Replace the hidden composer prefill with a visible scope affordance: when
the Coach is launched narrowed to a metric, the hero shows a scope pill
naming that metric plus a tappable seed question, and the drawer sources
rail surfaces the same scope line. When the Coach opens unscoped, resolve
today's most notable derived signal server-side and offer it as a tappable
opener; fall back to the neutral greeting when no signal crosses the gate,
so the blank box is never cold and never fabricated.
Add an internal GET /api/insights/coach/seeded-question that reuses the
briefing's derived-signal detector to select the opener server-side; the
client renders the resolved DTO.
The correlation discovery matrix gained two non-MeasurementType channels
(medication compliance, symptom severity), but the Coach get_correlations
reader still filtered its measurement query by hand and never built the new
channels. The channel keys leaked into the Postgres enum cast and threw for any
user with medication or illness data.
Extract the route's two channel-series fetchers into a shared module
(correlation-channel-series.ts) so both consumers build the channels identically,
then wire the reader to derive its type filter from discoveryMeasurementTypes and
fold the fetched compliance + symptom series into the discovery input. The reader
now humanises the channel keys to "medication adherence" / "symptom severity".
Render daily medication-compliance rate and illness symptom-severity as
first-class series in the FDR discovery engine, so the adherence-dip →
symptom-flare link (and compliance → vital drift) can finally surface.
Compliance pools every active medication's dose-history ledger into a per-day
taken/(taken+missed) rate, re-keyed to the user's display timezone. Symptom
severity reads the illness day-log functional impact, zero-filling healthy days
only across real episode spans so a user with no episodes yields an empty
series rather than a constant. Both flow through the existing n >= 20, BH-FDR,
effect-size, and shrinkage gates unchanged; a sparse series degrades to absent.
Each channel forms its own metric family (no self-lag tautology) and keeps the
association-not-causation framing. Add discoveryMeasurementTypes() so every
caller drops the non-measurement channels before a Prisma type filter.
The Coach may point at a published /learn guide, but only a slug in the
catalog. That was a prompt instruction with no enforcement — a model could
hallucinate /learn/lower-your-cortisol and ship a dead link. Add a deterministic
post-filter on the assembled reply that keeps a published slug verbatim and
strips any reference whose slug is not in the catalog, tidying the prose around
the removed link. Makes the catalog's impossible-by-construction claim an
enforced guarantee.
Each retrieval tool re-scoped the snapshot to a single source, so its cache key
never matched the inventory's full-source build — a four-tool turn paid four
extra full snapshot builds on top of the inventory's. Thread the inventory's
probe scope to every tool so a per-tool read lands the one cache entry the
inventory already primed, collapsing the turn to a single build (a window
override still gets its own correct build). Raise the loop's round cap from two
to three so a sequential cross-metric why-chain isn't starved; the per-round
token budget is unchanged and the absolute ceiling tracks one above so the
final round still forces prose.
The tool-mode path dropped the launch scope's sources: the inventory probes the
full source set, so a Coach opened from a metric page saw the whole inventory
and could roam, while only the no-tools path honoured the narrowing. Inject a
one-line FOCUS hint naming the launched domain(s) into the tool-mode user turn
so the model prioritises that metric and fetches its figures first, with every
other domain still reachable if the question leads there. Empty on a generic
open, so the prompt prefix is unchanged on that path.
The daily AI-token gate was a flat 25,000/day for every provider. That cap
exists to bound the OPERATOR's API bill, but a ChatGPT-OAuth (Codex) or BYOK
chain egresses on the USER's own plan/key and costs the operator nothing, so
gating it on the operator-cost ceiling is wrong. A single gpt-5.x reasoning
turn legitimately reports 20k-40k gross total_tokens (re-sent system prompt +
inventory + tool defs per round + hidden reasoning, summed across rounds), so
one or two turns exhausted the flat cap and locked the user out of a plan they
pay for.
