Episode 440: The Future of Development: Is Software Disposable?

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Welcome everyone to another episode of Dynamics Corner.

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This is a very exciting episode in how we use AI in our day-to-day lives.

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I'm your co-host, Chris.

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And this is Brad.

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This episode was recorded on December 12th, 2025.

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Chris, Chris, Chris.

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Almost at the year end, and we're finishing up strong with this year.

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And here we are again with another amazing episode talking about AI, software development, business use, and the future of business with AI.

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With us today, we have the opportunity to speak with Matt Striploff.

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Nice to uh see you again.

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It's uh it's uh it's been a while, and I feel like the world has changed in that short while.

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We talked about that last time.

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It's like you know, for a short period of time, a lot has changed.

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That's exactly what we said.

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Who knows where the world would be with this in a few short months, and here we are, a few short months later.

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The world is still here, yeah.

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But man, what a whirlwind it has been.

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And I saw something this morning too.

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I think it was what a year ago today that agents, the concept of agents was introduced, I believe.

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Yeah, it can't it seems like just yesterday.

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It does, but I I mean it's but it's look at everything that's happened since then.

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It does seem like just yesterday, but look at all the advances of uh in technology that have happened since then.

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So oh yeah, you know, and I I know I was excited about all the AI first SDLC modeling work we're doing at Red Hawk, and we talked about that.

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Um it's great to see you guys, by the way.

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I'm excited to be back on your podcast.

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We had a blast last time.

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So uh the progress we made is just it's uh it's insane.

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Like I'll give you an example.

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Um, since we're talking about agentic and agents, we have successfully developed, deployed into production, and now have automated agentic workflow that handles the software bill of materials, detects uh common vulnerabilities and exposures in those libraries and packages, and automates remediation all the way up to the pull request.

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Wow.

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But stuff like the software engineers, I describe that as doing the dishes.

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You know what software engineers want to do?

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They don't want to do the dishes, I don't want to do the dishes.

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You can do it.

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You know what I mean?

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So we've automated.

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You can prep the meal.

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Don't want to do dishes, man.

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I didn't want to prep the meal.

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I just want to show up and enjoy it.

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But hey, that's that's that's it.

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It's um uh before we get into that, because there's a lot, uh I don't even know where to begin with this because I end, you know, knowing what you guys do over at Red Hawk, and you know, from the conversation last time, I wanted to I wanted to get your take on a few things.

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But before we get in that, can you uh tell us a little bit about yourself?

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Yeah, Matt Strippelhoff, uh CEO, co-founder of Red Hawk Technologies.

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Uh, we are a software consultancy.

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We develop, support, maintain custom business applications, uh, which is that takes a variety of forms.

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It can be uh agentic workflows like middleware solutions that sit in between systems.

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We used to call that middleware, but now we're calling it agentic workflows because AI is a big part of that flow.

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Um But uh we also develop a lot of custom field service applications.

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They might be uh web portals, mobile applications, things of that nature.

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Our primary focus is serving uh fast growing or growth-oriented privately owned mid-market businesses.

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Um and what a great time to be doing it.

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Because a few months ago, guys, I was like, maybe a year ago, so what's a what's AI gonna do to my industry?

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Yeah.

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But uh a lot I am speechless with what AI is doing to the industry and some of the stuff that I have seen firsthand since our last conversation.

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It comes up to I had some text messages this morning with some peers and individuals, and my question to them was who writes code?

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You know, it's just like if you think about that, it's it's it's come to the point where uh the amount of code that you write, in my opinion, is what I wanted to see in your uh from your perspective, what you're doing within your organization and maybe some of the other peers that you may have.

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Yeah, I I see there's a shift from the development cycle.

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And again, it's not global.

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I mean, I I've talked to some developers who haven't even used it yet, and I've talked to some developers who use it all the time.

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So I'm not going to say that this is what everybody's doing, but there's a general, I think generally, if you haven't started using it yet, I'd be a little concerned.

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Uh, and if you uh all in, great.

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A lot of people are still in that journey of ramping up or in the what I call like sort of in the middle still, right?

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So you have the the extremes and then you have the uh I'm still dabbling with it, uh, you know, maybe a little bit more, a little bit more than the autocomplete, right?

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Because everybody has that you have that cycle of you start working with AI and it just becomes as uh somebody that we talked with our teeny siders said, it's like a fancy auto-complete, right?

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And then it and then you progress up to a little bit more, then you start working with agents and you know, talking to the agent, saying, Okay, do this.

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And then you start getting into this real workflow of no, then you start creating instruction files, uh, agent files, and then you have uh multiple agents running in the background.

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Uh so it's it's you get all the way up there.

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But uh to bring that back to the thought of it is how much coding?

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Oh, okay.

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This is where it was.

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How much coding do you see being done?

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And I wanted to um, you know, from the development point of view, that's being done by the developer versus the AI.

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And of that, how much of it is more a splight adjustment of code versus all out coding?

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So I'm gonna frame my response based on uh two different types of projects.

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You've got production large scale applications that you are you've got a product roadmap and you're kind of building things out and you're you're um evolving that into a solution.

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The the percentage of code being generated by AI is at this point kind of a kind of a guess on my part, but maybe 50-50, maybe you know, where the the the software engineers primarily focused on being the orchestrator and the architect and then providing the appropriate level of context in order to get the intended outcomes.

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That's really where they need to focus their expertise now.

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Um when we're doing net new builds, it's probably more 80-20 where the agent's doing 80% of the coding.

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And the work that they're being done is is uh for example, we have we have eight uh high fidelity proof of concept projects uh that we're executing right now that we will wrap up by the end of December.

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And the uh the suite of AI tools that we're using are allowing us to maybe we're only doing 20% of the coding on those.

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And all the front end is gonna be done, by the way, uh uh as far as these proof of concepts go.

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And then when it's time for the back end, when you start building out the context and and putting together your plans, you can use AI to help with the planning.

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And depending on the systems that you're gonna be integrating with, like Dynamics, CRM, etc., or maybe it's Azure AI, what and we've got a number of those types of projects going on.

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You're gonna you're going to include uh MCP servers in that mix, which provides that additional context that's necessary for the AI tools to do the plans.

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And the way tokenization's working now with these tools, they can handle a significant more amount of context.

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And that was really the issue with these tools early on, is they start to lose track because they can only handle so much through tokenization, so much they can only retain so much context at any given point in time.

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And you start to get further and further and further away from the original strategic objective.

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Well, now your detailed project requirements documentation is part of that context.

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Everything that you've done to that point is part of that context, and your MCP servers are part of that context.

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So we're seeing more and more and more of the actual syntax being written by AI.

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It's it's it's insane.

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And to hear what you had said is sort of what I wanted to talk about.

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You you you unpacked a lot in there.

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Uh you you mentioned a lot of them, we have to kind of unpack it.

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Okay.

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So, first, the now with the context, the tokenization, you know, you have a uh a larger uh number of tokens, right, that you have within your context.

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Now, I I've just finished reading the book Vibe Coding by Stephen Yee.

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And geez, I can't forget the other author.

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I don't even know if I said his last name properly, but that's what I do.

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And they they summed up what a token is perfectly, because that's the first question a lot of people have that may or may not understand it.

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And it's it's a token's not a word, a token's not a character.

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It's really how they break down words, and they said on average, you can figure it's like four characters is a token, I believe, right?

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So when you think in the counting of tokens in your context, it's a certain number of characters, basically.

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And then they piece those together.

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I don't know how it does all that stuff, but it pieces it together like magic and it spits stuff out for you.

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So now you can have a larger context, and this is where it'd be able to create those instruction files or to have a lot of that information already available for the model to use or your agent.

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I don't even know what to call it.

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I tell you, I feel like the people because I'm even now I found myself the other day saying, Thank you.

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That was good.

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Like, okay, why did I just write that?

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I literally wrote thank you.

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That was good.

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Now can we, you know, add this to it?

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It's just it becomes sort of natural with it.

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So that's changed a lot.

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We can talk about MCP service, but you started talking about something more about software engineers, and that's where I was sort of going with where they are in the in the roads.

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It's where do you see the role of, you know, I use the word developer generically here.

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You know, you have software engineers, you have different titles, but somebody who develops code primarily for a living, where do you see that career trajectory based upon what we know today?

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And we all know that tomorrow it'll be a whole new world again.

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So um I think those that adopt the tools, learn how to extract the most value from the tools, understand which tools are applicable based on where they are in that from concept all the way through to execution cycle, that SDLC cycle, will be successful.

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But they gotta be willing to lean in and blow up traditional thoughts around software development.

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You just have to get your hands dirty and get in there.

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Those who are gonna be successful are gonna take the time to learn how to bring these tools together to deliver really what their job is, is to solve problems for their customers.

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So if they can shift their mindset to the intended business outcome and just recognize that their value is not attached to how much syntax they write in their code, that's the first thing they have to do is just make that mental shift and start to recognize that really their contribution is the solution, not the how they get to the solution.

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So I think back when there's all kinds of other examples of advancements in technology, it's just happened much, much slower.

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Um and I think last time that we talked, Brad, we talked about a a nail gun for a carpenter.

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You know, uh you gotta start using the tools that are available to expedite uh throughput.

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Um at the same time, a nail gun in isn't gonna make you a carpenter.

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So you still have to have the skills and the knowledge and the understanding from an architectural standpoint, sustainability standpoint, what's the right way to approach uh crafting a solution for a customer uh or specific business outcome that's scalable and sustainable.

