Overview
This video demonstrates building a complete AI-powered search engine application using Google’s Antigravity IDE and Stitch design tool. The tutorial shows how to create a full-stack application with frontend, backend, authentication, payments, and deployment without writing any code through AI automation and visual tools.
Key Takeaways
- AI development tools can now autonomously handle full-stack development from UI design to backend implementation without manual coding
- Modern no-code solutions enable production-grade applications with complex features like search ranking, authentication, and payments through visual interfaces
- Database integration has evolved to AI-native architectures where agents can directly manage schemas, queries, and data operations through MCP protocols
- Visual planning and implementation separation allows non-technical users to create detailed project specifications that AI agents can execute systematically
- Search functionality can be embedded directly in databases using extensions like BM25, eliminating the need for separate search infrastructure and data synchronization
Topics Covered
- 0:00 - Introduction to Antigravity and Stitch: Overview of Google’s free AI development tools and their capabilities for full-stack development
- 0:30 - Technology Stack Overview: Breakdown of tools being used: Stitch for UI, Antigravity for development, Tiger Data for backend, Stripe for payments
- 2:00 - Building an AI Search Engine: Demonstration project: creating a personal AI search engine powered by PostgreSQL with BM25 ranking
- 3:00 - Prerequisites and Setup: Required accounts and initial setup for Google, Stripe, Tiger Data, and deployment platforms
- 4:00 - Using Stitch for UI Design: Creating frontend components with AI-powered design tool and connecting to Antigravity via MCP
- 7:00 - Antigravity Implementation Planning: Setting up autonomous AI agent with detailed implementation plan for full-stack development
- 8:30 - Backend Setup with Tiger Data: Configuring PostgreSQL database with AI-native features and MCP integration
- 11:00 - Integrating Search Technology: Implementing BM25 text search extension directly in PostgreSQL for intelligent ranking
- 12:30 - Application Execution: Running the autonomous build process and demonstrating the completed application
- 14:00 - Testing and Refinement: Adding authentication, payment integration, and testing search functionality with live data