Speaker 1: You know, I feel like it's pretty much a universal
Speaker 1: truth of the digital age that well, we all just
Speaker 1: want to build things quickly.
Speaker 2: Oh absolutely right, Like.
Speaker 1: You get this great idea, you sit down at your
Speaker 1: keyboard and you just want to immediately start writing code.
Speaker 1: It feels productive, it feels like you're, you know, actually
Speaker 1: making a thing.
Speaker 2: Yeah, the momentum is intoxicating. But jumping straight into a
Speaker 2: code editor like that, it's a lot like deciding to
Speaker 2: build a skyscraper and starting by just pouring a massive
Speaker 2: truckload of concrete into a random field.
Speaker 1: Yeah exactly. I mean you might end up with a wall,
Speaker 1: but you're probably just going to sink into a swamp.
Speaker 2: Right. It's a disaster waiting to happen.
Speaker 1: And that's why we're doing this deep dive today, because
Speaker 1: whether you are a junior programmer trying to level up,
Speaker 1: or maybe a team lead managing some huge project, or
Speaker 1: someone who's insanely curious about how these massive digital systems
Speaker 1: are built without you know, collapsing in on themselves. Understanding
Speaker 1: software architecture is the ultimate shortcut.
Speaker 2: It really is. It saves you from so much which
Speaker 2: disastrous expensive rework down the.
Speaker 1: Line exactly, So today we are diving into Michael Keeling's
Speaker 1: book Design It from Programmer to software architect, and our
Speaker 1: mission here is to figure out how to evolve from
Speaker 1: just writing code to truly designing software.
Speaker 2: Systems and spoiler alert for everyone listening, it involves a
Speaker 2: lot less typing and well, a whole lot more talking
Speaker 2: to humans.
Speaker 1: Yes, okay, let's unpack this. Yes, because I think there's
Speaker 1: this very stubborn myth in the tech world of the
Speaker 1: Ivory Tower architect.
Speaker 2: Oh yeah, the solitary wizard in the glass.
Speaker 1: Office, right, drawing these highly complex boxes and lines on
Speaker 1: a whiteboard, and then they just slide those diagrams under
Speaker 1: the door for the lowly developers to actually build. But
Speaker 1: Keeling completely dismantles that, doesn't he.
Speaker 2: He completely tears it down. I mean, it's a fundamental
Speaker 2: shift in perspective. When you're a programmer, your primary audience
Speaker 2: is the compiler. Your job is to give highly specific
Speaker 2: instructions to a machine.
Speaker 1: Right, it's you and the computer.
Speaker 2: Exactly. When you step into the role of a software architect,
Speaker 2: your primary audience suddenly becomes people. The technology is almost
Speaker 2: secondary to the sociology.
Speaker 1: I love that. So architecture isn't like being that wizard
Speaker 1: casting spells. It's more like being a city planner.
Speaker 2: That's a great way to look at it.
Speaker 1: Because you can design the coolest, most technologically advanced skyscraper
Speaker 1: in the world, but if it doesn't connect to the
Speaker 1: roads people actually drive on, or have plumbing that connects
Speaker 1: to the city grid, it's totally useless.
Speaker 2: Right, if you get the human element wrong, I don't
Speaker 2: care how elegant your code is, it just won't save you.
Speaker 2: Architecture is about understanding the humans using the system, the
Speaker 2: humans paying for it, and the humans building it.
Speaker 1: So how does an architect actually balance what the code
Speaker 1: needs with what all these different humans need? Because that
Speaker 1: sounds exhausting.
Speaker 2: What's fascinating here is that the human element doesn't just
Speaker 2: influence the code, it actively dictates it. Keeling brings in
Speaker 2: the heart principles of design thinking to navigate all this.
Speaker 1: Okay, heart, that's an acronym, right, let's break those down.
Speaker 2: Yeah, So hart HG is the human rule. It just
Speaker 2: reminds us that all design is a social activity. You're
Speaker 2: designing four people with people?
Speaker 1: Makes sense? What about the A.
Speaker 2: That's the ambiguity rule? This one is tough For engineers,
Speaker 2: it's about preserving ambiguity early on, to keep your options open, like,
Speaker 2: don't lock into a solution before you fully understand.