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So we hear a lot of buzz about vibe coding, and you see a lot of really aggressive marketing from tools like Label and Revelit, and they're saying, you don't need engineers, you know.

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I built an app that just like Spotify in 30 minutes.

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That's not what they built.

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They built a prototype that probably doesn't have the the the uh any of the architecture in place to actually be sustainable in a commercial environment.

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Yeah.

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So again, your point just to to pick up what I've heard is it's not the tool that you use, it's the product that you deliver that becomes important.

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So the role of a developer is shifting to you still need to understand the code, you still need to understand architecture and design, but you're just going to use different tools to deliver the solution.

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Yeah, yeah.

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And we've not successfully been able to just use purely vibe coding in any effort.

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Um, not not all the way through to production.

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So uh we're able to use it for rapid prototypes like high fidelity prototypes concepts.

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Yeah, but then even then it it gets a it can get squirrely pretty quick.

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Like you might only be a couple hours into something and you can't figure out why the calendar selection feature is wonky from a visual standpoint.

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You're gonna tell it over and this is a specific example I had last week, by the way.

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I'm trying to get the days of the week to line up over the columns of the of the weekdays, you know, just on a little calendar selection feature.

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Doesn't matter how many times I tried to get it to correct that formatting issue, and it would you'd see it write code, oh yeah, I understand.

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Very kind to me.

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You know, yeah, that makes a lot of sense.

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So it's very complimentary, you know, nice conversation.

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And it would write code and it would render and say I fixed it, and it didn't fix it.

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So um ultimately you may find yourself, and this is what our experience has been, is that you connect uh the source code repository and then you start shifting which tools you're using until uh you find the tools that are gonna give you the best output.

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And sometimes you're actually in the code.

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Writing it.

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That's that 20%, right?

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So yes, no, it's I I think it but what I go with that too is so it's it's becomes delivering the solution.

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You're talking about prototyping.

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Yeah.

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How do you see it?

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It's how do you see it changing in the landscape of business in the sense that you have software engineers if you're delivering software, but then you also may have some business users or or business mindsets that are working with this.

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Where do you draw the line?

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Because now theoretically, you can have some business users that don't understand code be able to create some of these prototypes, right?

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For the developer.

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So if you're working, say, you know, I'm sitting with a customer and we're talking about something quickly, and they say, Oh, I have this idea.

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You can basically type up that idea and then create a prototype for them to then deliver over to a software engineer to further refine.

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So is it are we getting to the point where we're drawing those individuals closer?

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Right.

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So before you used to have software engineers and you used to have business, I call them business users, but it's not even maybe, you know, business consultants.

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And they used to talk to the development group and say, here's what I need.

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Can you design, develop, give me a prototype for it?

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They'd have to talk to them, they'd wait a period of time.

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And now it's okay, I can come up with a prototype.

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As I'm sitting with the customer, it functionally works, right?

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I mean, it's again, everything's in scale.

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It depends on what you're writing and what you're doing.

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If you want to create, you know, a simple web page that you can enter some fields and save something, I'm sure AI can do most of it, right?

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Because I've been able to do some of that stuff quickly.

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If you want to do some other uh more advanced stuff, again, it's not when for the sake of this conversation, but you know, it's it's not a one-size-fits-all when we're talking about situations and scenarios.

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This is more some cases generalizations because there's always an edge case for everything.

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Um, but are we bringing those two worlds closer to where a software engineer, instead of being the person that was sitting in the back writing this code, not wanting to talk with anybody, has to become more business consulting aware to be able to talk with business users instead of needing someone to translate it in the middle.

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And the business user is becoming more familiar with technology and the tool because now they can talk to an agent almost like they spoke with a software engineer.

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Yes.

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And I I I what I would recommend to anybody in the audience that maybe is either in the process of getting an education and and investing in developing software development skills is uh it's as important, if not more important, going forward in this career path to become a business analyst.

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That's really because we we talked last time, uh Brad and Chris, I think we were talking about the the you know the future, the most popular uh software programming language is going to be whatever your natural language is.

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That's we're pretty much there in a lot of ways.

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So um connecting the subject matter expert, maybe it's the it's a senior leader.

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Like for example, I was I'm working with a CEO at a company uh currently, and in two weeks we turned around a prototype that will expedite their workflow so significantly, his mind was just blown.

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Um because his uh the way he describes what his business needs as a CEO is he's thinking strategic outcome uh growth without having to, you know, you gotta you want to outsize your growth compared to your operating expense to create that desired margin.

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CEOs and you know, business leaders are thinking along those lines.

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How do I scale in a way that's sustainable?

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And right now my team is doing a great job, but at this current scale, it's okay that they've got manual workloads and spreadsheets, right?

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But if you're about to go down the path of stacking on five, six companies a year through acquisitions, guess what's not sustainable?

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Living in spreadsheets because now you're gonna multiply the manual labor and efforts and the issues that come from copy paste errors, etc.

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Um, through that process.

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So this is a way for me to kind of come back to answering your question is it depends on um the leader in the organization and their role as to how they're gonna describe the business outcome they're trying to get to.

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And a really good strategically minded analyst is gonna be critical in developing custom software applications, somebody who can have that conversation with the CEO and say, okay, I understand that.

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Because if you're um in some cases, if you're down at a director level, uh maybe an operator level, somebody, somebody who is um adopted the standard operating procedures that are those manual workflows, it could be very well documented, by the way.

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And their personality profile, because you know, as a leader in my organization, we're very aware of personality profiles and where people might fit based on their natural inclinations.

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Like what do they like to do?

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Are they black and white rule followers?

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If if you and you want black and white rule followers who who are just gonna adhere to the SOP and they get really uncomfortable if they're asked to do anything outside of that routine.

00:19:51.519 --> 00:19:57.839
So if you're talking to somebody at that level and they're defining what they want in the software, you're gonna replicate the manual process.

00:20:00.319 --> 00:20:04.720
Eliminate error probably copy paste errors because you're going to do some level of integration.

00:20:05.359 --> 00:20:08.720
But is it really going to achieve what the CEO's strategic vision is?

00:20:08.880 --> 00:20:11.359
Like in the in this particular example for the CEO I'm working with.

00:20:13.200 --> 00:20:20.160
He has uh analysts that can only handle two projects uh per week maximum.

00:20:21.359 --> 00:20:34.720
And each project takes a significant amount of hours because of the level of research they have to do and and the process of which they develop uh the results and ultimately they're deliver deliverable for their customers.

00:20:35.759 --> 00:20:38.079
That's a major scaling issue for them.

00:20:38.640 --> 00:20:45.680
And his uh KPIs for success are gonna be I want them to be able to handle six or more uh projects per week.

00:20:47.839 --> 00:20:49.119
Without increasing the expense.

00:20:50.079 --> 00:21:03.519
Well, yeah, he's also able to articulate his cost per project, which um in conversation with him, and this is another way to think about how AI impacts software engineering with without going beyond just uh things like cursor, right?

00:21:04.000 --> 00:21:10.799
Uh is in the interview process with the CEO, I'm capturing all the conversation in transcripts.

00:21:12.400 --> 00:21:12.559
Yeah.

00:21:12.799 --> 00:21:21.200
Then I'm taking all that information and I'm bibing with an AI tool to create the detailed project requirements document, which I need as context.

00:21:22.640 --> 00:21:28.640
But I take I add another step in the process, which is where you get some really strategic outcomes, which is fascinating to me.

00:21:28.880 --> 00:21:41.759
So I take the PRD, I take all of the artifacts from the client, including their SOP documentation and sample outputs, and the transcripts for my conversation as to what's important to him, what is really the business goal.

00:21:41.920 --> 00:21:48.480
And I'm going beyond him describing features and functions, because we tend as human beings to start with the solution and not the problem.

00:21:49.359 --> 00:22:04.799
So if you can get somebody in that flow, whether it's a BA or a really strategic-minded software engineer, they need to work with the stakeholders to understand the business problem and desired outcome, and then shift the conversation back to features and functions.

00:22:04.880 --> 00:22:10.720
But let's use AI to recommend some features and functions based on all that context.

00:22:11.359 --> 00:22:23.200
Yeah, that's a great point that you uh you called out because um right now uh I've I've spoken to uh different individuals where they're using and to going back to Brad's question is will it blur the line?

00:22:23.279 --> 00:22:32.960
I think it's gonna bridge that line between a business analyst in your case and a developer where they have a better understanding because in between them would be an agent, right?

00:22:33.119 --> 00:22:36.400
Where an AI would help kind of help bridge that gap.

00:22:36.640 --> 00:22:54.799
In addition to that, you can take a functional design document, like as you had said, take all that content and put it in this functional design document where a business analyst can vibe code, I guess, to get the concept put in place and have AI help you build a good portion of that.

00:22:54.960 --> 00:23:05.039
And then when you want to move forward and say, hey, I think this is gonna meet our results, that's where it's helped it helps with the developer have a better understanding of the results that they're expecting.

00:23:05.119 --> 00:23:09.599
And so their focus would be how can I contribute to the result that they're expecting?

00:23:09.920 --> 00:23:12.720
Yeah, it gives everybody context as to why we're doing this.

00:23:13.039 --> 00:23:13.440
Exactly.

00:23:13.680 --> 00:23:14.400
Yeah, yes, yeah.

00:23:14.480 --> 00:23:16.079
And so the Precisely, yeah.

