Do I need an Apple Silicon Mac to use this skill?

The MLX implementation requires Apple Silicon for GPU acceleration, but the JAX-based features are cross-platform and will work on any environment that supports JAX.

How does this skill handle color generation?

It maps specific PRNG seeds to hues in the HSL space and converts them to RGB, ensuring that the same seed always produces the exact same color across different devices.

What is the benefit of using SplitMix64 in this skill?

SplitMix64 provides a fast, functional, and splittable PRNG that ensures identical results regardless of hardware, which is vital for reproducibility in scientific computing and simulations.

Can this be used for neural network initialization?

Yes, it includes specific implementation patterns for deriving deterministic weight initialization seeds and embedding color into latent spaces using MLX.

MLX-JAX SplitMix64 Deterministic Generator

Name: MLX-JAX SplitMix64 Deterministic Generator
Author: plurigrid

byplurigrid

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Data Science & ML

Generates high-performance, deterministic SplitMix64 pseudo-random sequences for MLX and JAX workflows on Apple Silicon.

About

This skill bridges the gap between functional PRNG design and high-performance computing by implementing the SplitMix64 algorithm within both JAX and MLX frameworks. It enables perfectly reproducible results across CPUs, GPUs, and disparate machines, which is essential for deterministic color generation, GF(3) conservation systems, and consistent neural network initialization. By leveraging MLX for Apple Silicon GPU acceleration and JAX for vectorized parallel execution, it provides a robust foundation for researchers and developers working on topological computing, generative art, and complex system simulations.

Key Features

2 GitHub stars
Vectorized batch processing for high-throughput seed generation
GPU-accelerated PRNG specifically optimized for Apple Silicon
Deterministic color and hue generation with HSL/RGB mapping
GF(3) trit conservation and balanced triad generation support
Functional SplitMix64 implementation for JAX and MLX

Use Cases

Building cybernetic immune system models requiring precise self/non-self discrimination
Generating deterministic, algorithmically consistent color palettes for UI or simulations
Ensuring reproducible machine learning experiments across different Apple Silicon hardware

About

Key Features

2 GitHub stars
Vectorized batch processing for high-throughput seed generation
GPU-accelerated PRNG specifically optimized for Apple Silicon
Deterministic color and hue generation with HSL/RGB mapping
GF(3) trit conservation and balanced triad generation support
Functional SplitMix64 implementation for JAX and MLX

Use Cases

Building cybernetic immune system models requiring precise self/non-self discrimination
Generating deterministic, algorithmically consistent color palettes for UI or simulations
Ensuring reproducible machine learning experiments across different Apple Silicon hardware