I went through the menus of 41,249 delivery restaurants on Lieferando. 13,588 of them had a plain Margherita with a stated size between 28 and 32 cm. I then normalized the prices to 30 cm.

The idea for the map came from a recent Big Mac post on r/de: https://www.reddit.com/r/de/comments/1xf4yvd/je_nachdem_wo_man_wohnt_kostet_derselbe_big_mac/ Unlike a Big Mac, of course, not every pizza is the same, but I still find the pattern interesting.

More detailed breakdowns are on https://pizzameter.de/karte/preise, including other toppings.

▎ Tools: Python + PostgreSQL for scraping and cleaning, d3-geo + Vue for the map.

Source: every Wordle answer from #0 to #1,955 (through Oct 26, 2026), checked against Wordle's original 2,309-word answer list. "Already used" = original-list words that have been an answer at least once; "still unused" = the 452 that haven't. Tools: Python and matplotlib. I ran the numbers with Claude's help.

What stands out: the editor seems to have used the "easy" words first. Words ending in E made up 20% of the used ones but only 12% of what's left, while words ending in Y went from 14% to 23%. Double letters and S-starts are also over-represented in the leftovers.

A few days ago I posted which subreddits each age group is most overrepresented in (https://www.reddit.com/r/dataisbeautiful/comments/1xf6gt3/). The pushback I got most: people on Reddit lie about their age, self-reported data can't be verified, it's just noise.

Fair point. So instead of arguing, I checked.

Chart 1: do people age on schedule? If people made their ages up, there'd be no pattern over time. So I took everyone who stated their age in two posts or comments at least a year apart, 2.3 million people, and checked whether their age went up by the time that actually passed. 49% aged exactly on schedule and 68% were within a year. The outlined bars are the same people with their second age swapped for a random stranger's, which is what noise looks like: 3.5% and 7%. Gender too: 5.4M people stated it twice and 96% said the same thing both times.

First, what isn't a lie. Plenty of numbers look like ages and aren't: "I'm 99 farming" is a RuneScape skill level, "91F" is an army job code, "Female, 26, nurse" is someone guessing about another person. I filter that out wherever I can spot it, and nobody's age comes from one comment anyway: it's the middle of everything that person has said, so a stray number gets outvoted.

Chart 2: so where do everyday Redditors lie? I flagged every age claim 10+ years off from what that same person usually says, then read 20 random flagged posts in each of the 100 biggest everyday subreddits. Three AI reviewers per post, and a lie only counts when all three agree. Result: about 1 in 270 age claims is a lie. Even the "worst" one, r/indiasocial, is around 2%. When people do it, they mostly add years: 38 in the post, 26 everywhere else. Think a r/tifu story told by a 50-year-old from an account that's 27 in every other post, or 30-somethings saying they're 15 in r/teenagersnew.

Chart 3: where it spikes. Dating subs are even more honest than average (0.15%). Lying only takes off in a few adult subs: 8.4% of age claims in cheating and cuckold subs, 3.5% in "mature women" ones. 91% of those posts are written as a woman and 65% come with a Snapchat handle. Promo accounts posting as a 45-year-old woman, who are 28 in their other posts.

So: most people just say how old they are.

Full write-up with five more checks, where this method is weaker, and the CSVs: https://kissmypost.com/research/reddit-self-reported-age-gender-accuracy

Source and tools in the first comment.

This is part of my overall analysis to find out whether companies that talk more about AI tend to perform better than their quieter peers in the stock market. Full interactive piece, with the rest of the analysis: https://milesmeters.com/form-10-k-ai/

Source: 2,438 annual reports (10-Ks) filed with the SEC - every S&P 500 company's last report before ChatGPT launched (Nov 30, 2022) plus every report since. I counted AI terms per 10,000 words and labeled each mention hopeful (selling, using, or investing in AI) or worried (risks like competition, regulation, and security).

Data: Copernicus ERA5-Land monthly means (ECMWF), total precipitation converted to mm/day and averaged by calendar month over 2011–2025. Shown on 0.1° grid

What’s moving: the tropical rain belt (the Intertropical Convergence Zone) follows the sun. In June–August it sits north of the equator, bringing the Indian and West African monsoons. In December–February it moves south over the Amazon, southern Africa and northern Australia.

Tools: Python (xarray/numpy) to process the data into per-month grids, rendered with deck.gl (WebGL) in my own web map. Basemap © MapTiler © OpenStreetMap contributors.

Caveats: This is a 15-year average, not a 30-year climate normal. ERA5-Land is a reanalysis (a model fitted to observations), not rain-gauge data. It covers land only, so the oceans are blank.

Interactive version (temperature, sunshine, snow, beach score; click any spot for its whole year): https://www.leidora.com

Interactive live version with all updates

Sources:

• EPA ratings
• EIA prices: gas and diesel (week of Oct 19), home electricity (August 2026 residential, 18.16¢ a kWh)
• The 25 best-selling models from January to September 2026, from the carmakers' own sales reports, plus the next 5 best-selling EVs and the Prius for comparison. Each row is a version, not a model, so models sold as gas, hybrid, plug-in hybrid or diesel get a row for each. Plug-in hybrids split their miles between battery and gas by EPA's estimate for an owner who charges every night.

Tools: Kadoa for the data, Python, HTML and SVG for the chart. Claude did the car outlines from each model's published length, height and wheelbase.

EDIT:

• Prices vary a lot by state, so I added a state picker to the live version.
• The cities are only a ruler for distance/for scale.
• Added plug-in hybrids and the Prius. At 53 mpg the Prius goes 1,237 miles on $100, just behind the slowest EV (Rivian R1S, 1,278).
• Added a toggle for fast charging vs home charging
• metric units added

What you're looking at:

• Top: BepiColombo's path seen from above the Sun. The rings mark the flybys.
• Bottom: its distance from the Sun over time. The wobble getting tighter and dropping toward Mercury's orbit is the spacecraft working its way in.

Why "on average": Venus comes closer at its nearest, but it also spends long stretches on the far side of the Sun. Mercury never strays far from the Sun, so it never gets very far from us either.

And yet it's one of the hardest places to get to. Falling toward the Sun speeds you up, so the whole trip is about getting rid of speed. That took one flyby of Earth, two of Venus and six of Mercury itself, each one handing a little of the spacecraft's energy to the planet. Orbit insertion is planned for 21 November.

The Earth flyby was in April 2020, which makes BepiColombo one of the few visitors anyone had that spring. And even spacecraft run late: a thruster problem in 2024 added a year.

Interactive version, where you can drag the timeline and change the speed: https://dojo.amcharts.com/demos/bepicolombo/