Collaboration Software

In the AI-assisted development era, tool selection matters more than ever

In AI-assisted development workflows, collaboration tools are not just “assistants” — they’re critical components for ensuring code quality, accelerating iterations, and maintaining project context. Choose the right tools, and AI transforms from “occasional helper” to “full-time collaborator.”

Version Control: Git

Why Git is Essential

In AI-assisted development, Git’s importance is amplified:

• Prevent AI from Breaking Code - AI might mistakenly modify critical code; Git lets you roll back anytime
• Quickly Compare Changes - When AI generates large amounts of code, Git diff helps you spot issues fast
• Safe Experimentation - Use branches to let AI experiment freely without affecting the main branch
• Foundation for Collaboration - Team collaboration and CI/CD are built on Git

Recommended Configuration

# Set up a commit template
git config --global commit.template ~/.gitmessage
 
# Enable automatic spelling correction
git config --global help.autocorrect 1
 
# Global ignore file
git config --global core.excludesfile ~/.gitignore_global

Git Client: Fork

Why Fork is Recommended

In fast-iterating AI projects with frequent code changes, Fork’s visualization capabilities are especially valuable:

• Quick Diff Review - Visual comparison helps you rapidly review AI-generated code changes
• Timeline Preview - In rapid iterations, intuitive commit history helps you understand project evolution
• Clear Branch Management - AI often needs to switch between branches for experiments; Fork makes this intuitive
• Friendly Conflict Resolution - When AI-generated code creates conflicts, visual tools greatly reduce resolution difficulty

Alternatives

• GitKraken - Feature-rich, supports team collaboration
• Sourcetree - From Atlassian, free to use
• Command line - Most direct approach; AI can also generate Git commands

API Testing: Bruno

Why Bruno Works Well with AI

Bruno’s biggest advantage is file-based Git management, allowing AI to directly participate in API documentation maintenance:

• Git-Friendly - API requests are stored as .bru files, directly version-controlled
• AI-Maintainable - AI can read and write .bru files, automatically updating API documentation
• Local-First - No cloud account required; you have full control over your data
• Team Sync - Share API collections via Git, ensuring the team uses the same test cases

collections/
├── api/
│   ├── users/
│   │   ├── get-user.bru         # AI can update directly
│   │   └── create-user.bru      # AI can update directly
│   └── orders/
│       └── list-orders.bru
└── environments/
    ├── local.bru
    └── production.bru

Documentation Tools: Nextra

Why Documentation Tools Matter

In AI-assisted development, documentation isn’t just for humans — it’s key to providing project context for AI:

• Rapid Project Understanding - AI can read Markdown docs to quickly grasp project architecture and conventions
• Context Transfer - Attach documentation files to AI conversations to provide necessary background
• Knowledge Accumulation - Good documentation helps AI generate code that better fits project conventions
• Version Sync - Markdown files can be version-controlled alongside code, maintaining consistency

Why Nextra is Recommended

Nextra  is a Next.js-based documentation framework, particularly suitable for AI-assisted development:

• MDX Support - Embed React components in documentation; AI can maintain both docs and components
• File-based Routing - File structure defines navigation structure; AI can easily understand and modify
• TypeScript-Friendly - Configuration files use TypeScript, providing type hints for AI-generated code
• Built-in Search - Automatically generates search index with no extra configuration
• Fast Hot Reload - Based on Next.js, excellent development experience

AI Collaboration Advantages

When using Nextra, AI can:

1. Edit Documentation Directly - Attach .mdx files in conversations for AI to update content
2. Sync Navigation Updates - Modify _meta.tsx files; AI understands navigation structure
3. Auto-generate Examples - Embed interactive code examples in documentation
4. Maintain Multilingual Docs - AI can maintain Chinese and English docs simultaneously with consistent structure

Alternatives

• Docusaurus - React-powered, from Meta, feature-complete
• VitePress - Vue-powered, lightweight and fast
• GitBook - Team knowledge base, suitable for non-technical users

Documentation Best Practices

1. Docs as Code - Write in Markdown/MDX, version control with code
2. Structured Organization - Clear directory structure helps AI locate information
3. Keep Updated - Have AI update related docs when modifying code
4. Context Attachment - Attach relevant documentation files when conversing with AI to provide context
5. Use Frontmatter - Add metadata at the beginning of docs to help AI understand document types

Design Collaboration: Figma + MCP

Advantages of Figma MCP

Through Figma MCP, AI can directly read design files:

• Design to Code - AI understands elements, styles, and layouts in design files
• Maintain Consistency - Automatically extract colors, fonts, and other specs from the design system
• Accelerate Development - Reduce time converting from design to code

See the MCP Tools section for details.

Summary

Choose the right collaboration software to make AI not just a “code writing tool,” but truly integrated into your development workflow.