How to easily vibe code an AI App using Google AntiGravity ?

Explore anti-gravity's LLMs, select Gemini 3 Pro High, and outline a minimal viable SaaS for AI agents using Convex backend and a plain html/css frontend deployed to Vercel.

Identify and align tasks with an implementation plan to proceed with an ai app, reading the full response and coordinating planning before moving forward.

Plan a convex project to build a vanilla front end (html, css, js) and a TypeScript back end with schema.ts and api functions for agents and users, plus seed data.

Build a lightweight ai agent sas MVP with a convex backend, real-time database, and serverless functions, using a vanilla html css js frontend via cdn es modules, without a framework.

Learn to build an ai app with convex backend, defining agents, stores, and sessions in a root ai agent sas project, and render with index.html, app.js, and style.css.

Kick off by starting back end with npx convex dev for your AI app, under Google AntiGravity, then serve the front end and review the implementation plan and task plan.

Follow the Anti-Gravity and Gemini 3 Pro plan, accept changes, and submit updates to see the implementation plan take effect.

Discover how to vibe code an ai app by using a content delivery network, es modules, and esm.sh to load convex in the browser without a build step.

Vibe code an ai app using Google AntiGravity by testing Convex as an alternative to SuperBase, building basic HTML CSS JS front-end, and planning back-end integration.

Initialize a Convex back-end while building front-end core files, including index.html, style.css, and app.js, and install dependencies to start the Convex development environment while validating tasks and the implementation plan.

Troubleshoot language mismatches and npm script errors while vibing code an AI app using Google Antigravity, addressing npm.ps1, Node.js restrictions, and blocked script execution.

Learn to set up a convex backend for an ai app with npm install, audit, and manual initialization, then run npx convex dev after logging in via the browser.

Initialize the convex backend manually by logging in via the browser with npx-convex-dev, save progress to status.md and todo.md, then update app.js and implement the backend schema and function.

Refactor the project from convex to a local PostgreSQL backend using a lightweight Python FastAPI server. Connect a vanilla HTML/CSS frontend to PostgreSQL and plan moves to convex or super-base.

Verify Python 3.13.9 and PostgreSQL versions, recognize errors like object not found and command not found, and troubleshoot by verifying access paths and providing database credentials.

Switch to SQLite for now to run a fully local, no-install database that mimics PostgreSQL logic. SQLAlchemy handles the difference, supporting convex-based AI app development.

Set up the Python backend with FastAPI and SQLite, create and seed the database with initial agents, and connect the frontend to the new backend.