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b1fb3c5285
Stop drip-feeding scenarios: extend the harness to cover the latencies that actually dominate perceived speed, and measure them on a REAL production build. - --prod: build a production renderer with the probe included (VITE_PERF_PROBE=1, off in normal builds) and launch it from dist/. Measures minified React, so numbers are representative shipped figures instead of ~3x-inflated dev ones. - cold-start scenario (tier "cold"): launch → CDP → driver → first paint, via a fresh isolated spawn per run. Captures spawn_to_cdp_ms, spawn_to_driver_ms, fcp_ms. - first-token scenario (backend tier): Enter → first assistant token painted — the TTFT latency an agent app is uniquely judged on. - run.mjs gained --prod (build once), cold-start fresh-spawn loop, and gates ci+cold tiers against the baseline. Baseline re-captured on a PRODUCTION build (median of 5), darwin-arm64 — all green. Representative numbers: cold-start spawn→interactive ~1.6s, FCP ~0.5s stream frame p95 22ms, 1 longtask keystroke p50 2ms, p95 8.7ms transcript mount 145ms, 82ms longtask (400-msg open) The prod build also settled the open question from the dev numbers: the transcript-mount "lead" (221ms longtask in dev) is only ~72-82ms in prod — not actionable. Measurement did its job. |
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| .. | ||
| cold-start.mjs | ||
| first-token.mjs | ||
| index.mjs | ||
| keystroke.mjs | ||
| profile-switch.mjs | ||
| session-switch.mjs | ||
| stream.mjs | ||
| submit.mjs | ||
| transcript.mjs | ||