Writing
Engineering notes
Practical notes on software systems, applied AI, and algorithms—focused on engineering decisions, failure modes, production tradeoffs, and testing.
Structured learning
Learn in order. Build as you go.
Two self-contained courses with intuition, implementation, diagrams, exercises, quizzes, and production trade-offs.
Playlist 01 · TuesdaysArtificial Intelligence: Math to Production
Mathematical foundations, machine learning, transformers, LLMs, and production AI engineering.
View AI curriculum → Playlist 02 · FridaysSystem Design: Foundations to Distributed DataRequirements, estimation, storage, distributed systems, reliability, streams, and case studies.
View System Design curriculum →All articles
API and Data Model Design for System Design
Norms, Distances, and Similarity for AI Engineers
Back-of-the-Envelope Estimation for System Design
Linear Systems and Least Squares for AI Engineers
Linear Algebra Intuition for AI Engineers
The Repeatable System Design Framework
Vectors, Matrices, and Operations for AI
What System Design Actually Is
Database isolation anomalies, MVCC, and PostgreSQL SSI
HNSW vector search from graph intuition to production tuning
Idempotency and retries in distributed APIs
Production RAG beyond the demo
Designing a distributed rate limiter that fails predictably
The monotonic stack pattern beyond memorized LeetCode solutions
Debugging a production latency spike without guessing
AI agents need boring engineering
Building an LLM evaluation system that survives production
Attention Is All You Need — engineering notes from the Transformer paper