SparkyFitness is a large, self-hosted fitness and health tracking platform. It is primarily an application: a full-stack monorepo with an Express + PostgreSQL backend, a React web frontend, a React Native/Expo mobile app, and a Python FastAPI Garmin microservice, all coordinated by a shared TypeScript package containing Zod schemas, constants, and domain algorithms. The product covers nutrition, workouts, sleep, fasting, mood, cycle/pregnancy, medications, symptoms, water intake, mindfulness, and health-provider data from Garmin, Fitbit, Withings, Oura, Polar, Strava, Google Health, and others. It also includes an AI chatbot with tool calling, barcode scanning, CSV/FIT imports, and native HealthKit/Health Connect sync. The dominant data flow is client hooks calling typed API service layers, which hit Express routes backed by PostgreSQL with row-level security. External wearable integrations refresh OAuth tokens under row locks and use provider sync claims to avoid duplicate imports. The mobile clients also directly read/write platform health stores, and a large migration set evolves the schema with careful backfills and deduplication. The repository is not a data-research repo; there are no analytical notebooks or dbt-style pipelines. Its character is clearly a production application with an unusually wide feature surface. The codebase shows strong engineering discipline: shared Zod schema triples, consistent TanStack Query key factories, central cache invalidation hubs, provider sync concurrency controls, and extensive Vitest/Jest tests, including regression tests pinned to GitHub issue numbers. The main risks are breadth-driven complexity: many external integrations, native iOS/Android/watch targets, manual patches to generated schemas, and some duplicated logic across web and mobile. Overall it is actively maintained, well-tested, and offers several genuinely reusable patterns.