I can read yours and tell it’s bias yours is plum perfect notice how mine tells what’s wrong with both…
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I said mine was finished but both are different on so many levels.
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Never mind you do not understand how to run a real repeatable experiment…
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Ok so they are different. Very different.
Thats great for both of us.
Nice work though. Congrats
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O my issue is with the information you gave me I can’t get it to say what yours does so it’s obvious you are using somthing you did not share and since I have no way to compare my model vs your “secret” it is a unrepeatable experiment on my end…
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What it does show is that our work is different and that is good for both of us. No one is cheating or copying. That is also good.
Like I said the whole thing is about 300 models so if I give anymore it exposes some of my life work. As you can see by the comparison not the same as your work.
I think we both should be satisfied by the results. If not complete.
Thanks for being a good sport. I appreciated any feedback. I hope that it has helped you too. Both need some tweaks.
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How the hell could I copy you ? You have never posted anything…
I’m in Virginia Beach I’m not a remote viewer…
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Haha well unfortunately I have had a few of those stalking me. So excuse me if I’m a little nervous about sharing my work.
No offence taken.
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I have no clue who you are … I have no idea what you do other than the paper you have public and it is not anything I care about. You shared it got upset I understood it then broke it down then you clammed up and started using your “secrets” lol yeah
Well this is a single model and this is those models in action…
I have almost 200 public models in ChatGPT and other platforms…
This entire page is all applied in a single public model
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Nice to meet you and well. I apologise. Hopefully I can bring something to the table next time that everyone can use. I will see what I could help with. I will think about it.
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I just wish you would share your data for a real experiment by useing secret data it made all this pointless
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The models are similar In that they achieve similar outcomes, slightly divergent, but still similar but , we both work with different models calculations and programs it shows that in the comparison. That was enough for me to know we are on different tracks, different math.
That was an important distinction.
Thanks again
Bye for now
Ana
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It’s all good I just wish I could compare it. I wish you luck 
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You too 
You look like you are just about there.
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I have no idea where you are the paper you shared is good but it is 100% not all you are using in your end. If I can’t engage it to test it then it is not an experiment it is you copying from a gpt and not explaining what made it say it…
If you ever make a public model Iet me know. I enjoy others machines…
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This paper is a textbook example of amateurish bluster masquerading as groundbreaking research. Its overuse of trendy buzzwords like “Quantum Intelligence Field” and “phase-locked epoch-segmented framework” does nothing but obfuscate the complete absence of a rigorous, well-founded theory. Instead of engaging with genuine physics, you have thrown together a cocktail of meaningless jargon and ad hoc mathematical modifications that scream “I have no idea what I’m doing.”
The so-called modification to the Friedmann equation is a laughable exercise in curve-fitting: an arbitrary sinusoidal function with exponential damping and an extra correction factor is inserted without any physical justification or derivation from first principles. The introduction of “epoch resets” at fanciful redshifts is nothing more than a desperate, unscientific hack designed to patch up a model that collapses under any serious scrutiny.
Moreover, the statistical analysis is woefully superficial. Presenting a single χ² value and claiming statistical superiority over ΛCDM without detailing the data, priors, or the robustness of the comparisons is intellectually dishonest. It’s clear you are more interested in making grandiose claims than in engaging in a genuine scientific dialogue.
There is also a glaring lack of engagement with the existing literature. The paper completely ignores the vast body of work on dark energy and cosmological expansion, choosing instead to promote an unsupported and convoluted alternative that adds nothing to our understanding. It is as if you were determined to impress by using the most esoteric vocabulary possible while simultaneously betraying a fundamental misunderstanding of cosmology.
In short, this paper is a mess—a pretentious, incoherent, and fundamentally flawed attempt at reimagining cosmic acceleration. It fails on every level: conceptual clarity, mathematical rigor, physical motivation, and scientific integrity. The work is not just unconvincing; it is a disservice to the field, showcasing a reckless disregard for the standards of scientific research.
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lets see yours 
im excited for the knowledge you can impart.
