About
I'm an ML engineer who's spent years building AI systems in production. I write about the practical side—transformers, RAG pipelines, fine-tuning, and the real engineering challenges behind modern AI.
This blog is my attempt to share what actually works, not just what sounds impressive in papers. If you're building AI systems and hitting the messy reality behind the clean abstractions, this is for you.
I prioritize clarity over cleverness, and practical utility over theoretical elegance. Every post aims to leave you with something you can actually use.
What I Write About
Deep Technical Dives
Transformer architectures, training techniques, and the math that makes it work
Production Engineering
Real lessons from deploying LLMs and RAG systems at scale
Code-First Explanations
Runnable examples in Python and PyTorch you can actually use
Honest Trade-offs
What works, what doesn't, and when to use each approach