1 00:00:01,080 --> 00:00:05,679 How'd you like to listen to dot NetRocks with no ads? Easy? Become 2 00:00:05,679 --> 00:00:09,839 a patron for just five dollars a month, you get access to a private 3 00:00:10,000 --> 00:00:14,439 RSS feed where all the shows have no ads. Twenty dollars a month will 4 00:00:14,439 --> 00:00:19,039 get you that and a special dot NetRocks patron mug. Sign up now at 5 00:00:19,039 --> 00:00:24,160 Patreon dot dot NetRocks dot com. Hey Carl and Richard here with your twenty 6 00:00:24,199 --> 00:00:29,320 twenty four NDC schedule. We'll be at as many NDC conferences as possible this 7 00:00:29,440 --> 00:00:34,240 year, and you should consider attending no matter what. The Copenhagen Developers Festival 8 00:00:34,320 --> 00:00:41,560 happens August twenty sixth through the thirtieth. Tickets at Cphdevfest dot com. Ndcporto 9 00:00:41,679 --> 00:00:46,520 is happening October fourteenth through the eighteenth. The early discount ends June fourteenth. 10 00:00:46,840 --> 00:01:03,000 Tickets at Ndcporto dot com. We'll see you there, we hope. Wow, 11 00:01:03,079 --> 00:01:07,959 you did it again. You found the last build show at dot net 12 00:01:07,040 --> 00:01:11,000 rocks. I'm Carl Franklin and I'm Richard Campbell, and Mark Brown's here. 13 00:01:11,000 --> 00:01:14,760 We're going to talk to him very soon. But anything that you want to 14 00:01:15,280 --> 00:01:19,599 tell the people about our week here at Build you know, it's fun to 15 00:01:19,719 --> 00:01:23,280 be back in person. You know, everybody's getting better at conferences by bad, 16 00:01:23,319 --> 00:01:30,280 Like we're remembering how the conference. Yeah but yeah, I get Microsoft 17 00:01:30,280 --> 00:01:33,400 asked me to put together a podcasting space, so we brought in a bunch 18 00:01:33,439 --> 00:01:34,840 of other podcasters as well. Yeah. Then I figured, you know, 19 00:01:34,879 --> 00:01:38,760 we could make some dnrs while we were here. Yeah, and you know, 20 00:01:38,879 --> 00:01:41,359 great line of guests, and this is the last of them. Yeah, 21 00:01:41,359 --> 00:01:44,120 the published wise, it's actually get a published in order, so it's 22 00:01:44,159 --> 00:01:47,159 all the last when we're recording. Yeah, third day, so it should 23 00:01:47,159 --> 00:01:49,079 be fun. Yeah, but I don't have any whiskey here, which is 24 00:01:49,159 --> 00:01:53,719 usually the last show of the day, the rituals. We're on the third. 25 00:01:53,760 --> 00:01:56,159 I'm a little bruised. Well you are, Yeah, I'm not. 26 00:01:56,359 --> 00:01:59,840 However, I've been a good boy. You're a responsible individual. I appreciate 27 00:01:59,879 --> 00:02:05,120 it. I wouldn't say that. Let's not get crazy now, all right, 28 00:02:05,200 --> 00:02:15,400 roll the crazy music for better? No framework? Man, what do 29 00:02:15,439 --> 00:02:17,039 you got so better? No framework? For those who don't know, this 30 00:02:17,159 --> 00:02:21,319 is it started out as I would, you know, point my finger at 31 00:02:21,319 --> 00:02:27,360 a namespace in the framework. This you know, the any you know classes 32 00:02:27,680 --> 00:02:30,639 in dot net that we may or may not know about that kind of thing. 33 00:02:30,639 --> 00:02:32,560 But that got old real quick, and I started googling stuff and finding 34 00:02:32,680 --> 00:02:36,520 eventually, do run out of framework? Do you kind of got through the 35 00:02:36,560 --> 00:02:38,240 framework? Yeah, it took a while, but you got there exactly. 36 00:02:38,919 --> 00:02:45,120 And nobody wants to hear, you know, a lesson on the bowels of 37 00:02:45,159 --> 00:02:50,280 the common language run time. Well, anyway, so Mark Brown is here, 38 00:02:50,560 --> 00:02:53,599 and Mark you can join in because I picked this toy for you. 39 00:02:53,159 --> 00:02:57,639 Yeah. Yeah, So who's a toy boy now right? I know I've 40 00:02:57,639 --> 00:03:02,199 become the toy boy, right. I know Mark is into a lot of 41 00:03:02,240 --> 00:03:09,280 things, you know, technology wise, but also electronic music, and I 42 00:03:09,360 --> 00:03:15,599 know this about you. So I found I'm sharing a device that I have 43 00:03:15,680 --> 00:03:17,879 and I have a link for it on sweetwater dot com, which is where 44 00:03:17,879 --> 00:03:23,639 I bought it. Did not find it on Amazon, but they're out of 45 00:03:23,639 --> 00:03:27,080 stock. So this is going to be fun. When you know ten people 46 00:03:27,080 --> 00:03:30,479 who listen to dot net rocks, I'll call up and slam the back orders, 47 00:03:30,560 --> 00:03:34,599 slam the back lid. So this is a Kenton l N d R 48 00:03:35,000 --> 00:03:38,759 MIDI line driver. It's basically two boxes. It's a ballan. You know 49 00:03:38,800 --> 00:03:44,319 what a ballan is? A way to use one transport for a different protocol, 50 00:03:44,439 --> 00:03:46,240 right, And there's a lot of Cat five, Cat six ballons where 51 00:03:46,240 --> 00:03:50,120 you just string a network cable between two things. You can do for video, 52 00:03:50,159 --> 00:03:53,000 for audio, for whatever. Well that's what this is. So MIDI 53 00:03:53,560 --> 00:04:00,000 Musical Instrument Digital interface has a problem that when the cables get over even close 54 00:04:00,039 --> 00:04:04,719 to twenty feet long, the latency gets high. It's a kind of serial 55 00:04:04,719 --> 00:04:09,879 protocol. It's a very yeah, it's a serial protocol, old school, 56 00:04:10,520 --> 00:04:14,560 and so this solves the problem. It's always been a problem for me because 57 00:04:14,599 --> 00:04:18,240 in my studio, the piano is typically on the other side of the room 58 00:04:18,360 --> 00:04:24,120 from the workstation where I'm recording, because I want isolation and everything, and 59 00:04:24,959 --> 00:04:29,399 I've always had a problem with this, and so I've tried Bluetooth adapters and 60 00:04:29,439 --> 00:04:32,560 they're two Like, the latency is too much. This is the only thing 61 00:04:32,600 --> 00:04:36,639 that works. So I've basically hardwired this into my studio, one side on 62 00:04:36,680 --> 00:04:41,040 the piano and the other side of my workstation. I'm taking a switch between 63 00:04:41,040 --> 00:04:45,399 either. It's litter just because Ethernet lends at a higher frequency. Yep, 64 00:04:45,519 --> 00:04:47,399 you could haliday a heckup a lot faster, that's right, okay, and 65 00:04:47,480 --> 00:04:50,959 maybe you did put a switch and you'd be fine. You're still talking milliseconds, 66 00:04:51,079 --> 00:04:56,839 yeah, exactly. So the piano that I have is the Yamaha Baby 67 00:04:56,839 --> 00:05:00,120 gran that has made built into it, and there's a Selenite under every key, 68 00:05:00,240 --> 00:05:03,560 so if you plug a MIDI keyboard into it and play, the keys 69 00:05:03,600 --> 00:05:08,680 go up and down right. And it's also a great MIDI controller. So 70 00:05:08,839 --> 00:05:13,639 I just have it now hardwired in so at any time somebody wants to play 71 00:05:13,680 --> 00:05:17,560 the piano and record it. Let's say there's a drummer playing right and the 72 00:05:17,600 --> 00:05:21,680 piano playing at the same time, I can just have the piano player record 73 00:05:21,759 --> 00:05:30,120 MIDI and with no sound, and then edit it right to fix the mistakes, 74 00:05:30,600 --> 00:05:32,040 and then play it back through the piano and record it. Wow. 75 00:05:32,160 --> 00:05:36,279 So that's a trick that my clients love. Very clever, do that now? 76 00:05:36,920 --> 00:05:40,519 Yeah. Anyway, it's one hundred and sixty nine bucks. And if 77 00:05:40,519 --> 00:05:44,399 you struggle like me, yeah for the pair. If you struggle like me 78 00:05:44,439 --> 00:05:46,759 with this problem, this is a solution. Whenever they get back and stock 79 00:05:47,439 --> 00:05:50,959 and ask for Mike God Love, he's my guy at Sweetwater. Yeah, 80 00:05:51,040 --> 00:05:54,839 and they have everybody orders a bunch of them then maybe they'll rush the back 81 00:05:54,959 --> 00:05:57,959 order back. Yeah. Maybe, yeah, yeah, maybe they'll offer me 82 00:05:58,000 --> 00:06:00,560 some free stuff. Who knows stuff is good. Free stuff is good. 83 00:06:00,680 --> 00:06:03,199 Bought some stuff from Sweetwater. They're good people. They are all right, 84 00:06:03,240 --> 00:06:05,720 all right. Richard, who's talking to us today, grabbed a comment. 85 00:06:05,759 --> 00:06:09,639 Let's be clear, you found this comment, but yeah, grabbed a comment 86 00:06:09,680 --> 00:06:14,120 off of mast done. Yeah, it's only because we've already read all the 87 00:06:14,160 --> 00:06:18,160 COSMOSCB related kind of off of the of the website and so yeah, plus 88 00:06:18,360 --> 00:06:21,720 other issues, but that's not important now. This comment comes from Hassen Savarn, 89 00:06:21,759 --> 00:06:27,439 who said it looks like the AI theme is replacing data topics and conferences. 90 00:06:27,720 --> 00:06:30,519 I get it. AI is everywhere lately. But AI depends on data. 91 00:06:30,720 --> 00:06:33,959 Yeah, you still need good data models, optimized databases, data quality. 92 00:06:34,000 --> 00:06:38,319 It all still matters. So organizers, please don't remove data topics from 93 00:06:38,319 --> 00:06:41,879 your upcoming events or net topics for that matter. Correct. Why would they 94 00:06:41,920 --> 00:06:44,360 do that? I no, no, no, everybody thinks it's Python now 95 00:06:44,399 --> 00:06:47,160 with the AI think so on the other side is like his gigo ever meant 96 00:06:47,240 --> 00:06:51,560 more than it means right now, garbage in, garbage out right like that 97 00:06:51,680 --> 00:06:57,519 was an old joke of computing decades ago. But what is a large language 98 00:06:57,519 --> 00:07:02,040 model but a garbage amplifier and garbage it spreads it everywhere, or a small 99 00:07:02,079 --> 00:07:08,040 language model for that, Yeah, hassan, great comment, Thanks so much, 100 00:07:08,079 --> 00:07:11,199 and I'll respond to you a masadon he send you a copy of music 101 00:07:11,199 --> 00:07:13,160 go by, And if you'd like a copy of music, go buy, 102 00:07:13,399 --> 00:07:16,240 write a comment on the website at dot at rocks dot com, or on 103 00:07:16,279 --> 00:07:20,120 the facebooks or on masd on Twitter. All of those work. Well, 104 00:07:20,199 --> 00:07:23,040 read them all and if I read them on the show, I'll send you 105 00:07:23,040 --> 00:07:26,319 a copy of music. Go by. Yeah, blues guy, I'm a 106 00:07:26,639 --> 00:07:29,319 guy. Yeah, I heard they just got DMS on there. There you 107 00:07:29,399 --> 00:07:30,600 go. Well, you can find all the ways to get in touch with 108 00:07:30,639 --> 00:07:34,920 me anyway at Carl Franklin dot com, including all those things that Richard mentioned. 