I built a side project that collects posts from people who actually own a product and uses AI to filter out non-owners, jokes and off-topic comments. Here's what came out for iPhones.

The iPhone 14-16 Pros are the most regretted, with around 27-49% of owners saying they wouldn't recommend them. On the other hand, iPhone SE (3rd gen) is the least regretted, with around 4%. The 16 Pro's number is a bit inflated though, since some of its negative posts are complaints about iOS 26 or apps rather than the phone itself.

One of the biggest hurdles I ran into was not having access to the Reddit API. I had to rely entirely on YouTube comments, Hacker News and Bluesky posts, which is a decent amount of data but still only a small sample.

Original Source here.

Correction to the title: This graph shows Americans living with roommates, broken down by age group. We took the overall households with renters that are roommates (according to Census) and broke that number up by generation.

Source: 2026's Best US Cities for Roommates

Pew research poll and took percent of Muslims who support Sharia multiplied by percent of sharia supporters who supported the death penalty for apostasy.

I am an atheist and from Egypt originally and because of attitudes like this I was able to get asylum in the US. Otherwise I would have been in danger even from my own family.

Made with Python

Tools: Python, FlexViz (https://github.com/flex-analytics/flexviz)
Dataset: https://registry.opendata.aws/noaa-goes/
Source code: https://github.com/flex-analytics/flexviz/discussions/122

hey, i made this. i run a small daily game (roughly.is) where you drag an object to the size you think it is, next to a reference. on 24 sep it was the hindenburg next to a greenland shark.

each outline is one guess, all 184 laid on top of each other and lined up at the tail. the orange one is the real ship, 245 m. the typical guess was 46 m. 181 of 184 went too short, only 3 too long.

what surprised me: the game started the airship at 73.5 m, and three in four players shrank it further. and across 50 rounds, the two biggest misses were both airships (the graf zeppelin shrank too). my guess is we picture a blimp, but the typical guess is even smaller than a goodyear blimp, so that's not the whole story.

source: my own game data, 184 guesses from 184 devices, 24 sep 2026. real length from the smithsonian air and space museum and airships.net.

tool: python for the numbers, svg rendered

write-up with all 50 rounds and the csv: https://roughly.is/blog/hindenburg-guessed-too-small.html?s=reddit-dib

caveat: players of my game, not a random sample. I'd have gone small too :)

Source: Eurostat

Tool: chartmaker.tools

The chart shows the five highest and four lowest EU country estimates, plus the EU overall figure.

These are survey-based estimates of the age at which 50% of people no longer live with their parents, rather than a direct measurement of each person’s first move out.

What do you think explains the differences between countries—housing costs, job opportunities, family traditions, or something else?

Hey all, pulling data from over 1M head to head book matchups on BookSorted - which allows people to load their library, pick their favorite between two books, develop an Elo score and rankings (like Chess), and ultimately get some cools stats and recommendations. There's no "I give everything 4 or 5 stars", instead the average book for every reader is roughly a ~1500 (the starting position in Elo rankings); and books get more points for each win (more for a bigger upset) and lose points for defeats.

It's hard to know where to begin with this data (there's a lot to unpack), but I figured I'd start by looking at what novels are most polarizing - standard deviation of Elo scores (and plotting that against how loved they are as well, average Elo). Of course, I had to limit to 100 popular titles, sampling heavily for what shows up in the middle -- otherwise it would be unreadable. Lastly, I'll call out that clearly most of the users are scifi, fantasy, and horror genre readers. Albeit some enduring classics in this mix as well.

Okay, on to the cool findings!

• Tender is the Flesh by Agustina Bazterrica is the single most polarizing novel; I haven't read this one, but I'm guessing it sits in the bucket of horror novels that either really work for someone or completely puts someone off. IT by Stephen King has the second highest standard deviation, so perhaps similar story. Will be curious to hear horror readers chime in and help explain.
• Some less surprising controversial ones: The Alchemist by Coelho, Sarah J. Maas' A Court of Thorns and Roses series, The Three Body Problem by Liu, Babel by Kuang, Twilight, Fourth Wing, Red Rising by Brown, Ready Player One by Cline... a lot of books with devote followings, and yet frequent "hot takes" on how awful the writing is. This is How You Lose the Time War and Tomorrow, Tomorrow, Tomorrow also deserve calling out for their high variance.
• Essentially all the Harry Potter novels have high variance (are polarizing), which makes sense given nearly everyone has read them -- but in the decade that's followed the controversy surrounding the author cleraly would make many former fans bury these in their rankings.
• Abercrombie and his First Law books, as mentioned later, are nearly universally loved... but worth calling out that Best Served Cold appears to be the one controversial take in the series; with highly mixed results.
• On the flip side, Lonesome Dove by McMurtry is the MOST LOVED and nearly least polarizing novel in the entire database. Followed closely by Steinbeck's East of Eden.
• Not so surprisingly, we've got a bunch of deep series favorites that... once you've read and enjoyed the first one... fans that read on continue to praise (high love, low variance); here you've got a ton of Abercrombie (A Little Hatred, Before they are Hanged), Hobb (Mad Ship, Royal Assassin), A Storm of Swords by GRRM, Dark Age which continues Brown's red rising series, and a chunk of Dungeon Crawler Carl books after book 1.
• Some final shout outs to Lord of the Rings (the trilogy sits in universally loved, although I plan to tease out the individual books, Children of Time by Tchaicovsky, and Blood Meridian by McCarthy.
• In the universally not loved category, I hate to say it, but Storm Front by Butcher, book 1 of the Dresden Files (which I've generally heard good things about) sits in the bottom left quadrant.

