City decision cards rendered Chart.js canvases and AI evidence bodies before the user could see or expand those sections. This keeps the card shell visible while delaying chart data/canvas work until the section nears the viewport and skipping the AI evidence body while its details panel is collapsed.
Constraint: Preserve existing decision-card layout and evidence copy.
Rejected: Add list virtualization in the same pass | card-level render costs should be reduced before changing list mechanics.
Confidence: high
Scope-risk: narrow
Reversibility: clean
Tested: npm run build
Not-tested: Runtime scroll benchmark on a large production city set.
Market scan state was still tracked as separate nullable payload and status strings. This moves the hook internals to RemoteData<MarketScan> so loading and error paths can preserve previous data while keeping the existing marketScan and marketStatus return values for current card consumers.
Constraint: Preserve the existing useCityMarketScan public compatibility fields.
Rejected: Update all card UI to consume marketRemote immediately | keeping the compatibility bridge avoids a broad rendering diff while the request state model lands.
Confidence: high
Scope-risk: narrow
Reversibility: clean
Tested: npm run build
Not-tested: Forced live market-scan failure with stale previous quote.
The city-card data hook still contained localStorage serialization and TTL eviction helpers alongside AI fallback and UI state transitions. Moving those helpers into scan-terminal-cache creates a reusable cache boundary for the scan terminal request layer without changing cache keys, TTLs, or payload shapes.
Constraint: Preserve existing localStorage keys and expiry behavior.
Rejected: Migrate all caches to RemoteData in this commit | cache-helper extraction is a safer intermediate step before query policy changes.
Confidence: high
Scope-risk: narrow
Reversibility: clean
Tested: npm run build
Not-tested: Browser private-mode quota edge cases beyond existing guarded behavior.
The AI city forecast hook still owned stream queueing and in-flight request dedupe after the first request-client pass. Moving that policy into scanTerminalClient keeps network concurrency, queued progress, and requestKey reuse in the request layer while leaving the hook responsible only for cached UI state and progress rendering.
Constraint: Preserve existing two-stream concurrency limit and queued user-facing progress copy.
Rejected: Move localStorage cache at the same time | cache policy should be separated from stream transport policy to keep this refactor reviewable.
Confidence: high
Scope-risk: narrow
Reversibility: clean
Tested: npm run build
Not-tested: Live multi-city SSE under production latency.
The scan terminal hooks were each carrying their own fetch, SSE parsing, error normalization, previous-data handling, and market request behavior. This adds a small scanTerminalClient plus RemoteData helpers so terminal data, city detail, market scans, and AI city streams share one request boundary without introducing React Query.
Constraint: Do not add dependencies or change backend API contracts.
Rejected: Introduce React Query immediately | too broad for this release and would force larger UI state rewrites.
Rejected: Move localStorage caches in the same pass | safer to first isolate network and stream IO before cache policy migration.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Live SSE cancellation against production latency.
The city card had accumulated independent checks for AI status, market availability, stale observations, breakouts, peak timing, model consensus, and next-bulletin waits. This introduces a CityDecisionState builder so UI components consume one coherent recommendation, urgency, evidence quality, AI status, market status, badges, and primary reason.
Constraint: Keep the current card copy and badge priority behavior while moving decision-state ownership out of the component.
Rejected: Refactor calendar and opportunity lanes in the same commit | the shared model should land behind the city card first, then other views can migrate with smaller visual-risk diffs.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Browser visual regression and dedicated unit tests for every decision-state combination.
Dashboard.module.css mixed base shell styles with the entire scan terminal surface, making every decision-card or calendar styling pass risky. This extracts the scan terminal layer into its own CSS module and attaches that module root beside the existing dashboard root so global class selectors keep their current behavior.
Constraint: Preserve existing global scan-* class names and visual cascade.
Rejected: Rename scan classes into scoped module keys | too much DOM churn for a behavior-preserving CSS split.
Rejected: Split card/calendar/mobile rules in the same pass | safer to establish the scan-terminal layer first before finer component CSS ownership.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Pixel-level browser comparison across dark and light themes.
AiPinnedForecastView had become a mini application that owned list state, card shell, freshness, market, AI evidence, and model evidence rendering in one place. This split keeps the workspace wrapper thin and gives the decision card explicit product-component seams for future recommendation reasons, risk levels, and mobile-specific layout work.
Constraint: Preserve existing weather, market, AI read, chart, refresh, remove, collapse, and mobile behavior.
Rejected: Rewrite the decision-state construction in the same pass | this component split should stay behavior-preserving before a state-model refactor.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Browser visual regression on physical mobile devices.
AiPinnedForecastView still owns the city-card data assembly, but its header, decision band, and AI evidence rendering now live in focused presentational sections. This keeps the next decision-state refactor smaller while preserving the current UI and interaction behavior.
Constraint: Keep weather, market, and AI evidence calculations unchanged in this pass.
