A few days ago I posted which subreddits each age group is most overrepresented in (https://www.reddit.com/r/dataisbeautiful/comments/1xf6gt3/). The pushback I got most: people on Reddit lie about their age, self-reported data can't be verified, it's just noise.

Fair point. So instead of arguing, I checked.

Chart 1: do people age on schedule? If people made their ages up, there'd be no pattern over time. So I took everyone who stated their age in two posts or comments at least a year apart, 2.3 million people, and checked whether their age went up by the time that actually passed. 49% aged exactly on schedule and 68% were within a year. The outlined bars are the same people with their second age swapped for a random stranger's, which is what noise looks like: 3.5% and 7%. Gender too: 5.4M people stated it twice and 96% said the same thing both times.

First, what isn't a lie. Plenty of numbers look like ages and aren't: "I'm 99 farming" is a RuneScape skill level, "91F" is an army job code, "Female, 26, nurse" is someone guessing about another person. I filter that out wherever I can spot it, and nobody's age comes from one comment anyway: it's the middle of everything that person has said, so a stray number gets outvoted.

Chart 2: so where do everyday Redditors lie? I flagged every age claim 10+ years off from what that same person usually says, then read 20 random flagged posts in each of the 100 biggest everyday subreddits. Three AI reviewers per post, and a lie only counts when all three agree. Result: about 1 in 270 age claims is a lie. Even the "worst" one, r/indiasocial, is around 2%. When people do it, they mostly add years: 38 in the post, 26 everywhere else. Think a r/tifu story told by a 50-year-old from an account that's 27 in every other post, or 30-somethings saying they're 15 in r/teenagersnew.

Chart 3: where it spikes. Dating subs are even more honest than average (0.15%). Lying only takes off in a few adult subs: 8.4% of age claims in cheating and cuckold subs, 3.5% in "mature women" ones. 91% of those posts are written as a woman and 65% come with a Snapchat handle. Promo accounts posting as a 45-year-old woman, who are 28 in their other posts.

So: most people just say how old they are.

Full write-up with five more checks, where this method is weaker, and the CSVs: https://kissmypost.com/research/reddit-self-reported-age-gender-accuracy

Source and tools in the first comment.

Interactive live version with all updates

Sources:

• EPA ratings
• EIA prices: gas and diesel (week of Oct 19), home electricity (August 2026 residential, 18.16¢ a kWh)
• The 25 best-selling models from January to September 2026, from the carmakers' own sales reports, plus the next 5 best-selling EVs and the Prius for comparison. Each row is a version, not a model, so models sold as gas, hybrid, plug-in hybrid or diesel get a row for each. Plug-in hybrids split their miles between battery and gas by EPA's estimate for an owner who charges every night.

Tools: Kadoa for the data, Python, HTML and SVG for the chart. Claude did the car outlines from each model's published length, height and wheelbase.

EDIT:

• Prices vary a lot by state, so I added a state picker to the live version.
• The cities are only a ruler for distance/for scale.
• Added plug-in hybrids and the Prius. At 53 mpg the Prius goes 1,237 miles on $100, just behind the slowest EV (Rivian R1S, 1,278).
• Added a toggle for fast charging vs home charging
• metric units added

This is part of my overall analysis to find out whether companies that talk more about AI tend to perform better than their quieter peers in the stock market. Full interactive piece, with the rest of the analysis: https://milesmeters.com/form-10-k-ai/

Source: 2,438 annual reports (10-Ks) filed with the SEC - every S&P 500 company's last report before ChatGPT launched (Nov 30, 2022) plus every report since. I counted AI terms per 10,000 words and labeled each mention hopeful (selling, using, or investing in AI) or worried (risks like competition, regulation, and security).

This had some counterintuitive findings. I expected cities like NY and SF to top the list. The conventional wisdom is that rent should be around 30% of one's gross (pre-tax) income.

Analysis: In 2024 the typical renter household in the Miami metro spent 38.6% of its income on rent and utilities, the most of the 25 US metros with the most renters (next: Orlando 35.8%, Tampa 35.6%). Nationally the figure barely moved, 31.6% in 2010 vs 30.9% in 2024, because median gross rent rose 74% ($855 to $1,487 a month) while median renter household income rose 78% ($30,671 to $54,446 a year). Rent took a bigger share of income in 9 of the 25 metros, most in Las Vegas (+3.7 points; rent +76% vs renter income +52%) and Houston (+3.1; +74% vs +55%), and a smaller share in 15, most in Detroit (−2.2) and Chicago (−2.1). Part of why renter incomes kept pace may be who rents: Harvard's Joint Center for Housing Studies (America's Rental Housing reports) has noted growth in higher-income renter households since 2010. This data can't separate that shift from raises for people who were already renting.

Caveats: the figure is a median over renter households paying cash rent, so half pay more and half less; it doesn't show how many renters are severely burdened. Each value is a survey estimate with a margin of error of up to about a point for a single metro (Miami ±0.7), so small changes may be noise. Dollar figures are current dollars, not adjusted for inflation. 2024 is the latest 1-year ACS release.

Sources: [US Census Bureau, American Community Survey 1-year estimates](census.gov/programs-surveys/acs), 2010 and 2024: tables B25071 (median gross rent as a percentage of household income), B25064 (median gross rent), B25119 (median household income by tenure) and B25003 (renter households), read from the public ACS summary files at www2.census.gov. The 25 metros are those with the most renter households in 2024 (Puerto Rico excluded), named by their first city. Hollow dot = 2010, filled dot = 2024; red = rent took a bigger share of income than in 2010.

Tool: Artio for the data pipeline and chart; the chart is SVG drawn in Python.

Link: https://www.artio.so/dataviz/pages/2026-10-26-rent-vs-income

Blue represents net gains for Bolsonaro while red represents net gains for Lula.

The data show a net shift of approximately 2.29 million votes in Lula's favor between the 2022 election and the 2026 estimates.

The movement is geographically uneven. São Paulo (+677,000), Rio de Janeiro (+644,000), and Pernambuco (+443,000) together account for approximately 77% of the nationwide net shift toward Lula.

Conversely, the largest shifts toward Bolsonaro are concentrated in the South and Center-West, particularly Rio Grande do Sul (−208,000), Goiás (−109,000), and Santa Catarina (−106,000).

Overall, the data suggest that the national shift toward Lula is driven primarily by gains in the Southeast, especially São Paulo and Rio de Janeiro, while the South and parts of the Center-West move in the opposite direction.

Notably, Minas Gerais, the second most populous state in the country showed very little movement between these two elections demonstrating that a large population does not guarantee a large difference in absolute raw vote totals.

Data Pipeline: Apple Watch -> Marrow Health -> InfluxDB -> Visualization

Random insights:

• 3.3 Million steps, max of 29,649 in a day
• Spent 29% of the year asleep
• 21% of my sleeps started after 2 am 🥀
• Burned the most calories between 6-7pm
• Sound is 7-8 dB higher on Friday/Saturday afternoons than other days
• 167 hours over 85 dB, the level where long exposure damages hearing 😔

The python code to make the visualization is open source if you’d like to see your own rings. Marrow Health is developed by me, but you can use your own exporter as well.

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.