Building My Personal YouTube-to-Podcast Pipeline: Solving Real Problems with AI and Code

Building My Personal YouTube-to-Podcast Pipeline: Solving Real Problems with AI and Code

There’s something deeply satisfying about building a solution for a problem you actually have. Not a theoretical problem, not a “wouldn’t it be cool if” project, but something that genuinely makes your daily life better. That’s exactly what happened with my latest creation: a YouTube-to-podcast server that transforms my favorite YouTube content into a personal podcast feed I can listen to during my daily drives to and from daycare.

The Problem: YouTube Addiction Meets Real Life

I love YouTube. There, I said it. From tech tutorials to deep-dive documentaries, from conference talks to maker videos, YouTube is my primary source of educational and entertainment content. But here’s the thing: I spend 40 minutes every day driving to and from my daughter’s daycare, and I wanted to use that time productively instead of just listening to the same old radio. The obvious solution would be YouTube Premium’s background play feature, but that still requires active phone interaction, data usage, and doesn’t give me the podcast-like experience I craved. What I really wanted was a way to queue up interesting YouTube videos at home, have them automatically converted to audio, and consume them like a personal podcast during my drives. So I built exactly that.

The Vision: Personal YouTube Podcast Server

The concept was straightforward:

• Download YouTube videos as high-quality MP3 files
• Generate an RSS feed from the downloaded content
• Serve everything from my home server
• Access it like any other podcast feed Simple in concept, but as anyone who’s built self-hosted solutions knows, the devil is always in the details.

  The Tech Stack: Modern Tools for a Classic Problem

  Core Components

• Python 3.9 for the server application
• yt-dlp for YouTube downloading (the modern successor to youtube-dl)
• ffmpeg for audio processing and conversion
• HTTP server built into Python’s standard library
• Docker for containerization and easy deployment
• CasaOS as the hosting platform

Building with AI: Claude as My Development Partner

This project became a fascinating case study in AI-assisted development. I had the vision and the domain knowledge, but I leveraged Claude (Anthropic’s AI) extensively for implementation details, best practices, and problem-solving.

What I Built by Hand

• Core application logic: The main server architecture and request handling
• RSS feed generation: XML formatting and metadata handling
• Docker configuration: Container setup and port mapping
• API design: RESTful endpoint structure

  Where AI Accelerated Development

• yt-dlp command optimization: Getting the perfect flags for high-quality MP3 extraction with metadata
• HTTP server implementation: Proper request routing and error handling
• Dockerfile best practices: Multi-stage builds and security considerations
• Documentation creation: Comprehensive README and usage examples The collaboration was seamless. I’d describe what I wanted to achieve, and Claude would suggest implementation patterns, catch edge cases I hadn’t considered, and help debug issues as they arose.

Containerization: Docker + CasaOS = Easy Deployment

One of my requirements was easy deployment on my CasaOS home server. Docker was the obvious choice, and the containerization process revealed some interesting challenges:

Server Binding Challenge

The biggest gotcha was getting the Python server to accept external connections. The default localhost binding doesn’t work in Docker containers. The solution:

# Wrong: Only accepts localhost connections
socketserver.TCPServer(("localhost", 8080), handler)
# Right: Accepts all connections
socketserver.TCPServer(("0.0.0.0", 8080), handler)

Docker Compose Configuration

version: '3.3'
services:
  youtube-podcast-server:
    build: .
    container_name: youtube-podcast-server
    restart: unless-stopped
    ports:
      - "5757:8080"
    volumes:
      - /DATA/AppData/youtube-podcast-server/downloads:/app/downloads

Testing: From Development to Production

The moment of truth came when testing the deployed system. Using my CasaOS server, I could:

1. Send download requests via curl:

   curl -X POST http://100.102.219.105:5757/download \
     -H "Content-Type: application/json" \
     -d '{"url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"}'
   

2. Access the RSS feed from any device:

   http://100.102.219.105:5757/rss
   

3. Add to podcast apps and enjoy seamless audio playback

   The Daily Workflow: Automation in Practice

   My typical workflow now looks like this: Evening (Queue Setup):

   • Browse YouTube and find interesting videos
   • Copy URLs and send quick POST requests to download them
   • Content processes overnight Morning (Podcast Consumption):
   • Podcast app automatically syncs new episodes
   • Drive to daycare listening to fresh content
   • Return trip with different content Weekend (Maintenance):
   • Review and clean up old episodes
   • Add new channels or content types

Lessons Learned: Building for Real Problems

This project reinforced several important principles: 1. Solve Real Problems: Building for actual needs creates better software 2. Keep It Simple: 150 lines of Python beat complex frameworks for this use case 3. AI as Accelerator: AI tools excel at implementation, humans excel at vision 4. Test Early: Real-world testing reveals issues no amount of theory can predict 5. Document Everything: Future you will thank present you

Open Source and Community

The complete project is available on GitHub for anyone interested in building their own YouTube-to-podcast pipeline. The code is simple enough to understand and modify, and the Docker setup makes deployment straightforward. While the technical implementation is interesting, the real value is in solving a genuine daily problem. Sometimes the best projects aren’t the most complex onesΓÇöthey’re the ones that quietly make your life a little bit better, 40 minutes at a time.

Conclusion: The Joy of Purpose-Built Solutions

There’s something deeply satisfying about using a tool you built to solve your own problem. Every morning when my podcast app automatically downloads new episodes from my personal feed, I’m reminded that the best technology doesn’t have to be complicatedΓÇöit just has to work. This YouTube podcast server isn’t going to change the world, but it changed my morning commute. And in a world full of over-engineered solutions to imaginary problems, sometimes building something simple that actually improves your daily routine is the most rewarding project of all. Whether you’re driving to daycare, walking the dog, or just want to turn YouTube into audio content, the fundamental approach works: identify a real problem, build a focused solution, and don’t overcomplicate it. Now if you’ll excuse me, I have a Rick Astley song to never give up listening to on my drive home. — Project Repository: github.com/maxrenke/youtube-podcast-server
Tools Used: Python, yt-dlp, Docker, CasaOS, Claude AI
Topics: Self-hosting, Productivity, AI-Assisted Development, Problem Solving