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“I never had a doctorate, so I had to make do with Quicksort.” —Sir Tony Hoare (unpublished interview for Algorithms to Live By)

This is only half correct.

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).

It is possible to reproduce one of the key claims in this post -- the "Russian tail" in the early voting tallies -- straight from the raw data hosted on the Clark County, NV website. This code can be run in a Colab notebook:

  # 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()

This produces a figure identical (up to histogram bucketing) to the one at the end of the linked article.

Vint Cerf & Bob Kahn (TCP/IP), Paul Baran (packet switching), Tim Berners-Lee (WWW), Marc Andreesen (Netscape), Brewster Kahle (Internet Archive), Douglas Engelbart (hypertext), Aaron Swartz (RSS, Creative Commons), Richard Stallman (GNU, free software movement), Van Jacobson (TCP/IP congestion control), Jimmy Wales (Wikipedia), Mitchell Baker (Mozilla), Linus Torvalds (Linux)...

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

Peter Eckersley (1978-2022) was posthumously inducted into the Internet Hall of Fame for his founding work on Let’s Encrypt. The Internet is a better place because of Peter (and his many collaborators and colleagues).

My understanding of the evidence is that it suggests the mechanism was made in Rhodes.

Watch movements are generally sensitive to magnetic fields, and can become magnetized and lose accuracy. Some watch models explicitly advertise their level of resistance to magnetism, for instance the Rolex Milgauss, which is designed to withstand 1,000 ("mille") gauss.

For the donation of a single dollar, you are given access to all of his videos. I would not describe that as "exclusive," although I understand what you are saying.

Current title ("Peter Norvig Leaves Google to Join Stanford HAI") seems to overstate slightly. According to Peter: "I still have my affiliation with Google, but will spend most of my time at Stanford."

Brian Christian here. Honored to be the guest on the Ezra Klein show this week and by Ezra’s kind words: "Brian Christian’s recent book The Alignment Problem is the best book on the key technical and moral questions of A.I. that I’ve read."

Happy to take any questions, etc., if folks are interested! AMA.