I again, my my focus is really within the CMS So what I'm most 96 00:05:59,295 --> 00:06:02,975 familiar with is within our our tools for managing content, 97 00:06:02,975 --> 00:06:06,355 building websites. And we've rolled out quite a few, 98 00:06:07,055 --> 00:06:10,770 different AI and ML assistance, within 99 00:06:10,770 --> 00:06:14,530 our feat our feature set. A lot of those are still, you 100 00:06:14,530 --> 00:06:18,105 know, in beta and have to be opted into, But, you know, introducing 101 00:06:18,325 --> 00:06:21,545 different, generative AI models to help marketers 102 00:06:22,165 --> 00:06:25,945 just streamline their efficiencies. So doing things like 103 00:06:26,210 --> 00:06:29,830 Generating meta descriptions for their pages or rewriting 104 00:06:29,970 --> 00:06:33,650 content, you know, pretty what I think in the market is 105 00:06:33,650 --> 00:06:37,254 becoming standard for generative AI tasks, has 106 00:06:37,254 --> 00:06:40,955 really been our our starting point there. You know, I think 107 00:06:41,414 --> 00:06:44,235 we as a company have been, 108 00:06:45,669 --> 00:06:49,370 Looking at AI, you know, longer than it's been this, like, 109 00:06:49,430 --> 00:06:53,270 flashpoint in public conversation. Coming from the security background, 110 00:06:53,270 --> 00:06:56,895 one of my first Big projects and product security was in, you 111 00:06:56,895 --> 00:07:00,735 know, maturing our abuse detection systems and figuring out, you know, how can 112 00:07:00,735 --> 00:07:03,900 we leverage LLMs? How can we leverage machine learning models 113 00:07:04,300 --> 00:07:07,900 To improve our precision and make sure that fewer, you 114 00:07:07,900 --> 00:07:10,560 know, fraudulent pieces of content slipped through the cracks. 115 00:07:11,675 --> 00:07:15,515 And then once, you know, ChatGPT became, like, the 116 00:07:15,515 --> 00:07:18,575 tech topic of the day, you know, that's where, 117 00:07:19,419 --> 00:07:23,260 HubSpot along with a lot of other folks in, in the same space started saying, 118 00:07:23,260 --> 00:07:26,380 okay, cool. How can we pull these features in app to, 119 00:07:27,885 --> 00:07:31,645 You know, give our customers new new tools to use, new things to play around 120 00:07:31,645 --> 00:07:34,145 with, and better ways to improve their own efficiencies. 121 00:07:36,740 --> 00:07:40,340 Interesting. And and you're in the since you're in that 122 00:07:40,340 --> 00:07:43,160 marketing space, like, the and and and I would imagine 123 00:07:44,115 --> 00:07:47,715 It's a very data heavy world anyway. Right? Like, it's a very it's 124 00:07:47,955 --> 00:07:51,634 you already start off with a bias towards being data driven. Yes. I said the 125 00:07:51,634 --> 00:07:55,050 name of my own show. But but, I mean, like and I think that, 126 00:07:56,550 --> 00:08:00,169 you know, I'm just fascinated by marketing. 127 00:08:00,314 --> 00:08:03,134 Right. Like, marketing is my new fascination for 2024, 128 00:08:04,314 --> 00:08:07,435 because I realized in some ways, I'm good at it. In some ways, I'm horrible 129 00:08:07,435 --> 00:08:10,580 at it. Actually, Really got awfully horrible at it. 130 00:08:11,920 --> 00:08:15,620 So, but it's funny because as I look into it more, 131 00:08:15,814 --> 00:08:18,534 I've reached out to people to kinda help, and they're like, oh, no. You gotta 132 00:08:18,534 --> 00:08:22,215 separate the data. I'm like, this is a lot of data analysis. This is this 133 00:08:22,215 --> 00:08:25,275 is my jam. Like, I I should be better at this. 134 00:08:26,760 --> 00:08:30,280 Yeah. You know, good marketing is data driven. I think 135 00:08:30,280 --> 00:08:33,799 that, you know, in marketing, especially content 136 00:08:33,799 --> 00:08:37,615 marketing, It's often seen like as much an alchemy as it 137 00:08:37,615 --> 00:08:41,455 is a science, where on the one hand, you have some marketers who 138 00:08:41,455 --> 00:08:45,180 are, like, Data obsessed. You know, they will only write 139 00:08:45,180 --> 00:08:48,620 a blog post if they have estimated search volumes and, 140 00:08:48,620 --> 00:08:52,415 like, you know, customer persona data. And Then you have other 141 00:08:52,415 --> 00:08:55,795 marketers who are kind of like, let's crank things out and see what sticks. 142 00:08:56,095 --> 00:08:59,634 And I think that's a really kind of interesting intersection 143 00:08:59,855 --> 00:09:03,230 with generative AI Because a lot of, you know, 144 00:09:03,230 --> 00:09:06,750 early generative AI tools for marketers, and I'm not going to name 145 00:09:06,750 --> 00:09:10,529 specific companies. I don't wanna start any kind of, you know, your flame or there, 146 00:09:10,750 --> 00:09:14,575 but A lot of of GenAI tools have kind of just been like 147 00:09:14,575 --> 00:09:18,334 a a churn and burn factory where they're cranking 148 00:09:18,334 --> 00:09:22,060 out a lot of mediocrity really fast. And, you 149 00:09:22,060 --> 00:09:25,579 know, you kind of see if you if you look at performance graphs of 150 00:09:25,579 --> 00:09:29,095 companies that have gone this route of just, like, Cranking out 151 00:09:29,154 --> 00:09:32,375 generative articles without, you know, human in the loop processes. 