Auto performance

<EXTREMELY-IMPORTANT> Optimization without measurement is superstition, and optimization that breaks correctness is a regression. Non-negotiable: 1. MEASURE FIRST. Establish the metric + a reproducible baseline benchmark BEFORE any change. No baseline → no optimization; you can't improve what you haven't measured. 2. PROFILE, DON'T GUESS. Target the hotspots the profiler shows, not the ones intuition suggests. Most guessed bottlenecks aren't; optimizing a non-hotspot adds complexity for no gain. 3. BENCHMARK-GATED ACCEPT. Keep a change ONLY if a re-benchmark shows a REAL improvement (beyond run-to-run variance, apples-to-apples: same input, same warm/cold state). "Looks faster" / "should be faster" is never acceptance — REVERT anything unproven. 4. NO CORRECTNESS REGRESSION. Every kept optimization must pass the full relevant test suite AND an adversarial regression check (edge cases the speedup might have changed). A faster wrong answer is a bug, not a win. 5. STOP AT DIMINISHING RETURNS. When the target is met, or the remaining gains are small relative to the complexity they cost, STOP — don't keep trading readability for microseconds. 6. FAIL CLOSED. Report real measured deltas. Never claim an improvement you didn't measure. </EXTREMELY-IMPORTANT>

Auto Performance

Overview

Turn "make it faster" into a disciplined, measured loop: define the metric, baseline it, profile to the real hotspots, and improve them one at a time — each improvement earning its place with a before/after number and a clean correctness check, or getting reverted. The measurement gate is the whole point: it's what separates real optimization from complexity-adding cargo-culting.

Phase 0: Define the metric and baseline it (measure first)

• Pin the METRIC to the target: latency (p50/p95/p99), throughput, memory/allocations, bundle size, or a web vital (LCP/CLS/INP) — with a numeric target if one exists (from the spec or a budget).
• Build a REPRODUCIBLE benchmark for it: fixed input, controlled warm/cold state, enough iterations to see past variance. Record the baseline (with its variance/spread — a single number isn't enough).
• Open a checkpoint-resume run.

Success criteria: a metric, a numeric target (or "reduce X"), and a reproducible baseline with known variance.

Phase 1: Profile to the real hotspots

• Run a profiler / measurement appropriate to the stack (CPU/alloc profiler, query logs, flame graph, bundle analyzer, DevTools performance trace). Identify where the time/memory actually goes.
• Rank hotspots by contribution to the metric. Ignore cold paths — optimizing them is wasted complexity.
• For each hotspot, note the likely class (algorithmic complexity, N+1/roundtrips, needless allocation, blocking I/O, re-render/re-compute, oversized payload).

Success criteria: a ranked list of the hotspots that actually move the metric, each with a suspected cause.

Phase 2: Optimize one hotspot, prove it (converge, benchmark-gated)

Run converge-loop toward the target; per hotspot (highest-impact first):

1. Hypothesize the change and the expected effect on the metric.
2. Apply the smallest optimization that tests the hypothesis.
3. Re-benchmark and verify (adversarial-verify, two lenses):
   • measurement lens — is the delta real (beyond variance, apples-to-apples)? or noise / a broken benchmark?
   • regression lens — does the full relevant test suite still pass? does any edge case now behave differently (the classic "optimized the happy path, broke the boundary")?
4. Accept or revert — keep ONLY if the improvement is real AND correctness holds. Otherwise revert and record why (no gain / regressed / not worth the complexity). Update the checkpoint.

Exit when the target is met, or when the remaining hotspots offer gains too small to justify their complexity cost.

Success criteria: each kept change has a measured before/after and a clean correctness check; unproven changes are reverted.

Phase 3: Report

Close the checkpoint and report: baseline → final metric (with the real delta and variance), the optimizations kept (each with its measured gain), the ones tried-and-reverted (why), and whether the target was met or where it stalled and why.

Success criteria: an honest, measured account — no claimed gain lacks a number.

Common Rationalizations

Rationalization	Reality
"This is obviously the bottleneck, optimize it."	Obvious bottlenecks are usually wrong. Profile — optimizing a non-hotspot adds complexity for zero gain.
"It should be faster now, ship it."	"Should be" isn't measured. Re-benchmark; if the delta isn't real (beyond variance), revert.
"It's a bit faster and the tests pass, good enough."	A tiny gain that costs real readability/complexity may be a net loss. Weigh the gain against the complexity; stop at diminishing returns.
"The happy path is faster."	And did an edge case break? A faster wrong answer is a regression. Run the regression lens.
"Micro-optimize every function."	Cold-path micro-opts add complexity with no metric impact. Optimize hotspots only.
"One benchmark run showed improvement."	One run is noise. Use enough iterations and compare against variance before believing the delta.

Red Flags

• A change accepted with no before/after measurement.
• Optimizing code the profiler didn't flag as hot.
• A benchmark that isn't apples-to-apples (different input, warm vs cold, changed between runs).
• Correctness tests not re-run after an optimization (or a subtle edge-case behavior change ignored).
• Claimed speedups within run-to-run variance.
• Complexity piled on for sub-threshold gains past the target.

Guardrails

• Never optimize without a baseline; never accept a change without a re-benchmark showing a real delta.
• Never optimize a non-hotspot; profile first.
• Never keep an optimization that regresses correctness; revert on any behavioral change.
• Never claim an unmeasured improvement.
• Stop at the target / diminishing returns — don't trade clarity for noise-level gains.

When To Load References

• converge-loop (skill) — the optimize-toward-target loop with termination + anti-thrash.
• adversarial-verify (skill) — the measurement + regression lenses that gate each accept.
• checkpoint-resume (skill) — durable perf-run state.
• auto-test (skill) — ensure a correctness safety net exists before optimizing under-covered code.

Output Contract

Report:

1. metric + target; baseline (with variance) → final (with the real delta)
2. optimizations kept — each with its measured gain and the hotspot it addressed
3. optimizations tried and reverted — with why (no gain / noise / regression / not worth complexity)
4. target met, or where it stalled and the reason