I started this blog to turn scattered learning into reusable knowledge.

My work and interests sit at the intersection of machine learning systems, distributed training, GPU performance, and reinforcement learning infrastructure. These topics are often explained either too abstractly or with too much implementation detail. I want to write explanations that connect the underlying ideas to practical engineering decisions.

I also want this blog to serve as a record of growth: what I learned, what assumptions changed, and how I approach difficult infrastructure problems.

Topics I plan to write about include:

  • How to reason about GPU bottlenecks
  • Distributed training trade-offs
  • Training and inference systems
  • Reinforcement learning infrastructure
  • Compiler and kernel optimization
  • Lessons from building reliable ML platforms

Everything here reflects my personal views and uses only information that is appropriate to share publicly.