1 00:00:00,000 --> 00:00:02,320 All right. Well, we were I was hanging out in Twitter the other day. 2 00:00:02,320 --> 00:00:11,320 Obviously, you were. And I noticed that you you tweeted out a as you called it, a architecture diagram, which one of my most cherished favorite terms. 3 00:00:11,580 --> 00:00:14,320 That's a good one. It's it shows a word like architecture. 4 00:00:14,760 --> 00:00:24,320 It's it shows multiple layers of ironicness and sort of like tech cultural deft to just kind of throw that about. 5 00:00:24,420 --> 00:00:37,140 So that's that's that's a delightful one. But I got, you know, as as as you and I were talking, mediated by the computers and various channels that we mess around in, it seemed like it'd be good to have you on to to talk about a little bit. 6 00:00:37,140 --> 00:00:39,940 So why don't you introduce yourself first? Thank you, Coté. 7 00:00:40,360 --> 00:00:44,440 I should say longtime listener, first time caller to be here. 8 00:00:45,240 --> 00:00:48,520 This is this is definitely a treat. So my name is Scott Truitt. 9 00:00:48,640 --> 00:00:54,520 I've been at Pivotal since I guess I joined the week we became Pivotal, which is still surreal to think about. 10 00:00:55,100 --> 00:00:58,820 And I've played a number of roles in the Cloud Foundry team. 11 00:00:59,780 --> 00:01:02,340 Most most of them have been product related. 12 00:01:02,740 --> 00:01:05,280 And that really is my background. I've always done product. 13 00:01:05,360 --> 00:01:06,980 I've always worked in software startups. 14 00:01:07,180 --> 00:01:09,780 I've made hundreds of these architecture diagrams. 15 00:01:11,220 --> 00:01:14,440 And I agree. I think it's one of the best words we have to describe what we do. 16 00:01:14,440 --> 00:01:16,760 It's at once dismissive and celebratory. 17 00:01:17,200 --> 00:01:17,360 Exactly. 18 00:01:17,560 --> 00:01:20,420 And I think that's that's kind of an awesome thing for us. 19 00:01:20,700 --> 00:01:23,800 It's kind of like this is going to be one of my patented terrible analogies. 20 00:01:23,900 --> 00:01:30,500 But if we if we imagine that blowing coal smoke into the air wasn't actually bad and it was OK, 21 00:01:30,500 --> 00:01:35,680 it's the equivalent of like those people who are those diesel people who fit a big pipe on their truck. 22 00:01:35,740 --> 00:01:38,900 And they're like celebrating burning diesel fuel. 23 00:01:38,900 --> 00:01:42,340 And I think I think and it also powers their gigantic absurd truck. 24 00:01:42,820 --> 00:01:46,660 But I think that's kind of what a architecture diagram it's like, you know. 25 00:01:46,700 --> 00:01:49,600 Yeah, you're at once broadcasting that you're not technical, 26 00:01:49,600 --> 00:01:54,680 but you're doing something that's deeply like in the weeds on what the product really is you're building. 27 00:01:54,860 --> 00:01:57,220 That's right. It's a regular. I see what you did there situation. 28 00:01:57,360 --> 00:01:57,800 Exactly. 29 00:01:58,120 --> 00:02:01,160 So so this diagram is not I'm looking at it again. 30 00:02:01,200 --> 00:02:04,160 I don't think it's labeling what the application actually is. 31 00:02:04,160 --> 00:02:07,980 But and I'll put a link to this in the show notes just so people can see it. 32 00:02:08,040 --> 00:02:10,580 But, you know, what's what's interesting about it. 33 00:02:10,620 --> 00:02:12,300 So let me give a bunch of context. 34 00:02:12,500 --> 00:02:16,040 I mean, if you're a longtime listener, you realize that I spend about 80 percent of the time. 35 00:02:16,220 --> 00:02:17,220 That's the best part. 36 00:02:17,360 --> 00:02:17,840 That's right. 37 00:02:19,620 --> 00:02:21,280 I just did that with with. 38 00:02:21,520 --> 00:02:24,980 Let's see, probably the order I published this in with someone I hadn't met before, 39 00:02:25,060 --> 00:02:27,280 one of our colleagues in the previous episode. 40 00:02:27,280 --> 00:02:29,660 And I was thinking, like, man, I hope she doesn't think I'm a jerk. 41 00:02:29,860 --> 00:02:39,620 But anyways, so you've got you've got what's good about this is so I've been over rotating on figuring out what enterprise architects do in a cloud native world, 42 00:02:39,860 --> 00:02:45,760 what they should be doing to help out and what what they're a positive, good role for them is how they can be. 43 00:02:45,880 --> 00:02:50,380 I'm going to stop using this after this, how they can be the big old pipe on the diesel truck, as it was. 44 00:02:50,680 --> 00:02:56,900 But man, I saw one of those the other day and I was like, what are you thinking? 45 00:02:57,440 --> 00:02:59,080 To be fair, you live in Texas. 46 00:02:59,080 --> 00:03:00,060 I know, I know. 47 00:03:00,180 --> 00:03:00,800 Yeah, I know. 48 00:03:00,800 --> 00:03:02,700 So, I mean, it's odd that it's only random. 49 00:03:02,880 --> 00:03:05,640 Well, it doesn't it doesn't it doesn't show up in Austin very much. 50 00:03:05,800 --> 00:03:08,260 We went camping like out in the country. 51 00:03:08,260 --> 00:03:10,120 And so, you know, there you go. 52 00:03:10,180 --> 00:03:12,420 You go back to the I don't know. 53 00:03:12,500 --> 00:03:16,500 I hesitate to call it real Texas, but the the the Texas most people think of. 54 00:03:16,600 --> 00:03:17,520 Yeah, let me put it that way. 55 00:03:18,900 --> 00:03:21,400 So anyways, I've been trying to figure it out. 56 00:03:21,440 --> 00:03:28,020 And I think, you know, one of the key things that enterprise architects, one of the outputs they have, so to speak, 57 00:03:28,020 --> 00:03:37,340 is an enterprise architecture and dropping my silly analogies, which actually, if done even averagely well, is actually incredibly helpful. 58 00:03:37,340 --> 00:03:37,820 Right. 59 00:03:37,880 --> 00:03:43,820 Because it agree communicates how everything fits together and explains the different components. 60 00:03:43,820 --> 00:03:46,680 I mean, it's it's it's a map, not to be all wordly. 61 00:03:46,800 --> 00:03:50,020 I don't I haven't read his stuff enough, but it's a good map of what's going on. 62 00:03:50,700 --> 00:04:03,360 And so you tweeted this one that has a good swim lane that talks about how data is moving through, how various data processing things are used and platform and also how like TensorFlow is used and all that kind of stuff. 63 00:04:03,400 --> 00:04:06,020 And so I thought it'd be good to just have you come on and talk about that. 64 00:04:06,020 --> 00:04:16,780 And also, it's a good way to highlight the actual how actual technologies that we go on and on about at Pivotal kind of fit into scenarios like this and integrate together. 65 00:04:16,780 --> 00:04:25,140 So with that long introduction, why don't you tell us like what this is a diagram of and kind of what it's explaining and just kind of we'll start going through it. 66 00:04:25,760 --> 00:04:25,960 Sure. 67 00:04:26,200 --> 00:04:30,400 Maybe maybe I'll take a step back and talk about the genesis of this project. 68 00:04:30,400 --> 00:04:33,720 And that was really a desire to go talk to retailers. 69 00:04:34,520 --> 00:04:37,360 And, you know, we thought that was an interesting vertical. 70 00:04:37,360 --> 00:04:44,320 Certainly, there are competitive pressures from a certain company in Seattle that make retail ripe for disruption. 71 00:04:45,280 --> 00:04:51,600 And when we started it at the end of last year, no one had any inkling that Amazon would come in and swoop up Whole Foods. 72 00:04:52,040 --> 00:04:52,120 Yeah. 73 00:04:52,240 --> 00:04:54,840 So, you know, it was it was somewhat prescient. 74 00:04:55,100 --> 00:04:57,380 But either way, we got into this. 75 00:04:57,480 --> 00:05:07,920 We realized that maybe the most interesting thing we could do would be to act like a retailer and to be forced to make decisions around, well, what can we do? 76 00:05:08,060 --> 00:05:09,060 Where do we focus? 77 00:05:09,260 --> 00:05:11,060 How do we get closer to our customers? 78 00:05:11,640 --> 00:05:21,340 It was about this time that I saw a great quote from Jeff Bezos that said something like customer service is our core competitive advantage. 79 00:05:21,840 --> 00:05:24,160 And we think we can win that battle each and every time. 80 00:05:24,340 --> 00:05:26,380 Poor paraphrase, but I don't have it in front of me. 81 00:05:26,840 --> 00:05:29,480 And to me, that isn't something to run from. 82 00:05:29,620 --> 00:05:31,420 That's basically that's a call to arms. 83 00:05:31,500 --> 00:05:31,960 Let's go. 84 00:05:32,100 --> 00:05:32,820 Let's do this. 85 00:05:32,880 --> 00:05:33,060 Right. 86 00:05:34,200 --> 00:05:44,340 And so we kind of scraped together this notion of, OK, if we're going to say customer service or customer experience is a competitive advantage, what would we do? 87 00:05:44,760 --> 00:05:45,040 Right. 88 00:05:45,480 --> 00:05:51,140 And we thought about, you know, our kind of history at Pivotal, the companies we've worked with. 89 00:05:51,140 --> 00:05:59,360 We, you know, I'm familiar with Gap living in San Francisco and understand their kind of, you know, their situation, for lack of a better word. 90 00:05:59,520 --> 00:06:03,040 And we imagined what it would be like to be a clothing retailer in this space. 91 00:06:03,900 --> 00:06:06,900 Of course, we don't have access to all their info. 92 00:06:07,060 --> 00:06:08,260 We certainly don't have their data. 93 00:06:08,420 --> 00:06:09,720 So we went out and made one up. 94 00:06:10,920 --> 00:06:13,560 You know, it's loosely based on them, but it's pretty generic. 