Research tools and frameworks for evidence-based analysis across technology, health, economics, and human performance.
A personal research lab for exploring complex topics through rigorous methodology. The repository contains:
- Research tools - Python scripts for transcription, audio processing, cover art generation, and content publishing
- Research outputs - Reports, analysis, and synthesized findings on various topics
- Podcast episodes - AI-generated audio discussions based on research findings
- Educational frameworks - Learning protocols based on neuroscience research
All research follows these principles:
- Prioritize authoritative sources - Peer-reviewed studies, meta-analyses, regulatory documents, official data
- Evaluate methodology - Study design, sample size, control groups, potential biases
- Report effect sizes - Practical significance matters more than p-values
- Note limitations - Study populations, generalizability, contradictory findings
- Identify conflicts - Funding sources, industry relationships, potential incentives
- Cite specific studies - Enable verification and deeper exploration
Health & Performance - Cardiovascular health, HRV, VO₂ max, sleep optimization, exercise protocols, supplementation
Technology & Blockchain - Protocol design, stablecoins, DeFi mechanisms, consensus algorithms, regulatory frameworks
Economics & Markets - Monetary policy, market dynamics, financial regulation, economic mechanisms
Learning & Development - Educational frameworks, Montessori principles, memory consolidation, attention management
Research findings are synthesized into podcast episodes using NotebookLM for AI-generated audio discussions.
Each episode includes:
- Full research report with citations
- Complete transcript
- Chapter markers for navigation
- Validated source links
research/
├── podcast/
│ ├── feed.xml # RSS feed
│ ├── episodes/ # Episode files and research
│ │ └── YYYY-MM-DD-slug/
│ │ ├── prompts.md # All prompts used
│ │ ├── research-results.md # Raw research outputs
│ │ ├── sources.md # Validated source links
│ │ ├── report.md # Final research report
│ │ ├── cover.png # Episode cover art
│ │ ├── *.mp3 # Audio with chapters
│ │ └── *_transcript.json # Full transcript
│ └── tools/ # Processing scripts
├── learning/ # Educational frameworks
└── index.html # Landing page
Located in podcast/tools/:
- transcribe_only.py - Local Whisper transcription (no API needed)
- generate_cover.py - DALL-E 3 cover art generation
- add_logo_watermark.py - Brand overlay for cover images
- generate_chapters.py - Chapter marker generation
Research reports and analysis are shared for educational purposes. Source materials retain their original licenses.