Reclaim your digital autonomy by introducing controlled chaos into recommendation algorithms.

OffBalance aims to disrupt and reset the algorithmic patterns that digital platforms use to predict and influence your behavior. By introducing controlled randomization, we help you break free from filter bubbles and recommendation loops.

Reset your music recommendations with sophisticated pattern disruption:

• Creates random playlists across genres
• Simulates natural listening patterns
• Switches between different market regions
• Randomizes track attributes and interactions
• Optional preservation of your existing preferences

Planned features for breaking the video recommendation algorithm:

• Random video watching patterns
• Duration-based interactions
• Comment and like randomization
• Channel subscription cycling
• Watch history manipulation

Strategies for resetting the For You Page:

• View time randomization
• Interest pattern disruption
• Interaction randomization
• Regional content mixing
• Hashtag diversity injection

Approaches to diversify your feed:

• Post interaction randomization
• Friend content prioritization changes
• Group content mixing
• Advertisement preference reset
• Timeline algorithm confusion

Methods to reset the Explore page:

• Story view patterns
• Post interaction randomization
• Hashtag exploration
• Reels algorithm disruption
• Discovery page reset

Techniques for search result depersonalization:

• Search history randomization
• Click pattern disruption
• Location-based result mixing
• Language preference cycling
• Device fingerprint variation

bash

git clone https://github.com/yourusername/offbalance.git

pip install -r requirements.txt

python -m offbalance.spotify # Currently available

Each platform module operates on three core principles:

1. Pattern Disruption

   • Randomized interaction timing
   • Varied content engagement
   • Geographic location mixing

2. Controlled Chaos

   • Preserved user preferences (optional)
   • Scheduled randomization
   • Monitored impact

3. Natural Simulation

   • Human-like behavior patterns
   • Realistic timing variations
   • Context-aware actions

• No data collection
• Local operation only
• Open source code
• Transparent processes
• API-compliant actions

python

PLATFORMS = { 'spotify': { 'status': 'Available', 'risk_level': 'Low', 'effectiveness': '90%' }, 'youtube': { 'status': 'Development', 'risk_level': 'Medium', 'effectiveness': 'TBD' },

}

Each module provides:

• Before/After comparisons
• Recommendation diversity scores
• Pattern break effectiveness
• Algorithm reset confirmation
• Content variety metrics

We welcome contributions! See our Contributing Guide for details.

This project is licensed under the MIT License - see the LICENSE file for details.

This tool is for educational purposes and personal algorithm management. Use responsibly and in accordance with each platform's terms of service.