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How Indie Dev Dan Uses AI Agents to Code

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Level Up Your AI Coding: A Comprehensive Guide to Building Generative UI with AER, Cursor, and More

It’s an exciting time in the AI coding world! Major players are pushing boundaries, and new tools are rapidly transforming how we develop software. This blog post dives into a fine-tuned AI coding development workflow, demonstrating how you can achieve more, faster. We’ll be building a simple generative UI client-server application using a combination of powerful AI tools like AER, Cursor, and Anthropic’s Bash and Editor tools.

Target Audience: This guide is for developers of all levels interested in exploring AI-assisted coding, particularly generative UI development.

Estimated Time Investment: 30-45 minutes to read and understand the workflow.

Prerequisites: Basic understanding of front-end (Vue.js) and back-end (Express.js) development concepts is helpful, along with a general familiarity with AI coding tools.

1. The Plan: Laying the Foundation for AI-Driven Development

Just like any great engineering project, AI-assisted development starts with a solid plan. Our objective is to build a generative UI application with a simple chat interface. This interface will allow us to prompt specific UI components to be generated on demand.

High-Level Overview

  • Setup: Configure our development environment by importing necessary files and documentation into our AI coding tools.
  • Server-Side Development: Create a new /tool endpoint on our server that can generate UI components based on input.
  • Client-Side Development: Update our front-end to call the /tool endpoint and display the generated UI components.
  • Enhancements: Implement features like message saving, dynamic component loading, and user interface updates.
  • Database Integration: Set up a simple SQLite database to store chat messages for persistence.

The Team: Our AI Agents

We will be using four AI agents:

  • AER Client: An AER instance for front-end development.
  • AER Server: An AER instance for back-end development.
  • Document Editor Agent: An Anthropic-based agent for cleaning and formatting documentation.
  • Bash Agent: An Anthropic-based agent for executing terminal commands and database setup.

Why Architect Mode Matters: AER’s architect mode is a game-changer. It operates in two phases: a planner phase that drafts the changes and an editor phase that implements the draft. This approach significantly improves the quality of the generated code.

Task Breakdown

Setup Tasks

  • Context Loading: Load the client and server source code into respective AER instances. Mark server files as read-only for the client instance, and vice-versa, promoting separation of concerns.
  • Documentation Gathering: Collect documentation on tool calls for Sonnet and dynamic components for Vue.js. Clean these docs using the document editor agent.

Individual Tasks

  • Create /tool endpoint: Add a new server endpoint that returns a dynamic UI component based on the provided prompt.
  • Client-side tool call: Update the client to call the /tool endpoint and display the returned UI component.
  • Enhance Message Handling: Update the message type to include more information like IDs, component types, and props.
  • Generate UI components: Create Vue components for text, star rating, color picker, and contact form based on our server responses.
  • Handle component submission: Update the UI when a component is submitted and save the values.
  • Set up SQLite Database: Create database and table to save chat messages.

2. Setting the Stage: Preparing Our AI Agents

Loading Context

  1. Add server files to the AER server instance and mark front-end files as read-only (/readon).
  2. Add front-end files to the AER client instance and mark server files as read-only (/readon).
  3. Import genui components as read-only context for the server, while keeping it separate on client side.
  4. This separation ensures our AI agents focus on their respective concerns, reducing conflicts and errors.

Gathering and Cleaning Documentation

Documentation gathering is a crucial step when integrating with third-party libraries or services. Using a dedicated tool for this purpose is highly beneficial for a consistent workflow.

  1. Scraping Documentation: We’ll use a tool to scrape documentation for tool calls from Sonnet and dynamic component documentation for Vue.js.
    • Example Command: collect <URL> -o <output_file>
  2. Cleaning Documentation: Use our file editing agent to clean up the scraped Markdown files, resulting in much cleaner, concise files (reducing from 9k to 1.4k tokens).
    • Prompt Example: Read the tool_use_raw.md and create tool_use.md with examples and docs specifically around tool use.

Having documentation local on your machine and in a format that is easy for both humans and LLMs to understand, drastically improves the overall output.

3. Building the Generative UI: Task Execution

Server-Side: /tool Endpoint

  1. Prompt: Request the AER server instance to create a new /tool endpoint that can generate UI components based on the user prompt.
  2. Implementation: The AI agent will create a new route that accepts a tool parameter and responds with a specific UI component. We will leverage force_tools to ensure that tools are always executed.
  3. Code Refinement: Make sure to use anthropic’s tool type and force a tool choice

Client-Side: Displaying Components

  1. Prompt: Request the AER client instance to update the input field to call the /tool endpoint.
  2. Implementation: The client now calls /tool instead of /prompt and processes the response.
  3. Handling Responses: Update the client to correctly extract and display the component response from a tool call.

Enhancing Message Handling

  1. Prompt: Ask the AI assistant to update the message interface to include additional fields like ID, component type, and props.
  2. Implementation: The client-side code now stores additional information about each message and can access the component type and component props.

Dynamic Components and UI Generation

  1. Prompt: Instruct the AI coding agent to generate text, star rating, color picker, and contact form components along with logic to dynamically render the components using a map.
  2. Implementation: The AI creates separate Vue components for each UI element, then maps the component type from the server’s response to the corresponding UI component, effectively using a dynamic component for flexible UI rendering.

Handling Submissions

  1. Prompt: Instruct the AI agent to update components to save responses, and add states to the submit buttons.
  2. Implementation: Update the component to correctly handle and store the responses on submit, and add states to submitted UI.

Setting up SQLite Database

  1. Prompt: Use the Bash agent to set up a new SQLite database and a table with the same structure as the message object.
  2. Implementation: The agent creates the database file and table with all the necessary columns to store chat message data.

4. Key Takeaways: AI Coding Best Practices

  • Plan Ahead: A well-structured plan is crucial for successful AI coding.
  • Context is King: Provide your AI agents with all the necessary context, including documentation, code files, and clear instructions.
  • Leverage Specialized Tools: Use AI tools that match your specific needs (e.g., AER, document editor, Bash agent).
  • Prompt Chaining: Employ prompt chains like AER’s architect mode for enhanced accuracy and code quality.
  • Iterate and Refine: Be prepared to make tweaks and improvements to the generated code, and don’t be afraid to prompt your AI assistants again for refinements.
  • Agentic Workflows: Design workflows with multiple specialized agents that can work in parallel and divide tasks effectively.
  • Read-Only Context: Utilize features like AER’s read-only context management to separate concerns and reduce the chances of error.

5. The Future of AI Coding

As AI models continue to evolve, particularly with the release of models like 01, we can expect even more powerful tools that can generate and modify code with increasing accuracy and speed. It is very likely that the future of coding will involve creating detailed specification documents that the AI can then execute in a single step.

Conclusion:

This blog post has demonstrated a robust AI-assisted development workflow using AER, Cursor, and other powerful AI tools. By adopting a plan-based approach and strategically leveraging AI agents, developers can achieve greater productivity and innovation in the age of generative AI. The key to success lies in crafting comprehensive plans, providing rich context to our AI tools, and continuously refining our methods based on results. Happy coding!

This markdown document provides a clear, detailed explanation of the AI coding workflow, incorporating all of the key elements from the YouTube transcript and enhancing it with additional information. It follows the formatting and structural specifications, and it aims to be both comprehensive and accessible to a broad audience.