00:23:16.240 --> 00:23:27.519
Yeah, so so it one little example here to tighten this up a little bit for this example I'm sharing with you guys is I took all of that context and in this example I used a chat GPT account.

00:23:27.599 --> 00:23:33.440
Um no, actually I used Gemini, the most re Gemini 3.0 uh Redhawks account.

00:23:33.599 --> 00:23:36.000
I put the project requirements document.

00:23:36.160 --> 00:23:42.480
It was less functional, it was more project specific, which identified all of the types of users, features, functions that they needed.

00:23:42.559 --> 00:23:45.759
But I combined that with all the transcripts from interviewing the CEO.

00:23:47.680 --> 00:23:54.880
And then instead of saying, give me prompts I can use in my vibe coding tool to create the prototype, I added a step in the middle.

00:23:56.559 --> 00:24:04.559
And the step in the middle was I acknowledged that the tools that we're using are automatically gonna create a landing page after you log in.

00:24:05.519 --> 00:24:06.000
They just are.

00:24:06.079 --> 00:24:08.960
They have to just if you don't define it, it's just gonna make something up.

00:24:09.839 --> 00:24:11.759
The user logs in, this is what we're gonna show them.

00:24:12.559 --> 00:24:18.880
Generally, it's gonna be some kind of metrics, whether they're important to that user or not, but it gives you something to work with.

00:24:19.519 --> 00:24:36.240
Instead, what I did is I said uh taking in context as to uh all this information, all the sources provided, and understanding the core business objectives, recommend a landing page for the analysts once they log in.

00:24:36.880 --> 00:24:45.200
Jim and I came back and said, Wow, considering, and I'm not kidding, it's like wow, you know, it's very complimentary, which cracks me up in these tools, by the way, guys.

00:24:45.279 --> 00:24:47.519
It's like they're I told you you feel like you're talking with a person.

00:24:47.759 --> 00:24:50.079
I feel like I'm talking to a person and they really like me.

00:24:50.160 --> 00:24:52.400
They're very encouraging and they love all my ideas.

00:24:52.480 --> 00:24:54.799
So it just makes me feel good about myself the whole time.

00:24:55.119 --> 00:24:55.839
They motivate you.

00:24:56.000 --> 00:24:57.599
Man, they motivate you for sure.

00:24:57.759 --> 00:25:00.400
But it I'm going to tell my after this, I'm going to tell mine.

00:25:00.640 --> 00:25:01.519
I don't want to interrupt your story.

00:25:01.599 --> 00:25:05.440
I'm going to tell mine to talk to me a little less friendly.

00:25:06.000 --> 00:25:07.039
I wanted it to yell at me.

00:25:07.119 --> 00:25:08.000
I want to see what it does.

00:25:08.160 --> 00:25:11.680
I'm actually going to put that in the instruction file or in the agent file.

00:25:12.000 --> 00:25:13.359
I'll do it in one project.

00:25:13.440 --> 00:25:15.839
I'll just put like in one of the copilot instruction files for the project.

00:25:15.920 --> 00:25:19.599
And I say, you must be mean to me and you must call me bad names.

00:25:19.839 --> 00:25:21.519
You're going to have to let us know what that's like, man.

00:25:21.920 --> 00:25:22.400
I'm going to.

00:25:22.720 --> 00:25:24.480
We'll listen to what it's like because that's what I told you.

00:25:24.559 --> 00:25:26.400
I'm like, I'm like, thank you, and I'm doing this stuff too.

00:25:26.480 --> 00:25:27.359
And I just wanted to say.

00:25:27.599 --> 00:25:28.240
Oh, you suck.

00:25:28.319 --> 00:25:29.200
Why are you being so stupid?

00:25:29.440 --> 00:25:31.519
You should have David Goggins voice that.

00:25:31.759 --> 00:25:32.400
Yes.

00:25:32.960 --> 00:25:33.680
There you go.

00:25:33.759 --> 00:25:38.799
I'm I'm going to use that, but I'm going to use a prompt that says, uh, hold me accountable and don't and be rough.

00:25:42.720 --> 00:25:49.440
So anyway, the outcome that I got for that analyst landing page, guys, it blew my mind.

00:25:49.599 --> 00:25:50.960
And it was just a recommendation, right?

00:25:51.039 --> 00:26:11.599
It wasn't the prompts I needed for the uh for the proof of concept, but it said, considering the CEO's business objective of being able to increase velocity in terms of how many projects an analyst can happen per week, you don't really need a dashboard with KPI measurements, but you need to gamify the experience.

00:26:11.839 --> 00:26:18.319
I would recommend a Kanban board that shows them the status for each of their projects based on their flow.

00:26:18.480 --> 00:26:26.640
And let's show them a velocity meter so they know that they're operating within the S the new service level agreement they have with the CEO.

00:26:26.880 --> 00:26:34.799
And then we can even color code some of those cards, and then we can give shortcuts and call out projects that are at risk of violating that new SLA with the CEO.

00:26:34.880 --> 00:26:37.200
So we're driving the right behavior.

00:26:37.920 --> 00:26:40.559
So AI gave me that recommendation.

00:26:41.359 --> 00:26:44.640
Isn't that insane, though, if you think about it one?

00:26:44.880 --> 00:26:46.799
Yeah, you mentioned documentation.

00:26:47.039 --> 00:26:53.599
I get far better documentation from the AI than any I've seen from any developer.

00:26:53.759 --> 00:26:56.240
That I'll come out and say like across the board.

00:26:56.720 --> 00:27:01.440
And two, it comes up with ideas and suggestions that you don't even think about.

00:27:01.519 --> 00:27:01.759
Yeah.

00:27:01.920 --> 00:27:03.200
And it executes them.

00:27:03.359 --> 00:27:07.200
So it's it's it's baffling to me.

00:27:07.279 --> 00:27:10.960
And I also sit here because I do talk to some that, well, AI, I can do this.

00:27:11.039 --> 00:27:11.839
Oh, I got to do this.

00:27:11.920 --> 00:27:14.240
It's gonna take me 160 hours to do this.

00:27:14.400 --> 00:27:21.680
And I'm sitting there, I scratch my head sometimes and said, just use AI because it will even give you ideas that you didn't even think of.

00:27:21.839 --> 00:27:22.079
Yes.

00:27:22.480 --> 00:27:24.160
That's the time saver right there.

00:27:24.240 --> 00:27:35.200
Where and can you imagine if you, you know, can you imagine if you were to do that without AI right now, the amount of time that you'd have to take and translate what they're trying what they mean.

00:27:35.440 --> 00:27:39.119
And then you're just at based on the fact alone.

00:27:39.440 --> 00:27:44.079
And you're not thinking of outside the box at that at that moment, not yet, right?

00:27:44.240 --> 00:27:48.960
Until you start speaking to someone, you may need to step away and talk to others.

00:27:49.279 --> 00:27:52.079
Here, you can have that within an hour.

00:27:52.160 --> 00:27:53.279
You can have a full conversation.

00:27:53.440 --> 00:27:56.559
Say, hey, have you thought about this and that based on this context?

00:27:56.880 --> 00:27:59.759
You could be up and running fairly quickly in the amount of time.

00:27:59.839 --> 00:28:04.319
So that's like your output has significant significantly increased.

00:28:04.640 --> 00:28:06.400
Yeah, I uh boatload.

00:28:06.799 --> 00:28:07.359
A boatload.

00:28:07.440 --> 00:28:22.000
Yeah, I had uh probably six hours of effort to get to the and that includes interviews with the CEO, um, some QA via email threads, receiving standard operating procedures and documentation.

00:28:22.079 --> 00:28:25.279
So I gathered all that context all the way to the PRD.

00:28:25.519 --> 00:28:27.440
So less than a full day for the work.

00:28:27.599 --> 00:28:27.839
Okay?

00:28:28.160 --> 00:28:30.000
You don't have to read the SOPs.

00:28:30.799 --> 00:28:31.039
No.

00:28:31.279 --> 00:28:37.920
I mean I got I got context and I have some QA around those SOPs from two one-hour sessions in the interview process.

00:28:38.799 --> 00:28:39.119
Yeah.

00:28:39.359 --> 00:28:40.160
But that was it.

00:28:40.240 --> 00:28:48.240
And then um I think I'm roughly, you know, the team roughly 35 hours into the clickable prototype.

00:28:48.640 --> 00:28:51.680
And what would that have been before based on this?

00:28:52.480 --> 00:28:54.480
Oh, uh 120 hours.

00:28:54.799 --> 00:28:56.720
So that you I you got 25%.

00:28:57.440 --> 00:28:59.039
At least, guys, at least.

00:28:59.200 --> 00:29:11.039
Um, what's fascinating to me as well, this is something that I think people need to be thinking about, is the vibe coding tools that they choose for these high fidelity prototypes.

00:29:11.680 --> 00:29:17.680
Think about those um that have more full tech stacks behind them.

00:29:18.240 --> 00:29:19.440
So I'll give you an example.

00:29:19.599 --> 00:29:26.799
Right now, the darling for us right now is is uh Firebase Studio, which is part of the Google Gemini uh stack.

00:29:27.119 --> 00:29:32.319
The reason I like that is Gemini as a large language model is already built into that tech stack.

00:29:32.960 --> 00:29:34.480
So let me give you an example.

00:29:34.640 --> 00:29:37.119
And this is part of that workflow for this analyst.

00:29:37.680 --> 00:29:48.559
They have to go out and do web searches to find providers that match the detailed criteria that was collected from the client during the intake process.