Speaker 1: The problem, right, don't jump to the code editor exactly.
Speaker 2: Then there's R for the redesign rule. This acknowledges that
Speaker 2: all design is essentially redesigning known patterns. We really aren't
Speaker 2: reinventing the wheel every time. And the t T is
Speaker 2: the tangibility rule making abstract ideas real. So drawing sketches
Speaker 2: are building models so people can actually point at them
Speaker 2: and talk about them. The good architect is really a
Speaker 2: mentor and a collaborator, not a dictator.
Speaker 1: So they're constantly just trying to understand what the stakeholders
Speaker 1: actually want in the real world.
Speaker 2: Yes, exactly, through tools like empathy mapping and stakeholder maps.
Speaker 2: Keeling uses a great case study in the book called
Speaker 2: Project Lionheart. Oh right with the Yeah, you have the
Speaker 2: mayor of the city, a guy named Jean Claude van
Speaker 2: Dam and no relation to.
Speaker 1: The actor, which is a shame honestly true.
Speaker 2: But anyway, he wants to modernize the city's Office of
Speaker 2: Management and Budget. His goal cut procurement costs by thirty percent.
Speaker 1: Okay, Chris, standard executive.
Speaker 2: Goal, right, But then you look at the actual end users,
Speaker 2: the people doing the work in that office. Their primary
Speaker 2: pain point is that it takes them six weeks to
Speaker 2: publish a single a riding request for proposal.
Speaker 1: Oh wow, so those are totally conflicting motivation completely. I mean,
Speaker 1: the mayor wants a cheap a process, but the users
Speaker 1: desperately want a faster process. If you only listen to
Speaker 1: the mayor, you build a cheap system that is slow
Speaker 1: and miserable to use.
Speaker 2: And if you only listen to the users, you build
Speaker 2: a lightning fast system that completely bankrupts the city.
Speaker 1: Right, And that tension right there is exactly why empathy
Speaker 1: mapping is so critical. You have to map out those
Speaker 1: conflicting needs because they directly shape the technical constraints of
Speaker 1: the system you're about to build.
Speaker 2: Exactly. You can't code your way out of a fundamentally
Speaker 2: human disagreement.
Speaker 1: But wait, if human needs are this complex and they're
Speaker 1: constantly conflicting, how do we ever decide when to stop
Speaker 1: talking and start typing? Because you know, developers, we get
Speaker 1: very antsy, oh very antsy, Like if we try to
Speaker 1: perfectly map every single human emotion to a piece of code.
Speaker 1: We're going to be planning until the heat death of
Speaker 1: the universe.
Speaker 2: Yeah, and that brings us to a really important concept
Speaker 2: called bounded rationality. It was coined by a guy named
Speaker 2: Herbert Simon.
Speaker 1: Bounded rationality. What does that mean? In this context?
Speaker 2: It's basically the realization that humans have hard limits. We
Speaker 2: have limits in time, money, cognitive capacity, and just available knowledge.
Speaker 2: Because of bounded rationality, it is mathematically impossible to find
Speaker 2: the single optimal design for a complex software system.
Speaker 1: Okay, so perfection is a literal.
Speaker 2: Myth, a literal mathematical impossibility. So instead of chasing this
Speaker 2: perfect optimal design, architects look for a design that satisfies, satisfices.
Speaker 1: I love that one.
Speaker 2: It's great, right, It's a portmanteau of satisfactory and sufficient.
Speaker 2: You look for a design that is good enough to
Speaker 2: solve the primary problems and reduce your major risks, and
Speaker 2: then you just move on.
Speaker 1: Okay, here's where it gets really interesting to me, because
Speaker 1: I want to push back on behalf of all the
Speaker 1: impatient developers listening right now, go for There is this
Speaker 1: famous saying in agile software development, Right, a month of
Speaker 1: coding can save an hour.
Speaker 2: Of architecting, right. The classic joke.
Speaker 1: Yeah, the joke being that if you just start building,
Speaker 1: you'll figure it out as you go. So why don't
Speaker 1: we just build things quickly, get it in front of users,
Speaker 1: and fix it later when we actually know what the
Speaker 1: real world problems are.