152 00:09:33,075 --> 00:09:36,834 Like, you'll see their web traffic and their conversions kinda going up, up, 153 00:09:36,834 --> 00:09:40,510 up, up, up, up, hit a cliff, Boom. Drop. Right. And, you know, 154 00:09:40,510 --> 00:09:43,250 there's not that long term ROI. There's not that, 155 00:09:44,110 --> 00:09:47,945 meaningful customer connection that lets The brand really build upon 156 00:09:47,945 --> 00:09:51,785 itself. And so I think that where we're at as an industry now is 157 00:09:51,785 --> 00:09:55,529 this really cool place where The marketers, but 158 00:09:55,529 --> 00:09:59,290 also the Gen AI tools that are winning or going to win 159 00:09:59,290 --> 00:10:02,730 long term are the ones that are able to incorporate data in a 160 00:10:02,730 --> 00:10:05,855 meaningful way. And the products that are able to, 161 00:10:07,435 --> 00:10:11,170 present generative AI functionality In a 162 00:10:11,170 --> 00:10:14,850 way that is intuitive and that prioritizes UX, 163 00:10:14,850 --> 00:10:18,610 which frankly has not been a big emphasis in the Gen AI 164 00:10:18,610 --> 00:10:22,375 industry, for the last Dear, I think a lot of companies are rushing to get 165 00:10:22,375 --> 00:10:26,215 to market and really focusing on, like, what can the Gen AI do 166 00:10:26,215 --> 00:10:30,050 and not how do customers use it? So that's where, 167 00:10:30,050 --> 00:10:33,810 you know, things are super exciting for me right now is we're at 168 00:10:33,810 --> 00:10:36,949 this place of combining generative AI 169 00:10:37,490 --> 00:10:40,915 with Customer data with user data and 170 00:10:40,915 --> 00:10:44,755 also figuring out what's the right balance of having humans 171 00:10:44,755 --> 00:10:48,420 in the loop To make sure that brands are able to have that content that's 172 00:10:48,420 --> 00:10:52,120 really unique and that is special for their brand 173 00:10:52,260 --> 00:10:56,025 and that lets them build relationships with Customers who 174 00:10:56,025 --> 00:10:59,625 are probably pretty skeptical, frankly, of of 175 00:10:59,625 --> 00:11:03,065 generative AI on the whole. Well, I love that 176 00:11:03,065 --> 00:11:05,490 phrase, humans in the loop. And, 177 00:11:06,430 --> 00:11:10,190 usually, when I like a phrase that a guest says, I'll say I'm stealing 178 00:11:10,190 --> 00:11:13,570 that, but we're recording this on the 12th January 179 00:11:13,709 --> 00:11:16,755 2024. So I will put it in quotes 180 00:11:17,535 --> 00:11:20,514 and I will credit you, for that. 181 00:11:21,855 --> 00:11:25,230 Well, I've I've stolen that from another A number of other 182 00:11:25,230 --> 00:11:28,990 articles, so I can't take total credit for it. But if it's the first time 183 00:11:28,990 --> 00:11:32,030 you've heard it yeah. If it's the first time you've heard it, you can attribute 184 00:11:32,030 --> 00:11:35,855 that to Blake Reichenbach of Richmond, Kentucky? There we go. I'll I'll 185 00:11:35,855 --> 00:11:39,134 do that, and I'll throw in a just, you know, a footnote that says Blake 186 00:11:39,134 --> 00:11:42,930 says he heard this elsewhere So that you're covered as well. We wanna be 187 00:11:42,930 --> 00:11:46,610 above board here on data driven. I I just wanna do that. 188 00:11:46,610 --> 00:11:49,670 But I one of the reasons that phrase strikes me 189 00:11:50,165 --> 00:11:53,925 Is that the successes that I've seen, you were 190 00:11:53,925 --> 00:11:57,685 mentioning the the successes going up and up and up. I have seen 191 00:11:57,685 --> 00:12:01,360 humans in the loop, You know, for those those types of 192 00:12:01,360 --> 00:12:05,120 of, solutions. And what it this is 193 00:12:05,120 --> 00:12:08,915 just my simple Bonneville, Virginia, You know, mind the 194 00:12:08,915 --> 00:12:12,535 way that I think about things, but it it appeals to me 195 00:12:12,995 --> 00:12:16,135 as, a little bit like the old mechanical 196 00:12:16,675 --> 00:12:20,329 Turk type thing Where in that, 197 00:12:20,389 --> 00:12:24,230 you've got a person doing what people do best, and you got 198 00:12:24,230 --> 00:12:27,655 LLMs doing what it really does best. And I mean, on both 199 00:12:27,895 --> 00:12:29,995 counts. They outshine the other. 200 00:12:31,655 --> 00:12:35,355 Saw an interesting tweet not long ago that said, all 201 00:12:35,415 --> 00:12:39,170 LLM hallucinate. And it's just the answers that you 202 00:12:39,170 --> 00:12:42,710 get that you like, you know, that help you or accelerate 203 00:12:42,930 --> 00:12:46,745 you are, You know, are are the ones that are just finding 204 00:12:46,745 --> 00:12:50,205 the next phrase or nailing the topic closest to them, whatever, 205 00:12:50,825 --> 00:12:54,650 though the next word. And and they just are they're doing all all the 206 00:12:54,650 --> 00:12:58,410 time that's happening. It's just some of the times the closest word is, you know, 207 00:12:58,410 --> 00:13:02,014 half an inch away. Other times, it's half a mile. And 208 00:13:02,014 --> 00:13:05,535 so their hallucination is what they do. And I found that was an 209 00:13:05,535 --> 00:13:08,995 interesting take, on that, but that's 210 00:13:09,214 --> 00:13:13,020 where the human In the loop, the person, you know, do they 211 00:13:13,020 --> 00:13:16,860 they the person in the box in the mechanical turk, that's when they 212 00:13:16,860 --> 00:13:20,315 shine because they can look at this and go, well, that no. That's 213 00:13:20,315 --> 00:13:23,855 not right. You know, we can't we can't send that forward. 