Amazon Q in Production: 72 Percent Time Saved

A developer at JPMorgan built an automated content pipeline that generates and uploads videos to nine YouTube channels daily, cutting production time by 72 percent. The system uses local AI models via Ollama/Gemini for scripting, Google Cloud TTS for voice, Pexels for images, and MoviePy for video assembly, all orchestrated by launchd plists. The entire setup runs at zero monthly cost.

Creating content manually for 9 channels would take 8+ hours daily. I have a full-time job as VP at JPMorgan. The math doesnt work. Trending Topic CSV ↓ AI Script Ollama/Gemini ↓ Google Cloud TTS Voice ↓ Pexels Images + MoviePy Video ↓ YouTube API Upload <plist version="1.0" <dict <key Label</key <string com.gyaaninfive.youtube</string <key ProgramArguments</key <array <string /usr/bin/python3</string <string /path/to/upload.py</string </array <key StartCalendarInterval</key <dict <key Hour</key <integer 18</integer <key Minute</key <integer 15</integer </dict </dict </plist One plist per channel. Runs at the exact scheduled time. Google Cloud TTS: Free 4M chars/month Pexels API: Free YouTube API: Free Ollama: Free local Total: ₹0/month 9 channels. Daily uploads. Zero manual work.