A (3:36)

Got it. Yeah.

C (3:37)

Most ideas, they don't get built not because they're bad, but because the implement, the implementation barrier is what stops them. So removing the bottleneck is just a natural place where I, you know, made sense with who I am and where I am in time. If that's a good answer, that's amazing.

A (3:58)

So tell us what is more Tech Foundry. I mean, I know it's the ability to be able to launch an app without writing any code. So just tell us exactly what it is.

C (4:08)

So what it is exactly is just like in the hook, it's agentic software delivery. So it's a full AI assembly line that takes your idea in plain English and even helps you, you know, surface the ideas, discover the unknown unknowns, so to speak. Because AI does have the power to do that. And then it returns working documented software. And it is also an intuitive process that works along with you. So you can kind of have as much hands on in the process as you'd like.

A (4:42)

Got it. And if I literally don't want to do anything, it will produce the software for me.

C (4:48)

Yes, yes. That's the idea that we're working towards is if you want to, you know, if you've never coded anything before and you just want this, you have a problem, you need it solved and it will just pop it out for you. Or you can actually in this, in the same way, if you commission a car from maybe Ferrari or something, you yourself can say, I want these particular specs, this engine, this amount of torque on these size of lug nuts and everything will be to your spec.

A (5:16)

So awesome. Awesome. Okay, so talk to us about the holy smokes moment. What is a time where a customer, a user, someone experienced it and it changed everything for them?

C (5:33)

So I've had quite a bit of experience with this. I was actually in Cognizant. I worked for a company named Cognizant. They had the world's largest generative AI coding competition. And it was really, I was actually able to be a subject matter expert for AI coding tools. And that was my holy smokes moment when I saw, you know, literally hundreds of people at the same time use my kind of manual version of the workflow, so to speak, to actually build out. And that whole competition, they delivered 30,000 some odd apps. And I, I did get the opportunity to advise several hundred teams. You know, obviously I couldn't be there for every single one in a complete capacity, but I did send them kind of of the proprietary things in this actual app and, and that belong to my company.

A (6:28)

Oh, wow. Amazing. And so we're very curious people. How does it work? For instance, if we were to open up the hood and really look at the brain of the tool, how does it all work?

C (6:41)

So inside the brain, what's actually happening is it's an assembly line instead of one giant AI. So each kind of agent has one job and a spe. And a lot of times it can be a specialization that creates reliability. So the pipeline is essentially, you know, and it's very similar to the software development life cycle or the agentic development life cycle, but in kind of a way that onboards non technical folks as well, or I shouldn't say non technical people who don't code or don't want to code. So it's essentially discovery, then architect and define the features. Then at that point that's when it'll be building, doing quality assurance, providing explanations and auditability. There's layers for that that keep the AI honest and help with its reasoning and other things like that. And then there's deployment and maintenance so that potentially under the hood, end to end, how it works.

A (7:46)

Awesome. And you know, I hear obviously with, you know, having guests on the podcast of folks that are having kind of like this building like these armies of agents in the background. Do your agents work together? Are they clearly like just specialization? I do my job, I do my job, only I do it really well. Are they working with each other?

C (8:07)

Yes. So they are fully autonomous and they do hand off information. And what I actually offer in my company and my, my actual proprietary kind of blend that I do is what I call the elastic workforce model. So it's where workers are added or subtracted based on demand. There they work in parallel, where they're coordinated and their dependency graphs manage sequencing. So it's kind of like smart, smart handoffs. It's truly, you know, it's truly taking the difference between like a factory making cars in the 1960s to you know, a very complicated assembly line for maybe like GPUs right now.

A (8:54)

Wow, that's amazing. Now obviously as the founder, right, you have developed this, you're working with it. Talk to us about that real world magic where you yourself was part of blown away by what you had created and the power of more tech foundry.

C (9:15)

So, you know, and there's kind of a Lot of ways that it's anecdotal for me to say, but yeah, personally where I really kind of crossed the line because this was for me a manual workflow that eventually evolved into an automated system. And where it really kind of crossed the threshold is like after so many projects, software, like you can brag, you got a computer science degree, you're a tech bro, and now software is not a problem for you, but now it's really not a problem. So now it's, you know, now what, what science do I want to pursue? What, what things like that are the, you know, rather than how, what do I want to do with my coding career? It's what do I want to do with my scientific, my academic or my industry career.

A (10:03)

Career. Got it. Oh, wow, that's amazing. And then how are you thinking about ethics? What are the guardrails that you're thinking about? You know, ethical policy is so important with AI, Governance is important with AI. So how are you thinking about ethics as you're building out Tech Foundry?

