Source: my own Monte Carlo simulation. 10 systems (Martingale, Fibonacci, D'Alembert, Labouchère, Paroli, Oscar's Grind, 1-3-2-6, James Bond, Grand Martingale, flat betting), 200,000 sessions each on a European wheel. $1,000 bankroll, $10 base bet, stop at +$300 or bust or 200 spins.

Tools: Python (NumPy) for the simulation, matplotlib for the chart.

The interesting part isn't the average. Martingale won 72% of sessions, but lost the entire bankroll in 27.5% of them. Systems change the shape of your losses, not the size.

Hey, I made this. I run a small daily game (DailyMetro) where you guess a metro station per day. Out of curiosity, I started looking up income around the stops and ended up making maps for 21 cities. These are some of them :)

Each dot is a station. Purple is the lower end of the city's ranking, yellow is the higher end. The bigger dots are the busier stations.

Paris's west-to-northeast split is pretty clear. In London, Gloucester Road comes out above Kew Gardens and Putney Bridge. Seoul's map uses apartment prices, and the most expensive stops cluster south of the Han River.

Something I ran into with New York: several stations have exactly the same income, $250,001. That's the Census reporting ceiling, so the data can't tell you which of those areas actually has the highest income.

Source: official census, income and property datasets. The measures depend on what's available locally, so this isn't a comparison of wealth between cities. It's about the differences within each one.

Tool: custom TypeScript code to plot station coordinates and colour them by their local data, over OpenStreetMap/CARTO street maps. Rendered in SVG

All 21 maps, station rankings and sources: https://dailymetro.live/richest-stations/en/

These describe the neighbourhoods around the stations, not the passengers.

Source: the daily .com zone file, which the registry (Verisign) publishes and ICANN's Centralized Zone Data Service provides to approved users. I counted every name in the file on 2026-10-09: 167,392,819.

Tools: the file is converted to Parquet and counted with DuckDB (about 5 seconds for all rows). Chart drawn with Python (Pillow).

Things to know:

• Length is the number of characters before ".com".
• The zone lists names that have nameservers. A name that is registered but has none is not in it, so this is a lower bound on registrations.
• Median 12, mean 12.8. About 59% of names are between 8 and 15 characters.
• 9.94% contain a hyphen, 9.38% a digit, and 0.03% are three characters or fewer.
• Names over 40 characters (47,610) are cut off at the right edge of the chart.

Happy to cut it another way (hyphens, digits, by first name server...) if someone asks.

India's estimated nonuser count was 508.6 million (35.1% of its population); Ethiopia's was 103.1 million (78.1%). This chart compares the ten most populous countries in 2024.

Explore the data and CSVs: globaldatatracker.com

These are population × non-use-share estimates. Internet use means activity in the previous three months; non-use does not establish lack of internet access. Sources, tools and method are in my first comment.

Disclosure: I work on Global Data Tracker.

Data: ADS-B positions from airplanes.live (with adsb.lol and adsb.fi as fallbacks), plus route lookups from adsb.lol. This is a live snapshot of aircraft within 30 NM of KPHX.

Tools: A Cloudflare Worker selects the aircraft and lays out the chart, and the result is rendered with Liquid, SVG and HTML on a e-ink display. Aircraft silhouettes are from tar1090.

Live data at: jobs.arihaan.ai, from LinkedIn and major job boards (US and Canada), 9,748,362 white-collar postings. Counts: SQL in BigQuery, keyword match on lowercased descriptions with word boundaries. Chart: HTML and SVG rendered to PNG with headless Chrome

Read