Vibe Engineering, Part I
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Point blank: I am getting busy.
Appearing on podcasts, speaking at conferences and investing in tech start-ups. I just co-founded an amazing AI company. All of this is also competing with the 3 publications I write:
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I wasn’t always a software building geek.
My first world was numbers, deals, and high-stakes finance. It was there, co-founding a hedge fund that used machines to read financial statements, that I got the bug. I saw firsthand how technology could create leverage, how a well-written algorithm could process information and find insights faster and more reliably than any team of analysts. That was it for me. Since 2011, I’ve been investing in and building technology, and I’ve never looked back.
This path, from finance to tech, has given me a unique perspective. I didn’t learn to code from a textbook; I learned by doing. I dove into machine learning, obsessed over Kaggle competitions, and spent countless hours at hackathons building systems to solve real-world problems. I know what it’s like to code from a place of pure intuition, passion, and a relentless drive to just make it work.
It’s a style of building that many of you might recognize: “vibe coding”.
Let’s be clear: vibe coding is not only valid, it’s often essential. It’s the raw, creative energy that turns an idea into a functioning prototype. It’s the spark that lets you build a quick script to automate a tedious task or spin up a proof-of-concept for a machine learning model over a weekend. It’s born from a deep, intuitive understanding of the problem you’re trying to solve. When you’re in the zone, you can feel the logic flowing, connecting the dots in a way that feels more like art than science. For a long time, that’s how I built things. It’s how I got better. It’s how many great founders and engineers get their start.
We recently hared a piece that explored how to build apps and digital properties in the age of AI:
But vibes have a limit.
I learned this lesson the hard way. The systems I build today for McDonagh Technologies aren’t just for hackathons. We work with everyone from bootstrapped start-ups to Fortune 500 firms, building computational intelligence and layering it into their core operations. Our mission is to translate cutting-edge science into performant, commercially successful tech. And when you’re powering a company’s revenue engine or deploying a machine learning system that a major firm relies on, “it worked on my machine” isn’t good enough. The system has to be powerful, efficient, and above all, reliable.
You can feel your way through a small project, but you can’t “vibe” your way to a scalable and maintainable application. The creative, intuitive approach that gets you from zero to one can lead to chaos as you try to get from one to one hundred. The code becomes a tangled web of dependencies, a small change in one place breaks something seemingly unrelated, and bringing on a new team member requires weeks of just explaining how the labyrinthine logic works.
Think of it like building a structure.
You can totally build a shed in your backyard on vibes.
You grab some wood, some nails, and you just start putting it together. You might make a few mistakes, but at the end of the day, you’ll have a shed. It will stand, and it will keep the rain off your lawnmower. But you would never, ever try to build a skyscraper that way. A skyscraper requires a blueprint. It needs a deep understanding of physics, materials science, and engineering principles. It requires a plan that ensures it can withstand high winds, support thousands of people, and last for a century.
This series is about giving you that blueprint. It’s about adding a set of powerful, proven organizing principles to your toolkit so that you can move from building sheds to building skyscrapers. We’re going to bridge the gap between vibe coding and intentional, disciplined software architecture. This isn’t about crushing your creativity with rigid rules.
This is about channeling that creativity into building something truly robust, scalable, and lasting.
SOLID Principles
When you start to study software architecture, one of the first things you’ll encounter is the set of principles known as SOLID. Coined by Robert Martin, these five principles are the absolute bedrock of modern object-oriented design.
A word of warning: at first, they can be a bit academic and abstract. But once you internalize them, they become a kind of professional intuition that guides you toward writing cleaner, more maintainable, and more flexible code.
Let’s break them down with practical examples, not jargon.
S - Single Responsibility Principle
“Do one thing and do it well.”
This is the simplest to understand but arguably the most important. It states that a class or module should have only one reason to change. Think about a system I might build to analyze financial reports. In a vibe-coded approach, I might have a single, massive script that does everything: it fetches the report from the SEC database, parses the text, extracts the key financial numbers, calculates ratios, and then generates a PDF summary.
