[OC] from my website FlightAtlas.org

🎂 Babies born on leap day (February 29) had the least common birthday in Canada from 1995 to 2025, while those born on September 20 had the most common.

Explore more live birth trends here.

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🎂 Les personnes nées un jour bissextil (29 février) avaient la date de naissance la plus rare au Canada de 1995 à 2025, tandis que celles nées le 20 septembre avaient la plus fréquente.

Examinez d’autres tendances relatives aux naissances vivantes ici.

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

HTTP status code 402, “Payment Required”, was reserved in the 1990s and then left almost unused. A newer standard called x402 finally puts it to work: a web API answers 402 with a price, the caller pays a small amount in USDC (a dollar-pegged token), retries, and gets the data. No account, no API key.

I run a small directory of these APIs, and its checker tries to buy from each one. This chart is the most recent attempt for every API it has tried: 4,235 of them. 58% sent back what they said they would. The group I find most interesting is the 288 that answered a payment by asking for payment again.

Hi sub,

This is the result of one month of border drawings on my game, Borderline.

You can explore the map in great details here: https://www.habibicode.org/thedrawnworld

Or add your lines to it here: https://www.habibicode.org/borderline

NOAA’s estimated within-year increase in global atmospheric CO₂ fell from 3.76 ppm in 2024 to 2.06 ppm in 2025, a 45.2% smaller increase. That is still growth: the 2025 annual mean was 425.62 ppm. The bars measure start-to-end-of-year concentration change, not the difference between annual means (2.83 ppm in 2025) or emissions mass.

These are global marine-surface measurements, not Mauna Loa alone. Whiskers preserve NOAA’s reported uncertainty; recent values can be revised.

Source: NOAA GML global CO₂ data, version 2026-10. Tools: Python/pandas/Matplotlib, with ChatGPT/Codex assistance.

I run Meplar. Data and methods.

I've decided to put together data of every 'regular' (900+ minutes played) player in the Premier League, La Liga, Serie A, the Bundesliga and Ligue 1 last season, counted by nationality and divided by the population back home. The map shows Europe, and the ranking underneath is the whole world.

Croatia comes out top: 5.4 players per million people. Denmark is right behind on 5.2, then Kosovo, Spain and Uruguay, the only country outside Europe in the top ten.

The big footballing nations sit much lower once you account for size. France is 2.6, England 1.9, Germany 1.6, Argentina 1.2, and Brazil only 0.28 (37th), even though 59 Brazilians played regular minutes. The USA is last of the 45 at 0.04.

Worth saying: countries that have one of these five leagues get a boost from playing at home, which is part of why Spain ranks so high. And a country needs at least five regulars to be ranked, so Iceland (four players, 10.4 per million) isn't on the list.

Football fans, I'd love to hear your thoughts on why small countries like Croatia and Denmark keep producing so much talent relative to their size?

Data Source: It's built from 1,594 players who played at least 900 league minutes in 2025/26, with populations from the World Bank (and the UK's own figures for England, Scotland and Wales). Pulled together with The Prism, a football analytics & scouting app I'm building, and charted in Python/matplotlib.

(If you want the rest of the world, I've put an interactive version up here: every country on a world map, with every player. You can hover any country, see who its players are and sort the full table!)

A 2018 study titled Talent vs Luck: The Role of Randomness in Success and Failure explored how luck and talent influence success using an agent-based model. One of its most interesting findings was that the most successful individuals in the simulation were not necessarily the most talented. Luck played a major role!

I found the study fascinating, so I decided to create my own Python simulation inspired by the model. I've shared a visualization of the results here. I also made a video discussing the original study and exploring the simulation in more detail: https://youtu.be/acE0-AxY-Ig . It's an eye-opening way to think about success.

I am interested to hear what you think about it?

Data: UN World Population Prospects via World Bank WDI. Age-share indicators: https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS and https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS . Total population selects the ten largest non-aggregate economies: https://data.worldbank.org/indicator/SP.POP.TOTL . Snapshot retrieved 14 October 2026.

Tools: Python, pandas, NumPy and Matplotlib. Each ratio divides the unrounded share aged 0–14 by the share aged 65+. Nigeria is 13.2006 and the United States 0.92994 before display rounding. A ratio of one means equal numbers in those two age groups. Countries are selected by population, then ordered by ratio. These are population estimates, not children per parent or family-size measures. No interpolation or imputation was added by this analysis.

Data license: CC BY 4.0, with World Bank additional terms: https://datacatalog.worldbank.org/public-licenses#cc-by .

Cleaned country-year CSV, original source snapshots, methodology and reproducible plotting code: https://meplar.com/datasets/country-age-profiles/

Source: U.S. Energy Information Administration, Monthly Energy Review, Table 8.1 (September 2026): https://www.eia.gov/totalenergy/data/browser/csv.php?tbl=T08.01

I made this chart for Meplar using Python, pandas and Matplotlib. Clean CSV, full source snapshot, methodology and reproducible code: https://meplar.com/datasets/us-nuclear-electricity/

1990 is the year with the most operable U.S. reactors in this EIA series. Each line is indexed to its own 1990 value, so all three share one scale. Generation is annual net output; units and net summer capacity are year-end snapshots. Operable includes some long shutdowns, and reactors differ in size. This is a descriptive comparison, not a causal claim. Values retrieved October 14, 2026; source data are public domain.

Interactive map + live filters (Genre, Venue Capacity, Heatmap vs. Pins) if you want to zoom into your city or check specific touring corridors: Lighthouse Live Music Map (Full breakdown of data sources, deduplication, and tech stack is in my comment below!)

The US elections seem to have grabbed the focus of many, but the US is still at war with Iran and President Trump is giving small statements multiple times per week on the status of the war. Most of the time, though, it's no more than saying the war will end "soon."

I've been tracking all of his statements (both spoken and written) on the status of the war and any negotiations, and it became obvious that he was saying similar things as time passes. Even months apart.

For example, in March Trump said, "It's going to be finished pretty quickly." And then yesterday, October 14th he said "It's going to be over very quickly." That's nearly word-for-word of what he said during the start of the war.

So the bar chart is a plain look at how many times he's said "soon" or "shortly" or other words when describing the end of the war or the imminence of some deal or agreement.

Data Source: http://artofnodeal.com/ (direct link to the JS object storing all the quotes here)

Tools: Vanilla JS