109 00:07:35,279 --> 00:07:41,399 So let's formally introduce Mark Brown. Been on the show several times, 110 00:07:41,439 --> 00:07:45,000 but not in a while. He's been in the software industry for more than 111 00:07:45,040 --> 00:07:50,480 thirty years old guy, when I was younger, ripersnappers. Today he has 112 00:07:50,519 --> 00:07:56,120 worked for a wide array of companies, from ISPs to dot COM's building applications 113 00:07:56,120 --> 00:08:01,079 that ranged from the largest of back end systems to the smallest the devices and 114 00:08:01,120 --> 00:08:07,120 everything in between. Mark leads the growth team for Azure Cosmos DP. Welcome, 115 00:08:07,240 --> 00:08:09,959 Thank you very much. Great to be back. It's been a while 116 00:08:09,000 --> 00:08:13,360 too long. And as you've you just caught me here and said, hey, 117 00:08:13,519 --> 00:08:16,759 we haven't talked about Cosmos debating around like you are correct, that is 118 00:08:16,839 --> 00:08:18,839 right. Are you doing any electronic music these days? You know it's been 119 00:08:20,040 --> 00:08:22,920 Uh no, I guess I'm just gonna say no, that's fine. Yeah, 120 00:08:24,360 --> 00:08:26,439 it's been hard to takes all it takes, you know, it does 121 00:08:26,560 --> 00:08:31,959 take time and dedication and yeah, and you've been busy, been very busy. 122 00:08:31,399 --> 00:08:35,720 Uh, life has not been the same. And I guess about eighteen 123 00:08:35,759 --> 00:08:43,000 months since the AI detonation. Yeah, like chat GPT landed on my head 124 00:08:43,039 --> 00:08:46,039 like a ton of bricks right over that November twenty two. Yeah. Yeah, 125 00:08:46,080 --> 00:08:52,720 and I spent all of my Christmas vacation looking at this thing and thinking, 126 00:08:52,960 --> 00:08:56,799 hey, we should be there's there's a there's a there's a place for 127 00:08:56,879 --> 00:08:58,960 us in all of this. So it's your fault, well not all may 128 00:09:00,200 --> 00:09:03,519 some of it, because here it build it's all AI. I mean everything 129 00:09:03,519 --> 00:09:07,080 in the keynote is AI and everything everywhere is all averything. Yeah, well 130 00:09:07,120 --> 00:09:13,039 you, let's face it, Microsoft has a great platform here. Yeah, 131 00:09:13,120 --> 00:09:18,600 yeah, jumped on right like I jumped in, and I started looking at 132 00:09:18,240 --> 00:09:22,320 l ms in this whole Jenai space and the technology here, and thought, 133 00:09:22,440 --> 00:09:26,919 you know, there's a place for Cosmos in building these types of applications, 134 00:09:28,240 --> 00:09:31,639 particularly when you look at things like chat. Like chat seems to be kind 135 00:09:31,679 --> 00:09:35,120 of the kind of the interface, if you will, a natural language interface 136 00:09:35,159 --> 00:09:39,120 into that. And you know, customers have been using Cosmos to build just 137 00:09:39,200 --> 00:09:43,240 regular chat bots and chat applications for years, and that was kind of the 138 00:09:43,279 --> 00:09:48,159 angle I took. I'm like, what's our role in that? And things 139 00:09:48,200 --> 00:09:52,360 have been snowballing. In fact, it wasn't just me that was thinking and 140 00:09:52,399 --> 00:09:56,799 looking about this, but also our own engineering team and even the open Ai 141 00:09:56,919 --> 00:10:05,360 folks. Very soon after they initially launched, before that really exploded, they 142 00:10:05,399 --> 00:10:09,399 migrated over from existing postcrists. They were running all of their chat GPT, 143 00:10:09,519 --> 00:10:11,759 all the chat history and everything on. There was running ont of some Postgrist 144 00:10:11,840 --> 00:10:18,039 servers and they were struggling as a numbers just a hundred million users in two 145 00:10:18,039 --> 00:10:22,039 months. Yeah, it doesn't just happen for free. It was overloading because 146 00:10:22,039 --> 00:10:24,840 they'd have to go and reshard their databases like constantly to try to keep up 147 00:10:24,879 --> 00:10:28,960 with the growth in there. And we reached out to him and said, 148 00:10:28,080 --> 00:10:31,039 hey, I think we've got a way we can help you with this, 149 00:10:31,919 --> 00:10:35,720 and we walked them through Cosmos and explained, hey, this is you know, 150 00:10:35,799 --> 00:10:39,480 this is scale out architecture here right, so beause postcrists is still in 151 00:10:39,600 --> 00:10:43,360 RDBMS. So did they rearchitect their data? They did not really need to 152 00:10:43,399 --> 00:10:48,879 re architect. Migration was not wholly difficult, and they were able to do 153 00:10:48,919 --> 00:10:52,519 it basically with the lights on. Wow for that. So they migrated over 154 00:10:52,519 --> 00:10:56,840 to us very early after they launched, and then we helped them ride from 155 00:10:58,080 --> 00:11:01,200 like a handful to I mean their peak was like over one hundred and eighty 156 00:11:01,240 --> 00:11:03,679 million daily active users. Wow. I think I remember the day that went 157 00:11:03,720 --> 00:11:09,240 into effect, because it went from you know, one word at time to 158 00:11:09,519 --> 00:11:13,559 just like yeah, yeah, And they did all of that with a single 159 00:11:15,000 --> 00:11:20,639 Cosmos instance. Wow. We just basically as users kept piling on and kept 160 00:11:20,879 --> 00:11:24,600 basically adding chats and sessions to it. You know, Cosmos just ended up 161 00:11:24,639 --> 00:11:28,559 just just scaling out. That's what it does. It's a scale out architecture 162 00:11:28,559 --> 00:11:33,279 in there, so was that the largest customer that demand wise that they're not. 163 00:11:33,480 --> 00:11:37,360 They're not our largest customer, not even not even close actually, but 164 00:11:37,360 --> 00:11:41,320 they're I mean they're large, yeah, for sure. And I will say 165 00:11:41,320 --> 00:11:48,440 this one thing that was unique was the rate of growth. For sure, 166 00:11:48,679 --> 00:11:52,200 the fastest app in history to reach those kinds of consecutive or daily acting. 167 00:11:52,360 --> 00:11:56,159 Us growing at that rate, I think it's it's pulled back from that one 168 00:11:56,240 --> 00:12:00,960 eighty, but now with four to zero out, it's it's spiking again, 169 00:12:01,200 --> 00:12:03,639 right, So they're they're likely going to go over that two hundred number, 170 00:12:03,679 --> 00:12:07,720 I'm sure at some point. Now. We've talked about AI a lot on 171 00:12:09,879 --> 00:12:13,600 this show since we've been a build and seeing all the great things coming in. 172 00:12:13,720 --> 00:12:20,559 I've mentioned on more than one occasion that I'm so for developers embracing AI 173 00:12:20,080 --> 00:12:24,559 and for you know, especially the offerings that you have get hub co pilot 174 00:12:24,679 --> 00:12:31,200 and even the co pilot that is now set to allow us to sort of, 175 00:12:31,440 --> 00:12:35,039 you know, reach into our enterprise and see, you know, and 176 00:12:35,120 --> 00:12:39,039 ask questions in real language. That's great and and so, but I was 177 00:12:39,080 --> 00:12:41,679 concerned on the first day. I'm looking at the keynotes and I'm saying, 178 00:12:41,679 --> 00:12:46,000 you know, there's somebody out there that's saying, you know, I'm going 179 00:12:46,080 --> 00:12:50,679 to use this to be my brain and you know, to make decisions for 180 00:12:50,720 --> 00:12:54,600 me. I'm just going to trust it. And of course you know that 181 00:12:54,600 --> 00:12:58,480 that's that's a problem. But then I was very excited to see on the 182 00:12:58,519 --> 00:13:03,840 second day the security layer that Microsoft ads over these large language models, so 183 00:13:03,879 --> 00:13:09,320 that you know, that's something that chat GPT doesn't have. If I'm not 184 00:13:09,440 --> 00:13:16,080 mistaken, it's this thing that is looking for hallucinations and looking for inaccurate data 185 00:13:16,279 --> 00:13:22,279 and even malicious use of it. So important, and I was very glad 186 00:13:22,320 --> 00:13:24,440 to see that. You want to talk about that a little bit. That's 187 00:13:24,480 --> 00:13:28,360 the whole responsible AI thing you're talking about there. That's a huge focus and 188 00:13:28,440 --> 00:13:35,159 emphasis on everything we do. Everything we produce and release internally at Microsoft, 189 00:13:35,159 --> 00:13:41,480 that's levernges of technology has to go through a pretty set of rigorous in seat 190 00:13:41,519 --> 00:13:43,480 of testing and controls and all that stuff to make sure that it's adhering to 191 00:13:43,519 --> 00:13:48,960 all those responsible AI principles that are within there. I'm really glad that we 192 00:13:50,120 --> 00:13:54,600 out of the gate are applying this to that, and not just for ourselves 193 00:13:54,639 --> 00:13:58,840 but also making sure customers that's applying those technologies, we have to keep them 194 00:13:58,840 --> 00:14:01,799 safe. That's right as well, and keep their customers safe. We don't, 195 00:14:03,039 --> 00:14:07,519 you know, think about the early debacles people misusing chat GPT. There 196 00:14:07,559 --> 00:14:13,159 was a lawyer that had the tool right assummation that referenced cases that simply didn't 197 00:14:13,159 --> 00:14:16,399 exist exist yah, and he didn't check. And now, oddly enough the 198 00:14:16,480 --> 00:14:20,240 judge doesn't go for the oh the software did it answer? Yeap. You 199 00:14:20,279 --> 00:14:24,960 know there was another case in Canada actually with their Canada Canada one. Yeah, 200 00:14:26,039 --> 00:14:26,960 you remember you know that one? Yeah, no, I've paid close 201 00:14:26,960 --> 00:14:30,759 attention to that. Where we'll tell this anyway. It was a chat bought 202 00:14:31,240 --> 00:14:35,480 that uh, powered by an LM that was answering questions about somebody when needed 203 00:14:35,480 --> 00:14:39,200 to make flight changes for a particular reason. I can't remember which reason it 204 00:14:39,279 --> 00:14:41,320 was. And they said, oh, yeah, there's no feast for that, 205 00:14:41,320 --> 00:14:43,120 that's fine. Yeah, And so they were one hundred bucks you could 206 00:14:43,240 --> 00:14:46,240 yeah, yeah, anyway they went or he was supposed to get I think 207 00:14:46,279 --> 00:14:48,440 he was supposed to get a refund, and then when he went to get 208 00:14:48,440 --> 00:14:50,039 the refund, they're like, oh, you know, we don't rEFInd that. 209 00:14:50,120 --> 00:14:52,679 It's like, well you told me, yeah, like, oh, 210 00:14:52,720 --> 00:14:56,720 that's that's a mistake in the software. Yeah, And they actually went all 211 00:14:56,720 --> 00:14:58,519 the way to court on it, and the court ultimately said they have an 212 00:14:58,559 --> 00:15:03,080 employee had said that you would be liable. You just because you use a 213 00:15:03,120 --> 00:15:05,919 tool to do something that employee might do, it doesn't mean you're not liable. 