Classify the chain's cost owner by its primary provider via resolveDailyCap:
an admin-openai primary (operator pays) keeps the operator-cost cap; every
user-egress primary (codex/openai/anthropic/local) gets a generous
abuse-only ceiling. Threaded into the Coach chat gate and both OCR-extract
modes.
Raise the operator-cost cap 25,000 -> 200,000 so the operator-key path also
survives a normal day of reasoning turns, keeping gross-token accounting
rather than reworking the reconcile math.
Subtract cached input tokens at reconcile: the Responses-API gross total still
includes prompt-cached input the user did not re-pay for. The codex client
already parsed cached_tokens; sum it through the tool loop and bill
total_tokens minus cached.
The Daily Briefing strips any number absent from its server-computed
figures; the Coach's tool path had no equivalent, so a transcription or
paraphrase drift (a tool returns systolic 128, the reply says ~138) could
ship. Collect the numeric leaves from every present tool result this turn,
extract the numbers the reply asserts, and soft-correct any that match no
fetched figure to a neutral placeholder — annotated, non-blocking, and a
no-op on a qualitative turn or the no-tools path where there is nothing to
grade against. The bounded retrieval loop now returns the present results'
payloads so the check has an authoritative figure set.
Thread the request abort signal through runCoachToolLoop into each
per-round completion so a mid-generation client disconnect tears down
the upstream call instead of paying the full token cost. Completes the
no-tools-path wiring to cover the tool-retrieval path as well.
Regenerate the OpenAPI spec for the trimmed OCR-capability reason enum.
The Coach SSE turn ran the provider call to completion before the stream
existed and never wired it to the request's abort signal, so a mid-generation
client disconnect still paid the full upstream token cost. Thread an optional
`signal` through CompletionParams and singleUserTurn into each client's
safeFetch (composed with the per-call timeout via AbortSignal.any), and pass
`request.signal` from the chat route's no-tools path. An early disconnect now
tears the upstream request down instead of generating into a closed
connection. Omitting the signal leaves behaviour unchanged (timeout-only).
buildComparisonSnapshotForUser read a user's entire measurement history
per type with no date floor, then fed each series to summarize(). The
snapshot only consumes avg30 / avg30LastMonth / avg30LastYear, and the
widest of those reaches into the [365, 395)-day window (ageMs < 395 days
in meanOfWindow), so nothing older than 395 days can change a result.
Add a 400-day measuredAt.gte lower bound to every per-type read in both
copies of the builder — the page-blocking POST /api/insights/generate
handler and the shared comprehensive-generate path the nightly
pregenerate cron drives. The fan-out runs all seven snapshot types via
Promise.all, so on multi-year accounts the unbounded scan was tens of
thousands of rows per type. 400 days keeps a 5-day floor over the
strict-less-than 395-day reach.
The OpenAI client pinned `response_format: { type: "json_object" }` on every
call. The Coach tool loop's forced-final round runs with `tool_choice: "none"`
and expects plain prose, but JSON mode coerced the reply into a JSON object —
the user saw a JSON blob or an empty reply instead of the coach's answer.
Send `response_format` only when the caller opts in with `responseFormat: "json"`
and no tools are present, mirroring the Anthropic client's prefill gate. The
JSON-consuming callers that relied on the implicit mode (insight generate +
briefing retry, medication extraction, the connection-test probe) now pass the
flag explicitly; the prose Coach path and every tool round stay out of JSON
mode. Codex already omitted the flag and is unaffected.
Replace snapshot-stuffing in the Coach turn with a small base context plus
on-demand retrieval. The model receives the full system prompt, a tool-mode
grounding addendum, and a tiny DATA INVENTORY manifest listing which domains the
user has logged and which tool fetches each; the figures themselves are pulled
only when a question needs them.
Six read-only tools wrap the existing server-authoritative snapshot builder:
get_metric_series (blood pressure / weight / pulse + the additive series),
get_glucose_panel, get_sleep, get_medication_compliance, get_labs, and
get_illness_recovery. Each returns a structured { present: false } for an absent
or opted-out domain rather than throwing or returning an ambiguous empty list,
so the model can always tell missing data from real data. Module and cycle gates
are enforced at the builder, so opted-out domains are unfetchable.