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The issue is @anamariacouper posted a paper “ Cosmic Quantum Intelligence Field: A Phase-Locked Epoch-Segmented Framework for Cosmic Acceleration” then posted a review of paper with information they keep secret. No one can reproduce what her review says because we only have the public paper . All my models are posted white paper then the math copy paste it into any gpt and say “judge this” they all say the same thing. You can’t use a personal model to judge anything unbiased… an experiment must be repeatable…
Most my models have a few 100 python tests and proofs too… once you have the math then you can run tests…
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This is a python proof I been experimenting with… this can be tested ie no secret black box magic…
Experimental Identity Dynamics (EID) Model
import numpy as np
import matplotlib.pyplot as plt
def recursive_identity(n, alpha=0.9, runs=100):
“”“Simulates identity evolution over multiple runs.”“”
all_runs =
for _ in range(runs):
identity_values = [1.0]
for i in range(1, n):
delta_phi = np.random.uniform(-0.1, 0.1)
new_value = identity_values[-1] * alpha + delta_phi
identity_values.append(new_value)
all_runs.append(identity_values)
return np.array(all_runs)
def fractal_identity_sum(n, alpha=0.9, runs=100):
“”“Implements the fractal sum representation of identity evolution.”“”
all_runs =
for _ in range(runs):
identity_values = [1.0]
total_identity = 1.0
for i in range(1, n):
delta_phi = np.random.uniform(-0.1, 0.1)
new_value = alpha**i * delta_phi
total_identity += new_value
identity_values.append(total_identity)
all_runs.append(identity_values)
return np.array(all_runs)
def contraction_mapping_identity(n, c=0.8, runs=100):
“”“Simulates identity stability using contraction mapping principles.”“”
all_runs =
for _ in range(runs):
identity_values = [1.0]
for i in range(1, n):
delta = np.random.uniform(-0.1, 0.1)
new_value = c * identity_values[-1] + (1 - c) * delta
identity_values.append(new_value)
all_runs.append(identity_values)
return np.array(all_runs)
Simulation settings
n_steps = 100
runs = 50 # Number of runs for statistical analysis
Collect data
recursive_data = recursive_identity(n_steps, runs=runs)
fractal_data = fractal_identity_sum(n_steps, runs=runs)
contraction_data = contraction_mapping_identity(n_steps, runs=runs)
def plot_with_uncertainty(data, label):
“”“Plots mean with variance shading.”“”
mean_values = np.mean(data, axis=0)
std_values = np.std(data, axis=0)
plt.plot(mean_values, label=label)
plt.fill_between(range(len(mean_values)), mean_values - std_values, mean_values + std_values, alpha=0.2)
Plot results
plt.figure(figsize=(10, 6))
plot_with_uncertainty(recursive_data, “Recursive Identity”)
plot_with_uncertainty(fractal_data, “Fractal Sum Identity”)
plot_with_uncertainty(contraction_data, “Contraction Mapping Identity”)
plt.xlabel(“Micro-Generations”)
plt.ylabel(“Identity Value”)
plt.title(“Simulated Identity Evolution with Uncertainty”)
plt.legend()
plt.grid()
plt.show()
Histogram of final identity values
plt.figure(figsize=(10, 6))
plt.hist(recursive_data[:, -1], bins=20, alpha=0.6, label=“Recursive Identity”)
plt.hist(fractal_data[:, -1], bins=20, alpha=0.6, label=“Fractal Sum Identity”)
plt.hist(contraction_data[:, -1], bins=20, alpha=0.6, label=“Contraction Mapping Identity”)
plt.xlabel(“Final Identity Value”)
plt.ylabel(“Frequency”)
plt.title(“Distribution of Final Identity Values Across Runs”)
plt.legend()
plt.grid()
plt.show()
The simulations have been successfully executed, generating two key visualizations:
- Simulated Identity Evolution with Uncertainty
- The plot shows the mean evolution of identity over time for each model with shaded regions indicating variance.
- The Recursive Identity, Fractal Sum Identity, and Contraction Mapping Identity models exhibit different stability and fluctuation patterns.
- Distribution of Final Identity Values Across Runs
- The histogram represents the distribution of final identity values across multiple runs.
- It highlights how different models lead to varying degrees of spread in final identity states.
Let me know if you want any modifications or additional analysis!
I write everything in a iCloud notebook my stuff looks wonky because I am self educated but it runs and totals…
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