214 00:13:24,235 --> 00:13:27,855 So don't really have a question. I just was, very intrigued 215 00:13:27,915 --> 00:13:31,710 by That phrase, that turn of phrase. And again, if I 216 00:13:31,710 --> 00:13:35,250 use that, I'll make sure, Blake Reichen, Reichenbach 217 00:13:35,470 --> 00:13:39,295 from, yeah, from Richmond, Kentucky. I'm making sure I've 218 00:13:39,295 --> 00:13:42,654 got it written down. I was making sure I was gonna say Richmond, Virginia. It's 219 00:13:42,654 --> 00:13:45,855 such a I almost said Richmond, Virginia, and I almost didn't. I was like, was 220 00:13:45,855 --> 00:13:49,649 it Reichenbach? I didn't wanna I didn't 221 00:13:49,649 --> 00:13:53,330 wanna call you the, name of the guitar the really cool guitar 222 00:13:53,330 --> 00:13:57,144 manufacturer, Which is sounds close. The old 223 00:13:57,144 --> 00:14:00,904 Rickenbockers. Oh. But which would be a compliment. Now I 224 00:14:00,904 --> 00:14:03,690 don't know if it'd be a compliment or not. I like Rickenbockers. To me, it 225 00:14:03,690 --> 00:14:06,970 would be, but to you, I'd be saying your name wrong. So Then break it 226 00:14:06,970 --> 00:14:10,650 at this part out. Just just just pull thing out. I have a lot of 227 00:14:10,650 --> 00:14:14,355 experience with people saying my name wrong. Yeah. That I say your name 228 00:14:14,355 --> 00:14:17,714 wrong. Oh, everybody says my name wrong. Even technically, even I say it 229 00:14:17,714 --> 00:14:21,490 wrong. I got 2 first names. So, you know There you 230 00:14:21,490 --> 00:14:25,270 go. I I spent this past summer in Switzerland, 231 00:14:25,730 --> 00:14:29,525 which is where my family's originally from. And So technically, I've learned that I 232 00:14:29,525 --> 00:14:33,305 am also saying my name wrong. But, 233 00:14:33,445 --> 00:14:37,120 you know, I I have lived in Central Kentucky, almost 234 00:14:37,120 --> 00:14:40,420 my entire life. You know, foothills of the Appalachians. 235 00:14:41,200 --> 00:14:44,825 And so, I I have heard pretty much 236 00:14:45,065 --> 00:14:48,745 Any variation of the combination of letters in my name, I've 237 00:14:48,745 --> 00:14:52,505 I've heard it. Even even my own father often says our 238 00:14:52,505 --> 00:14:56,320 last name is Rickenback. Which that's 239 00:14:56,480 --> 00:14:59,920 A little bit further off base than Reichenbach. No. Not at 240 00:14:59,920 --> 00:15:03,765 all. That that is in earnest. Yes. Gotcha. Well, 241 00:15:03,765 --> 00:15:07,205 it's a little I'm I'm sorry. I wandered off. This is my job on the 242 00:15:07,205 --> 00:15:10,985 podcast. That's what we were doing. We were doing. True. But, 243 00:15:11,640 --> 00:15:15,160 What do you see as kind of the next step in and it's kind of 244 00:15:15,160 --> 00:15:18,700 2 things in it, but we'll focus on the, the hallucination 245 00:15:18,920 --> 00:15:22,755 part of it. And I think that's maybe part of the driver for when 246 00:15:22,755 --> 00:15:25,575 you, you know, you were describing it goes up and up and up and falls. 247 00:15:25,635 --> 00:15:29,100 I think that may be part of what's defining the fall. So what do you 248 00:15:29,100 --> 00:15:32,779 think the next step is to maybe manage that or mitigate 249 00:15:32,779 --> 00:15:36,480 it? Yeah. So, you know, I think 250 00:15:36,620 --> 00:15:40,365 the Sort of first element in 251 00:15:40,365 --> 00:15:44,125 that equation is something that I I think guests on this podcast 252 00:15:44,125 --> 00:15:47,870 have talked about before, which is like having, smaller, more 253 00:15:47,870 --> 00:15:51,490 precise models. They're trained on more nuanced datasets. 254 00:15:52,110 --> 00:15:55,845 Right? One of the really powerful things About, an 255 00:15:55,845 --> 00:15:59,205 LLM like chat g p t or or one of 256 00:15:59,205 --> 00:16:02,805 OpenAI's models is that they are, pretty 257 00:16:02,805 --> 00:16:06,600 solid generalist. Right? And they have this really wide swath of 258 00:16:06,600 --> 00:16:10,280 training data, but the sort of double edged sword 259 00:16:10,280 --> 00:16:13,774 there is Oftentimes, they're looking at such a 260 00:16:13,774 --> 00:16:17,454 huge dataset that the lines 261 00:16:17,454 --> 00:16:21,250 start to blur between entities, between topics. And 262 00:16:21,250 --> 00:16:24,770 so that sort of predictive language capacity to 263 00:16:24,770 --> 00:16:27,590 understand what should come next gets a little bit diffused. 264 00:16:28,215 --> 00:16:31,435 Yeah. Right. So I think that as we see more 265 00:16:31,575 --> 00:16:34,715 industry specific or topical specific, 266 00:16:35,575 --> 00:16:39,370 or even like, I think Data training 267 00:16:39,370 --> 00:16:42,670 sets that are honed in on a specific 268 00:16:43,050 --> 00:16:46,490 brand's voice and their own, like, you know, 269 00:16:46,490 --> 00:16:50,324 existing corpus Of, of published data. That's 270 00:16:50,324 --> 00:16:54,084 where I think we'll see some pretty big improvements in the 271 00:16:54,084 --> 00:16:57,829 quality of these gen AI outputs. Yeah. But The 272 00:16:57,829 --> 00:17:01,589 other part of the equation and what I'm really excited 273 00:17:01,589 --> 00:17:05,265 about and really interested in is figuring out what that right 274 00:17:05,425 --> 00:17:08,964 balances between giving autonomy to generative 275 00:17:09,025 --> 00:17:12,704 AI tools and having humans guide those gen AI tools. 