Full Transcript for Are Developers Becoming AI Managers?

work, that they're coordinated, that they're getting the job done well, that you're defining the problem. 00:03:50.080 --> 00:03:53.280 Because one of the things we're learning right now is you can't outsource thinking. 00:03:53.680 --> 00:03:59.199 But you can outsource, but what I'm starting to conclude is we can outsource outsource doing. 00:03:59.439 --> 00:04:02.000 And I think we're gonna be able to do more and more of that. 00:04:02.159 --> 00:04:12.080 And so then as a business owner, it's it's how do we start to work with and prepare for our entry-level people and training them and getting them ready to be more managers? 00:04:12.159 --> 00:04:13.840 Because I think the entry level is gonna be managers. 00:04:14.080 --> 00:04:15.439 So there's the two tiers here. 00:04:15.599 --> 00:04:16.800 We've got managers. 00:04:16.879 --> 00:04:20.720 Um, so so uh doers become managers, correct? 00:04:20.959 --> 00:04:23.279 And then entry level, what's the opportunity for it? 00:04:23.519 --> 00:04:25.040 Well, entry levels become the managers. 00:04:25.199 --> 00:04:26.560 Managing is the entry level. 00:04:26.800 --> 00:04:29.519 So my question is what happens if you don't want to be a manager? 00:04:30.800 --> 00:04:37.519 Well, I my only response to that is what happens if you don't want to drive a car and you want to stay with a horse? 00:04:37.680 --> 00:04:38.639 There's still space for that. 00:04:38.879 --> 00:04:41.279 That becomes very, very specialized, doesn't it, right? 00:04:41.519 --> 00:04:48.800 Well, yeah, but see, what if we're not here's my here's my theory is what if we're not at the car yet? 00:04:49.920 --> 00:04:51.519 What if we're or what if we are? 00:04:51.600 --> 00:04:58.800 But what if we're at the car that they developed that didn't have windshield wipers, didn't have um, you know, didn't have you know all these things, you know, brakes. 00:04:59.040 --> 00:05:01.519 Yeah, brakes are important. 00:05:01.759 --> 00:05:06.160 There was no um there were no construction or what if or what if we're not? 00:05:06.319 --> 00:05:08.720 Like what if we're just somewhere in between? 00:05:08.879 --> 00:05:13.199 Like, I still like my philosophy is that we're still really, really early. 00:05:13.439 --> 00:05:19.600 So being a manager might be a stepping point to where we're headed, and it's it's where we are now. 00:05:19.680 --> 00:05:22.959 But is that what the thesis was in the keynote? 00:05:23.120 --> 00:05:24.480 Um, is that what they were saying? 00:05:24.560 --> 00:05:28.399 That uh No, this is certainly pieces that like shoes were dropping for me. 00:05:28.480 --> 00:05:30.000 I don't think that was their thrust. 00:05:30.240 --> 00:05:34.399 But it was like I was coming with this lens thinking about already where's that ceiling going? 00:05:34.480 --> 00:05:35.680 And I I want to agree. 00:05:35.759 --> 00:05:36.959 I don't think we're there yet. 00:05:37.360 --> 00:05:39.279 I think we are in some spaces. 00:05:39.439 --> 00:05:46.240 I think as developers and coders, we are more and more being pressed around should I be doing the coding anymore? 00:05:46.480 --> 00:05:48.079 Is that the right space for me? 00:05:48.240 --> 00:05:51.040 And more and more, it's gonna be the answer is gonna be no. 00:05:51.360 --> 00:06:00.000 Well, the the interesting thing about the timing of it, like so, Chris and Vince, like uh the the role that we all play is we're intermediaries between the technology and the cut and the customer, right? 00:06:00.240 --> 00:06:02.720 So are you guys already hearing from your customers? 00:06:02.879 --> 00:06:04.480 Is the tech there already? 00:06:04.639 --> 00:06:06.079 Like, is it should we jump in? 00:06:06.240 --> 00:06:07.839 I mean, first of all, are you getting inquiries? 00:06:07.920 --> 00:06:09.839 Actually, I'm very curious, Chris. 00:06:10.079 --> 00:06:14.319 How many of your customers are coming to you proactively asking about AI for starters? 00:06:14.639 --> 00:06:15.519 There's definitely interest. 00:06:15.600 --> 00:06:19.680 Uh Hedy and I had an interesting situation a few months ago, actually. 00:06:19.759 --> 00:06:25.279 Uh, another file maker developer in Michigan came to us because they were getting asked about AI interesting by their customer. 