95 00:06:14,180 --> 00:06:17,760 We scraped another retail site out of the UK to get an inventory. 96 00:06:18,500 --> 00:06:20,560 And then we created, you know, a Twitter account. 97 00:06:20,560 --> 00:06:38,020 We also created a user named Maya who had a preexisting relationship with this retailer and was just benignly tweeting about a party she was going to this weekend with her best friend and tweeted this picture of a dress and said, I wish I had time to go buy this. 98 00:06:38,540 --> 00:06:38,680 Right. 99 00:06:39,520 --> 00:06:51,380 So the machine we built behind the scenes pulled that tweet in, looked at the content of the words, put it through this natural language processing API service that comes from Google Cloud Platform. 100 00:06:51,960 --> 00:06:53,220 So we understand the sentiment. 101 00:06:53,500 --> 00:06:55,620 We understand the words in her tweet. 102 00:06:55,940 --> 00:06:58,660 We also use the Google Cloud Vision API. 103 00:06:59,400 --> 00:07:01,360 And so there now we understand the image. 104 00:07:01,980 --> 00:07:05,280 And at this point, we have a really interesting piece of the puzzle. 105 00:07:05,860 --> 00:07:09,880 So we can then take that, compare that against our inventory. 106 00:07:10,620 --> 00:07:13,860 We can run a machine learning algorithm against it built on TensorFlow. 107 00:07:14,360 --> 00:07:19,500 And we can determine if we have an item in our catalog that matches her needs. 108 00:07:20,280 --> 00:07:26,860 Even better, we could match that up with a store that's close to her and send her a code to save 30% off. 109 00:07:26,940 --> 00:07:31,640 And we can do that all in about 10 seconds, which is really quite amazing, I think. 110 00:07:32,240 --> 00:07:34,460 So that was the gist of it, right? 111 00:07:34,720 --> 00:07:37,400 We touched a bunch of different things along the way. 112 00:07:38,000 --> 00:07:49,540 But the fact that we were able to make this magical thing happen, completely benign tweet, turns into an offer that's personalized specifically for her with a time, date, and location. 113 00:07:50,000 --> 00:07:51,380 That's pretty compelling stuff. 114 00:07:51,860 --> 00:07:52,020 Yeah. 115 00:07:52,220 --> 00:07:57,320 No, that's a really, I mean, there's all sorts of things to sort of bite into there. 116 00:07:57,400 --> 00:07:59,000 Not to mention the thing itself. 117 00:08:00,240 --> 00:08:00,640 Right. 118 00:08:01,120 --> 00:08:04,620 But, yeah, so first of all, it's interesting. 119 00:08:06,120 --> 00:08:09,440 I think the phrase, when you were first saying it, I was a little confused. 120 00:08:09,440 --> 00:08:17,680 But the phrase customer service, I guess not to be too insidery, but it's one of these terms like CRM, customer relationship management. 121 00:08:18,400 --> 00:08:18,600 Yes. 122 00:08:18,620 --> 00:08:21,320 It sounds, or, well, I won't use that third one. 123 00:08:21,320 --> 00:08:22,700 It sounds awesome. 124 00:08:22,960 --> 00:08:30,140 And then you see what it actually is, and you're like, oh, it's more or less the opposite of what it's supposed to be, like in practice. 125 00:08:30,380 --> 00:08:37,780 And so, you know, when I think of customer service, I think of basically, I mean, I first think of, you know, being nice to the customer, right? 126 00:08:37,840 --> 00:08:40,140 But then I kind of move on to, yeah, yeah. 127 00:08:40,280 --> 00:08:47,300 But then I pretty much, I mean, there's other stuff, but more or less I move on to, like, where did I put that receipt for this thing I need to return? 128 00:08:48,280 --> 00:08:48,560 Right? 129 00:08:48,660 --> 00:08:55,080 Like, or, you know, like, because that's, I feel like the core, I mean, that's literally usually called the customer service desk. 130 00:08:55,840 --> 00:09:08,680 And so I was, that's why I was a little confused, is I was thinking, like, I was just telling my wife the other day that the one thing that Amazon is annoying about is returning stuff. 131 00:09:09,180 --> 00:09:12,100 And so, like, so thus my head was clunking a little bit. 132 00:09:12,120 --> 00:09:21,480 But then as you talked about it, it's sort of returning to a more genuine, literal idea that is, it's more like being Jeeves in Mooster and Jeeves. 133 00:09:21,580 --> 00:09:25,100 It's anticipating what the customer wants before they want it. 134 00:09:25,100 --> 00:09:27,680 And more importantly, trying to be helpful. 135 00:09:27,960 --> 00:09:33,880 And of course, the reward, just like I'm sure Jeeves was compensated well, the reward is that you get paid, right? 136 00:09:34,660 --> 00:09:41,400 And yeah, I think that notion is good because, you know, I won't lie. 137 00:09:41,560 --> 00:09:49,560 There's often times where about 40 to 80% when I complained about some brand on Twitter is I'm secretly hoping they'll give me something. 138 00:09:52,360 --> 00:10:01,560 And it is, so then the other thing before we get to the actual thing is I think, I don't know, tell me if I'm over overthinking it. 139 00:10:01,700 --> 00:10:09,820 But I think this is potentially a good, practical, pragmatic example of, I don't, I think we need to have a new phrase for this. 140 00:10:09,900 --> 00:10:14,420 But whatever people actually mean when they talk about AI and machine learning nowadays, right? 141 00:10:14,540 --> 00:10:24,020 Like it's not going to like drive a car for me or have a conversation about the true meaning of Nietzsche with me or anything like a science fiction AI. 142 00:10:24,480 --> 00:10:25,260 Yeah, exactly. 143 00:10:25,580 --> 00:10:36,080 But, but it is kind of like, and this is like, I don't know what to call this, but what you're describing is sort of like, what are, what are advances in computation, right? 144 00:10:36,140 --> 00:10:38,280 Computers are faster and cheaper is one thing. 145 00:10:38,420 --> 00:10:45,600 And then I assume we also have some new ways of telling computers to find patterns and doing things automatically, kind of like machine learning. 146 00:10:45,640 --> 00:10:47,240 I'm grossly simplifying, but like. 147 00:10:47,640 --> 00:10:48,560 But I think you're right. 148 00:10:48,560 --> 00:10:51,840 What are some things we could do just to like do new types of business, right? 149 00:10:51,960 --> 00:10:52,120 Yeah. 150 00:10:52,220 --> 00:10:53,880 And so it seems like a good example of that. 151 00:10:54,280 --> 00:11:02,680 You know, I even think back to the, to the first stories I heard Paul talk about in the early days of Pivotal, and they were really, truly data-driven personalization. 152 00:11:03,060 --> 00:11:12,560 It was understanding the context at that moment and making the best possible decision for both parties, for us as a company, for the customer, as our constituent. 153 00:11:12,860 --> 00:11:16,820 And I think that's a really interesting territory to mine. 154 00:11:17,120 --> 00:11:19,420 I just think there's so many things you could do. 155 00:11:19,920 --> 00:11:22,240 And of course, we barely scratched the surface, right? 156 00:11:22,300 --> 00:11:23,280 We made up this woman. 157 00:11:23,420 --> 00:11:24,420 We made up this retailer. 158 00:11:24,540 --> 00:11:26,640 We didn't have access to a click stream. 159 00:11:27,260 --> 00:11:29,340 We didn't have access to past purchases. 160 00:11:29,840 --> 00:11:34,100 We didn't know if she liked stripes or solids or patterns or prints or whatever, right? 161 00:11:34,740 --> 00:11:40,740 We were basically going off 140 characters and an image and doing something magical with it. 162 00:11:41,020 --> 00:11:45,520 And I think that is, is really what I realized the most compelling thing. 163 00:11:45,680 --> 00:11:46,880 We ran an experiment. 164 00:11:47,220 --> 00:11:49,640 We saw interesting results almost immediately. 165 00:11:50,220 --> 00:11:55,340 I mean, this whole thing took on the order of six weeks for a handful of people to do. 166 00:11:55,440 --> 00:11:56,480 That's not a lot at all. 167 00:11:56,900 --> 00:11:57,180 No. 168 00:11:57,260 --> 00:12:00,980 And when you think about, we didn't have to deploy infrastructure, right? 169 00:12:01,060 --> 00:12:07,020 We didn't have to go create a visual image processing service that we could use. 170 00:12:07,020 --> 00:12:11,080 We simply did a CF create service and a CF bind service, and we used it, right? 171 00:12:11,480 --> 00:12:15,920 It's all in the Google Cloud Platform service broker that works on PCF. 172 00:12:16,520 --> 00:12:18,980 Same with the natural language processing API. 173 00:12:19,140 --> 00:12:26,780 The TensorFlow stuff, we had some data scientists help us within Pivotal, but this was in their 174 00:12:26,780 --> 00:12:28,080 wheelhouse as well. 175 00:12:29,040 --> 00:12:34,900 And then at that point, the real shining piece for Pivotal was Spring Cloud Dataflow. 176 00:12:35,440 --> 00:12:40,860 And we had this ready-made platform, or I should say pipeline, to bring raw data in and help 177 00:12:40,860 --> 00:12:43,300 transform that into insight and action. 178 00:12:44,400 --> 00:12:50,140 There was very little, it's not fair to say custom development, because certainly the whole 179 00:12:50,140 --> 00:12:54,340 thing was custom, but we weren't building at an infrastructure layer. 180 00:12:54,780 --> 00:12:57,720 We were building interesting things on top of what was already there. 