Posts

The last decade of my career has been a journey of continuous learning and gradual transitions. It started in school with Visual Basic and Oracle SQL, moved into Java, C#, and PHP during college, and eventually led to the Java Spring stack in my early professional years. As I moved into mid-senior roles, my focus shifted to the frontend with jQuery, Angular, and React. Eventually, I branched out into Cloud-Native technologies like Kubernetes. Today, I’m working with Node.js, Python, and AI. Moving from greenfield product development to setting up build pipelines and observability has taught me a lot. Even with years of momentum, the rapid advancement of AI brings a fresh set of challenges. ...

Navigating the AI Roller Coaster: A Product Engineer’s Guide to Efficiency The past year has felt like a high-speed roller coaster. Every week, a “mind-blowing” AI model or application launches, promising to revolutionize how we work. For developers, this creates a phenomenon known as the Paradox of Choice. With so many tools—GitHub Copilot, OpenCode, and Claude Code—it is easy to succumb to decision fatigue. When building an application — from the frontend UI to the backend logic and database schema — the secret to staying productive isn’t jumping to every new tool the day it launches to reap it’s benefit. Instead, the real differentiator is how you manage context. The quality of the application you build is directly tied to the quality of the context you provide. ...

It’s wild to think about how a simple home computer—a machine that could barely run a text editor or a small 2D game—used to amaze us. Now, we’ve got Artificial Intelligence (AI) quietly handling tasks in our everyday devices, from suggesting which movie to watch next to helping doctors spot illnesses faster. It’s easy to forget just how rapidly AI has grown because so much of it happens behind the scenes. But if you peek under the hood, you’ll see that training those clever models and keeping them running is neither cheap nor straightforward. ...