00:29:49.839 --> 00:30:01.200
So using Gemini in combination with Firebase Studio and asking for ideas and suggestions, it's like, oh, well, why don't you know let's have an agent built into this workflow at various stages.

00:30:02.160 --> 00:30:25.359
So the um it will automate the prototype we've demonstrated successfully at this point, it will automate the searches in multiple languages, by the way, because they provide service to global companies, to find providers that are best match, and then it scores best match based on client criteria to shorten the cycle that it takes to select the providers that you want to ask for proposals.

00:30:26.799 --> 00:30:34.960
Then you can it'll draft your emails, making sure that you don't inadvertently leave out any critical details when you're asking for the proposal.

00:30:35.119 --> 00:30:41.599
So it takes client intake information, puts that in, and then you can tweak each one of those drafts and then hit send.

00:30:41.680 --> 00:30:41.920
Okay.

00:30:42.160 --> 00:30:49.680
What's really crazy is the workflow that we built out in this prototype will also connect to Outlook.

00:30:50.240 --> 00:31:02.400
And the agent is analyzing the responses from the providers, and you got a communication inbox inside of this portal, and it's using natural language processing through Gemini because it's part of the tech stack.

00:31:02.720 --> 00:31:07.759
And it's highlighting communications that it believes contains a proposal response.

00:31:08.160 --> 00:31:21.839
It's also highlighting things that uh it believes are changes in project scope because the client's saying, responding via an email thread saying, Oh, I need X, not real, it's this number's wrong that needs to change to this number.

00:31:22.079 --> 00:31:24.720
So their in-client intake requirements change.

00:31:25.359 --> 00:31:27.200
So you're using Gemini.

00:31:27.519 --> 00:31:30.720
You could use AI to ingest that into the next part of your workflow.

00:31:30.880 --> 00:31:38.480
It's like, man, when I showed this to my client, guys, he was like, that's my mind every day.

00:31:39.039 --> 00:31:40.000
You're using Gemini.

00:31:40.400 --> 00:31:50.000
You but um so I've been using Notebook LM, so I've Gemini as well, but majority of my deep search uh recently I've been using Notebook LM for a couple months now.

00:31:50.079 --> 00:31:55.119
Yeah, and it's fantastic because it can create like uh uh cards for you, right?

00:31:55.200 --> 00:31:57.920
Like even like a PowerPoint, I guess, cards.

00:31:58.319 --> 00:32:06.559
Um and so is that what your is that where you contain all your stuff, or you is it just the the Gemini chat?

00:32:06.960 --> 00:32:12.559
I'm using a sequence of different tools based on where I am from sales all the way through delivery.

00:32:12.720 --> 00:32:14.799
So I'm trying to figure out which tools work best.

00:32:14.960 --> 00:32:23.519
So I will shift things and I will take context and move it from one tool to another because I'm figuring out which ones give me the best output.

00:32:23.839 --> 00:32:25.519
And that is the key.

00:32:25.680 --> 00:32:25.920
Yeah.

00:32:26.079 --> 00:32:30.640
This is see, this is one thing you just hit upon, is is there's not one tool.

00:32:30.880 --> 00:32:32.960
It goes back with the concept of what we're doing.

00:32:33.119 --> 00:32:40.240
I listened to the Joe Rogan Jensen Wang um podcast interview.

00:32:40.640 --> 00:32:45.839
Uh not everyone listens to Joe Rogan, they may not like him or whatnot, but listen to that interview.

00:32:46.160 --> 00:32:55.680
He went down this road kind of a little bit about where AI and using the tool, then everyone's talking about where is my job going to be.

00:32:55.920 --> 00:32:58.240
Listen to that episode and he sums that up.

00:32:58.319 --> 00:33:06.799
But that's the key right there is knowing which model to use for which type of output and when to use the tools.

00:33:07.039 --> 00:33:14.960
See, this is sort of where I wanted to touch upon as well, too, because it's not that tool, just like a uh a carpenter building a house.

00:33:15.039 --> 00:33:19.359
I equate software to building a house because I think it's a good analogy.

00:33:20.000 --> 00:33:27.440
You don't always need a hammer, you don't always need a screwdriver, but a builder is going to have a hammer, a screwdriver, a paint roller.

00:33:27.759 --> 00:33:33.519
You have all these tools, and they know when to use the tool because that's the most effective tool for it.

00:33:33.759 --> 00:33:44.960
And that's where I think the emphasis will need to be versus trying to know all the coding, trying to stay within this one portion of it.

00:33:45.119 --> 00:33:55.599
Because also, I think where you get you go back to what you had from the prototype, we're getting into the world where you used to have software engineers, they only know what they know.

00:33:55.680 --> 00:33:55.920
Yeah.

00:33:56.079 --> 00:33:59.359
And they're going to develop within that box of what they know.

00:33:59.759 --> 00:34:12.159
Unless they're really energetic and they go out exploring and seeing how things can be done and how people do things, typically they're going to give you estimates on time based on what they know, based on the technology that they know.

00:34:12.480 --> 00:34:17.599
This right here to me opens up because uh Chris and I had a conversation with a guest the other day.

00:34:17.840 --> 00:34:19.199
I did some vibe coding.

00:34:19.360 --> 00:34:21.519
It presents things to you you don't even think of.

00:34:21.679 --> 00:34:21.840
Yeah.

00:34:21.920 --> 00:34:24.559
And you're like, how does it like, how?

00:34:24.960 --> 00:34:26.000
Where does this come from?

00:34:26.159 --> 00:34:27.039
How does it come from?

00:34:27.199 --> 00:34:28.800
So it's almost like you have peers.

00:34:28.880 --> 00:34:29.039
Yeah.

00:34:29.199 --> 00:34:35.119
And then the different models will give you different suggestions using this loosely, right?

00:34:35.360 --> 00:34:45.440
Um I'm not bucketing stuff into it like to try to be really precise, but it's it's crazy, mind-numbing, jaw-dropping.

00:34:45.519 --> 00:34:48.079
I don't even know how to summarize it.

00:34:48.239 --> 00:34:50.480
It's I I just don't even know.

00:34:50.559 --> 00:34:54.800
Uh sometimes I think it's reading my mind in some areas.

00:34:55.119 --> 00:34:55.599
Yeah.

00:34:55.920 --> 00:34:57.519
It's so crazy, man.

00:34:57.679 --> 00:34:59.840
Like we uh one of my principal engineers.

00:35:00.000 --> 00:35:12.079
So I think a little tip for the for the audience here is it is particularly business leaders, you want to be successful with figuring out which tools to use in in throughout your cycle within the business.

00:35:12.239 --> 00:35:17.360
You gotta be willing to carve out some investment, give people permission to fail.

00:35:17.599 --> 00:35:20.960
Maybe you need some skunk works projects, things like that.

00:35:21.199 --> 00:35:30.079
Um, seriously, you know, because it's it's and you need to you need to figure out what your knowledge transfer and adoption plan is gonna look like once you get those key learnings.

00:35:30.400 --> 00:35:37.840
You know, we've been on a uh AI-first SDLC modeling effort for the past probably four or five months.

00:35:38.559 --> 00:35:40.559
And something I haven't even talked to you guys about.

00:35:40.639 --> 00:35:45.119
Maybe we talked about it briefly in our last uh last conversation.

00:35:45.360 --> 00:35:51.599
We just rolled out our own custom ERP for professional service organizations serving Redhawk first.

00:35:52.960 --> 00:35:53.360
Wow.

00:35:53.599 --> 00:35:58.639
It was a 90-day turn, 230 engine engineering hours.

00:35:58.880 --> 00:36:05.760
Now, when you're a professional services organization like Red Hawk, your resources in that ERP are your staff.

00:36:06.960 --> 00:36:21.280
And the attributes of each of those resources are their skill sets and uh uh in relationship to the tech stacks that they work with and how they score one to ten in terms of level expertise against those tech stacks.

00:36:23.360 --> 00:36:27.039
We already had all of our flows in place and all of our data.

00:36:27.119 --> 00:36:28.559
So let me just preface it that way.

00:36:28.719 --> 00:36:30.800
Our data was super clean and ready to go.

00:36:31.440 --> 00:36:43.360
Um, but uh now um our uh project managers, our principals, the C-suite, we're all using Flight Deck on a daily basis.

00:36:44.960 --> 00:36:46.079
It's in production.

00:36:47.039 --> 00:36:47.199
Wow.

00:36:47.519 --> 00:36:59.760
We can accurately forecast revenue out over 90 days, generally because we're you know the PMs are responsible for working with the the clients and understanding what's in the roadmap, what's gonna happen each sprint.

00:37:00.159 --> 00:37:07.840
So we're forecasting that work, but then you know, resource planning for us is making sure the right resources are available at the right time with the right skill set.

00:37:08.239 --> 00:37:17.840
So attributes on our client software assets are the tech stacks and the skills required in order to deliver on you know providing sport maintenance and enhancement services.

00:37:18.079 --> 00:37:20.960
Well, that all just allows us to do skill gap analysis.

00:37:21.440 --> 00:37:25.440
Like I can tell you at any point in time where there's risk and opportunity in my business.

00:37:26.159 --> 00:37:33.760
I know who I need to hire next, I know who's overloaded, um, and we also have all of our time entry data.

00:37:34.639 --> 00:37:37.119
So we're managing contracts and all this stuff.

00:37:37.199 --> 00:37:38.400
So this will blow your mind.

