Vibe Engineering, Part II

In the first part of this series, we laid the foundation. We talked about the journey from “vibe coding” to a more disciplined, architectural approach.

We established the need for a blueprint, much like you’d need for a skyscraper versus a backyard shed. We covered the bedrock principles of SOLID, DRY, and KISS, the fundamental rules that guide us toward writing code that is robust, maintainable, and clean. These principles are the “why” of good code, the core truths that underpin great software design.

Now, we move from the “why” to the “how.”

If principles are the laws of physics for software, then design patterns are the engineering techniques and tools you use to build the structure.

You don’t have to re-derive the formula for a load-bearing arch every time you build a bridge; you use a proven, established design. Similarly, in software, countless engineers before us have faced the same common problems and have developed elegant, reusable solutions.

These solutions are called design patterns.

At first they overwhelmed me, now they are source of incredible comfort. Launching points to solve problems.

This isn’t about memorizing a catalog of rigid formulas. That’s what I thought at first, and why I initially resisted software engineering. Now I see it differently.

It’s about building your infinite toolkit.

Think of a master chef. They don’t just “vibe” their way through every dish. They have a deep understanding of foundational techniques: a mother sauce, a perfect pastry dough, the right way to sear a steak. Design patterns are our mother sauces. They are recipes for solving recurring design problems that you can adapt, combine, and use to build more sophisticated and flexible systems. Knowing them frees you from reinventing the wheel and allows you to focus your creative energy on the unique challenges of the business problem you’re actually trying to solve.

These patterns fall (loosely, but I did my best!) into three categories:

• Creational Patterns: These provide ways to create objects while hiding the creation logic, rather than instantiating objects directly using the new operator. This gives you more flexibility in deciding which objects need to be created for a given use case.

• Structural Patterns: These are about how classes and objects are composed to form larger structures. They help ensure that if one part of a system changes, the entire system doesn’t need to change with it.

• Behavioral Patterns: These are concerned with communication between objects, identifying common communication patterns and realizing these patterns.

Let’s stock our toolkit with some of the most essential patterns from each category.

Ready to truly become a builder?

Creational Patterns: How Objects Are Made

The way you bring objects into existence has a profound impact on your application’s flexibility and complexity. These patterns help you manage the creation process with intention.

Singleton: The One and Only

The Problem: Sometimes, you need to ensure that a class has only one instance, and you need to provide a single, global point of access to it. Think of a system-wide configuration manager, a connection pool to a database, or a logging service that all parts of your application need to write to. Having multiple instances of these would be inefficient at best and catastrophically inconsistent at worst.

The Solution: The Singleton pattern restricts the instantiation of a class to a single object. The first time the singleton is requested, the class creates a new instance of itself and stores it privately. On every subsequent request, it simply returns that stored instance.

In my world, when building a system that pulls data from a financial API, I need to manage my API key and rate limits carefully. I would encapsulate this logic in a FinancialDataService class. If different parts of my application created their own instances of this service, I might quickly exhaust my API call limit or have trouble managing authentication. By implementing it as a Singleton, I guarantee that every part of the system goes through the exact same instance, which can intelligently manage the connection, cache results, and handle rate-limiting for the entire application.

A word of caution: The Singleton is a powerful pattern, but it’s also one of the most easily abused. It can make testing more difficult because it introduces a global state into your application. Like any sharp tool, it should be used deliberately and only when you are absolutely certain that there must be one, and only one, instance of an object.

Factory Method: Let the Subclasses Decide

The Problem: Imagine you have a framework that needs to create objects, but the exact type of object to create will vary. You don’t want the framework’s code to be tightly coupled to a bunch of specific class names. You want to delegate the responsibility of creation to the code that uses the framework.

The Solution: The Factory Method pattern provides an interface for creating objects in a superclass but lets subclasses alter the type of objects that will be created.

Let’s say I’m building a system for processing financial documents. The system needs to handle various types of filings: 10-K (annual report), 10-Q (quarterly report), and 8-K (current event report). The overall process is similar for each: fetch the document, parse it, and extract key information. However, the parsing logic is completely different for each document type.

The naive approach would be a giant if-else statement: if doc_type == ‘10-K’: parser = TenKParser() elif doc_type == ‘10-Q’: .... This is brittle. When a new document type comes along, I have to modify this central piece of logic.