00:06:25.360 --> 00:06:27.120 I think they sell hot tubs or something like that. 00:06:27.199 --> 00:06:36.000 They had five stores and they had this whole laundry list of stuff like I wanted to do our 401k contributions and calculate payroll and stuff like that. 00:06:36.079 --> 00:06:37.920 I was like, that's all real deterministic stuff. 00:06:38.079 --> 00:06:39.360 Yeah, bad choice for this. 00:06:39.519 --> 00:06:41.439 But one of the that's important, by the way. 00:06:41.600 --> 00:06:42.639 Let's put a pin in that too. 00:06:43.279 --> 00:06:45.839 One of the use cases, though, was reviewing sales calls. 00:06:45.920 --> 00:06:48.000 You know, that's a lengthy process. 00:06:48.079 --> 00:07:00.959 There's hours of calls, and their provider actually had a thing where it could summarize the sales call and say, here's the points they hit, or do the analysis for them if they say make sure you mention the promotion or whatever it is that they wanted to do coaching on. 00:07:01.120 --> 00:07:03.040 And so there was one great use case. 00:07:03.199 --> 00:07:05.040 So we thought, well, our work here is done. 00:07:05.120 --> 00:07:12.720 We've told them you have deterministic stuff, hire your file maker developer to uh add some uh workflow logic in. 00:07:12.959 --> 00:07:22.879 And then just last week we talked to the same developer again and said they got open claws and stuck Mac Minnie's in all the stores and just YOLO'd the whole thing, didn't listen to a word of advice about it. 00:07:23.600 --> 00:07:25.920 Well, at least hopefully you're not culpable. 00:07:26.319 --> 00:07:27.439 You didn't sign anything, did you? 00:07:28.240 --> 00:07:29.439 But I

don't know what to make of that. 00:07:29.600 --> 00:07:36.720 It's like open clause probably not the best fit for someone who's not technical and well doesn't know what they're doing, and they just did not care. 00:07:36.959 --> 00:07:40.399 As a bit of a T first of all, great framing for is AI there yet? 00:07:40.560 --> 00:07:42.240 It might be too there in some of those cases. 00:07:42.319 --> 00:07:47.920 And great framing for this episode, we're gonna finish off the last segment with some some hot open claw talk. 00:07:48.079 --> 00:07:52.160 Um, but um, and so if for anybody's that might not familiar, we'll get into defining what that is. 00:07:52.240 --> 00:07:58.240 But I think for the purpose of this conversation, that's the most like aggressive AI there is, basically, right? 00:07:58.480 --> 00:08:00.399 So when we talk about is AI there yet? 00:08:00.560 --> 00:08:06.879 Well, the combination of maybe not totally and it being completely in YOLO mode is probably quite dangerous. 00:08:07.199 --> 00:08:12.879 But Chris, this ties in as well to, you know, the owner's job was to manage the AI. 00:08:13.040 --> 00:08:15.519 They didn't do a good management job there, right? 00:08:15.680 --> 00:08:18.720 They didn't use the right tool to get the job done. 00:08:19.040 --> 00:08:30.879 And that's, I think, coming back to more and more of the skill set that we need to be learning and we need to be providing solutions for is that, you know, you're not supposed to use a sledgehammer when you need a hammer. 00:08:30.959 --> 00:08:33.679 You're not supposed to use a hammer when you need a sledgehammer. 00:08:33.759 --> 00:08:35.759 And and that becomes management tasks. 00:08:36.000 --> 00:08:41.600 But I think that's the interesting role that all of us here play is we're the ones that know the difference between the hammer and the sledgehammer, right? 00:08:41.759 --> 00:08:44.960 Um like Vince, it's are you having these conversations with your customers too? 00:08:45.039 --> 00:08:46.480 Are they coming to you proactively? 00:08:46.559 --> 00:08:51.120 And are you, do you find yourself responsible for determining like what AI can actually do? 00:08:51.440 --> 00:08:58.000 I mean, to some level, I'm dabbling in it and I'm learning it as everybody is, and that's growing very quickly. 00:08:58.080 --> 00:09:03.519 Uh, but there's a lot of other people on our teams that are just doing a lot more uh in that space. 00:09:03.679 --> 00:09:05.600 And so uh that's that's happening. 00:09:05.759 --> 00:09:09.360 There's there's definitely demand, there's definitely interest, etc. 00:09:09.759 --> 00:09:20.000 I think going back to the to the keynote or the or the panel discussion, it was interesting to see that it was kind of like uh you know, very positive oriented. 00:09:20.159 --> 00:09:23.120 It was eight, nine, and ten, like when they were gauging. 00:09:23.200 --> 00:09:24.159 What was the the question? 00:09:24.559 --> 00:09:28.000 The whole theme

