{"id":42524077,"url":"https://github.com/victoriacheng15/personal-reading-analytics","last_synced_at":"2026-01-28T15:36:27.459Z","repository":{"id":220866717,"uuid":"752796726","full_name":"victoriacheng15/personal-reading-analytics","owner":"victoriacheng15","description":"Zero-infrastructure reading analytics pipeline. 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Runs entirely on GitHub (Actions + Pages).  \n- **Fully automated** → Scheduled GitHub Actions keep data fresh, utilizing CI/CD governance for human-in-the-loop code review and merging.  \n- **Observability first** → Uses an Event Sourcing pattern (MongoDB) to decouple extraction from analytics, ensuring full auditability and health monitoring.\n- **Cost-effective** → Uses only free tiers (GitHub, Google Sheets API, MongoDB Atlas), proving powerful automation doesn’t require budget.\n\n---\n\n## 🏗 Architecture \u0026 Documentation\n\nUnlike typical \"script-based\" scrapers, this system is architected for scale and maintenance using an **Event Sourcing** pattern.\n\n- **Ingestion**: Python scripts harvest content and emit standardized events to a MongoDB immutable log.\n- **Observability**: An external hub consumes these events to populate Grafana dashboards for system health monitoring.\n- **Visualization**: Go binaries process the event stream, generate AI-powered summaries for historical context, and produce the static site.\n\n### 📈 System Observability\n\nTo demonstrate operational maturity, I maintain a public **[Observability Hub](https://victoriacheng15.github.io/observability-hub/snapsots.html)**.\nThis separate dashboard visualizes the \"health\" of this pipeline (ETL status, error rates, latencies) without requiring Grafana authentication.\n\nFor deep technical details, architectural diagrams, and operational guides, please visit the **[Documentation](docs/README.md)**.\n\n---\n\n## 🛠 Tech Stacks\n\n![Go](https://img.shields.io/badge/Go-00ADD8.svg?style=for-the-badge\u0026logo=Go\u0026logoColor=white)\n![Python](https://img.shields.io/badge/Python-3776AB.svg?style=for-the-badge\u0026logo=Python\u0026logoColor=white)\n![Google Sheets API](https://img.shields.io/badge/Google%20Sheets-34A853.svg?style=for-the-badge\u0026logo=Google-Sheets\u0026logoColor=white)\n![MongoDB](https://img.shields.io/badge/MongoDB-47A248.svg?style=for-the-badge\u0026logo=MongoDB\u0026logoColor=white)\n![Google Gemini](https://img.shields.io/badge/Google%20Gemini-4285F4.svg?style=for-the-badge\u0026logo=google-gemini\u0026logoColor=white)\n![Docker](https://img.shields.io/badge/Docker-2496ED.svg?style=for-the-badge\u0026logo=Docker\u0026logoColor=white)\n![GitHub Actions](https://img.shields.io/badge/GitHub%20Actions-2088FF.svg?style=for-the-badge\u0026logo=GitHub-Actions\u0026logoColor=white)\n\n---\n\n## 📊 What It Shows\n\n**Key Metrics Section:**\n\n- **Total articles**: Tracking total articles across currently supported sources\n- **Read rate**: Percentage of articles completed with visual highlighting\n- **AI Delta Analysis**: Multi-dimensional analysis of reading **Velocity** (pace), **Backlog Health** (clearing old debt vs. new noise), and **Chronology** (era of content focus) to provide narrative context beyond raw numbers.\n- **Reading statistics**: Read count, unread count, and average articles per month\n- **Highlight badges**: Top read rate source, most unread source, current month's read articles\n\n**7 Interactive Visualizations (Chart.js):**\n\n1. **Year Breakdown**: Bar chart showing article distribution by publication year\n2. **Read/Unread by Year**: Stacked bar chart with reading progress across years\n3. **Monthly Breakdown**: Toggle between total articles (line chart) and by-source distribution (stacked bar)\n4. **Read/Unread by Month**: Seasonal reading patterns across all months\n5. **Read/Unread by Source**: Horizontal stacked bars comparing progress per provider\n6. **Unread Age Distribution**: Age buckets (\u003c1 month, 1-3 months, 3-6 months, 6-12 months, \u003e1 year)\n7. **Unread by Year**: Identifies which years have the most unread backlog\n\n**Source Analytics:**\n\n- Per-source statistics with read/unread split and read percentages\n- Substack per-author average calculation (total articles ÷ author count)\n- Top 3 oldest unread articles with clickable links, dates, and age calculations\n- Source metadata showing when each provider was added to tracking\n\n### Supported Sources\n\nCurrently extracting articles from:\n\n- freeCodeCamp\n- Substack\n- GitHub (Added 2024-03-18)\n- Shopify (Added 2025-03-05)\n- Stripe (Added 2025-11-19)\n\n---\n\n## 📖 How This Project Evolved\n\nLearn about the journey of this project: from local-only execution, to Docker containerization, to automated GitHub Actions workflows.\n\n- [Part 1: From Pi to Cloud Automation](https://victoriacheng15.vercel.app/blog/from-pi-to-cloud-automation)\n- [Part 2: From Links to Reading insights](https://victoriacheng15.vercel.app/blog/from-links-to-reading-insights)\n- [Part 3: From Metrics to Milestones](https://mehub-git-fix-rss-escaping-victoriacheng15s-projects.vercel.app/blog/from-metrics-to-milestones)\n\n---\n\n## 🚀 Ready to Explore?\n\nDon't just take my word for it, interact with the real data.\n\n👉 **[Launch Personal Reading Analytics](https://victoriacheng15.github.io/personal-reading-analytics/)**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvictoriacheng15%2Fpersonal-reading-analytics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvictoriacheng15%2Fpersonal-reading-analytics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvictoriacheng15%2Fpersonal-reading-analytics/lists"}