We’re living in a time where AI can generate code with just a prompt. The rise of tools like GitHub Copilot, ChatGPT, and others has introduced a fascinating new way of writing software—“vibe coding.”
Vibe coding is the art of writing software guided more by intuition, code completions, and AI-generated suggestions than by deliberate architectural decisions or deep understanding of the system. It’s about “going with the flow”—letting the AI fill in the blanks while you steer the direction.
This new style is enabling developers to:
Prototype applications quickly without deep knowledge of syntax or frameworks
Explore unfamiliar languages and libraries on the fly
Generate working demos in minutes
Learn through interaction, rather than documentation
It’s changing how we write software. But it’s also raising new expectations.
At its best, vibe coding makes you feel productive. It helps you:
Break out of blank-page paralysis
Avoid boilerplate and syntax errors
Iterate faster
Explore possibilities without friction
And in many scenarios—like prototyping or side projects—it works beautifully.
But what happens when you try to take that code to production?
Over the weekend, I tried to set up a TLS connection over TCP using SChannel APIs in Windows with C++. It was my first time manually upgrading a raw TCP socket to a secure TLS channel. In the past, I had always relied on frameworks or configurations that abstracted this complexity away.
This time, I thought—why not vibe code my way through it with AI?
So I did.
I spent hours letting the AI guide me—structuring the code, handling certificates, and attempting the handshake. But after a while, I noticed a pattern: the AI kept repeating the same steps, suggesting the same APIs, just with slight variations. It was stuck in a loop—hoping that doing the same thing differently would eventually work.
It didn’t.
The TLS handshake kept failing. I was probably 80% there—but the last 20%? That required domain expertise, deeper knowledge of how SChannel works, and an understanding of the nuances of certificate handling and security context setup.
That’s when it hit me: AI can accelerate your journey—but it can’t replace the fundamentals.
While AI is making massive strides, production code demands more than just compiling and running.
It requires:
Correctness across all edge cases
Readability for team collaboration
Maintainability over time
Performance under load
Resilience against failures
Security against real-world threats
Testability with reliable outcomes
Vibe coding doesn’t teach these principles. In fact, it can hide complexity behind quick fixes—making it easy to skip the learning needed to build scalable systems.
AI won’t replace developers who understand software deeply. It will amplify them.
The developers who thrive in this new era will:
Ask better questions to AI
Validate and refine AI-generated code with confidence
Spot subtle bugs or inefficiencies
Know when to trust the AI—and when not to
Design systems that are robust, secure, and elegant
To get there, you must invest in the fundamentals. There are no shortcuts.
Here are a few books that have shaped how I think about code:
Clean Code by Robert C. Martin – For writing readable and maintainable code
The Pragmatic Programmer by Andrew Hunt and David Thomas – For timeless development principles
Designing Data-Intensive Applications by Martin Kleppmann – For system design and scalability
Refactoring by Martin Fowler – For improving the structure of existing code
Code Complete by Steve McConnell – For comprehensive best practices across the software lifecycle
Most of what I know has come from books—not from AI prompts.
If you've come across great video tutorials, blog series, or courses that helped you strengthen your fundamentals—please drop them in the comments! I’m always looking to learn more, and I’d love to share those resources with others too.
Let’s not just vibe with code. Let’s build with understanding.
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