214 00:15:07,000 --> 00:15:09,440 You've got to payback good on them. Yeah, it is good, 215 00:15:09,519 --> 00:15:13,759 and you know, it kind of highlights an important aspect with building these types 216 00:15:13,799 --> 00:15:18,200 of applications, which is, uh, you need to capture those interactions that 217 00:15:18,279 --> 00:15:24,919 happen between customers and users and this technology, these lms, just like you 218 00:15:24,039 --> 00:15:30,159 have and say any customer service scenarios that you always everybody's conversation be recorded. 219 00:15:30,159 --> 00:15:33,720 It's trading purpose exactly right, you know what you must do the same. 220 00:15:33,000 --> 00:15:39,159 Even more important because you're having a computer talk to you're experimenting this new technology, 221 00:15:39,360 --> 00:15:43,960 right, Like I think we scrutinized customer service calls much more closely when 222 00:15:43,000 --> 00:15:46,759 they were new than we do today. That this is new, so it 223 00:15:46,799 --> 00:15:50,759 needs to be closely screwedin. You've got to make sure that your customer service 224 00:15:50,759 --> 00:15:52,799 agents are on script and giving the correct information the same as you do with 225 00:15:52,840 --> 00:15:58,399 your lms. And this is a key kind of workload that we're finding with 226 00:15:58,559 --> 00:16:03,440 Cosmos these days and building these types of applications is Yeah, you can in 227 00:16:03,480 --> 00:16:07,039 the past have used these things to build chat applications and chatbots and also just 228 00:16:07,200 --> 00:16:11,519 chat assistance where you're talking and chatting with a human. Even more important now 229 00:16:11,519 --> 00:16:17,440 where you have lms that are generating responses sometimes had a whole cloth if they're 230 00:16:17,480 --> 00:16:21,440 not properly grounded to the data that they should be, you need to capture 231 00:16:21,440 --> 00:16:23,000 those interactions. If for no other reason, then you need to be able 232 00:16:23,000 --> 00:16:27,480 to audit that information. But you can take that information and also feed that 233 00:16:27,879 --> 00:16:32,879 into maybe an mL model and do some kind of sentiment analysis. You can 234 00:16:32,879 --> 00:16:37,399 feed that into some kind of clustering. You can do all kinds of analytics 235 00:16:37,440 --> 00:16:41,759 on top of that after the fact, to make sure that your data is 236 00:16:41,799 --> 00:16:45,000 better, make sure that the information it's better, the prompts are better. 237 00:16:45,600 --> 00:16:48,799 That's super important, and you can't do it unless you capture that data to 238 00:16:48,840 --> 00:16:52,360 begin with. Yeah, and so this has been a key workload for us 239 00:16:52,200 --> 00:16:57,519 for Cosmos over the last eighteen months, is using Cosmos as part of building 240 00:16:57,559 --> 00:17:03,279 that chat interface and capturing that information. And now with build, we just 241 00:17:03,319 --> 00:17:10,839 announced our native vector search for Cosmos dB as well as something I'm super excited 242 00:17:10,839 --> 00:17:14,839 about, which is this new suite of technologies called disc an N. So 243 00:17:14,880 --> 00:17:18,160 an N is approximate nearest neighbor, right, and in the vector space. 244 00:17:18,200 --> 00:17:21,519 That's essentially how you do these types of queries is you don't do a keyword 245 00:17:21,559 --> 00:17:25,720 match or a full text match or search. You use vectors and vectorize data 246 00:17:25,759 --> 00:17:30,559 and words and whatnot to to find the semantic meaning and al also find those 247 00:17:30,599 --> 00:17:34,160 related terms within there. And so now we've got this capability built natively into 248 00:17:34,160 --> 00:17:38,519 Cosmos. So customers that are using Cosmos to say, store and capture all 249 00:17:38,519 --> 00:17:44,720 that chat interactions can now do vector search vector database correct, so you automatically 250 00:17:44,759 --> 00:17:48,519 have a large language model or a small language. Yeah. And now not 251 00:17:48,599 --> 00:17:52,599 just for the chat but also you know, vectorize your data like RAG pattern 252 00:17:52,680 --> 00:17:53,640 is a big thing now, right. You want to be able to have 253 00:17:55,000 --> 00:17:57,759 an LM that you don't need to necessarily retrain. You can provide it and 254 00:17:57,839 --> 00:18:03,039 augment that information that it generates for a user using data out of your database. 255 00:18:03,079 --> 00:18:07,599 And the best way to get relevant information is using these vector search technologies. 256 00:18:07,599 --> 00:18:11,359 So take your data, your transactional or whatever data it is, vectorize 257 00:18:11,400 --> 00:18:14,400 its stored in cosmos, and then do a vector search over that and then 258 00:18:14,440 --> 00:18:18,240 have it enrich and augment. I like things like operations manuals, like documentation 259 00:18:18,359 --> 00:18:22,920 about the business, about how we operate. Those kinds of things are easy 260 00:18:23,200 --> 00:18:29,160 and obvious. But there are other things too that you might want to vectorize 261 00:18:29,680 --> 00:18:33,640 that you might not be thinking of. Yeah, like things like operation manuals 262 00:18:33,680 --> 00:18:37,079 and static documents. I mean you can do those, you can put them 263 00:18:37,119 --> 00:18:41,519 in like a cognitive search or AI search. The harder challenge is how do 264 00:18:41,599 --> 00:18:45,000 you do that over high volume transactional data. How do I do that? 265 00:18:45,119 --> 00:18:49,519 If I'm trying to capture I don't know, actions in a shopping cart, 266 00:18:49,640 --> 00:18:55,240 or what pages people are browsing or other things where you have an enormous volume 267 00:18:55,279 --> 00:19:00,680 of data vector databases in the market today, and even search engines capable of 268 00:19:00,720 --> 00:19:03,119 capturing that kind of data. You need an operational or a transactional data store, 269 00:19:03,200 --> 00:19:07,480 especially a scalable one to be able to capture that. This is what 270 00:19:07,599 --> 00:19:10,519 was unique about the announcement. And I'm not sure landed it build here, 271 00:19:10,559 --> 00:19:15,519 but the disc and en technology with Cosmos really allows you to take and build 272 00:19:15,559 --> 00:19:19,559 these types of scenarios over high volume transactional data and you just you can't do 273 00:19:19,599 --> 00:19:23,519 that with existing like ahnsw types of data. We used to think that the 274 00:19:23,599 --> 00:19:27,920 OLAP cube was like the end all be all of finding related data. That 275 00:19:29,200 --> 00:19:34,279 wasn't you know, easily accessible by regular RDM myths. And now this takes 276 00:19:34,319 --> 00:19:37,519 too all new level, doesn't it. You need? So really all you 277 00:19:37,599 --> 00:19:38,759 have to do on that, I say all you have to do, but 278 00:19:38,839 --> 00:19:44,359 if you use Cosmos dB as you're a data store, now all that data 279 00:19:44,400 --> 00:19:49,200 becomes searchable, yes, and in fact natural language, and not just within 280 00:19:49,319 --> 00:19:52,440 Cosmos. But also you know, we have a feature, you know, 281 00:19:52,480 --> 00:19:56,759 fabric is a brand new YE or analytics one like data Live platform o. 282 00:19:56,799 --> 00:20:00,480 The word fabric has been reused a couple of times, but I did notice 283 00:20:00,480 --> 00:20:04,559 the logo on my shirt to yeah, that's the new fabric, the new 284 00:20:04,640 --> 00:20:08,440 fabric. Yeah, but we have a we have this feature called Cosmos Mirror 285 00:20:08,440 --> 00:20:14,240 which will take any data you write into Cosmos and then automatically etl that into 286 00:20:14,440 --> 00:20:17,519 your one leg So if you want to take that data, yes it's already 287 00:20:17,559 --> 00:20:19,839 vectorized, but you can store it in the one leg and then spin up 288 00:20:21,000 --> 00:20:25,119 a new workspace and create new mL workbooks and you can do all kinds of 289 00:20:25,119 --> 00:20:27,680 stuff like maybe do basic clustering in there, to do sentiment analysis over what 290 00:20:27,720 --> 00:20:33,680 your customers are saying everywhere. You can go anywhere with this and do it 291 00:20:33,720 --> 00:20:37,319 in a big data analytics platform to augment all of that in there and get 292 00:20:37,319 --> 00:20:41,079 those insights and then use that to feedback in and that I think is the 293 00:20:41,119 --> 00:20:45,680 promise and what's coming next is you can build these applications. Customers can come 294 00:20:45,720 --> 00:20:48,720 and use them and they can you know, you can ground your data and 295 00:20:48,799 --> 00:20:53,359 make sure it's providing good, sensible answers. But you really need is that 296 00:20:53,480 --> 00:20:56,480 three sixty. So I want to take that data, I want to do 297 00:20:56,559 --> 00:21:00,799 analytics