You can explore possible scenarios here https://realsize.world/en/time/continental-drift?mode=future
The interactive version also has a globe view.

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

Strasbourg’s 2024/25 recorded season age averaged 20.6 years when weighted by domestic-league minutes. It was the lowest of 2,814 eligible club-seasons in this fixed 1996/97–2024/25 snapshot of the Premier League, LaLiga, Bundesliga, Serie A and Ligue 1. Lille 2017/18 was next at 22.0; the median club in each season stayed around 26.

This uses recorded season ages, not age on each matchday, and requires at least 25,000 valid player-minutes per club-season. Goalkeepers are included; cups and national teams are excluded. The finding applies to this dataset, not an all-time European record. The source snapshot date is not documented, and the current upstream release differs.

Source: Meplar’s stored Transfermarkt-derived snapshot; upstream dataset and collection project. Tools: Python/pandas/NumPy/Matplotlib, with ChatGPT/Codex assistance.

I run Meplar. Data and methods.

English: https://thelongswell.com/floods/how-a-flood-is-made/
Spanish: https://thelongswell.com/floods/asi-se-forma-una-inundacion/

Source: ERA5 reanalysis (Copernicus Climate Change Service), 2026 preliminary; AEMET station observations (Barcelona Fabra, station 0200E). Anomalies against the 1991–2020 baseline.
Tool: Built with Claude (Anthropic) as a coding assistant. It wrote the code that downloads and processes the ERA5 data and renders the maps and animations. All layers are plotted from the measured data, nothing is AI-generated imagery.

Data: USGS ANSS Comprehensive Earthquake Catalog (ComCat), retrieved 14 October 2026: https://earthquake.usgs.gov/data/comcat/

This selection contains 8,556 cataloged magnitude 4.5+ earthquakes with origin times in calendar year 2025 UTC, including aftershocks. Depth groups contain 6,942 shallow events (<70 km), 1,328 intermediate (70 to under 300 km), and 286 deep (300 km or more). Their shares round to 81.1%, 15.5% and 3.3%, totaling 99.9% after rounding.

Tools: Python, pandas, NumPy and Matplotlib. Coastline: Natural Earth, public domain: https://www.naturalearthdata.com/about/terms-of-use/

The lower panel collapses latitude into longitude versus depth. It is not a physical cross-section. Equal-size dots show events, color shows depth, and overlaps hide some points. Detection and preferred magnitude types vary; this is not all earthquakes or a map of risk. Depth and magnitude estimates can be revised.

Frozen event CSV, source snapshot, full query, methodology and plotting code: https://meplar.com/datasets/global-earthquakes-2025/

It's almost always 7.

**This list does not include every partnership or ownership stake.

Disclosure: I run Football Edge Lab. This chart compares observed home win shares across six leagues and 21 seasons (49,615 recorded completed matches).

Full research, sample counts, sources and limitations: https://footballedgelab.com/research/home-advantage-trends

CSV: https://footballedgelab.com/research/league-comparison.csv?view=seasons

Endpoint changes do not establish a continuous trend or a crowd effect. A top-level comment provides the calculation method and tools.

Youtube version | Full dataset

Every US location of the 20 fast-food chains with the most stores, put on a map.

Source: Collected from each chain's store locator with Kadoa in October 2026.

Tools: Python and matplotlib, ffmpeg for the GIF

Every sector has one fixed inventory-days value and every route has one fixed transit-days value, so this is the full 8x8 grid: each cell is one sector on one route, shaded by whether that sector’s average inventory would outlast a clean shipment on that route, and whether it would also outlast a Red Sea-scale delay on top. The number in each cell is the buffer, inventory days minus transit days.

Source: Datastripes Supply Chain + OECD I/O Matrix

Tool: Datastripes

Source: Energy Institute, Statistical Review of World Energy 2025, via Our World in Data.

Tools: D3, Canvas, WebGL shader for the watercolour (edges based on Tyler Hobbs’ "How to Hack a Painting"), Claude Opus 5.5.

Sources are stacked by size each year, smallest on top, so overtakes show as crossings. The watercolour bleeds mostly along the time axis, so band heights stay close to the data.

Interactive version: https://gitnoise.github.io/energywatercolour/

I recently completed an analysis of software-development employment trends from 2022 (when ChatGPT was introduced) to the present. This analysis, which is summarized here, found that employment has continued to increase since 2022, though unemployment rates have increased as well. It also appears that employment increases have been stronger for more seasoned developers (e.g., those aged 30-44) than those in their 20s.

I plan to update this analysis in the coming months and years as new Current Population Survey data becomes available. The underlying code has been released under the MIT License, so you're welcome to use it as the basis for your own research.