Detailed Prompting and Context #
- Provide excruciating detail in the initial prompt rather than vague descriptions.
- Specify color palettes (e.g., dark blues/purples for a dark mode) and UI styling preferences.
- Clearly define functional requirements, such as using specific AI models (Gemini 3) for niche tasks (dietician persona).
- Mention explicit tech stacks for front-end (Next.js) or request the AI to research the best tools for the job.
- Upload reference images of professional UI designs and instruct the AI to mimic that aesthetic.
Roadmapping Before Execution #
- Instruct the AI to analyze the request and create a detailed step-by-step roadmap without writing code initially.
- Review the implementation plan to ensure it covers design, front-end architecture, back-end management, and AI integration.
- Approve the roadmap before allowing the AI to execute, ensuring the model tackles small, manageable steps to improve accuracy.
Version Control and GitHub Integration #
- Immediately save stable versions of the app by pushing code to GitHub once an MVP (Minimum Viable Product) is reached.
- Use the "Source Control" tab within the IDE to manage repositories.
- Maintain a fallback point to restore the app if subsequent AI changes break the code.
Automated Troubleshooting #
- Avoid manual debugging; instead, copy-paste error messages or buggy code directly back into the chat.
- Leverage multiple LLMs for debugging; if Gemini 3 fails to fix an error, swap to Claude 3.5 Sonnet or GPT-4.
- Provide context on what the specific goal was and ask the AI to identify and fix the discrepancy.
Model Selection Optimization #
- Planning Phase: Use Gemini 3 Pro for its large context window and speed when creating roadmaps and analyzing large files.
- Execution Phase: Switch to Claude 3.5 Sonnet in "Fast Mode" for writing high-quality code and implementing specific functions.
- Match the model's strengths to the current stage of development.
Antigravity Agent Management #
- Utilize the "Agent Manager" for a higher level of automation where the agent can write, execute, and test code autonomously.
- Allow the agent to perform terminal commands and interact with the app interface to identify and fix bugs without manual intervention.
Parallel Agent Workflows #
- Run multiple agent conversations simultaneously to skyrocket productivity.
- Dedicate one agent to competitor research and feature ideation while another researches the best tech stack.
- Task one agent with building the front end while a separate agent concurrently builds out back-end functions and database schemas.
Summary #
The video outlines a high-speed development workflow for building production-ready apps using the Antigravity IDE. The core strategy shifts from manual coding to "AI Orchestration," focusing on front-loading context with detailed prompts and reference images, enforcing a "plan-before-code" roadmap, and utilizing Git for safety. The workflow emphasizes using specific AI models for specific tasks (Gemini for planning, Claude for coding) and leverages autonomous agents to synchronously build, test, and research different components of an application simultaneously.
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