The YouTube Automation Hackathon is a build event for anyone who wants to build something for creators in order to have them spend less time on repetitive creator busywork and more time actually making content. Over the course of the event, you'll design and build a tool, script, or workflow that automates some part of the YouTube content pipeline, thumbnail generation, metadata and SEO, upload scheduling, analytics reporting, comment moderation, clip generation, you name it. Bring a team or fly solo, pick a problem you've actually run into as a creator or editor, and ship something that works by the end.
This hackathon is an independent, community-run event. It is not sponsored, endorsed, or affiliated with YouTube or Google in any way. "YouTube" is used here only to describe the subject matter of the challenge.
Build a tool that automates any part of the YouTube creator workflow. Ideas that fit this mold include auto-generating thumbnails from video frames, batch-scheduling uploads with optimized posting times, pulling analytics into a dashboard that flags underperforming videos, auto-captioning and translating videos, or using AI to draft titles and descriptions from a transcript. If you're working with the official YouTube Data API, stay within its terms of use; this is about building smart tools on top of the platform, not scraping or circumventing it. Projects will be judged on creativity, technical execution, and how useful the tool would actually be to a real creator.
Solo projects and team projects (up to 4 people) are both welcome. Your project should solve a genuine pain point in the YouTube content pipeline, actually run and produce a real result, and be built during the hackathon window.
By the deadline, each team submits: - Project repo— a link to your code (GitHub, GitLab, etc.), with a README that explains what the tool does and how to run it - Demo video (optional)— a short video (2–4 minutes) walking through your project and showing it actually working - Short write-up— a few sentences covering: the problem you solved, how your tool works, and what tech you used - Team info— names of everyone on the team and who built what (helps judges and helps us give credit fairly)