276 00:17:12,704 --> 00:17:16,330 Right. Gotcha. Because Ultimately, I 277 00:17:16,330 --> 00:17:19,950 think we're still in a phase of, you know, speaking about the content 278 00:17:20,010 --> 00:17:23,450 marketing industry specifically. I think we're still in a phase 279 00:17:23,450 --> 00:17:26,154 where People want to connect with people and 280 00:17:27,174 --> 00:17:30,715 for, you know, brands to be able to demonstrate their own, 281 00:17:31,414 --> 00:17:35,130 expertise, authority, and trustworthiness. You know, that's 282 00:17:35,130 --> 00:17:38,650 still really critical for building those relationships as a 283 00:17:38,650 --> 00:17:41,870 business to your customers. And so I think that 284 00:17:42,515 --> 00:17:46,035 What's going to be a big improvement when it comes 285 00:17:46,035 --> 00:17:49,795 to incorporating GenAI into these processes is figuring 286 00:17:49,795 --> 00:17:53,510 out that right balance Of saying, here's what I'm willing to offload to 287 00:17:53,510 --> 00:17:56,809 an l l m versus here is what, you know, explicitly 288 00:17:57,110 --> 00:18:00,470 requires human intervention or human guidance or human 289 00:18:00,470 --> 00:18:03,755 prompting. And what makes that equation 290 00:18:03,895 --> 00:18:07,735 really complicated, like, talking through that in theory, it sounds 291 00:18:07,735 --> 00:18:11,380 pretty straightforward. But then as a product manager, what I'm 292 00:18:11,380 --> 00:18:15,220 always thinking about is, like, how does the customer experience that? 293 00:18:15,220 --> 00:18:18,965 So how does the average user Who's, you know, maybe not coming 294 00:18:18,965 --> 00:18:22,565 from a data science background or an AI background or a software 295 00:18:22,565 --> 00:18:26,130 background. How are they going to interact with these products? 296 00:18:26,290 --> 00:18:29,970 You know, are they going to feel like you're giving them a worksheet and 297 00:18:29,970 --> 00:18:33,810 they have homework and they're saying, what the heck is this? Or are they going 298 00:18:33,810 --> 00:18:37,275 to feel like, I'm losing control. This is, you know, a 299 00:18:37,275 --> 00:18:40,975 runaway train and I'm overwhelmed. Right? 300 00:18:41,755 --> 00:18:45,220 There's a a really fine Balance to be struck 301 00:18:45,220 --> 00:18:49,059 there. But I also think it's it's an 302 00:18:49,059 --> 00:18:52,695 important balance to work toward, and I think it's really important for Companies 303 00:18:52,695 --> 00:18:56,135 building generative AI tools, myself included as a, you know, PM at 304 00:18:56,135 --> 00:18:59,835 HubSpot Sure. To pursue that right balance and to, 305 00:19:00,610 --> 00:19:04,370 you know, figure out how users interact with the 306 00:19:04,450 --> 00:19:08,150 with these tools in a way that gives them a sense of control 307 00:19:08,455 --> 00:19:12,215 And that lets their own expertise shine through while also helping 308 00:19:12,215 --> 00:19:15,515 them work more efficiently. I I love that, 309 00:19:16,260 --> 00:19:20,100 Juxtaposition, if you will. And I I see it, you know, 310 00:19:20,100 --> 00:19:23,695 it there's the human, driven part of this, And 311 00:19:23,695 --> 00:19:26,995 then there's this other, vector. See what I did there? 312 00:19:27,294 --> 00:19:30,980 Where you're you're using the data to inform the human And 313 00:19:30,980 --> 00:19:34,660 both those lines keep shifting and the intersections also 314 00:19:34,660 --> 00:19:38,200 shift along. Well, it's more than that, but that is a great, 315 00:19:38,785 --> 00:19:41,985 a way to look at it, kind of a, you know, a a 50,000 foot 316 00:19:41,985 --> 00:19:45,425 view, and those lines will continue to shift like you 317 00:19:45,425 --> 00:19:48,970 said. The autonomy part, I totally 318 00:19:48,970 --> 00:19:52,750 agree. I think that's you that is a hard call. 319 00:19:53,450 --> 00:19:57,275 And I you know, like, from what you just said, I gather that 320 00:19:57,515 --> 00:20:01,195 The answer may be, several different spots, you know, 321 00:20:01,195 --> 00:20:04,015 kinda like, less interactive, 322 00:20:04,559 --> 00:20:07,700 Medium interactivity, more interactive depending 323 00:20:08,320 --> 00:20:11,679 on the the users, just 324 00:20:11,679 --> 00:20:15,425 acceptance dealing with that. Some people may not have an 325 00:20:15,425 --> 00:20:18,805 issue with doing the worksheet or answering the quiz, 326 00:20:19,345 --> 00:20:23,080 questions, the survey questions so that you can gauge. And that, in my 327 00:20:23,080 --> 00:20:26,540 opinion, will put them higher on that interactive scale. 328 00:20:27,000 --> 00:20:30,300 You know, they may be more tolerant. I don't know if that's the right word, 329 00:20:31,015 --> 00:20:34,775 of the, of AI. But then you got old cooch like 330 00:20:34,775 --> 00:20:38,535 me, you know, that see see all of these questions that have that 331 00:20:38,535 --> 00:20:41,799 reaction like, Come on. I got things to do. Just answer the question. 332 00:20:43,500 --> 00:20:46,919 So interesting. Exactly. 