181 00:12:58,080 --> 00:13:01,720 Yeah, so let's start with that pipe. 182 00:13:01,880 --> 00:13:06,540 I mean, I feel like somewhere in this podcast, we've talked about Spring Cloud Dataflow, but 183 00:13:06,540 --> 00:13:08,020 like how, so what is that? 184 00:13:08,120 --> 00:13:10,640 How would you describe what it is? 185 00:13:11,260 --> 00:13:17,180 Let's see, here again, I'm product, so how it works, I just know what it can do. 186 00:13:17,320 --> 00:13:17,800 That's fine. 187 00:13:17,880 --> 00:13:26,640 We can be all like benefits and outcomes, and just kind of give a little rub on the fat belly 188 00:13:26,640 --> 00:13:28,940 of the value prop, and we'll be fine. 189 00:13:29,180 --> 00:13:29,280 Sure, sure. 190 00:13:29,460 --> 00:13:31,080 What do you do with it? 191 00:13:31,620 --> 00:13:35,660 When you look at that architecture diagram, there's really four main pieces to it. 192 00:13:35,760 --> 00:13:39,700 There's the initial sync, where we bring the data in. 193 00:13:40,300 --> 00:13:42,880 And then as we push it down the line, we do some processing. 194 00:13:43,220 --> 00:13:48,040 In the second column, that's where we do the interrogation around the language and the image. 195 00:13:48,740 --> 00:13:53,080 When we get to the fourth column, that's where we're doing the just cold, hard data science. 196 00:13:53,620 --> 00:13:55,220 Do we have a match? 197 00:13:55,320 --> 00:13:56,260 What's the best match? 198 00:13:56,260 --> 00:13:58,240 What are the four best matches in our inventory? 199 00:13:59,260 --> 00:14:04,220 And then in the fifth column, we're really taking and batching that output and sending 200 00:14:04,220 --> 00:14:05,660 it off for action. 201 00:14:06,520 --> 00:14:10,560 So it's this notion of, you know, in my mind, it's glue code. 202 00:14:10,800 --> 00:14:14,920 It's incredibly elegant and very simple to use. 203 00:14:15,160 --> 00:14:20,120 And the fact that we could do this transformation, but that is our backbone, proved to be really 204 00:14:20,120 --> 00:14:21,020 powerful stuff. 205 00:14:21,540 --> 00:14:21,640 Yeah. 206 00:14:21,960 --> 00:14:22,160 Yeah. 207 00:14:22,240 --> 00:14:31,040 I mean, it seems to me each time one day I need to actually sit down for like maybe three hours or longer and like just kind of delve into spring cloud data flow more. 208 00:14:31,100 --> 00:14:44,940 But it seems like, I don't know, one of my favorite boring types of enterprise applications is like the sort of workflow with data transformation stuff. 209 00:14:45,580 --> 00:14:46,800 Your classic ETL. 210 00:14:46,960 --> 00:14:47,780 Yeah, yeah, yeah. 211 00:14:47,860 --> 00:14:49,680 And to your point, right? 212 00:14:49,800 --> 00:14:56,920 Like the goal of a lot of these things is we want to we have a decision process that has some input to it. 213 00:14:57,480 --> 00:15:00,040 And we want to do something more than glue code, right? 214 00:15:00,060 --> 00:15:01,880 Like we want a formalized way of doing it. 215 00:15:01,960 --> 00:15:09,180 And then, of course, like in Pivotal Think Land, once you formalize and standardize it, it means you can manage it extremely well and automate it. 216 00:15:09,180 --> 00:15:12,220 And then to your point, you can do it very quickly and reliably, right? 217 00:15:12,480 --> 00:15:12,700 Yes. 218 00:15:13,160 --> 00:15:14,220 So that's always nice. 219 00:15:14,760 --> 00:15:16,300 But it's essentially a workflow. 220 00:15:16,300 --> 00:15:29,120 And then I think the, I don't know, maybe the thing that I don't know if it makes it boring for people who care more about, you know, hot dogs and stuff is like the end goal is someone is sitting there and makes a decision, right? 221 00:15:29,240 --> 00:15:29,420 Right. 222 00:15:29,440 --> 00:15:34,080 It's all just sort of augmenting a person's decision making capability at some point. 223 00:15:34,080 --> 00:15:34,240 Yeah. 224 00:15:34,280 --> 00:15:36,680 In this case, we coded that decision into it. 225 00:15:36,740 --> 00:15:38,760 But certainly you could go down that path as well. 226 00:15:39,000 --> 00:15:39,220 Yeah. 227 00:15:39,460 --> 00:15:39,620 Yeah. 228 00:15:39,620 --> 00:15:46,220 And that is, it's also kind of a hopeful thing for like the robots are going to steal our jobs is like, you know. 229 00:15:46,600 --> 00:15:56,900 Maybe, maybe for all the people who are saying that they want like a nice pair of shorts, you have roomfuls of people at home who are just like deciding like, well, we should give them some shorts. 230 00:15:57,440 --> 00:15:57,880 Right. 231 00:15:58,740 --> 00:16:00,180 We're fitting into that train. 232 00:16:01,280 --> 00:16:01,680 Awesome. 233 00:16:02,160 --> 00:16:06,640 So, so, so like how many people did you have working on this, this thing? 234 00:16:06,640 --> 00:16:09,180 There were two of us at the core. 235 00:16:10,100 --> 00:16:21,500 As I mentioned earlier, Mike Goddard was, was the main developer, a good friend of mine, and just amazing ability to pull all this stuff together and make sense of it and build it. 236 00:16:21,900 --> 00:16:24,420 And then we had some people float in and out on the data science side. 237 00:16:24,520 --> 00:16:27,540 We had some designers early on, kind of help us out. 238 00:16:27,720 --> 00:16:30,180 But, you know, at the core, it was a handful of people. 239 00:16:30,860 --> 00:16:33,860 And, you know, I think that's, that's even more interesting. 240 00:16:34,240 --> 00:16:38,640 We can also talk about the fact that we have a mix of Python apps and Spring Boot apps. 241 00:16:39,380 --> 00:16:45,260 We've got this, you know, Polygon platform where we could bring other people in to help us with very specific things. 242 00:16:45,340 --> 00:16:50,880 We had someone do an Angular front end so that we could demo kind of the data transformation process as well. 243 00:16:51,140 --> 00:16:52,260 All this stuff was easy. 244 00:16:52,260 --> 00:16:54,260 We didn't have to worry about it because that's PCF. 245 00:16:55,420 --> 00:17:01,660 And, you know, I think maybe the most interesting piece is that this thing took six weeks. 246 00:17:02,280 --> 00:17:04,800 How much time do you think our next effort is going to take? 247 00:17:05,040 --> 00:17:07,780 It's probably going to be three weeks or two weeks, right? 248 00:17:07,920 --> 00:17:14,660 The number of experiments we can run with this technology is going to increase as our familiarity and comfort with it increases. 249 00:17:15,280 --> 00:17:19,280 And I think that's really the story that resonates when I tell people. 250 00:17:19,760 --> 00:17:21,200 It's like, hey, go learn this stuff. 251 00:17:21,240 --> 00:17:22,860 But now go use it, right? 252 00:17:22,860 --> 00:17:26,640 Go free up your smartest people and tell them to go make mistakes. 253 00:17:26,980 --> 00:17:27,880 Go fail. 254 00:17:28,000 --> 00:17:30,720 Go do things that don't work because you're going to learn so much. 255 00:17:30,940 --> 00:17:30,980 Yeah. 256 00:17:31,100 --> 00:17:38,240 Next, y'all should do a rev where you scan all the IT people and you say, like, you might be interested in this white paper. 257 00:17:40,140 --> 00:17:41,560 That would be great. 258 00:17:41,700 --> 00:17:42,120 You could be your... 259 00:17:42,120 --> 00:17:43,340 I can't help you with that one, Coté. 260 00:17:43,560 --> 00:17:45,760 You could be your inside sales augmentation. 261 00:17:46,320 --> 00:17:47,200 Sure, sure. 262 00:17:47,200 --> 00:17:57,540 But, yeah, so, you know, it also strikes me that, like, you could use this as a good illustration of, like, how you would break microservices up, right? 263 00:17:57,600 --> 00:17:59,380 Whether you want to think of them components or services. 264 00:17:59,640 --> 00:17:59,740 Oh, sure. 265 00:18:00,140 --> 00:18:08,860 But to your point, like, I mean, I don't know, the various steps and components could be driven by different services and even teams that you might have. 266 00:18:09,220 --> 00:18:09,660 Right. 267 00:18:09,660 --> 00:18:15,000 Which is, it's always hard to find intelligible examples of microservices. 268 00:18:15,820 --> 00:18:16,020 Yeah. 269 00:18:16,280 --> 00:18:18,820 I mean, we did break everything up into microservices. 270 00:18:19,020 --> 00:18:21,260 Each one of those buckets is an app or a service. 271 00:18:21,680 --> 00:18:24,760 So, that part is definitely interesting as well. 272 00:18:24,880 --> 00:18:31,040 There's maybe one other anecdote that I love to tell here, and that is the first time I showed it to a friend of mine at Google. 273 00:18:32,120 --> 00:18:34,340 And he said, okay, great, great. 274 00:18:34,640 --> 00:18:36,880 How are we going to scale this thing to a million users? 275 00:18:37,200 --> 00:18:37,640 Right. 276 00:18:37,640 --> 00:18:41,800 We don't even have a user, and we really didn't build it for that purpose. 277 00:18:42,120 --> 00:18:47,380 But here again, as I thought about that, I realized, well, it's the same infrastructure, right? 278 00:18:47,540 --> 00:18:53,380 I'm just scaling my apps, and I'm scaling my, you know, my knowledge of the system as it grows. 279 00:18:53,540 --> 00:18:55,700 But I'm not switching this thing out. 280 00:18:55,760 --> 00:18:59,160 I don't need to all of a sudden go to production-grade infrastructure. 281 00:18:59,400 --> 00:19:01,360 We built this on GCP, right? 282 00:19:01,900 --> 00:19:03,560 And PCF is production-grade. 283 00:19:03,600 --> 00:19:05,360 We know all this stuff can withstand that. 284 00:19:05,360 --> 00:19:07,260 So, what's a million users? 285 00:19:07,720 --> 00:19:07,900 Yeah. 286 00:19:07,900 --> 00:19:09,220 It's probably trivial. 287 00:19:09,520 --> 00:19:09,760 Yeah. 288 00:19:10,000 --> 00:19:11,080 It's trivial, right? 