of the thing was what's the future of software engineering? 00:09:28.320 --> 00:09:30.240 Is it like is it dead or what do you think? 00:09:30.399 --> 00:09:41.279 Oh, and so for humans to be software engineers and so it was sort of a doom index, like on a scale of you know one to ten, with the ten being the future is bright and one being world doomed. 00:09:41.519 --> 00:09:42.000 What would you do? 00:09:42.159 --> 00:09:48.480 It was JSON actually was zero to ten, but it was it was very high, ranging from like seven to ten. 00:09:48.960 --> 00:09:49.440 Oh, really? 00:09:49.679 --> 00:09:51.919 Was the gamut of four panelists, was it? 00:09:52.080 --> 00:09:54.559 Well, and Replit CEO was ten out of ten. 00:09:54.720 --> 00:09:55.039 Yeah, yeah. 00:09:55.200 --> 00:09:59.279 The one who was who would so Replit is like automated coding. 00:09:59.440 --> 00:10:04.320 And he was like, absolutely AI engineers are our code software engineers are. 00:10:04.879 --> 00:10:07.200 Well, he was speaking to his book though, in fairness, right? 00:10:07.360 --> 00:10:08.639 I mean, he needs that to be true. 00:10:08.879 --> 00:10:10.080 And I'm not saying it's not. 00:10:10.320 --> 00:10:19.279 Um you know, Kate, you and I had a conversation the last time we did an episode together where we were talking about like the doom scale, like what you know, where you kind of put yourself on there. 00:10:19.440 --> 00:10:20.559 Let's switch it up a little bit. 00:10:20.720 --> 00:10:24.559 Where do you see software coding on as far as like done being 10? 00:10:24.639 --> 00:10:27.120 Is that what the framing was uh during the panel? 00:10:27.279 --> 00:10:29.200 Uh software is just done? 00:10:29.600 --> 00:10:34.639 No, the the higher was the the brightness of the future, zero being the future's doomed. 00:10:34.879 --> 00:10:35.200 Interesting. 00:10:35.440 --> 00:10:37.120 Ten being there's it's a really bright future. 00:10:37.519 --> 00:10:37.840 Oh, wow. 00:10:39.919 --> 00:10:41.440 For human engineering engineers. 00:10:42.080 --> 00:10:42.879 Let's talk about that. 00:10:43.039 --> 00:10:43.200 Yeah. 00:10:43.919 --> 00:10:45.279 Okay, where are you at on that? 00:10:45.600 --> 00:10:51.759 I mean, I think I think it's like it how we're we're changing how we define a software engineer. 00:10:52.159 --> 00:10:53.440 That's basically what they're meaning. 00:10:53.759 --> 00:11:11.360 And and to the point of the the panelists, and and I did this so so the way I went through the the conference today was I was I used Claude and I, you know, took some notes, uploaded you know, screenshots of the slides I was seeing, and then had Claude research each speaker, you know, where which company to work for, what could their angle possibly be? 00:11:11.440 --> 00:11:15.360 Because you realize anybody that's gonna go speak at a conference, they're gonna have an angle, right? 00:11:15.519 --> 00:11:19.200 So of course, thinking back that, oh yeah, all these people would

rate it a high rating. 00:11:19.360 --> 00:11:22.000 Yeah, I mean that that how could if they said one, right? 00:11:22.080 --> 00:11:25.039 Well then opposition researchers. 00:11:25.600 --> 00:11:27.279 Opposition research is like that. 00:11:27.600 --> 00:11:41.120 So I think that there could be something that they're saying, yes, it's highly, you know, optimistic for software engineers, but I think software engineers is gonna mean something completely different, you know, even in a few years than what we think of a software engineer today. 00:11:41.360 --> 00:11:44.159 We're just you know, or maybe it's gonna take on a new turn. 00:11:44.240 --> 00:11:47.360 I mean, we do, we have uh context engineer prompt engineers, right? 00:11:47.440 --> 00:11:55.279 But but I I don't know, you know, because software is now like AI is software, like that's that's it, you know, in our current state, you know, we are. 00:11:55.600 --> 00:12:00.879 Marcus, uh so this conference is put on by Andrew Ng from uh Deep Learning AI amongst America. 00:12:01.039 --> 00:12:02.159 He's like basically a celebrity. 