For more than 18 years, I built large-scale systems, and turn traditional products into ML/AI-powered solutions that created multi-million-dollar impact. Along the way, I filed 30+ patents and published 17+ research papers.


Then, in 2021, the AI wave hit.


And even with all my experience, I found myself trying to keep up — learning new architectures, new terms, and a completely new way of building software.



So I did what I’ve always done when the ground shifts:
I dug in.


Since then, every weekend I have spent studying AI systems in depth — not just the theory, but how things actually work in real engineering environments. I built prototypes, read papers, redesigned workflows, worked on agentic systems, and constantly tested how these ideas behave in practice.


And something became very clear:

Engineers aren’t struggling because AI is too hard.
They’re struggling because it’s explained terribly.


Most content out there is:
too abstract
too shallow
too hyped
or too research-heavy to be useful


But engineers don’t need more buzzwords.
They need clarity.
They need mental models they can rely on.
They need someone to translate the chaos into something they can use.


That’s why I started AIEdTalks.


Now I run AIEdTalks, which helps engineers become AI-ready through clear, practical explanations delivered weekly.



When I’m not writing, I work full-time as a research engineer exploring agentic systems and new AI ideas. I also love marathons and swimming—and hope to complete an Ironman someday.