[FEATURE] AI-powered dependency conflict prediction

[mikejmorgan-ai]

Problem

Library dependency conflicts are the #1 cause of failed installations. Current tools (apt, dpkg) report errors AFTER the fact rather than preventing them.

Solution (from Ed's feedback)

AI-powered dependency management that:

• Predicts conflicts BEFORE installation starts
• Understands transitive dependencies
• Suggests resolution strategies
• Learns from successful/failed installs

Different from #164

Issue #164 is about optimizing the dependency tree. This issue is about AI-powered PREDICTION of conflicts before they happen.

Example UX

$ cortex install tensorflow
⚠️  Conflict predicted: tensorflow 2.15 requires numpy<2.0
    Your system has numpy 2.1.0 (installed by pandas)
    
    Suggestions (ranked by safety):
    1. Install tensorflow 2.16 (compatible with numpy 2.x) [RECOMMENDED]
    2. Downgrade numpy to 1.26.4 (may affect pandas)
    3. Use virtual environment (isolate tensorflow)
    
    Proceed with option 1? [Y/n]

Technical Notes

• Parse /var/lib/dpkg/status for current state
• Build dependency graph from apt-cache
• Train model on common conflict patterns
• Consider integrating with libraries.io API for broader package ecosystem

Acceptance Criteria

• Dependency graph analysis before install
• Conflict prediction with confidence scores
• Resolution suggestions ranked by safety
• Integration with apt/dpkg dependency data
• Works with pip packages too (major pain point)
• CLI output shows prediction and suggestions

Bounty: $150 (+ $150 bonus after funding)

Paid on merge to main.