In an era where AI can generate software in seconds, building a real-life production environment from scratch matters more than ever.

I started building this website not just to have a personal portfolio, but with a much bigger goal in mind: to engineer a "real" product from scratch.

These days, almost anyone can build a website or generate a software product using AI. But there is a massive gap between theory and execution. The notions of "I can do it" and "I actually did it" are entirely different. I engineered this website to prove to myself and others that I can handle the complexities of a real-life production environment.

The Tech Stack: Next.js, Tailwind CSS, and TypeScript

To bring this project to life, I chose a modern and robust stack. But why these specific technologies? As an engineer, my goal is to use software as a tool to solve problems, not to get bogged down by them.

Here are the core reasons behind my architectural choices:

Mastering the Next.js Architecture: This was perhaps the most important reason. I wanted to deeply understand how modern web routing works, how to manage components, and how to build a scalable architecture that goes beyond traditional React.
Escaping CSS Complexity and HTML Insufficiency: Plain HTML is often insufficient for dynamic real-world applications, and traditional CSS can quickly turn into a complex, unmaintainable mess. Tailwind CSS allowed me to style components directly and systematically, keeping the codebase clean.
Streamlined Feature Implementation: Using TypeScript provided the strict type safety I needed to catch errors during development rather than in production, making it much easier and faster to implement new features with confidence.

The Engineering Mindset

Building this portfolio reinforced my core philosophy: Software development and mathematics are not the end goals. They are simply the most powerful tools we have to optimize systems and engineer real-world solutions.

Anyone can build a site with a prompt. But figuring out the architecture, managing the deployment, and building a system that you fully control—that is what makes you a deep-learner.