A bounded loop runs the call -> tool-result -> answer cycle: at most two rounds
with a hard cap of three that forces a final answer, parallel tool execution,
and only the final round streamed. The day's budget reserves the per-call
ceiling times the round count and reconciles the summed tokens; the atomic
reserve and reconcile primitives are unchanged. All inbound gates (rate limit,
refusal, replay scan, consent, budget) stay before the loop.
The legacy snapshot path stays alive as the no-tools fallback: tool mode is on
only when every provider in the chain supports tool-calling, so a chain that
includes a local model falls back to the prior behaviour verbatim. The tool
trace is persisted onto the assistant message provenance.
Replace the CompletionParams { systemPrompt, userPrompt } string pair with a
structured { system, messages[] } request across all five AI clients. One
refactor unlocks three things: provider prompt-caching (Anthropic cache_control
on the stable system prefix, OpenAI/Codex automatic prefix cache, with
cachedInputTokens surfaced for observability), the tool-calling wire surface,
and clean multi-turn.
Each client maps the new shape to its own wire and degrades unsupported
features silently: the local OpenAI-compatible server never receives a tools
field (most reject unknown keys), and prompt-caching is best-effort everywhere.
CompletionResult gains toolCalls, finishReason and cachedInputTokens; the tool
types and per-provider mapping ship now, the tool-use loop does not.
Vision inputs fold into the user turn's content parts via singleUserTurn, with
each provider's pre-existing block ordering preserved. The corrective-retry
paths append to the last user message rather than a flat string. The Coach
keeps assembling one user turn so its snapshot-once and grounding behaviour is
unchanged. No wire schema visible to API consumers changed.
Behaviour is unchanged across every surface; the full suite stays green.
The full SNAPSHOT block (every cluster plus labs, illness, cycle and the
reference-grounding lines) was interpolated into the prompt on every turn,
so a one-word follow-up re-paid the entire grounding prefix. Ship it only on
the first turn, or once the history window starts eliding turns; on a cheap
follow-up send a short pointer back to the snapshot the model already received
earlier in the same conversation. The prior turns ride the transcript, so
grounding and cross-metric correlation are preserved while a follow-up turn
sheds about 6,000 tokens. Record snapshotSent and promptChars on the reply
annotation so the cut is observable.
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.
The v1.18.11 briefing now folds illness-episode and cycle state into the
prompt via buildBriefingIllnessCycleContext, which resolves module gates
through memoizePerRequest and queries cycleProfile. The generate route test
mocks neither getEvent nor the cycle/illness models, so every case threw once
the new call path ran. Stub the context module to no output and add the
getEvent export to the logging-context mock so these cases stay scoped to the
provider and cache behaviour they assert.
Bound the comprehensive briefing's feature read to a 400-day window instead of
walking the full measurement history on every generation. All-time min/max/avg
are sourced from one grouped aggregation over the whole history, so the
allTime* fields stay honest while the unbounded row materialisation is gone;
the oversize downgrade ladder remains as the safety net.
Fold illness-episode and cycle state into the briefing prompt, reusing the
server-authoritative builders the Coach already assembles. Both are
module-gated and cost nothing for users without them, so a mid-illness or
mid-cycle user finally gets context-aware prose.
Apply the outbound safety screen to the assembled Coach reply before
persistence and streaming, with a per-turn audit row on a block.
createSseStream gains a cancel handler + a shared AbortSignal: a client
disconnect now flips the signal so the token-emit loop stops enqueuing
into a closed connection and any provider fetch wired to the signal is
torn down. The chat emit loop honours it between frames.
The daily-briefing block (paragraph / signalsOfDay / keyFindings) was
schema-shaped but never number-checked the way recommendations[] are: a
restated figure could drift from the snapshot with no server rejection.
