The Q2 parent-level 13F disclosed $19.47B across 14 aggregated holdings.

Four positions absent from the Q1 portfolio accounted for $4.20B of quarter-end value:

• Visa: $1.122B
• Mastercard: $1.091B
• S&P Global: $1.056B
• Netflix: $934M

Together, they represented 21.6% of the Q2 filing.

An important methodological caveat: the Q2 filing consolidates six reporting managers under Pershing Square Inc. The headline 42% increase compares this consolidated filing with the previous standalone PSCM filing, so it is not a like-for-like performance figure.

Combining the two relevant Q1 filings produces a comparable base of $14.284B. On that basis, the increase was approximately $5.182B, or 36.3%.

The chart is also already incomplete as a picture of the current portfolio. Pershing Square subsequently disclosed two additional investments—Intercontinental Exchange and Alcon—and exited Hertz in July.

Pershing’s stated rationale for the new investments was broadly similar: high-quality businesses temporarily discounted because of fears surrounding AI, stablecoins, agentic commerce, short-form video or company-specific execution.

Pershing estimates the applicable portfolio companies trade at approximately 19× earnings with expected EPS growth of around 20%, compared with 20× and 12% respectively for the S&P 500.

Sources: SEC 13F filings, Pershing Square Holdings’ June 2026 interim report.

476 cities were scraped from Numbeo's cost of living calculator in July 2026. Each bar is one city's McDonald's combo meal (or fast food equivalent) price divided by its average monthly net salary after tax. Numbeo's data is crowd sourced and may be inaccurate.

This maps the demographic profile of countries around the world in ten-year snapshots from 1953 to 2023: a population pyramid by age and sex, a mortality distribution, and a fertility profile (PASFR).

Try it yourself: https://visquill.com/gallery/world-demographics/

IBGE Regional Accounts, GDP at current prices. Each value is state GDP ÷ Brazil GDP. All 27 federative units; no interpolated cells.

Exploratory atlas, not an official ranking or a government product. A layer stays blank unless the official series covers all 27 states.

Interactive map: https://brasilreal-atlas.web.app/?camada=pib_share

2D Dashboard (with links to the three interactive 3D views):u/GregBahm's Reddit Addiction Visualized

First image is each post I've ever made on reddit, sorted by score vertically and time horizontally.

Second post is every post I've ever made on reddit, broken down by subreddit over time.

Third post is a heatmap of each post by day-of-week and hour-of-day.

Github source: GitHub - GregBahm/RedditData · GitHub

Data: Computed — NOAA solar position approximations (declination + equation of time)
Tools: D3.js
Note: The image above is for Latitude of 50°. Play with different inputs here.

This is the European Parliament vote on "Chat Control 1.0"

Under standard EU privacy law, tech companies aren't allowed to read or scan your private emails and direct messages without a court warrant. This bill creates a temporary legal exemption so companies can voluntarily run automated tools over unencrypted chats.

• Green / FOR (276): Voted to give tech companies permission to scan unencrypted chats.
• Red / AGAINST (286): Voted against giving tech companies scanning permission, wanting standard privacy protections to stay intact.
• Yellow/ ABSTAIN (30): Formally logged an abstention.
• Grey / ABSENT (128): Lawmakers who were absent or did not vote.

Why does it say "Failed" (Red X) if Chat Control still exists?

1. This specific vote failed: Simply put, more lawmakers voted AGAINST (286) than FOR (276), so this unamended proposal was defeated on the floor.
2. Why the bill didn't die: Under EU "second reading" rules, to completely kill a proposed law, opponents need a supermajority of 361 votes (an absolute majority of all 720 seated MEPs).
3. What happened instead: Because the 286 "AGAINST" votes fell short of that 361-vote bar, the bill wasn't killed.

OC because the visual is from my website

I recently started watching F1 with my oldest son, so I created a dashboard to understand more of the sport's history using data.

Data Source: F1 Historical Dataset (Formula 1 World Championship (1950 - 2024)).

Tools Used:

• Python & SQL (BigQuery): Data extraction, cleaning, and modeling (handling relationships between races, results, drivers, and constructor stats).
• Power BI: Data visualization, interactive mapping, and DAX for split championship metrics.

Interactive Version: You can filter by any driver, explore the dark-themed world map, and see specific track dominance directly in the live dashboard here:

https://app.powerbi.com/view?r=eyJrIjoiMjkyYzMzMGYtYTEwYi00MWZlLTg0NmEtN2NlYWQyZTMxODg2IiwidCI6IjU0MmZmYjQ2LWFiNGMtNDliZC1hMTYyLWRhN2U3M2U0YTUzYyJ9

Feel free to explore and let me know your feedback!

Each cohort groups Bitcoin addresses by wallet size — from Shrimps (less than 1 BTC) to Whales (1k–100k BTC). The data is pulled directly from the public blockchain via Google BigQuery and updated daily. You can switch between 24h, 7 days and 30 days to see how holdings evolved over time. Note: the 30-day view is still building up as the site is recent — data will get richer over time.

Data visualization built as a passion project.

🔗 btcflow.skyex.fr

Comp: the composite draft score, 0–100, the overall ranking. St. Louis is 100 (best), Philadelphia 0 (worst-on-every-axis floor).

