SnapStock
AI-Powered Community Stock Photography
A web platform where creators upload and explore high-quality stock photos with AI-based content moderation ensuring safety and quality at scale.
The Challenge
User-generated content platforms face the dual challenge of encouraging community contributions while maintaining quality and safety standards without expensive manual moderation.
• Manual content moderation doesn't scale with user growth
• Inappropriate or low-quality uploads damage platform reputation
• Traditional stock photo sites have high barriers to contributor entry
• No real-time feedback for uploaders on content quality
• Expensive moderation teams cut into platform profitability
The Solution
SnapStock combines open community contributions with AI-powered moderation, automatically filtering inappropriate content and providing quality scores to maintain high standards.
Key Features:
AI content moderation for safety and quality scoring
Automatic tagging and categorization using computer vision
Advanced search with filters for style, color, and composition
Contributor reputation system based on upload quality
Firebase-powered fast image delivery and storage
Community curation and featured collections
Design & Development Process
ML Model Research
3 weeksEvaluated pre-trained models for content moderation and implemented custom training for photography quality assessment
Platform Architecture
2 weeksDesigned scalable image processing pipeline with Firebase Storage and MongoDB metadata
UI/UX Design
2 weeksCreated Pinterest-style masonry layout with efficient lazy loading for image browsing
Development & ML Integration
11 weeksBuilt platform with real-time ML processing pipeline for uploads
Project Showcase
Technical Implementation
Serverless architecture with ML processing pipeline, optimized for high-volume image uploads and delivery
Technologies Used:
Key Challenges & Solutions:
Real-time ML inference on image uploads
Implemented serverless functions with batched processing and progressive quality checks during upload
Scalable image storage with fast CDN delivery
Leveraged Firebase Storage with automatic image optimization and WebP conversion for performance
Accurate AI moderation without false positives
Multi-model ensemble approach combining content safety, quality scoring, and manual review fallback for edge cases
Results & Impact
User Feedback:
"SnapStock's AI moderation let me launch a UGC platform without hiring moderators. It just works."
Key Learnings & Takeaways
ML moderation requires human fallback for edge cases—never fully automate controversial decisions
Image optimization is critical—uncompressed images kill mobile performance
Quality scoring helps surface best content but needs calibration per category
Contributor feedback loops improve upload quality over time
Interested in working together?
I'm always open to discussing new opportunities and exciting projects.
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