There is a better way to understand computational design.
A reference library for learning the reasoning behind computational-design methods, so you can understand why they work, recognise when to use them, and adapt them to real projects.
Learning computational design should not be this messy.
There are too many ways to solve the same problem. How do you know which approach to use, or what to learn from it?
A problem with the current script sends the learner to YouTube or their course notes. Both can lead to asking AI, while the notes can also lead to an old project file. From AI they browse forums, copy a random snippet, and read the documentation. The documentation or old project file can lead to a hacked-together solution, which can send them back to YouTube again.
Scripository brings structure to learning computational design.
Concepts help you understand what you're working with. Principles and patterns shape how you think. Techniques and workflows put that thinking into practice.
Together, they give you a framework for understanding computational design as a whole.
Concept
Understand the ideas you're working with.
Pattern
A recurring structure for a class of problems.
Principle
A guiding rule for making decisions.
Technique
A script or component that solves a specific problem.
Workflow
A repeatable process built from different techniques.
See how Scripository breaks down the problem of
Breaking problems down this way turns one solution into knowledge you can reuse elsewhere.
Reduce the search space
Discard candidates that cannot affect the result.
Index once, query many
Build one index, then reuse it for every search.
Search with a Grasshopper RTree
Use Grasshopper's RTree components to return nearby candidates.
Find and verify nearby geometry
Prepare the geometry, query the RTree, and verify the candidates.
Learn the thinking behind the scripts and how techniques put that thinking into practice.
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