I’m starting a series of practical projects to demonstrate that clean, simple architecture is the best way to build software. Here is the plan.
In my last post, I talked about why Simple Architecture is a superpower.
But talk is cheap. Code is what matters.
To prove that you don't need overengineered systems to build high-quality software, I’m starting a series of small, real-world projects that I’ll be sharing on GitHub.
My goal is twofold:
- To show that a clean, well-tested "boring" stack is often better than a complex trendy one.
- To help other developers see how to structure projects that are easy to maintain and scale.
Here is the "Mental Framework" I’m using to choose my tools and why this matters for your career too.
1. The "Boring Technology" Rule
For these projects, I’m avoiding the "flavor of the week" frameworks.
I’m choosing technologies that are stable, have great documentation, and a strong community. Whether it's Node.js, Python, or Go, the focus isn't on the language itself, but on how I solve problems with it.
Why? Because companies don't want a developer who experiments with their production code. They want someone who delivers reliable solutions.
2. Focus on "Core Logic" first
In the upcoming projects, you will notice that the business logic is separated from the framework.
If I’m building a CLI Tool (my first project!), the logic that calculates a task shouldn't care if it's running in a terminal or a web server. This is the heart of simple architecture.
3. README as a First-Class Citizen
A project without a good README is like a book without a cover.
For every repo I share, I’m focusing on:
- The "Why": What problem does this solve?
- The "How": Clear installation and usage instructions.
- The "Tests": Showing that the code works as expected.
How AI is Helping Me Prototype Faster
I'm using AI to speed up the "setup" phase of these projects. Here’s how:
- "Boilerplate Generation": Instead of spending an hour setting up a folder structure, I ask AI to generate a clean, modular structure based on my architectural rules.
- "Documentation Drafts": I feed my code to the AI and ask it to write the first draft of the README. I then refine it to ensure it has my "voice" and technical accuracy.
AI allows me to focus on the decisions, while it handles the repetition.
What’s Coming Next?
The first project will be a CLI Tool designed to solve a common developer pain point. It will be small, fast, and 100% open-source.
I want to show you that a professional repository isn't about the number of files, but the quality of the decisions inside them.
What do you value most when you look at someone's GitHub? The complexity of the code or the clarity of the documentation? Let's talk below!
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