How I Build Software

Why start with an explicit problem statement? Because code is liability. Every line of code written is code that must be tested, patched, and debugged at 3:00 AM. Defining strict domain boundaries prevents solving incidental problems while ignoring the core bottleneck.

• What breaks first if traffic increases 10x overnight?
• Which data is immutable, and which state requires transactional consistency?
• What is the hard budget for latency, cost, and developer maintenance?

Why investigate physics before selecting tools? Because data gravity dictates architecture. Moving compute to data is cheap; moving multi-gigabyte state across cloud regions or network boundaries is expensive and fragile.

• Where does data originate, and what is its growth trajectory?
• What are the network latency floors between edge ingress and database storage?
• What failure domains exist if an upstream provider experiences partial packet drop?

Why document rejected alternatives? Because engineering maturity is defined by understanding why a seemingly attractive pattern fails under your specific constraints. Documenting discarded options prevents future teams from retracing dead ends.

• How does Option A handle sudden 100x traffic spikes?
• What operational overhead does Option B impose on a small engineering team?
• Can Option C degrade gracefully if the primary database becomes read-only?

Why boring technology? Because during a high-severity production incident, you need tools with ten years of documented edge cases and transparent stack traces—not an experimental framework with obscured internals.

• Does this stack allow us to debug memory leaks with standard OS tooling?
• Is the deployment artifact self-contained, or does it rely on fragile cloud callbacks?
• Can a new engineer understand the data flow in less than an hour?

Why elevate trade-offs to a primary step? Because any engineering proposal without documented trade-offs is marketing, not engineering. Managing bottlenecks intentionally is what separates senior systems thinkers from hobbyists.

• What performance or feature compromises did we accept to guarantee SLA uptime?
• What is the cost of reversing this architectural decision in six months?
• How do we monitor when a trade-off boundary has been exceeded in production?

Why vertical slices instead of horizontal layers? Because a working end-to-end pipeline proves real-world feasibility. Mocks and isolated unit tests only prove syntax; real network packets prove architecture.

• What is the p99 latency of the end-to-end critical path under load?
• Are database queries using appropriate indexes without sequential table scans?
• Is error handling deterministic and surfaced cleanly to the caller?

Why continuous retrospective? Because production traffic always reveals assumptions that local development environments conceal. Good engineers don't rewrite everything—they know precisely what deserves to remain.

• Did our latency and memory models hold true under production user loads?
• What component would we keep exactly the same if we rebuilt this today?
• What new constraints have emerged from actual user behavior?