C (10:28)

So a big part of the ethics, and I've been thinking about this from kind of the beginning and really the art, like some of the hardest part about software is creating novel solutions for non trivial problems. So the way that we account for that as well as just handling, you know, for example, like secure information or adherence to whatever protocols or standards we imbue strict protocols for auditability and human in the loop. So even though you are, let's say you want to just put in your request for an app and then get one out. Well, even if you go and commission something, they usually might ask, even if you go buy a car brand new, they'll ask, well, what color do you want? What, what are you expecting? You know, AI, no matter how powerful it gets, it won't be able to simply read your mind. You will still have to at some point take, tell it what you want.

A (11:25)

Well, that's great, Damian. And what's so fascinating about it is that it's something you mentioned that you think about all the time you thought about recently. And it seems as if you definitely have kept ethics at the forefront as you started, you know, More Tech Foundry. And that's so important and something that our listeners are very, very interested in. So let's talk about the future of, of More Tech Foundry. You know, the future changes an hour from now will be very different from now, a year from now. But if you had to think to, I don't know, 2030, Damien, how do you see the future of More Tech Foundry? You know, an hour from now can be very different from current, right? As we're talking now, a year from now, there are just so many different changes. So let's 2030, how do you see more Tech Foundry evolving and what kind of impact do you see it making globally?

C (12:24)

Well, personally, I've always wanted to do something significant and be significant. So I hope that more Tech Foundry evolves with technology and with humanity and science, academia as it goes. And in 2030, I think that there's a couple of things that happen that will happen that kind of people aren't predicting. So the first is that software stops being the resource constraint for expansion. So businesses grow. Today a car dealership wants to get into tires, they need capital, et cetera. So every new domain, My point is that every new domain that a business gets into, the bottleneck is always going to be execution capability and it's where it relates to software. This will eliminate that.

A (13:17)

Awesome. Awesome. That's amazing. Particularly for a lot of, you know, the examples that you've given throughout our discussion has been brick and mortar businesses and how, like you said, there are just so many limitations and restrictions to be able to scale. And more Tech Foundry being at the front of that is absolutely amazing. Okay, so I have a question for you. I call it from one genius to another. So one of my previous guests asked a question for you, Damien, and that is, what's the belief about AI you used to hold that you no longer believe and why?

C (13:57)

So I used to believe that that AI would follow when you say a jack of all trades is useless, like a master of one trade is the best. I, I personally kind of have been moving away from that. I used to believe that it's better to have it be kind of a master of all and accumulate knowledge and context over time. But now I believe that decentralization and accumulation of individual kind of places of context is going to be the future.

A (14:33)

Wow. Okay, I love that. I love that. Great question. And so then like I said, a lot of our listeners are very curious. They want to touch and feel if this week they want to be introduced to more Tech Foundry, what's the best way for them to get pull up their sleeves and get their hands dirty?

C (14:56)

Well, it depends on their level of expertise, but there's something for all levels of seniority. So I would say if you have some, you know, even crazy app idea that you just arbitrarily thought of and the AI tools of now just quite don't do it because it's not AI generation, it's not a landing page. You know, you might need something that actually does something complicated for you. So if you wanted to get your hands dirty, we have a few ways that we're trying to offer services. So one, we can either do it for you and you can just talk to our kind of chatbot and then get ownership of the code. And then the other way is that developers can call it through an API to our, to our developer kit. So those are a couple of ways that you can actually dive in with it.

A (15:46)

Got it. And give us the website or what source do we need to go to?

C (15:51)

Yeah, the website. Right now we're still kind of building out and just doing like beta testers. So if they wanted to do that, our website is actually more techfoundry.com we are getting that up and going right now to, you know, accept the beta testers. So please bear with us. But yeah, beta testers. And then it will be open for everybody. And it is model agnostic. So any model of, you know, whatever you would like, even locally run models. And that's no problem.

A (16:22)

Awesome. So that's more tech. Foundry.com you'll see it below. But also M O O R E T e c h foundry.com. awesome. And, and as our listeners know, we have founders whom we feature that are all along the spectrum of in development, looking for or either looking for or soon to look for beta testers all the way to deployment and implementation. So that is great there. So we're now going to move to our rapid round, rapid fire round and I have four questions I'm going to ask. I'm going to ask the question and then you give me the first response that comes to mind, starting with what is the most overrated tech or AI trend?

C (17:10)

I think this might be, you know, maybe a cheap shot. But prompt engineering as a career, certainly a skill set, but not a career. You know, there's a lot of hats one has to wear when acting as a developer or scientist. And you know, I think that's kind of under a certain umbrella. Not an umbrella, if that makes sense.

A (17:30)

Got it. Now let's elaborate on that a little bit now because I hear some people saying, oh, you should definitely make sure that you are an expert in prompt engineering. And what you're telling me is not so much.

C (17:40)

Well, so you should be an expert at prompt engineering, but with AI, part of, part of what makes it so powerful is that it has the ability to tell you the unknown unknowns and articulate for you what you cannot. So by, you know, Kind of doing a static prompt engineering and leaving that to humans. You're not really. You're not. You're not giving the opportunity for things like the Ralph Wiggum loop where it can look back and see if that was the best way and then see if there's maybe a better way to articulate that, if there's a better way to optimize so that, you know. And again, it's not that it's not important, it's just as a whole specific person at the industry, I think that might be a little much.

