Benchmark algorithmic optimizations

Always validate performance improvements with benchmarks rather than assuming algorithmic optimizations provide benefits. Many seemingly faster approaches may not deliver measurable gains or may even perform worse in practice.

When proposing algorithmic changes for performance reasons:

1. Provide benchmark evidence comparing the old and new approaches
2. Test with realistic data sizes and conditions
3. Consider maintainability costs versus performance gains
4. Implement “fast path” optimizations only when measurements justify the complexity

Example from a filtering optimization attempt:

// Proposed "faster" in-place filtering
function turboFilterInPlace(data, predicate) {
  for (let i = data.length; i--; i >= 0) {
    if (!predicate(data[i], i, data)) {
      const lastItem = data[data.length - 1]
      if (i < --data.length) data[i] = lastItem;
    }
  }
  return data;
}

// Result: Benchmark showed it's "not faster than the existing implementation"

Before implementing complex optimizations, measure whether simpler approaches like using appropriate data structures (Set for deduplication, Map for lookups) provide sufficient performance gains with better code clarity.