What happens when the SEC changes its API? The whole script might break. What if we want to change the output from a PDF to a web dashboard? We have to dig into the same massive file. The script has too many responsibilities.
SRP tells us to break this up. We should have one module whose only job is to fetch data. Another module’s only job is to parse text. A third’s only job is to perform calculations. And a final one’s only job is to generate the output. Now, if the data source changes, we only have to update the data-fetching module. The other parts of the system are unaffected. Each piece is simple, focused, and easy to understand.
O - Open/Closed Principle
“Open for extension, but closed for modification.”
This principle means that your code should be designed so that you can add new functionality without changing the existing, working code. Changing code that already works is risky; it can introduce bugs into a stable system.
Imagine we have that financial report generator. Let’s say it initially supports generating reports as PDFs. The naive approach would be to have a function with a big if-else block: if (format == ‘pdf’) { ... } else if (format == ‘csv’) { ... }. What happens when management wants to add HTML reports? Or JSON? We have to go back and modify that function, adding another else if. This violates the Open/Closed Principle.
A better way is to design a system where new formats can be added like plugins. We could define a ReportExporter interface with a method called export(). Then we create a PdfExporter class that implements this interface. When we need to add CSV exports, we simply create a new CsvExporter class. The core logic that orchestrates the report generation never needs to change. It’s open to new types of exporters but closed to modification.
L - Liskov Substitution Principle
“Don’t break the parent’s promise.”
This one sounds complex, but the idea is simple. If you have a base class and a subclass that inherits from it, you should be able to use the subclass anywhere you could use the base class without causing any unexpected behavior. The subclass must honor the “contract” of the parent class.
A classic (and somewhat humorous) example is the square-rectangle problem. Mathematically, a square is a type of rectangle. So, in code, you might create a Square class that inherits from a Rectangle class. A Rectangle has setWidth and setHeight methods. But a square must have equal sides. So, if you implement the Square class, you might make the setWidth method also change the height, and vice-versa, to maintain the “squareness.”
Now, imagine you have a function that takes a Rectangle object and does this: rect.setHeight(10); rect.setWidth(5); assert(rect.getHeight() == 10);
This function works perfectly for a Rectangle. But if you pass in your Square object, the assertion will fail! Setting the width to 5 would have also changed the height to 5. The Square subclass breaks the promise made by its Rectangle parent. This is a violation of LSP. It tells us that inheritance should be about behavior, not just “is-a” relationships from the real world.
I - Interface Segregation Principle
“Don’t force me to use what I don’t need.”
This principle is about keeping your interfaces lean and focused. It’s better to have many small, specific interfaces than one large, general-purpose one. Clients should not be forced to implement methods they don’t use.
Let’s go back to my world of machine learning. Imagine we have a generic MachineLearningModel interface. A “vibe-coded” version might include methods like train(), predict(), tune_hyperparameters(), explain_prediction(), and save_to_disk(). This seems fine, but not all models can do all these things. A simple, pre-trained model might only need the predict() and save_to_disk() methods. A complex deep learning model might need all of them. An unsupervised clustering model might not even have a predict() method in the traditional sense.
Forcing every model class to implement all those methods is cumbersome. Many would just be empty or throw an error. ISP tells us to break this “fat” interface into smaller, more focused ones, like Trainable, Predictor, Explicable. A class can then implement only the interfaces that make sense for it. This makes the system more flexible and the roles of each class clearer.
D - Dependency Inversion Principle
“Depend on abstractions, not on concretions.”
This final principle is about decoupling your code. It states that high-level modules (your core business logic) should not depend on low-level modules (like a specific database, file system, or network connection). Both should depend on abstractions (like interfaces).
