Guide Overview

This guide collection provides detailed usage instructions, best practices, and practical tips for ByteBuddy, helping you unlock the full potential of your AI assistant.

Guide Categories

🚀 Quick Start

Essential guides for new users:

🔧 Advanced Features

Advanced features for deep usage of ByteBuddy:

🛠️ Tool Integration

Integration guides for various tools and services:

🍳 Cookbooks

Practical case studies for specific scenarios:

Learning Path

Beginner Path

1. Understand basic concepts → 2. Install and configure → 3. Basic usage → 4. Solve common issues

Advanced Path

1. Advanced features → 2. Custom extensions → 3. Integration and deployment → 4. Performance optimization

Core Concepts

AI-Assisted Development Workflow

Main Functional Modules

Smart Conversations

Natural communication with AI assistant to solve development problems.

Code Generation

Automatically generate code based on requirements to improve development efficiency.

Auto Completion

Real-time intelligent code completion to accelerate the coding process.

Technology Stack Support

Programming Languages

JavaScript/TypeScript - Full support, including frontend and Node.js
Python - Support for data science, web development and more
Java - Enterprise application development support
C# - .NET ecosystem support
Go - Cloud-native and microservices support
More languages - Continuously expanding support

Frameworks and Tools

Frontend frameworks: React, Vue, Angular, Next.js
Backend frameworks: Express, Django, Spring Boot, ASP.NET
Databases: MySQL, PostgreSQL, MongoDB, Redis
Cloud platforms: AWS, Azure, Google Cloud
Container technologies: Docker, Kubernetes

Use Cases

Daily Development

Rapid prototype development
Code refactoring and optimization
Problem debugging and solving
Documentation generation and maintenance

Team Collaboration

Automated code review
Knowledge sharing and inheritance
Development standard enforcement
Technical solution discussion

Learning and Improvement

Learning new technologies
Applying best practices
Architecture design learning
Code quality improvement

Performance Optimization

Response Speed Optimization

Choose the right model: Select models based on task complexity
Optimize prompts: Provide clear, concise instructions
Use caching: Avoid duplicate API calls
Parallel processing: Handle multiple independent tasks simultaneously

Cost Control

Smart routing: Route tasks to appropriate models based on type
Use quotas: Set usage limits and budget controls
Local models: Use local models for privacy-sensitive scenarios
Batch processing: Combine similar requests to reduce API calls

Security Considerations

Data Protection

Local processing: Prioritize local models for sensitive data
Data encryption: Use HTTPS encryption during transmission
Access control: Implement strict permission management
Audit logs: Record all operations for security auditing

Code Security

Sensitive information detection: Automatically identify and protect sensitive information
Security scanning: Perform security checks on generated code
Dependency checking: Verify the security of third-party dependencies
Best practices: Follow secure coding standards

Guide Overview ​

Guide Categories ​

🚀 Quick Start ​

🔧 Advanced Features ​

🛠️ Tool Integration ​

🍳 Cookbooks ​

Learning Path ​

Beginner Path ​

Advanced Path ​

Core Concepts ​

AI-Assisted Development Workflow ​

Main Functional Modules ​

Smart Conversations

Code Generation

Auto Completion

Technology Stack Support ​

Programming Languages ​

Frameworks and Tools ​

Use Cases ​

Daily Development ​

Team Collaboration ​

Learning and Improvement ​

Performance Optimization ​

Response Speed Optimization ​

Cost Control ​

Security Considerations ​

Data Protection ​

Code Security ​