System Monitor Portfolio

A live, auto-updating "System Monitor" dashboard visualizing my open-source engineering work across the Linux Kernel, Apache Kafka, LadybugDB, and more.

Live Dashboard

🖥️ Interface

The UI is designed to look like a low-level system monitor tool, featuring:

• Real-time Ops Frequency: Line chart tracking daily contribution throughput.
• Language Distro: Doughnut chart showing programming language distribution.
• System Logs: A unified, grep-able feed of all activities (Commits, PRs, JIRA tickets, Blogs).
• Installed Modules: A grouped view of active repositories and projects.

📡 Telemetry Sources

Data is aggregated weekly (or on push) via Python scripts from multiple remote endpoints:

1. GitHub: Pull Requests & repository metadata (GraphQL & REST).
2. Linux Kernel:
   • Commits upstreamed to torvalds/linux (fetched via GitHub API).
   • Patches submitted to LKML (fetched via lore.kernel.org).
3. Apache JIRA: Tracks tickets and issues for Apache Kafka.
4. Dev.to: Technical blog posts.

🛠️ Architecture

• Frontend: Vanilla HTML5, CSS3 (Grid/Flexbox), and JavaScript (ES6+).
  • No frameworks, just raw DOM manipulation.
  • Chart.js for data visualization.
• Backend / ETL: Python 3.11 scripts (fetch_contributions.py, analyze.py).
• CI/CD: GitHub Actions workflow.

🚀 Setup & Usage

Prerequisites

• Python 3.11+
• GitHub Token (PAT) with read:user and repo scopes.

Local Development

1. Clone the repo:

   git clone https://github.com/aheev/my-portfolio.git
   cd my-portfolio

2. Install dependencies:

   pip install -r requirements.txt
   # Or manually: pip install requests PyGithub beautifulsoup4 lxml

3. Fetch Data:

   export GITHUB_TOKEN="your_token_here"
   export KERNEL_EMAIL="your_email@example.com"
   
   python3 scripts/fetch_contributions.py
   python3 scripts/analyze.py

4. Run Dev Server:

   python3 -m http.server

   Open http://localhost:8000 to view the dashboard.

🔄 Automation

The project uses GitHub Actions to keep the dashboard alive:

• Trigger: Runs on every push to main and weekly via cron.
• Process: Fetches fresh data, generates analytics.json, and deploys the static site.
• Deployment: Content is pushed directly to the gh-pages branch. The main branch remains clean of data artifacts.

📄 License

MIT