00:12:02.320 --> 00:12:07.279 Anytime you can't get through the exhibit area, it's because he's there and all the fans are taking pictures of him. 00:12:08.799 --> 00:12:09.759 Oh, for sure he did. 00:12:10.000 --> 00:12:19.519 But he also um has a very aggressive stance on, you know, when people ask him whether software, you should still learn software, like young people should still learn learn software. 00:12:19.600 --> 00:12:21.279 He says yes, emphatically, yes. 00:12:21.759 --> 00:12:23.919 Um, so what what's your take on that? 00:12:24.159 --> 00:12:30.000 Somebody knew right out of the, you know, right out of the box, should should learning uh software be a thing, a priority? 00:12:30.320 --> 00:12:36.000 Well, I'm gonna go around a little bit and then come back to what is saying, what, what you're what you're asking. 00:12:36.159 --> 00:12:45.759 Um what somebody said on the panel that um a junior developer is gonna get more more complex tasks than a junior would have got five years ago. 00:12:46.080 --> 00:12:51.039 Because they have the knowledge right at their fingertips, you can give them a more complex task. 00:12:51.200 --> 00:12:57.840 You don't have to worry about because they're because they are embellished by the tool sets, whatever harness they're using. 00:12:57.919 --> 00:13:00.559 And and so because of that, you can give them more complexity. 00:13:00.639 --> 00:13:02.320 And this has been my personal journey. 00:13:02.399 --> 00:13:08.720 I mean, you and I met a year and a bit ago, and I was like a neophyte in this, in this space, right? 00:13:08.879 --> 00:13:12.559 And and in a year and a half, I feel like I've come a long way. 00:13:12.799 --> 00:13:22.639 And and this is what they're talking about that you the the journey from junior to senior gets compressed because you can start to interact with these models that teach you so much. 00:13:22.879 --> 00:13:32.320 So do I I'm a better at languages now than I started a year and a half ago because of how much I'm looking at the code that that that the AI is generating. 00:13:32.559 --> 00:13:33.759 So I think you take that away. 00:13:33.919 --> 00:13:38.000 I couldn't write, I'd be really, really struggled to write a basic Python. 00:13:38.480 --> 00:13:52.639 You've gone through immersion, like immersion learning, like you would learn a language, you just go to that that country and and this concept reminds me of when I first got started in the computer industry, I was really interested in hypercard and I didn't know how to write hyper talk or all that stuff. 00:13:52.960 --> 00:14:02.000 And I just started taking apart example hypercard stacks that already existed, and I learned by deconstructing existing stuff. 00:14:02.159 --> 00:14:06.320 And I think there's a real opportunity for new software developers to do the same thing. 00:14:06.639 --> 00:14:11.679 Have the LLM build something for you and then pick it apart and say, now why does this work? 00:14:11.840 --> 00:14:22.080 And so for those uh people who are curious about you know what how why does the clock tick, kind of a thing, I think there's a real opportunity for them to learn by osmosis, as it were. 00:14:22.559 --> 00:14:31.120 What's been foundational for me though is the knowledge I brought from FileMaker because FileMaker teaches you how to how to think about data, how to structure data. 00:14:31.360 --> 00:14:42.639 You you as a FileMaker developer know the full stack and you can see that layer and how it interacts with the front end and how the front end you don't call it that in FileMaker, but but that's really what you're seeing. 00:14:42.720 --> 00:14:50.639 And so that ability to visualize the data has really translated well to now me writing in JavaScript and Python, right? 00:14:50.720 --> 00:14:52.320 Um there's no doubt. 00:14:52.399 --> 00:15:01.840 Um, I mean, I can share with you my observation from like the first couple of years of doing services is that we were just thinking, yeah, plug your data into the language model and amazing things will happen. 00:15:01.919 --> 00:15:04.559 And then we realize all it does is amplify bad data. 00:15:04.799 --> 00:15:04.960 Right. 00:15:05.200 --> 00:15:11.360 And one of the things, like you know, the file maker databases are sort of like the HTML code of databases, like they're very forgiving. 00:15:11.519 --> 00:15:18.480 Like you can have a lot of garbage in there and a lot of columns you never needed, or just kind of sort of forget about things and stack on top of it and it'll just still work. 00:15:18.879 --> 00:15:22.320 But it's not so forgiving when you get down when you're connecting language models to that. 00:15:22.399 --> 00:15:34.320 So I I think on some level, like as a file maker developer for many years, I think one thing that's really fascinating to me, when especially when we got

