ML/AI, distributed systems, data infrastructure
AI systems, data infrastructure, and engineering tradeoffs.
I write to turn technical learning into durable notes: concise explanations, implementation details, and ideas worth revisiting across ML/AI, distributed systems, and data platforms.
-
One Agent or a Team of Agents?
It feels obvious that a team of AI agents should beat a single one. Three recent studies say: usually not — and the exceptions are where it gets interesting.
-
Encoder vs. Decoder Today
Quick notes on a common AI interview question: what encoders and decoders do, when to use them, and what changed in the ChatGPT era.
-
Best Resources for Understanding Attention and Transformers
Best resources that helped engineers like me understand transformer attention, from the math intuition to the full architecture.
-
3-Min Summary: How OpenAI Scaled PostgreSQL to Millions of QPS
What I learned from OpenAI's PostgreSQL scaling post: protect the primary, scale reads, and keep operations disciplined.