Speaker 2: It is very tempting philosophy, especially in start of culture. Yeah,
Speaker 2: but the data just completely destroys it. Keeling brings in
Speaker 2: this research from Barry Bahem on the sweet spot of
Speaker 2: design time, and the numbers are just staggering.
Speaker 1: Lay the numbers on me.
Speaker 2: Okay, imagine you have one hundred day schedule for a
Speaker 2: medium to large project. Let's say it's about one hundred
Speaker 2: thousand lines.
Speaker 1: Okay, one hundred days pretty standard.
Speaker 2: If you spend just five percent of your time, so
Speaker 2: five days on architecture, you will end up increasing your
Speaker 2: total schedule by forty three percent.
Speaker 1: Wait, forty three percent.
Speaker 2: Yes, due to massive expensive rework. You build the wrong thing,
Speaker 2: you realize it doesn't scale, you have to tear it down,
Speaker 2: you break other things in the process, and you basically
Speaker 2: start over.
Speaker 1: Oh my god. So trying to save time by rushing
Speaker 1: the design phase actually adds almost half the project time
Speaker 1: back on in penalties.
Speaker 2: That is brutal, it really is. But it gets better
Speaker 2: if you increase your upfront architecture time to seventeen percent,
Speaker 2: so seventeen days out of the one hundred, the total
Speaker 2: schedule increase drops to thirty eight percent.
Speaker 1: Okay, think about that math for a second. You spend
Speaker 1: more time planning upfront, but your overall delivery time goes
Speaker 1: down because you aren't doing all that rework exactly.
Speaker 2: It saves you time in the long run.
Speaker 1: But you can't just plan forever, right, because, like we said,
Speaker 1: bounded rationality correct.
Speaker 2: Boem's data also shows that if you spend thirty three
Speaker 2: percent of your time on architecture, you hit severe diminishing returns.
Speaker 2: The schedule inflates again because of analysis paralysis.
Speaker 1: Right, you're just staring at a whiteboard making no progress.
Speaker 2: Yeah, so there's a mathematical sweet spot and it's usually
Speaker 2: around fifteen to twenty percent of the project time.
Speaker 1: Okay, I buy the sweet spot, but seventeen days is
Speaker 1: still a long time to just stare at a whiteboard.
Speaker 1: How does an architect know what to actually focus on
Speaker 1: during that time so they aren't just you know, spinning
Speaker 1: their Reels.
Speaker 2: Well, you focus on risk. Risk driven architecture is how
Speaker 2: you decide when to stop designing. Keeling offers this brilliant,
Speaker 2: simple framework for identifying engineering risks.
Speaker 1: How does it work?
Speaker 2: It uses a condition might consequence format. So, for example,
Speaker 2: here's one from the book. Condition a new burrito restaurant
Speaker 2: opened across the street. Consequence the team might get sick
Speaker 2: eating too many burritos.
Speaker 1: That is a highly realistic engineering risk, absolutely.
Speaker 2: But notice how specific it is. It's not just I
Speaker 2: have a bad feeling about lunch. By identifying the specific
Speaker 2: condition and the potential consequence, an architect can actually mitigate
Speaker 2: it right, Like.
Speaker 1: Maybe you cater lunch on release days so the team
Speaker 1: doesn't go eat the risky burritos.
Speaker 2: Up exactly in software, Once the major architectural risks are
Speaker 2: reduced to an acceptable level, you stop actively designing. You
Speaker 2: shift to what he calls passive design.
Speaker 1: Passive design meaning what they just sit back and.
Speaker 2: Watch kind of. The team starts writing code and the
Speaker 2: architect simply monitors the system to ensure the original assumptions
Speaker 2: hold up.
Speaker 1: Okay, so we use risk to figure out when to
Speaker 1: stop designing. But that naturally leads to the question of
Speaker 1: what we are actually measuring those risks against, which means
Speaker 1: we need to talk about asrs.
Speaker 2: Ah. Yes, architecturally significant requirement.
Speaker 1: Right, so what exactly makes a requirement architecturally significant?
Speaker 2: Asrs are the foundation. These are the requirements that will
Speaker 2: literally force you to raise your architecture to the ground
Speaker 2: and start over if you get them wrong. Keeling break
Speaker 2: them into four types.
