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normalized data type in `lib/trends-data.ts` that exports a `TrendsSnapshot` combining all the data shapes, and setting up `lib/trends-source.ts` to handle server-side API fetching with environment variables for the endpoint and key, falling back to sample data if those aren't configured, while also calculating growth and momentum metrics. Then I'll build the API route handler to return the snapshot with metadata flags, create a context provider using SWR that manages the refresh interval and exposes controls, refactor the components to pull from context instead of static imports, add a settings panel in the nav for interval selection and manual refresh, and prompt for environment variables through SystemAction. Since this is all one cohesive feature around data configuration, I'll skip breaking it into separate tasks and just work through it as a single implementation. Defining the snapshot type... I'll set up a server-only fetcher module that reads the API credentials from environment variables, falls back to sample data if no URL is configured, and includes a flexible normalizer to handle the external API response shape gracefully. The focus is on computing growth metrics from the data, whether that comes from the live API or the sample fallback. Writing the fetcher module... Adding helper functions... I'm refocusing on what the user actually needs: a configurable data source that pulls from an external API with a fallback to sample data, an API route for the client to poll, and auto-refresh capabilities. The timeSeries computation can stay tied to the trends' spark arrays aggregated by category, and I'll keep the chart flexible enough to handle dynamic series keys without over-engineering it right now. Writing the momentum helper... For each point in the series, I'll scale the spark value by the ratio of current volume to the last spark reading, giving me values in k-mentions. Then I'll sum across all trends in a bucket to get the momentum for that category. I'm setting up 7 points for the 7-day range, 4 for 30-day, and 6 for 90-day, sampling from the spark array proportionally. 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Now I need to compute the trend metrics: growth from the item or derived from history, spark data with at least two points, status based on growth, rank by volume descending, and volume from the item or the last history value. For category stats I'll group by category, sum volumes, and calculate percentage shares. The summary needs total tracked count, average growth, top category by volume, and distinct source count. Then I'll build the time series data by grouping trends into momentum buckets. Writing momentum grouping logic... Now I'm implementing the snapshot builders — I'll keep the sample data as-is with its curated numbers for the demo, but compute snapshots fresh for live data to stay honest. Both functions return the same TrendsSnapshot structure with trends, category stats, time series, and summary. 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I'll build a configurable data pipeline: an external-API data source (via env vars) with graceful fallback to the sample data, a "],["code",{},"/api/trends"],["text",{}," route the client polls, and a SWR-backed provider that auto-refreshes on an interval with live status and manual refresh. 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