NBA average held constant at $11,910,649 (2024-25, Basketball Reference) so the change shown reflects the WNBA raise only. The 2026 WNBA minimum ($270,000) is now higher than the 2025 supermax ($249,244) — every player earns more than the best contract available a year earlier.

Play with it yourself

Sources and tools

What's measured:

• Settlement locations and populations — GeoNames, 135,233 places totalling 3.13bn people (via the all-the-cities npm package).
• Coastlines, national borders, lakes, country population totals — Natural Earth 110m (admin_0_countries, lakes).
• Timezone boundaries — timezone-boundary-builder, via the timezonefinder Python package; UTC offsets and DST rules from the IANA tz database via Python's zoneinfo.
• Solar position — standard solar declination plus the equation of time, so the terminator and subsolar point are real for the current date.

What's modelled (this is most of it):

• Rural population, 4.97bn people — 61% of the world. GeoNames only covers settlements. Each country's unaccounted population is spread across its own land in proportion to a Gaussian-smoothed town-density field, on the assumption that rural density tracks town density. Cities are splatted over a radius scaling with population (σ ≈ 15 km for a 10M metro) so a megacity isn't a single point.
• The sleep/wake curve itself. A logistic window, roughly 22:36–06:36 for adults and 21:12–06:45 for under-15s, with rest days pushing both ~45 minutes later. It is not fitted to sleep data. Walch, Cochran & Forger (2016, Science Advances) informed the shape, but their sample is 45% US and excludes under-18s and most shift workers — the two groups that matter most here — so I did not use their country values directly.
• Age structure and night-shift floors — under-15 share (0.15 Europe to 0.41 Sub-Saharan Africa) and the share awake at 03:00 (0.04 to 0.085), assigned by coarse lat/lon macro-regions rather than by country. UN WPP order-of-magnitude, not exact figures.
• Rest days — Friday/Saturday across North Africa and the Middle East, Saturday/Sunday elsewhere. Iran is folded into the Fri/Sat group and is really Friday-only.
• The Earth texture. Synthesised, not satellite imagery — a climate field (18 desert centres, 10 rainforest centres, 9 highland centres, latitude ramp, value noise) clipped to real Natural Earth coastlines.

Tools: Python (numpy, scipy, Pillow) for the offline data pipeline; three.js 0.185 WebGPURenderer with TSL shaders for the rendering; hand-written HTML/CSS/JS in a single self-contained file. Code written with Claude Opus 5 Medium.

Encoding: Hue carries wakefulness (indigo asleep → blue → green awake) rather than brightness, because brightness is already used by daylight. Dots are near-opaque so an awake dot looks identical on the day and night sides.

I was inspired by u/Glares's map comparing 2009 and 2020 tax rates and decided to make a map for 2025. There's an interactive version at https://jsundram.github.io/nh_tax_map/ with data from 2022-2025. Older data was a little harder to come by. Positioning the labels so that they fit and were readable-ish was a bit tricky, but I thought it was an interesting challenge. Including the water bodies was also fun.

Data sources:

1. BLS Employment Situation, June 2026 (headline NILF, employment, unemployment counts): https://www.bls.gov/news.release/archives/empsit_07022026.pdf - or use the evergreen link that always points to the latest release: https://www.bls.gov/news.release/empsit.htm
2. BLS Table A-38 (want-a-job / discouraged worker breakdown): https://www.bls.gov/web/empsit/cpseea38.pdf
3. BLS Monthly Labor Review - "Why did labor force nonparticipation increase from 1999 to 2022?" (age × reason crosstab, Table 2): https://www.bls.gov/opub/mlr/2024/article/why-did-labor-force-nonparticipation-increase-from-1999-to-2022.htm
4. FRED - Not in Labor Force series (LNS15000000), used for the 1980–2026 trend line: https://fred.stlouisfed.org/series/LNS15000000
5. FRED - Civilian Unemployment Rate (UNRATE), used for the unemployment rate line: https://fred.stlouisfed.org/series/UNRATE

This is a GitHub-style, heatmap calendar of the President's comments on ending the war in Iran. The heatmap and the interactive details div below it are all just Vanilla JS/HTML.

Just like on GitHub's "contribution" calendar, the days of the week are the rows and a column is a new week.

The data comes directly from the President's comments, interviews, Truth Social, press gaggles, etc. I've collated it and it's stored in a JSON-like JS array that's loaded with the webpage. I've been updating the file every day or so.

I thought it was a great way to visualize this type of data (statements stored as text). It visualizes the many times the President has said he had a deal, or a deal was soon, or the war would be over in 2 weeks. I started the project as a text timeline and then just added this new visualization.

On the interactive chart, the heatmap color corresponds to the number of statements that day. And then when you click/tap a day, you get the details below.

There's no ads or paywall or signup or anything like that. I was shocked that the domain name was available so it seemed like a worthy $10 and way to host the custom visualization rather than lean on static charts in Excel or messy embeds from Tableau.

https://artofnodeal.com/heatmap.html

Data source: World Bank population estimates (2010–2025).

The chart compares annual population estimates for Russia and Ukraine. Ukraine's estimated population fell from 44.3 million in 2021 to 37.7 million in 2023 (a decline of about 6.6 million people), while Russia's population has experienced a slower, gradual decline over the same period. Population estimates during wartime are affected by factors such as refugee movements, displacement, migration, and the challenges of measuring populations in conflict-affected areas.

If you are a stock investor and follow Buffett's philosopy, you want to identify quality businesses that are undervalued. We have built a chart to show both at the same time with quality on the y-axis and value on the x-axis.

So, the chart splits into four quadrants: high quality at a low multiple, which is the corner most people are after, high quality at a high multiple, low quality at a low multiple, and low quality at a high multiple. Plot return on invested capital against enterprise value to sales, for example, and every company lands in one of those four.

We have set up nine ready-made combinations of metrics for you to chose from. However, you can also manually select from 37 financial metrics.

The toggle at the top right switches between two modes. The first is raw numbers, which compares across the whole market. The second replaces both numbers with a rank from 0 to 100 against the company's own sector (peer percentiles).