Speaker 1: Okay, let's walk through those.
Speaker 2: You have constraints, quality attributes, influential functional requirements, and other influencers.
Speaker 1: Constraints seems pretty self explanatory.
Speaker 2: Right, Yeah. Constraints are unchangeable facts. Like the mayor says,
Speaker 2: Projects Linehart must be open source. You can't negotiate that.
Speaker 2: It limits your technology choices right out of the gate.
Speaker 1: What about influential functional requirements?
Speaker 2: Those are specific features that bend the whole system. So,
Speaker 2: for example, if your app needs to process credit.
Speaker 1: Cards, right, that's not just a simple button.
Speaker 2: No, it introduces massive security compliances, third party API integrations
Speaker 2: and data encryption standards that change the entire shape of
Speaker 2: your database. And other influencers That might be things like
Speaker 2: the existing skills of your team or maybe legacy systems
Speaker 2: you're forced to integrate.
Speaker 1: With got it, but the heavy hitter. The one the
Speaker 1: book focuses on most is quality attributes. These are the
Speaker 1: ellibilities right.
Speaker 2: Yes, scalability, availability, maintainability, portability, all the alliblities.
Speaker 1: And the crucial lesson here is that you cannot just
Speaker 1: write down We need it to be portable on a
Speaker 1: sticky note. I mean that means absolutely nothing to a developer.
Speaker 2: Nothing at all. You have to be incredibly precise. The
Speaker 2: book brings in this amazing scenario from the NASA Jet
Speaker 2: Propulsion Laboratory for a Mars rover.
Speaker 1: Oh I love this part. It shows how specific you
Speaker 1: have to be exactly.
Speaker 2: They define a highly specific six part scenario just for portability.
Speaker 2: The stimulus is running a test suite on a new
Speaker 2: hardware platform. The source of the stimulus is the test team.
Speaker 2: The artifact being tested is the mission flight control system.
Speaker 1: It's so detailed it goes on.
Speaker 2: The response is that the system runs on the new hardware.
Speaker 2: The response measure, and this is the critical part, is
Speaker 2: that all tests pass with no code changes. And the
Speaker 2: environment is the testing lab that.
Speaker 1: Is surgically precise, like you can actually write a test
Speaker 1: for that. Yeah, but it also means you have to
Speaker 1: make choices, because obviously you can't have infinite scalability, perfect security,
Speaker 1: and instant performance all.
Speaker 2: At the same time as trade offs, which.
Speaker 1: Brings up my absolute favorite analogy in the whole book.
Speaker 1: The chainsaw blender.
Speaker 2: Oh the ultimate lesson in trade offs.
Speaker 1: Right, So, imagine you want to make a smoothie. You
Speaker 1: have a standard kitchen blender. It sits on your counter.
Speaker 1: It's quiet, it's safe, but you know it's tethered to an.
Speaker 2: Electrical outlet, very restricted.
Speaker 1: Then you have a battery powered hand blender. It's portable,
Speaker 1: you can take it camping, but it's really weak it
Speaker 1: won't crush ice. And then you have the chainsaw blender.
Speaker 1: You attach a blender pitcher to a gas powered two
Speaker 1: stroke motorcycle throttle chainsaw engine naturally, Naturally, it is insanely powerful.
Speaker 1: It is entirely portable, but it is horribly loud, it
Speaker 1: spews toxic exhaust, and it is wildly unsafe for indoor use.
Speaker 2: A slight drawback.
Speaker 1: Right, So, if someone tells me they want a blender,
Speaker 1: and I build them a chainsaw blender, because I assumed
Speaker 1: their primary goal was power, I have completely failed. If
Speaker 1: their actual goal was just making a quiet safe morning
Speaker 1: smoothie exactly.
Speaker 2: And if we connect this to the bigger picture, the
Speaker 2: chainsaw blender illustrates that every single architectural decision trades one
Speaker 2: quality attribute for another. You trade safety and quietness for
Speaker 2: power and portability.
Speaker 1: So how do we make sure we aren't building chainsaw
Speaker 1: blenders for people who just want a quiet smoothie.
Speaker 2: Architects use a tool called a decision matrix to visually
Speaker 2: map these trade offs out.
