Vibe Planning: The Smarter Way to Code with AI Agents

Detailed Summary

Introduction to AI Coding Approaches (0:00 - 0:58)

• Initial attempts at building software with coding agents often result in hundreds of files, complex architecture, and frequent failures.
• Specs-driven development, where the agent plans and implements, works better but limits the developer's learning.
• A new approach, "Vibe Planning," is recommended for new software developers.

Vibe Planning Methodology (0:58 - 2:28)

• Treat the LLM or coding agent as a collaborator or partner, not just for offloading tasks.
• Brainstorm ideas with a planning agent, asking for feedback and clarifying questions to aid learning.
• Use a "divide and conquer" strategy: break the application into atomic parts (features or phases) instead of planning the whole application at once.
• Plan each feature separately, considering the big picture.
• Implement one feature at a time using a separate coding agent (e.g., Claude Code).
• Test and verify the implemented feature using the initial planning agent.
• Iterate this process to ensure no performance regressions and avoid touching working implementations.

Introducing Traycer for Workflow Management (2:28 - 3:42)

• The Vibe Planning workflow can be followed with almost any coding agent or specialized tools like Traycer.
• Traycer helps build plans for each feature, uses independent agents for implementation, and verifies the implementation.
• Traycer is a VS Code extension that simplifies the workflow.
• The video is sponsored by Traycer, highlighting its smooth and effortless integration.
• Users need to install the Traycer extension in VS Code and sign up for free (with a 7-day pro trial).

Practical Demonstration: Building an Image Generation Web App (3:42 - 6:38)

• The demonstration begins with the Traycer extension in VS Code, offering options to chat with AI for intent clarification or create a detailed file-level plan.
• The presenter chooses to converse with the AI to build a plan for a web app.
• The web app will use HTML for the frontend and Google's Nano Banana image model (Gemini 2.5 flash image) for the backend to generate four images based on user prompts.
• The user should be able to select an image and iterate on it with new prompts.
• The app will use a REST API, not an SDK, requiring the agent to look up Nano Banana documentation.
• Traycer performs a Google search for the Nano Banana API endpoint, a key feature.
• The agent asks clarifying questions about backend technology (Python chosen) and image generation count (four images, using candidate count if possible).
• Temporary in-memory storage for images is specified.

Detailed Planning and Implementation with Traycer (6:38 - 9:47)

• Traycer generates a detailed plan with multiple phases, including an implementation plan down to individual files and their content.
• The plan is then executed using Claude Code, with Traycer tracking and verifying the implementation.
• The backend implementation for the Gemini REST API client is detailed, including Python files, API structure, parameters, and expected output.
• Incremental testing is performed after each phase to track progress and test components.
• The agent identifies and helps fix issues during implementation, providing context for necessary changes.
• After fixing a bug, the API endpoint is tested, confirming functionality.
• The process continues to the next stage, with Claude Code executing the plan and Traycer verifying no regressions.

Testing the Web Application (9:47 - 11:46)

• The app is run, revealing a text input for prompts and a button to generate images, along with an option to iterate with a selected image.
• A prompt is entered: "Create an image of a cat on a skateboard on a beach. There is a sunset scene behind the cat."
• Four images are generated, and the multimodal Nano Banana model also provides accompanying text.
• One image is selected, and a new prompt is given: "Put a hat on the cat while it's drinking orange juice on a sofa watching TV."
• Four new images are generated, showing similar cats, demonstrating the iterative functionality.
• New features can be added by defining new phases within Traycer.

Conclusion and Call to Action (11:46 - 12:42)

• Traycer is presented as a powerful tool for planning and verifying AI-assisted coding applications.
• The presenter praises Traycer's specs-driven development with verification.
• Users can get started with Traycer for free with a 7-day pro trial, with a link in the video description.
• The video's goal was to demonstrate a complete technique for breaking down projects into manageable tasks for AI coding, providing better context to the coding model.

Notable Quotes

"You should think of the LLM or coding agent as a collaborator or partner rather than a software engineer that you just offload tasks to." — [Presenter]

"Rather than planning the whole application, divide it into atomic parts and plan those atomic parts." — [Presenter]

"This will ensure there are no regressions in performance or it's not going to potentially touch implementations that are already working." — [Presenter]

"Tracer makes it a lot more smooth and effortless." — [Presenter]

"Tracer is an extremely powerful tool that can help you do the planning for your five coding applications." — [Presenter]