SpinStep

Quaternion graph traversal and control system https://github.com/VoxleOne/SpinStep

Quaternion libraries have work to do now. Positive potential: Simplified “undo” mechanism: this result suggests that a given traversal (sequence of rotations) might be “reset” (i.e., returned to origin) using a simpler method than computing a full inverse sequence. That could simplify any functionality in libraries, like SpinStep[0], that deal with “returning to base orientation” or “undoing steps.” The libraries could include a method: given a sequence of quaternion steps that moved from orientation A to orientation B, compute a scale factor λ and then apply that scaled sequence twice to go from B back to A (or A to A). This offers a deterministic “reset” style operation which may be efficient. Orientation‐graph algorithms: in libraries used in robotics/spatial AI, the ability to reliably reset orientation (even after complex sequences)

Very interesting. I’ve been working on a small library called SpinStep — it's a Python-based quaternion traversal framework that steps through trees based on orientation thresholds, rather than positional hierarchy. I designed it with spatial decision-making in mind, and while it's still early-stage, it might complement Ketu’s orientation handling or be interesting for modeling formation logic. Totally open-ended — no expectations at all. Just thought I’d share it in case it aligns with any future ideas you’re exploring. https://github.com/VoxleOne/SpinStep

I'm trying to approach spatial reasoning by introducing quaternions to navigate graphs. It is a change in the unit of traversal — from positional increment to rotational progression. This reframing has cascading effects. It alters how we model motion, how we think about proximity, and ultimately how systems relate to space itself. The traditional metaphor of movement — stepping from point A to point B — is spatially intuitive but semantically impoverished. It ignores the continuity of direction, the embodiment of motion, and the nontriviality of turning. Quaternion-based traversal reintroduces these elements. It is not just more precise; it is more faithful to the mechanisms by which physical and virtual entities evolve through space. In other words objects 'become' the model. https://github.com/VoxleOne/SpinStep/blob/main/docs/index.md

Very cool. I just wanted to say how much I enjoyed reading through your detailed flight logs — the way you’ve documented your experience, from distances and time in the air to the nuances of roles (P1, P2, PICUS), was fascinating. As someone concerned with these matters — developing SpinStep, a quaternion-based library for modeling orientation and vector state evolution in physical systems — I found myself unexpectedly inspired by your data. It got me thinking: could these kinds of spatiotemporal logs, with their emphasis on direction, roles, and environmental influences, be approached through something like rotational state modeling? For example: .Aircraft headings and orientation changes could map naturally to quaternions. .Role transitions (e.g. P1 ↔ P2) resemble discrete state changes within a continuous system. .Wind effects or fligh

Great subject, thanks. I recently built SpinStep[0], a tool for visualizing and stepping through SCNN computations. It lets you upload a model, then see—layer by layer—how inputs are transformed, which kernels activate, and how feature maps evolve. It's a hands-on exploration of what’s actually happening under the hood in Spherical CNNs. For anyone who's been frustrated by the opaque "black‑box" nature of CNNs, SpinStep might be a fun way to poke around and build intuition. [0] https://github.com/VoxleOne/SpinStep/blob/main/docs/index.md

Neat approach! For this kind of mesh animation on a sphere’s surface, another path worth exploring is quaternion-based orientation traversal. I’ve been experimenting with this in SpinStep[0] — a Python library that uses quaternions to step through spatial structures based on orientation rather than position. It’s particularly helpful when you want smooth, rotation-aware transitions (like gliding across a sphere’s surface) without relying solely on angles or coordinate systems. Could be interesting to try a similar traversal heuristic in a Three.js context using Quaternion.slerp(). [0]https://github.com/VoxLeone/SpinStep/blob/main/README.md

>>Just wait until these things can move through space with physical/gyro sensors on their own, and better guidance software. Yeah, there's a lot of room for improvement "Traditional waypoint navigation assumes movement through a series of Cartesian positions. But in pursuit dynamics, for example, what matters is directional alignment over time" https://github.com/VoxleOne/SpinStep/blob/main/docs/01-ratio...

Fascinating article – a great example of J's array-processing power for concise, performant geometric computation. It got me thinking about how different paradigms could complement this. I've been working on a Python project[0], which is a framework for quaternion-driven traversal of tree-like structures based on orientation rather than just position or order. Essentially, J handles the low-level "how" of vector math at scale, while SpinStep-like concepts could provide a higher-level, more semantic "what" and "why" for decisions driven by explicit orientation sets and angular relationships. It's an interesting thought experiment on combining the raw power of array languages for geometry with more specialized frameworks for orientation-based reasoning. [0] https://github.com/VoxleOne/SpinStep

Well done — really enjoyed this. We could use this kind of optimization in our library[0], which builds differentiable logic networks out of gates like AND, XOR, etc. It focuses on training circuit-like structures via gradient descent using soft logic semantics. The idea of compiling trained models down to efficient bit-parallel C is exactly the kind of post-training optimization we’ve been exploring — converting soft gates back into hard boolean logic (e.g. by thresholding or symbolic substitution), then emitting optimized code for inference (C, WASM, HDL, etc). The Game of Life kernel is a great example of where logic-based nets really shine. [0]https://github.com/VoxLeone/SpinStep/tree/main/benchmark