When I’m building a revenue engine for a company, the high-level logic might be “calculate customer lifetime value.” A naive implementation might have this logic directly call a specific PostgreSQL database to get the data it needs. The code would be full of SQL queries.
What happens when the company decides to migrate to a different database, like Snowflake? Or what if we want to run a simulation using data from a CSV file instead of the live database? We’d have to rewrite our core business logic. This is brittle.
DIP advises us to create an abstraction, say a CustomerRepository interface. This interface would have methods like getCustomerById() or getAllPurchases(). Our high-level business logic would use this interface. Then, we create a concrete class, PostgresCustomerRepository, that implements the interface and contains the specific SQL code. If we switch databases, we just create a new SnowflakeCustomerRepository class. The core business logic—the most valuable part of our system—never has to change. It depends on the abstract idea of a “customer repository,” not the concrete implementation.
The Golden Rules: DRY and KISS
Beyond the five SOLID principles, there are two simpler, more universal rules that should always be at the forefront of your mind. When you layer SOLID with DRY and KISS, you build the framework of a successful software engineer.
Don’t Repeat Yourself (DRY)
This is one of the most fundamental principles in software engineering.
Every piece of knowledge in a system should have a single, unambiguous, authoritative representation. In simpler terms: don’t copy and paste code.
If you find yourself writing the same logic in multiple places, you need to abstract it into its own function or class.
When you have duplicated code, you create a maintenance nightmare. If you find a bug in that logic, you have to remember to fix it in every single place you pasted it. Inevitably, you’ll miss one, leading to inconsistent behavior and maddening bugs. Creating a single source of truth by putting that logic in one place means you only have to fix it once.
This is as true in software as it is in Master Data Management for a company’s revenue operations, a single source of truth is the key to reliability.
Keep It Simple, Stupid (KISS)
Engineers sometimes fall in love with complexity.
In fact, you will see “resume-driven development” and worse from engineers.
We want to build clever, intricate systems that show off our technical prowess. The KISS principle is a powerful reminder that this is usually a mistake. The best code is simple, readable, and straightforward. It’s easy to understand, easy to debug, and easy to modify.
When I build systems, my focus is on creating powerful, efficient, and reliable engines. Complexity is the enemy of all three. A complex system is harder to reason about, which makes it less efficient to maintain and less reliable in production. Resisting the urge to over-engineer is a sign of maturity and professionalism.
The goal is not to write the most clever code.
The goal is to solve the problem in the simplest, most robust way possible.
Introduction to Architectural Patterns
Finally, let’s zoom out.
The principles above guide how you write individual classes and modules. Architectural patterns guide how you structure your entire application. Just as there isn’t one single corporate strategy that works for every company, there isn’t one software architecture that’s right for every project.
The choice depends on the team, the budget, the timeline, and the specific problem you’re solving.
Monolith: This is the traditional all-in-one application. The user interface, business logic, and data access layer are all contained within a single codebase and deployed as a single unit. For a bootstrapped start-up or a new project, this is often the fastest and simplest way to get started. You don’t have to worry about the complexities of distributed systems.
Microservices: This pattern involves breaking the application down into a collection of small, independent services. Each service is built around a specific business capability, has its own codebase, and can be deployed independently. For a large company like a Fortune 500 firm, this allows different teams to work on different services autonomously, using the best tech stack for their specific job. It offers scalability and resilience but comes with significant operational overhead.
Understanding these high-level patterns is the first step in making conscious, strategic decisions about the structure of your software, rather than just letting it “happen.”
Moving from vibe coding to architected solutions is a journey.
It’s about evolving from an intuitive artist to a disciplined craftsperson who is also an artist.
The principles we’ve discussed today are the foundation of that craft.
They are the blueprint that will allow you to take your passion and intuition and build something truly powerful, reliable, and enduring.
In the next part of this series, we’ll get more tactical and look at the specific “recipes,” or design patterns, that you can use to solve common problems in your code.
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