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🚀 Showcase

Congratulations! You have successfully run your first AI inference call. Now, let’s demonstrate how to build a real AI chat application through a complete project example.

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📋 Project Overview

APUS Chat Attestation Example is a complete full-stack application example that demonstrates how to:
  • Deploy AI chat agents on the AO network
  • Integrate APUS AI inference services
  • Build frontend interfaces to interact with AO agents
  • Implement message attestation and verification mechanisms
Project URL: https://github.com/apuslabs/apus-chat-attestation-example

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🏗️ Project Architecture

apus-chat-attestation-example/
├── ao_agent.lua          # AI chat agent on AO network
├── frontend/             # Frontend application
│   ├── src/
│   │   ├── components/   # React components
│   │   ├── hooks/        # Custom hooks
│   │   └── utils/        # Utility functions
│   └── package.json
└── README.md

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🔧 Core Components Analysis

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1. AO Agent (ao_agent.lua)

This is a smart contract deployed on the AO network, responsible for:
  • Receiving user messages
  • Calling APUS AI inference services
  • Generating AI responses
  • Handling message attestation
Key Features:
  • Uses APUS AI library for inference
  • Implements message queue management
  • Supports message attestation and verification
  • Error handling and retry mechanisms

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2. Frontend Application

User interface built with React, providing:
  • Chat interface
  • AO Connect integration
  • Message sending and receiving
  • Real-time status updates

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🎯 Developer Learning Guide

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Phase 1: Understanding AO Agent Architecture

Learning Objective: Master the basic structure of AO Agent and APUS AI integration methods Key Code Snippets: 

-- Core logic for handling messages
Handlers.message = function(msg)
    -- 1. Validate message format
    -- 2. Call AI inference
    -- 3. Generate response
    -- 4. Send response
end
Learning Points:
  1. Message Processing Flow: Understand how AO Agent receives, processes, and responds to messages
  2. AI Integration Pattern: Learn how to elegantly integrate APUS AI services
  3. State Management: Master AO Agent’s state management mechanisms
  4. Error Handling: Understand error handling and retry strategies

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Phase 2: Frontend Integration Patterns

Learning Objective: Master how frontend interacts with AO Agent Key Concepts:
  1. AO Connect: Learn how to connect to AO network
  2. Message Sending: Learn how to send messages to AO Agent
  3. Response Handling: Master how to handle AI responses
  4. State Synchronization: Understand frontend state synchronization with AO Agent

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Phase 3: Project Customization Development

Development Steps:
  1. Project Initialization 
    # Clone example project
git clone https://github.com/apuslabs/apus-chat-attestation-example
cd apus-chat-attestation-example

# Install dependencies
npm install
  2. AO Agent Customization
    • Modify business logic in ao_agent.lua
    • Adjust AI prompts and parameters
    • Add custom functionality modules
  3. Frontend Interface Customization
    • Modify UI components and styles
    • Add new interactive features
    • Integrate other services or APIs
  4. Testing and Deployment
    • Local testing of AO Agent
    • Deploy to AO network
    • Frontend deployment and configuration

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💡 Development Tips and Suggestions

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For Development

You can fully leverage the AI assistant:
  1. Code Learning Mode 
    Prompt: "Please analyze this AO Agent code and explain its architecture and key functions"
    • Let AI learn the code structure of ao_agent.lua
    • Understand APUS AI integration patterns
    • Master AO Agent best practices
  2. Frontend Code Migration 
    Prompt: "Please analyze this frontend code and explain how to call AO Agent"
    • Let AI learn frontend interaction patterns with AO Agent
    • Understand AO Connect usage methods
    • Master state management and error handling
  3. Project Customization Guidance 
    Prompt: "Based on this example, help me design a [Your Project Type]"
    • Let AI customize new features based on the example project
    • Provide code refactoring suggestions
    • Optimize performance and user experience

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Development Best Practices

  1. Modular Design
    • Modularize AO Agent functionality
    • Use clear function naming and comments
    • Implement reusable components
  2. Error Handling
    • Implement comprehensive error handling mechanisms
    • Add logging functionality
    • Provide user-friendly error messages
  3. Performance Optimization
    • Optimize AI call frequency
    • Implement message caching mechanisms
    • Reduce unnecessary network requests
  4. User Experience
    • Add loading state indicators
    • Implement real-time message updates
    • Provide responsive design

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🎉 Next Steps

  1. Explore Example Project: Carefully read and understand the example code
  2. Local Testing: Run and test the project in local environment
  3. Custom Development: Develop your own AI applications based on the example
  4. Community Engagement: Join APUS Discord for support and feedback
Now you have the foundational knowledge to build AI applications - it’s time to start your creative journey! 🚀