Add a grounding cross-check that asserts every number the briefing prose
states traces to a features.signalsOfDay figure (latest / avg / delta)
within a rounding tolerance. Wire it into both generation sites with one
corrective retry, then strip the briefing rather than persist a
fabricated number. Also gate the higher-temperature paragraph reroll on
the same check so a phrasing reroll cannot drift a figure.
Rebuild the /coach surface as a centred-hero chat with a word-by-word fade, a
single thinking disclosure, and a per-message token footer; emit token usage on
the done frame and persist it via migration 0182; plumb an additive reasoning
frame with an elapsed-time fallback for non-reasoning providers.
An account with no AI provider configured still reads the last cached
briefing — the read path serves it without ever touching a provider — so
the days-old report rendered as current. The pregenerate cron runs fine;
the cache simply can never refill without a provider. Report provider
availability from the read GET and pair the briefing's relative age with a
discreet 'this can't refresh — connect a provider' hint linking to
Settings -> AI, on both the overview hero meta row and the briefing card
footer. The hint only shows when a briefing is actually rendered; the
empty-state already owns its own no-provider branch.
Also align the briefing card footer caption to the 11px scale the
adjacent period-in-review card uses, so the two overview footers read at
one size.
Carry an additive usage envelope on the SSE done frame
({ totalTokens, model }) and persist tokensUsed + model onto
coach_messages (migration 0182, nullable, no backfill) so the
per-message token footer survives a conversation reload. Add an
optional reasoning frame the client can render inside the thinking
disclosure when a reasoning-capable provider emits a summary; providers
without reasoning emit none. Extend CoachMessageDTO and regenerate the
OpenAPI contract. Server-authoritative: clients display the count, never
recompute it.
One-shot prettier pass so the new format:check gate starts from a clean
baseline. Formatting only — no behaviour change. Generator-owned and
vendored artifacts stay untouched via .prettierignore.
The daily token cap was read before the provider call and recorded only
after a successful reply, so concurrent requests could all read
`spent < cap` and all hit the provider, and an empty/sentinel reply
recorded zero spend despite burning upstream tokens.
Reserve the budget atomically before the call: a single upsert-increment
returns the new total, so concurrent requests serialise on the row and
cannot both pass the cap; over-cap refunds the reservation and surfaces
`coach.budget.exceeded` as an SSE error frame. Reconcile the reservation
to the actual token count immediately after the provider returns —
before any empty/sentinel short-circuit — so burned tokens are always
recorded. An integration test proves two concurrent requests cannot both
exceed the cap and that an empty reply still records its cost.
Replace the scattered `?? 0.3` / `?? 1000` temperature and maxTokens
magic numbers across the status provider, Coach route, comprehensive
generator and period-narrative generator with a single typed
`ai-budgets.ts` map. Each entry documents the output contract that
fixes its token ceiling.
Right-size the per-metric status output from 1000 to 250 tokens: the
card renders one `{ "summary": "..." }` of 2-4 sentences (30-60 words),
so 1000 was an order of magnitude over the real ceiling.
Add a discreet unread dot to the Coach FAB when the Coach has something
the user has not opened yet, and clear it when they open the Coach.
The signal is server-authoritative: a new coach_last_seen_at column on
users records the last time the Coach was opened, and the launcher's
status endpoint reports unread when the newest assistant message is
newer than that stamp — so the dot works even with no push channel and
clears across every device, web and iOS. Opening the Coach (FAB or the
/coach page) stamps the column via a new /api/insights/coach/seen
endpoint; the launcher keeps a local stamp only for an instant paint.
The dot is a calm cyan, not an alarming red, per the card-status rule.
Surface the population reference bands behind the metrics a Coach turn
carries as a compact, citation-aware grounding block appended after the
SNAPSHOT. For each present metric the block states the headline band, its
source (blood pressure on the ESH 2023 line, US/ESC framings as context
only), and where the user's value sits via the shared four-state
placement contract.
Framed strictly as general guidance: the block opens and closes with a
not-a-diagnosis caveat, never overrides the existing refusal/dose/drug-
level safety discipline, and carries no commercial brand name. Glucose
respects the diabetes opt-in — the tighter ADA management goal only when
the user has declared it, otherwise the general non-diabetic band.
