Blog
Notes on GPU engineering, ML infrastructure, and systems programming — written as I learn and build.
Mixture-of-Experts (MoE): Papers to Read
A bottom-up reading list for understanding sparse Mixture-of-Experts models — from the 1991 origins through modern sparse LLMs (Mixtral, DeepSeek-V3, Grok), routing algorithms, load balancing, scaling laws, and the systems that make expert parallelism fast.
Reasoning Models: Papers to Read
A bottom-up reading list for understanding reasoning in LLMs — from chain-of-thought prompting through process reward models, RL-trained reasoners (o1 / R1 style), test-time compute scaling, and self-improvement.
Recommendation Systems: Papers to Read
A bottom-up reading list for understanding recommendation systems — from classic CF through industrial-scale deep learning, sequential models, graph networks, and LLM-era generative rec.
Useful GenAI and Systems Reads
A short collection of practical GenAI and systems resources I want to keep handy.
Hello, World
Why I'm starting a blog, and what to expect here.