📅 2025-08-04 — Session: Developed YouTube Video Processing and Web App Roadmap
🕒 14:40–15:05
🏷️ Labels: Youtube Api, Python, Web App, Content Orchestration, Automation
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary goal of this session was to outline a roadmap for a web application focused on content orchestration and to enhance YouTube video processing capabilities.
Key Activities
- Web App Content Orchestration: Developed a high-level roadmap for setting up a web application using Next.js, GitHub, and Vercel for CI/CD. This included steps for selecting a frontend framework, defining a content model, automating content generation, and managing deployment processes.
- YouTube Video Processing: Implemented Python scripts for backfilling YouTube channel videos using yt-dlp and explored API alternatives for optimizing video fetching. This involved using the YouTube Data API v3 and leveraging an Invidious instance’s JSON API for efficient data retrieval.
- Datetime Handling: Addressed timezone awareness issues in Python datetime comparisons by ensuring offset-naive datetimes are made timezone-aware.
- Retry Logic in Video Fetching: Developed a Python function with retry logic for fetching YouTube videos, incorporating timeout handling to prevent indefinite request blocking.
- Streaming Video Fetcher: Created a streaming video fetcher with real-time progress logging, allowing for immediate processing of video records.
- Metadata Retrieval Enhancement: Enhanced YouTube video metadata retrieval by incorporating additional fields such as thumbnails, channel information, and engagement statistics.
Achievements
- Successfully outlined a comprehensive roadmap for a web app focused on content orchestration.
- Developed robust Python scripts for efficient YouTube video processing and metadata enrichment.
Pending Tasks
- Further testing and integration of the developed scripts into existing workflows.
- Exploration of additional metadata fields for potential analytics enhancements.