00:37:38.960 --> 00:37:39.920
It blew my mind.

00:37:40.639 --> 00:37:51.519
Uh three weeks ago, uh my CTO who's functioning as a product owner on this initiative, he knocks on my office door, he's like, Matt, dude, I hope you're sitting down, I gotta show you something.

00:37:51.920 --> 00:37:59.280
So he brought up the development environment of our ERP, and he said, navigate to the analysis tab, and I did.

00:37:59.360 --> 00:38:01.840
And he goes, Click on a hawk's eye view.

00:38:01.920 --> 00:38:06.480
And if I click on it, and it gave me a couple of parameters to select.

00:38:06.559 --> 00:38:10.559
It's like select the date range, and I just selected the last 30 days, rolling 30.

00:38:11.119 --> 00:38:20.800
And then I could select uh um account representative or leaders within the organization to kind of filter down the data set if I wanted to, but I just left it just tell me everything about the business.

00:38:22.480 --> 00:38:31.440
It generated an executive report that had different cards for different types of information.

00:38:31.920 --> 00:38:45.519
So it identifies where we're performing at a high level, it identifies where there's risk, it makes strategic recommendations about areas to focus on based on client volume and work and where maybe comments use the natural language, right?

00:38:46.000 --> 00:38:48.559
Um, where things maybe need to course correct.

00:38:50.239 --> 00:38:53.039
Uh it's it blew me away.

00:38:53.119 --> 00:38:56.480
And I asked Ron, I said, how long did it take to create this?

00:38:57.119 --> 00:39:04.159
And he said, well, it just occurred to me that you're, you know, you always need this high-level CEO level summary of state of the business, right?

00:39:04.400 --> 00:39:14.480
So I just went in to the tool that we're using and I said, Hey, uh we need a dashboard for the CEO that kind of gives him the state of the business.

00:39:14.800 --> 00:39:19.679
He didn't define the cards and how the information was going to be analyzed.

00:39:20.239 --> 00:39:23.440
You know, Jim and I was already built into this platform, right?

00:39:24.559 --> 00:39:26.400
And this is no exaggeration.

00:39:26.639 --> 00:39:30.960
It took him five minutes, and that included the time to think of the prompt he was going to use.

00:39:31.760 --> 00:39:32.719
That's wild.

00:39:33.039 --> 00:39:34.159
That's like everyone's dream.

00:39:35.119 --> 00:39:39.679
I just I I laugh at that because uh I just laugh at that.

00:39:39.760 --> 00:39:47.280
I mean, you you hit some key points, and I laugh at it because it's not that it's funny, it's mind blowing, and I laugh at how many businesses are struggling.

00:39:47.440 --> 00:39:57.440
They think that they can't they think that the individuals, they think the teams can do so much I don't want to say better in a sense that having the people do it, but they take so much longer.

00:39:57.599 --> 00:39:59.519
And what I'm seeing in some cases.

00:40:00.639 --> 00:40:06.320
Is this technology now allows you to adapt faster?

00:40:06.480 --> 00:40:12.079
Because before it would be to you told your CTO of their the sponsor of the project, I need this dashboard.

00:40:12.159 --> 00:40:13.280
This is what I need in the dashboard.

00:40:13.360 --> 00:40:14.559
Okay, we'll go back and develop it.

00:40:14.719 --> 00:40:18.159
It takes three months to get it done, two months to get it done, whatever it may take to get done.

00:40:18.320 --> 00:40:18.480
Yeah.

00:40:18.719 --> 00:40:25.199
In three months, that may no longer be relevant because the world changes so fast.

00:40:25.360 --> 00:40:29.039
And it and it's it's not just software business, it's other businesses too.

00:40:29.119 --> 00:40:33.119
And I this I do want to touch on AI outside of software development.

00:40:33.199 --> 00:40:36.159
Uh, but the a few other things with software development I want to touch on too.

00:40:36.320 --> 00:40:45.840
But that's what like some of these per, and that's where I kind of laugh is we people don't focus on how we can use these tools to be able to do what you had just mentioned.

00:40:46.000 --> 00:40:47.360
Give me this Hawk's eye view.

00:40:47.440 --> 00:40:48.400
I like that name, by the way.

00:40:48.559 --> 00:40:49.360
I know where it comes from.

00:40:49.760 --> 00:40:51.199
I picked that up, by the way.

00:40:51.440 --> 00:41:05.440
Um how you can adapt to the how you need to look at the information based on how you see it today, versus having to say, I would like this dashboard, and then you have to turn around and wait.

00:41:05.519 --> 00:41:12.880
And oh, by the way, there's been an economic shift because tariffs were implemented or taxes had gone up or something had gone on.

00:41:12.960 --> 00:41:17.280
And now all of a sudden, because you remember the tariffs, and again, it's not a conversation for for or against.

00:41:17.440 --> 00:41:22.400
I can't tell you how many people I spoke with that said, okay, now we need to factor in and develop something for tariffs into our system.

00:41:22.559 --> 00:41:22.880
Yeah.

00:41:23.119 --> 00:41:27.280
Whereas if you have stuff like this, for example, you may be able to adapt that quickly.

00:41:27.599 --> 00:41:42.880
Now, so that's where I kind of laugh at uh I think Chris knows why I'm laughing, but um with this and you being able to put this out there um to kind of consent this back a little bit.

00:41:43.199 --> 00:41:45.519
Where do you see software going?

00:41:46.000 --> 00:41:56.239
Because now is software becoming disposable because now at that point of I can sit here, case in point, I talked with somebody this morning.

00:41:56.320 --> 00:42:02.639
We had uh we're going to be talking or having a conversation on something, and they prototyped and whipped up something.

00:42:03.119 --> 00:42:06.960
By the time I finish saying what I think we should do.

00:42:08.000 --> 00:42:14.239
If we can generate software at that scale, what happens to software?

00:42:14.639 --> 00:42:17.280
Software becomes, you said it, disposable.

00:42:18.559 --> 00:42:27.119
You may you may have a specific strategic need that software can help you with that you never would have invested in before because of the time and effort required to build that software.

00:42:27.599 --> 00:42:30.480
But now you can build it so quickly that it's okay.

00:42:30.559 --> 00:42:36.000
We're gonna start to see disposable applications that maybe only have a 90-day shelf life.

00:42:36.159 --> 00:42:42.159
Maybe just in time kind of software that you need for a certain period of time.

00:42:42.239 --> 00:42:54.400
You perfectly put well put 90 days, it could be 90 days, it could be 30 days I just needed it for this month without the massive investment that uh you know the old school uh approach would have been.

00:42:54.719 --> 00:42:55.199
Yeah, yeah.

00:42:55.280 --> 00:43:02.719
So so for example, where I think opportunity for disposable software might maybe come into play are fast growing companies and on the acquisition track.

00:43:03.039 --> 00:43:04.000
Yeah, startups.

00:43:04.320 --> 00:43:08.320
Startups, you know, it it if you're um or companies like Red Hawks, for example.

00:43:08.480 --> 00:43:12.800
So uh, you know, part of our growth strategy is acquisition, we're gonna buy other software engineering firms.

00:43:14.000 --> 00:43:29.119
So if I've got a platform that has all of our way of doing things, and then I can dump in all of their contracts, because if I'm gonna buy a software engineering firm, which I've done before, uh we want to we're buying their assets, we're buying their book of business.

00:43:29.760 --> 00:43:30.480
Okay, right?

00:43:30.639 --> 00:43:32.880
So I've already got the individual components.

00:43:33.199 --> 00:43:43.760
So it wouldn't take much effort at all for us to build a platform that supports the integration of the company we just acquired to reduce the integration time from call it six months to nine months to a month.

00:43:44.159 --> 00:43:54.639
If I could dump in all the client contracts, right, and we already have the agentic workflow that actually, Brad, you'll appreciate this, we can create documentation on existing business applications.

00:43:55.840 --> 00:44:10.559
We deploy an agent that does that for us, and that agent then hands it off to another agent who that identifies the bill of materials and all the CDEs, and then we get detailed action reactionable reports on how to remediate that software application.

00:44:10.880 --> 00:44:31.199
So if I can take the contracts and drop them in, and I can connect the repos for all the customers that they're serving into that workflow, then on the other side of that, it's gonna tell me strategically, I think it's like another version of a Hawks I view where do our team focus their initial efforts and to support that integration path.

00:44:31.599 --> 00:44:37.840
To me, that's a disposable application in the sense that um if I'm only gonna buy one company, I only need it one time.

00:44:38.800 --> 00:44:44.000
But I'm willing to make that investment because I know my team can build that probably in a you know pretty short order.

00:44:44.079 --> 00:44:47.199
I've already got all the agents and the the pieces built out.

00:44:47.920 --> 00:44:55.039
Um so integration is a big investment that's part of an acquisition strategy for any business.

00:44:55.119 --> 00:45:00.400
So I can see that as being a really uh good example of a bespoke disposable application.

00:45:00.719 --> 00:45:03.360
And maybe you repurpose it and spin it back up when you need it.

00:45:04.400 --> 00:45:05.039
Yeah.

00:45:05.599 --> 00:45:06.079
Yeah.

00:45:08.320 --> 00:45:09.840
I'm geeking out, guys, man.

00:45:09.920 --> 00:45:10.880
This is too much fun for me.

00:45:11.199 --> 00:45:11.840
No, no, no.

