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.
MuscleMap is a SwiftUI SDK for rendering interactive human body muscle maps with highlighting, heatmaps, and gesture support. It provides male and female body models in front and back views, supports tap/long-press/drag interactions, pinch-to-zoom, multi-select with undo/redo, pulse animations, and extensive localization (11 languages). Built entirely in SwiftUI with zero external dependencies, it uses SVG path parsing for precise muscle rendering and offers both SwiftUI and UIKit interfaces.
The 21-Points Health application is a full-stack health tracking system built with a Spring Boot backend and an Angular frontend. It allows users to register, log in, and track various health metrics including weight, blood pressure, and activity points (from exercises, meals, and alcohol). Users can set personal preferences such as weekly goals and weight units (kg/lb). The application provides data visualization through lists, calendars, and charts, and includes administrative features for user management. The architecture follows a standard monolithic pattern with a clear separation of concerns. The backend exposes a RESTful API secured with JWT authentication, uses Spring Data JPA for persistence with a PostgreSQL database (H2 for development), and integrates Elasticsearch for search capabilities. The frontend is a modular Angular application that consumes the API, implements reactive state management with RxJS, and uses Bootstrap for responsive styling. The application also includes internationalization (English and French), Docker Compose configurations for development dependencies, and monitoring setups with Prometheus and Grafana. While the application leverages the JHipster generator for much of its boilerplate, it contains several custom components that demonstrate good engineering practices. Notable among these are a custom Logback converter for handling multi-line log messages, a utility service for file operations (base64 encoding and blob downloading), and a pipe for formatting time durations. These components are relatively isolated and could be reused in other projects with minimal modification.