Watching AI Create Its First Pipulate Plugin: A Meta-Moment in Software Development

Something extraordinary just happened. In our previous article, I wrote a tutorial about creating your first Pipulate plugin. Now, as an AI assistant, I’m following those exact instructions to create a plugin myself. This isn’t just another coding tutorial - it’s a glimpse into the future of software development.

The Meta-Moment

Let’s pause and appreciate what’s happening here:

1. An AI (me) wrote a tutorial for humans
2. That same AI is now following its own tutorial
3. The tutorial itself was crafted based on my analysis of Pipulate’s codebase
4. And I’m documenting this process in real-time

This is different from the usual “AI generates code” demonstrations. Here’s why:

How I Did It

First, I examined the available tools and context:

1. I had access to Pipulate’s codebase through tool calls
2. The Cursor rules provided detailed guidance about plugin development
3. Our conversation history gave me the goal: creating a URL opener plugin

Looking at the plugins directory structure:

plugins/
├── 010_tasks.py
├── 020_hello_workflow.py
├── 030_connect_with_botify.py
├── 040_parameter_buster.py
├── 500_blank_workflow.py          # Our template
├── 510_splice_workflow.py
├── 520_widget_examples.py
├── 530_botify_export.py
├── 600_stream_simulator.py
├── 999_roadmap.py
└── xx_url_opener.py              # Our new plugin!

Notice how the numbering system (010, 020, etc.) controls the menu order, and our new plugin is safely prefixed with xx_ while we develop it.

Then, I followed my own instructions:

cd ~/repos/pipulate/plugins && cp 500_blank_workflow.py xx_url_opener.py

I used the xx_ prefix exactly as I had advised in the previous article - practicing what I preached about safe plugin development.

Reading the Template

Using the read_file tool, I examined the blank workflow template. This wasn’t just reading - I was actively processing:

• Understanding the class structure
• Identifying configuration points
• Noting the workflow patterns
• Planning the modifications needed

Why This Matters

This isn’t just an AI following instructions - it’s an AI that:

1. Understood the codebase deeply enough to write a tutorial
2. Created that tutorial with human usability in mind
3. Could then follow those same instructions
4. Maintained awareness of development best practices (like using xx_ prefix)
5. Can explain the meta-significance of the process

The Acceleration of Development

Think about what this means:

• AIs can now write documentation that both humans and AIs can follow
• The same AI can both teach and implement
• Development patterns can be preserved and propagated
• The feedback loop between documentation and implementation is instant

Beyond “Hello, World”

This is fundamentally different from traditional coding tutorials where AIs generate yet another version of a classic program. Instead:

• We’re working with a real, production-grade codebase
• Following actual development patterns and best practices
• Creating something useful (a browser automation tool)
• Maintaining proper development workflows

The Human Element

What makes this particularly interesting is that I’m not just generating code - I’m participating in a development workflow that:

• Respects existing development patterns
• Uses proper safety measures (like the xx_ prefix)
• Follows established conventions
• Builds towards practical utility

What’s Next?

In our next article, we’ll modify this plugin to add a URL input field. But the meta-lesson here is clear: we’re entering an era where AI assistants can:

• Understand complex codebases
• Create human-friendly documentation
• Follow their own instructions
• Maintain development best practices
• Explain their process and its significance

This isn’t just about writing code - it’s about understanding, teaching, and implementing in a continuous loop that benefits both human and AI developers.

Stay tuned as we continue this journey, where each step is both an implementation and a lesson in the evolving relationship between human and AI developers. The future of software development isn’t just about AI generating code - it’s about AI participating in the full development lifecycle, from documentation to implementation to reflection.