Source-as-Course — Precursors: Origins of Folklore

The complete source code for an artificial life game.

Real genetics. Real biochemistry. LLM cognition. Three.js rendering. ECS architecture. A full production codebase — yours to study, fork, and build on.

Buy Source — $1,000 Education — $350

Hosted on Gitea at git.multiversestudios.xyz. 10% royalty on derivatives. Full license terms.

What You Get

Six production systems. One codebase.

🧬

Genetic Engine

Real genome encoding with alleles, dominance, crossover, and mutation. Every creature carries heritable traits that affect biochemistry and behavior.

Biochemistry Simulation

Chemical reactions, hormonal systems, organ simulation, hunger, fatigue, and aging. Behavior emerges from chemistry, not scripts.

🧠

LLM Cognition

Creatures think using language models. Their decisions integrate biochemical state, memory, social context, and personality traits encoded in their genome.

🏗

ECS Architecture

Entity-Component-System design handling 1000+ entities. Components for genetics, biochemistry, cognition, rendering, physics, and social behavior.

🎨

Three.js Renderer

Custom rendering pipeline with procedural creature generation, biome shaders, day/night cycles, and a 14,400-pixel scrolling world.

Build Pipeline

Complete development environment: TypeScript, Vite, testing, CI/CD, deployment. Everything you need to fork and ship your own version.

Course Modules

Three modules. Deep dives into each system.

MODULE 1

The Living World

ECS architecture, entity lifecycle, world generation, biome systems, physics, and the Three.js rendering pipeline. How to build a world that supports emergent life.

MODULE 2

Genetics & Biochemistry

Genome encoding, allele expression, crossover and mutation, biochemical reactions, hormonal systems, organ simulation. How life emerges from chemistry.

MODULE 3

AI Cognition

LLM integration, prompt engineering for creature minds, memory systems, social modeling, personality traits, and decision-making architecture. How thought emerges from biology.

Sample Excerpt

What the code looks like.

// Genetic Engine — Crossover with positional bias
export function crossover(parentA: Genome, parentB: Genome): Genome {
  const offspring = new Genome();
  for (let i = 0; i < parentA.chromosomes.length; i++) {
    const crossPoint = Math.floor(Math.random() * parentA.chromosomes[i].length);
    offspring.chromosomes[i] = [
      ...parentA.chromosomes[i].slice(0, crossPoint),
      ...parentB.chromosomes[i].slice(crossPoint),
    ];
  }
  return mutate(offspring, MUTATION_RATE);
}

Simplified for display. The actual implementation includes dominance hierarchies, linked genes, and epigenetic markers.

Tech Stack

What's in the repo.

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Choose your tier.

$1,000
INDIVIDUAL

Full source access for one developer. Learn, build, and ship your own projects.

10% royalty on commercial derivatives

Buy Individual
$100,000
STUDIO

Full source access for your entire team. Build commercial products at scale.

10% royalty on commercial derivatives

Buy Studio
$350
EDUCATION

Full source access for students and educators. Learn from production code.

10% royalty on commercial derivatives

Buy Education

No refunds. 10% royalty on all commercial derivatives. See Developer License for full terms. Can't pay? Earn access instead.