It is true that some/many Rolex AD’s will allocate the most desirable watches to customers with an existing purchase history, and that some customers therefore buy less desirable models in order to earn goodwill with the AD.

However, it is not the case that the most desirable watches are necessarily (or even on average) the most expensive models. For instance, it is generally the steel models that are the most desirable and command the highest markup from MSRP on the secondary market. The Submariner, the Daytona, the GMT-Master II: almost all of Rolex’s most iconic, most in-demand, most "flippable" watches are the full steel versions, which are the cheapest versions of those model families.

To give a concrete example, it is generally considered easier to get a full-gold GMT (~$43k) or a two-tone (half steel, half gold) GMT (~$18k) at an Authorized Dealer than it is to get the full steel version ($11k).

  # Download and extract zip file
  import requests
  import zipfile
  import io

  # Get raw data from Clark County website
  zip_url = "https://elections.clarkcountynv.gov/electionresultsTV/cvr/24G/24G_CVRExport_NOV_Final_Confidential.zip"

  # Download the zip file
  response = requests.get(zip_url)
  zip_file = zipfile.ZipFile(io.BytesIO(response.content))

  # Extract to the current working directory
  zip_file.extractall()

  # Close the zip file
  zip_file.close()

  import pandas as pd
  import matplotlib.pyplot as plt
  import numpy as np

  # Read the actual data, skipping the first three header rows and excluding downballot races
  df = pd.read_csv('/content/24G_CVRExport_NOV_Final_Confidential.csv', skiprows=3, usecols=range(21), low_memory=False)

  # Find the Trump and Harris columns
  trump_col = "REP"
  harris_col = "DEM"

  # Convert to numeric
  df[trump_col] = pd.to_numeric(df[trump_col], errors='coerce')
  df[harris_col] = pd.to_numeric(df[harris_col], errors='coerce')

  # Filter for early voting
  early_voting = df[df['CountingGroup'] == 'Early Voting']

  # Group by tabulator and calculate percentages
  tabulator_stats = early_voting.groupby('TabulatorNum').agg({
      harris_col: 'sum',
      trump_col: 'sum'
  }).reset_index()

  # Calculate total votes and percentages
  tabulator_stats['total_votes'] = tabulator_stats[harris_col] + tabulator_stats[trump_col]
  tabulator_stats['harris_pct'] = tabulator_stats[harris_col] / tabulator_stats['total_votes'] \* 100
  tabulator_stats['trump_pct'] = tabulator_stats[trump_col] / tabulator_stats['total_votes'] \* 100

  # Create subplots
  fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

  # Plot Harris histogram
  ax1.hist(tabulator_stats['harris_pct'], bins=50, edgecolor='black', color='blue', alpha=0.7)
  ax1.set_title('Distribution of Harris Votes by Tabulator (Early Voting Only)')
  ax1.set_xlabel('Percentage of Votes for Harris')
  ax1.set_ylabel('Number of Tabulators')

  # Plot Trump histogram
  ax2.hist(tabulator_stats['trump_pct'], bins=50, edgecolor='black', color='red', alpha=0.7)
  ax2.set_title('Distribution of Trump Votes by Tabulator (Early Voting Only)')
  ax2.set_xlabel('Percentage of Votes for Trump')
  ax2.set_ylabel('Number of Tabulators')

  plt.tight_layout()
  plt.show()

For others who don't know what the "Russian tail" is, here is the link within the PDF: https://www.rferl.org/a/georgia-election-manipulation-russia...

None of them I have ever heard of. Whatever that may mean.

Edit: On the whole list https://www.internethalloffame.org/inductees/all/ I spotted maybe seven names. Still a single digit percentage.

...but you’re missing the point of my comment, which is simply to acknowledge and honor (my late dear friend) Peter.

My point was not do criticize the achievements of the work of any of those people.

1. I was not actively aware that this hall exists

2. I am mostly critical to such awards in general. I have noted that several companies receiving the "Export company of the year" here in this country (doesn't matter which one) have went bust a couple of years later. I received the "hacker of the year" award at my workplace some years ago. It was supposed to hang with all previous awards in the cafeteria. I did not like that and "forgot" it at home. I quit the company a year later anyway.

Edit: Forgot that I worked for the "software product of the year" twice in my life. One needed heavy, painful architectural rework 3 years later. The other was Series 60. People old enough know how that went, killed a global market leader.

These harms can be diffuse at massive scale, and acute at small scale.

One example of each: (1) https://www.science.org/doi/abs/10.1126/science.aax2342 One of USA’s largest health insurers builds ML system for patient triage. It optimizes for a proxy metric of health need (namely, cost) rather than health need itself; consequently it deprioritizes and systematically excludes millions of people from access to health care.

(2) https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg Autonomous Uber car builds their braking system on top of a vision model that optimizes for object classification accuracy using categories of {"pedestrian", "cyclist", "vehicle", "debris"}; consequently it fails to determine how to classify a woman walking a bicycle across the street, as a result killing her.

In both cases, optimizing for a naively sensible proxy metric of the thing that was truly desired turned out to be catastrophic.

Personally, I use it to support public content, because I have the means to do so now, and because I didn’t always.

I come across this a lot when trying to pay for things with Cash App or Coinbase Card, as they are both classed as prepaid cards for reasons I don’t understand.

Meanwhile, https://wallstreetonparade.com/2020/09/3-count-felon-jpmorga...

> secret documents leaked from FinCEN, the Financial Crimes Enforcement Network, a unit of the U.S. Treasury. The documents “show that five global banks — JPMorgan, HSBC, Standard Chartered Bank, Deutsche Bank and Bank of New York Mellon — kept profiting from powerful and dangerous players even after U.S. authorities fined these financial institutions for earlier failures to stem flows of dirty money.”

In comparison to 7 figure flows, Patreon restrictions on already-limited prepaid cards are AML theater.

To your point about small amounts, I think that is also monetizable with fake/stolen accounts cashing in on referral and promotional “free money,” combined with widespread flip/swap scams. I wonder if patio11 could enlighten us with some hard numbers on the dollar values of attempted fraudulent purchases. I suspect that actual card swipe fraud to be high dollar items sent to dead drops, and low dollar fraud utilizing unauthorized access to others’ accounts and money transfer apps, and so-called “friendly fraud.”

On a related note, scam rap is so lit right now. I’ll just leave this right here. Teejayx6 really gets the subculture, and as a child who grew up learning how to hack in the wild 90s, he speaks truth. He may have created the subgenre, but raps about ill-gotten gains are as old as soul music.

> I just made a fake GoFundMe someone send donations

https://genius.com/Teejayx6-gofundme-lyrics

https://en.wikipedia.org/wiki/Teejayx6

x100 channels I may watch.

I can't afford to be a patron for all the channels I like. Maybe I'm in the minority for my salary but $100 or $200 / month is not a thing I can afford, or even half that.

edit: in a way it's the paywall of videos