The Factory Method pattern offers a cleaner way. I can create an abstract DocumentProcessor class. This class has a method that defines the overall workflow, like process(), but it declares an abstract create_parser() method—the factory method. Then, I create concrete subclasses like TenK_Processor and TenQ_Processor. Each of these implements the create_parser() method to return its specific parser instance (TenK_Parser or TenQ_Parser).

My main application code can now work with the generic DocumentProcessor and be completely unaware of the specific parser classes. It decouples the “what” (processing a document) from the “how” (parsing a specific format).

Structural Patterns: How Objects Relate

Once you have your objects, you need to assemble them into something useful. Structural patterns are about composing these objects into larger structures while keeping those structures flexible and efficient.

Adapter: The Universal Translator

The Problem: You need to make two incompatible interfaces work together. You have a component that works perfectly fine, but its interface doesn’t match what the client code is expecting. You can’t change the component (perhaps it’s a third-party library), and you don’t want to change all your client code.

The Solution: The Adapter pattern acts as a bridge between two incompatible interfaces. It’s a special object that wraps one of the objects to make it compatible with the other.

This happens all the time when I’m layering new technology into existing companies. A company might have a well-established internal system for customer analytics that expects data in a certain format, let’s say with a method analyze(customer_data). I want to plug in a powerful new machine learning model I’ve built, but my model’s interface is predict_churn_probability(customer_features). The interfaces are incompatible in both name and the data they expect.

Instead of rewriting my model or their system, I create an AnalyticsAdapter. This adapter implements the analyze(customer_data) method that the legacy system expects. Inside that method, it transforms the customer_data into the customer_features format that my model needs, calls my model’s predict_churn_probability method, and then formats the result back into whatever the legacy system expects. It’s like a diplomat translating between two parties who speak different languages, allowing for productive communication without forcing either one to change.

Decorator: Adding Layers of Responsibility

The Problem: You want to add new functionality to an object without altering its source code. You might want to add this functionality to some objects of a class but not others, and you might want to do it at runtime. Using inheritance to create subclasses for every possible combination of features would lead to a “class explosion” and become unmanageable.

The Solution: The Decorator pattern allows you to attach new behaviors to objects by placing them inside special wrapper objects that contain the behaviors. The wrapper has the same interface as the object it’s wrapping, so from the client’s perspective, nothing has changed.

Imagine a simple object whose job is to fetch data from a database. Now, we decide we need to add logging to every data request. Then, we need to add caching to avoid hitting the database too often. Then, we need to add a layer that checks user permissions before allowing the data fetch.

Instead of creating a LoggingCachingPermissionedDataFetcher class, we use decorators. We have our core DataFetcher object. We can then wrap it in a LoggingDecorator. This decorator will log the request and then delegate the actual fetch call to the DataFetcher object inside it. We can then take that combined object and wrap it in a CachingDecorator. This decorator will first check its cache. If the data is there, it returns it; if not, it calls the fetch method on the object it’s wrapping (the LoggingDecorator), which in turn calls the original DataFetcher.

You can stack these decorators like Russian nesting dolls, combining functionalities in any order you want, all without ever touching the code of the original DataFetcher.

Behavioral Patterns: How Objects Communicate

Great systems are not just about well-structured objects; they’re about how those objects collaborate and communicate. Behavioral patterns focus on the assignment of responsibilities and the algorithms of communication between objects.

Observer: The “Subscribe and Notify” Pattern

The Problem: You have a “one-to-many” relationship between objects. When the state of one object (the “subject”) changes, multiple other objects (the “observers”) need to be notified and updated automatically. You want to do this without coupling the subject to the observers. The subject shouldn’t need to know anything about its observers other than that they implement a certain interface.

The Solution: The Observer pattern defines a subscription mechanism. The subject maintains a list of its observers. When its state changes, it automatically iterates through this list and calls a notification method on each observer object.

In the world of finance, a MarketDataFeed is a perfect subject. It receives a constant stream of new stock prices. Many different parts of an application might be interested in these price updates. A TradingAlgorithm needs to know the new price to decide whether to buy or sell. A PortfolioManager needs it to re-calculate the portfolio’s value. A DashboardUI needs it to update the charts and tickers on a screen.