started with 42U and you were part of that journey, right? 00:15:34.799 --> 00:15:42.480 You meet these people and they come and they get grav, they gravitate towards it because it's like, wow, it's multidisciplinary. 00:15:42.639 --> 00:15:48.240 It's like it, I get to do, I just I get to not just do code, but I get to interact with people. 00:15:48.399 --> 00:15:51.200 And it's like, so it's not for everybody. 00:15:51.360 --> 00:15:55.360 And so for some people, it's like, hey, just give me the inputs, I'll give you the outputs. 00:15:55.440 --> 00:16:02.320 That's uh, that's all I want, you know, like just slide the pizza under the door and and I don't have to worry about it to interact with people. 00:16:02.480 --> 00:16:16.320 But I think for some people, there's there's a there's a beauty in, you know, coming into work and you're working for uh Joe's garage one day and you're working for a financial services institute the next day, you get to be like an actor. 00:16:16.399 --> 00:16:22.879 And that's so rewarding because it's like it's all dynamic, it's different, it's changing. 00:16:23.200 --> 00:16:31.759 It's it's it's something that a lot of the people that I've come across who were talented, that were talented in that multidisciplinary thing. 00:16:31.919 --> 00:16:47.919 And I think that has a big impact on our future because being not just like a coder and being more broad gives us the ability to have empathy with the customer, have understanding in terms of how you solve those their problems, etc. 00:16:48.159 --> 00:16:52.720 I think that's that's gonna have a lot more longevity for for you know our background. 00:16:52.879 --> 00:16:56.639 Well, I there's no question if we're wondering if you have all those skills. 00:16:56.720 --> 00:16:57.120 Yeah, right. 00:16:57.200 --> 00:17:00.480 Let's just say that's your harness, like your career harness, and you have all those skills. 00:17:00.559 --> 00:17:05.279 Well, then you're thinking about which ones peel away in the AI era and which are which are gonna be serve me well. 00:17:05.759 --> 00:17:07.039 And you're talking about human skills. 00:17:07.200 --> 00:17:07.920 I'm talking about humans. 00:17:08.240 --> 00:17:16.960 The thing is, to Kate's point, like not everybody wants to talk to people and you know, interact with the customer and gather requirements and you know, do the people things. 00:17:17.119 --> 00:17:21.920 There are some slide the piece pizza onto the door folks, and like what what future does that hold? 00:17:22.160 --> 00:17:27.839 So I think that's really kind of where the what why they I mean, first of all, that was the first topic out of the gate at the conference, right? 00:17:27.920 --> 00:17:35.920 So obviously this is on the top of minds of a lot of folks, but there's easily 3,000 people there who are building and involved in the the industry itself. 00:17:36.079 --> 00:17:41.680 Um, also with that in mind, what what were some of the other uh topics that you guys heard uh uh bopping around? 00:17:41.759 --> 00:17:50.960 I would say that last year at this conference, I uh really started to hear context engineering for the first time, and that was like almost de facto in every one of the sessions. 00:17:51.200 --> 00:17:53.440 Um, any other things that popped out as you guys? 00:17:54.960 --> 00:17:59.680 Sort of another kind of word that was thrown around a lot during the keynote was taste. 00:18:00.079 --> 00:18:01.279 Oh, yeah, taste, yeah. 00:18:01.359 --> 00:18:05.279 Yeah, we bring the taste, so we're still important enough to be involved. 00:18:05.359 --> 00:18:09.519 We get a cookie, you know, that kind of thing. 00:18:09.759 --> 00:18:18.160 As far as the theme goes, though, uh much has been made of the cloud's nice and all, and the cloud is useful, but the cloud can go down sometimes. 00:18:18.319 --> 00:18:21.440 So the best approach is a hybrid approach. 