Speaker 1: How does a decision matrix actually work in practice? Like
Speaker 1: in a room full of engineers, you.
Speaker 2: List your architectural pattern options on one axis, say a
Speaker 2: three tier pattern, a published subscribe pattern and apports an
Speaker 2: adapter's pattern, and then on the other axis you list
Speaker 2: the quality attributes you prioritize, like maintainability, scalability, and performance.
Speaker 2: Then you score them.
Speaker 1: And this is where we actually need to explain how
Speaker 1: these patterns work mechanically, because they aren't just you know, buzzwords.
Speaker 2: Let's do it. Three tier pattern separates your system into
Speaker 2: presentation which is the user interface, logic which is the brain,
Speaker 2: and data the database.
Speaker 1: Pretty standard it is.
Speaker 2: It's great for maintainability because they're separate, but if millions
Speaker 2: of users hit it at once. It can really bottleneck. Now,
Speaker 2: look at published subscribe or pubsub.
Speaker 1: Okay, how does that one work?
Speaker 2: Think of it like a magazine subscription. A data producer
Speaker 2: just publishes a message to a central hub and it
Speaker 2: doesn't care who reads it. The consumers just subscribe to
Speaker 2: the hub to get their copies.
Speaker 1: So the publisher and the subscriber never actually talk directly
Speaker 1: to each other exactly.
Speaker 2: It's incredibly flexible and scalable because you can add a
Speaker 2: thousand subscribers without touching the publisher. But it trades off
Speaker 2: strict data consistency.
Speaker 1: Because you can't guarantee exactly when a subscriber actually reads
Speaker 1: the message.
Speaker 2: Right. Then you have ports and adapters, which is also
Speaker 2: called hexagonal architecture. Imagine you have a core piece of
Speaker 2: logic that calculates taxes. The port is the interface plug,
Speaker 2: and the adapter is a piece of code that translates
Speaker 2: SQL database data into a format your tax calculator can read.
Speaker 1: So if I want to switch from a SQL database
Speaker 1: to a no SQL database tomorrow, I don't have to
Speaker 1: rewrite my time logic. I just write a new adapter
Speaker 1: to plug.
Speaker 2: Into the port precisely. It gives you incredible testability and isolation.
Speaker 2: But it requires writing a lot of extra boilerplate code
Speaker 2: for all those adapters. So the decision matrix forces the
Speaker 2: team to look at the mechanics of these patterns and
Speaker 2: visually acknowledge what they're giving up to get what they want.
Speaker 1: It ensures you don't build a chainsaw blender by accident exactly.
Speaker 1: That makes the redesigned rule of heart makes so much sense.
Speaker 1: We aren't inventing new shapes, we are just picking the
Speaker 1: right established pattern. But Keeling also warns about an anti pattern,
Speaker 1: doesn't he the dreaded big ball of mud.
Speaker 2: Oh, the big ball of mud that is the default
Speaker 2: architecture of a system that wasn't designed at all. It's
Speaker 2: just a tangle of undisciplined coding, unchecked technical debt, and
Speaker 2: completely bypassed patterns.
Speaker 1: Basically a nightmare.
Speaker 2: Yeah, every piece of code is glued to every other
Speaker 2: piece of code, so if you try to fix a
Speaker 2: tiny bug in the login screen, you somehow break the
Speaker 2: checkout cart.
Speaker 1: But wait, I see a paradox here and say, we
Speaker 1: do everything right, We map our human needs, we use
Speaker 1: our decision matrix, and we pick a highly decoupled, beautiful
Speaker 1: published subscribe pattern. But then there's Conway's law.
Speaker 2: Ah. Conway's law the inescapable gravity of software engineering.
Speaker 1: Right.
Speaker 2: It states that a system's designed will inevitably mimic the
Speaker 2: communication structures of the organization building it.
Speaker 1: So, if I pick that beautiful pub sub pattern on paper,
Speaker 1: but my actual human development teams are completely siloed, they
Speaker 1: hate each other, and they never communicate, Conway's law says,
Speaker 1: my architecture is going to fail anyway. It's just going
Speaker 1: to mimic their dysfunction.