The MarketDataFeed doesn’t know or care about any of these components. It just allows them to “subscribe” for updates. When a new price for Google comes in, it notifies all its subscribers, passing them the new price data. The TradingAlgorithm can then execute its logic, and the DashboardUI can redraw itself. If we want to add a new AlertingSystem that sends an SMS if a stock drops by 5%, we just create a new observer and subscribe it to the feed. We don’t have to change the MarketDataFeed at all.

Strategy: Making Algorithms Interchangeable

The Problem: You need to perform a certain task, but there are multiple different algorithms or ways to do it. You want to be able to switch between these algorithms easily, perhaps even at runtime, without your client code being full of if-else statements.

The Solution: The Strategy pattern suggests you identify a family of related algorithms, encapsulate each one in its own separate class, and make their objects interchangeable. The main object, called the “context,” holds a reference to one of the strategy objects and delegates the work to it.

When I’m building a machine learning system to optimize a company’s go-to-market strategy, I might experiment with different modeling approaches. For predicting which customers are likely to buy a product, I could use a Logistic Regression model, a Gradient Boosting model, or a Neural Network. Each of these is a different “strategy” for solving the same problem.

My core application logic shouldn’t have to know the details of each model. Instead, I can define a common PredictionStrategy interface with a predict(data) method. Then I create LogisticRegressionStrategy, GradientBoostingStrategy, etc., each implementing this interface. My main application is configured to use one of these strategy objects. When it needs a prediction, it just calls strategy.predict(data). I can easily swap out the Gradient Boosting model for the Neural Network by simply changing which strategy object is configured, all without touching the core application code.

This makes A/B testing models or upgrading them in the future incredibly simple.

Tactics for Cleaner Code

Principles and patterns provide the high-level structure, but true craftsmanship is also found in the details. These are the day-to-day habits that elevate your code from merely functional to truly professional.

The Power of Meaningful Names

Code is read far more often than it is written.

Vague or misleading names force the next person who reads your code (which is often your future self) to waste mental energy trying to decipher your intent. A variable named d is meaningless.

A variable named elapsed_time_in_seconds is perfectly clear. A function called processData() is a mystery. A function called calculate_quarterly_revenue_from_sales_data() tells you exactly what it does. In finance and corporate strategy, precision in language is paramount; the same applies to code.

Good naming is a hallmark of a professional developer.

Comments That Explain the “Why”

A common mistake is to write comments that explain what the code is doing.

For example, // a = a + 1. This is useless; the code itself is perfectly clear. Good code should be self-documenting in what it does. Where comments become invaluable is in explaining the why. Why was a particular design choice made? What business rule or constraint led to this seemingly odd piece of logic?

A comment like // We’re using a linear search here because extensive testing showed the customer list rarely exceeds 50 items, and the simplicity outweighs the performance gain of a more complex algorithm is worth its weight in gold to the next developer.

Also: stops the next guy from wasting everyone’s time “hey, I’ve got an idea!”.

Graceful Error Handling as a Feature

In the rush of vibe coding, it’s easy to assume the “happy path”. Easy mode. Assuming that the network will always be available, the file will always exist, and the user input will always be valid.

Professional, reliable systems are built on the opposite assumption: things will fail. Error handling shouldn’t be an afterthought; it should be an integral part of your design. Anticipate potential failures, use try-catch blocks, provide clear error messages, and have a defined strategy for recovery. This is what transforms a fragile prototype into a reliable revenue engine.

These patterns and tactics are your toolkit for moving beyond the initial spark of an idea. They provide the structure and the vocabulary to build complex systems intentionally. They allow you to stand on the shoulders of giants, using proven solutions to solve common problems so you can focus your unique creative energy on what truly matters: delivering powerful, reliable, and efficient systems that solve real business challenges.

In the final part of our series, we’ll look at the craft of building for the long haul. How to use testing, version control, and automation to ensure your software is not just well-designed, but also professionally managed throughout its entire lifecycle.

This is how you elevate from a craft builder to a true engineer.

A vibe engineer.. but an engineer nevertheless.

Next time out we’ll talk about the practical approach to using vibe coding tools to build enterprise grade software.

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