A (18:25)

Okay, got it. Awesome. What about the most underrated AI tech trip?

C (18:31)

I think the underhyped AI breakthrough is obviously I'm going to be biased and say the one that I'm here promoting is the context architecture. So everyone debates kind of which model is the smartest, which capacity does it have, but nobody really talks about how the system preserves what it knows and, and how across a long, complex project. How does a brilliant AI lose everything between sessions? And that's useless if it does that, right? If it loses everything between sessions, or if you're building, let's say you're building something. And on feature one, well, that's fine. Feature two, that's a little bit much with all the decisions, all the change log, all the everything. By the time you get to feature 10, you have accumulated too much context to proceed. And normally, what people do in the real workforce is we disseminate and delegate and we keep notes and we track the state of projects, even scientific or even in a lab, it doesn't matter. You will keep track of the state of that in notes and in documents. And so that is, to me, it's as far as academia goes, research and industry, it. It is far behind, clean behind.

A (19:49)

I love that perspective. Now, what about a book we should all read and why?

C (19:56)

One book that I think everyone should read is the Innovator's Dilemma by Clayton Christensen. It's not about AI, but it's about how incumbents get displaced. So the technology, it always looks like trivial at first, but the disruption is. Is always obvious in hindsight. So we're in the looks trivial phase right now.

A (20:17)

Yeah, the Innovator's Dilemma. I love that book. I have read that and I concur. That is an amazing book.

C (20:23)

Awesome. Awesome.

A (20:24)

Yeah. And if y' all can go and look at any of his videos on YouTube or what have you. Amazing, too. Yep, absolutely amazing. All right. Scare us, make us think. What is one of the biggest, boldest predictions you have? Hmm.

C (20:41)

So I think the biggest, boldest predictions that I have are, honestly, I hate to say it. And if, you know, if we really want something scary, I think it will be the lack of barrier to entry for regular people to autonomous battle, battle drones. So now people can. With this. I mean, in theory, you could train a swarm of drones to do search and rescue, go look through an avalanche and go see this and that. But just inversely, you can hook guns up to them or even just something simple, make them run into somebody or anything in between, so we have no idea. And at scale, if people accumulate that, that will be a problem.

A (21:29)

So now that is an interesting prediction because some people, particularly when I have conversations with colleagues or friends about AI and like, AI for bad and is that possible, et cetera. And so you definitely are saying that is possible, right?

C (21:44)

Oh, yeah.

A (21:44)

Just like with anything, you can develop that. But with that prediction, what do you think about, like, I don't know, police surveillance or, you know, do you think that there's just as much muscle to kind of find that or find the culprits?

C (22:02)

No. So I think that with AI, I don't know if anyone watching or you are a fan of. It's called Mindhunter.

A (22:12)

Okay.

C (22:13)

It's about a man named John Douglas who worked for the FBI and he worked in their. He. He worked in their science division, basically for psychological sciences. And what they deduced by, you know, they interviewed, like, serial killers, and they interviewed people convicted of violent crimes. And what they deduced is that profiling isn't the answer. Profiling is not an effective way to preemptively stop crimes. So I think with AI, it's just going to inflate that. It's not going to. All of a sudden, profiling will become more of a valid way to preemptively stop things. Because with profiling and AI and training data, you are. You're limited by the. By the knowledge that you haven't had yet. And that's where intelligence comes from, is what one has to know, the depth of their ignorance to truly be intelligent.

A (23:09)

Got it. I love that. Absolutely love that. So, Damon, this has been a wonderful discussion. You're doing so much with more Tech Foundry, and even with your answer with the last question and this drone war fighting, etc, that really did leave us with a lot to think about. So how do we get in contact with you? What are your social media handles? Tell us all of that if we want to stay in contact, learn more about you as the founder, learn more about the company.

C (23:39)

So across the board, it'll be more Tech Foundry on all social medias more techfoundry.com and then I personally I like to make it a point to be in contact with my customers and with anyone looking to collaborate. So you can reach me it's on the website but you can reach me personally at damien@moretechfoundry.com and I will maybe can't get to every single request but I will field as many as I can.

A (24:06)

Awesome. I love when our guests, the founders themselves are open to being contacted so that's good. So again Damian thank you so much for being here. Can't wait to continue to hear how More Tech Foundry is changing the world and it has been a wonderful conversation. Thank you.

C (24:21)

Thank you absolutely.

A (24:24)

And everyone until next time remember to lead with AI. Bye.

B (24:29)

Thanks for tuning in to lead with AI. I'll see you next time as we continue exploring the cutting edge innovations shaping AI across the public and private sectors. Until then keep leading with AI.