on it, and then I want to apply that back to help refine 298 00:21:00,839 --> 00:21:04,880 the system prompts that are within there, help refine all of the all of 299 00:21:04,960 --> 00:21:08,680 the knobs and dials for how those things interact with customers in there, and 300 00:21:08,720 --> 00:21:12,720 help optimize those things. Boy, that's just creating a whole bunch of new 301 00:21:12,759 --> 00:21:17,279 jobs, isn't it. I mean, if you think about the expertise required 302 00:21:17,319 --> 00:21:21,359 to do that, you know and do and hit those tweak those knobs and 303 00:21:21,400 --> 00:21:25,200 dials. You know, that's that's like a whole new thing. Yeah, 304 00:21:25,440 --> 00:21:29,200 but you know what it's Microsoft. What do we do is we take and 305 00:21:29,279 --> 00:21:33,480 democratize things that are hard and make them accessible for developers. Right. Yeah, 306 00:21:33,559 --> 00:21:36,079 you mentioned one like off the cuff there and we've been talking about a 307 00:21:36,160 --> 00:21:40,519 run ass because we talk more about data analytics over there. But just as 308 00:21:40,119 --> 00:21:44,559 it's one drive for data. Yeah, right, Like you think about when 309 00:21:44,599 --> 00:21:45,519 you want to build a model like this, you need to pull from a 310 00:21:45,519 --> 00:21:48,960 bunch of different data sources and so you know, you need different considentials for 311 00:21:49,000 --> 00:21:52,200 every SQL server. Like just even finding all that stuff, it's a nuisance 312 00:21:52,240 --> 00:21:56,359 and one like really comes at it. Of all the data is here, 313 00:21:56,400 --> 00:21:59,519 and it's not a repository like you dump off a copy of the data here. 314 00:22:00,039 --> 00:22:06,160 It's the data so it's being continuously updated as well. And it just 315 00:22:06,240 --> 00:22:11,400 makes a consistent way for resources to get access to data. So like the 316 00:22:11,440 --> 00:22:15,039 evolution of the data lake, isn't it. Yeah, And in fact it 317 00:22:15,079 --> 00:22:18,720 doesn't have to just sit there, Like you can analyze and run jobs on 318 00:22:18,799 --> 00:22:22,119 this data that's in there and then use something like a Spark to write it 319 00:22:22,200 --> 00:22:26,319 back into like a cosmos or to a hot store or something. It should 320 00:22:26,359 --> 00:22:29,079 also be available in the lake, right right, it is, right, 321 00:22:29,200 --> 00:22:32,960 so it doesn't have to just sit there and just gather dust, right, 322 00:22:33,039 --> 00:22:36,440 you know, take and analyze and process that data and then feed it right 323 00:22:36,480 --> 00:22:40,519 back in again. Sure. Wow, yeah, mind is being blown well 324 00:22:40,599 --> 00:22:42,200 here this week. Just wait, it gets crazier than that. I did 325 00:22:42,200 --> 00:22:45,920 this show with a run already on run as and I'll put a link to 326 00:22:45,000 --> 00:22:49,920 it. But you can put symbolic links into the lake to data stores that 327 00:22:49,960 --> 00:22:53,200 are not in Azure. You can link to Oracle databases. You can look 328 00:22:53,240 --> 00:22:56,720 up to S three, so all of the data appears visible in the lake, 329 00:22:56,920 --> 00:23:02,440 even if he's even in the lake. But it has the credentials, 330 00:23:02,480 --> 00:23:04,720 so if you have the rights to access that, then it deals with the 331 00:23:04,720 --> 00:23:07,599 credential calling to go over to a WS and pull the data as part of 332 00:23:07,640 --> 00:23:11,799 your search. Like oh so check out this big fish that I pulled out 333 00:23:11,839 --> 00:23:17,160 of Lake Superior. Oh no that wasn't lake. You'reon whatever, like whatever 334 00:23:17,240 --> 00:23:21,160 lake you want. You're good, right, You're fine. Before we get 335 00:23:21,200 --> 00:23:23,759 to the break and we're it's coming up. But Cosmos dB when we talked 336 00:23:23,799 --> 00:23:30,480 about in near early days was really about h geodistributed high velocity database and that 337 00:23:30,599 --> 00:23:34,000 really to me said certain categories of projects are going to need this, Like, 338 00:23:36,319 --> 00:23:38,920 so is that still the case? Like does it make sense for most 339 00:23:38,920 --> 00:23:42,160 folks? You don't want to experiment with copilots that sort of thing that they 340 00:23:42,160 --> 00:23:48,880 would go Cosmos, You know, it was never that was never the first 341 00:23:48,880 --> 00:23:52,559 foot forward, right. It was always where you could take it if you 342 00:23:52,640 --> 00:23:56,200 needed to write. That was always the promise of Cosmo says you could it 343 00:23:56,200 --> 00:24:00,440 could you could use it for any size workload. But if if you needed 344 00:24:00,480 --> 00:24:03,559 to do stuff that you could not do with a relational database, Cosmos could 345 00:24:03,599 --> 00:24:06,960 take you there right right. You can always start small, and in fact, 346 00:24:07,000 --> 00:24:11,799 a lot of the engineering an effort we've been putting focused on is really 347 00:24:11,839 --> 00:24:15,559 designed to help improve that developer experience starting at a small scale. You know, 348 00:24:15,599 --> 00:24:22,039 we have a serverless option now and actually just recently we announced the availability 349 00:24:22,079 --> 00:24:26,680 for being able to take and seamlessly migrate from serverists to say an auto scale 350 00:24:26,720 --> 00:24:29,200 and then if you want, you can go back again in there. But 351 00:24:29,279 --> 00:24:32,559 that makes it easy if you start with like a serverless option. That's kind 352 00:24:32,599 --> 00:24:34,599 of how you should do your dev test right, Like why pay for a 353 00:24:34,680 --> 00:24:37,960 database? Why pay for compute if you're not using it? Right, it's 354 00:24:37,960 --> 00:24:41,359 a dev right, or it's a test environment when you go into production. 355 00:24:41,559 --> 00:24:45,480 Great. Then migrated over to an auto scale allow it to elastically scale in 356 00:24:45,519 --> 00:24:52,880 and out, and that's great. Other things as well have always been I 357 00:24:52,880 --> 00:24:55,759 guess, you know, for designing for a scale out database can be a 358 00:24:55,759 --> 00:24:59,119 little tricky, you know, yeah, absolutely, Then I was perfectly happy 359 00:24:59,119 --> 00:25:03,279 with that viewpoint that. Look, I've tried to scale document data stores across 360 00:25:03,359 --> 00:25:06,319 multiple sides. That's hard, and you don't want to own that. It's 361 00:25:06,400 --> 00:25:11,000 plumbing, right, So the idea that there was a knob in Azure around 362 00:25:11,000 --> 00:25:14,880 causes Dbut that's put it in these geographies. Keep these syncs at this rate, 363 00:25:15,119 --> 00:25:18,400 like you pay for it, but you don't have to own that complexity. 364 00:25:18,519 --> 00:25:22,880 That's right. But it may begged the question like do I ever want 365 00:25:22,920 --> 00:25:26,519 this in one node mode? But you're describing one node scenarios, you're at 366 00:25:26,599 --> 00:25:30,559 least initially knowing you would go to multiple nodes they if the case arise, 367 00:25:30,000 --> 00:25:33,799 it always starts small, yeah, right, So, and it needs to 368 00:25:33,839 --> 00:25:38,559 be approachable and easy to use, and also it needs to be mutable in 369 00:25:38,599 --> 00:25:41,079 a way, right. Like, one of the things that's been challenging or 370 00:25:41,200 --> 00:25:47,480 is challenging for building distributed scale A databases is you typically have to make very 371 00:25:47,799 --> 00:25:51,799 very complex design decisions very early on, like how do you partition data so 372 00:25:51,880 --> 00:25:55,480 that it scales out? And in the past that was always a challenge because 373 00:25:55,559 --> 00:25:57,640 in many cases you were asking a developer to predict how it was going to 374 00:25:57,640 --> 00:26:00,359 be used once it was in production. Only thing worse than that prediction was 375 00:26:00,400 --> 00:26:03,640 trying to change it afterwards. And that was the thing that we realized is 376 00:26:03,720 --> 00:26:08,599 we needed to make it easy for a developer to change that because you guys 377 00:26:08,640 --> 00:26:11,920 have all done this for a long time. You all know you get an 378 00:26:11,920 --> 00:26:15,920 app into production and you're surprised by how customers use it, and then you 379 00:26:15,960 --> 00:26:18,880 need to go back and back to the drawing board and I need to refactor 380 00:26:18,920 --> 00:26:22,039 a bunch of this stuff. And that from a scale out point of view, 381 00:26:22,359 --> 00:26:25,880 if you do not have your partition design correct, you cannot scale. 382 00:26:25,960 --> 00:26:30,359 It is the key to scaling, and that's another recent innovation is we've made 383 00:26:30,359 --> 00:26:33,319 it way easier for customers to go back and say, give me a big, 384 00:26:33,319 --> 00:26:36,480 great easy button that I want to push and allow me to repartition my 385 00:26:36,559 --> 00:26:38,920 data so that I can scale right and then maybe change it again. Like 386 00:26:40,160 --> 00:26:42,960 absolutely, the data size grows and new features come in, you need to 387 00:26:44,000 --> 00:26:48,440 repartition or have multiples. This is another innovation as well, is providing a 388 00:26:48,440 --> 00:26:52,680 material easy way to do a materialized view on that data. This is common 389 00:26:52,720 --> 00:26:56,680 with like say a CQRS type of pattern right where you have ingesting of lots 390 00:26:56,680 --> 00:26:59,359 of data and you need to be able to scale that out. Well. 391 00:26:59,599 --> 00:27:02,400 Rarely the case that you're going to read the data the same way you write 392 00:27:02,400 --> 00:27:06,359 it. You may have data coming in you want to do reports by state, 393 00:27:06,480 --> 00:27:07,920 or you want to do a report by some category, or you want 394 00:27:07,960 --> 00:27:11,720 to do some kind of dashboard over here, and you can't run queries over 395 00:27:12,119 --> 00:27:15,799 a large scale out database like that and get good performance on your repath. 