Streamline & Scale Newsletter: Issue 7: Podcast

Alright. Picture this scenario for me.

Alright.

It's, 2:00 PM on a Thursday. You're in one of those high stakes leadership meetings.

I know this room. I have been in this room many, many times.

The air conditioning is humming, the coffee on the table's getting cold. You know the kind, I mean the stuff that's been sitting there for three hours.

Oh yeah. The carafe coffee,

but the energy in the room, it is just electric.

On the big screen, someone is clicking through a prototype. And it looks slick. I mean, the buttons work, the screens transition smoothly.

The flow feels intuitive.

Exactly. And the stakeholders are nodding.

It feels like you finally crossed the finish line. The hard part is done right.

It's a great feeling. That moment of alignment is so powerful. Everyone agrees on the vision.

Yeah.

Here is the cold bucket of water. I have to throw on that fire immediately.

I had a feeling you were gonna ruin the moment.

You always do.

That moment. That applause that is at the finish line, that is barely the starting gun. And if you treat it like the [00:01:00] finish line, you are setting yourself up for a very, very expensive disaster.

Because what looking at a picture of a house isn't the same as actually living in it.

Exactly. A picture doesn't have plumbing, that leaks a picture doesn't have a foundation that cracks and that distinction is exactly what we're unpacking today.

Okay.

We are doing a deep dive into the streamline and scale newsletter. Specifically issue seven from Red Hawk Technologies. It's titled After the Clickable Prototype. And it's fascinating because it focuses entirely on that dangerous gap between, Hey, this looks great, and this actually works in the real world.

So today isn't about the design phase, it's about the the morning after the party.

Right? We're gonna talk about the three distinct paths leaders can take after that meeting. And spoiler alert, just build it is only one of them.

Interesting.

We're gonna look at how to structure the delivery so the thing doesn't fall apart six months later.

And then, and this is the part I'm really excited to get into. We're gonna look at a case study on using an AI first [00:02:00] approach to build software four times faster,

but without all the hallucinations and messy code we usually hear about

precisely. It's a masterclass in discipline over momentum. The central idea here is really simple, but you know, often ignored approval authorizes investment, but it does not guarantee production confidence.

Production confidence. Okay, let's put a pin in that term because I think that's our north star for this deep dive. But let's go back to that conference room. The prototype is approved. Everyone is high fiving. What usually happens,

Is a rush to code. The adrenaline takes over. You hire the team, you start to sprints, you burn the budget, but the source material suggests we need to, um.

Hit the brakes and view the prototype not as a green light, but as a decision gate.

A decision gate. Okay. Let's unpack that because usually if I show you a prototype and you say you like it, I assume the decision is go

right. So that's the instant.

So why would we stop if we just got a yes?

Because you know, I like the look of, this is not the same thing as this business [00:03:00] model scales.

The newsletter outlines three paths. Path one is the one you're expecting. Proceed to build.

Okay. So that is an option. We aren't just here to crush dreams today.

It is an option.

Yeah.

But even here. The mindset has to shift. You move from hope, just hoping users will like it to confidence. Mm. The conversation changes from stakeholders asking abstract questions like, what if we did this to very concrete logistical questions like, when can we launch this?

So the abstract debate is over. We're not brainstorming anymore.

Okay. So that's the happy path. That's when everything goes right. But you said there were three paths. Yeah. So what happens if the prototype was good, that maybe it raised some eyebrows?

That brings us to Path two.

Hmm.

Refine the idea.

Mm-hmm.

This is where discipline really kicks in. Let's say the prototype reveals that while the interface is pretty, the actual business case isn't airtight.

Or maybe the user flow raises some adoption questions like, [00:04:00] will people actually click this 12 times to get a result?

I feel like a lot of companies would just ignore that. They'd say, oh, we'll fix it in post, or we'll train the users to like it, just to keep the momentum going.

You hit the nail on the head.

Momentum is a drug in the corporate world, but the aha moment here is a delaying delivery. To clarify the problem isn't failure,

right?

It's protection. You refine the idea before you commit the big capital. You might go back to the drawing board for two weeks to save two years of wasted development.

Which brings us to Path three, the one that I think is the hardest for human beings to actually do

pause or pivot,

actually stopping, killing a project that people are excited about. That sounds painful.

It feels rare, doesn't it? It feels like a defeat. But the source argues that stopping based on evidence is a massive success.

It prevents what economists call sunk cost escalation,

throwing good money after bad just because you've already spent some money.

Precisely.

There was a specific story in the source [00:05:00] material about this that I found really telling the custom IoT application.

Yes. This is a perfect example of why the prototype phase is a test, not just a demo.

