Douglas Seo
서경덕 - Co-Founder and Engineer of Azetta AI
My grandfather learned English in the soup kitchens of the war that reduced his childhood home to ash and rubble. His work in city government built the Korea we know today. My father learned English through RF textbooks, then globalized Silicon Valley MEMS sensors to Asia. He left on a cattle drawn carriage, and returned with a Tesla. I stand on the shoulders of giants, and with the selfless love of my mother and grandmother learned English in Kindergarten in sunny Southern California. I dreamt to be an inventor and build a sustainable, better future for all. Berkeley sharpened the tools; founding and shipping did the rest. I built some cool stuff, but I'm most proud of popper, an app that pays you for hanging out with friends, and makes some money for small businesses along the way—10k downloads, 50+ businesses, $20k monthly revenue generated for our client businesses, and an extremely effective system that handled 300k read/writes with sub-400ms load times, proof that the inventor dream can ship.
Now, I am building with Taha Bouhsine. Curiosity pulls me toward the frontier of AI research; competition drives me to pioneer the technical landscape of modern history. Fluent in English, Korean, Python, Spanish, & JavaScript. Learning Japanese, LaTex, C++, and Machine Learning. When I'm not building, you can find me playing with a ball or facetiming my gf. I'm always open to discussing new ideas and potential collaborations. I nerd out over physics, math, spirituality, philanthropy, and hacking the money system.
Experience
Founding Engineer (Employee #1 and Technical Lead) at Werkflow
Founder & CEO at Popper
Software Engineering Intern at Chirp Microsystems (TDK)Education
B.S. in Electrical Engineering and Computer Science (EECS)Résumé/CV
The Tokens You Don't See
Sequence packing, intra-document masking, and why the invisible data engineering of your training pipeline shapes what the model learns as surely as any architectural choice.
Small Models, Sharp Instincts
Before you can train big, you need to know how to read what your model is telling you. Optimizers, the batch size equation, and learning to diagnose training from the charts.
The Gravity of Learning
Machine learning borrows its vocabulary from biology. The math underneath it is linear algebra. But the thing it might actually be describing is physics — specifically, gravity.