396 00:27:15,920 --> 00:27:21,039 You need to have that data kind of repactored and pivoted so that you can 397 00:27:21,079 --> 00:27:26,680 do efficient dashboards, efficient reports, whatnot. So providing that capability just another 398 00:27:26,720 --> 00:27:30,119 big read easy button for developers you don't and then so you you know, 399 00:27:30,279 --> 00:27:34,960 I do not like pre optimizing. I've been burned by it too many times. 400 00:27:36,680 --> 00:27:38,079 And I'm not saying I want to have the problem first, but I 401 00:27:38,119 --> 00:27:42,480 kind of want to have the problem first, right because often those optimizations create 402 00:27:42,519 --> 00:27:47,119 more problems than they solve. So it's better to watch the data flow in 403 00:27:47,799 --> 00:27:51,599 and see where it's getting rough because you've got good instrumentation and go, okay, 404 00:27:51,839 --> 00:27:55,319 well if you're protuction like partition like this will reduce the impact, and 405 00:27:55,599 --> 00:27:59,519 then you try your benchmarkt like now you're describing as any area where I'm willing 406 00:27:59,519 --> 00:28:03,000 to express now to say, okay, let's try a partition strategy and see 407 00:28:03,000 --> 00:28:07,279 what the instrumentation. But this is key for a developer experience, right is 408 00:28:07,319 --> 00:28:10,759 you need to have that flexibility to be able to come back and approach it 409 00:28:10,839 --> 00:28:12,440 in a different way or solve it in a different way. Folks ask us 410 00:28:12,440 --> 00:28:15,039 for a lot of certainty, and we don't have it. Man, Yeah, 411 00:28:15,559 --> 00:28:18,240 yeah, I appreciate you state. It's like I don't have to decide 412 00:28:18,319 --> 00:28:22,480 that right now. Let's wait till we see how the data flows before we 413 00:28:22,559 --> 00:28:26,400 decide make a decision, and then have the flexibility to revise. Yeah, 414 00:28:26,480 --> 00:28:30,759 we guessed here, it's a guess. We're going to measure it like this 415 00:28:30,119 --> 00:28:33,359 and revisit this at some point and maybe adjust it. Yeah, yeah, 416 00:28:33,440 --> 00:28:37,680 well I appreciate it. So make it easy for developers to run a small 417 00:28:37,720 --> 00:28:40,640 scale they want to run on a single note. That's great, But help 418 00:28:40,680 --> 00:28:44,640 them make it easier for them to achieve that kind of scale that you just 419 00:28:44,680 --> 00:28:47,960 can't get that anywhere else. That's why customers use us. It's always been 420 00:28:48,000 --> 00:28:52,039 Microsoft's mo And with that, I'm going to interrupt for one brief moment for 421 00:28:52,079 --> 00:28:59,279 this very important message. Attention dot net developers looking for the ultimate SDK to 422 00:28:59,359 --> 00:29:06,519 handle a lot electronic document processing. Meet tx text Control. Txtext controls your 423 00:29:06,519 --> 00:29:12,119 go to solution for seamless PDF generation, secure electronic signatures, and efficient digital 424 00:29:12,160 --> 00:29:18,720 forms processing, all within your dot net applications. Empower your products with robust 425 00:29:18,759 --> 00:29:25,400 document management capabilities, boost productivity, and deliver top notch solutions to your clients. 426 00:29:25,920 --> 00:29:30,880 Trusted by developers worldwide, including me and Richard, txtext control is the 427 00:29:30,039 --> 00:29:37,000 SDK that makes a difference. Check out demos dot textcontrol dot com for live 428 00:29:37,079 --> 00:29:42,519 online demos and see it in action. Hey Carl here, tired of the 429 00:29:42,559 --> 00:29:48,960 debugging grind, Let's talk about a breakthrough that's here to help ray guns AI 430 00:29:48,319 --> 00:29:53,759 error resolution isn't just another tool. It's like having a seasoned developer right by 431 00:29:53,799 --> 00:30:00,119 your side, suggesting fixes and explaining errors in real time. If you're curious 432 00:30:00,279 --> 00:30:04,680 as to how it can smooth out your debugging day, visit raygun dot com 433 00:30:04,680 --> 00:30:10,960 slash dot net rocks that's raygun r A y g u n dot com slash 434 00:30:11,319 --> 00:30:15,160 do O t n E t r O c k s for your free fourteen 435 00:30:15,240 --> 00:30:22,440 day trial to explore the way reygun is revolutionizing debugging. And we're back. 436 00:30:22,480 --> 00:30:25,559 It's not a Rocks. I'm Richard Canvill. Let's call Franklin Yo. Chat 437 00:30:25,599 --> 00:30:29,759 with our friend Mark Brown in the latter stages of the twenty twenty four build 438 00:30:29,799 --> 00:30:33,920 in Seattle. Gray day as usual, Seattle. That's how this place works. 439 00:30:33,759 --> 00:30:37,119 It's I mean, it's cloudy and gray. It's just another day. 440 00:30:37,160 --> 00:30:45,240 It's just another day. We call that Thursday. Well, I mean it 441 00:30:45,240 --> 00:30:51,200 does seem like these LM workloads were made for you guys, Like, but 442 00:30:51,359 --> 00:30:55,440 what's the shape of the data that they're typically managing. It's the vector data 443 00:30:55,480 --> 00:30:57,680 stores, right, It could be anything, right because as a Jason data 444 00:30:57,680 --> 00:31:00,359 store, we largely don't care. Yeah, you can take anything, right, 445 00:31:00,559 --> 00:31:04,559 You can have hierarchy, you can do whatever. Yeah, it's it's 446 00:31:04,599 --> 00:31:10,279 whatever. You need to go back to why we're in the space and what 447 00:31:10,319 --> 00:31:12,839 we were doing. You know, vector databases are super hot, and there 448 00:31:12,839 --> 00:31:15,119 are a lot of them out there now and they're getting a lot of attention, 449 00:31:15,359 --> 00:31:21,000 and they're very important to building efficient workloads on these alboraks got an implementation 450 00:31:21,079 --> 00:31:25,000 of a vector store inside of it now or will they will? Yeah, 451 00:31:25,000 --> 00:31:27,920 So they've been doing a brute force thing lately. So you just basically put 452 00:31:27,920 --> 00:31:32,279 an array of vectors into a into a into a column and then call a 453 00:31:32,359 --> 00:31:36,359 cosign function on that thing til you can get basically it's it's an exact match, 454 00:31:36,400 --> 00:31:38,200 right, or a brute force not doing approximate nearest neighbor. So I 455 00:31:38,240 --> 00:31:41,759 got a story for it towards the beginning of the year, when you know, 456 00:31:41,799 --> 00:31:45,680 before any of this was available and nobody even knew how to make a 457 00:31:45,920 --> 00:31:51,440 vector database or what they were. I was approached by a colleague to put 458 00:31:51,480 --> 00:31:53,720 together some training on AI and put you know, how to use chat, 459 00:31:53,759 --> 00:31:59,319 GPT, how to use the tools, the APIs and and and it was 460 00:31:59,400 --> 00:32:02,359 just because of was such a hot topic. And I said, after a 461 00:32:02,400 --> 00:32:06,160 while when things changed so rapidly, It's like, you know, this stuff 462 00:32:06,200 --> 00:32:08,359 is changing so fast, this is not the time. We just got to 463 00:32:08,400 --> 00:32:13,359 wait, got to wait this out and see what major tools shake out, 464 00:32:13,400 --> 00:32:15,799 because they will be available. And turns out I was right, Yeah, 465 00:32:16,000 --> 00:32:19,880 yeah, it only gets easier. Yeah, all these things that we had 466 00:32:19,920 --> 00:32:23,519 to Can you imagine going into a training class like in I don't know, 467 00:32:23,720 --> 00:32:27,519 right before Windows came out, and somebody's teaching you how to write your own 468 00:32:27,559 --> 00:32:30,839 windowing system, right, and the Windows comes out, It's like, well, 469 00:32:30,880 --> 00:32:34,839 what the hell was all that then? You mean like prior to like 470 00:32:34,920 --> 00:32:37,880 Jeff Richter's book or something like that, Like, please give me some MFC. 471 00:32:38,160 --> 00:32:42,880 Yeah, I'm old me too with you. It's been crazy for the 472 00:32:42,920 --> 00:32:45,799 last eighteen months. The everything, all the APIs for open Ai and everything 473 00:32:45,799 --> 00:32:50,279 built on top of it and around it, whether it's things like a semantic 474 00:32:50,319 --> 00:32:53,640 kernel or a lying chamber law index, these things are changing like weekly. 475 00:32:53,960 --> 00:32:58,039 They would go and push new builds and you'd break half of everything that's right 476 00:32:58,039 --> 00:33:02,880 there. And a lot of customers I was working with were frustrated because there's 477 00:33:02,920 --> 00:33:07,200 a lot of center of gravity around using these orchestration SDKs for llms right, 478 00:33:07,200 --> 00:33:10,799 like, they do provide a good kind of single sheene if you will try 479 00:33:10,839 --> 00:33:15,519 and help connect all these pieces together. But man, they were changing so 480 00:33:15,640 --> 00:33:17,960 fast, so fast that it was a lot of customers are like, you 481 00:33:19,000 --> 00:33:21,839 know what, I'm just going to set those aside for a bit because I'm 482 00:33:21,839 --> 00:33:25,079 trying to get something built here and if my billd breaks every single day because 483 00:33:25,079 --> 00:33:29,079 you guys are changing, then I need to kind of take my own ownership 484 00:33:29,079 --> 00:33:31,039 of that. Ryan McKay and I were doing a YouTube show called The AI 485 00:33:31,119 --> 00:33:35,039 Bought Show, and it just kind of was like that, you know, 486 00:33:35,119 --> 00:33:39,039 he would and in fact, he would go and test some things on against 487 00:33:39,160 --> 00:33:42,799 I don't know if it was chat Gypt or just something in open AI. 488 00:33:42,920 --> 00:33:45,519 Maybe it was like the you know, the playground, I think it's called 489 00:33:45,599 --> 00:33:51,200 right, and he would test it all and then get the response, and 490 00:33:51,200 --> 00:33:55,559 then we'd record the next day and the response was completely dead, and it's 491 00:33:55,640 --> 00:33:59,640 like, oh, they fixed this because he was showing like how to break 492 00:33:59,680 --> 00:34:02,559 it or whatever. Now it doesn't break. And the next day it was, 493 00:34:02,720 --> 00:34:07,720 oh, well this kind of didn't work yesterday. It's amazing. It's 494 00:34:07,759 --> 00:34:09,239 just just goes to show you, you know, just calm down. This 495 00:34:09,280 --> 00:34:15,440 is a good time right now to investigate what Microsoft is doing because they they 496 00:34:15,480 --> 00:34:17,400 are the tool builders. They are the tool buildings. I'll pull some links 497 00:34:17,400 --> 00:34:22,440 for like lang chain and the other semantic kernel. This is something that we 498 00:34:22,480 --> 00:34:25,159 haven't really talked about all that much on dot net rocks. Maybe we could 499 00:34:25,840 --> 00:34:30,360 just give us an elevator pitch for what that is. Yeah, So Samanic 500 00:34:30,400 --> 00:34:36,320 kernel is just among kind of these group of these orchestration SDKs for building l 501 00:34:36,599 --> 00:34:38,920 ms and kind of the equivalent of these things that people may have heard of 502 00:34:38,920 --> 00:34:42,800 would be lang Chain. It's really the big one I think out there. 