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I built a free chrome extension to stop procrastinating on youtube
r/SideProject
7/2/2025
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| Rebound AI YouTube Video Filter | https://chromewebstore.google.com/detail/rebound-ai-youtube-video/jobklgkelpcjokedkmmdnapkhdkipkab | Productivity/Content Filter | Filters YouTube videos based on user-defined categories using LLM analysis of title and description |
| Ingest | https://ingest.707x.in | Content Aggregation | Creates feed of recent videos from user-defined channels/playlists, updates every 4 hours |
| EMBARK | bcworks.in.net/embark | Learning Platform | Curated educational content with articles, quests, and videos (in testing phase) |
USER NEEDS
Pain Points:
- Procrastination on YouTube
- Difficulty avoiding distracting content
- Lack of self-discipline with blocking tools (users ignore "go back" prompts)
- Time wasted on unproductive content
Problems to Solve:
- Filtering unwanted YouTube content automatically
- Creating focused content feeds
- Building discipline to avoid distractions
- Removing distracting videos before they appear
Potential Solutions:
- Browser extensions for content filtering
- AI analysis (LLM) of video metadata for categorization
- Curated content platforms
- Aggregation tools for specific channels/playlists
- Removing distracting elements at source (suggested in comments)
- Transcript analysis for pre-filtering
GROWTH FACTORS
Effective Strategies:
- Building free tools to solve common problems
- Targeting specific platforms (YouTube, Reddit)
- Iterative development based on user feedback
Marketing & Acquisition:
- Sharing on relevant subreddits (r/SideProject)
- Offering free access to build user base
- Beta testing with target audience (e.g., classmates)
Monetization & Product:
- Free pricing model (mentioned as 100% free)
- Potential expansion to other platforms (Reddit suggested)
- Feature development based on user requests (e.g., transcript analysis)
- Product-market fit: addressing procrastination market
User Engagement:
- Community feedback during testing phase
- Public sharing for early adoption
- Responding to technical questions in comments