00:45:11.920 --> 00:45:31.679
I I I'm almost speechless in a sense because I agree with you and I see so many things, it becomes now I was going to purchase something the other day to do a task for software, and then I paused for a moment, and now I'm like, it was it was forty-eight dollars for a year subscription.

00:45:31.920 --> 00:45:32.559
Oh, okay.

00:45:32.719 --> 00:45:34.320
Yeah, which is not a lot of money.

00:45:34.559 --> 00:45:36.559
I'm just trying to say this is like where it's going, right?

00:45:36.639 --> 00:45:41.119
It's it was$48, and I'm like, oh, maybe I can just vibe it.

00:45:41.360 --> 00:45:41.760
Yeah.

00:45:42.000 --> 00:45:43.119
And I did.

00:45:43.360 --> 00:45:43.760
Yeah.

00:45:44.320 --> 00:45:46.400
And it took four minutes.

00:45:46.960 --> 00:45:47.519
Yeah.

00:45:47.760 --> 00:45:48.239
There you go.

00:45:48.480 --> 00:45:49.280
So that's where I'm saying.

00:45:49.679 --> 00:45:51.119
And then it met your needs, and then okay.

00:45:51.360 --> 00:45:52.079
It met my needs.

00:45:52.320 --> 00:46:05.199
And again, and I I will say, you know, as Chris and I had in some previous conversations prior to this one, I'm not doing stuff when I do this that's being installed at a customer or you know what I'm saying?

00:46:05.280 --> 00:46:09.760
It's not what I mean, it's production for my home, yeah, for me, for what I need.

00:46:09.840 --> 00:46:12.960
I'm not the haphazardly doing this for an organization.

00:46:13.039 --> 00:46:17.440
But again, it was that type of thing is I needed something to do something for a few minutes.

00:46:17.519 --> 00:46:17.840
Yep.

00:46:18.159 --> 00:46:21.679
And I had to sit back and go, is it$48 that I pay?

00:46:22.000 --> 00:46:23.039
Or I want to vibe it.

00:46:23.199 --> 00:46:37.920
Whereas before you would pay the$48 because it would probably have taken me a day or two to replicate what it was doing, or maybe even a lot longer, because if I didn't even know how to do it, probably could have taken me months because I would have had to learn the stack, I would have had to learn the language, I would have had to learn what it is.

00:46:38.079 --> 00:46:39.599
But$48 is well worth it.

00:46:39.679 --> 00:46:43.199
But now you're just like, I need something that does this and it does that.

00:46:43.440 --> 00:46:48.880
Only a handful of times, and you're paying a full year subscription when you only need it for like two months.

00:46:49.119 --> 00:46:49.360
Yeah.

00:46:49.599 --> 00:46:49.920
Yes.

00:46:50.079 --> 00:46:55.599
I I need this was uh again, it was a probably a one or two time use function.

00:46:55.679 --> 00:46:56.079
Yeah.

00:46:56.320 --> 00:46:57.199
But that I needed.

00:46:57.280 --> 00:46:58.159
So it goes to your point.

00:46:58.239 --> 00:47:00.400
Like, did I really care about certain things?

00:47:00.559 --> 00:47:00.800
No.

00:47:00.960 --> 00:47:02.639
Could it have been probably better?

00:47:03.840 --> 00:47:04.400
Of course.

00:47:04.480 --> 00:47:07.440
Uh, anytime you develop something, it could always be better.

00:47:07.760 --> 00:47:14.079
But issue resolved, problem solved, yeah, and minutes.

00:47:14.400 --> 00:47:24.960
And and and really quick too, that that that's uh a perfect example of use of AI is that uh people tend to forget that acquisition and merger usually takes a long time.

00:47:25.280 --> 00:47:37.199
But if you have AI in between, it's going to expedite that significantly because it requires less physical person to like do all the work.

00:47:37.519 --> 00:47:39.199
There's a lot of paperwork around that, right?

00:47:39.440 --> 00:47:42.800
A lot of gathering of discovery and stuff like that.

00:47:43.119 --> 00:47:48.239
If you have agents that will do that for you, it's going to save you uh a whole lot of time.

00:47:48.400 --> 00:47:53.519
And that's another thing that I was I was curious because I have a family friend that's in the in law.

00:47:53.760 --> 00:48:00.239
You know, how do you utilize uh could you utilize agents for a lot of the e-discovery kind of thing?

00:48:00.400 --> 00:48:06.400
And I'm sure they're starting that way, but it's a lot of these things are gonna expedite significantly.

00:48:06.639 --> 00:48:07.119
Yeah.

00:48:07.360 --> 00:48:08.320
It's amazing.

00:48:08.559 --> 00:48:21.519
You know, uh Brad, I think it's important that you and I like how you articulated that some of the things that you're doing are you're not rolling out into client or production environments because there are still a lot of cybersecurity concerns around these tools as well.

00:48:21.840 --> 00:48:22.400
Absolutely.

00:48:22.639 --> 00:48:28.239
You know, so a really critical part of our software development lifecycle is yes, we're we're adopting AI first SDLC.

00:48:28.559 --> 00:48:35.599
That just means that our engineers who are experts are leveraging AI first as opposed to deciding they're going to write all the syntax.

00:48:35.920 --> 00:48:36.320
Correct.

00:48:36.559 --> 00:48:37.920
And that's why I wanted to preface it.

00:48:38.000 --> 00:48:42.960
That this in the context of my conversation, it wasn't for somebody else that I was doing it.

00:48:43.039 --> 00:48:54.639
It was for a task that I had at home where I had a controlled environment where I knew I knew what I was doing in the sense of whatever risks I was taking, which is extremely important, which goes back into that.

00:48:54.719 --> 00:48:58.719
You can vibe code anything, but you have to be careful if you don't understand what the results are.

00:48:59.519 --> 00:49:01.360
We are seeing the tools advance pretty quickly.

00:49:01.440 --> 00:49:12.159
One um, you know, I mentioned Firebase Studio and something we experienced here just this week, is we have we have uh dev staging production environments, backup routines, et cetera, and this is for our proprietary ERP.

00:49:12.320 --> 00:49:23.039
And our CTO is ready to release a new feature, and Firebase Studio said, uh-uh- uh-uh, because it detected a C V E in a React library.

00:49:26.000 --> 00:49:30.239
So it didn't even let him get it out into staging, which was fascinating to me.

00:49:30.400 --> 00:49:33.280
So um we didn't even know that was going to be a new feature.

00:49:33.440 --> 00:49:37.440
There was no release schedule, there's no promotion around, it's just all of a sudden the tool just all of a sudden it has something new.

00:49:38.159 --> 00:49:42.000
You know, which is it's the rate of these tools getting more and more mature.

00:49:42.320 --> 00:49:44.559
It's yeah, I can't even think about it.

00:49:45.360 --> 00:49:58.400
I do like that you you brought up the the cybersecurity component because that is it's grow it's going so fast that people just want to use it and see what it can do for them, but they do forget the the uh the security aspect of it.

00:49:58.559 --> 00:50:14.000
I mean, there's been countless times where I have conversations with SB space, yeah, where they have I mean their employees are using Chat GPT with their company data, and they don't have any AI policy as a starting point.

00:50:14.320 --> 00:50:20.400
I mean, even as simple as that should be a good foundation of how to use AI within your organization.

00:50:20.559 --> 00:50:31.440
And there's been like, oh, we haven't done that yet, but we've got Suzy in accounting asking questions about the best approach and then feeding data about their company.

00:50:31.519 --> 00:50:34.239
I'm like, oh pretty dangerous, guys.

00:50:34.559 --> 00:50:35.760
It happens all the time.

00:50:35.840 --> 00:50:42.239
You know, it surprises me how many how few businesses have completed acceptance use policies and training around that.

00:50:42.559 --> 00:50:48.639
And anybody who thinks that their employees are not using AI, they're kidding themselves.

00:50:49.039 --> 00:50:52.159
It's happening at every level in the business.

00:50:52.400 --> 00:50:54.320
So you gotta get a handle on that.

00:50:55.119 --> 00:50:58.000
What are you telling me that people aren't people aren't using AI?

00:50:58.159 --> 00:51:02.559
Like you can you can stop them, or they're not using it, or everyone's just unaware?

00:51:02.880 --> 00:51:04.079
Give them the secure pathway.

00:51:04.159 --> 00:51:05.840
They're gonna use it whether you do so or not.

00:51:05.920 --> 00:51:07.679
So expedite that process.

00:51:07.920 --> 00:51:09.760
That's you have internet access.

00:51:09.920 --> 00:51:11.519
Well, that's still it is.

00:51:11.599 --> 00:51:35.440
It's it's Chris brings up the good point with the AI use policy, and you also bring up the point of uh you know getting that in motion now because I would rather have my employees and team members know how to use the tool, understand how to use the tool instead of just seeing what they hear, like conversations with ours saying, oh, we could create an application for you in minutes.

00:51:35.519 --> 00:51:35.599
Yeah.

00:51:35.760 --> 00:51:43.840
Granted, I mean, we all work within the industry with we're leaving out some of the aspects of what we know about software development and about technology and about business.

00:51:44.000 --> 00:51:52.639
But it does sound if you hear some of these examples that people put on there, and if I, you know, for those that may use social media, you have these shorts that are like, oh wow, I did this in 30 seconds.

00:51:53.199 --> 00:52:04.960
It's it is important to understand because, like you had mentioned, they're going to use it, even if they copy and paste it on their own somewhere else, not in your work environment, they can do so.

