Google Antigravity: Build SaaS Apps with AI

Explore the editor view, generate calculator code with control plus i, preview in the browser, and manage html, style.css, and scripts.js while adding braces, divide, power, and modulo.

Learn the manager view in antigravity, switch between workspaces and playgrounds, manage agents, generate an apple-like website clone with an html code, and use knowledge, browser, and mcp servers.

Explore task templates and predefined tasks like MSBuild and Maven, detailing labels, types, commands, and arguments, to enable build orchestration and CI workflows in anti-gravity.

Master how to use the documentation to leverage antigravity tools and agent managers, configure editors, track changes, and streamline workflows through parallelization and knowledge management.

Install and configure essential VS Code extensions—GitLens, Prettier, ESLint, Docker, and Funder Client—to enable inline blame, auto format on save, real-time linting, API testing, and containerized development.

Create and manage an agent space workspace, use focus editor and prompts with model variants to generate and refine interactive SaaS app code.

Explore the anti-gravity agentic workflow, from mission and prompt to context loading, plan generation, implementation, and verification, including three iterative code updates with human approvals.

Define a clear mission with objectives, constraints, and measurable acceptance criteria before planning. Then implement in small steps, seek approval before execution, run and observe results, and verify against criteria.

Generate a full-fledged Aura Brew coffee shop website from a single prompt, producing index.html with style.css, menu.html, about.html, contact.html, and main.js, with inline css, smooth transitions, and a professional footer.

Explore frontend development fundamentals, from CSS foundations and responsive design to style.css updates, typography improvements, and interactive elements like navigation, gradients, and a hamburger toggle.

Identify the root cause of blocked tasks using targeted questions like five-whys and mapping dependencies to forecast impact, then design escalation, parallel task shifting, risk assessment, and transparent communication.

Refine navigation, reveal animations, smooth scrolling, and contact form handling in an AI-driven workflow, treating each as testable features edited by agentic prompts to improve performance.

Iterate with ai by repeating iterations for the same prompt, refining css and fonts, test changes incrementally, and achieve clearer, high-contrast ui text and layout.

Compare autonomous planning with prompt-driven edits, showing how agents handle tasks through fast system edits or planning subproblems, tools, and workflow orchestration.

Learn how to plan and implement a pink theme for the AuraBrew website, updating CSS variables, colors, images, and layout, with automated and manual verification.

Explore automated browser testing in the Google antigravity IDE, where an AI agent dynamically generates end-to-end tests in a real browser, navigating pages, submitting forms, and validating rendering and navigation.

Explore self-verification in agent-based development, where autonomous agents implement changes, run verification checks, diagnose failures, and automatically correct and reverify to ensure outcomes match objectives.

Maintain traceable change history with who, when, and why to ensure accountability and transparent documentation; build a living, version-controlled knowledge base with structured taxonomies, decision logs, and visual process maps.

Discover how antigravity skills enable AI agents to perform tasks efficiently in a development environment using reusable modules and GitHub integration.

Turn a repetitive workflow into a reusable skill with clear metadata and instructions, enabling a git commit formatter to automate changes in the OpenAI Codex model.

Learn to structure skill instructions and metadata for reliable ai-powered workflows. Understand skill.md metadata, name and description, and clear inputs and outputs for correct triggering and reuse.

Test and debug ai skills in anti-gravity environments by verifying discovery, trigger accuracy, and output rules, then validate metadata and formatting with a git commit formatter example.

Package modular, self-contained skills with defined inputs and outputs, document them for reuse, distribute through marketplaces, monitor performance, and enable cross-project portability.

Explore model context protocol (MCP) as an interoperability standard that securely links AI models to external tools via MCP servers, enabling controlled execution and modular, auditable integrations.

Connect to the first MCP via GitHub integration, configure a personal access token, install Docker, and generate and manage GitHub repositories within the model context protocol.

Explore how AI agents interpret natural language to orchestrate tool actions, translate intent into tasks, and automate repository workflows with MCP and GitHub integrations.

Connect Stitch with antigravity to design UI with AI, generate screens from text, and manage projects via the Stitch MCP, including creating a smart canteen app and a login screen.

Strengthen credentials with multi-factor authentication to thwart phishing and brute-force attacks, and enforce role-based access, least privilege, secure secret storage, and token rotation for robust governance.

Learn front-end and back-end coordination by building a simple to-do app with a shared plan and API bridge, guided by agent-driven, beginner-friendly workflows that generate plans and screenshots.

Design and refine a front-end for a to-do app by iterating a three-layer structure: layout, style, behavior, displaying backend data and allowing task creation with responsive user experience.

Practice iterative improvement in the back-end by starting with a simple working server and progressively adding validation, error handling, structure, and security, with antigravity agents guiding refinements.

Explore agent permissions and safety controls in Google Antigravity, applying least privilege, role-based access, and monitoring with validation, sandboxing, and logging to safeguard automated agents.

Learn how workflows define structured steps to automate coding, debugging, testing, and documentation in Google Antigravity, enabling reusable procedures with safety checks and validation before execution.

Learn backup and version control best practices for AI-driven SaaS projects, using git and GitHub to track commits, branch safely, and protect against data loss.

Clarify requirements early to prevent misinterpreted tasks and reduce ambiguity. Apply structured requirement discussions, feedback loops, context alignment, reframing, assumption validation, and iterative scope validation to sustain alignment.

Review rate limits and quotas across models and plans in Google Antigravity, and learn how refresh cycles and access extensions via keys or organizational integrations affect usage.

Master efficient use of ai agents to code, debug, and manage projects in antigravity environments by breaking tasks into steps and using clear, structured prompts.

Apply modular architecture and separate front-end, back-end, and data layers to improve scalability. Automate builds and testing, monitor performance, and incrementally scale infrastructure with caching and optimized queries.

Practice continuous user testing to validate assumptions early, driving fast feedback loops through rapid iterations, incremental releases, and real-time analytics and performance dashboards guiding feedback-driven refinement.