Garmin data archive – local, offline, Ollama-ready (github.com)

A new project allows users to archive and analyze their Garmin Connect data locally, eliminating reliance on cloud services and third-party servers. Developed using Python, this solution enables individuals to ask AI questions about their personal health metrics—such as sleep, heart rate variability (HRV), and recovery—without compromising their data privacy. The project consists of several Python scripts that download, store, and summarize data, creating offline analytical dashboards that run on users' local machines. This advancement holds significant implications for the AI/ML community, particularly in the realm of personal data privacy and local computing. By facilitating local data analysis, users can harness AI capabilities without exposing sensitive information to external platforms. The project integrates seamlessly with local AI models like Ollama or Open WebUI, providing users with a robust, personalized AI assistant for health insights. Furthermore, the setup and technical specifications are designed to be accessible, requiring only basic Python knowledge, thus broadening the appeal to non-developers seeking to enhance their understanding of their health data without sacrificing privacy.