Covers Markowitz’s mean-variance model as the mathematical foundation for setting asset weights, the two optimal points on the efficient frontier (Min-Variance and Tangency), and the practical adjustments that compensate for the model’s weaknesses.
MSA is a decision about which criterion to use to decompose the system. Domain boundary, data ownership, scale pattern, failure isolation — the chosen criterion creates the service boundaries, and those boundaries decide communication and data in turn.
ES and CQRS address how a system’s source of truth is shaped and how its views are separated from it. Adoption cost spreads across the system, so I lean toward adopting only when the value can be stated explicitly.
Distributed transactions are about how a single ACID transaction decomposes across services and how its pieces are reassembled. The roles and trade-offs of 2PC, Saga (Choreography vs Orchestration), and Outbox.
settings.json, CLAUDE.md, slash commands, subagents, hooks. Claude Code’s customization surface settles into four layers once you pick one criterion: when does each one step in?
alacritty + tmux + nvim + zsh + Claude Code in a single screen. The choices and structure behind a terminal-centric development environment.
A good engineer isn’t someone who knows technology well, but someone who can share that knowledge with their team.
Building my first ML Lifecycle — a three-tier architecture for an ad Fallback CTR prediction — as a backend engineer without an AI background. The technical decisions I made, and what I learned through running it.
A retrospective on column-level encryption of sensitive data in a running service. Envelope encryption, DEK granularity decisions, the WHERE clause constraint that led to HMAC, and the migration automation Skill that spread the work across the org.
A retrospective on the internal hackathon. How an idea I proposed evolved with the team into a 1st-place project and an internal launch — and the starting point for using AI tools in earnest.