Rejected: Move business-state construction at the same time | mixing behavior extraction with view extraction would make regressions harder to isolate.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Browser manual regression on mobile expand/collapse states.
ScanTerminalDashboard had accumulated terminal fetching, theme persistence, local clock updates, and AI pinned-city hydration in one component. Moving those responsibilities into focused hooks keeps the screen component as the composition layer while preserving the existing UI and data flow.
Constraint: Refactor must not change the current decision-card or scan-terminal behavior.
Rejected: Split visual card components in the same commit | too much surface area for one safe refactor pass.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Browser manual regression across map, calendar, and pinned-card interactions.
Users needed reassurance that unavailable quotes and long AI evidence are normal states, not broken systems. This adds a v1.5.5 upgrade announcement, softens market-unavailable copy, surfaces a one-line recommendation reason, and makes mobile cards prioritize observed temperature, expected high, peak timing, AI expansion, and a separate market line.
Constraint: Keep existing dashboard data contracts and avoid backend schema changes.
Rejected: Hide unavailable market rows entirely | users still need to know weather evidence remains usable without a quote.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Browser visual QA on physical mobile devices.
Users need to know whether a city can be acted on before reading the full explanation. City cards now expose METAR or official-observation freshness, model update timing, market quote freshness, and AI state in a dedicated trust block, while the calendar view groups rows into action-oriented timing buckets with a single reason per city.
Constraint: Keep the existing card and calendar data contracts; derive freshness and action reasons from fields already present in the frontend payload.
Rejected: Add another long explanatory paragraph | it would repeat the same trust problem instead of making the first glance clearer.
Confidence: high
Scope-risk: moderate
Reversibility: clean
Tested: npm run build
Not-tested: Live browser visual QA with stale METAR and delayed quote examples.
Users saw fast-rule evidence, partial streams, DeepSeek completion, and fallback states as one blended AI status. The city evidence panel now names the current stage directly so a loading stream reads as fast judgment complete, a successful response reads as AI bulletin read complete, and incomplete responses explain that rule evidence is being used.
Constraint: Keep the existing fast evidence path visible while DeepSeek streams in.
Rejected: Label fallback as an AI failure | that incorrectly implies the card is broken even when rule evidence is valid.
Confidence: high
Scope-risk: narrow
Reversibility: clean
Tested: npm run build
Not-tested: Manual browser timing of partial stream transitions.
City decision cards already had enough evidence, but the first screen still forced users to read the longer explanation before seeing why a card mattered. The header now caps status chips at three high-priority signals and uses product-facing labels for observed breakouts, stale METARs, AI loading, missing market prices, strong model agreement, and next-report waits.
Constraint: The card should stay lightweight and avoid another explanatory section.
Rejected: Keep six mixed freshness chips in the header | too much noise for first-glance scanning.
Confidence: high
Scope-risk: narrow
Reversibility: clean
Tested: npm run build
Not-tested: Browser visual QA across all city states.
The decision card now exposes deterministic guard state, data freshness, AI readiness and market sync directly in the card header so users can understand whether they are looking at fresh evidence, a fallback, or a stale/exception case before reading the full explanation.
Constraint: Keep the first useful read available while DeepSeek airport/HKO details are still streaming.\nRejected: Add another expanded evidence panel | header-level badges are faster to scan and avoid increasing card depth.\nConfidence: high\nScope-risk: narrow\nReversibility: clean\nTested: npm run build\nNot-tested: Browser visual QA on production data.
City-card fallback reads now stop using stale METAR or official observations as strong live anchors. A stale observation no longer forces high/low revisions, and both backend and browser AI cache keys include the observation fingerprint so updated report times, receipt times, temperatures, or stale status invalidate old AI text.
Constraint: Cached city AI reads must not survive a material observation update
Rejected: Let stale METAR trigger observed-break revisions | stale reports can be older than the active temperature path
Confidence: high
Scope-risk: moderate
Tested: pytest tests/test_web_observability.py -q
Tested: npm run build
The prior label said the weather decision layer had no market price connected, which could be read as a broken market integration. The card now says weather-first read with market prices shown separately.
Constraint: Market price layer is already rendered below the weather decision band
Rejected: Keep 'no market price input' | accurate internally but misleading in user-facing Chinese copy
Confidence: high
Scope-risk: narrow
Tested: npm run build
Not-tested: Visual screenshot review
City AI can return a partially streamed JSON string when the provider truncates output. The fallback previously kept an unfinished clause such as '但TAF显示', which made the forecast explanation look broken even though earlier evidence was usable.
Constraint: Provider JSON can be truncated after useful fields have already streamed
Rejected: Drop all partial AI text | would lose valid METAR interpretation already returned before truncation
Confidence: high
Scope-risk: narrow
Tested: pytest city AI truncation regression tests
Tested: npm run build
Not-tested: Live DeepSeek provider response
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