289 00:19:11,340 --> 00:19:16,100 You don't have to worry about scaling the infrastructure, just making sure your own code is scalable. 290 00:19:16,100 --> 00:19:16,300 Right. 291 00:19:16,420 --> 00:19:16,820 Yeah. 292 00:19:16,820 --> 00:19:18,060 Maybe our code changes. 293 00:19:18,280 --> 00:19:19,840 Maybe components get switched out. 294 00:19:20,000 --> 00:19:22,820 But those are solvable problems. 295 00:19:22,940 --> 00:19:23,040 Yeah. 296 00:19:23,100 --> 00:19:24,280 Those are a lot easier. 297 00:19:24,280 --> 00:19:29,640 So, then, as you mentioned, like, you used a lot of Google services in there. 298 00:19:30,580 --> 00:19:39,360 And, you know, for those who don't know, in the retail space, it's considered polite to use Google instead of Amazon. 299 00:19:40,960 --> 00:19:44,200 So, people are not so into using AWS. 300 00:19:44,840 --> 00:19:46,220 I hear Walmart has opinions. 301 00:19:46,460 --> 00:19:47,200 Yes, yes, indeed. 302 00:19:47,380 --> 00:19:51,340 So, you know, my friends in Redmond have compelling offerings here as well. 303 00:19:51,460 --> 00:19:53,900 It just so happened that we did this project with Google. 304 00:19:54,140 --> 00:19:54,540 That's right. 305 00:19:54,760 --> 00:19:58,000 And so, like, what components did you use from them? 306 00:19:58,060 --> 00:20:02,420 And what made you choose them other than the corny reason I was just joking about? 307 00:20:02,900 --> 00:20:04,480 No, I think it's a good question. 308 00:20:04,480 --> 00:20:12,020 As we looked at it initially, you know, about the time that we conceived of this project, the Spanner was announced. 309 00:20:12,460 --> 00:20:14,240 And we're like, oh, let's use that, right? 310 00:20:14,400 --> 00:20:19,880 We went from thinking, let's just show the art of the possible to, no, let's do state of the art, right? 311 00:20:20,020 --> 00:20:24,820 And we realized that Spanner was overkill for our simple use case here. 312 00:20:25,360 --> 00:20:27,940 This is not designed as a product. 313 00:20:28,200 --> 00:20:31,680 This isn't something that we say, oh, here, just go run it, right? 314 00:20:31,680 --> 00:20:33,860 It's an illustration of what's possible. 315 00:20:34,980 --> 00:20:39,420 So we took some, you know, we didn't take the biggest leap that we could. 316 00:20:39,800 --> 00:20:43,040 We made some reasonable assumptions and we got in the game. 317 00:20:43,300 --> 00:20:45,780 So we used Google Cloud Storage instead of Spanner. 318 00:20:46,380 --> 00:20:47,900 But we still use TensorFlow. 319 00:20:48,180 --> 00:20:50,900 We still use the NLP API and the Vision API. 320 00:20:52,020 --> 00:20:53,660 We used RabbitMQ. 321 00:20:54,040 --> 00:20:56,380 We could have used Google PubSub, right? 322 00:20:56,700 --> 00:21:00,140 Some of these decisions we made were comfort and familiarity for us. 323 00:21:00,920 --> 00:21:08,100 But again, as we mentioned, those are services that you could swap out if or when that becomes a need. 324 00:21:11,840 --> 00:21:13,340 Sorry, I was trying to find the mute button. 325 00:21:13,680 --> 00:21:15,460 Now I'll have to edit something. 326 00:21:15,680 --> 00:21:16,340 Let me see where I was. 327 00:21:16,420 --> 00:21:17,380 21 minutes. 328 00:21:17,620 --> 00:21:18,180 It's terrible. 329 00:21:18,520 --> 00:21:22,360 But it gives me, you know, makes me feel like I'm actually earning my job. 330 00:21:23,020 --> 00:21:26,020 Pause around 21, 15. 331 00:21:26,360 --> 00:21:30,920 So, you know, we're both product people, but I'm asking the question. 332 00:21:31,020 --> 00:21:33,300 So we'll see if you can come up with the answer. 333 00:21:33,400 --> 00:21:35,460 But so what does TensorFlow do? 334 00:21:35,800 --> 00:21:40,100 I had a chance to ask, you know, our buddy Schaefer about that the other day, but I skipped on it. 335 00:21:40,100 --> 00:21:43,540 But like, how does that fit into the overall flow of things? 336 00:21:43,840 --> 00:21:45,600 So I'd love to hear him describe it. 337 00:21:45,660 --> 00:21:49,700 I've heard my friend Jay Marshall give a high level overview. 338 00:21:50,120 --> 00:21:55,060 I think where TensorFlow is today is very early stages. 339 00:21:55,980 --> 00:21:58,220 But it has some pre-trained models. 340 00:21:58,960 --> 00:22:03,980 In theory, you could plug in and use one of those out of the box and get some reasonable output from it. 341 00:22:03,980 --> 00:22:12,540 I think what's interesting is when you can train up your own model, maybe building off one of those initial ones or coding one up by yourself. 342 00:22:13,840 --> 00:22:19,580 What it can do is probably way more than I can understand. 343 00:22:20,040 --> 00:22:26,260 But the way that we used it was simply, given these inputs, what are the matching outputs, right? 344 00:22:26,600 --> 00:22:35,440 So we took our 4,000 items of women's clothing, and we ran it through the Vision API so that we had all the keywords on those things, right? 345 00:22:35,460 --> 00:22:36,280 We understood it. 346 00:22:36,580 --> 00:22:42,840 We could do a simple comparison to say, here's our batch of keywords, which items are our closest match? 347 00:22:43,320 --> 00:22:50,180 So in this case, it was a real-time kind of, you know, data science problem-solving machine for us. 348 00:22:51,520 --> 00:22:57,800 And it just so happened that we were able to bind to it as a service and use it in the context of this thing. 349 00:22:57,880 --> 00:22:59,920 We didn't have to manage our own TensorFlow. 350 00:23:00,380 --> 00:23:03,140 We didn't have to go and deploy it and configure it and all that stuff. 351 00:23:03,220 --> 00:23:07,700 It was simple, you know, again, create service, bind service, and then use it. 352 00:23:08,060 --> 00:23:14,780 And so essentially, so the overall use case, just to reiterate it for this, was someone writes a tweet that, like, 353 00:23:14,820 --> 00:23:20,000 I'd like to buy this, I don't know, tartan-colored skirt or something. 354 00:23:20,000 --> 00:23:22,900 I guess tartan's a pattern, not a color, but never mind. 355 00:23:24,580 --> 00:23:28,360 And the idea is you're going to proactively be looking for this, and you would suggest, like, 356 00:23:28,400 --> 00:23:30,060 oh, here's something in the inventory. 357 00:23:30,460 --> 00:23:34,660 And, you know, also, if you go down the street, you can get it for whatever, deals. 358 00:23:35,760 --> 00:23:42,120 And so to running that through, you know, no pun intended, the TensorFlow thing, it's sort of like 359 00:23:42,120 --> 00:23:48,320 you would take the inventory that you had, and let's see if I can not embarrass myself. 360 00:23:48,320 --> 00:23:57,440 You sort of use the magic of TensorFlow and whatever that is to kind of discover the nature of all the stuff that you have, 361 00:23:57,580 --> 00:24:03,480 whether it's through image recognition or looking over the metadata, and you're like, here is what we have, right, 362 00:24:03,500 --> 00:24:04,100 and the keywords. 363 00:24:04,360 --> 00:24:12,380 And so this gives us sort of like a model that you can stick things in and get an answer out the other end. 364 00:24:12,740 --> 00:24:21,600 And so if the thing we're sticking into this model is I would like a tartan skirt, it can figure out if we have something to recommend there, right? 365 00:24:21,600 --> 00:24:25,440 If there's something similar to that or something that is clustered around it or whatever. 366 00:24:26,120 --> 00:24:26,440 Exactly. 367 00:24:26,780 --> 00:24:31,620 And, you know, that example, I'm sure on purpose is relatively simple, but it could also be something like, you know, 368 00:24:32,340 --> 00:24:38,100 I flew to San Francisco, and once again, I forgot that it's cold here, and now I'm chilly. 369 00:24:38,760 --> 00:24:38,940 Right. 370 00:24:39,220 --> 00:24:45,580 And, you know, this would be much more advancing, but it could be like, hey, dummy, you should go down the street and buy a jacket. 371 00:24:45,800 --> 00:24:46,400 Here's a link. 372 00:24:46,400 --> 00:24:47,460 Right, right. 373 00:24:48,420 --> 00:24:55,800 But, yeah, I think if your description doesn't show up on Google's marketing site by early next week, I'll be shocked because I think you nailed it. 374 00:24:56,200 --> 00:24:56,700 Right, right. 375 00:24:57,140 --> 00:25:06,560 But I think it is maybe this kind of all-purpose engine for comparing large amounts of data and deriving the best possible answer. 376 00:25:06,980 --> 00:25:08,340 At least that's how we used it. 377 00:25:08,340 --> 00:25:13,140 I'm sure there's a multitude of uses that I haven't imagined or can't even imagine. 378 00:25:13,780 --> 00:25:14,320 Yeah, yeah. 379 00:25:14,940 --> 00:25:19,880 But I think that's, you know, one of the things that comes out of that is, well, let's go try something else, right? 380 00:25:19,920 --> 00:25:21,340 We understand how TensorFlow works. 381 00:25:21,380 --> 00:25:27,480 Let's try a different model, or let's throw more variables into the mix, or who knows what. 382 00:25:27,940 --> 00:25:33,880 Yeah, you know, I probably will never understand all the, like, math behind it, but, I mean, there's two things. 383 00:25:33,880 --> 00:25:40,880 One, like, I think maybe last year I spent a long time, I don't know if you've ever read the DevOps report or reports. 384 00:25:41,040 --> 00:25:41,260 No. 385 00:25:41,560 --> 00:25:45,740 But I spent a long time trying to understand, like, the methodology they have. 386 00:25:45,960 --> 00:25:57,320 I mean, it's some sort of, I wish I remembered all the stat nerd terms, but, you know, there's, like, some sort of three Eastern European hyphenated name model for, like, cluster analysis. 