A client came to Red Hawk, ready to invest heavily in a custom application enabled by IoT devices,

Internet of Things, so like sensors in a factory or trackers on a fleet of trucks,

right? Big hardware and software integration. Expensive stuff.

Very,

so they built a prototype to test the concept, but during that process, because they were testing rigorously, they realized two things.

One, the device behavior was unreliable. The hardware itself wasn't doing what the brochure said it would.

And the second thing,

the algorithm needed extensive onsite calibration. It wasn't something you could just plug and play.

So if they had just started coding immediately after the PowerPoint presentation,

they would've spent months and probably hundreds of thousands of dollars building software for devices that didn't work.

Mm-hmm. For a system that couldn't be deployed at scale. Oh, exactly. So they realized it wouldn't [00:06:00] scale. And they stopped. They didn't spend the big money, that decision to stop, saved them from a complete disaster.

Wow.

The takeaway is that the goal isn't just to build software, it's to avoid building the wrong software.

That is such a crucial distinction. It's risk management, not just code generation.

Okay, so let's say we're on path one. We have the green light, the prototype works, the business case is solid. We're ready to write code

now we build. But this raises the next big question from the newsletter, how

this is where we get into delivery models.

And I think for a lot of people, especially non-technical founders, the assumption is, well, we just hire some developers or an agency and tell 'em to get to work. Why does the model matter so much? It matters because production confidence is a lifecycle. It involves ownership, funding, risk governance. It's not just about writing lines of code.

It's about who owns the house when the pipes burst at 3:00 AM on a Sunday,

right? The newsletter breaks this down into two main models. Let's start with the one that sounds a bit [00:07:00] like a subscription, software development as a service.

This is model A, and you should think of this for organizations that might have a great business idea, say a logistics company or a healthcare provider, but they don't have a basement full of internal engineers or DevOps specialists,

so they don't have their own tech army.

Correct. In this model, you bundle the build and the ongoing support and maintenance into one engagement.

What's the strategic benefit there? Why not just hire a freelancer?

It manages risk. You as the client own the software, the intellectual property, the IP is yours, but Red Hawk in this case manages the operations.

It avoids the risk of owning a complex system that you technically own but can't actually support.

It's like buying a Formula One car. Sure you own it, but do you know how to change the tires in two seconds? Do you have the pit crew,

a very sophisticated pit crew? Exactly. The executive mindset here is we want this built and evolved.

But we don't wanna stand up an entire [00:08:00] internal software delivery organization.

Okay. That makes sense. For the non-tech companies. So what is Model B?

Model B is time and materials. This is for organizations that prioritize internal ownership and control. Maybe they have a mature IT team, but they need specific expertise or just extra hands to hit a deadline.

This feels more like the traditional contracting model. We pay you for your hours.

It is, but it's flexible. We can scale up or down. The executive mindset here is we want control without losing visibility or predictability. They wanna drive the car themselves.

Mm-hmm.

They just need someone to build the engine.

So is one better than the other? Should everyone be doing the SDaaS model?

No. And that's the key. So what?

Yeah,

there is no right answer. Only the answer that fits the company's internal maturity. If you don't have an IT team, time and materials might leave you with a product. You can't maintain a zombie app.

If you have a huge IT team, software as a service might feel too hands off. You have to match the model to your reality.

It sounds like [00:09:00] self-awareness is the most important asset here. You have to know what you're actually good at.

Exactly.

But regardless of the model, whether you rent the pit crew or build your own, once the software is live, the scary stuff starts.

The newsletter had a phrase that stood out to me, real users. Real data, real change.

This is where the rubber meets the road. And this brings us to a topic that usually makes people's eyes glaze over until they get hacked. Software Composition Analysis or SCA.

Okay, keep it accessible for us. They used an analogy about an ingredient list.

It's exactly that. It the industry. It's known as an SMOB, a software bill of materials. Here's the reality. A lot of people don't realize. Modern software isn't written from scratch, like a novel,

right?

It's assembled like a car, use libraries, frameworks, third party tools.

So it's like baking a cake. I write the recipe, but I'm using pre-made mix.

Store-bought frosting and sprinkles from a jar.

Precisely. And you need to know if the factory that made the sprinkles just announced a recall because they [00:10:00] contain lead.

That took a dark turn, but I get the point if the sprinkles, the third party code have a vulnerability. My software has a vulnerability,

and the scale of this problem is massive.

The source provides a staggering statistic. Approximately 47,000 new vulnerabilities are disclosed a year.

47,000 a year.

That breaks down to roughly 130 every single day.

130 a day. That is terrifying.

It is. And hackers know this attacks target these dependencies. They don't just bang on your firewall, they look for a weak lock in a window frame you bought from a third party vendor.

So Red Hawk SCA isn't critiquing the code I wrote. It's checking the safety of the blocks I built with.