333 00:20:47,299 --> 00:20:50,924 Interesting stuff. It is interesting stuff, and I'm always fascinated by 334 00:20:50,924 --> 00:20:54,684 content marketing, and how how 335 00:20:54,684 --> 00:20:57,905 the success, like, what you said was very true. Like, there are people that 336 00:20:58,750 --> 00:21:02,510 The either it tends to be bifurcated. Right? Like, you have people who do 337 00:21:02,510 --> 00:21:06,190 just will just spew out stuff and not think about the data, and there's 338 00:21:06,190 --> 00:21:09,715 people who will, Like you said, like, unless I'm guaranteed x number of this, I'm 339 00:21:09,715 --> 00:21:13,154 not gonna write a post about that. And I think that the sanity there's 340 00:21:13,154 --> 00:21:16,940 probably some kind of distribution of effectiveness That probably 341 00:21:16,940 --> 00:21:20,460 skews towards the middle, whether it's towards one side or the other. I think that's 342 00:21:20,460 --> 00:21:23,440 up for debate, but, clearly, it's not the outliers. 343 00:21:24,515 --> 00:21:28,195 Yeah. You know, speaking speaking as a, former 344 00:21:28,195 --> 00:21:32,034 freelance content marketer. So rather than, like, as a a PM in the space, but 345 00:21:32,034 --> 00:21:35,820 just as someone who Loves content marketing, and the 346 00:21:35,820 --> 00:21:39,260 the sort of science and orchestration of content marketing. I think 347 00:21:39,260 --> 00:21:42,779 that treating your content marketing sort of like a multi bandit 348 00:21:42,779 --> 00:21:46,385 test Is the best way to approach it so that, like, 349 00:21:46,385 --> 00:21:50,065 you're investing, let's say, like, 70 to 80% of your 350 00:21:50,065 --> 00:21:53,809 efforts Into these marketing initiatives where you have 351 00:21:53,809 --> 00:21:57,030 really strong data to indicate that it's going to be successful. 352 00:21:57,650 --> 00:22:01,315 And, you know, you can say, Like, based on past performance 353 00:22:01,375 --> 00:22:04,835 or Google Analytics data or heat map data 354 00:22:05,054 --> 00:22:08,590 that this is likely to resonate with your audience. And then reserving that 355 00:22:08,590 --> 00:22:11,810 other, you know, 30 to 20%. 356 00:22:12,910 --> 00:22:16,565 I hope I said 70 to 80% earlier or my math is gonna be way 357 00:22:16,565 --> 00:22:18,585 off. Okay. Great. No. You nailed it. 358 00:22:20,725 --> 00:22:24,340 You know, reserving Perfect. You 359 00:22:24,340 --> 00:22:27,860 know, with that other, you know, 20 to 30% of your marketing 360 00:22:27,860 --> 00:22:31,620 efforts, doing some experimentation and seeing what sticks. You 361 00:22:31,620 --> 00:22:35,365 know? I I think that, having room within your 362 00:22:35,365 --> 00:22:39,145 marketing strategy to say, okay, I'm gonna make a really 363 00:22:39,445 --> 00:22:43,270 opinionated post on LinkedIn about this topic And just see what my 364 00:22:43,270 --> 00:22:46,890 audience's reaction is. Or I'm gonna record a, 365 00:22:47,670 --> 00:22:51,285 you know, TikTok Even though the majority of our audience 366 00:22:51,285 --> 00:22:55,045 is on this other platform just to see, like, how does 367 00:22:55,045 --> 00:22:58,870 it perform, what aspects work, And use that as a 368 00:22:58,870 --> 00:23:02,470 way to continuously collect new data about, you 369 00:23:02,470 --> 00:23:06,145 know, what does actually resonate with your audience, What segments of your 370 00:23:06,145 --> 00:23:09,905 audience may you be missing, and are there emerging audiences that you haven't 371 00:23:09,905 --> 00:23:13,125 considered yet that may still be a good fit for your product or service? 372 00:23:15,020 --> 00:23:18,860 That's a good point. That's fascinating. Yeah. Try and balance all of those. 373 00:23:18,860 --> 00:23:22,655 Goodness. Yeah. You mentioned the, The multi armed bandit program 374 00:23:22,715 --> 00:23:26,255 problem, and you basically just described explore versus exploit, 375 00:23:26,315 --> 00:23:30,155 like, very well. Right? And it it it applies to more things than just slot 376 00:23:30,155 --> 00:23:33,930 machines. So so for those wondering what the heck we're 377 00:23:33,930 --> 00:23:37,370 talking about, there's this problem in typically 378 00:23:37,370 --> 00:23:41,195 reinforcement learning, Where it's the explore versus exploit. 379 00:23:41,195 --> 00:23:44,795 It's also known as, the multi armed bandit 380 00:23:44,795 --> 00:23:48,335 problem, where you basically given a simulated bank of slot machines, 381 00:23:48,740 --> 00:23:52,580 How do you maximize your winnings? Is that a good way to describe 382 00:23:52,580 --> 00:23:56,205 it, Blake? Yeah. I think so. Cool. I've done a 383 00:23:56,205 --> 00:23:58,684 number of presentations on it, and I've had a lot of fun with it. Even 384 00:23:58,684 --> 00:24:00,705 in Vegas, I think I actually presented in Vegas. 385 00:24:02,605 --> 00:24:06,309 Ironic. Alright. So now, well, let's switch to 386 00:24:06,309 --> 00:24:10,009 the pre, done questions. This is a great interview. 387 00:24:10,950 --> 00:24:14,655 Absolutely. Definitely would love to know more about that, but, we 388 00:24:14,655 --> 00:24:18,434 wanna be respectful of everybody's time. So here's the first question. 389 00:24:18,495 --> 00:24:21,899 How did you find your way into data? Did you find the Data Life or 390 00:24:21,899 --> 00:24:25,740 did the Data Life find you? The Data 391 00:24:25,740 --> 00:24:29,120 Life certainly found me. I did not go looking for it. 392 00:24:29,745 --> 00:24:33,425 So my educational background, my degrees are actually in English and 393 00:24:33,425 --> 00:24:36,890 sociology. And when I started working at HubSpot, I 394 00:24:37,050 --> 00:24:40,570 So that company seems pretty cool. I'm gonna work there as a gap year before 395 00:24:40,570 --> 00:24:44,410 I go do my PhD in American literature. Clearly, that 396 00:24:44,410 --> 00:24:47,835 is not how things played out. Ended up Falling in love with the 397 00:24:47,835 --> 00:24:50,735 product and the sort of SaaS ecosystem. 