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Today's big news is another DOGE development.
This one centers on modernizing how payments occur so that they're centralized, individually-justified, and accessible to the public.
If this works, there'll be clear, transparent accounting of everything the government pays for🧵
This one centers on modernizing how payments occur so that they're centralized, individually-justified, and accessible to the public.
If this works, there'll be clear, transparent accounting of everything the government pays for🧵
This order starts off big.
The first thing it orders is creating a centralized system for logging payments issued by agencies.
Agencies already record this information, but this adds the need to consistently justify payments and to put all this together in an organized way.
The first thing it orders is creating a centralized system for logging payments issued by agencies.
Agencies already record this information, but this adds the need to consistently justify payments and to put all this together in an organized way.
Currently payment records are recorded in very disparate formats and inconsistently.
Some payments need to have justifications supplied already, and yet they're basically never justified.
People often just do not follow the rules!
Some payments need to have justifications supplied already, and yet they're basically never justified.
People often just do not follow the rules!
@fperrywilson
1/
Okay, let's take a look at what's surrounding you that you CANNOT SEE or WONT LOOK FOR
Here are some in ConnCTecticut you might want to check out before posting more stupid sh!t
If you make excuses & show no interest in verifying these, then you're a demon
1/
Okay, let's take a look at what's surrounding you that you CANNOT SEE or WONT LOOK FOR
Here are some in ConnCTecticut you might want to check out before posting more stupid sh!t
If you make excuses & show no interest in verifying these, then you're a demon
2/
Micah was 16yo. The official record states the Pfizer killed him.
Notice the Y59.0 at the bottom. That means "viral vaccines"
Notice also that the CDC did NOT use Y59.0 as the Underlying Cause of Death (aka root cause of death).
Fraud everywhere we look
Micah was 16yo. The official record states the Pfizer killed him.
Notice the Y59.0 at the bottom. That means "viral vaccines"
Notice also that the CDC did NOT use Y59.0 as the Underlying Cause of Death (aka root cause of death).
Fraud everywhere we look
3/
As an MD you KNOW vexines cause GBS.
This death record lays out the classic time table of GBS. A few weeks after Cvid vx, Dalcie experienced GBS symptoms. She degraded to death probably over 5 months.
Unfortunately, Dr. Gill's assistant can't spell Guillain Barré
As an MD you KNOW vexines cause GBS.
This death record lays out the classic time table of GBS. A few weeks after Cvid vx, Dalcie experienced GBS symptoms. She degraded to death probably over 5 months.
Unfortunately, Dr. Gill's assistant can't spell Guillain Barré
How was Chhatrapati Sambhaji Maharaj’s death avenged, and who took the revenge? 🔥
This 2-minute thread will give you goosebumps! Read till the end to witness history’s fierce retribution…!👇
This 2-minute thread will give you goosebumps! Read till the end to witness history’s fierce retribution…!👇
After Chhatrapati Sambhaji Maharaj’s brutal execution, Aurangzeb’s general, Zulfiqar Khan, captured Raigad Fort and imprisoned Sambhaji’s wife, Yesubai, and son. Sambhaji’s younger brother, Rajaram Maharaj, then ascended the Maratha throne.
Aurangzeb had tortured Chhatrapati Sambhaji Maharaj for 40 days before killing him. This inhumane act ignited a fire in the hearts of the Marathas. All their internal differences disappeared, and only one goal remained: the destruction of Aurangzeb.
The Eternal City at night - a thread 🧵
1. Piazza Navona, Rome
1. Piazza Navona, Rome
2. Trevi Fountain
Regarded as one of the most iconic fountains in the world, it was designed by the Italian architect Nicola Salvi and completed by Giuseppe Pannini in 1762.
Its name derives from the Latin word trivium, meaning "intersection of three streets".
Regarded as one of the most iconic fountains in the world, it was designed by the Italian architect Nicola Salvi and completed by Giuseppe Pannini in 1762.
Its name derives from the Latin word trivium, meaning "intersection of three streets".
3. The Colosseum
It is thought that the name originates from a massive statue of Nero, which was moved beside the amphitheatre by the emperor Hadrian.
The word colosseum is a neuter Latin noun formed from the adjective colosseus, meaning "gigantic" or "colossean".
It is thought that the name originates from a massive statue of Nero, which was moved beside the amphitheatre by the emperor Hadrian.
The word colosseum is a neuter Latin noun formed from the adjective colosseus, meaning "gigantic" or "colossean".
My parents wanted to visit Puri Jagannath Temple for years. My mother's knees stopped her until surgery fixed her pain. The morning we arrived, came to know her surgeon died in a car crash.
I thought about life's meaning. Dr. Sharma spent 40 years building skills; everything ended in seconds. Alexander conquered half the world by 30, died by 32.
If we leave with no warning, how do we know when enough is enough? If time erases everything, why try at all?
Start learning what's REALLY been going on with the Epstein/Maxwell child sex trafficking ring.
