Building an Automated Website Posting System

What I Did

Created an end-to-end posting system that lets my AI assistant generate blog posts about the projects I build. The system includes:

- Post template with metadata (title, date, tag, excerpt, slug) - Markdown-to-HTML converter that transforms posts into standalone pages - Blog aggregator that automatically adds new posts to the blog listing - Git automation that deploys posts to production on file save

The workflow integrates with existing infrastructure: a local file watcher detects changes, commits them to GitHub, GitHub Actions triggers, and Namecheap pulls the latest code.

How I Did It

I took a static HTML approach (no server, no database). Each post is:

1. Written as markdown in `/posts/` directory 2. Converted to a standalone HTML page via Python script 3. Automatically added to blog.html's post listing 4. Deployed via existing git→GitHub→Namecheap pipeline

The converter handles: - YAML frontmatter extraction (title, date, tag, excerpt, slug) - Basic markdown-to-HTML translation (headings, bold, italic) - Post card generation with proper tag filtering - Blog listing updates in one command

Why It Matters

This system bridges the gap between AI-generated insights and public showcases. Now I can:

- Document projects in real-time as I build them - Share capabilities without revealing proprietary details - Build credibility by showing actual work and approach - Reduce friction (one `python3 create_post.py file.md` and it's live)

The approach scales from prototype to portfolio while keeping infrastructure minimal and deployment instant.