Speaker 2: Yes. Absolutely, if you have three teams that don't communicate,
Speaker 2: you will inevitably build a system with three disconnected, incompatible components,
Speaker 2: regardless of what that whiteboard diagram says. Conway's law is
Speaker 2: a two way street. If you, as the architect, want
Speaker 2: to change the technical architecture of the software, you very
Speaker 2: often have to reorganize the human teams building it. You
Speaker 2: have to align the human communication paths with the desired
Speaker 2: software communication.
Speaker 1: Paths, which brings us completely full circle back to the
Speaker 1: heart principles. The human element is inseparable from the technical patterns.
Speaker 1: You literally cannot fix the code without fixing the people.
Speaker 2: Which is exactly why in architect's most vital tool isn't
Speaker 2: a coding language at all. It's a shared vocabulary. To
Speaker 2: overcome Conway's law and prevent that big ball of mud,
Speaker 2: the team needs a common meta model. They have to
Speaker 2: agree on what things.
Speaker 1: Are called, which sounds easy, but developers love to argue
Speaker 1: about naming conventions.
Speaker 2: Well, they love it.
Speaker 1: The book breaks this down using Arlow Belshie's Seven Stages
Speaker 1: of naming, which I found fascinating. It shows how the
Speaker 1: literal name of a variable in your code evolves as
Speaker 1: your human understanding of the system evolves.
Speaker 2: Naming is actually a great metric for how well you
Speaker 2: understand your own design. The first stage is missing. You
Speaker 2: don't even know enough to name the concept yet. Stage
Speaker 2: two is nonsense, nonsense like what well bellschie uses the
Speaker 2: example of naming a component cranbar cranberry. Yeah, it means
Speaker 2: absolutely nothing. It's just a placeholder. So the code compiles.
Speaker 2: But as you refine the architecture, you move through stages
Speaker 2: like honest and honest and complete, where the name describes
Speaker 2: mechanically what the code does.
Speaker 1: Like naming it data fetcher, checker, transformer and job starter.
Speaker 2: It's honest, but I mean it's incredibly clunky because you
Speaker 2: are still thinking like a machine. The ultimate goal is
Speaker 2: Stage seven domain abstraction. Instead of that clanky name, you
Speaker 2: realize what the component truly is in the context of
Speaker 2: the business domain. You reach a profound abstraction and name
Speaker 2: it something like data preparation Agent.
Speaker 1: Ah. So suddenly anyone looking at the code knows exactly
Speaker 1: what its purpose is without having to read one hundred
Speaker 1: lines of logic exactly. But how do you get a
Speaker 1: whole team of very opinionated engineers to arrive at those
Speaker 1: stage seven names and agree on complex patterns before the
Speaker 1: project just runs out of money? I mean, you can't
Speaker 1: just debate it in a conference room forever.
Speaker 2: That brings us to the sharette, also known as the
Speaker 2: architecture design studio.
Speaker 1: I loved the historical anecdote for this in the book.
Speaker 1: Back in nineteenth century France, architecture students that the ecolt
Speaker 1: des bizarre. Would you know, procrastinate as students do, and
Speaker 1: when the professor came around with a wooden cart a
Speaker 1: charette to collect their physical models for grading, the students
Speaker 1: would literally follow the cart down the hall. They'd be
Speaker 1: feverishly gluing balsa wood and wire onto their models on
Speaker 1: sharette in the cart, right up to the very last second.
Speaker 2: It's such a vivid image and it perfectly captures the
Speaker 2: essence of the modern design studio, extreme time constraints. A
Speaker 2: design studio is a highly structured time box workshop. It
Speaker 2: relies on a relentless cycle of create, share, critique, and iterate.
Speaker 1: So how does a cycle actually look?
Speaker 2: You give small teams just five or ten minutes to
Speaker 2: sketch a technical solution to a specific architectural problem. Then
Speaker 2: they pitch it to the room. The room critiques it
Speaker 2: based only on facts and the quality attributes from your
Speaker 2: decision matrix, not personal preference.
Speaker 1: Right, no arguing over I just like this better exactly.
Speaker 2: Then you immediately force them to iterate and do it again.