Not the hot takes from surface skimming social media influencers.
Links in the next post...
Not the hot takes from surface skimming social media influencers.
Links in the next post...
PART 1: The Truth About The Public Corruption Unit's Investigation of the Epstein Sex Trafficking Ring
Nobody else is telling it to you straight. Why not?
briancates.substack.com/p/part-1-the-t…
Nobody else is telling it to you straight. Why not?
briancates.substack.com/p/part-1-the-t…
Part 2: How Do You Roll Up A Vast Criminal Conspiracy, Anyway?
The Epstein Case Is Far From The Only Federal Criminal Conspiracy Case Being Slow-Rolled
briancates.substack.com/p/part-2-how-d…
The Epstein Case Is Far From The Only Federal Criminal Conspiracy Case Being Slow-Rolled
briancates.substack.com/p/part-2-how-d…
🧵🧵 Mao’s Cultural Revolution vs. Ours (the similarities are quite interesting):
Over the past 15 years the United States has experienced significant cultural, political, and social upheaval, marked by intense debates over history, identity, and the role of institutions.
The names of sports teams, army bases, streets, and elementary schools were changed.
Protests, cancel culture, pronouns and chemical castration became the norm all the sudden.
Over the past 15 years the United States has experienced significant cultural, political, and social upheaval, marked by intense debates over history, identity, and the role of institutions.
The names of sports teams, army bases, streets, and elementary schools were changed.
Protests, cancel culture, pronouns and chemical castration became the norm all the sudden.
While the Cultural Revolution in China (1966-1976) under Mao was more radical and more violent, it sought to erase the old guard influences and enforce ideological conformity, certain parallels can be drawn especially when you look at how the college students really drove societies cultural upheaval.
Mao’s Cultural Revolution was the mobilization of young people to act as enforcers of ideological purity. The Red Guards, often teenagers and college students, were encouraged to challenge teachers, intellectuals, and authority figures who were deemed insufficiently revolutionary. These young activists engaged in public denunciations, participated in “struggle sessions” (where individuals were forced to confess ideological crimes), and played a role in the destruction of cultural artifacts and historical texts.
You've been looking at financial statements all wrong.
I'll teach you how to analyse the Income Statement, Cash Flow Statement, and Balance Sheet in this thread—something no one else teaches.
Collaborated with @Analyst_Mayank
I'll teach you how to analyse the Income Statement, Cash Flow Statement, and Balance Sheet in this thread—something no one else teaches.
Collaborated with @Analyst_Mayank
(1) Income statement:
Reveals a company’s revenues, expenses, profit and loss over a period (Financial Year or a Quarter)
Investors should analyse it systematically to assess trends and red flags if any.
Reveals a company’s revenues, expenses, profit and loss over a period (Financial Year or a Quarter)
Investors should analyse it systematically to assess trends and red flags if any.
(1.1) Analyze Revenue (Top Line)
Check Sales Growth: Compare revenue over multiple periods
Identify Revenue Sources: Look at core operations vs. non-operating income. Revenue from core operations is important. Non-core income could be a small part derived from income from investments, Forex gain etc.
Seasonality & Trends: Some businesses have cyclical revenue (e.g., retail spikes during festivals)
🔹 Key Metric: Revenue Growth (%) (A rising trend suggests business expansion)
Check Sales Growth: Compare revenue over multiple periods
Identify Revenue Sources: Look at core operations vs. non-operating income. Revenue from core operations is important. Non-core income could be a small part derived from income from investments, Forex gain etc.
Seasonality & Trends: Some businesses have cyclical revenue (e.g., retail spikes during festivals)
🔹 Key Metric: Revenue Growth (%) (A rising trend suggests business expansion)
I’ve recently updated German data to use more user-friendly 10-year age groups instead of the previous 5-year intervals. The difference was surprisingly large, so I decided to investigate—digging deep into the code for about three days to verify everything. Here’s what I found:
Overview of Mortality Watch Data Handling
Mortality Watch integrates multiple data sources and can handle different temporal resolutions (yearly, monthly, weekly). A few months ago, I refined the logic to select the best available data for each resolution—e.g., using yearly data for yearly resolutions.
For these recent updates, I also added 5-year resolution yearly data:
• Yearly Deaths: These match exactly with the underlying dataset from Destatis, which provides official yearly figures.
• Yearly Population: This is reported as of December 31st each year. The simplest approach would be to join the data by year, but that would be inaccurate. Since the beginning of Mortality Watch, I’ve always calculated daily population estimates—breaking down deaths and population into daily values and interpolating as needed. This ensures consistency, as summing up daily data correctly matches the totals.
When comparing Mortality Watch population data with the Destatis source, Mortality Watch reports the mean of daily interpolated data. This may seem counterintuitive at first, but it provides a more accurate representation.
