Quantified Self

Quantified Self is a personal data aggregation and analysis system designed around the philosophy of owning your data while providing meaningful context for AI systems and self-improvement tracking.

Philosophy

The core principles of this quantified self approach are:

AI Context Enhancement

Rich personal data serves as contextual information for language models, enabling more personalized and relevant AI interactions based on actual behavioral patterns rather than assumptions.

Data Sovereignty

The system emphasizes personal data ownership rather than feeding information into surveillance platforms or data silos. Users maintain control over their information and can choose how to utilize it.

API-First Approach

Priority is given to services that offer data egress capabilities, avoiding platforms that create data lock-in. This ensures long-term access to personal information regardless of platform changes.

Technical Architecture

The system is built around a personal API located at a centralized endpoint that aggregates data from various services. The architecture employs intelligent caching strategies:

• Health data: Updated every 4 hours
• Development activity: Hourly updates
• Music listening: 30-minute refresh cycles
• Other metrics: Variable based on data volatility

Data Categories

Health & Fitness

• Apple Health metrics including steps, exercise minutes, and heart rate variability
• Long-term movement pattern analysis (10+ years of data)
• Sleep quality and recovery tracking

Productivity & Development

• GitHub contribution patterns and commit history analysis
• RescueTime productivity analytics and time allocation
• Coding time distribution across different projects and languages

Skills & Gaming

• Chess rating progression and puzzle-solving statistics
• Programming problem-solving metrics from platforms like LeetCode
• Typing speed improvements and accuracy tracking
• Gaming performance analytics across different platforms

Creative Output

• Photography platform statistics and engagement metrics
• Music listening history with decade-plus tracking through Last.fm
• Writing output monitoring and productivity patterns

Implementation Goals

The primary objectives of the system are:

1. Improvement Tracking: Monitoring metrics that can demonstrate progress over time
2. AI Context: Providing language models with actual behavioral data for more accurate responses
3. Personal Insights: Understanding patterns in behavior, productivity, and health
4. Data Independence: Maintaining control over personal information

Privacy and Control

The system is designed with privacy as a foundational principle. Data aggregation occurs on personally controlled infrastructure, and sharing is selective and intentional rather than automatic or required by platform terms of service.