Developer Builds Local AI Dashboard for BMW News Using Ollama, FastAPI, and React

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A developer has published architectural insights behind the BMW AutoTrend Dashboard, a locally hosted platform that scrapes, classifies, and visualizes BMW-related automotive news. The system runs entirely on local infrastructure, using Ollama for AI-powered article summarization and entity extraction, with a rule-based regex engine as a fallback when the LLM is unavailable. The stack combines a React and TypeScript frontend served via Nginx with a FastAPI backend, SQLite FTS5 for full-text search, and APScheduler for automated data ingestion and analytics snapshots. Web scraping is handled through subprocess calls to a Node.js CLI tool, while Clean Architecture principles keep business logic decoupled from external dependencies. The platform is designed for extensibility, allowing new scraping adapters to be added via a unified abstract interface without modifying core logic.

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