Strategic technology adoption analysis tracking 27 technologies with multi-source validation
Organizations struggle to separate genuine technology adoption from marketing hype. Traditional analysis relies on single data sources, missing cross-market patterns and providing incomplete adoption signals.
A comprehensive analysis system that tracks 27 technologies across multiple data sources (GitHub, npm, PyPI) to detect hype signals, reveal cross-market patterns, and provide high-confidence adoption insights.
Multi-source validation system (GitHub + npm + PyPI) with hype detection algorithms. Strategic selection of 27 technologies for depth over breadth to reveal cross-market patterns.
Phase 1A: Data Collection - GitHub, npm, PyPI API collectors with rate limiting and exponential backoff
Phase 1B: Analysis & Insights - Multi-source validation, velocity calculations, divergence-based hype detection
Phase 1C: Report Generation - Professional markdown reports, matplotlib visualizations, comparative analysis
Phase 2: Autonomous Deployment - GitHub Actions for continuous tracking (planned for future automation)
Transferable skills and capabilities beyond the technical implementation
Single data source unreliable (GitHub stars can be inflated). Built validation requiring GitHub + npm + PyPI agreement for HIGH confidence. Caught 2 hype signals: LangChain (15.6x divergence), Zipline.
Could track 100 technologies superficially but chose 27 strategically for deep analysis. Dual-dimensional (enterprise AI + fintech) revealed patterns: fintech adoption lags enterprise by ~6 months.
PyPI API returned 429 errors. Implemented exponential backoff, graceful degradation (system continues with incomplete data), proper logging from day 1 for autonomous operation.
Premium services cost $500-1000/month. Selected free-tier APIs (GitHub, npm, PyPI) with proper engineering patterns. Proved free tiers viable for production quality with smart architecture.