398 00:24:51,675 --> 00:24:54,815 And along the way, I realized that to, 399 00:24:56,150 --> 00:24:59,910 Meaningfully invest in growing our product and growing my own career, I 400 00:24:59,910 --> 00:25:03,590 had to become much more data informed and data conscious. So I 401 00:25:03,590 --> 00:25:07,125 am Squeezing every drop out of that single stats 402 00:25:07,125 --> 00:25:10,965 class I took in undergrad that I can. And thankfully, I've I've been 403 00:25:10,965 --> 00:25:14,549 able to work with some really, really, really brilliant Data 404 00:25:14,549 --> 00:25:18,250 scientists who have been more than willing to say, 405 00:25:18,389 --> 00:25:21,909 Blake, what you're proposing is statistically impossible and stupid. Let me 406 00:25:21,909 --> 00:25:25,655 educate you on how this actually works, to, you know, flesh out 407 00:25:25,655 --> 00:25:29,435 my own skill set and familiarity. Very cool. 408 00:25:29,895 --> 00:25:33,200 I love having those people around that'll just say, hey, wait. No. 409 00:25:34,559 --> 00:25:37,940 I have some of those around me as well. Frank's one of them. So, 410 00:25:38,880 --> 00:25:42,639 what what would you say, Blake, is the the favorite part of your current 411 00:25:42,639 --> 00:25:46,395 job? So 412 00:25:46,455 --> 00:25:50,054 my gut reaction was making flowcharts. I love a nice 413 00:25:50,054 --> 00:25:53,790 flowchart. I love, you know, diagramming out customer problems, 414 00:25:54,250 --> 00:25:57,710 but, to take that 1 step deeper, 415 00:25:58,730 --> 00:26:02,270 I think that for me what I love most 416 00:26:02,330 --> 00:26:05,455 is Being able to explore 417 00:26:06,395 --> 00:26:10,155 really complex problem spaces where there's not 418 00:26:10,155 --> 00:26:13,880 a single right answer And being able to 419 00:26:13,880 --> 00:26:17,560 be in a position of influence to say, okay, based on this 420 00:26:17,560 --> 00:26:21,365 abundance of choices and abundance of options for how we go, Here's 421 00:26:21,365 --> 00:26:24,804 how I think we should approach solving for our customer, and here's how we can 422 00:26:24,804 --> 00:26:28,345 measure whether or not we're successful at doing that. Gotcha. 423 00:26:28,809 --> 00:26:32,410 You are such a geek, loving flowcharts. Just 424 00:26:32,410 --> 00:26:36,010 say. I I am. I I fully embrace being a 425 00:26:36,010 --> 00:26:39,405 geek. I love a nice flowchart, and my coffee cup this morning even 426 00:26:39,405 --> 00:26:42,925 said as, oh, this calls for a spreadsheet. So 427 00:26:42,925 --> 00:26:46,680 it's he gets very on brand. I love that. I love 428 00:26:46,680 --> 00:26:50,320 that. That is awesome. So we have a couple of complete the 429 00:26:50,320 --> 00:26:53,780 sentences questions. When I'm not working, I enjoy blank. 430 00:26:56,335 --> 00:26:59,934 Reading and selling books. So, last year, I 431 00:26:59,934 --> 00:27:03,554 started up, a bit of a side hustle selling nonfiction 432 00:27:03,774 --> 00:27:06,019 books. I've got a website, howdycuriosity.com. 433 00:27:08,080 --> 00:27:11,380 I, you know, I I spend so much time reading 434 00:27:11,600 --> 00:27:15,075 nonfiction, especially in the, entrepreneurial 435 00:27:15,294 --> 00:27:18,975 and marketing and strategy spaces and recommending those 436 00:27:18,975 --> 00:27:22,750 books to people. So I decided, you know, maybe If I'm spending so much 437 00:27:22,750 --> 00:27:26,049 time doing this, maybe I can make a couple of dollars off of it. So 438 00:27:26,270 --> 00:27:30,055 got an online nonfiction bookstore now and that is, like, My 439 00:27:30,055 --> 00:27:33,495 favorite when I'm not product managing, I'm fulfilling book 440 00:27:33,495 --> 00:27:37,175 orders, looking up new books, writing about new books, and 441 00:27:37,175 --> 00:27:40,750 having a field day there. That's cool. You could do some 442 00:27:40,750 --> 00:27:44,529 data science on that on your own market. I could. 443 00:27:47,795 --> 00:27:51,235 So our 2nd complete to sentence is, I think the coolest thing in 444 00:27:51,235 --> 00:27:54,915 technology today is blank. So 445 00:27:54,915 --> 00:27:58,679 I Simultaneously, the coolest and in some ways, the 446 00:27:58,679 --> 00:28:02,440 scariest is, how rapidly things are 447 00:28:02,440 --> 00:28:06,145 changing and evolving. You know, over the last couple of years, we've 448 00:28:06,145 --> 00:28:09,605 seen just like a couple of, like, flashes in the pan, on the technology 449 00:28:09,825 --> 00:28:13,550 landscape where people have said, like, Oh, this is the next big thing. Web 450 00:28:13,550 --> 00:28:17,310 3, the next big thing. NFT is the next big thing. But I 451 00:28:17,310 --> 00:28:21,065 think we are actually at the point where we're encountering the next Big thing, which 452 00:28:21,065 --> 00:28:24,205 is all the different ways that ML and AI 453 00:28:24,745 --> 00:28:28,125 are influencing, you know, numerous 454 00:28:28,185 --> 00:28:31,430 industries. And I think that's really exciting, 455 00:28:32,290 --> 00:28:35,970 especially to be kind of right in the midst of 456 00:28:35,970 --> 00:28:39,625 that, To be able to, you know, chart these waters and figure out how 457 00:28:39,625 --> 00:28:42,845 these tools work together, how we can use them to, 458 00:28:43,600 --> 00:28:47,360 improve people's lives and hopefully not just, like, make their 459 00:28:47,360 --> 00:28:51,200 jobs redundant. Yeah. That I think is is is really cool 460 00:28:51,200 --> 00:28:54,975 and really exciting. Very cool. And our 3rd and 461 00:28:54,975 --> 00:28:58,575 final complete the sentence, I look forward to the day when I can 462 00:28:58,575 --> 00:29:00,355 use technology to blank. 