503 00:34:43,480 --> 00:34:46,000 La Index is another one of these things. And you know what they do 504 00:34:46,079 --> 00:34:51,559 is Python, right, Yeah, they all support like Python and JavaScript for 505 00:34:51,920 --> 00:34:54,840 the most part, like lang chain for sure. Samanic kernel offers a dot 506 00:34:54,920 --> 00:34:59,559 net sdk. They also have a Python option in there as well. They 507 00:34:59,559 --> 00:35:02,000 may have a JavaScript also that they're in there, but anyway, you know, 508 00:35:02,039 --> 00:35:05,480 these things are changing so fast, they might have something else in there 509 00:35:05,480 --> 00:35:12,400 as well. But the value of what these things do is they provide a 510 00:35:12,440 --> 00:35:15,519 way to easily kind of plug in lots of different things. It could be 511 00:35:15,559 --> 00:35:20,039 different lams. You can build kind of an agent's on top of these things. 512 00:35:20,079 --> 00:35:22,880 They all have these notions of kind of planners or chainers within there, 513 00:35:22,920 --> 00:35:28,519 so you can really build kind of these complex LLM workloads and do it in 514 00:35:28,599 --> 00:35:31,639 a kind of an easier abstracted interface, right, so you don't need to 515 00:35:31,679 --> 00:35:36,199 get down into the weeds of native SDKs to connect to all of these different 516 00:35:36,519 --> 00:35:42,079 vector databases or llms or embedding models. You can just provide kind of that 517 00:35:42,159 --> 00:35:45,719 single user interface for that. That's the promise of these things. The reality 518 00:35:45,719 --> 00:35:49,400 for the last year and a half is they keep changing so fast that it's 519 00:35:49,440 --> 00:35:52,159 hard to keep them and use them. But that's their promise, and I 520 00:35:52,199 --> 00:35:58,880 think they do a good job of doing that. They are starting to solidify, 521 00:35:59,239 --> 00:36:02,239 if you will, some level is a semantic kernel after these things that 522 00:36:02,280 --> 00:36:07,480 were announced to build considered like a low level tool compared to like I don't 523 00:36:07,519 --> 00:36:13,199 know, the co pilot studio or any of these other high level tools for 524 00:36:13,199 --> 00:36:15,920 sure. I mean those are look at the copilots too. Is kind of 525 00:36:15,920 --> 00:36:21,239 a low code way of going and building these types of of applications, for 526 00:36:21,280 --> 00:36:23,679 sure. If you're going to go and roll your own and you wanted to 527 00:36:23,760 --> 00:36:27,840 be able to bring together lots of different bits and pieces of these things. 528 00:36:27,840 --> 00:36:31,239 You would use something like a semantic kernel or maybe a combination of like native 529 00:36:31,360 --> 00:36:36,199 SDKs and semantic kernel to go and build these types of things. So that's 530 00:36:36,239 --> 00:36:38,599 their value and being able to do that provide that pluggable interface, if you 531 00:36:38,639 --> 00:36:45,079 will, for doing these things. I can feel people listening, going agents, 532 00:36:45,519 --> 00:36:50,119 What what's that? Why do I need one of those or a series 533 00:36:50,159 --> 00:36:55,360 of these? Are these like little experts in a particular silo that maybe talk 534 00:36:55,440 --> 00:37:00,559 to each other or maybe have areas of ex ortise. Yeah, it's exactly 535 00:37:00,599 --> 00:37:04,239 it it So let's just I mean, I could kind of give you some 536 00:37:04,280 --> 00:37:08,159 a rough scenarios. If I'm in an e commerce type of application and I 537 00:37:08,239 --> 00:37:12,360 want to do i don't know, product search or something or asked about products, 538 00:37:12,360 --> 00:37:15,280 I may have an agent specifically tailored to doing product searches. Right. 539 00:37:15,599 --> 00:37:22,840 I may have another agent tailored specifically to refunds or to some kind of customer 540 00:37:22,960 --> 00:37:25,480 profile or updates or anything, I mean, anything that you need. Like, 541 00:37:27,039 --> 00:37:29,760 all of these things kind of operate around the notion of you have a 542 00:37:29,840 --> 00:37:32,840 system prompt that kind of defines the behavior and also grounds it to specific data. 543 00:37:34,199 --> 00:37:37,199 Right, And So the idea of the agent as you've created this kind 544 00:37:37,199 --> 00:37:40,840 of encapsulated whole that takes inputs and is specialized around a particular thing, maybe 545 00:37:40,840 --> 00:37:45,760 grounded to some specialized data, and then provide specialized outputs for the thing that 546 00:37:45,840 --> 00:37:51,119 Richard hates the most is that's an easy way to answer promorphize an expert software 547 00:37:51,400 --> 00:37:54,880 thing. You can give them their own personalities and you can tell them to 548 00:37:54,960 --> 00:38:00,559 answer every question like a sixties stoner or something. Yes, you know, 549 00:38:00,679 --> 00:38:05,559 okay, dude, let me tell you what's going on. I did something 550 00:38:05,679 --> 00:38:09,000 very similar. I created a demo that I've used at the Fabric conference that 551 00:38:09,119 --> 00:38:15,159 was at actually it was a night last year, and it was built and 552 00:38:15,199 --> 00:38:19,920 grounded to the old old Adventure Works twenty seventeen data based from SQL service. 553 00:38:19,920 --> 00:38:22,480 So it's like a bike store, right. So I created this AI agent 554 00:38:22,679 --> 00:38:28,719 and I called him Wheelie, and I went to the Being image generator and 555 00:38:28,760 --> 00:38:32,320 I created an image looked like it looked like Clippy. Ironically, could have 556 00:38:32,400 --> 00:38:37,039 like a little like a little cartoon avatar on a bike doing a big wheelie 557 00:38:37,079 --> 00:38:39,719 or whatever, and he had a big smile on and stuff like that. 558 00:38:39,760 --> 00:38:43,719 So I created a whole persona around this thing. The T shirt that said 559 00:38:43,800 --> 00:38:45,760 keep on truck, and I should keep the T shirt for this thing keep 560 00:38:45,760 --> 00:38:50,519 on wheeling or biking or whatever. But no, but it's a retail bike 561 00:38:50,559 --> 00:38:53,119 store, right, I want to create an AI assistant that's really into biking. 562 00:38:53,159 --> 00:38:58,079 So the system prompt for this thing is you were super enthusiastic about biking, 563 00:38:58,119 --> 00:39:00,679 and you really just want to help people find all the biking and accessories 564 00:39:00,679 --> 00:39:04,480 that they need so they can go have great biking adventures. Right, and 565 00:39:04,559 --> 00:39:07,599 you should have all this energy and all that stuff. So what's interesting is, 566 00:39:07,840 --> 00:39:08,800 you know, I go when you start testing this thing, and it's 567 00:39:08,800 --> 00:39:12,800 exactly what it comes back with this And I find if in the prompts you 568 00:39:12,880 --> 00:39:16,800 use word like brilliant you know, or just like master of you know, 569 00:39:17,000 --> 00:39:22,239 it will actually be smarter, right in terms of you know, or the 570 00:39:22,360 --> 00:39:24,880 perception will be smarter. So you can provide all of that stuff, you 571 00:39:24,880 --> 00:39:29,199 can give it that personality, you can you know, it's a great way 572 00:39:29,239 --> 00:39:30,880 to represent your brand if a way, right, if you have kind of 573 00:39:30,880 --> 00:39:35,119 a specific vibe for who you are. As long as it doesn't tell you 574 00:39:35,159 --> 00:39:38,800 this refunds that don't exist. Yes, that's right. Accuracy is a feature 575 00:39:38,880 --> 00:39:45,679 too, and safety. Here's a coupon. Yeah, it's it's interesting to 576 00:39:45,880 --> 00:39:47,840 you know, we did I did this great show with Steph A. Warre 577 00:39:47,880 --> 00:39:52,760 as we talked about it. It's a language computer. It fits out language 578 00:39:52,760 --> 00:39:55,719 for you based on your instructions. Yep. Ye, And it's just humans 579 00:39:55,719 --> 00:40:01,280 that project understanding into that. Yes, you always have to remember your instructions 580 00:40:01,320 --> 00:40:05,280 and also the data you give it as well. Right, it's only smart 581 00:40:05,400 --> 00:40:09,159 enough for whither what's been trained for or the information that is provided. Well. 582 00:40:09,559 --> 00:40:15,079 Yeah, and it needs testing. You know. The conversation over and 583 00:40:15,079 --> 00:40:19,719 over again is that you need experts in the data sets to test it and 584 00:40:19,760 --> 00:40:22,599 say, oh that is correct, that's incorrect, and sort of work your 585 00:40:22,599 --> 00:40:25,159 way through it. It's it's it's certainly not a reason not to use it, 586 00:40:25,199 --> 00:40:29,559 but it's like it's just like if you're gonna work in a dynamic language, 587 00:40:29,760 --> 00:40:32,639 you need testing around it. Right, you know, you you you 588 00:40:32,679 --> 00:40:36,599 have to do the whole thing, You have to own the whole problem, 589 00:40:37,079 --> 00:40:40,599 and you can make these things work and be incredible labor saving devices. You 590 00:40:40,599 --> 00:40:45,880 know, they talk about response times dropping down to seconds and you know, 591 00:40:45,000 --> 00:40:50,639 making customers happy as long as it's correct. I had this argument with a 592 00:40:50,679 --> 00:40:53,679 friend, you know, when I was talking about hallucinations and stuff and wrong 593 00:40:53,760 --> 00:40:59,920 answers, and the argument was to defend this behavior. Well, I know 594 00:41:00,079 --> 00:41:04,519 people like that too. It's like, yeah, my uncle Joe is a 595 00:41:04,719 --> 00:41:08,880 complete bs er, but I'm not going to hire him to run my company. 