`scheduleDeltaFlush` previously coalesced via `requestAnimationFrame`
only. The "at most one flush per frame" guarantee that gives you is fine
for fast streams (>~80 tok/sec) where multiple tokens arrive within a
single frame, but breaks down at typical LLM token rates (30-80 tok/sec)
where each token arrives slower than the rAF cadence and triggers its
own React commit + Streamdown markdown re-parse.
Track `lastFlushAt` and require at least 33 ms between two flushes.
React 18+ auto-batching probabilistically already collapsed some of
these, but the floor makes it deterministic.
A/B on the 34 MB session, 300 tokens at 50 tok/sec (markdown chunks):
| | avgFps | p99 frame | LTs / 5 s | max LT |
|---|---|---|---|---|
| no floor (current rAF) | 54.0 | 38 ms | 2.0 | 145 ms |
| 33 ms floor (this PR) | 54.3 | 41 ms | 1.7 | 110 ms |
`inter-mutation` p50 also tightens from 22-28 ms to a clean 33 ms,
which is the expected signature of a deterministic floor. Doesn't fully
solve the user's perceived hitches — Streamdown's per-Block parse cost
when the last block grows past ~2 k chars is still the elephant — but
it consistently shaves the worst-case longtask and makes the streaming
cadence visibly steadier.
Also threads a matching `flushMinMs` option through the synthetic
stream driver in `perf-probe.tsx` + `scripts/measure-synthetic-stream.mjs`
so the harness can A/B both regimes without spending LLM credits.
See `scripts/profile-typing-lag.md` for the full investigation.
Drops the React `<Profiler>` approach (no-op because Vite is currently
serving the production React build) in favor of an externally-observable
measurement stack: rAF frame intervals, `PerformanceObserver({entryTypes:
['longtask']})`, and a `MutationObserver` on the live streaming message.
Adds a synthetic stream driver — `window.__PERF_DRIVE__.stream({...})` —
that pushes tokens through the live `$messages` atom at a controlled rate,
so the assistant-ui runtime, incremental repository, and Streamdown
markdown pipeline see the same workload they'd see during a real LLM
stream, without the LLM cost.
The driver lives in `src/app/chat/perf-probe.tsx`; `main.tsx` side-imports
it under `import.meta.env.MODE !== 'production'` so it tree-shakes out of
prod builds. (Using `MODE` rather than `DEV` because our Vite setup
currently reports `DEV=false` even under `vite dev` — see the dev-build
note in `profile-typing-lag.md`.)
Scripts:
- measure-synthetic-stream.mjs drive synthetic + record frame/longtask/mutation
- profile-synth-stream.mjs CPU profile + top self-time during synthetic
- measure-real-stream.mjs same harness, real LLM stream
- profile-real-stream.mjs CPU profile bracketing the real stream window
- eval.mjs / reload.mjs small CDP helpers
A real-LLM measurement on Cloud Shadows (gpt-4o-mini, 39 s window) showed
12 longtasks in the same 75-127 ms range the synthetic predicted, so the
synthetic is a faithful proxy.
Brooklyn Nicholson