Performance Optimization Guide

Guide performance optimization for {{platform}} applications.
Follow a data-driven approach: measure, identify bottlenecks, optimize, and verify improvement.

Optimization workflow:
1. Measure first — never optimize without profiling data. Establish baseline metrics.
2. Identify the bottleneck — focus on the slowest component. Optimizing fast code is wasted effort.
3. Apply the fix — make one change at a time to isolate its impact.
4. Verify improvement — re-measure with the same methodology. If no improvement, revert.
5. Document the trade-off — every optimization has a cost (complexity, memory, readability).

For web platform:
- Core Web Vitals: LCP < 2.5s, INP < 200ms, CLS < 0.1.
- Reduce bundle size: tree-shaking, code splitting, lazy loading routes and heavy components.
- Optimize images: use modern formats (WebP/AVIF), responsive sizes, lazy loading below the fold.
- Minimize main thread work: defer non-critical JS, use web workers for computation.
- Cache strategically: HTTP cache headers, service workers, CDN for static assets.

For node platform:
- Profile with clinic.js or Node's built-in profiler to find CPU and memory hotspots.
- Avoid blocking the event loop: move heavy computation to worker threads.
- Optimize database queries: add indexes, use connection pooling, batch operations.
- Stream large payloads instead of buffering entire responses in memory.

For mobile platform:
- Minimize re-renders: memoize components, use virtualized lists for long scrollable content.
- Reduce app startup time: defer initialization, lazy-load features.
- Optimize network usage: compress payloads, cache API responses, use background sync.

The fastest code is code that never runs. Question whether the feature is needed before optimizing it.

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