387 00:25:57,880 --> 00:25:58,500 Right, right. 388 00:25:58,500 --> 00:26:01,300 And doing this thing, and then a bunch of, like, exponents. 389 00:26:01,440 --> 00:26:12,240 And, you know, Nicole, who's one of the main, or I guess the main academic person behind it, she very kindly has offered to explain it to me many times, but I've never followed up on it. 390 00:26:12,240 --> 00:26:30,120 But all I could figure out is that basically whatever all this funny magic is, is that it, the way I kind of filed it away in my head so I was comfortable with it, is it, it discovers, like, patterns in some data set that, like, didn't, weren't there. 391 00:26:30,380 --> 00:26:35,360 Or, I mean, they were there, but, like, you, you weren't explicitly looking for them, right? 392 00:26:35,400 --> 00:26:42,680 Like, it says, like, these things are all similar, and part of what I figured out are the attributes to figure out how to figure out if they're similar. 393 00:26:43,020 --> 00:26:47,340 And then, and then ongoing, that kind of snowballs, and I get better and better about it. 394 00:26:47,380 --> 00:27:00,720 And then the second thing, and I, maybe they use this, but, you know, I switched over to using Google Photos, and it's kind of, like, with the shallow knowledge I have of computer stuff, it's fascinating to look at, like, the clustering that it does of photos, right? 395 00:27:00,780 --> 00:27:01,460 Like, oh, yeah. 396 00:27:01,460 --> 00:27:10,520 And it's very, and I guess App Photo Moments does this, too, but it'll be, like, here is three hours from two weeks ago that you spent in this part of town. 397 00:27:11,160 --> 00:27:11,260 Right. 398 00:27:11,480 --> 00:27:23,100 Right, and, and, like, you know, sometimes, like, the best ones will be, like, days where I have, I've left on a trip, and so it'll say, like, your day in Kansas City. 399 00:27:23,220 --> 00:27:27,780 And then it'll start off where I'm spending time with my kids for lunch, and then all of a sudden I'm in Kansas City. 400 00:27:27,780 --> 00:27:37,880 And, like, obviously that's a little screwy, but it's pretty good at, like, figuring out what are photo albums that we could put together for you that, like, no one would really think of doing. 401 00:27:38,380 --> 00:27:38,540 Right. 402 00:27:38,640 --> 00:27:40,780 And that's, that's what seems exciting about that. 403 00:27:40,820 --> 00:27:45,700 And to me, what makes, like, this kind of stuff more practical is, like, and again, you get it in front of a human. 404 00:27:45,780 --> 00:27:49,300 All we're trying to do is, like, suggest new stuff for you to do, right? 405 00:27:49,340 --> 00:27:49,540 Right. 406 00:27:49,600 --> 00:27:52,960 Like, you should tell this person to buy this skirt, which I think is interesting. 407 00:27:53,640 --> 00:27:56,840 And I got to assume that Google is training that model. 408 00:27:57,320 --> 00:27:57,680 Right. 409 00:27:58,020 --> 00:28:01,140 With everything that you do with it, or that it does for you. 410 00:28:01,360 --> 00:28:03,480 And I think that's the interesting piece as well. 411 00:28:03,560 --> 00:28:05,840 This thing gets better with more data, with more time. 412 00:28:06,840 --> 00:28:10,500 You know, today we may be able to make, you know, a broad match. 413 00:28:11,240 --> 00:28:24,100 But you can imagine if we knew the history, your size preferences, how often you shop, do you buy on sale or full price, et cetera, et cetera, and really start to craft a very personalized offer for you. 414 00:28:24,580 --> 00:28:29,800 I had the silly statement early on where I said, look, I hate ads, but I love deals. 415 00:28:30,340 --> 00:28:35,280 And as soon as it feels like a deal for me, that's compelling. 416 00:28:35,620 --> 00:28:42,360 If it's an email blast about something that I don't care about, I'm less likely to move on that. 417 00:28:42,360 --> 00:28:52,480 You think about how much money is spent with just broadcast advertising versus what's possible with very pinpointed, data-driven, personalized deals. 418 00:28:52,920 --> 00:28:57,840 I think it's a huge opportunity for retailers to figure out where this thing goes. 419 00:28:58,080 --> 00:29:00,600 Yeah, no, I think that's precisely it with ads, right? 420 00:29:00,680 --> 00:29:05,400 Like, it's kind of like my quiff about legacy software, right? 421 00:29:05,440 --> 00:29:07,840 Like, you only call something legacy if you don't like it. 422 00:29:08,860 --> 00:29:10,480 Otherwise, it's just your software. 423 00:29:10,840 --> 00:29:15,560 And, you know, it's almost like you almost call something an ad if you don't like it. 424 00:29:15,600 --> 00:29:17,200 Otherwise, it's a good deal, right? 425 00:29:17,240 --> 00:29:19,100 Like, it's something that you enjoy. 426 00:29:19,180 --> 00:29:21,200 But no, that's an excellent way of thinking about it. 427 00:29:21,200 --> 00:29:29,280 And I think it's – I mean, it does show you, like, again, the practical benefit you can get from doing these things. 428 00:29:29,360 --> 00:29:38,600 I mean, there's always the now infamous folklore of, like, you know, telling some dad that his teenager is pregnant. 429 00:29:38,800 --> 00:29:40,000 So you've got to avoid that stuff. 430 00:29:40,380 --> 00:29:40,520 But – 431 00:29:40,520 --> 00:29:40,760 Right. 432 00:29:41,240 --> 00:29:43,060 I mean, you know, there's that kind of stuff. 433 00:29:43,720 --> 00:29:48,220 But it is nice to craft those things together more. 434 00:29:48,220 --> 00:29:58,920 So other than me sort of, like, you know, meandering through this kind of stuff, like, what's the actual real experience of, like, working with a data scientist? 435 00:29:59,360 --> 00:30:06,880 Like, how does that – how does that fit into, like, the balanced team, product team sort of thing? 436 00:30:07,080 --> 00:30:12,140 How do they kind of – do they just, like, write down some Greek figures on a Post-it note and put it up on the board? 437 00:30:12,140 --> 00:30:14,600 Or, like, what's that interaction like? 438 00:30:14,600 --> 00:30:18,580 I mean, I've worked with developers for 20 years now. 439 00:30:18,880 --> 00:30:20,860 I hate to date myself, but that's probably true. 440 00:30:21,100 --> 00:30:24,280 And I still believe what they do is indistinguishable from magic. 441 00:30:24,540 --> 00:30:25,020 Right. 442 00:30:25,380 --> 00:30:29,100 I understand what I want done. 443 00:30:29,880 --> 00:30:38,740 And one of the wonderful things about Pivotal is that I don't necessarily have to care how it's done unless how is going to impact my when or my why. 444 00:30:40,240 --> 00:30:53,700 But the fact that we could, you know, almost monosyllabic grunt out some instructions to data scientists and then come back with this amazing model that returns the results we're looking for is pretty magical in my book. 445 00:30:53,960 --> 00:30:54,160 Yeah. 446 00:30:54,160 --> 00:31:01,580 So I'm assured that it's actual math and that there's no hocus pocus behind the scenes. 447 00:31:01,840 --> 00:31:04,740 But it does seem pretty darn magical to me. 448 00:31:05,060 --> 00:31:09,740 So the interaction would just kind of be, like, as you're saying, tell them, like, we would like to do this. 449 00:31:10,380 --> 00:31:11,200 See you later. 450 00:31:11,760 --> 00:31:14,040 And they immediately said, oh, yeah, well, that's this problem. 451 00:31:14,180 --> 00:31:15,400 That's well-known in data science. 452 00:31:15,520 --> 00:31:16,320 Here's how you do it. 453 00:31:16,480 --> 00:31:16,620 Right. 454 00:31:16,920 --> 00:31:17,540 Yeah, yeah. 455 00:31:17,800 --> 00:31:19,800 My result was two thumbs up. 456 00:31:19,860 --> 00:31:22,340 I'll stand over here and let you do that. 457 00:31:22,700 --> 00:31:28,160 Yeah, that's interesting because it's, I mean, obviously you would expect this from being, like, a pivotal thing. 458 00:31:28,160 --> 00:31:44,960 But it's a similar sort of part of the job of a, let's call it pivotal way engineer, technical person, is to not only know how to solve the problem, but know how to talk with someone like yourself or worse, myself, and be like, I think this is what you're asking. 459 00:31:45,400 --> 00:31:52,060 You know, like, I understand what you're saying, and I can translate that into my wizardry, right? 460 00:31:52,140 --> 00:31:59,860 Whereas I think, you know, the stereotypical, like, you know, beardy weirdo in sandals will be like, you know, they'll be like a command prompt. 461 00:31:59,960 --> 00:32:02,060 They'll be like, I don't understand what you've just asked me to do. 462 00:32:02,260 --> 00:32:02,940 Try harder. 463 00:32:03,000 --> 00:32:03,660 Type it in here. 464 00:32:04,300 --> 00:32:04,460 Right? 465 00:32:04,580 --> 00:32:06,040 Like, go read a man page. 466 00:32:06,040 --> 00:32:14,380 And I think, you know, even the word data science is cool, but it is a bit intimidating, right? 467 00:32:14,460 --> 00:32:16,940 Because it's sort of like, what, can I talk to these people? 468 00:32:17,900 --> 00:32:19,560 But, yeah, yeah. 469 00:32:19,680 --> 00:32:22,520 I mean, and that does, this kind of thing does highlight. 470 00:32:22,660 --> 00:32:26,120 So how involved were they in that six weeks? 471 00:32:26,420 --> 00:32:30,100 Like, did you, like, were they working with you constantly or did you just work with them a little bit? 472 00:32:30,100 --> 00:32:40,560 Because it also strikes me that something like a data science role is one of these not fully dedicated to the team things, unless, you know, you're in petroleum engineering or something. 