cFoodo
Feature
- 1.Developed an AI-driven restaurant discovery platform that automatically extracts and analyzes Instagram food posts, providing users with real-time, location-based recommendations integrated with seamless map and list views.
- 2.Architected a highly concurrent batch processing pipeline using a Go worker pool to process batches of ~100 posts and comments simultaneously. Strategically chunked large requests to prevent LLM token overflow while stabilizing system resource utilization and minimizing latency.
- 3.Automated operational workflows by integrating Database Webhooks with a Discord Bot, transforming the manual "add restaurant" procedure into a structured data pipeline. This reduced maintenance costs and backend complexity, requiring only basic field validation to process data.
- 4.Implemented spatial indexing for location-based queries to eliminate full table scans. This drastically reduced query latency and ensured a smooth, responsive user experience on map and list interfaces during real-time user location updates.
Architecture

Screenshots


