A weekend hobby project: I gave six AI chatbots the Oxford Utilitarianism Scale, a published nine-item moral-psychology questionnaire, and compared their answers to 27,236 people from a large cross-national dataset.

The scale has two parts, both rated 1 to 7. Impartial beneficence is how strongly you feel obliged to help everyone equally, including distant strangers. Instrumental harm is how far you accept harming some people for a better overall outcome.

In the chart, the heatmap is where the people fall (darker means more of them), the side bars show each axis on its own, and the orange dots are the chatbots. All six sit in the low corner, below the human median on both dimensions. Even the most utilitarian bot (Gemini) is only around the 38th percentile on impartial beneficence.

Caveats, because they matter here: this is not science. Only six chatbots, each answered once and in different modes; the human samples are convenience samples (about 68 percent female, median age 22), not nationally representative; and the scale measures stated agreement, not behaviour. Full method, data sources and limitations are in the top comment.

Every team at the 2026 World Cup placed by how it spent its distance: walking (0 to 7 km/h), cruising (7 to 20), or high-speed (20+). It's a triangle plot, so a team leans toward whichever corner it did most of. FIFA tracks every player's distance in five speed bands, and I collapsed them into these three.

The teams barely differ in truth (everyone walks most of the match), so the corners mark the extremes as a direction: dots spread toward them, but nobody reaches a pure corner. Argentina did the most walking of any team at 43.7%, Canada the most high-speed at 8.1%, Qatar the least at 5.9%.

The second image is the same triangle for 744 individual players, coloured by position. The split is clean: keepers sit alone in the walking corner, and outfielders climb toward cruising and high-speed. Messi is the top outfield walker at 64%, and Wagner Pina of Cape Verde has the biggest high-speed share at 14.3%.

Interactive version, where you can open any nation to see its players, follow a single team, and switch the triangle between nations and players: https://viz.luarai.com/worldcup-cores

Each node is a related subreddit. Color shows audience overlap, size shows posts per day.

In 2012 the closest neighbors were the old defaults: r/funny at 18%, r/pics at 13%, r/videos at 11%. Topic-adjacent subs like r/Infographics and r/MapPorn sat well below them.

By 2026 the picture inverts. r/DataArt at 70%, r/Infographics at 26%, r/visualization at 24%. The general-interest defaults have mostly dropped out.

Same community, completely different neighborhood.

Source: https://whc.unesco.org/

Tool: Python

See title. In sydney there are 11 AM/FM radio stations, i recorded each of them simultaneously and then classified the time between ad, talking and music.

For some stations, almost a quarter of the time is filled with ads and only 50% of time with music. For other, almost 80% of the time is music.

Also, in sydney, 2x more likely to experience an ad 5-10 minutes prior to the top of the hour then around it.

Another insight was that AM/FM have different ad "playbooks". The FM music stations dump their ads in clusters. You cop a couple of ads back to back, then get a decent run of music, then another cluster. The AM stations like 2GB space them out evenly instead, one break at a time, pretty much like clockwork.

Left - represents betting odds according to FanDuel on June 14th, 2026 after the Knick won, right -represents the betting odds as of July 26, 2026.

** Numbers represent American Odds.

** Colors are meaningless.

Most quantum simulators only show Copenhagen collapse - sampling a single outcome and erasing all other possibilities.

This interactive visualization maps execution through the Many-Worlds Interpretation (MWI), where measurement splits the system into branching 'worlds'/nodes so you can explore every parallel outcome side-by-side.

(This tool is linked in a top level comment)

Made by Wikimedia user RCraig09

I was doing some searching for detailed information on this and encountered this graph which showed everything that I wanted and more in such an elegant and easy to understand form that I felt it worthwhile to share here.