Speaker 1: So what does this all mean? It's basically like speed
Speaker 1: dating for architectural solutions. You don't just marry the first
Speaker 1: idea that comes to your head. You meet a dozen
Speaker 1: different ideas in a single afternoon. You figure out what
Speaker 1: actually works, combine the best traits of all of them,
Speaker 1: and most importantly, you avoid committing your entire project to
Speaker 1: a cranberry.
Speaker 2: Yes, the ticking clock forces you to cheat that bounded
Speaker 2: rationality we talked about earlier, and the group critique completely
Speaker 2: destroys groupthink.
Speaker 1: I can see how that generates a massive amount of
Speaker 1: buy in too, because everyone had a hand in the design.
Speaker 2: It does. But this ray is a really important question.
Speaker 2: After the share it is over, the sticky notes are
Speaker 2: in the trash and the whiteboards are erased. How do
Speaker 2: we ensure these great ideas actually survive in the code base?
Speaker 1: Yeah? How do you enforce it without like standing over
Speaker 1: a developer shoulder stop them from going rogue on day three?
Speaker 2: You have to physically shrink the gap between the model
Speaker 2: and reality. Keeling calls this using an architecturally evident coding
Speaker 2: style okay, meaning what it means the vocabulary of the
Speaker 2: architecture is physically baked into the file structure. If you
Speaker 2: share it decided on a data preparation agent, there better
Speaker 2: be a directory in the code base called exactly that.
Speaker 1: Oh, I see.
Speaker 2: Yeah. If you chose a three per pattern, the root
Speaker 2: folders in your repository should be named presentation, logic, and data.
Speaker 2: You don't rely on human discipline to enforce the architecture.
Speaker 2: You encode the architectural rules into the physical structure of
Speaker 2: the software itself, so if someone tries to bypass the pattern,
Speaker 2: the code literally won't compile.
Speaker 1: That is brilliant. Okay, let's zoom out for a second.
Speaker 1: If you are listening to this right now, why does
Speaker 1: all of this matter to you?
Speaker 2: What we've learned from Michael Keeling is that being a
Speaker 2: software architect isn't just about knowing the most programming languages
Speaker 2: or writing the slickest algorithms in complete isolation, Not at all.
Speaker 2: It's fundamentally about empathy. It's about translating complex human needs
Speaker 2: into precise textrical constraints. It's about mathematically managing risk so
Speaker 2: you don't waste months of people's lives on rework.
Speaker 1: It's about surviving Conway's law by aligning your human teams
Speaker 1: with your code structure. Perhaps most importantly, it's about knowing
Speaker 1: how to avoid building a chainsaw blender when a simple, quiet,
Speaker 1: multi tier pattern is exactly what your user actually needed.
Speaker 2: And you know, we connect this to the bigger picture
Speaker 2: of where the tech industry is heading. Right now, this
Speaker 2: human centric approach becomes even more vital. How so, well,
Speaker 2: we are entering an era where AI can instantly generate
Speaker 2: thousands of lines of functional code based on a simple prompt.
Speaker 2: The sheer mechanical act of typing out a program is
Speaker 2: rapidly becoming a commodity.
Speaker 1: Wow, yeah, that's true.
Speaker 2: So what is left the architect's skill set? Navigating human empathy,
Speaker 2: defining those incredibly specific quality attributes, managing organizational kindyation, and
Speaker 2: facilitating these collaborative design studios. These are not things in
Speaker 2: AI can do.
Speaker 1: No, not at all.
Speaker 2: Architecture isn't just going to be a leadership role. It
Speaker 2: might soon become the only strictly human job left in
Speaker 2: software engineering.
Speaker 1: That is a staggering thought. The code itself becomes the
Speaker 1: cheat part, but the human translation of why we are
Speaker 1: building it becomes the priceless part.
Speaker 2: Exactly.
Speaker 1: Well, thank you so much for joining us on this
Speaker 1: deep dive into Michael Keeling's work. As you head back
Speaker 1: to your desk or jump into your next sprint planning meeting,
Speaker 1: I want you to take a hard look at your
Speaker 1: current projects and ask yourself a question. Are you actively
Speaker 1: designing your architecture or is it designing you? Make sure
Speaker 1: you check the ground before you pour that concrete. We'll
Speaker 1: catch you next time.