Creative Coding

Creative Coding — The Algorithm Palette

Algorithms are brushes. Noise is paint. The canvas is a computational space where simple rules produce infinite variety. This skill provides the technical vocabulary for building artifacts that generate, simulate, or compute their visual output.

Core Concepts

Noise — The Foundation of Organic Computation

Noise functions produce pseudo-random values that vary smoothly across space and time. They are the primary tool for creating organic, natural-feeling procedural output.

Perlin noise:

• noise(x) — 1D: smooth random values along a line
• noise(x, y) — 2D: smooth random values across a surface (terrain, textures)
• noise(x, y, t) — 3D: animated 2D noise (add time as third dimension)
• Scale controls frequency: small input range = smooth; large range = detailed
• Always returns values in a consistent range (0-1 in p5.js, -1 to 1 in raw implementations)

Fractal Brownian Motion (fBm): Layer multiple octaves of noise for natural-looking detail:

value = 0
amplitude = 1
frequency = 1
for each octave:
  value += amplitude * noise(x * frequency, y * frequency)
  amplitude *= 0.5 (persistence)
  frequency *= 2 (lacunarity)

More octaves = more detail. 4-8 octaves is typical.

Domain warping: Feed noise output back as input coordinates for surreal, organic effects:

warpedX = x + noise(x, y) * warpStrength
warpedY = y + noise(x + 5.2, y + 1.3) * warpStrength
finalValue = noise(warpedX, warpedY)

Multiple layers of warping produce increasingly psychedelic results.

Randomness — Controlled vs. Chaotic

Seeded randomness:

• Use a seed value to make randomness reproducible
• Same seed = same output every time
• Different seed = different output, same character
• Essential for generative art: allows curation of output

Distributions:

• Uniform — Equal probability everywhere (raw random)
• Gaussian/normal — Clustered around center (use for natural variation)
• Power law — Few large values, many small (use for organic size variation)
• Poisson disk — Random but evenly spaced (use for point placement without clumping)

The Animation Loop

All creative coding artifacts share a core structure:

setup():
  Initialize state (once)
  Create canvas/renderer
  Set initial parameters

draw() / update():
  Update simulation state (physics, rules, time)
  Render current state to canvas
  requestAnimationFrame(draw)

Frame independence: Use deltaTime to make animations frame-rate independent:

position += velocity * deltaTime

Algorithm Families

Read references/algorithm-catalog.md for detailed algorithm specifications and implementation patterns.

Quick reference:

Rendering Technology Selection

Read references/performance-guide.md for detailed performance optimization.

Decision tree:

Is it 3D?
  YES → Three.js (WebGL). Consider WebGPU for compute-heavy.
  NO ↓

Is it pixel-level computation (shaders, reaction-diffusion)?
  YES → WebGL fragment shaders or Canvas ImageData
  NO ↓

How many moving elements?
  < 100    → CSS transforms or SVG
  100-5K   → Canvas 2D
  5K-100K  → WebGL (instanced)
  > 100K   → WebGPU compute

Shader Basics (GLSL)

Shaders run per-pixel on the GPU. Key concepts:

Fragment shader structure:

uniform vec2 u_resolution;  // Canvas size
uniform float u_time;       // Elapsed time
uniform vec2 u_mouse;       // Mouse position

void main() {
  vec2 uv = gl_FragCoord.xy / u_resolution;  // Normalize to 0-1
  // ... compute color for this pixel ...
  gl_FragColor = vec4(r, g, b, 1.0);
}

Key GLSL techniques:

• SDFs (Signed Distance Fields): Define shapes as distance functions, combine with min/max/smooth operations
• Ray marching: Step through 3D space using SDFs to render scenes
• Domain repetition: mod(position, cellSize) repeats shapes infinitely
• Noise in GLSL: Implement Perlin/simplex noise for procedural textures
• Smooth blending: smoothstep() and mix() for gradual transitions

When to Consult This Skill

The Master Artificer or Simulation Smith should consult this skill when:

• Selecting an algorithm for a generative or simulation artifact
• Choosing between rendering technologies
• Implementing noise-based effects
• Designing shader-based visual effects
• Optimizing computationally intensive artifacts
• Understanding parameter spaces for algorithmic art