Gemini Builder: NEW Powerful Autonomous AI Coding Agent Can Build & Design ANYTHING & IS FULLY FREE!

Key Concepts

• Stitch: An AI coding agent for UI/UX design, capable of generating front-end code for mobile and web applications based on natural language descriptions or visual references (wireframes, mockups, images).
• Jules: An asynchronous AI coding agent designed for backend development, logic implementation, integrations, testing, and debugging. It can read project context and autonomously work on code.
• AI Software Assembly Line: The concept of combining Stitch and Jules into a streamlined pipeline for full-stack application development, where Stitch handles the front-end and Jules handles the back-end.
• Google AI Studio Build Mode: An alternative AI coding agent that can be used with exported zip files from Stitch, though it may not perform as well as Jules for complex backend tasks.
• Gemini 2.5 Pro: The AI model powering Stitch's experimental mode for UI component generation.
• Scalekit: A sponsor mentioned, providing an OAuth stack specifically built for AI applications, focusing on authentication for AI agents, servers, APIs, and human users.

Combining Stitch and Jules for Full-Stack Development

The video introduces a powerful new workflow that integrates Google's AI coding agents, Stitch and Jules, to create a comprehensive AI software assembly line for full-stack application development. This combination allows users to leverage Stitch for generating front-end code and then seamlessly transition to Jules for backend logic, integrations, testing, and debugging, ultimately enabling the deployment of products to production. The entire process is highlighted as being accessible and free to start.

Stitch: Front-End Generation

Stitch is presented as an AI-powered UI/UX design tool that can generate front-end code for mobile and web applications.

• Functionality: It can create components based on provided images, wireframes, mockups, or natural language descriptions.
• Example: Airbnb Clone: The presenter demonstrates Stitch's capability by creating an Airbnb clone. By providing an image from Google Images, Stitch successfully replicated all the components of the UI.
• Example: Crypto Trading Platform: In a more detailed demonstration, Stitch was tasked with building UI components for a crypto trading platform, including a real-time order book, live price feed, and candlestick chart.
  • Prompt: The user described the desired components and provided a reference chart from Google, requesting Stitch to mimic it.
  • Execution: The process involved planning by the AI, followed by the generation of components within a canvas.
  • Speed: The initial generation of the crypto trading exchange UI, mimicking the provided photo, took approximately 40 seconds.
  • Features: The generated UI included requested components like a live chart, candlestick chart, and an order book, structured according to the wireframe.
• Iteration and Refinement:
  • Multiple Variants: Stitch can generate multiple design options for a given prompt, allowing users to choose from different formats and component arrangements. The presenter generated three additional variants.
  • Annotation and Editing: Users can select specific components within the canvas for further iteration or editing.
  • Theme Adjustment: The ability to change themes (e.g., to green or light mode) is available.
  • Export Options: Generated code can be viewed, downloaded as a zip file, or exported directly to Figma.
• Experimental Mode: Stitch's experimental mode utilizes Gemini 2.5 Pro for UI component development, offering advanced capabilities.
• Limitations and Quality: While Stitch is powerful, the presenter notes that not all generated variants are perfect. Some may have design flaws (e.g., incorrect gradients, formatting issues). The presenter identified two preferred options from the generated variants for further development.

Integrating Stitch with Jules for Backend Development

The core innovation lies in the seamless integration of Stitch's front-end output with Jules for backend development.

• Exporting to Jules: After generating front-end components with Stitch, users can export the canvas to Jules.
  • Process: This is done by clicking the three dots on the canvas and selecting "export to Jules."
  • Integration: The exported code can be directly integrated into a selected repository within Jules.
• Jules' Role: Jules takes over the backend development, focusing on:
  • Building the backend structure.
  • Implementing logic.
  • Debugging components.
  • Expanding the app's functionality.
  • Connecting backend components with the front-end.
• Task Creation in Jules:
  • Description: Users describe to Jules what they want it to build on, specifying backend development, front-end polishing, and app expansion.
  • Repository Selection: A repository for Jules to work within is chosen.
  • Task Creation: A task is created for Jules to execute.
• Jules' Execution:
  • Planning: Jules first plans out the steps to tackle the backend task.
  • Approval: The user approves the plan.
  • Execution: Jules then proceeds to code out every component to make the app fully functional, ensuring seamless integration with the front-end developed by Stitch.
• Example: Crypto Trading Platform Backend: Jules was tasked with building the backend for the crypto trading platform.
  • Outcome: The final application, developed using the free tiers of Stitch and Jules, was fully functional.
  • Features Implemented: The live order book was functional, positions were added as requested, live trades were displayed, and users could place limit or market orders.

Alternative: Google AI Studio Build Mode

The transcript mentions an alternative for utilizing exported zip files from Stitch.

• Functionality: Google AI Studio Build Mode can be used with these zip files to build integration or backend components.
• Process: Users attach the file and describe their desired components.
• Comparison: While functional, it is noted that this method may not perform as well as using Jules for complex backend tasks.

Sponsor: Scalekit

The video includes a sponsorship from Scalekit, a company providing an OAuth stack for AI applications.

• Focus: Scalekit addresses the complexities of authentication in B2B AI products.
• Features:
  • Complete OAuth infrastructure for authenticating AI agents.
  • Secure MCP servers and APIs with a drop-in OAuth layer.
  • Protect human users with passwordless flows (magic links, passkeys) or enterprise SSO.
• Flexibility: Scalekit's modular design allows integration with existing OAuth providers (like Auth0 or open-source solutions) without requiring a full stack rewrite. This flexibility is highlighted as their "secret sauce."
• Benefit: Enables securing every layer of an AI app and scaling without headaches.

Getting Started and Conclusion

• Accessibility: Both Jules and Stitch can be accessed for free by creating an account with a Google account.
• Overall Value: The combination of Stitch and Jules is presented as a highly effective and accessible tool for building complex applications, offering capabilities beyond what is typically expected from AI models.
• Recommendation: The presenter highly recommends exploring these Google Labs AI coding agents.
• Call to Action: The video encourages viewers to subscribe, join the Discord, follow on Twitter, and watch previous videos.

Synthesis/Conclusion

The video effectively demonstrates a revolutionary approach to software development by showcasing the synergistic capabilities of Google's AI coding agents, Stitch and Jules. This integration forms a powerful "AI software assembly line" that streamlines the creation of full-stack applications. Stitch excels at rapidly generating sophisticated front-end UIs from simple prompts or visual references, while Jules takes over the complex backend development, logic implementation, and debugging. The entire process is accessible for free, making advanced AI-assisted development a reality for a wider audience. The presenter emphasizes the impressive functionality and efficiency of this combined workflow, highlighting its potential to significantly accelerate product development cycles and enable the creation of robust, production-ready applications. The mention of Scalekit further underscores the growing ecosystem of tools supporting AI application development, particularly in the crucial area of secure authentication.