596 00:41:09,639 --> 00:41:16,760 Joe's not processing my refund. No, those aren't the folks you use. 597 00:41:17,400 --> 00:41:20,960 So now fair enough, Like, what do you want from your software? 598 00:41:21,000 --> 00:41:23,760 What are you expecting? I just wonder for you know, trying to 599 00:41:23,800 --> 00:41:29,199 imagine the next couple of years in the transformation of the user interface, like 600 00:41:29,519 --> 00:41:32,800 a lot less buttons and knobs and things and a lot more just to free 601 00:41:32,840 --> 00:41:37,960 flowing texts. I think it's the natural language processing is kind of the going 602 00:41:38,039 --> 00:41:42,480 to be the interface for these things, right, not typing your talking and 603 00:41:42,599 --> 00:41:45,719 learning. Yeah, I also wonder about machine learning models for vision, Like 604 00:41:45,840 --> 00:41:49,360 I've always thought it would be great to have an assessment of frustration on the 605 00:41:49,400 --> 00:41:52,480 operator's part, right, but the software could see that you're struggling with it, 606 00:41:53,000 --> 00:41:55,519 just you mean, like feed in a like a jiff of that guy 607 00:41:55,559 --> 00:42:00,159 banging the hell out of keyboard or something anymore like most of these machines have 608 00:42:00,159 --> 00:42:05,440 a camera on already. Yeah, Like, could I actually recognize your struggling 609 00:42:05,440 --> 00:42:08,480 to achieve something in the software and then affect the software? Like do you 610 00:42:08,519 --> 00:42:12,559 need help? Can I put up a prompt? You know? Or I've 611 00:42:12,559 --> 00:42:15,280 noticed you only use these six features? Could I put them front the center? 612 00:42:15,320 --> 00:42:19,360 Like? What if the UX was dynamic to the way the user used 613 00:42:19,360 --> 00:42:22,079 it? Or please place that hamburger in the bag before I call and keep 614 00:42:22,119 --> 00:42:27,119 this light for somebody to come over. Oh yeah, So I heard someone 615 00:42:27,320 --> 00:42:30,800 on in one of the keynotes, and I can't remember who it was say 616 00:42:30,880 --> 00:42:35,719 that many people would rather talk to an AI than a real person. That's 617 00:42:35,760 --> 00:42:39,719 the first time I had ever heard that argument, because in my part of 618 00:42:39,760 --> 00:42:45,159 the world, most people don't want to go to a website and chat with 619 00:42:45,239 --> 00:42:49,039 a bot, you know, they want a real person. But is there 620 00:42:49,119 --> 00:42:52,480 this growing population of people who would rather speak to an AI than a real 621 00:42:52,519 --> 00:42:57,679 person. I guess it depends on the capabilities of that AAI. Let's start 622 00:42:57,679 --> 00:43:00,159 with the initial thing is would you rather go to a website to get the 623 00:43:00,199 --> 00:43:04,360 information? Would rather call the company? Right? Like I tend to not 624 00:43:04,480 --> 00:43:07,440 want to pick up the phone anymore. Because it takes longer looking for the 625 00:43:07,519 --> 00:43:15,239 least amount of time to get the things I need. And so you know, 626 00:43:15,559 --> 00:43:17,840 the reason you've been avoiding calling tech support and things like that is it 627 00:43:17,880 --> 00:43:22,000 takes a while and you have to keep explaining yourself in some ways. If 628 00:43:22,039 --> 00:43:27,039 the software is the interface for that, it's less frustrating and you're talking usually 629 00:43:27,079 --> 00:43:30,159 to someone who really doesn't understand what you're doing. But it's just taking your 630 00:43:30,159 --> 00:43:34,199 answers and plugging them into an expert system and anyway, you know. Anyway, 631 00:43:34,280 --> 00:43:36,920 so what you have as a transcribed version of a chatbod, like, 632 00:43:36,960 --> 00:43:40,000 why is that absolutely right about that? Yeah? I think it's gonna whether 633 00:43:40,039 --> 00:43:43,280 they want to do it or not is going to depend on how good the 634 00:43:43,760 --> 00:43:45,920 experience is. Okay, well, here's a great example. Things that I 635 00:43:46,000 --> 00:43:52,280 used to Google for I now ask chat gipt. So you basically believe in 636 00:43:52,280 --> 00:43:57,119 the I Feel Lucky button on Google because you're just getting on answer. Yeah, 637 00:43:57,559 --> 00:44:00,360 you're talking about school search. It's you know, you put in a 638 00:44:00,360 --> 00:44:01,760 search term, you look at the array of answers, right, You're like, 639 00:44:01,800 --> 00:44:05,920 I'll let me rEFInd their search term, right, but I will ask 640 00:44:05,960 --> 00:44:09,840 a question like, you know, is there a coin operated laundry facility at 641 00:44:09,840 --> 00:44:15,800 this hotel that I'm going to. It's much faster and easier for chatting through 642 00:44:15,840 --> 00:44:17,800 a site for that, the calmbing through a site and trying to find it, 643 00:44:19,280 --> 00:44:22,840 you know, or calling them or definitely calling them. Yeah, I 644 00:44:22,960 --> 00:44:27,159 use the chatchipt on being like maybe five six times a day. Yeah, 645 00:44:27,480 --> 00:44:31,159 it's replaced my search. Yeah, it's also replacing things like Google Translate, 646 00:44:31,280 --> 00:44:36,039 for example, because now with four to oh and not for zero, but 647 00:44:36,079 --> 00:44:39,199 four oh chpt four oh. You know, you can use your voice and 648 00:44:39,400 --> 00:44:44,880 you can just talk to somebody and it will say, all right, translate 649 00:44:45,119 --> 00:44:49,440 the English to Spanish, the Spanish English. We'll just do that. Yep. 650 00:44:50,280 --> 00:44:53,719 It's powerful. It's powerful. Stuff's actually I always had a conversation with 651 00:44:53,840 --> 00:44:59,920 someone's adjacent but not the same thing. Whose husband is waiting for a cock 652 00:45:00,000 --> 00:45:01,039 clear implant. His hearing has gone that bad. He says, I don't 653 00:45:01,079 --> 00:45:04,239 even want to talk to him or I have to shout and I always sound 654 00:45:04,280 --> 00:45:07,559 angry. And I pulled out Live Trends, I grabbed her phone, installed 655 00:45:07,559 --> 00:45:09,960 Live Transcribe on it, and then we just kept talking and she's like the 656 00:45:10,000 --> 00:45:13,159 words are coming out. It's like just put this in front of him, 657 00:45:13,159 --> 00:45:15,000 like, what are you doing? You've had the tool in your pocket the 658 00:45:15,039 --> 00:45:19,079 whole time and you didn't know. Isn't that funny? Yeah, But the 659 00:45:19,159 --> 00:45:22,679 fact the idea of walking into a shop in France when your frendsh is bad, 660 00:45:22,800 --> 00:45:28,079 my friend is bad, and being able to just have it spew frenchise 661 00:45:28,199 --> 00:45:37,320 Frenches just slightly off. Yeah. They maybe bring the world closer together. 662 00:45:37,440 --> 00:45:40,639 Yeah. Yeah. It was a while ago that Google did some keynote thing 663 00:45:40,639 --> 00:45:46,159 where they were where they were doing live transcription and translation and voice and and 664 00:45:46,239 --> 00:45:50,280 you supposed to get a product for that, but it's really good. There's 665 00:45:50,320 --> 00:45:52,760 a browser plug in for Google. Well that's the truth. I think it's 666 00:45:52,760 --> 00:45:54,880 now no longer you're not going to buy dedicated hard for that. Your phone 667 00:45:54,920 --> 00:45:58,079 will do it. Yeah. It just is a thing now, it's a 668 00:45:58,119 --> 00:46:00,519 thing, a new capability being added to the phone again because it didn't stock 669 00:46:00,599 --> 00:46:05,119 like, it didn't do enough. So the doomsayers will say, you know, 670 00:46:05,320 --> 00:46:09,320 all these things are being replaced by a single app and a single interaction. 671 00:46:10,159 --> 00:46:15,880 What you know is everybody else doomed? You know, is this so 672 00:46:15,039 --> 00:46:20,920 disruptive that it's going to cause mayhem? I don't think it's anywhere disrupted than 673 00:46:20,920 --> 00:46:24,400 anything else. Yeah, I agree, you're appreciating the choir, But you 674 00:46:24,400 --> 00:46:28,119 know, there's certainly people out there who were You know, the story of 675 00:46:28,159 --> 00:46:30,880 the lad Heites, you only go as far as the Lades were actually a 676 00:46:30,920 --> 00:46:36,559 group of people who very technical people by the way making They were hand manufacturing 677 00:46:36,559 --> 00:46:40,119 cloth, right with very simple tools. And in came the first steam powered 678 00:46:40,119 --> 00:46:44,760 looms and they smashed them up. And the thing they never talked about after 679 00:46:44,760 --> 00:46:47,400 that is after that the employer came back and they talked through how they were 680 00:46:47,400 --> 00:46:52,480 going to train, and all those folks started running those automated looms. And 681 00:46:52,519 --> 00:46:53,960 the biggest thing to happen is, yeah, you need a fewer people making 682 00:46:54,039 --> 00:46:59,039 cloth, But cloth became so much less expensive that people bought more clothes, 683 00:46:59,199 --> 00:47:01,159 right, yeah, and then many more people employed. Well, the term 684 00:47:01,199 --> 00:47:05,760 ludite is thrown around as someone who doesn't understand or like technology, and that's 685 00:47:05,800 --> 00:47:08,119 not the case. The Luddites were very technical. They were they were the 686 00:47:08,199 --> 00:47:13,079 high tech, but they were they were frightened by the change, by the 687 00:47:13,159 --> 00:47:15,840 change. I have a more recent example, and that is just the kind 688 00:47:15,880 --> 00:47:19,440 of the emergence of cloud I used to see a similar kind of reaction from 689 00:47:19,679 --> 00:47:22,559 your old I T pro guys that were out there in the loading dot grabbing 690 00:47:22,559 --> 00:47:27,079 all those