What is seed-chaining in this context?

Seed-chaining is a process where a 64-bit seed is transformed using XOR operations, a golden ratio constant, and a trit (value of -1, 0, or 1) to produce the next unique state in a sequence.

What does the Unworld skill actually do?

It replaces the concept of 'time' with 'derivation.' Instead of events happening at clock intervals, states are derived mathematically from a genesis seed, making the entire sequence deterministic and verifiable.

Why would I use Unworld instead of standard timestamps?

Unworld allows for frame invariance and perfect parallelism. Since any state can be computed if you know its position and the genesis seed, you don't need to wait for previous steps to finish or rely on an observer's clock.

What is GF(3) conservation?

It refers to Galois Field 3 logic where trits are balanced so their sum equals zero mod 3. This ensures algebraic stability and integrity across complex derivation chains.

Unworld

Name: Unworld
Author: plurigrid

byplurigrid

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Data Science & ML

Implements deterministic seed-chaining and color derivations to replace temporal sequencing with verifiable mathematical logic.

About

The Unworld skill provides a sophisticated mathematical framework for replacing traditional temporal sequencing with derivational succession. By utilizing seed-chaining—where each state is deterministically derived from the previous using color trits and the golden ratio—it enables the creation of frame-invariant, parallelizable, and verifiable logic chains. This skill is essential for tasks involving topological computing, GF(3) conservation, and game-theoretic simulations like Nash equilibrium modeling, allowing Claude to operate within a purely algebraic state-space free from external clock dependencies.

Key Features

Self-inverse involution chains for bidirectional logic verification
Best response dynamics for deriving Nash equilibrium states
Deterministic seed-chaining using SplitMix64 and golden ratio constants
GF(3) balanced color chain derivation for state visualization
2 GitHub stars
Triadic and 3-MATCH chain generation for complex multi-stream logic

Use Cases

Simulating game-theoretic equilibria in multi-agent systems
Developing frame-invariant protocols for distributed topological computing
Building deterministic state machines that require mathematical verification

About

Key Features

Self-inverse involution chains for bidirectional logic verification
Best response dynamics for deriving Nash equilibrium states
Deterministic seed-chaining using SplitMix64 and golden ratio constants
GF(3) balanced color chain derivation for state visualization
2 GitHub stars
Triadic and 3-MATCH chain generation for complex multi-stream logic

Use Cases

Simulating game-theoretic equilibria in multi-agent systems
Developing frame-invariant protocols for distributed topological computing
Building deterministic state machines that require mathematical verification