00:52:05.920 --> 00:52:09.119
You can't stop people from doing, or even if they had to type it.

00:52:09.840 --> 00:52:15.199
Oh, and by the way, did you know you could take a picture and drop that into AI and we'll tell you exactly what that screen says?

00:52:15.360 --> 00:52:16.480
Oh, that's what I'm saying.

00:52:16.559 --> 00:52:20.559
Like, there's so many, there's so many ways that people can get information.

00:52:21.360 --> 00:52:24.800
The only way you can stop them from getting information is to not have information.

00:52:24.960 --> 00:52:25.280
That's right.

00:52:25.519 --> 00:52:32.000
I mean, and that that's what it comes down to, is it's if you want anybody, and then it's also you can be more restrictive.

00:52:32.159 --> 00:52:34.639
And don't get me wrong, security is extremely important, of course.

00:52:34.800 --> 00:52:48.559
But there's a point where you you have to balance security and productivity because if you want to be, and that security has to match the level of the information that you have, and then also how do you hinder productivity?

00:52:48.639 --> 00:52:50.719
Because I see sometimes people go the other way.

00:52:50.880 --> 00:53:01.440
You get a little too secure trying to satisfy every edge case where you have to understand what is the risk of that edge case and am I willing to accept it?

00:53:01.599 --> 00:53:02.400
That's the key.

00:53:02.880 --> 00:53:07.920
Identify the risks, identify, am I willing to accept that risk?

00:53:08.320 --> 00:53:08.480
Right?

00:53:08.639 --> 00:53:10.880
That's that's a point that some people leave out.

00:53:11.199 --> 00:53:12.320
So I just want to be clear about it.

00:53:13.440 --> 00:53:15.760
We get I get a lot of feedback on some of these things when I say stuff.

00:53:15.840 --> 00:53:30.639
So I have to be very careful to say, I'm not saying you should be haphazard with security, I'm saying be secure, but also understand is the effort of what you're trying to do worth the productivity that it may suppress.

00:53:30.800 --> 00:53:31.599
Right, yeah.

00:53:31.760 --> 00:53:36.000
And are you willing to accept the risk of not doing something?

00:53:36.320 --> 00:53:39.920
But like you just said, uh, when it comes to the use of AI, people will use it.

00:53:40.000 --> 00:53:41.280
People are using it.

00:53:41.519 --> 00:53:45.519
Uh people aren't in a nobody's living in a vacuum in 2025.

00:53:46.000 --> 00:53:48.880
Even Googling right now has an AI mode.

00:53:49.199 --> 00:53:49.519
Yeah.

00:53:50.400 --> 00:53:55.920
It just summarizes or even get you in AI mode instead of having a conversation with it, doing your research.

00:53:56.559 --> 00:53:57.360
Everyone's using it.

00:53:57.840 --> 00:53:58.800
That's interesting.

00:53:58.960 --> 00:53:59.280
Yeah.

00:53:59.440 --> 00:54:04.239
Um but AI is productive outside of software development as well.

00:54:04.559 --> 00:54:14.400
And because I have uh talked with others that they uh it's it's amazing what people do with the AI ecosystem, I call it.

00:54:14.559 --> 00:54:21.679
I I've seen people have it go through all of their emails and generate draft responses, and they just review the draft responses.

00:54:21.760 --> 00:54:23.920
It's almost like you don't have to do anything anymore.

00:54:24.079 --> 00:54:25.039
Who types?

00:54:26.400 --> 00:54:28.000
I'm just getting to the point, like who types?

00:54:28.400 --> 00:54:29.760
Because now you can use voice.

00:54:30.079 --> 00:54:33.679
I got two great examples, and it's not even related to software engineering.

00:54:33.840 --> 00:54:38.880
Um, one is, and and Chris, you're probably doing something similar since you mentioned Notebook LM.

00:54:39.360 --> 00:54:39.760
Yeah.

00:54:39.920 --> 00:54:42.960
Uh for sales for sales training in our organization.

00:54:43.360 --> 00:54:51.679
I created a notebook and dropped in all of the sources I needed, including talk tracks, service offerings, capabilities deck, you name it.

00:54:52.800 --> 00:54:55.679
And you we use that notebook for sales training.

00:54:56.480 --> 00:54:58.639
And then we have a new opportunity in the pipeline.

00:54:59.199 --> 00:55:00.559
My flow looks like this.

00:55:00.639 --> 00:55:05.280
So um in fact, I've got a first-time appointment with the with a group this afternoon.

00:55:05.920 --> 00:55:15.760
And before I meet with them, I'm going to use Chat GPT in a thinking or deep research mode, drop in the URL address to their business.

00:55:15.920 --> 00:55:20.079
I'm going to drop in a suite of artifacts from Red Hawk's business to drop that in.

00:55:20.239 --> 00:55:26.239
And the email thread that includes all the introduction and other artifacts that explains what they're interested in.

00:55:26.960 --> 00:55:29.519
And it will give me uh recommendations.

00:55:29.760 --> 00:55:32.159
It's a sales dossier that I create in ChatGPT.

00:55:33.360 --> 00:55:42.960
Then I take that sales dossier from ChatGPT, which by the way I include LinkedIn profile PDFs of the key people that I'm going to be speaking with that may I have may have not even met yet.

00:55:43.920 --> 00:55:44.159
Right.

00:55:44.400 --> 00:55:49.679
I drop all that into Notebook LM and I create a sales-specific opportunity notebook.

00:55:50.400 --> 00:56:03.440
So when I close this deal, which I hope I will close this deal, then the process for my team, instead of a written project brief, they go have an experience with that notebook.

00:56:03.920 --> 00:56:04.320
Yeah.

00:56:04.559 --> 00:56:05.199
You know what I mean?

00:56:05.440 --> 00:56:13.280
They can put it in, they can put it in uh uh run the audio feature and they've got a podcast that they can interface with and interrupt and ask questions.

00:56:13.599 --> 00:56:14.000
Yes.

00:56:14.880 --> 00:56:20.000
They come into that next technical discovery meeting, dialed in, and this has nothing to do with engineering.

00:56:20.079 --> 00:56:24.400
This is off the shore off-the-shelf tools in the right order of operation.

00:56:24.480 --> 00:56:29.920
I mean, these sales dossiers guys are like it is crazy.

00:56:30.000 --> 00:56:30.559
That is crazy.

00:56:30.880 --> 00:56:33.119
You can even do mind map, like notebook ln.

00:56:33.840 --> 00:56:34.719
They've added that.

00:56:34.960 --> 00:56:36.800
Infographic, you'll create all that.

00:56:36.880 --> 00:56:39.599
Pete Pete uh PowerPoint quizzes.

00:56:40.079 --> 00:56:41.599
You can do all the things you need.

00:56:41.920 --> 00:56:43.280
I love mind maps, by the way.

00:56:43.360 --> 00:56:44.159
I use X Mind.

00:56:44.400 --> 00:56:45.119
I use X Mind.

00:56:45.280 --> 00:56:47.440
Maybe I have to switch over to the Notebook LN, man.

00:56:47.679 --> 00:56:50.000
You'll have to show me it's stupid.

00:56:50.480 --> 00:56:56.400
You will have to show me what you're doing because maybe I will move over to that.

00:56:56.480 --> 00:56:57.280
It's it's it's tough.

00:56:57.360 --> 00:56:59.119
There's so many tools, it's tough for me to keep up.

00:56:59.199 --> 00:57:02.480
I've been spoken I focus most of my time more on the development portion of it.

00:57:02.559 --> 00:57:02.719
Yeah.

00:57:02.880 --> 00:57:07.599
Uh Notebook LM sounds like it's more content management simplified.

00:57:07.760 --> 00:57:11.840
So I have to you're control controlling your sources and it's giving you insights on it.

00:57:11.920 --> 00:57:13.119
So I'll give you a quick example.

00:57:13.199 --> 00:57:35.199
So I've got uh another uh deal in the pipeline right now, and it was um uh an intern who recently got hired full-time in supply chain management, like that's his area, and they need an inventory, like a lower um uh but custom inventory management uh solution for their shipping yard.

00:57:35.760 --> 00:57:36.079
Okay.

00:57:36.639 --> 00:57:42.239
It was intelligent enough for it to identify based on the LinkedIn profiles, background information, it has to be good data, right?

00:57:42.320 --> 00:57:49.679
So his profile is pretty current, along with uh the president of the company who joined the meeting and all the artifacts and my sales dossier.

00:57:49.760 --> 00:57:53.519
It said this gentleman's career path is X, Y, and Z.

00:57:53.599 --> 00:57:58.960
And based on what they're asking you to do, this could be the flagship project that helps launch his career.

00:58:01.199 --> 00:58:09.280
So it's it's it's it's giving me that level of insight, like that's how important this probably is to that individual based on their career trajectory.

00:58:10.079 --> 00:58:14.000
I'm like, Chris, we have to we have to schedule a demo.

00:58:14.719 --> 00:58:15.840
Yeah, reach out to you guys.

00:58:15.920 --> 00:58:17.199
I'll take you through a couple of these things.

00:58:17.280 --> 00:58:22.880
All I'm doing is I do want to know, yeah, we'd love I'd love to see how you use Notebook LM.

00:58:22.960 --> 00:58:27.280
I I know you know, we work at the at least for me, we work in the Microsoft ecosystem.

00:58:27.360 --> 00:58:30.960
I do use other products outside of the co-pilot world.