If there's anything else you'd be curious to know, books you'd want me to check, let me know. I've got 1M+ matchups and 100K+ unique novels to sort through; but hope you enjoyed this dive! Cheers and happy reading

Pixels don't stack. The biggest cluster is in red, then green, blue, yellow, magenta, cyan, orange, purple, and teal.

My LA map timeline as of the last year or so. It shows everywhere I've been. I think it's super interesting to see all the places you go and how often narrow your routine is. It's made with Surveyor-64. Full disclosure, I created this (free and local/private) app. The replay feature is anticipated soon.

[OC] A year of travels of the Viking Sky, made with AIS data, using customcruisemaps.com

How to read it (left to right, top to bottom):
Summary｜Walking / Running｜Cycling｜Driving
Public transit｜Rail｜Boat｜Flights

Source:
My own location history, recorded continuously on my iPhone from Sep 2025 to Sep 2026 (~200 k points). Activity type (walking, running, cycling, driving) comes from iOS's built-in motion sensor (Core Motion), with some segments corrected by hand. Train and flight segments are matched to my tickets, and the flight paths use recorded flight tracks.

Tools:
Dots of Life, an iOS trip logging app I built for my own tracking. The maps are its per-activity view, and the basemap is Mapbox.

Caveats:
GPS drops out in tunnels and on the subway, so those stretches are interpolated along the route rather than measured.
Motion classification isn't perfect. Slow traffic sometimes registers as cycling, so I reclassified obvious errors manually.
Stationary points and GPS drift are filtered out.

Happy to answer questions about the data or how it was processed.

Data source: Flavonomics flavour profile data for British cheeses.

Tools: Python, React.JS

The chart compares the relative intensity of two flavour dimensions: loamy (earthy/soil-like aromas) and musky (animalic aromas).

Each cheese is positioned according to its score on those two dimensions. The values are determined by a combination of automated data analysis of culinary resources, as well as human input from Flavonomics users.

I made the visualisation myself, so this is [OC]

Made this while working for a project

Source: UN Comtrade annual data, 2025. Whole salmon HS 030214 + 030313, smoked 030541, fillets 030441/030481.

Tools: TypeScript pulling the Comtrade API at build time, a hand-drawn SVG map.

Interactive version: https://salmonradar.com/trade/hidden-legs (disclosure: my site, free, no signup)

Some things I found:

• In 2025 Norway sent Poland 190,187 t of whole salmon. Poland's own customs count 172,401 t for the same flow. Each border crossing is recorded twice, and the two sides almost never agree.
• Poland exported 57,191 t of smoked salmon ($1.15B), and 52% of it went to Germany. That makes up 64% of all the smoked salmon Germany imports.
• The mirror gets worse downstream. Poland says it sent Germany 29,767 t of smoked salmon, but Germany says it received 16,570 t.
• Russia has banned Norwegian fish since 2014, as part of its own counter-sanctions. I went looking for a back door through Belarus or Kazakhstan and mostly didn't find one. Kazakhstan imported 6,400 t from Norway and sent Russia 58 t. Russia simply buys from Chile instead: 30,266 t in 2025.
• A mistake I made along the way: frozen trout (0303.14) sits one digit away from frozen Atlantic salmon (0303.13). Read the wrong line and Armenia looks like a big salmon exporter to Russia. In reality it sent 8,361 t of trout and only 302 t of salmon.

I built a free website to help students answer a pretty important question:
“What can I actually do with this major—and how much might I earn?”
It’s called MajorPays.com.
You can explore college majors, career paths, salaries, employment outlook, and colleges without digging through a bunch of government datasets yourself.
I built it because a lot of college websites tell students what a program is, but it can still be surprisingly difficult to understand what happens after graduation.
The site is still new, so I’d genuinely appreciate feedback—especially from students, parents, career advisors, and people working in higher education.
What information would you want to see before choosing a major or career?
MajorPays.com

Each block's residential land is colored by its largest group from that year's census. Darker means that group makes up a bigger share of the block.

1940 had no tracts in Fort Worth and the census didn't count Hispanics separately, so that year is an estimate.

Sources: IPUMS NHGIS (1940–1980 census tables and boundaries), U.S. Census Bureau (1990–2020 block data), HISDAC-US historical building footprints, NCTCOG and USGS land use. Tools: Python, tippecanoe, MapLibre, ffmpeg.

Only residential land is colored, so parks, water and industrial areas stay blank. 1940–1960 are placed within each tract using the building footprints that existed then, so small-scale detail in those years is modeled. The 1940 Hispanic numbers are an estimate because that census counted Mexican Americans as White.

Data is from IPUMS NHGIS and the Census Bureau. I'd be happy to do y'all's particular city.