00:18:21.759 --> 00:18:29.440 And the models are getting small enough and capable enough that we really need to take seriously the idea of having models running at the edge. 00:18:31.920 --> 00:18:35.279 Uh uh local and uh pushing the cloud, no question. 00:18:35.680 --> 00:18:41.839 Several companies, Redis is talking about let's have our storage being close to where the data is being generated, not up in the cloud. 00:18:42.160 --> 00:18:47.039 So is there talk about like is does local equal trust in those did it was anybody talking about it? 00:18:49.759 --> 00:18:55.359 It's like you know, if you have a data lake, some of that data is old and you can mine it for trends and things like that. 00:18:55.440 --> 00:19:06.799 But if you're trying to find out is my equipment about to fail, you need real-time data and to have it, you know, trucking you know, terabytes of data up into the cloud and bringing it back down is expensive. 00:19:07.119 --> 00:19:18.160 So saying, okay, I have this big gob of data here locally on-prem, get my uh AI infrastructure wrapped around it locally, so the latency is really sl uh really tight. 00:19:18.319 --> 00:19:22.720 Yep, and I can get real-time uh determinations of whatever I'm trying to figure out. 00:19:23.039 --> 00:19:33.359 I'm shocked that people aren't hammering the trust thing more, honestly, because that was literally the only reason anybody ever used an open source model in 2021 is because you didn't trust open AI, basically, right? 00:19:33.519 --> 00:19:35.279 Um, and there's definitely an advantage to that. 00:19:35.359 --> 00:19:42.720 Uh Vince, what were some of the I mean, I I I attended the last session that was talking about uh hurt uh you know agents as hurting cats. 00:19:42.799 --> 00:19:43.039 Uh-huh. 00:19:43.279 --> 00:19:46.079 And uh it was band.ai, I think it was. 00:19:46.240 --> 00:19:49.519 And this fellow from uh from Greece was giving that talk. 00:19:49.759 --> 00:20:05.200 It just fascinated me because at one point he had a slide that he was talking about the

evolution of agents and how agents evolved uh for from being like something you you directly interact with to all the way to to the point where they have this uh kind of network. 00:20:05.359 --> 00:20:22.160 I don't know if anybody else uh participated in that talk where uh they they kind of like a a broker of uh of agents that you would talk to and it would find the right agent that you could talk to, and then you know, you would have that communication with all those different agents. 00:20:22.480 --> 00:20:28.319 So uh yeah, that was that was fascinating to me just to see that whole evolution and where that's gotten. 00:20:28.480 --> 00:20:47.440 And to the point where it's like I could see uh, you know, maybe maybe where it's heading in terms of, you know, uh if you're a company and you want to build a product or a service, and you would put your agents into the pool or a mix, and uh then it brings it begs the question of like how good is each agent? 00:20:47.519 --> 00:20:50.079 You know, it's like almost as the same as other people, right? 00:20:50.240 --> 00:20:52.000 You rely on this person for a job. 00:20:52.240 --> 00:20:54.640 Is that person gonna do the right thing, et cetera? 00:20:54.960 --> 00:20:57.680 Where those specialties come from and and and whatnot. 00:20:57.920 --> 00:21:05.359 And again, we're gonna have another segment here coming up in just a few minutes where we're gonna dive into the claw discussions and we'll kind of explore some of those things. 00:21:05.519 --> 00:21:10.880 Uh Marcus, what was uh you mentioned some of the stuff you talked about outside of the opening keynote stuff, what were uh some of the keywords? 00:21:11.200 --> 00:21:17.039 I think another key theme was was um memory uh memory con memory context, right? 00:21:17.279 --> 00:21:18.480 Or memory engineering. 00:21:18.640 --> 00:21:21.680 So we certainly context engineering was a buzzword a year ago, right? 00:21:21.759 --> 00:21:24.640 Now they're talking memory engineering was huge. 00:21:24.720 --> 00:21:26.960 And then we're just starting to talk about harness engineering. 00:21:27.119 --> 00:21:32.400 We haven't heard a lot of that, but I think that's the word the phrase to watch for in the future. 00:21:32.559 --> 00:21:37.440 But a lot of presenters and booths on memory management. 00:21:37.920 --> 00:21:39.759 I find that really an intriguing idea. 00:21:40.000 --> 00:21:47.759 I I I will say that my second observation uh out from the services world is like uh data matters, but then also, are you kidding me? 00:21:48.000 --> 00:21:50.880 Everyone's like blown away by memory, like statefulness? 00:21:51.039 --> 00:21:56.960 Like we like we've been living with in state, trying to figure out what to do with state for 20, 30 years, right, in our in our careers. 00:21:57.119 --> 00:21:58.880 And here this community is like, wait, you mean? 00:22:00.240 --> 00:22:03.359 More things and I can retrieve them in another conversation. 00:22:03.519

