mlx-jax-splitmix
MLX on Apple Silicon with JAX-style SplitMix64 PRNG. Deterministic color generation with GPU acceleration.
npx skills add majiayu000/claude-skill-registry --skill mlx-jax-splitmix --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MLX + JAX SplitMix64 Skill > *"Same seed, same colors — whether on CPU, GPU, or across machines."* ## 1. Core Insight JAX's PRNG design is **functional and splittable** — perfect for Gay.jl's deterministic coloring: ``` JAX: key, subkey = jax.random.split(key) Gay: seed₂ = splitmix64(seed₁) ``` MLX brings this to Apple Silicon with native GPU acceleration. ## 2. SplitMix64 in JAX/MLX ```python import jax import jax.numpy as jnp from functools import partial # SplitMix64 constants (same as Gay.jl) GOLDEN = jnp.uint64(0x9E3779B97F4A7C15) MIX1 = jnp.uint64(0xBF58476D1CE4E5B9) MIX2 = jnp.uint64(0x94D049BB133111EB) @jax.jit def splitmix64(z: jnp.uint64) -> jnp.uint64: """Pure functional SplitMix64 - JIT compiled.""" z = z + GOLDEN z = (z ^ (z >> 30)) * MIX1 z = (z ^ (z >> 27)) * MIX2 return z ^ (z >> 31) @jax.jit def seed_to_trit(seed: jnp.uint64) -> jnp.int8: """GF(3) trit: {-1, 0, +1}.""" return jnp.int8((seed % 3) - 1) @jax.jit def seed_to_hue(seed: jnp.uint64) -> jnp.float32: """Hue in [0, 360).""" return jnp.float32(seed % 360) # Vectorized version for batch processing splitmix64_batch = jax.vmap(splitmix64) seed_to_trit_batch = jax.vmap(seed_to_trit) ``` ## 3. MLX Implementation
- 1. Core Insight
- 2. SplitMix64 in JAX/MLX
- 3. MLX Implementation
- 4. JAX Key Splitting ↔ Gay.jl Derive
- 5. GF(3) Conservation with JAX
- 6. Parallel Color Generation
- 7. MLX + Neural Network Integration
- 8. Immune System Integration
- 9. Benchmark: JAX vs Pure Python
- 10. Commands
- 11. Dependencies
- 12. GF(3) Triads
- 13. References
- 14. See Also
Run JAX SplitMix64 demo uv run python scripts/jax_splitmix64.py MLX color generation uv run python scripts/mlx_colors.py --seed 1069 --count 100 Benchmark JAX vs MLX uv run python scripts/benchmark_splitmix.py Immune system with JAX acceleration uv run python scripts/jax_immune.py --verify 1069
What does the mlx-jax-splitmix skill do?
MLX on Apple Silicon with JAX-style SplitMix64 PRNG. Deterministic color generation with GPU acceleration.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlx-jax-splitmix --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.