Precisely. It's about managing technical debt and security risk as a continuous process, not a one-time check. You need to know the moment one of your ingredients goes bad.

This actually segues perfectly into our next segment because Red Hawk realized they needed to rebuild their own internal SCA tool.

They needed to make it pretty, usable and fast.

This [00:11:00] is where we get to the AI-First case study. This is the part that really blew my mind regarding efficiency. This ties together the build and the maintenance concepts we've just discussed, but it supercharges them with AI.

Everyone talks about AI coding, but usually it's, oh, I asked a chat bot to write me a snake game.

This sounds different. Let's look at the numbers they needed to rebuild this tool. What was the traditional timeline?

The estimate was eight weeks. That's a standard, you know, reasonable timeline for a project of this scope

and the AI-First timeline.

Two weeks.

Wait, what? Two weeks?

Two.

That's four times faster.

Now I know what the listeners think. Oh, great. They just asked chat GPT to write the code and now it's probably full of bugs and hallucinations

and that is exactly what they didn't do. The source is very specific here. This was a disciplined AI-First SDLC software development lifecycle.

There's a quote from Jason Wilson, a principal at Red Hawk that really stuck with me.

He said, tell it to write the test before it writes the code. Why does that matter so [00:12:00] much?

That is the golden rule. In software engineering, we talk about test driven development or TDD.

Yeah.

The idea is you write a test that fails, like the system should accept a username.

Mm-hmm.

And then you write the code to pass the test.

Why doesn't everyone do that? It sounds so logical,

because it's tedious. Humans hate writing tests. It feels like chores. It feels like paperwork. AI, AI loves structure. It doesn't get bored. So Red Hawk had the AI generate over 800 unit tests before the core coding even started.

800 tests that would take a human how long?

Days, maybe weeks of just grinding the AI did it. Almost instantly. And because the tests were written first, the AI then wrote the code specifically to pass those rigorous tests. It created a guardrail against the hallucinations you mentioned,

so the AI built the safety net and then it built the trapeze artist

in a way.

Yes. It also generated the documentation and the front end code.

So did the humans just sit back and drink coffee? What is the role of the [00:13:00] developer in this world?

That's the critical distinction. The source emphasizes that AI augmented the developers. It did the repetitive heavy lifting, the 800 tests, the boilerplate code, the documentation, scaffolding.

This freed the human engineers to focus on the high level architecture and the user experience. The UX.

So the humans are the architects. The AI is the brick layer.

Exactly. And the result was a dashboard that provides real-time vulnerablity visibility and automated remediation summaries. But the key insight isn't just speed.

It's control at higher velocity.

Control at higher velocity. I like that. It's not just driving fast, it's driving fast with better rates.

AI creates leverage.

Yeah.

But only if you apply it inside a system that already values clarity. If you use AI in a messy process, you just get messy code faster. Red Hawk used it within a discipline structure.

Write the test first, so they got quality code faster.

It really circles back to the theme of the whole newsletter discipline, whether it's choosing to pause a prototype, choosing the right delivery model, or using [00:14:00] ai, it's about being intentional.

Absolutely. The shiny object, the prototype, the new AI tool is never the answer in itself.

The answer is how leadership manages those tools.

So let's bring this all together. We've covered a lot of ground from the boardroom to the server room. What are the big takeaways for someone listening to this right now?

I see three main pillars. First. Treat the prototype as a decision gate, not a launchpad.

You have three options. Build, refine, or pause. All three are valid.

Oh,

don't be free to pause.

Production confidence requires a choice. You have to actively choose a delivery model, software developement as a service, or time and materials based on your internal capabilities, not just what's cheapest or fastest.

Mm-hmm.

You have to decide who owns the risk.

AI accelerates delivery, but it demands architectural discipline. You can move four times faster, but only if you have a system like write the test first to keep the rails on the track.

I wanna ask the listener a question based on this. If you are looking at a prototype right now, maybe you just came out of that meeting we described at the start, are you [00:15:00] asking can we build this

or are you asking the harder question?

Should we build this? And who is gonna own it when? Not if, but when it breaks.

Hmm. That's the difference between a project that launches and a project that lasts.

I wanna leave the listener with one final thought from the source material that I found really provocative. It challenges the hustle culture.

We're also used to,

let's hear it

Better software doesn't come from working harder. It comes from making the decision to stop, refine or build before the code is written.

That is powerful. It's not about the code you write, it's about the decisions you make before you touch the keyboard.

Precisely. Measure twice, code once.

Oh, well this has been a fascinating look behind the executive curtain. If you wanna dive even deeper, check the show notes for the link to the full Streamline and Scale newsletter from Red Hawk Technologies. It is well worth the read. Thanks for listening. See you in the next deep dive.