GitHub - JJardine919/voodoo-aoi: Training-free, token-free topological collapse engine. No training, no tokens, no neural net. Runs offline. AGPLv3. · GitHub
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JJardine919
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Voodoo AOI — a training-free, token-free collapse engine
A topological "collapse" engine that reads structure in signals with no training,<br>no tokens, and no neural network. It runs offline on a laptop — if you don't have a<br>quantum sampler it falls back to local simulated annealing automatically, no account<br>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<br>copyleft. Institutions optimize for what they can measure; a lot of value falls<br>through the cracks they can't see. This is a small tool for leveling that field.
Reproducible results — run them yourself
These demos are training-free topological analyses (persistent homology). No model<br>is fit to any data. The same mathematical tool is applied across three unrelated<br>domains. We show you where it fails, too — that's the point.
1. Reference-free MRI quality (6 brains)
As MRI k-space is undersampled, image loop-topology (Betti-1) degrades monotonically —<br>no reference image, no training. Replicated across 6 brains (OpenNeuro ds000102):
acceleration<br>1×<br>2×<br>2.9×<br>4×<br>6×<br>8×
Betti-1 (mean)<br>112<br>94<br>71<br>56<br>34<br>23
python demos/mri_qc.py
2. LIGO glitch discrimination (Gravity Spy, O1) — a narrow positive
On the two most look-alike glitch classes, Blip vs Koi Fish, where peak amplitude<br>barely separates them (AUC 0.24), topology does (AUC 0.84 ).<br>Honest limit: on the easy, already-separable pairs a trivial spectral-bandwidth<br>feature beats topology. This is a narrow result, not a universal one.
python demos/ligo_glitches.py
3. Battery capacity fade (NASA PCoE) — descriptive, not predictive
Topology tracks capacity fade strongly (r ≈ −0.7 to −0.9 across cells).<br>Honest limit: it does not forecast future capacity beyond what you'd get from<br>current capacity + recent trend (partial r ≈ 0.04). Descriptive positive, predictive null.
python demos/battery_predict.py
The engine
engine/ is the full 96D octonion-collapse organism: entropy gating, Fano/octonion<br>projection, Jordan-Shadow decomposition, 33 transposable-element families,<br>Monster-moonshine grading, and chain-complex homology. Token-free, no LLM, runs offline.
[..] 0.xx 0.xx SimulatedAnnealing">import numpy as np<br>from aoi_collapse_96d_dwave import aoi_collapse_96d_dwave
out = aoi_collapse_96d_dwave(np.random.normal(size=96))<br>print(out["betti"], out["intent"], out["chaos"], out["backend"])<br># -> [..] 0.xx 0.xx SimulatedAnnealing
Install
pip install -r requirements.txt<br>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.
License
AGPLv3 (see LICENSE). You may use, modify, and redistribute freely; if you run a<br>modified version as a network service, you must release your source. Every copy carries<br>attribution (see NOTICE).<br>Commercial licensing (to use it without AGPL obligations): james@lattice24.com .
Citation
James Jardine, AOI Shell v1.1: Token-Free Domain-Agnostic Signal Organism via 96D<br>Octonion Collapse. DOI 10.5281/zenodo.20200607
Author
James Jardine — Lattice24 / VoodooAOI · james@lattice24.com<br>Built in collaboration with Claude (Anthropic).
About
Training-free, token-free topological collapse engine. No training, no tokens, no neural net. Runs offline. AGPLv3.
Resources
Readme
License
AGPL-3.0 license
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