How to ACTUALLY organize your Grasshopper scripts (Pancake Plugin)
Most Grasshopper scripts work fine at a small scale.
But once you try to scale them, reuse them across projects, collaborate with others, or connect them to external tools… things can become messy very quickly.
In this video I explain why I use the Pancake plugin for Grasshopper as one of my core scripting paradigms for building more scalable computational design systems.
Pancake allows you to structure and destructure data using semantic logic, inspired by concepts from software engineering and object oriented workflows.
Instead of relying purely on datatrees, you can start defining clearer information structures and schemas that are easier to understand, maintain, reuse, and integrate.
And that becomes increasingly important as computational workflows move beyond isolated scripts into larger ecosystems involving BIM, databases, APIs, AI agents, dashboards, and collaboration platforms.
→ In this video:
• What Pancake actually is and how it works
• Why semantic data structures matter in Grasshopper
• The limitations of scaling large datatree workflows
• How schemas improve interoperability and reusability
• Why structured data becomes important for AI workflows
• How Pancake integrates well with tools like Elefront, Speckle, Rhino 8 Blocks, JSON workflows, Notion, Miro, and external systems
• Why I think data driven design is ultimately about information, not geometry
This is part of a broader series on computational design, parametric BIM, workflow systems, and AEC technology.
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I’m Marijn — architect & computational designer.
I build systems that turn design into scalable workflows using tools like Grasshopper, Rhino, Speckle, AI, and custom software development.
→ Follow for:
• Grasshopper tutorials
• Parametric BIM workflows
• Computational design systems
• AI integration in AEC
• Workflow automation
• Lightweight BIM
• Design technology and interoperability
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⏱️ Chapters
00:00 Intro
00:57 Channel
01:10 Example structure
03:17 Key components
04:38 Explaining dictionaries and JSON
08:07 Basic data organization with Pancake components
13:10 Deepdive
17:20 Workflow with Block instances
19:41 Categorizing data
21:28 Thanks for watching
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