Antigravity vs Cursor... The Truth
Quick Overview
The creator successfully integrates Google's new Gemini 3 Pro Image model via CometAPI into a Zapier workflow to automate the creation of custom fashion looks, demonstrating that the agent-driven development environment (Antigravity) handles complex multi-modal tasks involving API calls, database storage (Supabase), and front-end display seamlessly.
Key Points: The video demonstrates building an AI Fashion Stylist application using Google's Gemini 3 Pro Image model accessed through CometAPI, integrated via Zapier. The Zapier workflow successfully triggers image generation using the prompt and selected celebrity data, passes the Base64 image output to Supabase for storage, and displays the results in the custom Lovable app. Key features of Gemini 3 Pro Image demonstrated include generating realistic images based on multiple inputs (a user photo and up to 10 clothing item URLs) from a single prompt. The resulting images successfully combine the user's face with various clothing items, such as a colorful shirt, jeans, and socks, in different scenic backgrounds (e.g., European street, park). The Antigravity AI coding environment is shown planning, coding, debugging (fixing Next.js errors), and managing the entire development pipeline hands-free. The entire process, from planning the implementation to testing the final application, is completed with minimal manual intervention, showcasing the power of AI-driven development tools.
Context: The video showcases the capabilities of Antigravity, an AI-driven development environment, by building a complex web application called 'AI Fashion Stylist'. This application leverages the newly released Google Gemini 3 Pro Image model, accessed via the CometAPI service, to generate personalized fashion looks by combining a user's photo with URLs of clothing items. The entire build process, including Zapier integration for workflow automation and Supabase for database storage of generated images, is managed by the Antigravity agent.