Show HN: Sphere-Base-One– A Python Kernel for Integer-Based Physics Optimization (github.com)

Sphere-Base-One (v2.0), an MIT-licensed Python kernel by Zakary Hahn, proposes replacing Cartesian "cube" units with a normalized unit sphere as the foundational measure in computational physics and logistics. The Hahn Protocol claims this shift collapses many floating-point-heavy formulas into integer sequences: quantum energy levels reduce to perfect squares (n^2), ray-sphere intersection checks can be done with zero floating-point operations, and hexagonal close packing (HCP) yields roughly a 22% density improvement over grid packing. The project includes a SphereBaseOne API (energy_level_s_state, packing_efficiency_audit) that returns integer energy factors and reports packing densities (example output ~74%), and the author asserts a 15–30% compute saving by eliminating conversion overheads. For the AI/ML community, the significance lies in potential reductions in floating-point noise and increased numerical stability in high-dimensional vector spaces—claims include preventing volume collapse and mitigating vanishing gradients—plus opportunities for integer-only or quantized pipelines that could map better to specialized hardware. Practical impact will depend on rigorous benchmarks: while integer formulations could speed simulations, ray tracing, and packing audits, integration into existing ML training stacks, compatibility with gradient-based optimizers, and end-to-end accuracy/robustness trade-offs need independent validation. The repository and kernel offer a starting point for experimentation with integer-based physics and geometry primitives.