The block is deterministic and inspectable: built purely from the
reference-range backbone over the same scalars the snapshot already
surfaces, so it can never cite a value or band the snapshot does not
carry.
The targets route held a ~1.4k-line domain function in the handler body.
Move buildTargetsResponse into src/lib/targets/build-response so it sits
beside the other targets helpers and can be unit-tested without the route
shell; the route is now auth -> cachedSwr(buildTargetsResponse) -> apiSuccess.
Follow the as-needed compliance source pin to the new file location.
Add a calm, value-free Rest Mode indicator to the dashboard score card and
the recovery insight so the web matches what iOS already shows: when an
illness/condition episode is active the score and recovery are framed, never
penalised, and the surface now says so. The new banner is neutral and
untinted, self-gates on an active episode, and reveals only the onset date —
never a decrypted note or episode label. The dashboard reads the annotation
from the snapshot (resolved server-side alongside the score); the recovery
section reads it from the analytics payload.
Give the sleep page one assessment per distinct chart group: the
duration/architecture assessment now sits with its charts, and the
sleep-quality block carries its own SLEEP_SCORE assessment, matching the
"multiple charts, multiple texts" structure the recovery page meets.
Add the steps explainer (insights.subPage.explainer.steps*) and pass
explainerMetric="steps", and demote the recovery per-block title to an h3 so
the heading outline nests cleanly under the page heading and group heading.
The recovery page rendered a grid of compact device-score tiles plus a
redundant cross-link to the recovery-score anatomy at the top. It now
follows the same structure as every other metric page (blood pressure,
pulse, the HealthKit scaffold): each present device-native signal renders
a block carrying a short explainer, the max / median / mean stat strip,
the shared chart with the identical 7 / 30 / 90 / All toggle, and a
per-chart assessment. Multiple charts now carry multiple matching texts.
Register the seven device-native recovery / strain signals (ANS charge,
day / workout strain, cardio load, whole-day average / max heart rate,
energy expenditure) in the generic metric-status registry so each chart
gets a generated assessment, gate them on the recovery module, and give
each its own chart-overlay slot. Drop the duplicate top score-link block
(this surface is reached from the overview already).
Regenerate the OpenAPI contract for the widened metric-status enum.
The Coach can append a ---SUGGEST-REMINDER--- sentinel to propose a
measurement cadence (weight daily, the ESH/AHA 7-2-2 home-BP protocol,
structured glucose). The server resolves the cadence from a closed catalog
and emits an additive 'suggestion' SSE frame the chat renders as a one-tap
action card; accepting it creates a MeasurementReminder with origin COACH
through the same engine the Vorsorge surface uses (server-authoritative
schedule, finite course window self-expires via endsOn).
Non-naggy by construction: a frequency-cap cooldown, dismissal memory, a
master opt-out under Coach settings, and an explicit 'I measure enough'
stop path, all in the coach-prefs blob. Triple dedup at suggest, render,
and create so a cadence is never double-suggested or double-created, and
suggestions for a disabled module never surface. RHR/HRV stay passive --
the Coach surfaces the baseline, it never nudges to 'go measure' them.
The reminder DTO now echoes origin and endsOn for the iOS client.
Add `src/app/api/insights` to the inventory's walked trees so every
`route.ts` beneath it must classify: the 14 AI-narrative routes as
insights-module-gated, the Coach sub-tree as coach-delegated, and the
AI-settings / provider-chain / tile-layout / feedback / targets /
glp1-timeline routes as explicit infra-and-config EXEMPT entries with
documented reasons. A future ungated insights AI route now fails the
inventory BY NAME rather than leaking the surface over a Bearer token when
the account turned insights off.
Add representative insights-off → 403 module.disabled coverage to the
blood-pressure-status and correlations route tests (alongside the existing
insights-on → 200 path).