racks of hardware and then going and racking and loading all that stuff 691 00:47:27,079 --> 00:47:29,280 in, and then they're like, wait a minute, now that's my job. 692 00:47:29,320 --> 00:47:31,679 My job is doing that. Now you got some guy with a web 693 00:47:31,679 --> 00:47:37,239 page pushing a button. You have friends that are fit that I think you 694 00:47:37,280 --> 00:47:39,239 know, I'm talking. We have several front and I told him, I'm 695 00:47:39,280 --> 00:47:44,079 like, look, I go that piece goes away. But there's a whole 696 00:47:44,119 --> 00:47:45,800 ton of other stuff now that you were going to learn and do, and 697 00:47:46,159 --> 00:47:49,920 you were getting to the bottom of your to do list, right, That's 698 00:47:50,000 --> 00:47:52,440 true. It's not a thing. It's not a thing. There's so many 699 00:47:52,480 --> 00:47:55,639 more problems to solve now. I got to I say this on every dot 700 00:47:55,639 --> 00:47:59,079 and I rocks show we've done since we've been here. And I'll say it 701 00:47:59,079 --> 00:48:04,559 again. The chances of you being replaced you as a developer being replaced by 702 00:48:04,559 --> 00:48:07,159 an AI are slim to none. But the chances of you being replaced by 703 00:48:07,159 --> 00:48:15,239 a developer that uses it are high. Not high maybe, but significant enough 704 00:48:15,840 --> 00:48:20,639 so. And there's no excuse for developers there's no you know, there are 705 00:48:20,639 --> 00:48:24,840 no safety concerns about using an AI to help you write code, to look 706 00:48:24,840 --> 00:48:29,320 things up, to do research. You know, they just don't ask it. 707 00:48:29,480 --> 00:48:31,400 Whether you should ask your boss for a raise, you know, that's 708 00:48:32,280 --> 00:48:36,480 that's not ken If you want it's just the you still have to live with 709 00:48:36,519 --> 00:48:40,360 the consequences, that's right. Yeah, you may get bad advice, but 710 00:48:40,760 --> 00:48:44,280 you won't get bad code. And if you get bad code, just ask 711 00:48:44,360 --> 00:48:46,480 again. Yeah, try it again. I've been here's a trick, ask 712 00:48:46,519 --> 00:48:51,320 it to comment your code that it generates. I'm all in. Yeah, 713 00:48:51,440 --> 00:48:53,880 the last eighteen months I even used it, and the good HELB copilot is 714 00:48:53,920 --> 00:48:59,719 a great example. I needed to I needed to retrain learning Python. Yeah. 715 00:49:00,119 --> 00:49:02,480 I've been a c sharp developer for you know, as long as it's 716 00:49:02,480 --> 00:49:06,239 been around in two thousand and two, I guess right, and even before 717 00:49:06,280 --> 00:49:08,679 then, of course other Microsoft technologies, and I go back to VB back 718 00:49:08,679 --> 00:49:13,519 in the nineties, but me too. This AI space has put a lot 719 00:49:13,519 --> 00:49:16,440 of focus on Python right because, and I see this with the customers. 720 00:49:16,480 --> 00:49:21,559 A lot of these projects will start within kind of the data engineering and machine 721 00:49:21,599 --> 00:49:23,800 learning space because it seems to be that's the center of gravity for a lot 722 00:49:23,800 --> 00:49:28,119 of this machine learning and this space. It is a great language, but 723 00:49:28,159 --> 00:49:30,960 I didn't I was not very fluent in it, but I needed to learn 724 00:49:30,000 --> 00:49:34,920 it. And I've been using gethub, copilot and the tools in visual Studio 725 00:49:34,920 --> 00:49:37,760 and vis code and elsewhere to help me retrain, to help me retool myself 726 00:49:37,800 --> 00:49:42,320 so that I could be more productive writing Python. So you have this assistant 727 00:49:42,360 --> 00:49:45,880 effectively, that's coaching you on writing your running better. Take this C sharp 728 00:49:45,880 --> 00:49:49,880 code converted Python. Yeah, tell me comment it, tell me what it's 729 00:49:50,000 --> 00:49:52,920 it's actually briant. You know what I do now is I write a comment 730 00:49:52,960 --> 00:49:54,760 as to what I wanted to do, and then the Python comes out, 731 00:49:54,880 --> 00:49:59,199 and then it shoots out Python. I'm like, yeah, that's great. 732 00:49:59,320 --> 00:50:00,840 Yeah. Yeah. I can write the code and have it commented, or 733 00:50:00,840 --> 00:50:04,920 I can write the comment. It'll write the code. Yeah, you have 734 00:50:04,960 --> 00:50:07,440 a choice. Is that like schema first versus code first? Yeah. I 735 00:50:08,440 --> 00:50:13,599 write the comment and it writes code that also has comments. Yeah, ma'am, 736 00:50:13,920 --> 00:50:15,920 I used it, and I told you this. I used it for 737 00:50:15,639 --> 00:50:22,000 you know. It would spit out link statements that were some not readable by 738 00:50:22,039 --> 00:50:25,800 my customer, and so I basically asked it to you know, split this 739 00:50:25,840 --> 00:50:30,920 out into multiple for loops or whatever and comment it. And I took that, 740 00:50:30,320 --> 00:50:35,360 commented it. Yeah, and so you know, above the liuery, 741 00:50:35,440 --> 00:50:37,280 above the link query, this is what this link query does. Great, 742 00:50:38,079 --> 00:50:43,719 So not only is it solving a problem, but it's educating my customers, 743 00:50:43,760 --> 00:50:47,639 right. Better documenting, yeah, much better documentation. Yeah. I think 744 00:50:47,639 --> 00:50:52,519 it was twenty twenty two. My GitHub annual reports that the primary language I 745 00:50:52,519 --> 00:50:54,199 was writing in that year was Python. Really, I didn't think I was 746 00:50:54,199 --> 00:50:58,159 writing that much Python. As you know, I munch a lot of data 747 00:50:58,159 --> 00:51:00,960 in my life and buy golly pythons a bunch of data and so just you 748 00:51:00,960 --> 00:51:04,719 know, use the tool of them. I mean, the second was YAML, 749 00:51:04,760 --> 00:51:07,920 which makes me sadder, and the third was c sharp. Thank goodness. 750 00:51:07,159 --> 00:51:12,320 Yeah, I'm sorry for your YAML. Yeah. You know, I 751 00:51:12,480 --> 00:51:15,920 run a lot of infrastructure too, So Yamil's my friends there, you go, 752 00:51:15,199 --> 00:51:19,239 that's a good job. Yeah, oh well I got I got that 753 00:51:19,239 --> 00:51:21,920 they complain about. It's all good, right. And then it's also so 754 00:51:22,039 --> 00:51:24,639 this reality, it's like you are not the language you programming, right, 755 00:51:24,719 --> 00:51:28,679 You're the person who solves problems, and you know, the more tools y 756 00:51:28,719 --> 00:51:32,320 am your toolbox the easier gets that is the key is a ye. Yeah, 757 00:51:32,480 --> 00:51:37,519 yeah, I'm It's been a long time since I cared what language I 758 00:51:37,559 --> 00:51:39,039 was working. I'm just trying to get to the bottom of the problem. 759 00:51:39,079 --> 00:51:44,239 And now the tooling is super good at helping me. You know, why 760 00:51:44,280 --> 00:51:46,320 am I struggling with this? You know? And and it'll it'll get you 761 00:51:46,320 --> 00:51:50,280 over the hurdles. This is why developers, if for no other reason, 762 00:51:50,320 --> 00:51:52,519 should embrace this. Yeah, right, because it's a great robber duck. 763 00:51:52,639 --> 00:51:55,519 Holy man, it's such a good rubberd to make so much at very least, 764 00:51:55,679 --> 00:51:59,800 yeah, so much more productive. Yeah, I spent much time testing, 765 00:52:00,079 --> 00:52:04,280 less time just debugging stuff. I'm getting it just front to end. 766 00:52:04,440 --> 00:52:07,000 It's getting done faster. And here's the thing, like, you know, 767 00:52:07,000 --> 00:52:09,039 I don't be doomsday, but you know, if you don't embrace it, 768 00:52:10,239 --> 00:52:15,519 you may it may be to your detriment. So why not we work in 769 00:52:15,559 --> 00:52:17,800 technology? Why should we not be embracing that? Sure? Why are you 770 00:52:17,920 --> 00:52:22,360 using an it like you could just be? When you got into this gig, 771 00:52:22,480 --> 00:52:23,760 you knew it was going to be changing fast, right, that's what 772 00:52:23,760 --> 00:52:27,239 got you excited about it? Well, yeah, here it is again. 773 00:52:27,320 --> 00:52:30,159 We're turning the crank and it's changing fast. Using your chromium tip tweezers to 774 00:52:30,280 --> 00:52:36,880 light up electrons anymore either, right, like listen, shout out to right. 775 00:52:38,480 --> 00:52:43,920 Not organizing electrons one at a time anymore. I can hold B zero 776 00:52:44,000 --> 00:52:50,320 THEREO one zero one zero zero, just another tool, more automation, more 777 00:52:50,360 --> 00:52:52,400 productivity. Right, and you do. Maybe we'll get a little further down 778 00:52:52,400 --> 00:52:54,559 that list. The joke of courses. Every time we get these things, 779 00:52:54,679 --> 00:52:58,400 list gets longer. Yeah, there's more to do. There's so much more 780 00:52:58,400 --> 00:53:00,800 to do. Mark Brown, thank you. It's been a great pleasure talking 781 00:53:00,840 --> 00:53:04,880 to you, and this is great stuff. I love talking to you guys. 782 00:53:05,039 --> 00:53:07,679 Thanks for having me back on. I'm glad I could be your closer 783 00:53:07,760 --> 00:53:12,679 for this build for it absolutely. Thank you friend, all right, and 784 00:53:12,719 --> 00:53:37,679 we'll talk to you next time on dot net rocks. Dot net rocks is 785 00:53:37,679 --> 00:53:42,639 brought to you by Franklin's Net and produced by Pop Studios, a full service 786 00:53:42,719 --> 00:53:46,719 audio, video and post production facility located physically in New London, Connecticut, 787 00:53:46,920 --> 00:53:52,159 and of course in the cloud online at PW O P dot com. Visit 788 00:53:52,159 --> 00:53:55,440 our website at d O T N E t R O c K S dot 789 00:53:55,440 --> 00:54:00,719 com for r s S feeds, downloads, mobile app comments, and access 790 00:54:00,760 --> 00:54:06,159 to the full archives going back to show number one, recorded in September two 791 00:54:06,239 --> 00:54:08,800 thousand and two. And make sure you check out our sponsors. They keep 792 00:54:08,880 --> 00:54:13,519 us in business. Now go write some code. See you next time. 793 00:54:14,400 --> 00:54:22,000 You got jad middle Vans the