HackStack

Project Info

FitStreak

Devpost

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Inspiration

FitStreak is inspired by the challenge of maintaining long-term fitness habits. While many fitness apps focus solely on tracking metrics, we recognized that building and maintaining streaks can be a powerful motivator for consistent exercise and healthy habits. The gamification of fitness through streaks, combined with AI-powered insights, creates a unique approach to health and wellness.

What it does

FitStreak is a comprehensive fitness tracking and motivation platform that: Connects with wearable devices to track daily activities, sleep patterns, and body composition Uses AI to analyze fitness data and provide personalized insights and recommendations Implements a streak-based motivation system that encourages daily goal completion Tracks multiple fitness metrics including distance, time, and calories burned Provides detailed analysis of sleep patterns and body composition Offers a clean, modern interface for goal setting and progress monitoring

How we built it

FitStreak is built using a modern tech stack: Frontend: React.js with Redux for state management Backend: Python Flask server Integration with Terra API for fitness data collection OpenAI integration for intelligent fitness analysis Firebase for authentication and user management Real-time streak tracking and goal monitoring system Responsive design for both desktop and mobile use

Challenges we ran into

Implementing accurate streak tracking that accounts for timezone differences and edge cases Integrating multiple data sources (Garmin, sleep data, body composition) into a cohesive analysis Creating meaningful AI-powered insights that are both accurate and actionable Ensuring real-time synchronization between fitness data and streak updates Building a reliable authentication system that maintains user privacy

Accomplishments we're proud of

Successfully created an AI-powered fitness analysis system that provides personalized insights Implemented a robust streak tracking system that motivates users Built a clean and intuitive user interface that makes fitness tracking enjoyable Developed a scalable architecture that can handle multiple users and data sources

What we learned

Integration of multiple APIs (Terra, OpenAI) in a production environment Best practices for handling real-time fitness data Techniques for effective state management in a complex React application Methods for secure user authentication and data privacy Strategies for processing and analyzing fitness metrics effectively

What's next

Integration with additional fitness devices and platforms Agentic workout recommendations based on user progress Social features to allow users to compete and share achievements More detailed analytics and progress visualization tools Mobile app development for iOS and Android Implementation of personalized coaching features Integration with nutrition tracking platforms

Analysis

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Technology

Found in codeClaimed only
  • CSSIn code
  • FirebaseIn code
  • FlaskIn code
  • HTMLIn code
  • JavaScriptIn code
  • OpenAIIn code
  • PythonIn code
  • ReactIn code

8 of 8 appear in the indexed code.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

93 KB

Source files

28

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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