I’ve been curious about AI-powered development tools for a while now, and recently decided to give Anthropic’s Claude Code CLI a proper test drive. After a few hours of experimenting with it on my lab repository, I wanted to share some initial thoughts on what it’s good at and where it might fit into my workflow.

What I tested

I threw a couple of realistic tasks at Claude to see how it handles common development scenarios:

Documentation enhancement: Asked it to analyze my entire lab repository and create a comprehensive CLAUDE.md file for future Claude instances to understand the codebase. This involved understanding the project structure, identifying build commands, and documenting the architecture across multiple technologies (Docker, Kubernetes, Azure Bicep, Prefect).

README improvement: Had it enhance my repository’s README to better showcase the learning journey and technical skills - essentially making it more engaging for anyone stumbling across the repo on GitHub.

DevContainer configuration: Asked it to add the Claude CLI itself as a dependency in my devcontainer setup, requiring it to understand my current mise-based tool management approach and integrate smoothly with the existing workflow.

What worked well

Codebase comprehension: Claude impressed me with how quickly it grasped the overall structure and purpose of my repository. It didn’t just read individual files - it understood the relationships between different experiments and could articulate the learning progression across technologies.

Context awareness: When updating the README, it maintained the informal, “learning in public” tone I wanted while adding substantial technical depth. It understood that this was a learning repository, not a polished portfolio.

Tool integration: For the devcontainer setup, it properly analyzed my existing configuration (mise.toml, setup scripts) and chose the most appropriate approach rather than just adding another dependency management layer.

Interesting observations

The tool feels genuinely collaborative rather than just “generate code and hope it works.” It plans its approach, explains its reasoning, and asks clarifying questions when needed. When I interrupted one of its edits because I wanted to adjust the direction, it smoothly adapted.

It’s particularly good at tasks that require understanding context across multiple files and technologies. The CLAUDE.md creation was a good example - it had to synthesize information from package.json files, docker-compose setups, Azure Bicep templates, and various README files to create something coherent.

Where it fits

I can see Claude CLI being most valuable for:

  • Documentation tasks that require understanding the broader codebase context
  • Refactoring where you need to maintain consistency across multiple files
  • Environment setup where there are multiple valid approaches and you want someone to analyze the tradeoffs

It’s less of a “write this specific function” tool and more of a “help me think through this architectural decision” companion.

Next experiments

I’m planning to test it with some more complex scenarios in my lab - maybe having it help set up monitoring for my Kubernetes experiments or assist with some Azure Bicep template improvements. The real test will be seeing how it handles tasks where there’s less clear documentation and more trial-and-error involved.

Overall, pretty promising first impressions. It feels like having a knowledgeable pair programming partner who’s particularly good at seeing the big picture.