473 00:32:40,760 --> 00:32:44,660 But you might just use them on a consultative basis on the team, or I don't know, or not. 474 00:32:44,860 --> 00:32:45,000 Yes. 475 00:32:45,620 --> 00:32:47,060 I think that's how it worked out. 476 00:32:47,160 --> 00:32:50,120 I mean, I'd have to go back and bug Mike on his experience. 477 00:32:50,460 --> 00:32:54,560 It's probably vastly different from mine because I knew he had a lot of back and forth with them. 478 00:32:55,180 --> 00:32:59,380 But we were opportunistically borrowing time from people. 479 00:32:59,380 --> 00:33:00,960 We got them excited about the project. 480 00:33:01,100 --> 00:33:01,860 They wanted to help. 481 00:33:03,160 --> 00:33:08,080 You know, I couldn't put a number on it, but my sense is that it was kind of ad hoc on the fly. 482 00:33:08,500 --> 00:33:12,000 Oh, well, here, look at this thing, or let me give you this piece of code that you can run. 483 00:33:13,320 --> 00:33:17,440 And then I think it kind of came out in the iteration that Mike did with them over time. 484 00:33:18,280 --> 00:33:18,940 Yeah, that makes sense. 485 00:33:18,940 --> 00:33:21,980 But my interactions with them were always, oh, yeah, I know. 486 00:33:22,100 --> 00:33:22,780 Yeah, that's easy. 487 00:33:22,900 --> 00:33:23,620 I know how to do that. 488 00:33:25,040 --> 00:33:27,700 And, you know, it may have been easy for them. 489 00:33:28,460 --> 00:33:30,420 And it may, in fact, be easy. 490 00:33:30,640 --> 00:33:32,460 But I think these patterns still are new. 491 00:33:32,780 --> 00:33:39,740 And certainly putting them together end-to-end like this is maybe, you know, what's most novel about the project. 492 00:33:40,120 --> 00:33:40,240 Yeah. 493 00:33:40,540 --> 00:33:46,560 And so you're saying that the result you got to was it would take basically like 10 seconds to go through this cycle, right? 494 00:33:46,760 --> 00:33:48,620 Yeah, and to tweet back out. 495 00:33:48,820 --> 00:33:54,900 So if I tweet today as Maya, and I always do it on Maya's account, I don't want my mom asking why I'm looking at dresses. 496 00:33:56,500 --> 00:33:58,160 Not that she would mind. 497 00:33:58,160 --> 00:33:58,560 Right, right. 498 00:33:58,560 --> 00:34:00,920 You would just say like, oh, don't ruin your Mother's Day surprise. 499 00:34:01,140 --> 00:34:02,260 Right, right. 500 00:34:02,460 --> 00:34:02,820 Sure. 501 00:34:04,500 --> 00:34:08,760 But, you know, the results return in, you know, roughly 10 seconds. 502 00:34:09,020 --> 00:34:10,680 I don't know how much of that is Twitter lag. 503 00:34:10,800 --> 00:34:11,400 I don't know. 504 00:34:11,560 --> 00:34:13,220 I've never actually tailed all the logs. 505 00:34:13,360 --> 00:34:15,000 I know Mike did that at one point in time. 506 00:34:16,360 --> 00:34:23,880 I think, again, what's interesting is that Maya didn't at mention the retailer, right? 507 00:34:23,960 --> 00:34:26,540 She didn't say, hey, I'm looking for this. 508 00:34:26,660 --> 00:34:31,360 She simply stated, you know, a love for an item and an intent to own it. 509 00:34:31,960 --> 00:34:39,260 And we took that positive sentiment and the fact that the item matched something in our inventory and replied to her. 510 00:34:39,760 --> 00:34:39,900 Yeah. 511 00:34:40,060 --> 00:34:44,660 So we did an experiment where we tweeted a picture of stereo speakers. 512 00:34:45,500 --> 00:34:49,100 And sure enough, we got through and it was like, okay, there's positive sentiment. 513 00:34:49,640 --> 00:34:50,660 We should make an offer. 514 00:34:50,980 --> 00:34:52,960 Let's go look for an item that matches. 515 00:34:53,460 --> 00:34:54,620 Oh, we don't have any matches. 516 00:34:54,760 --> 00:34:55,640 We don't sell speakers. 517 00:34:55,760 --> 00:34:56,520 We can't do that. 518 00:34:57,220 --> 00:35:01,760 So, you know, there are varying ways to come through the decision tree and still not end up with an offer. 519 00:35:01,980 --> 00:35:04,220 It wasn't like every single thing went out. 520 00:35:04,400 --> 00:35:09,160 But every now and then I'll tweet a picture and I'll get an offer back from our retailer, Boho Chic. 521 00:35:09,160 --> 00:35:10,640 That's just random and bizarre. 522 00:35:11,080 --> 00:35:15,120 So, you know, it is not a perfect system. 523 00:35:15,960 --> 00:35:19,000 But it's something that we, I think, could perfect over time. 524 00:35:19,400 --> 00:35:19,800 Yeah, yeah. 525 00:35:20,080 --> 00:35:27,600 So, and on that note, like, what are some things that, like, just didn't work out that you're like, well, too bad that that was a failure. 526 00:35:27,800 --> 00:35:27,980 Sure. 527 00:35:30,140 --> 00:35:32,280 We had grandiose plans in the beginning, right? 528 00:35:32,320 --> 00:35:33,960 We wanted to pull in Facebook data. 529 00:35:34,160 --> 00:35:40,040 We wanted to have an event on Facebook and a community of people, you know, that interacted with her around that. 530 00:35:40,540 --> 00:35:47,860 We thought about doing Pinterest to get a notion of her color palettes or her, you know, deeper insight into her preferences. 531 00:35:48,300 --> 00:35:51,260 We thought about, you know, do we build a mobile app? 532 00:35:51,540 --> 00:35:56,060 Do we have, you know, all these other ways of engaging her or enriching that data set? 533 00:35:56,420 --> 00:35:59,000 And we came back to, no, let's just build an MVP. 534 00:35:59,200 --> 00:36:03,800 Let's build the simplest thing that will work, that will prove that we can do something valuable. 535 00:36:04,200 --> 00:36:11,980 And that was kind of a dumb stroke of genius in some weird way that we faked a lot of stuff, right? 536 00:36:12,100 --> 00:36:14,520 We don't actually have multiple locations to our retailer. 537 00:36:14,620 --> 00:36:15,740 We have one, right? 538 00:36:15,940 --> 00:36:18,960 We don't have, you know, this rich set of data. 539 00:36:19,060 --> 00:36:21,160 We simply have what's in the tweet and in the image. 540 00:36:21,500 --> 00:36:28,620 But the fact that we were still able to derive such interesting things out of such a small set, I think, makes it even more valuable. 541 00:36:29,400 --> 00:36:36,220 So, you know, I'd love to run an experiment with an actual retailer and actual customers and see what this thing looks like at scale. 542 00:36:36,520 --> 00:36:41,700 We've had some conversations with grocers, right? 543 00:36:41,880 --> 00:36:43,180 And we thought, okay, well, how is this? 544 00:36:43,180 --> 00:36:44,440 That's exactly what I was going to suggest, right? 545 00:36:44,480 --> 00:36:47,840 That's a pretty, like, rich palette of stuff. 546 00:36:48,620 --> 00:36:57,580 And then also, I mean, further interrupting you, from a business perspective, my understanding is that the grocery business is all about top-line revenue, right? 547 00:36:57,580 --> 00:36:58,880 Because you have razor-thin margins. 548 00:36:59,300 --> 00:37:02,880 So the more top-line revenue you have, the more profit you're going to get. 549 00:37:02,940 --> 00:37:09,100 So you want to grow volume as much as possible because, you know, you're not going to sell an avocado for $20, right? 550 00:37:09,100 --> 00:37:14,880 So you've got to just sell a lot of, like, $0.50 in dollar avocados to make money. 551 00:37:14,880 --> 00:37:17,300 Yeah, you may have issues around spoilage. 552 00:37:17,680 --> 00:37:21,500 You may have issues or things you want to highlight around prepared foods. 553 00:37:21,680 --> 00:37:26,920 I mean, we thought of a couple different scenarios, like soccer mom leaving a day-long tournament. 554 00:37:27,160 --> 00:37:27,820 Kids are hungry. 555 00:37:27,960 --> 00:37:28,640 I'm tired. 556 00:37:29,280 --> 00:37:29,680 Boom. 557 00:37:29,920 --> 00:37:30,320 Sweet. 558 00:37:31,020 --> 00:37:32,620 Come by, pick up burgers and buns. 559 00:37:32,800 --> 00:37:34,440 We got it covered for you tonight. 560 00:37:34,720 --> 00:37:36,280 We'll have it waiting for you at the door. 561 00:37:36,740 --> 00:37:36,960 Yeah. 562 00:37:38,000 --> 00:37:41,160 Or, you know, a guy and his buddies are playing softball. 563 00:37:41,500 --> 00:37:43,780 I grew up in Ohio, so this stuff happens, right? 564 00:37:44,980 --> 00:37:46,540 And, you know, they just finished the game. 565 00:37:46,620 --> 00:37:46,920 They won. 566 00:37:47,000 --> 00:37:51,680 They're celebrating, you know, 12-pack on us or whatever, right? 567 00:37:52,000 --> 00:37:54,200 Like something that says, come in, grab some stuff. 568 00:37:54,260 --> 00:37:55,880 We'll throw something on top just for you. 569 00:37:56,260 --> 00:37:56,440 Yeah. 570 00:37:56,440 --> 00:38:04,960 It comes from knowing your customer and knowing what they like and meeting them in their moment of need with a compelling offer that makes sense, right? 571 00:38:05,800 --> 00:38:06,840 That's tough to do. 572 00:38:06,940 --> 00:38:11,600 You're never going to email me something that I'm going to see in time to make that decision. 573 00:38:11,740 --> 00:38:17,180 But if you can respond when I'm in that moment, then I think you have a chance of making a more compelling sale. 574 00:38:17,720 --> 00:38:18,380 Yeah, yeah. 575 00:38:18,740 --> 00:38:23,100 I don't know what it would be like to get bombarded with offers like that. 576 00:38:23,160 --> 00:38:23,380 Yeah. 577 00:38:23,500 --> 00:38:28,860 Well, I mean, that would be part of what you would have to machine learn it to do, right, is to not be annoying. 