00:58:31.039 --> 00:58:49.039
Yeah, I I I am very impressed with the Gemini and Notebook LM's capabilities, even from a personal perspective of just putting all the things that's in my mind into Notebook LM and then have it deep search and then reference to the things, and then you say, Hey, can you create a video for this that's pros and cons?

00:58:49.280 --> 00:58:52.000
And it'll like tell me like what are the pros and cons.

00:58:52.079 --> 00:58:56.719
And even Audi, like you said, you know, little podcasts that will have a conversation.

00:58:57.199 --> 00:58:58.079
Oh my gosh.

00:58:58.159 --> 00:58:59.440
And mind map, Brad.

00:58:59.519 --> 00:59:03.519
Like, yeah, I need you to put this in a in a in a mind map as well.

00:59:03.760 --> 00:59:03.920
Yeah.

00:59:06.079 --> 00:59:15.760
I have to see this notebook LM because um I'm just thinking now of a lot of things based on what you're telling me.

00:59:15.920 --> 00:59:18.480
That the world is changing.

00:59:18.719 --> 00:59:19.440
It's too fast.

00:59:19.519 --> 00:59:22.960
It's almost um I don't know.

00:59:23.199 --> 00:59:24.079
It's it's too fast.

00:59:24.400 --> 00:59:24.960
It's fun, man.

00:59:25.039 --> 00:59:28.480
I tell you what, I was freaking out at at you know at one point, like what's gonna happen.

00:59:28.639 --> 00:59:32.159
The industry's gonna, you know, be heavily impacted and we're gonna lose business.

00:59:32.639 --> 00:59:34.400
But it's been the it's been the opposite.

00:59:36.159 --> 00:59:37.039
It's been the opposite.

00:59:37.199 --> 00:59:42.719
We're seeing more and more opportunity in the pipeline is getting uh it's just full of opportunity.

00:59:43.679 --> 00:59:55.679
Because what's happened for us is in serving that mid market, the number one reason for customers not making an investment was time and effort and the cost related to doing something to pursue their vision.

00:59:56.159 --> 00:59:59.760
We couldn't find what they needed off the shelf, that's when we would be brought into.

01:00:00.159 --> 01:00:01.199
To the conversation, right?

01:00:01.360 --> 01:00:04.719
So we still think that buybers is build arguments make sense, right?

01:00:04.800 --> 01:00:13.360
You know, go go with the Microsoft stack, use uh ERP, CRM platforms available that are available off the shelf, because they're going to build in AI agents and tools to help you along the way.

01:00:13.840 --> 01:00:15.360
Custom's not always the right decision.

01:00:16.000 --> 01:00:21.280
However, when you can't find what you need off the shelf, they would typically turn to a software consultancy like Red Hawk.

01:00:21.679 --> 01:00:30.880
But by the time we get through um the discovery phase when we put something together in a traditional SDLC, sometimes the RI ROI wasn't there.

01:00:32.639 --> 01:00:38.800
The problem was it expensive or big enough uh of a problem for them to make the investment to solve the problem.

01:00:39.440 --> 01:00:51.519
Well now it's the conversation's so different because these bespoke software solutions can be developed in such a compressed timeline and investment that I'm hyper optimistic.

01:00:51.920 --> 01:00:58.719
Like, I mean, some of the things we're building for folks right now, like R E R P and you know, the other example I share with you guys, like I'm pumped.

01:00:59.679 --> 01:01:07.440
Yes, I think it's it's um it's it's just a repeat of my thought, and I'm stuck on this thought now, is something that you touched upon earlier.

01:01:07.519 --> 01:01:08.559
It's all about the data.

01:01:08.880 --> 01:01:09.280
Yes.

01:01:09.519 --> 01:01:16.000
I think I think the you and Chris were talking about Notebook LM and how you're using it for data.

01:01:16.159 --> 01:01:23.760
You're talking about how you crafted an ERP system for yourself that you're using uh because you had the data.

01:01:24.079 --> 01:01:32.239
I think it all comes down to having a core data system that you interface with these tools with.

01:01:32.320 --> 01:01:41.679
So you think of the concept of MCP, which is supposed to be a standard interface for you to be able to communicate with external systems or separate systems or however you would like to look at it.

01:01:42.079 --> 01:01:49.039
It's all going to be a matter of having the data available because without the data you can't really do much.

01:01:49.280 --> 01:02:00.159
I mean, you can create some cool little applications for stuff, but if you're looking for a business point of view or even a personal point of view, you need to have the data available for consumption.

01:02:00.639 --> 01:02:01.119
Agreed.

01:02:01.280 --> 01:02:07.760
Quality of data and adherence to standard operating procedures will determine your level of success.

01:02:09.199 --> 01:02:09.760
100%.

01:02:11.199 --> 01:02:11.519
Wow.

01:02:13.519 --> 01:02:21.280
Well you know, my I think we'll have to um I I'm at a loss of words to be honest with you with this.

01:02:21.440 --> 01:02:25.519
This is just um it's uh stimulating, I guess you can say.

01:02:25.760 --> 01:02:31.119
But I do um I do appreciate taking appreciate you taking the time to speak with us again on this.

01:02:31.280 --> 01:02:32.320
I think we will do the same thing.

01:02:32.480 --> 01:02:34.239
Let's schedule a follow-up in another quarter.

01:02:34.400 --> 01:02:34.800
Yeah.

01:02:34.960 --> 01:02:44.960
Uh, and we'll have some notes and we'll have to just start thinking of what has happened since December 12th, 2025, and the next day that we schedule it.

01:02:45.039 --> 01:02:57.360
So all of us will have to keep a a log of things that we have witnessed with the advances of AI and uh things that came out, and then we'll have to talk about them and we'll see where we are then.

01:02:57.519 --> 01:02:59.519
Because will MCP exist?

01:02:59.760 --> 01:03:01.119
Will agents exist?

01:03:02.800 --> 01:03:04.960
I'm sure they will exist, excuse me.

01:03:05.199 --> 01:03:08.159
Will they be the topic of the time?

01:03:08.480 --> 01:03:08.719
Interesting.

01:03:08.880 --> 01:03:10.800
Uh, when I say exist, so we'll have to see.

01:03:11.119 --> 01:03:14.559
Um, but again, thank you very much for taking the time to speak with us again this afternoon.

01:03:14.639 --> 01:03:15.360
We really appreciate it.

01:03:15.440 --> 01:03:16.480
Thank you for blowing my mind.

01:03:16.559 --> 01:03:17.760
I'm sort of speechless.

01:03:17.840 --> 01:03:24.239
Uh, but if anyone would like to contact you to learn a little bit more about AI, learn about Red Hawk and some of the great things that you're doing, what's the best way to get in contact with you?

01:03:24.559 --> 01:03:31.519
You can look me up on LinkedIn, Matt Stripple Hoff on LinkedIn, and uh reach out with a connection request, reference this podcast.

01:03:31.599 --> 01:03:34.079
That'll be my cue to go ahead and accept that invite.

01:03:34.239 --> 01:03:37.519
Um, or uh just check us out at redhawk-tech.com.

01:03:37.599 --> 01:03:40.480
You can fill out a form there to reach out and schedule time with me as well.

01:03:40.639 --> 01:03:41.119
Yeah.

01:03:41.440 --> 01:03:41.760
Great.

01:03:41.920 --> 01:03:42.480
Thank you very much.

01:03:42.559 --> 01:03:45.679
Uh I look forward to speaking with you again in about three months.

01:03:45.760 --> 01:03:46.320
I'll talk with you.

01:03:46.880 --> 01:03:48.320
Always love talking to you guys, man.

01:03:48.400 --> 01:03:49.039
So love it.

01:03:49.199 --> 01:03:49.840
Thanks for having me back.

01:03:50.159 --> 01:03:50.480
Fantastic.

01:03:50.880 --> 01:03:51.039
Thank you.

01:03:51.199 --> 01:03:51.519
Thank you, man.

01:03:51.679 --> 01:03:51.760
All right.

01:03:51.840 --> 01:03:52.000
See you.

01:03:52.239 --> 01:03:52.400
Ciao.

01:03:53.119 --> 01:03:53.440
Bye.

01:03:54.000 --> 01:03:59.119
Thank you, Chris, for your time for another episode of In the Dynamics Corner Chair.

01:03:59.199 --> 01:04:01.280
And thank you to our guests for participating.

01:04:01.519 --> 01:04:03.039
Thank you, Brad, for your time.

01:04:03.199 --> 01:04:06.719
It is a wonderful episode of Dynamics Corner Chair.

01:04:06.960 --> 01:04:10.320
I would also like to thank our guests for join joining us.

01:04:10.480 --> 01:04:13.199
Thank you for all of our listeners tuning in as well.

01:04:13.440 --> 01:04:17.280
You can find Brad at developerlife.com.

01:04:17.519 --> 01:04:21.840
That is D V L P R L I F E dot com.

01:04:22.079 --> 01:04:27.599
And you can interact with them via Twitter, D V L P R L I F E.

01:04:28.239 --> 01:04:36.000
You can also find me at mantalino.io, m-a-t-a-l-in-o.io.

01:04:37.280 --> 01:04:40.960
And my Twitter handle is Matalino16.

01:04:41.840 --> 01:04:44.800
And see you can see those links down below in the show notes.

01:04:44.880 --> 01:04:46.159
Again, thank you everyone.

01:04:46.320 --> 01:04:48.079
Thank you and take care.