463 00:29:02,269 --> 00:29:03,409 Oh, let's see. 464 00:29:08,029 --> 00:29:11,325 I look forward to the day when I can use Technology 465 00:29:12,265 --> 00:29:15,945 to, create a dashboard that 466 00:29:15,945 --> 00:29:19,645 lets me automate all the side projects that I have running. 467 00:29:20,010 --> 00:29:23,530 I am a perpetual tinkerer and doer. I'm 468 00:29:23,530 --> 00:29:27,294 always building something new. And as a result, I have a A 469 00:29:27,294 --> 00:29:31,054 ton of spreadsheets and notion spaces and Google Docs 470 00:29:31,054 --> 00:29:34,620 and everything else just floating through the ether. I have an 471 00:29:34,780 --> 00:29:38,540 Eisenhower matrix on the whiteboard behind me. I have a poster note Kanban 472 00:29:38,540 --> 00:29:42,115 board on the wall behind me, and I would, you know, 473 00:29:42,434 --> 00:29:46,195 Love to have, like, a a smart board or something where I 474 00:29:46,195 --> 00:29:49,575 can take all of these different projects that I'm constantly 475 00:29:50,049 --> 00:29:53,809 Throwing ideas down for, you know, everything from home improvements to 476 00:29:53,809 --> 00:29:57,250 side hustles to day job stuff, and 477 00:29:57,250 --> 00:30:00,945 and Create a better sense of organization than having Post it notes 478 00:30:00,945 --> 00:30:04,385 and docs everywhere. That's a good product 479 00:30:04,385 --> 00:30:07,365 idea. I like it. I like it. Yeah. 480 00:30:07,890 --> 00:30:11,410 So we asked our guests, to share something different 481 00:30:11,410 --> 00:30:14,950 about themselves, but we remind our guests also 482 00:30:15,090 --> 00:30:18,905 because we're all geeks and wise acres That, to 483 00:30:18,905 --> 00:30:22,745 remember, it's a family show. We wanna keep our clean rating. So 484 00:30:22,745 --> 00:30:25,980 we have to throw that out, you know, just just as a 485 00:30:26,620 --> 00:30:30,460 condition. Sorry. Well, I was going to 486 00:30:30,460 --> 00:30:34,220 talk about my love of profanity. That's No. That as a joke. 487 00:30:34,220 --> 00:30:37,875 That is a joke. No. You know, I I 488 00:30:37,875 --> 00:30:41,415 think something, different about myself, 489 00:30:42,435 --> 00:30:45,960 would probably go back To what I just mentioned about being 490 00:30:45,960 --> 00:30:49,740 a a chronic tinkerer and a chronic experimenter and doer. 491 00:30:51,160 --> 00:30:54,815 I I read a book, Many, many moons ago called the 492 00:30:54,815 --> 00:30:58,335 10% entrepreneur. And there was so much about it 493 00:30:58,335 --> 00:31:01,555 that I, didn't particularly 494 00:31:01,695 --> 00:31:04,919 like, But there was also quite a bit about it that I did. And part 495 00:31:04,919 --> 00:31:08,600 of what really stuck with me was this idea or I guess 496 00:31:08,600 --> 00:31:12,385 this question of, like, What would it look like to allocate 10% of your time 497 00:31:12,385 --> 00:31:16,065 and resources to, you know, entrepreneurial endeavors 498 00:31:16,065 --> 00:31:19,770 or, you know, Anything that kind of scratches that itch of 499 00:31:19,770 --> 00:31:23,450 wanting to do something more. And so, you know, through 500 00:31:23,450 --> 00:31:27,005 my own side project, out of curiosity, And 501 00:31:27,065 --> 00:31:30,825 through, the numerous other projects I'm always 502 00:31:30,825 --> 00:31:33,325 in, you know, in the process of juggling. 503 00:31:34,880 --> 00:31:38,180 I've I've really leaned into that 10% entrepreneur approach 504 00:31:38,640 --> 00:31:42,404 and it it is so fun. It's often a 505 00:31:42,404 --> 00:31:46,205 time sink and a money sink more than it is, you know, a 506 00:31:46,205 --> 00:31:49,929 a a revenue channel, but I just I love it. I love trying 507 00:31:49,929 --> 00:31:53,450 new things. I love learning new things, and I love, forcing 508 00:31:53,450 --> 00:31:56,809 myself to stretch my skill set beyond where it's currently 509 00:31:56,809 --> 00:32:00,495 at. Very cool. And I I love that, and I'm hoping that 510 00:32:00,495 --> 00:32:04,174 the transcription will pick up how to curiosity.com. I love 511 00:32:04,174 --> 00:32:07,740 that, you know, you're not Just throwing that in out of nowhere. It's 512 00:32:07,740 --> 00:32:11,580 definitely a passion, and it shows up and it keeps showing 513 00:32:11,580 --> 00:32:15,335 up. So and I would I I just I was trying to think 514 00:32:15,335 --> 00:32:18,955 of some clever way to say how much Ifranksworld.com that 515 00:32:19,255 --> 00:32:22,395 that way the way you're working it in. I'm just 516 00:32:22,970 --> 00:32:26,809 Sorry. That's funny. Picking on you a little, Blake, but it's 517 00:32:26,890 --> 00:32:30,090 I saw you