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https://t.co/044i6CSmN9
Works same at the ⚛️scale!
Without messy Standard Model parameters we simply build the mass of the atom’s core using the exact same simple addition:
First, we get our basic "quark blocks" (derived from the electron/muon lepton ratio):
Up block (u) = 2.2 units
Down block (d) = 4.8 units
Then we build the proton and neutron by grouping the three blocks together and adding the vacuum's self-energy glue (Vbinding which is just adding a type of block value to itself):
Proton (uud): 2.2+2.2+4.8+929.1=938.3 units! (Actual proton: 938.3 MeV)
Neutron (udd): 2.2+4.8+4.8+927.8=939.6 units! (Actual neutron: 939.6 MeV)
(The tiny 1.3 unit gap is just the electromagnetic energy of the charged proton vs. the neutral neutron).
Whether orbiting massive planets in space or binding tiny quarks in a proton, the math always maths.
🌊 on a grid vibe it all out.
🌍If Earth's gravity whirlpool strength is 6653, the Sun's gravity whirlpool strength in this same grid system is simply scaled by its mass ratio:
6653×333,000 (Earth masses)≈2.2 billion units!
The exact same grid math scales all the way up to stars and galaxies.
The Eholoko Fluxon Model (EFM) represents a methodological departure from traditional theoretical physics.
It is rooted in the conceptual foundations of Dewey B. Larson's Reciprocal System Theory, but it stands on the shoulders of the vast empirical knowledge, datasets, and limiting-case frameworks established by the Standard Model, General Relativity, and ΛCDM cosmology.
I hold deep appreciation for the academic community and this work would be entirely impossible without the public datasets and observational catalogs that academia has painstakingly created.
Because of EFM’s non-traditional origins, I have chosen to bypass traditional peer-reviewed journals.
Instead, I am presenting this work under a fully open-source, reproducible computational standard.
I am not a formally trained mathematical physicist, and I do not present these papers as traditional analytical proofs (although some of the papers are framed that way in writing due to my prompting style).
My methodology is that of a computational phenomenologist: I formulate conceptual postulates, implement them into GPU-accelerated non-linear field solvers (utilizing AI assistance to architect the JAX and PyTorch code), and test all assumptions by running high-fidelity simulations.
The validation of EFM rests on the fact that these dimensionless simulations, when anchored to a single physical constant, output emergent scales—such as the speed of light, the hadron mass spectrum, atomic structure, the mass spectrum of the periodic table—that match observed public data with high concordance.
I welcome and invite rigorous skepticism, auditing, and attempts at computational falsification.
The entire codebase, simulation checkpoints (upon request as I prefer that researchers validate by conducting their own cosmogenesis simulations), and analysis notebooks are open-source and freely available for any researcher to run, modify, and test.
https://t.co/DFeSHLksip
A massive thank you to @AfroBitcoinOrg for sponsoring this amazing event which will now be done multiple times a year as we had the Man who started the Namibian Pool Federation in attendance as well.
He loved the Bitcoin Q&A section & is already looking forward to the next one🥳
Sooner rather than later companies will realize that Africa as a consumer is going to continue growing & such “oversights” will them you in the long run
Not only are Africans watching @antigravity@GoogleAI but so are all those building from @Kimi_Moonshot@deepseek_ai 🤷🏾♂️
Decided that '/molecular imaging' is probably the best name to house this paper under. It's a precursor read for the HiV protease inhibitor paper and opens the door to industrial usage of the solver across many sectors.
'FluxonChem: A First-Principles Continuous Scalar Fluid
Thermodynamic Engine for O(N log N ) Computational
Chemistry and Applied Pharmacology'
https://t.co/OQ6dmNdEQm
I have added a /applicable sciences folder to the /research folder which will house practical experiments on physical EFM applications.
I started with the analysis of the HiV-1 protease using the O(N) solver built off the Fermion paper to see if I could find an inhibitor and if so, there was application for practical novel drug research.
I believe there is.
https://t.co/Yj5h0S0qBE
Eholoko Fluxon Model establishes a method to visualize and test molecular bonds without Slater determinants or any Dirac scaffolding that is sizably more efficient than other current frontier algorithms in existence.
Ok so check this out: The universe is shaped like an onion made of energy with 8 layers.
We are in the third layer and crossing over to the fourth.
Boom.
We are in a new era.
The Eholoko Fluxon Model may be nascent but it blows the hinges off what we currently understand about computation.
This opens doors to innovations that were pure science fiction.
With zero empirical parameter fitting and zero machine-learning heuristics, the raw scalar thermodynamics of the EFM NLKG independently capture nearly 60% of the variance in aqueous solubility (R2=0.575), using only mass, specific phase friction, and scalar dipoles extracted from a pure vacuum simulation.
The remaining variance is strictly accounted for by the known absence of explicit solvent dielectrics and solid-state crystal lattice energies, which will be integrated into the next iteration of this software.
Meanwhile, the O(NlogN)O(NlogN) Mass Calibration (R2=0.955) proves the spatial topology engine is definitively flawless.
With zero empirical parameter fitting and zero machine-learning heuristics, the raw scalar thermodynamics of the EFM NLKG independently capture nearly 60% of the variance in aqueous solubility (R2=0.575), using only mass, specific phase friction, and scalar dipoles extracted from a pure vacuum simulation.
The remaining variance is strictly accounted for by the known absence of explicit solvent dielectrics and solid-state crystal lattice energies, which will be integrated into the next iteration of this software.
Meanwhile, the O(NlogN)O(NlogN) Mass Calibration (R2=0.955) proves the spatial topology engine is definitively flawless.