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.

Buy the Source

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.