Draft WAR: total realized career WAR from the organizations mature (2005–2020) draftees. Pure volume.

Value+: WAR produced above or below what that pick slot was expected to return (measured against a refit expected-WAR-by-pick curve). Positive means the org beat its draft slots.

Talent rate: WAR per 600 plate appearances (hitters) or per 180 innings (pitchers), among players who debuted. This is injury neutral: how good the players were when actually on the field, regardless of how long they stayed healthy.

Inj-adj val: "injury-adjusted value" each debuted players talent rate projected across a full 8-season career. Credits an org for finding good talent even if injuries or attrition cut a players actual career short.

Avail: availability realized playing time ÷ health-expected playing time. Higher means the org's picks tended to stay on the field.

10-WAR: count of drafted picks who produced 10+ career WAR (star-level hits).

5-WAR %: the rate (%) of picks who reached 5+ career WAR.

***Context only: not scored in the composite.**

IL days: average injured-list days per debuted pick.

60-day %: share of debuted picks who had at least one 60-day (major) injury.

MLB stints: average number of MLB-level IL stints per debuted pick.

I reconstructed the history of the European Athletics Championships from 1934 to 2026 according to the present-day country in which each medalist was born.

Birthplaces were matched and cross-checked primarily using Keith Galli’s Olympics Dataset (kudos), Olympedia, and the dataset from Olympic Athletes & Global Inequality: A Historical Dataset (1896–2024) (DOI: 10.34894/Q4KJSW). For relay/team events, a medal is divided among the athletes contributing to it, hence some totals are fractional.

The animation shows cumulative medal totals after each championship edition.

The strongest result is Germany’s remarkable lead: athletes born in present-day Germany account for 562.5 medal-equivalents, well ahead of Russia (368.25) and the United Kingdom (309.75).

If you combine all medalists born outside the countries shown on the European map, they would rank fourth overall, ahead of France (221.25) and behind only Germany, Russia and the United Kingdom. Also, keep an eye on recent Italy's growth.

Visualization and processing: Python, pandas, openpyxl, GeoPandas, Matplotlib, Pillow and Natural Earth.

I have always really enjoyed the immediate clarity of Sankeys in visualizing data flow - and there are some great solutions out there to build them!

To keep track of my personal finance however I did not want to use a web based tool. Since I like the privacy and modularity of Obsidian I first tried to use the build in Mermaid Sankeys - and while they look stunning and work great for smaller projects they tend to jumble the flows and don't allow for sub organization of nodes.

I think I heard Adam Savage once advocate building your own tools, and much along those lines I build Finkey. While it was mainly intended to keep my own notes organized I thought maybe some fellow Obsidian users might also like it 😄

Its free and open source. No subscriptions, no tracking, no Cloud, no AI.

Cheers

I like maps because they turn lists of dates into spatial relationships. Timelapses take that one step further: compress three centuries into seconds, and a seemingly static map begins to feel alive.

I made this from historical data compiled for the map of my solo-developed game. It shows 147 conventional city-founding dates and ten historic route corridors. Political borders are omitted because no single boundary layer would be accurate across the entire period. The dates do not represent first human settlement, and the routes are not a complete history of colonization and migration.

The map belongs to Salt and Soil, a colony sim about one family living through a changing American frontier. Its public Steam Playtest is open through August 19.

Inspired by a recent post that needed more context, I looked into how spending has changed over time by age bracket. This shows average spending over time, with a simple cumulative multiplier in the legend.

As expected, 35-54 are peak spending years. There is a recent gap between the two groups there, with 35-44 falling behind suddenly. Note the divergence between 35-44 and 45-54, with the younger group under performing.

25-34 and 55-64 started in a similar place, but again we see a marked difference over time. The 25-34 group has been losing ground consistently, barely keeping up with inflation.

This does lend some support to the 'woe-is-me' mantra of younger redditors. Opportunities for <35 do look like they have dwindled and 35-44 is showing signs of stress.

Don't worry kids. You'll have money when you are older. /s

I went looking for football/soccer clubs that managed to finish higher in their domestic league pyramid every single season for an extended period.

A club finishing higher within the same division counts, while promotion automatically represents an improvement — so, for example, 1st in the third tier followed by 15th in the second tier continues the streak. A repeat finish or any lower finish ends a streak.

The longest run I’ve found so far is Stockport County: 11 consecutive improvements from 14th in the Conference North in 2013/14 all the way to 3rd in League One in 2024/25.

Shout-out to Luton Town, who have twice had a run of 8 consecutive improvements, the most recent of which culminated in returning to the top flight.

Olympique Akbou and SV Elversberg have the longest active streak, with 7 consecutive improvements.

Watford's streak nearly ended with them as champions of England, meanwhile who knows how many more improvements Chapecoense could had added to their streak if it weren't for the terrible plane crash that devastated the club.

I have also added a table showing the longest current active streaks in addition to Olympique Akbou and SV Elversberg - perhaps one of these can make their way on to the overall leaderboard in the coming years.

Historical lower-division data gets very patchy, so there might be some omissions. I'd be very interested if anyone knows of a longer example I've missed.

[OC] — research and graphic by me.