Require the `insights` module (in addition to the existing assistant-surface
gate and any per-metric data-module gate) on all 14 narrative insights
routes: blood-pressure / bmi / pulse / weight / medication-compliance /
mood / metric / derived / derived-batch status, cards, correlations,
narrative, pregenerate, and rhythm-events. A 403 module.disabled envelope
now precedes any provider or cache work when the account has insights off.
The per-metric sleep/glucose/recovery/mood gates and the
requireAssistantSurface("insightStatus"|"correlations") checks are kept;
this adds the module layer on top of them.
Route tests that now resolve the gate mock @/lib/modules/gate
default-enabled so their existing data-shape and assistant-flag assertions
ride through unchanged.
The batch derived endpoint (the one the dashboard wellness strip reads)
computed Recovery/Strain/Stress unconditionally while the single derived route
already gated them, so disabling the recovery module did not hide the rings.
Reuse the single route's DERIVED_MODULE map (now exported, with READINESS added
since it equals the computed recovery score) and drop disabled-module metrics
from the batch payload per request, without mutating the cached map.
Per-route tests assert the two-layer Coach module gate: a disabled
module (operator availability OR per-user disableCoach) yields 403
module.disabled before any Coach work runs, and an enabled module falls
through to the normal path. Covers the chat POST + list GET, about-me
GET/PUT, the questions GET/DELETE, and the adopt POST short-circuit
ahead of the locked read-modify-write.
The streaming POST and the conversation-list GET enforced only the
legacy operator assistant flag (requireAssistantSurface("coach")),
missing the per-user disableCoach opt-out and the operator
module-availability layer. Both handlers now run the two-layer
requireModuleEnabled(userId, "coach") gate right after auth, returning
403 module.disabled when the account has Coach turned off — even with a
valid Bearer token. The legacy assistant-flag check stays in place.
The derived-score, metric-status and mood-status insight routes serve a mix
of core-vital and toggleable-module signals. Map the module-owned metric ids
(sleep/recovery/glucose) to their module key and refuse with the shared 403
module.disabled envelope when the account has that module disabled; mood-status
gates wholesale on the mood module, mirroring the cycle gate. Metrics absent
from the map are core vitals and stay open.
A PUT that omits sections (an iOS tiles-only reorder) reset the user's
section customization to defaults, and symmetrically a sections-only PUT
reset the stored tiles. serializeInsightsLayout now takes the resolved
stored layout and falls back to it — not to defaults — for whichever
dimension the body omits; the PUT handler loads the stored layout first.
A first-ever single-dimension PUT (no stored layout) still fills
defaults, so the v2 blob stays complete. Pins the merge behaviour with
three unit cases.
A medication can declare itself as-needed: no schedules, never due,
never reminded, excluded from every compliance rate — but intakes log
ad-hoc, consume inventory exactly like scheduled doses, and the
history stays. The wizard offers it as a cadence choice and round-
trips it on edit; card, table and detail show a calm marker where the
next dose normally sits, the take-all dialog never collects one, and
the supply runway stays unknowable by construction. (#316)
Stock only decremented on the web card's take button — the status
toggle, the phone sync, the Telegram buttons and the token API all
recorded the dose and left the inventory untouched, which is why a
fresh pack kept reading full. Consumption now stamps the intake row
itself: every path that turns a row taken consumes through one module
— open container first, then earliest expiry with auto-open, spilling
into the next pack and flooring at zero — and every path that takes it
back (undo, delete, a take turned skip, a converged duplicate)
refunds exactly what the stamp recorded. Replays and pending sync
echoes can never double-charge, and the nightly convergence pass
refunds the rows it tombstones. A medication can declare units per
dose, so two 2 mg tablets book as one 4 mg dose and stock reads in
doses with the unit count alongside; containers carry a named type
including tablet packs, hold up to a thousand units for inhalers, and
the GLP-1 card reads the same stock as the inventory tab instead of a
separate ledger. The inventory endpoints join the documented API.
