What will the WEF neo nazis do?

HAPPY ANNIVERSARY FLOODLIT

http://Floodlit.org acknowledging 3 years of documenting sexual abuse in the Mormon church

Floodlit launched three years ago today.

https://floodlit.org/3-year-anniversary/
#LighttheWorld2025 #LightTheWorld #CSA #MeToo https://x.com/floodlitorg/status/1995705151047295290

The Biraderi - Britain's Hidden Political Problem Exposed! - Raja Miah

ALL YOU JEW HATERS PAY ATTENTION:

This is the System of governance quietly gaining ground in the background. It's nothing to do with Jews and everything to do with the Islamist takeover of western civilisation.
YOU
ARE
BEING
USED!
WAKE
THE
FUCK
UP!!

https://youtube.com/watch?v=dX7zQtMH0BI&si=ucIZnWR2cyaqGq9Z

Judge unseals all Epstein grand jury files in Florida... putting new focus on dead detective's rape evidence https://www.dailymail.co.uk/news/article-15357587/Judge-unseals-Epstein-grand-jury-files-Florida.html

2025 Week 49 Spot the Symbolism 49 https://yabasta1.wordpress.com/2025/12/06/2025-week-49-spot-the-symbolism-49/ #symbolism #spotthesymbolism

AI responses may confabulate.
#SeanHross #Pharaoh

A total of
29 countries use red, white, and blue in their national flag.
Note that some sources indicate a slightly different number, often depending on whether additional minor colors (like gold trim on a coat of arms) are counted, or if territories are included.
Countries with red, white, and blue in their flags include:

Australia
Cambodia
Chile
Costa Rica
Croatia
Cuba
Czech Republic
Dominican Republic
Fiji
France
Iceland
Laos
Liberia
Luxembourg
Nepal
Netherlands
New Zealand
North Korea
Norway
Panama
Paraguay
Russia
Samoa
Serbia
Slovakia
Slovenia
Thailand
United Kingdom
United States

Simon Cowell says he's ‘aging backwards’ thanks to controversial blood-rinsing procedure https://www.foxnews.com/health/simon-cowell-says-hes-aging-backwards-thanks-controversial-blood-rinsing-procedure Simon Cowell is opening up about the unconventional wellness procedure that he claims is helping him age backwards — one that "rinses" and "filters" his blood before returning it to his body.

Sorry, you have to clean the Google ID corrupted links (copy link and edit out ID info before pasting into browser address bar) or else it will create a record that you got the link specifically from me.
#SocialCredit

AI responses may confabulate.

LLMs generally struggle with true
extrapolation (going beyond their training data) regardless of temperature, but lower temperatures (e.g., 0.0-0.4) make them more deterministic, sticking to learned patterns, which looks like following trends but isn't genuine reasoning; higher temperatures increase randomness and creativity but risk incoherence, while low temps focus on high-probability, "safe" (often repetitive) answers, which can be useful for mimicking consistent patterns but not for novel discovery, as they tend to repeat or produce generic outputs, missing the core of extrapolation. 
How Temperature Affects LLM Behavior
Low Temperature (e.g., 0.0 - 0.4):
Focus: Chooses the most probable next words, making outputs highly consistent, factual (within training), and deterministic.
Effect on Extrapolation: Mimics following a trend by repeating common patterns, but without true understanding; can become repetitive or overly generic, failing to generate novel, extrapolated ideas.
High Temperature (e.g., 0.8 - 1.0+):
Focus: Increases randomness, allowing less probable words to be chosen, leading to more creative or surprising text.
Effect on Extrapolation: More likely to "invent" beyond data but often results in hallucinations or nonsensical outputs, not reliable extrapolation. 
Why LLMs Struggle with Extrapolation
LLMs learn from patterns in vast datasets; they excel at interpolation (filling in gaps within known data) but are fundamentally limited in true extrapolation (predicting truly new scenarios or trends) because they lack causal reasoning and world models.
Temperature controls sampling from existing knowledge, not generating new knowledge beyond that scope. 
Best Practices for Trend-Following Tasks
Use low temperatures (0.0-0.4) for tasks needing high consistency, like data generation or formal writing where predictability is key.
For tasks needing a balance (like chatbots), a moderate temperature (0.6-1.0) is often used.
For complex forecasting, you might need techniques like few-shot learning, chain-of-thought, or specialized models (like those in LLMTime) that can better leverage existing data for forecasting, rather than relying solely on temperature settings.

https://youtu.be/OLEOCLpc0fA?si=38jNz4DehqYSUpXP


I get a very dark feeling about this guy & this house. It doesn’t feel like a home 🏡
Would love to hear what you think?
Much love x🕊️🤍