It’s not directly tied to geolocation, but it could integrate nicely with something like Astradia. Since Astradia provides high-fidelity attitude data without relying on GNSS, SpinStep could use that orientation stream to drive autonomous behavior trees, scanning patterns, or state transitions — all without depending on coordinates or maps. Basically: orientation in, logic out.

Would love to hear from others thinking about orientation-first autonomy or mapless navigation.

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

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

I'm a solo developer working on SpinStep. It is a proof-of-concept quaternion-based traversal framework for 3D graph, trees and orientation-based data structures. I'm actively developing it and looking for contributors, testers, and curious minds.

Appreciate your time — feel free to drop questions, suggestions, or PRs!