performance-scalabilitylisted

# Performance & Scalability Measure first. Averages hide tail pain, intuition hides the real hot path, and a synthetic microbenchmark does not prove production improvement. ## Measure - Define the user-visible metric, workload, percentile (p95/p99), environment, and acceptable threshold before editing. - Reproduce with representative data; isolate measurement noise; profile the dominant resource and trace the critical path. - Frontend: bundle size, render work, media loading, layout stability, interaction latency. Backend: tail latency, saturation, queue growth, lock contention, database pressure, connection pools, downstream limits. ## Optimize - Remove unnecessary work before caching, parallelizing, or changing infrastructure. - Fix algorithmic or query complexity (N+1, unbounded reads, missing indexes) before micro-optimizing syntax. - Bound caches by memory, lifetime, invalidation, and tenant/security scope. A cache that leaks across tenants is a security defect, not a performance win. - Treat concurrency as a trade-off involving contention, rate limits, ordering, and failure amplification. - Preserve readability unless the measured gain justifies complexity and tests protect it. Do not trade correctness, durability, or consistency for speed without an explicit, recorded decision. ## Verify - Compare before and after under the same workload; report variance, not only the best run. - Check correctness, cold/warm behavior, resource transfer (did the cost move elsewhe