Per-status texts check AI consent before the unchanged-data refresh can
re-stamp them, the comprehensive fingerprint folds in the comparison
baseline and generation language so toggling either regenerates once,
and profile, threshold and source-priority writes evict the analytics,
targets and derived caches they feed. CSV export neutralises spreadsheet
formula prefixes on free-text cells while numeric columns stay typed,
the derived batch validates its type token against the measurement enum
and carries a per-user rate limit, and a dose synced from the phone app
counts as the interactive write it is. Background cache refresh
failures log a structured event instead of vanishing, the warm queue
runs visit-triggered jobs alongside the nightly walk, the formatter
memo evicts one entry instead of flushing wholesale with its option
objects frozen, the targets cold build bounds its fan-out, and time in
bed unions every writer's envelope while stages stay with the richest
recorder.
Staleness can re-enter a generated text: measurement age is hashed as a
bucketed tier, so the fingerprint stays stable day to day but flips
exactly when a reading crosses the boundary the prompt caveat cares
about. The ingest debounce becomes a one-hour minimum gap with the
hash gate carrying the budget, so a notable morning reading is
narrated the same morning instead of after the next nightly run. A
manual regenerate now refills the status cards through the same
hash-gated pipeline — changed cards regenerate, unchanged cards
refresh free, no eviction touches the hash baselines. The user's
self-context joins the comprehensive fingerprint so a newly remembered
fact reaches the briefing, the sleep writer collapse covers every
caller that reads nights, the batched compliance build fans out
bounded instead of sequentially, and a failed compliance read renders
a quiet retry state in the exact skeleton footprint instead of
swallowing the error.
The dashboard reserves chart-row skeleton cells from the saved layout
before the snapshot resolves, so a cold load paints close to its final
footprint instead of growing by a screen when data lands; the tile
silhouettes count only tile-capable widgets with blood pressure as two,
the greeting line keeps its box through name personalisation, and the
getting-started checklist moves below the charts where its arrival
shifts nothing. On the insights overview the targets build runs its
eight reads in parallel with a one-year floor on the glucose scan
instead of strictly in sequence over an unbounded history, the derived
batch gains a server cache where it previously recomputed sixteen
metrics on every read, both serve stale-while-revalidate, and the
health-score column reserves its exact footprint while the analytics
payload is pending.
A sha256 fingerprint of the compacted feature snapshot is stored beside
every generated text; nightly, forced and ingest-triggered regeneration
all skip the model call when the fingerprint matches and refresh the
timestamp instead. Warms cover only the user's resolved locale and the
blanket per-status eviction after a comprehensive generation is gone —
a lazily-read second locale regenerates on its own miss path. The
ingest debounce widens from thirty minutes to six hours and no longer
deletes cache rows, the visit-triggered warm re-checks freshness at job
start, backs off for an hour after a failure and is capped at two
forced runs per user per day, and its dedup window now outlives the
client poll horizon. An active day drops from hundreds of model calls
to a handful tied to actual data change.
Stale-served insights responses now carry a revalidating flag; the
advisor and comprehensive queries poll on a bounded 25 s cadence until
the background warm lands, so an open session converges instead of
pinning yesterday's briefing for an hour. Interactive measurement
writes (single create, edit, delete, bulk-delete, restore) hard-evict
the analytics cache so a manually entered value is reflected on the
next read; background batch sync keeps the mark-stale path. The
visit-triggered force warm collapses to one job per user instead of
one per locale, and the umami script proxy failure now annotates a
named action with the failure reason instead of a bare flag.
The insights read path served a cold cache by generating inline — up to
ten seconds on first visit — and the nightly warm pass structurally
excluded evening visitors: their visit stamped the cache, the half-four
run skipped it as fresh, and it expired exactly at the next evening's
visit. Stale content now serves immediately with its timestamp while
one background job revalidates, the warm threshold drops to an hour,
forced warming fills both message locales, the analytics caches serve
stale-while-revalidate, the profile endpoint parallelises its reads,
the bug-report status drops a write per call, the umami proxy degrades
to a no-op script instead of an error, the insights route sheds its
heaviest static imports, and the medication page prefetches on intent.
Marc-André Bombeck