Can Google Antigravity 2.0 Pass the "Napkin Challenge"? 📝🚀

A developer used Google's Antigravity 2.0 and Gemini 3.5 to build and deploy a real estate investment advisor from a napkin sketch in under 40 minutes. The project, called the "Napkin Challenge," demonstrated that a coding agent can autonomously architect, build, test, and deploy a data-driven application using real-world census data and parallelized workflows. The developer proved that the barrier between an idea and a deployed product has virtually disappeared when combining the right context with powerful models.

🚀 From Napkin Sketch to data-driven real-estate advisor Agent in Under 40 Minutes? 🚀 Can a coding agent really work autonomously on complicated problems without human intervention? I decided to put Google’s new Antigravity 2.0 and Gemini 3.5 to the test: The Napkin Challenge. 📝 The goal: Build and deploy a real estate investment advisor - based on real-world data, starting with nothing but a rough sketch on a napkin. The Result? With the right context, yes I used: Antigravity 2.0 & Gemini 3.5: Architect and execute the plan Agent CLI Skills: Scaffold, build, test and evaluate the agent using ADK.3 Developer Knowledge MCP: Provided necessary context to connect to BigQuery MCP and integrate the census dataset for grounded investment advice. Parallelized Workflow using sub-agents: While the system ran the evaluation suite, it simultaneously deployed the agent to Cloud Run. The Outcome: The "Napkin Challenge" proves that when you combine the right context with powerful models, the barrier between an idea and a deployed product has virtually disappeared. I challenge you: What is your "napkin" project? Try it with Antigravity 2.0 and Gemini 3.5. 📝 🚀 How to join the challenge -