Technical Leadership & Engineering Practice

AI coding is remarkably good at getting projects off the ground, but robust software still depends on engineering judgment: structure, documentation, architecture, and knowing where things will break.

Microservices technical debt accumulates quickly. Weak domain boundaries create a distributed monolith with heavy coordination costs. Default to fewer interfaces until scale proves service splits.

Shipping early in deep tech reduces execution risk. Waiting for full clarity creates demo-ware and long feedback loops. Deliver thin end-to-end slices to validate constraints early.

Abstraction and performance are not enemies. Clean interfaces fail when data layout and optimization boundaries are ignored. Place optimizations at the right layer to keep code fast and maintainable.

Project architecture starts with iterative requirements. Treating greenfield design as one-shot creates brittle code and stakeholder drift. Use small PoCs to align scope, ownership, and evolvability.