Poland was home to roughly 3.3 million Jews before WWII, the product of centuries of communities, families, and cultural growth. Within six years, nearly 90% had been murdered. Seeing that loss as a single break in a 260-year demographic trend puts the scale of the Holocaust into a perspective that numbers alone rarely convey.

EDIT: IT GOT DELETED AGAIN WITHOUT ANY REASON

EDIT 2: I finally got in contact with the mods, they said it was an automatic removal by Reddit due to mass-reporting (what I thought). They manually approved my post for me this time.

Hi everyone,

Some of you may have seen an earlier version of this post. That version included fewer artists and was removed multiple times by the moderators without an explanation. I contacted the moderators several times, but I have not received a response or clarification. Same story on r/Music. The auto-mod refers me to the mod-comment with explanation but I never got those in the ~5 times this post got deleted. Some fishy stuff is going on. It really feels as if there may be a bot-driven reporting campaign causing posts to be mass-reported and automatically removed, but I cannot verify or prove that. So I am posting this again.

This updated version though! It includes new data through September 16 and adds three additional support artists: Finneas, Lukas Graham, and Beoga.

The previous version showed Macklemore gaining roughly 1 million followers. That increase has now more than doubled to +2.24 million. Ed Sheeran’s loss was previously around 123,000 followers, but has now grown to nearly 335,000, roughly three times the earlier figure. This is why the chart needed to be updated.

The chart shows absolute Instagram follower counts around the MetLife controversy and subsequent developments involving Ed Sheeran’s tour:

• Macklemore: +2,237,962
• Beoga: +281,358
• Finneas: +214,144
• Lukas Graham: +129,695
• P!nk: −146,128
• Ed Sheeran: −334,986

All panels use the same 2.4 million follower vertical range, with ticks 400,000 followers apart. Solid lines show the period before the marked event. Dotted lines show the period afterward.

The data comes from daily Social Blade snapshots. These are not continuous measurements, and the chart cannot establish that any event caused a follower increase or decrease. The unusually large changes for Macklemore and Beoga should therefore be interpreted cautiously.

Feedback on the data, methodology, or visualization is welcome.

CONTEXT

This chart relates to a series of publicly reported events connected to Ed Sheeran’s MetLife Stadium show and his tour.

On September 4, Macklemore performed at MetLife Stadium and made a pro-Palestine statement, including the phrase “Free Palestine.” In the following days, P!nk’s criticism of Macklemore was reported in the media. P!nk is included in the chart to show how her follower count changed during the same period.

On September 14, reports stated that Macklemore would no longer appear on the remaining dates of Ed Sheeran’s tour. Ed Sheeran addressed the situation on Instagram on September 15. Around the same period, support artists Finneas, Lukas Graham, and Beoga were also reported as withdrawing from the tour.

The purpose of this chart is to show how the artists’ Instagram follower counts changed around these events. It does not claim that any event caused the observed gains or losses.

At a $2.5 trillion market cap, SpaceX's now worth about as much as the 94 listed aerospace & defense companies combined.

Put another way: one company now makes up 50% of the entire $5.05 trillion listed aerospace & defense sector.

Is one company being half the sector a signal of where spaceflight is heading — or a fresh-IPO premium that won't hold?

Gay online dating app Grindr is generally recognized as one of the best-run consumer software companies in the world.

With fewer than 200 full-time employees, Grinder generates $3M revenue per FTE. As it turns out, it is ahead of (almost) all of big tech Magnificent 7 stocks.

Original source here. Compiled by myself, data comes from the valuation intelligence platform Multiples.vc, as of 31 August 2026.

Shows last actual fiscal year revenue and last reported employee number.

China is often the poster child for rapid reductions in poverty, and for good reason. In the early 1980s, over 90% of its population lived in extreme poverty, but by the early 2020s, that number had dropped to nearly zero.

Some might assume that China is the only reason why global extreme poverty has declined. But that’s wrong: many countries have seen dramatic reductions in poverty, too. Indonesia is one clear example; it’s shown alongside China on the chart.

