Linear regression in depth
Connecting Maximum Likelihood Estimation with Linear regression
Backend Engineer & AI Interpretability Enthusiast
Exploring the depths of software engineering and AI safety through mechanistic interpretability. Sharing insights from a decade of building distributed systems and recent deep dives into understanding how AI models really work.
Connecting Maximum Likelihood Estimation with Linear regression
A principled way to come up with loss functions
The right way to approach ML and why it is important to learn the Math
Distributed systems, microservices architecture, databases, and building scalable applications that serve millions of users.
Mechanistic interpretability, transformer circuits, AI safety research, and understanding what happens inside neural networks.
Real-world architectural patterns, performance optimization, and lessons learned from building production systems.
PyTorch, transformers, reinforcement learning, and practical applications of machine learning in production.