Skip to content

Code Pasting

Modern software development through articulate requirements and AI-assisted tooling.

Philosophy

In 2025, effective software development is fundamentally about clear communication of requirements. The traditional approach of typing code directly is being augmented (and often replaced) by a more refined process:

  1. Articulate Requirements - Express what you need clearly
  2. Document in Markdown - Use plain text that AI can understand perfectly
  3. Version Control with GitHub - Maintain organized, collaborative workflows
  4. AI-Assisted Development - Leverage modern tools like Claude Code
  5. Manage Dependencies - Handle the complexities of your tech stack

This approach is more effective than "vibe coding" because it enforces clarity, maintainability, and reproducibility.

Why "Code Pasting"?

The term captures the modern workflow:

  • You articulate your requirements in markdown
  • You paste them into an AI tool
  • The AI understands your needs from clear documentation
  • You paste the generated code into version control
  • You iterate with feedback and refinement

It's not about haphazard copying—it's about purposeful, documented development.

The Four Pillars

1. Markdown

The Foundation of Communication

Markdown is the universal language for documentation and requirement specification. When your requirements are in well-structured markdown, AI tools can: - Parse structure effortlessly - Understand context precisely - Generate code that matches your intent - Help you iterate on specifications

Topics: - Markdown syntax and best practices - Documentation patterns - Requirement specification formats - Creating effective prompts for AI

2. GitHub

Version Control and Collaboration

GitHub is the backbone of modern development: - Track requirements and changes - Collaborate with teams - Integrate with AI tools - Maintain history and accountability - Publish your work

Topics: - SSH authentication and setup - GitHub CLI for productivity - Git workflows and best practices - Collaboration patterns

3. Claude Code

AI-Assisted Development

Claude Code represents the next generation of development tools: - Understands complex requirements - Generates production-ready code - Operates within git repositories - Supports multiple AI models (Haiku, Sonnet, Opus) - Integrates with your AWS infrastructure

Topics: - Setup with AWS Bedrock - Model selection and usage - Integration with git workflows - Best practices for AI-assisted coding

4. Python

The Ecosystem of Science and AI

Python is the primary ecosystem for AI and scientific computing. While language choice varies by use case, Python's ecosystem deserves special attention: - ML/AI frameworks (PyTorch, TensorFlow, transformers) - Scientific computing (NumPy, SciPy, Pandas) - Data visualization and analysis - Quirks and best practices unique to Python

Topics: - Modern Python tooling (UV, Pixi) - Virtual environments and dependency management - Common gotchas and solutions - Integration with AI workflows

The Workflow

Step 1: Define Requirements (Markdown)

Write clear, structured requirements in markdown:

# Feature: User Authentication

## Requirements
- Support email/password login
- Implement password reset flow
- Add rate limiting

## Constraints
- Must support OAuth2
- Needs TOTP support
- Database: PostgreSQL

## Success Criteria
- All tests pass
- Performance <100ms for login

Step 2: Version Control (GitHub)

Commit your requirements to your repository:

git add requirements.md
git commit -m "Add user authentication requirements"
git push origin main

Step 3: Generate Code (Claude Code)

Paste requirements into Claude Code:

claude /path/to/project

Provide the requirements document to Claude—the tool understands markdown perfectly and can generate code that matches your specification.

Step 4: Iterate and Refine

  • Review generated code
  • Commit to GitHub
  • Update requirements based on learnings
  • Re-run Claude Code with updated specs
  • Merge and deploy

Why This Works

For AI Tools

  • Markdown is unambiguous
  • AI can parse structure reliably
  • Plain text avoids parsing errors
  • Requirements are self-documenting

For Developers

  • Clear specification prevents miscommunication
  • Requirements become documentation
  • Easy to iterate with AI
  • Entire workflow is versionable

For Teams

  • Markdown enables collaboration
  • GitHub provides visibility
  • AI tools democratize capabilities
  • Python ecosystem is well-established

Best Practices

  • ✅ Document first - Start with markdown requirements
  • ✅ Be specific - AI works best with clear specs
  • ✅ Use examples - Show what success looks like
  • ✅ Version everything - Git all requirements and code
  • ✅ Iterate together - Refine with AI feedback
  • ❌ Don't assume - Always specify constraints
  • ❌ Skip documentation - Markdown IS the foundation
  • ❌ Ignore edge cases - Specify them in requirements

Getting Started

  1. Start with Markdown to understand documentation best practices
  2. Set up GitHub for version control
  3. Configure Claude Code for AI-assisted development
  4. Explore Python if building AI/ML projects

The Future of Development

This approach represents how software development is evolving in 2025:

  • Less typing - AI handles code generation
  • More thinking - Clear specification is paramount
  • Better collaboration - Documentation enables team understanding
  • Faster iteration - AI suggests, you refine
  • Reproducible workflows - Everything is documented and versioned

By mastering these four pillars, you're not just keeping up with development trends—you're leading the way.