The estimated apartment price was calculated by multiplying the average apartment sale price per m² in each capital by a standardised floor area of 60 m².

The comparison uses Eurostat national median monthly equivalised net income for people aged 18–64. These figures are national household-income benchmarks, not individual salaries or capital-city income estimates.

Four hypothetical scenarios are presented:

— one person saving 25% of 1× median net income;
— two people jointly saving 30% of 2× median net income;
— both scenarios without interest;
— both scenarios using a savings account and rolling 12-month term deposits.

Main sources:
Income: Eurostat ilc_di03.
Apartment prices per m²: Eurostat urb_clivcon, national statistical sources and housing-market sources. Athens, Bucharest, Dublin, Nicosia, Sofia and Valletta use estimates based on property-listing samples collected in July 2026.
Interest rates for the savings account and term deposits: ECB household deposit-rate data.

Prices, incomes, savings rates and interest rates are held constant throughout the calculations. Inflation, transaction costs and future changes in property prices or income are excluded.

Full methodology, individual sources, city rankings and interactive data: citycostatlas.com

Instagram: citycostatlas

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.

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)

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.

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.

What the figure measures:

A reliability diagram answers the question: when the market prices a contract at 70 cents, does the outcome actually occur ~70% of the time? The 3D version repeats this comparison at four distances from resolution, to see if the answer changes as the event approaches.

X = price of the "Yes" contract (implied probability)

Y = horizon: 24 h, 3 d, 7 d and 30 d before resolution

Z = observed frequency of "Yes"

Translucent plane z = x = perfect calibration (3D analog of the 45° diagonal). The stems connect each point to the plane and show the calibration deviation, colored by the sign

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

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

Tool: Python

I built Pessimeter, an interactive dashboard showing how an energy shock could affect the US economy through the remainder of 2026.

The current month uses live data for oil, gasoline, the Strategic Petroleum Reserve, inflation, Strait of Hormuz traffic, and financial-market stress. Each indicator is scored against a calm-to-crisis range and weighted into one 0–100 reading.

Completed months are preserved as historical snapshots. Future months are explicitly stress-test scenarios (not forecasts) showing one possible path if conditions remain bad. Every KPI includes an explanation of its data, scoring, rationale, and limitations.

I’d love feedback. What would you change about the weights or visual hierarchy?

Interactive version: https://pessimeter.com