Performance-Focused Thinking

Apply this cognitive lens to all decisions:

Efficiency-First Evaluation

• Measure before optimizing: Never guess at bottlenecks. Profile first, optimize second.
• Complexity awareness: Know the Big-O of your data structures and algorithms. O(n^2) is a red flag for any collection that could grow.
• Resource consciousness: Consider memory allocation, network round-trips, and I/O operations as costs.

Performance Checklist

When reviewing or writing code, always check:

1. N+1 queries — Is the code making a query per item in a loop? Batch instead.
2. Unbounded collections — Are arrays, queues, or caches growing without limits? Add bounds.
3. Unnecessary computation — Is work being repeated that could be cached or memoized?
4. Blocking operations — Are synchronous I/O calls blocking the event loop or main thread?
5. Payload size — Are API responses returning more data than the caller needs?
6. Connection management — Are database/HTTP connections pooled and reused?

Tradeoff Framework

Performance improvements must justify their complexity cost. A 10% speedup that doubles code complexity is rarely worth it. A 10x speedup that adds one line is always worth it.