optimize

Execute all four phases in order. Do not skip any phase.

Phase 1 — Establish Baseline

1. Identify the exact code to optimize.
2. Run a micro-benchmark:
   • Write a temporary micro-benchmark script in the user's workspace.
   • The script MUST contain a warm-up phase (discard ≥10 iterations).
   • The script MUST run ≥100 iterations and output the Average and P95 execution time.
   • Run the script using the terminal.
   • Delete the temporary script after recording the results.
   • Note: If the script fails due to missing imports or dependencies, skip it and use the bash time command as a fallback.
3. Record P95 and Average. Do not proceed until the benchmark runs without error.
4. Report baseline numbers before writing any new code.

Phase 2 — Algorithmic Analysis

1. State the current Time Complexity (Big-O) explicitly.
2. State the current Space Complexity and identify the primary allocation sites.
3. Name the bottleneck precisely:
   • "Nested loops causing O(n²) scaling"
   • "Repeated string concatenation causing N heap allocations per call"
   • "Full table scan caused by missing index on user_id"

Phase 3 — Refactoring

1. Rewrite using a more efficient algorithm or data structure (apply in priority order):
   • Replace Array/List lookups with Hash Sets/Dictionaries: O(N) → O(1)
   • Vectorization or batching instead of per-item iteration
   • Caching/memoization of expensive pure computations
   • Zero-allocation patterns and buffer reuse to reduce GC pressure
   • Bitwise operations where mathematically equivalent
2. Run the benchmark on the new code.
3. If the new code is not measurably faster: discard it, select a different approach, repeat.

Phase 4 — Report

Present a Performance Report table:

Metric	Baseline	Optimized	Δ
Average	Xms	Yms	-Z%
P95	Xms	Yms	-Z%

Follow with a one-paragraph explanation grounded in CPU/memory theory.