●TypeScript + SaaS

Performance optimization is fundamentally about measurement. Intuition about what's slow is wrong often enough that optimizing without profiling wastes effort. The tooling stack makes performance visible—APM shows where time goes, database profiling reveals query inefficiencies, load testing exposes scaling limits. Most performance problems fall into predictable categories: database queries scanning entire tables, N+1 patterns fetching data one row at a time, missing caching for expensive computations, synchronous operations that could be async, unoptimized images. These patterns have known solutions—the skill is identifying which apply to your specific system. The goal isn't maximum performance; it's appropriate performance at reasonable cost. A 50ms response might be adequate; optimizing to 5ms might cost 10x engineering effort with no user benefit.

Performance optimization engagements begin with baseline measurements. I instrument the application with APM, enable database query logging, and set up synthetic monitoring for critical flows. This produces the data for informed decisions. The profiling phase analyzes production data to identify actual bottlenecks. I look at P50, P95, and P99 latencies—the P99 often reveals problems invisible in averages. Database query analysis identifies slow queries and missing indexes. From profiling data, I create a prioritized optimization plan. Each item has estimated effort and expected impact. Highest-impact, lowest-effort items come first. Implementation follows the measure-optimize-verify loop. Each optimization is measured before and after. Changes that don't measurably improve performance get reverted. Load testing validates that optimizations hold under production-like load.