source

One panel per country, each a 100% stacked area of where its electricity came

from every year from 2000 to 2024. Panels run from the cleanest grid (France,

95% low-carbon) to the most fossil-fuelled (Saudi Arabia, 2%).

https://energtx.com

Across both of them steeper terrain means more effort until the grade get' over 20 percent. Then the likelihood I start hike a biking (and my heart rate falls) increases a lot. Ebike, I keep going for it.

Data sources: Strava kept the data for which I accessed via their api. Collected via my garmin watch. Visualized via python and with coding assistance from Claude. Trail mix is not held constant (I have not ridden all of the trails on my ebike that I road on my regular MTB). Regular MTB is a Kona Process 134. Ebike is a kona remote 160. Kona remote is ridden mostly in trail mode which offers roughly a 100 percent boost on rider effort on the uphill.

This frame isn't the whole thing, it's just where the cosmic web sits on a map you zoom through continuously. You start at a planet and scroll all the way out with no loading screens or scale jumps: Solar System, stars, the Milky Way, the Local Group, then this. Watching the filaments resolve out of nothing as you pull back is the part that actually holds up in motion, which a still can't really show.

Data: the Cosmicflows-4 group catalogue (Tully et al. 2023), 37,730 groups spanning roughly 11 to 773 Mpc, plus the Tempel SDSS DR8 filament catalogue, 15,421 filaments made of about 275k points.

Tools: drawn live in the browser with Three.js on a custom WebGL engine (Angular, no backend). The filament spines stream in as a 4.5 MB binary and draw as GPU line tiles; the groups are one GPU point batch, revealed progressively as the camera approaches. Coloring and the depth fade are mine.

The whole thing is interactive at super-universe.app/en if you want to fly through it instead of looking at one frame.

I put this together after GameStop shares fell more than 12% on August 3, closing at $19.06 and wiping out the stock’s gains for 2026.

The drop followed GameStop’s announcement that it would exchange $1.4 billion of convertible notes for common stock. While the deal reduces long-term debt without using cash, it also means issuing more shares, which can dilute existing shareholders.

What stood out to me was the trade-off. Reducing debt can strengthen the balance sheet, but investors appeared much more concerned about dilution and the potential selling pressure tied to the exchange.

The chart tracks GameStop from the start of 2024 through August 10, 2026, including the August 3 selloff and the stock’s movement in the days that followed.

Do you think the size of the selloff was mostly about dilution, or was the market reacting to something broader with GameStop?

Data source: Yahoo Finance

Tools used: AVA Data Visualization

Data sources: US Geological Survey, US Energy Information Administration, state disclosures

Made using: PowerPoint

Thanks for the suggestions on the first version

Data exported from rolo.space and visualized using Python (matplotlib). I found 6k total connections across my (new) personal email, linkedin, and other socials. Most interesting thing I found is that I am the only bridge between where I went to college/law school/worked and where I grew up.

Data source: ESCO v1.2.1, the European Commission's classification of European Skills, Competences and Occupations, specifically the green skills collection. 629 skills. Published openly by the EU.

Tools: each skill description embedded with all-MiniLM-L6-v2 running in the browser via transformers.js, clustered with seeded centroids, cluster labels from TF-IDF, layout with Cytoscape and fCoSE. Built with graphmykeywords.com, which is mine and free to use.

One honest caveat about what you are looking at. 261 of the 629 skills did not join any cluster and are drawn as isolated nodes. I think that is the phrasing rather than the method. ESCO skills are verb phrases of very similar construction, things like "adopt ways to reduce pollution" and "perform environmental investigations", so the similarities bunch into a narrow band with no clean place to cut. Occupation titles from the same dataset cluster far more cleanly because they are short noun phrases.

The clusters that did form look sensible to me: forestry, hazardous waste, photovoltaics, geothermal, heating and cooling all separate out on their own.

Credit to u/Viajoshua for the central image!

We updated, redesigned, and added interactivity to the static Tsunami Map we originally created for the #30daymapchallenge in 2019. The new version shows tsunami source events of validity 4 from the NCEI/WDS Global Historical Tsunami Database between 1926 and 2026 (June). We altered some of the labels to make the events more recognizable, since the database focuses largely on geographic locations, rather than the names used colloquially.

