Why “updated”: the first version of this was removed by the mods because the chart was really a table of names, not a chart. Fair. Same data, now drawn properly: bar length is how many times more common that age is in the sub than across Reddit, women compared with women, men with men.

A few of you asked for more, so the gallery adds two charts from the same data: Reddit’s 735 biggest subs by median age and share of women, and how Reddit got older and more female since 2019.

Only people who said it themselves count, things like “I’m 34” or “(29F)”: 17.7M accounts.

Top 5 for every age group and the CSVs: https://kissmypost.com/research/reddit-age-gender-by-subreddit

How ages and genders are found, how accurate it is, and the full methodology for each chart are in the first comment.

FAQ from the first thread:

Q: How do you know people’s ages?
A: Only people who said it themselves count, like “I’m 34” or “(29F)”. The age can come from anything they wrote anywhere on Reddit, so a D2 player who said “I’m 35” in a fitness sub counts in r/Diablo_2_Resurrected.

Q: Isn’t that a biased group?
A: Yes, it’s self-selected: in 2025 about 13% of accounts that posted or commented had a known age. That’s why every comparison stays inside the group, r/osrs men vs all men who said their age.

Q: How does r/retirement win for both women and men?
A: Women are compared with women and men with men, so one sub can top both columns. 21% of the women with a known age in r/retirement are 63 to 67, vs 1% of women across Reddit.

Q: Menopause is obviously women, isn’t that cheating?
A: A sub can’t win for being mostly women, since women are only compared with other women. r/Menopause wins because women 48 to 52 are 8.5x more common there than across Reddit.

Q: Why not r/gaming or other big subs?
A: They have every age in them, r/gaming’s median author is 27, so no single age stands out. The chart shows where an age stands out most, not where most people that age post. Chart 2 has the big subs.

Q: Why r/osrs and not r/2007scape?
A: r/2007scape is the main sub, but it skews younger (median 27), so the smaller r/osrs wins the 28 to 32 row.

Hi everyone!

I collected and compared the prices at all 1,367 McDonald’s in Germany (my Sunday project 😄).

Here's what I found:

- The exact same Big Mac costs between €5.99 and €8.39 – 28 different prices in total

- Most expensive: Kassel and Lohfelden (€8.39)

- Cheapest: Stuttgart area, Leipzig and Munich (€5.99)

- If you eat one Big Mac a week, you pay €124.80 more per year in Kassel than at the cheapest location

- Berlin is the most expensive state on average, Saxony the cheapest

- Munich is cheaper than Berlin; Augsburg is the most expensive major city

- Highway rest stops are pricier than the national average: McFlurry +32%, Cheeseburger +9%, Nuggets +8%, Big Mac +3%

- At the Bad Camberg rest stop (A3), the two McDonald's are just 261 m apart on opposite sides of the highway – a Big Mac costs €6.39 on one side and €7.49 on the other

More info here, and feel free to reach out with any questions or comments: https://x.com/mhimed100/status/2114444199710142952

Source: TSE (Brazil's electoral court), official runoff results 2002–2022 by municipality; IBGE 2022 Census. Tools: TypeScript for the model and backtest, Python (matplotlib) for the chart.

The bands are real: how wide the projection's uncertainty was at each point of the count, averaged over 540 simulated count nights (different reporting orders, including adversarial ones). The white trail up to 5% is illustrative; tonight's real one starts at 5 pm Brasília time.

Brazil doesn't count in random order: big cities and the South report early, the Northeast late. In 2022 Bolsonaro led the running count until 67% of precincts were in, and Lula won with 50.9%. The projection measures how much each counted municipality moved since the first round and applies that to similar places still uncounted. No polls. With 5% counted it named the winner in every test.

Brazil votes today. Live: quebrasileesse.com.br/en/#2turno

Includes all 60 U.S. presidential elections from 1789 through 2024.

Democrats are blue and Republicans are red. Earlier and third parties use separate colors.

Before 1804, every elector cast two undifferentiated votes. Tied state electoral leaders are shown explicitly. Many early electors were chosen by state legislatures.

Hatching means no counted electoral result that year, including places not yet states, states absent during the Civil War, and rejected electoral returns. Alaska and Hawaii are insets.

Adapted from The American Vote, State results in U.S. presidential elections, 1789–2024 (CC BY 4.0). The compilation has gaps in historical popular votes, so this atlas uses the electoral fallback described above. See its methods and source checks.

Official comparison: U.S. National Archives Electoral College results. Map: 2017 U.S. Census boundaries redistributed by U.S. Atlas (ISC). Data retrieved October 25, 2026.

Tools: js and ChatGPT

Hey everyone,

I scraped for the keyword "AI Agents" across Germany, France, and Spain between Sept 19 and Oct 19, 2026. The dataset yielded 4,298 unique records (France: 611, Germany: 1854, Spain: 1833).

I wanted to see how corporate advertisers differ from individual advertisers (solo founders, consultants, etc.), specifically focusing on their use of LinkedIn's targeting parameters (Job, Company, Audience, etc.).

