I’m trying bois.
I think I’ve finally managed to derive the Einstein Field equations and standard model fields.
I thought I was on something big but information geometry really is just a small part of physics.
It now makes more sense to me, Why making up new math doesn’t make sense when the old math does what it’s supposed to do..
Can one of your big brain trolls tell me where I’m breaking down again?
By defining mass as the inverse of the internal clock variance of the six state register, the framework aligns the informational state of a tethered pixel with the de Broglie relation.
The included simulation shows that unitary evolution under non-commuting generators creates persistent temporal asymmetry, which sources the mass proxy.
I have provided a mathematical foundation for the second law of thermodynamics. It demonstrates that the computational cost to prevent a perturbation scales linearly while the cost to restore equilibrium scales at 3/2.
This strictly positive difference in informational cost creates an irreversible temporal gradient, offering a microscopic information-theoretic origin for the arrow of time.
Instead of requiring invisible, particulate, dark matter, the model uses geometric tension derived from the underlying lattice architecture.
With a single parameter, the IGSDF simulation produces flat velocity profiles at large radii that match astronomical observations, using only visible baryonic mass.
I’m fully aware that there are necessary expansions and evolutions, however I need to either minimize to a stable foundation or acknowledge some validity to these points specifically. I built an entire simulation notebook, and I would really appreciate any effective feedback.
Thanks for letting me bother you guys
Maybe something like fractal resonance??
Here is another one of those pieces of candy.
I have recently discovered a universal baseline debt (pi-3)pi and stability margin ~.730
I stumbled into these beautiful babies while using my IGSDF to build an ai
I’m just now starting with learning here so please be gentle.
Is this an unusual way to build an AI?
After years of watching scientists pretend like the LCDM model isn’t inherently flawed, and driven by the absolute desire to force feed the ‘physicists’ on this redit their own bologna.
Here is the correction to your Math.
Here is all your unanswered questions.
Here is the ai dribble you were too ‘smart’ to acknowledge.
In this publication you can find the predictions for: the mass of a neutrino , fundamental pixels and the correction to nuclear forces.
It’s really just a big beautiful cake, and I hope you eat so much you throw up :P
I’ve been spinning my little hamster wheel and using my little stick (ai) to draw in the mud.
After several separate failing models. I restructured how I was looking at the physics. I created a modular parameter set for the ai. I used this pre set structure to establish working foundational axioms from my work. I’m only saying this because of the drastic efficiency increase for structural processing by using this style of categorization when working with ai.
There are so many fundamental differences between how information is derived under current models and how I’m attempting to show it is derived that I essentially spent weeks playing in ai hallucination to produce fractional progress.
I would like to point out two things about my model.
Unfortunately, for me, the worst critiques you guys give me always lead to the best revisions.
Also… apparently we only have ~200 billion years leftHID Thesis