The eleven per-metric/derived-score status helpers (pulse, weight,
bmi, mood, blood-pressure, medication-compliance, general, the generic
metric card, biomarker, the shared feature cache, the derived-score AI
warm layer) computed todayKey from toBerlinDayKey(new Date()) before
resolving the user's own timezone — several fetched the profile only
for downstream day-bucketing, after the cache read had already run on
the Berlin key. A non-Berlin user's card regenerated at their local
evening (Berlin midnight) and, for biomarker series and the
derived-score prompt label, mis-keyed data or displayed a hardcoded
"(Europe/Berlin)" regardless of their actual zone.
Threads resolveUserTimezone()/userDayKey() ahead of every cache read
so the day-key and the data bucketing agree, and reuses the one
resolved value everywhere downstream instead of re-deriving it.
The comprehensive briefing's bounded feature read (extractFeatures) was
paid more than once where it should not be: the on-demand route and the
shared generator each run a downgrade ladder that re-reads with identical
arguments, and a nightly warm has no shared object across consumers in a
single tick.
Add a request/run-scoped feature cache: withFeatureCacheScope opens an
AsyncLocalStorage store, getCachedFeatures memoises the first compute per
(userId, day, includeRaw, sinceDays), gated on a cheap salient-input
fingerprint so a mid-scope ingest recomputes and a probe failure fails
safe. Outside a scope it is a pure passthrough. Wired into the
comprehensive generator, the on-demand POST handler, and the nightly
per-candidate loop + forceWarmUser. Coach keeps its own snapshot.
Marc-André Bombeck