A signal engine with no training, no tokens, no neural net URL

James Jardine released AOI Shell v1.1, a token-free signal analysis engine using 96D octonion collapse and topological methods without training or neural networks, under AGPLv3. The tool runs offline and demonstrates applications in MRI quality control, LIGO glitch classification, and battery capacity tracking, with honest reporting of limitations.

A topological "collapse" engine that reads structure in signals with no training, no tokens, and no neural network. It runs offline on a laptop — if you don't have a quantum sampler it falls back to local simulated annealing automatically, no account or key required. Clone it, run it, get the numbers below. Built over five months by one person and an AI, and given to the world under copyleft. Institutions optimize for what they can measure; a lot of value falls through the cracks they can't see. This is a small tool for leveling that field. These demos are training-free topological analyses persistent homology . No model is fit to any data. The same mathematical tool is applied across three unrelated domains. We show you where it fails, too — that's the point. As MRI k-space is undersampled, image loop-topology Betti-1 degrades monotonically — no reference image, no training. Replicated across 6 brains OpenNeuro ds000102 : | acceleration | 1× | 2× | 2.9× | 4× | 6× | 8× | |---|---|---|---|---|---|---| | Betti-1 mean | 112 | 94 | 71 | 56 | 34 | 23 | python demos/mri qc.py On the two most look-alike glitch classes, Blip vs Koi Fish, where peak amplitude barely separates them AUC 0.24 , topology does AUC 0.84 . Honest limit: on the easy, already-separable pairs a trivial spectral-bandwidth feature beats topology. This is a narrow result, not a universal one. python demos/ligo glitches.py Topology tracks capacity fade strongly r ≈ −0.7 to −0.9 across cells . Honest limit: it does not forecast future capacity beyond what you'd get from current capacity + recent trend partial r ≈ 0.04 . Descriptive positive, predictive null. python demos/battery predict.py engine/ is the full 96D octonion-collapse organism: entropy gating, Fano/octonion projection, Jordan-Shadow decomposition, 33 transposable-element families, Monster-moonshine grading, and chain-complex homology. Token-free, no LLM, runs offline. python import numpy as np from aoi collapse 96d dwave import aoi collapse 96d dwave out = aoi collapse 96d dwave np.random.normal size=96 print out "betti" , out "intent" , out "chaos" , out "backend" - .. 0.xx 0.xx SimulatedAnnealing pip install -r requirements.txt bash fetch data.sh downloads the public datasets into ./data/ MRI, LIGO, battery Then run any demo from the repo root, e.g. python demos/mri qc.py . AGPLv3 see LICENSE . You may use, modify, and redistribute freely; if you run a modified version as a network service, you must release your source. Every copy carries attribution see NOTICE . Commercial licensing to use it without AGPL obligations : james@lattice24.com . James Jardine, AOI Shell v1.1: Token-Free Domain-Agnostic Signal Organism via 96D Octonion Collapse. DOI 10.5281/zenodo.20200607 https://doi.org/10.5281/zenodo.20200607 James Jardine — Lattice24 / VoodooAOI · james@lattice24.com mailto:james@lattice24.com Built in collaboration with Claude Anthropic .