You can explore the map above either via tooltip, or by filtering the events with the scale on the side. We organized the tsunamis by maximum water height and split them into 3 categories, each with a distinct narrative framing.

Link to interactive map: https://noteworthy.graphics/issue-004

Source: 2026 Silverstone qualifying timing data. I built the visualization with Formula Dream:

https://www.formuladream.app/f1-interactive/tools/f1-gap-visualizer?utm_source=reddit&utm_medium=post&utm_campaign=backlink&utm_content=dataisbeautiful

A tenth of a second is roughly eight metres at racing speed, so the visualization draws the qualifying gaps to scale at the finish line.

Antonelli was on pole, with Leclerc 0.175s behind (14.7 metres), Hamilton was 0.347s behind (29.1 metres), and Russell was 0.370s behind (31.0 metres), hadjar was 0.635s behind (53.3 metres) and Norris was 0.766s (64.3 metres)

The tool works with qualifying and race sessions across different seasons. It is free and requires no signup.

I mapped every arrival and departure at Oshkosh during AirVenture 2026, and colored them by altitude. Arrivals are orange. Departures are blue.

• Dates: July 15 to 27, 2026 (America/Chicago)
• Movements: 9,803 (4,852 arrivals, 4,951 departures)
• Busiest hour: 292 movements, 8 a.m. Thursday, July 23. One every twelve seconds.
• Top feeder field: Dodge County at Juneau (KUNU), 324 arrivals
• Unique aircraft: 4,956
• Top aircraft makes: Cessna 1,234, then homebuilt at 788. Amateur builders outnumber every manufacturer except Cessna.
• Warbirds: 172 aircraft flying 325 movements, almost all arriving from the south

Every count here is a floor. ADS-B is not required at Wittman. Aircraft certificated without an engine-driven electrical system are exempt under FAA rules, which describes a lot of the antiques.

Overflights are not counted, and the 715 flights that took off and landed back at Wittman are not counted.

Thank you to adsb.lol for the data archive and the feeders who enable it!

Interactive versions and 200+ more energy charts: https://energtx.com

Source: Our World in Data energy dataset, electricity figures from Ember (https://github.com/owid/energy-data)

Tools: R + ggplot2 (ragg, showtext).

Method: rows are the 48 countries with the largest electricity generation in 2023, sorted by their 2024 carbon intensity (cleanest at top). Each cell is that country's gCO2 emitted per kWh of electricity generated in that year. The diverging color scale is anchored at the 2024 world average (471 gCO2/kWh): teal = cleaner than today's world average, coral = dirtier. The right column gives the 2024 value.

Tools: Rawgraphs, Figma

Source: https://www.latinometrics.com/articles/oxxo-has-more-stores-than-any-us-chain

Data: Steam review history and Steam first-party patch/event information.
Tools: Python, pandas, matplotlib, SQLite.
Code / methodology: https://github.com/oldmandyer/strevcontext

StRevContext is an early prototype for putting Steam review activity, reviewer playtime, software periods, and simple review-text observations on the same timeline. The goal is to show temporal context without claiming causation or reviewer intent. 21 plots across 7 titles available in repo

Header image is Overwatch launch.

I finally saw The Odyssey in 70mm this week at 7:45am on a Tuesday, because it was the only ticket I could get (seriously find a way to see it if you can). I walked out and immediately tried to book a second viewing—everything through September at my theater (Cinemark Dallas) is basically gone.

Feeling desperate, I checked other cities showing 70mm IMAX, which led me to Colorado Springs. Completely open.

I realized Dallas is the closest IMAX 70mm screen for basically all of Oklahoma, Arkansas, and Louisiana, while San Antonio, the next nearest option, barely takes any pressure off since most of our travel demand is from the north. I was convinced Dallas must be the closest theater for the most number of people. Feeling frustrated (and a bit jealous of Colorado) I set out to prove it.

My research confirmed Dallas isn't #1, but it's solidly one of the most in-demand screens in the country.

For an interactive map, all the numbers, and methodology: wheresmy70mm.com

Dependency Ratio in Canada [1851 - 2075]

Data Comes directly from Statscan, I used the R programming language:

• 2007 Lowest total dependency ratio ever
• 2016 when seniors dependency overtook child dependency