In 1984, 86% of Indonesians lived on less than $3 per day. By 2025, this had fallen to less than 4%. While it didn’t quite match China’s decline, it has still been impressive. The number of people living in extreme poverty has fallen from 140 million to about 10 million.

Note that the international poverty line is extremely low, defined as people living on less than $3 per day. But Indonesia has also made progress when measuring higher poverty lines.

Data source: World Bank Poverty and Inequality Platform (2026)

Tools used: Python, OWID Grapher, Figma

Hi everyone - I'm back again with the 2026 update to our password table! Computers, and GPUs in particular are not only getting faster, but AI can help us build setups in new and novel ways to crack faster than ever before. This table outlines the time it takes a computer to brute force your password, and isn’t indicative of how fast a hacker can break your password (especially if you reuse your passwords - please stop), but is the BEST case scenario for you. It’s a good visual to show people why better passwords can lead to better cybersecurity, but ultimately it’s just one of the many tools we can use to talk about protecting ourselves online!

Data source: Data compiled using independent data gathering and research from multiple sources about hashing functions, GPU power, and related data. The methodology, assumptions, and more data can be found at www.hivesystems.com/password

Tools used: Illustrator and Google sheets

In 1950, almost one in four children worldwide died before their fifth birthday. In Africa, one in three children died.

The chart shows the change since then. In every region, the mortality rate of children has declined.

In Africa and Oceania, the child mortality rate declined 5-fold, in Asia 10-fold, and in Europe about 27-fold.

Progress was not always steady. The sharp spike in Asia around 1960 was due to the “Great Leap Forward” famine in Mao’s China, the deadliest famine in recorded history.

And the progress is far from finished. The global average has declined to 3.5%, but this still means that about 4.7 million children younger than five years died in 2025 — 13,000 children every single day.

Data source: UN, World Populations Prospects (2024)

Tools used: Python, OWID Grapher, Figma

I asked GPT-4.1 to pick a random number between 1 and 100. 10k times.

This post is an "AI remix" of a very popular Reddit post here on r/dataisbeautiful where people were asked the same question: https://www.reddit.com/r/dataisbeautiful/comments/iiafkd/oc_i_asked_100_people_to_pick_a_number_between/

People also tend to not be very good random number generators.
I wanted to see if an AI model has similar biases or if instead it follows statistical rigor.

Some things I found interesting:

• 20, 30, 40 and other multiples of 10 were picked 0 times (except for 10 itself, which was picked once)
• 42 gets picked 4x expected uniform (Hitchhiker's Guide to the Galaxy reference)
• Numbers containing the digit 7 get over-picked (and yes, just like humans, 37 gets over-picked)
• 69 gets under-picked at 0.29x expected uniform (my hypothesis: safety guardrails during GPT's pre-training and post-training)

Definitely not a random uniform distribution. I ran a chi-square goodness-of-fit test against the uniform distribution and found χ² = 15,604, p ≈ 0.

You can see the full methodology and code in this open-source repo: https://github.com/exmergo/research-chatgpt-guesses-between-1-and-100

I used the OpenAI SDK to programmatically call GPT-4.1 10k times with the same prompt.

I used GPT-4.1 because it's a non-reasoning model that exposes a temperature parameter. I set temperature = 1.0; that's what makes the model's sampling distribution the thing I'm actually measuring. OpenAI's reasoning models restrict that parameter. It would be interesting to reproduce this experiment w/ reasoning models.

I used Viz, our own chart/dashboard AI Agent for the data visualization: Exmergo Viz

Last year, 4 out of every 10 grown ups in America didn't finish a single book. But a small group, about 1 in 10 people, read 25 or more books. That small group read so much that they probably account for about half of all the books read in the whole country. So basically, a few people are doing almost all the reading, while most people are barely reading at all.

EDIT - This includes audio books. The survey question was "Have you read or listened to ..."?

Also a graph for another day - most people who did read, read Mystery/Crime.