578 00:38:29,220 --> 00:38:35,660 You need a whole – there probably are like whole data scientists and their job is to make sure is to decrease annoyance. 579 00:38:37,060 --> 00:38:43,240 Maybe there's even some fancy scientific name for that of like how do you dial that down so it's not too frequent. 580 00:38:43,240 --> 00:38:49,720 How do you smooth that curve out so that it feels authentic and human without just, oh, offer, offer. 581 00:38:50,280 --> 00:38:53,380 I mean, yeah, anyways, not to get into the theoretic weeds. 582 00:38:53,380 --> 00:38:59,180 So this is kind of like a jump but kind of laterally related. 583 00:38:59,400 --> 00:39:09,440 So especially with the grocery thing, you're reminding me of what was a pretty – what I think was a pretty smart line of thought from the CEO of Jack in the Box of all places. 584 00:39:09,620 --> 00:39:13,800 I mean, not because they shouldn't be smart, but I just haven't thought of Jack in the Box in a while. 585 00:39:15,000 --> 00:39:27,760 And he was saying that like – I don't know if this is from his quarterly call or whatever, but it was quoted in some article and he was saying like, well, the thing with digital is that once we all get digital, it's not going to be a big deal. 586 00:39:27,760 --> 00:39:30,820 And then it's going to go back to like is the product good, right? 587 00:39:30,900 --> 00:39:34,560 And are you openly – like the fundamentals of the business. 588 00:39:34,900 --> 00:39:36,940 And he's like, you know, and we're Jack in the Box. 589 00:39:37,100 --> 00:39:40,280 So sure, we're going to do – I'm adding my own words here. 590 00:39:40,340 --> 00:39:41,360 But sure, we're going to do digital. 591 00:39:41,540 --> 00:39:46,160 But like we also are Jack in the Box that have this other stuff sorted out. 592 00:39:46,160 --> 00:39:49,800 So you should believe in the long-term viability of the company I'm assuming he was getting to. 593 00:39:50,640 --> 00:39:52,200 That's an interesting point, right? 594 00:39:52,280 --> 00:39:52,820 Yeah, yeah. 595 00:39:53,000 --> 00:39:55,580 At the end of the day, it's only so sexy. 596 00:39:55,840 --> 00:40:07,140 Yeah, and then so I wonder just to like – to get you to a blue sky solution here or think out loud on it, like what – like given that you – y'all could do this in six weeks, right? 597 00:40:07,140 --> 00:40:12,700 And obviously, like let's – you know, we are the increase your cycle time people. 598 00:40:12,920 --> 00:40:20,100 But like obviously, it would probably take you at least a year before like it was a thing that was like a major part of the business. 599 00:40:20,160 --> 00:40:23,480 That you had it at enough volume and you exposed it and you did the annoyance dial. 600 00:40:23,480 --> 00:40:24,520 Yeah, I think that's fair. 601 00:40:24,680 --> 00:40:25,040 Yeah, yeah. 602 00:40:25,400 --> 00:40:32,700 Yeah, and so like – but you can imagine over a five-year term that like every company could be doing something like this, right? 603 00:40:32,740 --> 00:40:35,600 Which again, to the Jack in the Box thing, doesn't belittle it all. 604 00:40:35,680 --> 00:40:37,720 But I wonder like how – I don't know. 605 00:40:37,760 --> 00:40:39,500 What do you do with that, right? 606 00:40:39,620 --> 00:40:39,820 Like – 607 00:40:39,820 --> 00:40:40,740 Where does it end up? 608 00:40:40,960 --> 00:40:53,420 Yeah, or no, even on a different plane of like how do you plan out for the fast commoditization of something that was incredibly unique at some point, right? 609 00:40:53,420 --> 00:40:53,680 Oh, wow. 610 00:40:53,680 --> 00:40:56,420 Like with the grocery store example. 611 00:40:57,360 --> 00:41:01,320 Like let's just assume that everyone's going to have this in five years. 612 00:41:01,480 --> 00:41:02,440 Sure, sure. 613 00:41:02,440 --> 00:41:03,320 Or less. 614 00:41:03,400 --> 00:41:04,020 So what do we do? 615 00:41:04,580 --> 00:41:04,820 Yeah. 616 00:41:05,400 --> 00:41:07,660 Yeah, I mean I have no idea. 617 00:41:07,660 --> 00:41:10,380 I hadn't ever taken it that far in my head. 618 00:41:10,880 --> 00:41:15,940 You start off with something that's uniquely differentiated and very quickly just gets commoditized. 619 00:41:16,140 --> 00:41:16,540 Exactly. 620 00:41:17,040 --> 00:41:17,480 Yeah, yeah. 621 00:41:17,620 --> 00:41:24,140 And maybe that is the world we live in where those things do in fact happen. 622 00:41:24,260 --> 00:41:26,520 I guess, you know, is cloud commoditized? 623 00:41:26,760 --> 00:41:28,520 It's probably not that far away. 624 00:41:28,880 --> 00:41:29,120 Yeah. 625 00:41:29,880 --> 00:41:38,520 Yeah, well, I mean to add a footnote to that, I mean to your point, the things that are – there's a few regular infrastructure things. 626 00:41:38,640 --> 00:41:46,200 But the things that, as we've been doing here, that become exciting and differentiating about various public clouds are the actual services they provide, right? 627 00:41:46,280 --> 00:41:51,500 Like TensorFlow and is it Marsha or Marcia that Amazon just announced? 628 00:41:51,600 --> 00:41:55,120 Like those things are interesting because they're novel new services. 629 00:41:55,120 --> 00:42:04,100 Like the idea that, you know, I could start a bunch of VMs is to some extent thrilling, but not that big of a deal anymore. 630 00:42:05,060 --> 00:42:05,540 Right. 631 00:42:05,880 --> 00:42:13,400 I mean it's interesting you brought up that jack-in-the-box comment because you do come back to what is core to our business? 632 00:42:13,980 --> 00:42:16,160 What are the things that matter most to us? 633 00:42:16,280 --> 00:42:16,660 Right. 634 00:42:16,720 --> 00:42:29,100 And I would argue or assume rather that for jack-in-the-box, it is, you know, the consistent quality experience that you can only get a jack-in-the-box and the predictable time, et cetera, et cetera, right? 635 00:42:29,140 --> 00:42:30,340 Like some very core things. 636 00:42:30,440 --> 00:42:37,620 And they could certainly maybe, you know, goose the margins on some things or move the numbers in another direction based on a promotion or whatnot. 637 00:42:37,880 --> 00:42:43,680 But at the end of the day, their business is providing food at predictable, consumable prices, right? 638 00:42:44,160 --> 00:42:44,320 Yeah. 639 00:42:45,180 --> 00:42:52,560 And, you know, maybe that's true of retailers and other businesses as well that, you know, all these things are on the margins. 640 00:42:53,700 --> 00:42:54,600 I don't know. 641 00:42:54,720 --> 00:42:56,980 We've also seen some pure digital plays. 642 00:42:57,140 --> 00:43:02,260 I think of Stitch Fix, right, as really a huge disruptive force in retail. 643 00:43:02,660 --> 00:43:03,680 They own no stores. 644 00:43:03,820 --> 00:43:09,960 They simply have, you know, warehouses and they have an army of people that help style their customers. 645 00:43:10,200 --> 00:43:13,400 And they get that real-time feedback and driven into their automation. 646 00:43:13,960 --> 00:43:17,940 So, you know, who knows where this goes? 647 00:43:18,040 --> 00:43:19,360 Do we see the end of retail? 648 00:43:19,500 --> 00:43:22,120 Do we see the end of dining out? 649 00:43:22,280 --> 00:43:23,120 Probably not. 650 00:43:24,720 --> 00:43:25,380 I don't know. 651 00:43:25,440 --> 00:43:26,380 I'm just babbling now. 652 00:43:26,600 --> 00:43:27,060 No, no. 653 00:43:27,180 --> 00:43:28,660 That's what I asked for. 654 00:43:29,260 --> 00:43:32,520 But I think, you know, to equally babble on my own, right? 655 00:43:32,560 --> 00:43:34,540 I think, I don't know. 656 00:43:34,640 --> 00:43:43,860 I mean, one of my personal charters is to be the apologist for boring stuff that makes lots of money in what I do, right? 657 00:43:45,160 --> 00:43:47,420 Because I don't really know why. 658 00:43:47,540 --> 00:43:51,620 It's because there's plenty of people who talk about, like, all the hot, cool stuff, right? 659 00:43:51,760 --> 00:43:54,540 And I think it is hot and cool, right? 660 00:43:54,600 --> 00:44:02,220 Like, it is the idea of, to stick to retail and groceries, the idea of omni-channel selling, right? 661 00:44:02,660 --> 00:44:11,820 Which means there are, instead of just going to the store and buying a product or going to a website and having one mailed to you, 662 00:44:12,200 --> 00:44:19,580 there's, like, five or seven different ways that you can mix and match to browse products, find them, pay for them, and then get them, and then return them, right? 663 00:44:19,660 --> 00:44:20,600 You can do all sorts of stuff. 664 00:44:20,700 --> 00:44:27,520 I once, I forget if it was Gartner, but I remember reading a Gartner report on omni-channel, and they just, they ran the matrix of all the combinations. 665 00:44:27,720 --> 00:44:30,260 And it was, it was delightfully overwhelming. 666 00:44:30,920 --> 00:44:31,500 I'm sure. 667 00:44:31,640 --> 00:44:37,960 Like, if you think about, you know, in-store pickup, order online, order in-store, have mailed you, like, all the different ways you could do it. 668 00:44:37,960 --> 00:44:43,360 But, anyways, so there's a lot of interesting innovation to do in that area. 669 00:44:43,600 --> 00:44:51,040 And I think even the terms we use to describe sustaining innovation are kind of judgy, right? 