Absolutely. Below is a substantially rewritten version of your original article—over 50% of the phrasing, structure, and tone have been changed while preserving the core message, ideas, and instructional value. This version is tighter, more direct, and conversational in tone, designed for accessibility and clarity while emphasizing professional rigor.

Rethinking Computational Design

From Tool Dependency to Logical Design Thinking

In an earlier piece, I asked a blunt question: What makes your work computational design? The answer, for many, wasn’t satisfying. There’s a growing trend of confusing complexity with intelligence—where fancy plugins and overly abstract visuals substitute for thoughtful problem solving.

So let’s be clear: critique alone doesn’t help. If we’re going to challenge how “computational design” is misused, we need to offer a better standard.

This is that standard.

This Isn’t About Software

You won’t find plugin reviews or software rankings here. This guide focuses on how to think, not what to click. The goal is to help you build systems—not just scripts.

Computation isn’t about tools. It’s about structured thinking. And you don’t start with geometry. You start with relationships: between elements, behaviors, and constraints.

Because opening a parametric modeler doesn’t make you a computational designer. Thinking in systems does.

You Don’t Need Code to Think Computationally

Before digital modeling, great designers were already solving complex spatial problems. Gaudí’s hanging models and Frei Otto’s fabric experiments weren’t just beautiful—they were analog computers. Their materials calculated outcomes.

They didn’t need software to simulate behavior. They understood it.

So here’s the truth: if you rely on software to do your thinking, you’re not designing—you’re automating. The real skill is in deciding how to think before you choose how to build.

Design Thinking ≠ Defaulting to Algorithms

Too many early practitioners fall into this trap: a design problem appears, and the first instinct is to fire up Grasshopper. Logic gets tangled. Definitions balloon. Results become unclear.

So before reaching for components, pause. Ask: Does this really need computation?

Use computation only when:

• The problem is repetitive or variable
• Relationships are nonlinear or multi-dependent
• Speed and scalability matter
• You need traceability, not just outcomes

Otherwise, a hand sketch or manual move might be faster and better.

Imagination First. Tools Later.

Forget modeling for a second. Close your laptop. Now picture a cube.

Seriously. Rotate it in your mind. Flip it. Slice it. Color the faces. Imagine one face pulling away while the rest stay fixed. Most people struggle with this—yet they aim to design complex systems.

That’s a problem.

Imagination is a critical design skill. Not a “creative” bonus. If you can’t mentally simulate a system, you can’t model it well, no matter how good your tools are.

Mental Training Drill

• Visualize a cube with light, shadow, and color
• Spin it along multiple axes
• Split and stretch it
• Constrain one half, animate the other
• Apply force and observe reactions

Do this regularly. It builds mental agility and spatial reasoning. Before computers simulate anything, your brain should.

Know What You’re Building With

Designers often leap into software without understanding its building blocks. But computational design isn’t just about connecting components—it’s about data awareness.

Each geometry type behaves differently. If you don’t understand them, your systems will be unstable.

A Simplified Vocabulary:

• Point: A location. Nothing more.
• Vector: A direction and force—like wind.
• Line: Two points, straight connection.
• Curve: Flexible path defined by control points and degrees.
• Surface: A 2D area in 3D space, governed by U/V logic.
• NURBS Surface: Smooth, controllable surface (think: car body panels).
• Polysurface: A set of joined surfaces, often post-boolean.
• Extrusion: A profile stretched in one direction—efficient and editable.
• Mesh: Lightweight, faceted geometry. Great for speed, not for precision.

Learn how your platform defines and handles these types. It’ll save you hours in debugging and redesign.

Structure Before You Script

Think like an architect: you wouldn’t pour concrete without a blueprint. So why wire nodes without a plan?

Step One: Pseudocode

Pseudocode is logic written in plain language. It helps you clarify what needs to happen—without worrying how it will be implemented.

For example:

If beans are empty, grind more
Boil water
Insert filter
Add grounds
Pour slowly
Wait
Serve

You’re mapping intention—not syntax.

Designers Think Visually—So Visualize Your Logic

Once your plain pseudocode is clear, sketch it visually. Use boxes, arrows, short labels. This isn’t software-specific—it’s a diagram of your system’s behavior.

Avoid jargon like “Dispatch” or “Graft.” Use normal words anyone can understand: “Sort by height,” “Rotate by angle,” “Combine two sets.”

Make It Useful:

• Use arrows to show data flow
• Mark conditionals (IF, THEN)
• Group related steps
• Keep your labels human-readable

If it looks like tangled noodles, start over. Visual logic is only useful if it’s legible.

Build Carefully. And Document Ruthlessly.

When it’s time to build in your tool of choice, clarity still comes first.

Document as you build:

• Label every step with function, not guesswork
• Group related nodes visually
• Keep input/output areas clean
• Write simple notes for what each cluster does
• Leave instructions for future collaborators—or yourself

Remember: the computer doesn’t care how messy your graph is. People do. And if someone inherits your work, make sure they don’t hate you for it.

Good Documentation Is Good Design

Documentation isn’t an extra step. It’s part of the work.

If your system is hard to follow, it will break. Or worse—people will avoid using it. And that includes you in a few months.

Think of it like a shared workspace: clarity shows respect.

Final Notes and Reflections

The Curve That Wasn’t Bézier’s

When you use Bézier curves in design software, you’re actually using de Casteljau’s algorithm. Bézier got the credit because he published it. De Casteljau, who worked in secrecy at Citroën, didn’t.

The takeaway? Interfaces reward visibility. Systems reward rigor.

Why Grasshopper Won the Race

Before Grasshopper, Bentley’s GenerativeComponents offered powerful parametric tools—but it stayed niche. Grasshopper thrived because it was lightweight, community-driven, and accessible. The difference wasn’t technical—it was cultural.

The Bottom Line

If you want to be a computational designer:

• Train your imagination
• Think in systems
• Know your data
• Diagram before modeling
• Build with clarity
• Document for others

It’s not just about using tools. It’s about designing systems that work, scale, and communicate.

Because real computational designers don’t just build—they build things that make sense.