--> 00:22:05.920 Uh Kate, your observations from uh some of the other sessions. 00:22:06.319 --> 00:22:12.079 Well, you know, so I again like I was using Claude, so so as I would did this, so I did a little I know, I know. 00:22:12.160 --> 00:22:16.240 So Claude and I, um, we came up with some themes, right? 00:22:16.319 --> 00:22:23.440 Of of what, but again, this is my personal experience of the conference, because we didn't go to all the same sessions or I was taking notes on certain things. 00:22:23.599 --> 00:22:26.480 So again, like this is this is the AI of one, right? 00:22:26.559 --> 00:22:32.880 But this is my personal, you know, but but again, like I I'm I'm coming from the responsible AI perspective. 00:22:33.119 --> 00:22:40.799 And to me, this was a conference of responsible AI, but nobody called it responsible AI because the topics were observability, reliability. 00:22:40.960 --> 00:22:43.359 I mean, I there were several sessions, security. 00:22:43.680 --> 00:23:02.640 But but what was interesting to me was going to multiple sessions about, you know, um, you know, using RAG, using um embeddings, using um, you know, graph um yeah, and and different ways that are all, you know, either can work, you know, in conjunction or apart to achieve the same goal is like having less hallucinations, right? 00:23:02.720 --> 00:23:09.759 Having more trustworthy AI, which again is like, and so I I've always thought like responsible AI, the word responsible is gonna go away. 00:23:09.839 --> 00:23:10.720 Like this is AI. 00:23:10.799 --> 00:23:12.720 This is table states though, right? 00:23:12.880 --> 00:23:13.039 Yeah. 00:23:13.279 --> 00:23:17.440 I mean, from your perspective, is this good that responsible AIs become AI? 00:23:17.839 --> 00:23:20.799 It's it's just, it's just, yeah, it's just this is what we talk about. 00:23:20.960 --> 00:23:27.200 And it is, it's bringing back in all the determinism that we're used to because you can't have just an LLM put it out there. 00:23:27.279 --> 00:23:29.920 It's like you need all the other infrastructure around it. 00:23:30.000 --> 00:23:34.079 And I think we're still at the, you know, there's sessions about, oh, you could do this, you could do that, you could do that. 00:23:34.160 --> 00:23:36.240 And I don't think there's one right or wrong way. 00:23:36.319 --> 00:23:41.440 It depends on the situation and then, you know, what tools you have at hand, and then of course what budget. 00:23:41.599 --> 00:23:42.880 Nobody was talking about budget here. 00:23:43.119 --> 00:23:44.079 Nobody talked about cost. 00:23:44.160 --> 00:23:46.640 And I was thinking, well, how much is it gonna cost me? 00:23:46.799 --> 00:23:57.200 So I think there's a lot of factors that that are still go into this, and and we as consultants, right, this is what we have to do is is know all the resources we need to know and have all the tools at hand. 00:23:57.440 --> 00:23:58.720 I think that's a great observation. 00:23:58.799 --> 00:24:01.680 And I know that's that's an area that you spend a lot of time in too. 00:24:01.759 --> 00:24:05.920 So it's uh particularly curious uh having that be, well, actually, what Claude told you. 00:24:06.240 --> 00:24:07.200 Yeah, yeah, yeah. 00:24:08.319 --> 00:24:11.680 So as we wrap up the segment here, we're just gonna do a just a rapid round here. 00:24:11.839 --> 00:24:19.200 What uh for all the other FileMaker folks, all our all our um, you know, friends in the community, uh, what's your top line thought, Chris? 00:24:19.359 --> 00:24:22.559 What would you tell them about what you're seeing out here in at an AI conference? 00:24:22.720 --> 00:24:26.880 What is something, you know, some advice or a takeaway that you would give to FileMaker people in particular? 