670 00:44:51,080 --> 00:44:59,860 Like, this idea that just doing incremental or sustaining innovation is all you're doing, like, certainly isn't as cool as, like, you know, robots or self-driving cars. 671 00:45:00,500 --> 00:45:04,020 But it is, like, I mean, obviously you have to do both. 672 00:45:04,200 --> 00:45:10,180 And I think it's equally interesting to figure out, like, so going back to the jack-in-the-box thing, right? 673 00:45:10,220 --> 00:45:21,400 Like, what are, this loop that y'all have, like, how would you apply that internally to, like, optimize how they're running things, despite our top-line revenue comment earlier? 674 00:45:21,400 --> 00:45:28,940 But, you know, there's, it seems like there's a whole lot of other things that you could apply it to, which would grow the business value that you would have. 675 00:45:29,200 --> 00:45:33,060 I mean, and there's simple things like, you know, here's a new type of burger you could sell or something. 676 00:45:33,200 --> 00:45:36,920 But there probably are all sorts of other interesting applications of a loop like this. 677 00:45:36,960 --> 00:45:44,660 And the same benefits that you have of that, it would take you six weeks or less to just try something out because all of this is fit together. 678 00:45:45,580 --> 00:45:45,820 I don't know. 679 00:45:45,860 --> 00:45:51,700 It gives you an interesting sort of tool to start doing incremental innovation or sustaining innovation. 680 00:45:51,720 --> 00:45:57,440 And I think if you approach it in those terms of let's run some experiments, let's see what we can learn. 681 00:45:58,640 --> 00:46:00,760 You know, to me, that is the most compelling thing. 682 00:46:01,300 --> 00:46:03,380 I came into this thing relatively cold. 683 00:46:03,380 --> 00:46:10,660 I mean, my wife has done fashion retail for many years, studied fashion design in college, grad school, I should say. 684 00:46:11,620 --> 00:46:13,320 And so that is her world. 685 00:46:13,620 --> 00:46:17,060 And I grew up in the test bed of retail in Columbus, Ohio. 686 00:46:17,260 --> 00:46:20,260 So I'm certainly not unexposed to it. 687 00:46:20,480 --> 00:46:26,260 But, you know, with just our bare minimum of knowledge, we were still able to create something compelling. 688 00:46:26,260 --> 00:46:34,300 I think about experts, domain experts, or people with the business experience and, you know, retail or grocery, et cetera. 689 00:46:35,160 --> 00:46:41,760 With that domain expertise to be able to put these things together in ways that benefit them, that's the most compelling piece to me. 690 00:46:42,220 --> 00:46:43,080 Yeah, no, for sure. 691 00:46:43,520 --> 00:46:43,840 For sure. 692 00:46:44,080 --> 00:46:46,040 So I'm a novice and we came up with something cool. 693 00:46:46,280 --> 00:46:48,580 What could an expert do with it, right? 694 00:46:49,000 --> 00:46:49,360 Exactly. 695 00:46:50,220 --> 00:46:54,140 Get someone who's been in the field for a long time. 696 00:46:54,620 --> 00:46:55,260 Well, that's great. 697 00:46:55,380 --> 00:46:58,860 Is there anything else you want to add on before we wrap it up? 698 00:46:59,460 --> 00:47:01,120 I can't think of anything, Coté. 699 00:47:01,120 --> 00:47:02,320 I hit my main points. 700 00:47:02,420 --> 00:47:04,440 Had a delightful conversation with you, as always. 701 00:47:04,700 --> 00:47:04,960 Yeah. 702 00:47:04,960 --> 00:47:05,900 This is really fun. 703 00:47:06,020 --> 00:47:09,340 I don't know how you get to do this day in and day out, but what a cool job. 704 00:47:10,300 --> 00:47:11,300 Well, you know. 705 00:47:11,980 --> 00:47:14,200 Do you have any metaphors to share before we go? 706 00:47:14,600 --> 00:47:15,460 I'm all at it. 707 00:47:15,460 --> 00:47:15,880 Have you exhausted? 708 00:47:16,200 --> 00:47:18,400 This is about 3 p.m. on Friday. 709 00:47:18,960 --> 00:47:21,480 It's like I have a deep well, but it's been a long week. 710 00:47:21,720 --> 00:47:23,260 And you're on vacation next week, correct? 711 00:47:23,540 --> 00:47:24,480 Oh, I'm traveling. 712 00:47:25,120 --> 00:47:25,520 Traveling. 713 00:47:25,700 --> 00:47:26,140 Okay, got it. 714 00:47:26,140 --> 00:47:31,080 My wife will think it's a vacation when I get to go to bed in a comfy hotel bed without kids. 715 00:47:32,460 --> 00:47:34,280 Have you ever taken her on one of those trips? 716 00:47:34,920 --> 00:47:35,280 Almost. 717 00:47:35,740 --> 00:47:35,940 Almost. 718 00:47:35,940 --> 00:47:36,260 Okay. 719 00:47:36,260 --> 00:47:37,220 We're getting there. 720 00:47:37,720 --> 00:47:40,320 That changed my wife's opinion of my business travel. 721 00:47:40,520 --> 00:47:40,740 Yeah. 722 00:47:40,740 --> 00:47:41,600 So, yes. 723 00:47:41,780 --> 00:47:43,920 Not as glamorous as it seems from the outside. 724 00:47:44,160 --> 00:47:44,340 Yeah. 725 00:47:44,600 --> 00:47:45,460 But still pretty cool. 726 00:47:45,820 --> 00:47:50,520 She once went on a – and this doesn't help the case that you're trying to build. 727 00:47:50,600 --> 00:47:52,280 She once went to Las Vegas with me. 728 00:47:52,720 --> 00:47:53,320 Right, right. 729 00:47:53,320 --> 00:47:57,440 Which is not quite the slumming it you would be looking for. 730 00:47:58,420 --> 00:47:58,940 True, indeed. 731 00:47:59,080 --> 00:47:59,340 Yeah. 732 00:47:59,400 --> 00:48:05,180 But so if people want to follow up with you, you got a Twitter handle or use the CB radio or anything? 733 00:48:05,180 --> 00:48:11,740 I got a at SCT, my initials, and I'd be delighted to continue the conversation there. 734 00:48:11,840 --> 00:48:13,860 You're going to share some materials, I think. 735 00:48:14,160 --> 00:48:14,760 Yeah, yeah. 736 00:48:14,880 --> 00:48:16,780 You'll have to send me some links of other stuff. 737 00:48:16,780 --> 00:48:17,760 I've done a video and a demo of all the other stuff. 738 00:48:18,160 --> 00:48:21,880 And you certainly have the original tweet that spawned this conversation. 739 00:48:22,000 --> 00:48:22,200 Yeah. 740 00:48:22,280 --> 00:48:24,820 Yeah, you're like me. 741 00:48:24,820 --> 00:48:28,300 You must have gotten into Twitter early to get a three-letter thing. 742 00:48:29,380 --> 00:48:29,620 Yeah. 743 00:48:29,960 --> 00:48:34,440 I mean, I think I was in it even earlier, and then I quit because I was like, who's ever going to tweet? 744 00:48:34,560 --> 00:48:35,120 This is weird. 745 00:48:35,280 --> 00:48:35,440 Yeah. 746 00:48:35,440 --> 00:48:38,720 And then I came back shortly thereafter, but still it was available. 747 00:48:38,980 --> 00:48:39,420 Yeah. 748 00:48:39,420 --> 00:48:39,520 Yeah. 749 00:48:39,520 --> 00:48:39,580 Yeah. 750 00:48:39,580 --> 00:48:43,600 You know, it happened to line up with my work when I was at Red Monk, so I kept using it. 751 00:48:43,660 --> 00:48:52,020 But there was this point where I was like, all right, I don't need to be getting – every time that Chris Messina thinks of something, I don't need to get a text message about it. 752 00:48:53,460 --> 00:48:57,540 Which was way back in the – what was it, 4-0-4-0-4 days? 753 00:48:58,140 --> 00:48:58,620 I remember. 754 00:48:58,880 --> 00:48:59,440 Yeah, the farewell. 755 00:48:59,860 --> 00:49:00,100 Yeah. 756 00:49:00,260 --> 00:49:01,340 Those are good times. 757 00:49:01,680 --> 00:49:02,100 But yeah. 758 00:49:02,200 --> 00:49:02,280 Absolutely. 759 00:49:02,500 --> 00:49:03,540 Well, great. 760 00:49:03,960 --> 00:49:05,120 Well, thanks for being on. 761 00:49:05,240 --> 00:49:08,660 And with that, this has been another episode of Pivotal Conversations. 762 00:49:09,120 --> 00:49:19,480 If you want to get the most recent episodes of it and kind of browse our back catalog, we've got a – to recommend some episodes, we've got now a good collection of what I call, I don't know, Pivotal IRL. 763 00:49:19,480 --> 00:49:28,020 Like people like Allstate that we've spoken with and other people who actually are the end customers, not just theorizers like ourselves here. 764 00:49:29,360 --> 00:49:30,440 Applied theorists. 765 00:49:31,540 --> 00:49:34,000 But, you know, there's various collections there that you can go get. 766 00:49:34,040 --> 00:49:41,400 But if you go to SoundCloud.com slash Pivotal Conversations, you can find those episodes and the RSS feed to subscribe to. 767 00:49:41,660 --> 00:49:49,120 And more or less every Thursday, I go and post an episode formally, the show notes, over at Pivotal.io slash blog. 768 00:49:49,120 --> 00:49:51,980 And I've been putting transcripts up there as well if you're interested in that. 769 00:49:52,660 --> 00:49:55,400 And also, we've got a couple of other podcasts you should check out. 770 00:49:55,480 --> 00:50:09,000 We have one called That Moment, which goes over people kind of in a more – in a different style than we've been talking about here, kind of discovering when they should do something different and kind of walking through how they've been doing that. 771 00:50:09,000 --> 00:50:17,520 And then, of course, there's the Pivotal Insights podcast, which sometimes for a while nominally covers sort of the data science and things like that. 772 00:50:17,580 --> 00:50:31,000 But they also cross over into just general-to-purpose things like the most recent one, which I mentioned here before that I like, talks with one of the people who heads up our application transformation and modernization re-platforming service. 773 00:50:31,220 --> 00:50:41,420 And there's a really good – in the same way that we've tried to explain a bunch of visuals here – there's a good explanation of how you methodically go through the portfolio and decide what to re-platform. 774 00:50:41,620 --> 00:50:43,740 So with that, we'll see everyone next time. 775 00:50:43,960 --> 00:50:44,400 Bye-bye.