Verifying the Accuracy of Mortality Watch
To ensure my calculations were correct, I conducted a deep dive and even discovered a small bug. The issue, which had only a minor impact, affected age-stratified CMR calculations:
github.com/MortalityWatch…
To further validate the methodology, I created an example script demonstrating different approaches to calculating ASMR. The results vary significantly based on the method used:
1️⃣ Simple year joins (joins deaths of 2020 to population of 12/31/2020), using Mortality.org age groups (0–14, 15–64, 65–74, 75–84, 85+)
2️⃣ Simple year joins, using 10-year age groups (0–10, …, 80+) – Currently used on Mortality Watch
3️⃣ Daily year joins (interpolated daily population joined on daily death data (evenly split)), using 10-year age groups (0–10, …, 80+)
4️⃣ Simple year joins, using single-year age groups (0–85)
5️⃣ Daily year joins, using single-year age groups
As you can see, the red values match MortalityWatch figures identically, (besides 2023 🥴).
It’s good news that there aren’t any bugs in the calculations. It’s also important to be aware of the sensitivity of the methodology used, and that we should always use the highest resolution possible. This analysis demonstrates how different age groupings and interpolation methods can have a significant impact on ASMR calculations.
Overview of Mortality Watch Data Handling
Mortality Watch integrates multiple data sources and can handle different temporal resolutions (yearly, monthly, weekly). A few months ago, I refined the logic to select the best available data for each resolution—e.g., using yearly data for yearly resolutions.
For these recent updates, I also added 5-year resolution yearly data:
• Yearly Deaths: These match exactly with the underlying dataset from Destatis, which provides official yearly figures.
• Yearly Population: This is reported as of December 31st each year. The simplest approach would be to join the data by year, but that would be inaccurate. Since the beginning of Mortality Watch, I’ve always calculated daily population estimates—breaking down deaths and population into daily values and interpolating as needed. This ensures consistency, as summing up daily data correctly matches the totals.
When comparing Mortality Watch population data with the Destatis source, Mortality Watch reports the mean of daily interpolated data. This may seem counterintuitive at first, but it provides a more accurate representation.
Verifying the Accuracy of Mortality Watch
To ensure my calculations were correct, I conducted a deep dive and even discovered a small bug. The issue, which had only a minor impact, affected age-stratified CMR calculations:
github.com/MortalityWatch…
To further validate the methodology, I created an example script demonstrating different approaches to calculating ASMR. The results vary significantly based on the method used:
1️⃣ Simple year joins (joins deaths of 2020 to population of 12/31/2020), using Mortality.org age groups (0–14, 15–64, 65–74, 75–84, 85+)
2️⃣ Simple year joins, using 10-year age groups (0–10, …, 80+) – Currently used on Mortality Watch
3️⃣ Daily year joins (interpolated daily population joined on daily death data (evenly split)), using 10-year age groups (0–10, …, 80+)
4️⃣ Simple year joins, using single-year age groups (0–85)
5️⃣ Daily year joins, using single-year age groups
As you can see, the red values match MortalityWatch figures identically, (besides 2023 🥴).
It’s good news that there aren’t any bugs in the calculations. It’s also important to be aware of the sensitivity of the methodology used, and that we should always use the highest resolution possible. This analysis demonstrates how different age groupings and interpolation methods can have a significant impact on ASMR calculations.
So here are the chart with the highest precision.
Since 2000. 3y reference and confidence interval with variance 1970-2019.
Some amazing sculptures of ancient civilizations on Earth 🧵
1. The Hunt Sculpture
1. The Hunt Sculpture
2. A Blend of Art
3. Sculpted Elegance
So for those who weren't following this for last two days, what happened in short is a federal district court judge ordered Trump Administration to pay out about $2 billion in money under contracts, etc. by midnight tonight purportedly to enforce Temporary Restraining Order. 1/
2/ Trump Administration basically said: a) we couldn't if we wanted to; b) this Court lacks authority to make us pay money on contracts b/c we have sovereign immunity (i.e., we didn't give you permission to sue us), you have to sue in Court of Claims.
3/ TROs aren't generally appealable but this was a court order to do something so it should be appealable now, but when Trump appealed to D.C. Circuit, the Court refused to hear appeal & tossed case. Trump had hedged bets & also asked SCOTUS to stop judge's unlawful order.
Jacques Ellul wrote one of the most important and clarifying books ever written on propaganda. In it, he insists a certain kind of person is the most susceptible to propaganda, giving three traits beyond his own ridiculous belief that he's immune to it. Let's take a look. 🧵
Ellul gives three traits that make someone not just susceptible to propaganda but also dependent on it (!). For us, they will be very unsettling. Before talking about those, though, he also explains that we generally misunderstand propaganda as being like tall tales and lies.
Not only are we susceptible to propaganda, then, we don't even have the ability to identify it. Sure, it's misleading, but it's more often misleading with truths than with outright lies. That's a first mistake most of us make in trying to recognize propaganda.