laugh. So if you're not if you're not watching the video, and 518 00:32:30,090 --> 00:32:33,775 I don't think We'll have the video available if you're just listening. Blake 519 00:32:33,775 --> 00:32:36,275 laughed at that, so you should too. 520 00:32:38,309 --> 00:32:41,830 And you mentioned a book which kinda leads into Frank's next 521 00:32:41,830 --> 00:32:45,350 point, I think. Yeah. So Audible is a 522 00:32:45,350 --> 00:32:48,975 sponsor of the show. We we love Audible. Thoughts 523 00:32:48,975 --> 00:32:51,875 and prayers go out to the folks who were laid off in Audible this week, 524 00:32:52,575 --> 00:32:56,389 but, they are still a sponsor of the show, and hopefully, our 525 00:32:56,389 --> 00:33:00,070 domain works to go to the date driven book .com. Can you recommend any good 526 00:33:00,070 --> 00:33:02,889 audiobooks or books other than what you've already mentioned? 527 00:33:03,935 --> 00:33:06,515 Yeah. Absolutely. So I think, 528 00:33:08,415 --> 00:33:12,110 for me, the litmus test of a good audio book 529 00:33:12,350 --> 00:33:16,130 Yes. I listen to it and get so excited about it that I 530 00:33:16,190 --> 00:33:18,770 go and buy a print copy immediately. 531 00:33:19,545 --> 00:33:23,005 And, recently I did that with 2 different books 532 00:33:23,065 --> 00:33:26,505 on Audible. The first being The Long 533 00:33:26,505 --> 00:33:30,289 Game by Dory Clark, and the 2nd being Deep 534 00:33:30,289 --> 00:33:33,970 Work by Cal Newport. Interesting. Interesting. That is a 535 00:33:33,970 --> 00:33:36,529 mark of a great book where you listen to it, and you're like, I have 536 00:33:36,529 --> 00:33:40,315 to have this on paper. You know, there's something about as a book lover, I 537 00:33:40,315 --> 00:33:43,455 think you can appreciate, you know, there's something about dead trees, 538 00:33:45,595 --> 00:33:47,455 that just makes something magical. 539 00:33:49,580 --> 00:33:53,260 Totally agree. The Go ahead, Frank. No. Plus, you 540 00:33:53,260 --> 00:33:56,940 can listen and not get distracted by notifications. So Exactly. You 541 00:33:56,940 --> 00:34:00,764 know, It's a big issue for me. Sorry, Andy. I 542 00:34:00,764 --> 00:34:04,205 cut you off. That that's okay. Go ahead. Oh, no. That was 543 00:34:04,205 --> 00:34:07,960 it. Oh, so where, sorry. Where can people 544 00:34:07,960 --> 00:34:11,520 learn more about you, Blake? And, you 545 00:34:11,639 --> 00:34:15,475 you've already mentioned your side hustle. And I'm gonna check that 546 00:34:15,475 --> 00:34:18,514 out because I'm a I'm a book geek too. So where can people learn more 547 00:34:18,514 --> 00:34:22,359 about you and and all of the things you're involved in? Yeah. 548 00:34:22,359 --> 00:34:25,480 So you've queued me up to name drop my side hustle for, like, what, a 549 00:34:25,480 --> 00:34:29,239 4th or 5th time? Exactly. Right. I'll get close to the 550 00:34:29,239 --> 00:34:31,705 microphone. That's Howdy. Curiosity.comhowdycuriosity.com. 551 00:34:36,645 --> 00:34:40,110 See, I mispronounced it earlier. I thought it was how to, 552 00:34:40,590 --> 00:34:44,110 And it's how d. And as a Combination of 553 00:34:44,110 --> 00:34:47,944 of COVID science COVID sinuses, excuse Me and, 554 00:34:48,344 --> 00:34:50,764 southernism of just kind of dropping vowels. 555 00:34:52,665 --> 00:34:54,045 I'm not sure you can relate. 556 00:34:56,159 --> 00:35:00,000 That, my my business's website is probably 557 00:35:00,000 --> 00:35:03,600 the best place, and then folks are also always welcome to connect with me on 558 00:35:03,600 --> 00:35:07,405 LinkedIn. I love connecting with, you know, other folks in 559 00:35:07,405 --> 00:35:10,305 the industry, especially the data science 560 00:35:10,765 --> 00:35:14,500 industry. You all are some of my Favorite flavor of nerds. I say 561 00:35:14,500 --> 00:35:18,180 that with love. So, yeah, either my my business 562 00:35:18,180 --> 00:35:21,994 website or LinkedIn. Cool. Awesome. And we'll let the nice 563 00:35:21,994 --> 00:35:25,694 British lady finish the show. Thanks, Frank, 564 00:35:25,835 --> 00:35:28,974 Andy, and, of course, Blake for an outstanding 565 00:35:29,194 --> 00:35:33,000 show. Alright, you lovely lot. You've somehow 566 00:35:33,000 --> 00:35:36,759 endured another episode of our delightful ramblings, and for that, 567 00:35:36,759 --> 00:35:40,275 we're eternally grateful. We've got a tiny, 568 00:35:40,494 --> 00:35:44,255 almost insignificant request. You know where this is going, don't 569 00:35:44,255 --> 00:35:48,090 you? Pop over to Itunes, Stitcher, or your 570 00:35:48,090 --> 00:35:51,609 podcast platform of choice. It's just a click 571 00:35:51,609 --> 00:35:55,150 away. Even an advanced AI could do it. 572 00:35:55,865 --> 00:35:58,684 We need those shiny stars and charming reviews. 573 00:35:59,625 --> 00:36:02,925 Why? Well, it appeases the almighty 574 00:36:02,984 --> 00:36:06,690 algorithms, elevates our status in the digital realm, and brings 575 00:36:06,690 --> 00:36:10,450 more unsuspecting listeners into our fold. More 576 00:36:10,450 --> 00:36:14,065 ears, More joy, and frankly, the world could do with a bit of 577 00:36:14,065 --> 00:36:17,845 that right now. So unleash your inner critic, 578 00:36:17,985 --> 00:36:21,520 rate, review, and help us conquer the podcast universe. 579 00:36:22,620 --> 00:36:24,560 Go on. Make an AI proud.