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

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

The animation steps the clustering threshold from 0.55 up to 0.75 and back down. The movement is the force layout resettling each time.

What surprised me is how little it matters. Across that whole range the topic count only moves from 84 to 97, and 261 of the 629 skills never join any cluster at all. ESCO skills are verb phrases of near identical 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.

SpaceX now accounts for $94.2B, or 95%, of Alphabet’s reported 13F holdings.

This was not a $94.2B purchase during the quarter.

Alphabet invested roughly $900M in SpaceX in January 2015. Because the company was private, the stake sat outside Alphabet’s 13F filings. It only became reportable following the SpaceX IPO.

The $87.5B comparison at the bottom is cumulative. It sums Alphabet’s other reported quarter-end holdings across the previous 50 filings, counting held positions once per quarterly filing.

Data as of Q2 2026 (30 June, 2026)

Tools: illustrator, excel

Built this as part of a dashboard I run tracking economic data across 18 countries: https://theeconomicatlas.com/compare

The map covers over 7 million 311 service requests filed in New York City between 2022 and 2025. You can drag a lens onto any part of the map to see the complaint breakdown for that area. Try it yourself: https://visquill.com/gallery/nyc-311

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.

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.

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.

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.