When people ask me “How I learned about everything I know” they often expect the answer to begin with a “Classroom”, but mine began with “Curiosity”. I did not have a research lab, a mentor, or a roadmap. Instead, I had a computer, an internet connection, and an endless number of questions that followed. Every project I attempted exposed another gap in my understanding and every failure became a lesson and every success revealed an even better problem waiting behind it.

What I started as learning Programming expanded into something bigger and better with time. I became fascinated not only by using AI models, but by understanding how they worked, how they could be trained, and how they could be improved. I found myself spending days reading research papers, implementing ideas from scratch, debugging training code, and rebuilding systems simply because I wanted to understand. Only because of one reason which was Curiosity and I love it.

This way of learning is what changed how I think. I stopped measuring my progress by how quickly I could reach an answer and started measuring it based on “How deeply I understood the problem” and “How I understood that problem”. When a model failed to train, I wanted to know the “Why”. When a system performed poorly, I wanted the “How” that led there. My process became less about programming and more about developing a habit of disciplined curiosity. But that curiosity also refused to stay inside just computer science. While building AI, I found myself exploring psychology, biology, neuroscience, history, philosophy, and more... I became fascinated to every “WHY, WHEN, WHAT and HOW” no matter if it was related to them or not. I started to see that all patterns were connecting them all no matter how different they looked despite the field.

That curiosity eventually grew into open-source work. As I began building tools, publishing datasets, releasing models, and contributing to projects. Seeing people I had never met download my models, datasets, use my software / training code, report issues, and suggest improvements, etc. changed my programming habit from a solitary activity to a collaborative one. Open source taught me that engineering is not just about writing code, but it is about creating work that other people can understand, reproduce, and build upon.

Working independently meant there was no one to tell me I was on the right path. Many projects took weeks or months before they produced meaningful and great results. Some ideas failed after realizing that my first design was flawed. Those moments taught me that persistence is not simply to “Continue despite the difficulty”; it is being willing to abandon your own assumptions when evidence shows they are wrong. In simple words, I learned that...

“I can be wrong sometimes. And this is how I shall improve it.”

Today, I am especially interested in multimodal models, where language, images, audio, and 3D data intersects. which I think would be the emergence for any form of a highly capable artificial intelligence. I enjoy building systems that connect research with practical engineering, because they demand both creativity and precision, which I already have. Every new project still begins the same way it did years ago...

“With a question I cannot yet answer”.

You can ask me and talk to me about anything which is related to “Langauge, History, Philosophy, Biology, Evolutionary Science, Psychology, Politics, Poetry, Programming, Writing, Love, Emotions, Space and Astronomy, etc. and more”... I’m okay with all of these or anything I forgot to list here, because I forget a lot.

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