00:24:27.200 --> 00:24:31.440 One aspect of FileMaker is that it's kind of a joyful thing to work in. 00:24:31.599 --> 00:24:35.440 You can move so quickly in some areas, and there's a lot of fun to it. 00:24:35.759 --> 00:24:36.720 The original vibe coding. 00:24:38.480 --> 00:24:47.039 And there's a lot, I mean, Google just ran through all their stuff and they made like a Hindi techno song about organic chemistry and just you know, just a world. 00:24:47.200 --> 00:24:47.359 Yeah. 00:24:47.599 --> 00:24:56.160 And so there is a lot of fun to be had just playing with, you know, get back to play and play with some of these tools because there's there's joy to be had there. 00:24:56.480 --> 00:24:56.960 I love that. 00:24:57.039 --> 00:24:57.599 Uh Vince. 00:24:57.839 --> 00:25:02.079 Yeah, explore curiosity, those are words that kind of ring in my head. 00:25:02.240 --> 00:25:08.880 But uh I I thought uh at some point, just like Chris was saying, we were doing this with FileMaker so many years ago. 00:25:09.039 --> 00:25:17.599 Like iteration was a thing back then, like layout mode, browse mode, layout mode, browse mode, and you have like the beauty for the customer is like, wow, you just did that. 00:25:17.759 --> 00:25:19.440 Oh, that's so so exciting. 00:25:19.680 --> 00:25:26.000 But you know, we we've dabbled there, and and AI is giving that to us in a very different level, you know. 00:25:26.160 --> 00:25:27.359 So I love that parallel. 00:25:27.680 --> 00:25:28.799 Marcus, your thoughts? 00:25:29.119 --> 00:25:30.079 This one stumped me. 00:25:30.160 --> 00:25:31.039 I'm glad they went first. 00:25:32.559 --> 00:25:33.359 But it's come together. 00:25:33.440 --> 00:25:40.720 I mean, when you when you approach AI in a playful way with your years of knowledge, because you can't outsource knowledge, right? 00:25:40.880 --> 00:25:45.119 When you're working with AI, you're still in charge of telling it what to do of managing it. 00:25:45.359 --> 00:25:52.799 When you take all of your experience as your FM developer and and approach AI playfully, magic seems to happen. 00:25:52.960 --> 00:25:54.000 And that's what got me hooked. 00:25:54.079 --> 00:25:59.359 That's what got me hooked in FileMaker, that's what got me so hooked with AI was it just it's so fun. 00:26:00.000 --> 00:26:08.000 But now I come to it with knowledge that that a vibe coder doesn't have because of my years in FileMaker, which is a superpower. 00:26:08.319 --> 00:26:08.559 Okay. 00:26:09.599 --> 00:26:12.400 I guess um, oh yeah, this is tough. 00:26:12.559 --> 00:26:30.480 Um I don't know, I think I the word trust is coming up for me, and I think that's maybe trust your gut, trust your intuition, trust your human intelligence, but also like learn, you know, and I think I think AI, yeah, it's it's a whole new world, right? 00:26:30.640 --> 00:26:40.079 And and um I guess I think you know, we all have to move at our own pace with it. 00:26:40.240 --> 00:26:51.759 And I think it's but it's also important to listen to people that have been like like us, like we're out there, we're listening, we're gaining information, and and there's a lot of experts, and there's a lot of there's a lot of movement to just make it better and better. 00:26:51.839 --> 00:26:55.440 And like there's a saying, like, today's AI is the worst AI you will ever use. 00:26:55.599 --> 00:26:59.359 And it's like that's true, you know, that that that we're just we're in a process with it. 00:26:59.440 --> 00:27:01.039 So Chris, what are your thoughts? 00:27:01.279 --> 00:27:10.960 Well, I would say um, you know, if you don't trust AI, maybe you can trust some of your your fellow constituents here from the AI community who've uh ventured out and you know dove into this space. 00:27:11.200 --> 00:27:14.960 The come on in, the water's warm uh is really what I would let everybody know. 00:27:15.039 --> 00:27:16.480 So you guys are really appreciate you. 00:27:16.720 --> 00:27:18.880 First of all, it's been a lot of fun being at the conference with you guys. 00:27:19.279 --> 00:27:20.240 Look forward to day two. 00:27:20.319 --> 00:27:22.160 We've got a whole bunch more observations. 00:27:22.240 --> 00:27:28.240 Uh and uh and then next we're gonna put together a segment on um uh open some hot open claw talk. 00:27:28.400 --> 00:27:29.440 So thanks a ton, guys. 00:27:29.519 --> 00:27:31.359 Uh, look forward to another day at the conference with you guys. 00:27:31.680 --> 00:27:32.160 Appreciate this.