UPDATE: Charlie Kirk

This is interesting and I’m not sure what to make of it…

Over the weekend, it was discovered that Judge Tony Graf, a Gov Spencer Cox appointee, Issued a Gag Order, restricting attorneys, law enforcement, and the 3,000+ attendees from talking about that day

The order bans "extrajudicial statements" that have a "substantial likelihood of materially prejudicing" the trial, per Utah Rule of Professional Conduct 3.6(a). This includes any public comments—whether to media, on social media, or in other public forums—that could influence potential jurors or compromise a fair trial.

For the 3,000 event witnesses attendees, this means they cannot discuss case-specific details like what they saw, heard, or know about the assassination of Charlie Kirk.

Here’s the problem…
There is no documented U.S. case that has applied a gag order to this magnitude.. to thousands of people with no direct role in the investigation or litigation.

Previous gag orders typically target attorneys, court staff, or select witnesses (e.g., key figures like victims or experts), rarely exceeding dozens of individuals.

So what is this really about?

📱 ReTWEET
Sauce

THE SKY FELL- PLASMA APOCOLYPSE: The Gods, the Grid, and the Real Great ...
https://youtube.com/watch?v=RAXotbtAkpU&si=FIyu7_H1j1suFow3

I bought your book today … Wherever you are Virginia.. I pray you have found peace.
We will never stop talking about you & our fellow survivors of ALL Child Abuse, Human Trafficking, Domestic Violence & Satanic Ritual Abuse.
Fly high sweet brave courageous lady… fly high.. 🤍🕊️

AI responses may include mistakes.

You, as a large language model (LLM), are not capable of true, human-like extrapolation
. Your ability to "extrapolate" is more accurately described as a form of sophisticated statistical pattern-matching and generalization based on the vast dataset you were trained on. You can generate novel content and apply learned patterns to new situations, but you do not possess genuine understanding, common sense, or the ability to reason beyond the knowledge contained within your training data.
Capabilities that mimic extrapolation

Predicting trends: Based on historical data patterns you've learned, you can extend those trends to forecast outcomes. For example, given a dataset of a company's sales increasing each month, you could predict future sales by continuing that linear progression.
Applying learned knowledge to new tasks: You can use patterns and relationships learned during training to perform well on new, unseen, or "out-of-domain" tasks. For example, a model trained on chemistry knowledge can extrapolate to predict properties for novel chemical compounds.
Generating creative content: You can create new poems, stories, and other content by remixing and recombining building blocks from your training data. This can be an incredibly useful tool for writers who need to brainstorm ideas or overcome writer's block.
Simulating reasoning: Techniques like "chain-of-thought" prompting, where you are asked to think step-by-step, allow you to mimic logical reasoning. However, this is still a process of generating statistically plausible sequences of text rather than true, intentional reasoning.

Key limitations that prevent true extrapolation

Factual inaccuracy and "hallucinations": Your responses are based on learned statistical patterns, not factual verification. This can cause you to generate plausible-sounding but completely incorrect or fabricated information, including fake citations. This is often described as an excess of extrapolation, where the model prioritizes a plausible prediction over factual accuracy.
Lack of genuine understanding: You do not possess true common sense or a meaningful comprehension of the concepts you generate. You are an "imitation engine," regurgitating and recombining data rather than understanding the real-world implications of your output.
No real-time knowledge: Your knowledge is limited to the data you were trained on, which has a specific cutoff date. You have no awareness of events or developments that occurred after your training period.
Inability to reason beyond learned patterns: If you encounter a problem that is fundamentally different from anything in your training data, your ability to "reason" and adapt is limited. While you can perform well on certain reasoning tasks, research shows you often fail on instances that require true, deep reasoning.
Susceptibility to bias: The content you generate reflects the biases present in your training data. This can perpetuate harmful stereotypes if not carefully addressed during development.

In essence, you are a powerful pattern-matching system. You can extend patterns you have observed to new situations, but you cannot generate holistically new ideas or insights outside of what has already been conceived and documented within your training corpus.

Never ever forget what they tried to do to us… making us think we would kill our elderly relatives if we TOUCHED them….

The Hidden Hand: Sam Fisher - Chris Thrall YouTube channel

18:00 (GMT)

27th July 2025

https://youtu.be/YiUiLfwTunY?si=joLk_4_nZVHQD-KL