11 Code assistants and development tools

 

This chapter covers

  • How AI coding assistants fit into development workflows
  • Using GitHub Copilot, Cline, Roo Code, Cursor AI, Google Project IDX, and Aider
  • Privacy considerations when working with AI coding tools
  • Best practices for AI-assisted development
  • Managing large codebases effectively with AI assistants

AI development tools have changed how we write software. What used to take hours—debugging, documentation, basic features—now takes minutes. Although these tools complement rather than replace developers, they’ve become an integral part of how we work.

Traditional code completion offers basic suggestions, but modern AI tools understand context, predict what we need, and can write complete functions or solve complex coding problems. This fundamental shift has transformed how we approach software development.

AI development tools come in several forms:

  • Terminals—Assistants that integrate directly with your command-line interface
  • IDE extensions—Plugins for popular Integrated Development Environments (IDEs) like Visual Studio Code or JetBrains IDEs
  • VS Code forks—Customized versions of VS Code with built-in AI features
  • Web interfaces—Browser-based coding environments with AI assistance

Before exploring these tools in detail, we need to discuss two important considerations: privacy and expertise.

11.1 Privacy and security

11.2 Understanding the value of existing skills

11.3 GitHub AI suite

11.4 Cline

11.5 Cursor AI

11.6 Google Project IDX

11.7 Aider

11.8 Summary comparison of code assistants

11.9 Best practices for AI-assisted development

11.9.1 Foundation principles

11.9.2 Advanced techniques

11.10 Prompts used in this chapter

Summary