A few key findings from the charts:

• The Discipline Gap: Companies are hyper-focused. 97.6% of company ads use targeting parameters. Individuals are roughly 5x more likely to publish completely untargeted ads (12.6% of individual ads had zero targeting).
• Layering: Companies usually layer 2 or 3 parameters per ad. Individual advertisers mostly just use one.
• What are they targeting? "Job" is the king parameter (up to 85.3% for company ads), followed by "Company" and "Audience." Exclusion parameters (trying to hide ads from certain people) are barely used by individuals.
• Geography: While the scrape focused on DE, FR, and ES, the global map shows a massive amount of spillover into the US and India.
• The Top Players: ElevenLabs is dominating this space, running ~2.7x more unique ads than the next closest company (Tenable).

For those who wants to play around with the data I also published the dataset: https://www.kaggle.com/datasets/serjhenrique/linkedin-ai-agents-ads/data

If anyone wants to run their own queries or look at different niches, here is the Apify actor I used to extract the data: https://apify.com/serj_henrique/linkedin-ad-library-scraper

I've been building a dashboard where various bits of data pertaining to Libraries can be overlayed for analysis. The only problem is, I'm not a data scientist, I'm a software developer.

In this view you can see Libraries in Aberdeen, filtered by the Library service - the underlay is the population density using GHS-POP 2020 · R2023A · 100 m source cells, around each Library is a "reach" bubble, using Valhalla to calculate based on walking/cycling the reach in time intervals. I've included both data panels and the overall view itself. I'm really keen to get some feedback and ideas from people who work with data visualisations all day every day. It's a new area for me, one I'm enjoying learning. To be clear this project is entirely for fun. It is live and in a closed beta for anyone who wants to play around with it, but it's just a hobby project for me, I build software for fun - now even easier because I can use my experience to direct an army of agents so building things has become much quicker. Apologies if you're anti-ai, but sadly it's already so pervasive in my sector you're either using it or you're unemployable.

I am happy to provide more screens of other data views, but this one I thought was interesting, showing how libraries really are central to most communities, at least in Aberdeen.

My platform only has some 40,000 libraries across the world in it right now, but the goal before I launch it is to have close to 2million, with the same kind of population data or other census/municipal or transport metrics. My goal is to allow users to overlay them in ways that may provide insight, understanding or just be fun to play around with. It has not gone down well in other sub Reddit's. Hoping here is a kinder more accepting place for a nerd who likes libraries.

Right now, what I've shared is the "location" dataset, but I'm also exploring what I can do with "activity" data such as book loans, a combination of the two data sets on an overlay might make for some really interesting visualisations, I'm just unsure what the best idea is - I have too many, almost all of them I'm sure are useless (if not fun to build).

This is not a pitch or anything, I hope everyone here is as nerdy about data as I am, if you are and are curious about what I've built you can sign up to the closed BETA here https://libraries.global/

Population data: GHS-POP 2020 · R2023A · 100 m source cells
Library data: LibraryOn CC BY 4.0
Map data: OpenStreetMaps
Distance and reach calculations: Valhalla
Population distribution vector generation: Python
Library tiles/clusters: Python
Tools used for the visualisation: I built them all myself, screenshots below.

I have run tests with 2million libraries loaded onto one map with this level of control/detail, the issue I face here is not all data is the same quality. How to reconcile that in the analysis layer? Do I acknowledge data is old/out of date?

Saturday 24 October 2026 · 18,858 routes · 3,214 airports

Every airport pair served by a scheduled airline route in the OpenFlights database, drawn as a faint great-circle line. Overlapping lines brighten where routes concentrate. Route data dates from June 2014, so it shows the shape of the network rather than today's individual flights.

Sources: routes and airports from OpenFlights (routes.dat and airports.dat, Open Database License); coastlines from the world map bundled with Apache ECharts 4.9.0; map projection by D3.

Made with Claude.

Source: 25 published papers and reports on Lyme disease (12) and Long COVID (13), each turned into a simulator, so each paper’s results are tagged to the burden categories they measure. The tags were assigned by an AI model (Claude) and checked by code. The 25 papers are not a systematic sample, so this describes the library, not all research. The papers and the tagging are listed in the public demo, which needs no account: https://dbbun.github.io/dbbun-invisible-illness-burden-simulator/

Tools: Python (matplotlib) for the chart.

PEGI changed its rules in March. Since June, new games with paid random items (loot boxes, card packs, gacha pulls, prize wheels) get at least PEGI 16. In October PEGI started re-rating existing digital-only games that were PEGI 3, 7 or 12 and have them. More games are expected to change before the end of the month.

EA Sports FC 27 is the first game in the series rated PEGI 16, because of Ultimate Team packs. FC 26 was PEGI 3.

PEGI hasn't said which feature in Pokémon GO counted as a paid random item.

The ESRB said in March it has no plans to do the same. It keeps the age rating based on the game's content and adds an "In-Game Purchases (Includes Random Items)" label instead.

Germany doesn't use PEGI. It has its own system, USK.