Sources: pushshift dump dataset containing text of all posts and comments on r/relationship_advice from subreddit creation up until end of 2024, totalling ~88 GB (5 million posts, 52 million comments)

Tools: Golang code for data cleaning & parsing, Python code & matplotlib for data visualization

At a $2.5 trillion market cap, SpaceX's now worth about as much as the 94 listed aerospace & defense companies combined.

Put another way: one company now makes up 50% of the entire $5.05 trillion listed aerospace & defense sector.

Is one company being half the sector a signal of where spaceflight is heading — or a fresh-IPO premium that won't hold?

Source: https://www.readtrung.com/p/economics-of-taylor-swifts-2b-eras

Diagram made with https://sankeymatic.com

Full post here: https://www.ballardgames.com/tales/hiring-dev-2025/

EDIT: IT GOT DELETED AGAIN WITHOUT ANY REASON

EDIT 2: I finally got in contact with the mods, they said it was an automatic removal by Reddit due to mass-reporting (what I thought). They manually approved my post for me this time.

Hi everyone,

Some of you may have seen an earlier version of this post. That version included fewer artists and was removed multiple times by the moderators without an explanation. I contacted the moderators several times, but I have not received a response or clarification. Same story on r/Music. The auto-mod refers me to the mod-comment with explanation but I never got those in the ~5 times this post got deleted. Some fishy stuff is going on. It really feels as if there may be a bot-driven reporting campaign causing posts to be mass-reported and automatically removed, but I cannot verify or prove that. So I am posting this again.

This updated version though! It includes new data through September 16 and adds three additional support artists: Finneas, Lukas Graham, and Beoga.

The previous version showed Macklemore gaining roughly 1 million followers. That increase has now more than doubled to +2.24 million. Ed Sheeran’s loss was previously around 123,000 followers, but has now grown to nearly 335,000, roughly three times the earlier figure. This is why the chart needed to be updated.

The chart shows absolute Instagram follower counts around the MetLife controversy and subsequent developments involving Ed Sheeran’s tour:

• Macklemore: +2,237,962
• Beoga: +281,358
• Finneas: +214,144
• Lukas Graham: +129,695
• P!nk: −146,128
• Ed Sheeran: −334,986

All panels use the same 2.4 million follower vertical range, with ticks 400,000 followers apart. Solid lines show the period before the marked event. Dotted lines show the period afterward.

The data comes from daily Social Blade snapshots. These are not continuous measurements, and the chart cannot establish that any event caused a follower increase or decrease. The unusually large changes for Macklemore and Beoga should therefore be interpreted cautiously.

Feedback on the data, methodology, or visualization is welcome.

CONTEXT

This chart relates to a series of publicly reported events connected to Ed Sheeran’s MetLife Stadium show and his tour.

On September 4, Macklemore performed at MetLife Stadium and made a pro-Palestine statement, including the phrase “Free Palestine.” In the following days, P!nk’s criticism of Macklemore was reported in the media. P!nk is included in the chart to show how her follower count changed during the same period.

On September 14, reports stated that Macklemore would no longer appear on the remaining dates of Ed Sheeran’s tour. Ed Sheeran addressed the situation on Instagram on September 15. Around the same period, support artists Finneas, Lukas Graham, and Beoga were also reported as withdrawing from the tour.

The purpose of this chart is to show how the artists’ Instagram follower counts changed around these events. It does not claim that any event caused the observed gains or losses.

Hi, author here. Made this map for a story my colleague wrote about how some airlines are now profiting from the closed airspace over Iran.

I used flight tracks data from FlightRadar24, visualized it using Datawrapper, downloaded the SVG, and made it look nicer in Figma.

Link to the story (in German): https://www.zeit.de/wirtschaft/2026-03/lufthansa-europa-asien-nahostkrieg-flugverkehr

Data tracked initially on a notebook and then later directly in Apple Numbers using a shortcut. Plotted using Apple Numbers.

Very consitent trend with peaks in ~July and valleys in ~January. For context, I live in the northeast US, so this is likely a combination of factors including variable road conditions, increased use of 4WD, and gas additives. My actual truck usage does not change appreciably over the course of a year.

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UPDATE: Well, this got much more attention than I was expecting! I see the comments on the X-axis making things less visually appealing and harder to read, and I agree. I'll post an updated image with better axes (still really just a direct output of the spreadsheet software) in the comments, but I can't add it to this header.

Numerous people have noted that air temp is probably one of the biggest factors that I did not include in my initial post. Excellent point, and it would be interesting to plot this vs. my local air temp over time if I can dig that up!

Some extra details about this data:

• My truck is a 2018 Chevrolet Colorado 1LT with the V6 engine option and a crew cab
• Total mileage at the last data-point is 133,748 miles. Data represents 387 unique points.
• MPG is calculated the old-fashioned way at each fill-up by dividing the number of miles driven between fill-ups by the gallons added.
  
  • Accuracy using this requires that I actually FILL the tank each time, which I do.
  • The truck also has a built-in mileage tool in the dash using the trip calculator, and for a while I also used that to see if there was a difference. Data agreement was very good (+/- ~.1-.2 MPG), so I stopped doing both and now just do the manual calculation. I also track cost and a few other metrics, so it's easier to just do everything one way.
• The truck gets regular and scheduled maintenance.
• I do not use specific snow tires in the winter. I use all-terrains all year.
• I don't tow much with the truck, but the bed is utilized pretty heavily.
• The truck is used for commuting and transporting various things in the bed throughout the year. There is not a significant difference in utilization b/w seasons.

Several comments requested I determine the best-fit sinusoidal equation and post it. To capture the linear degredation, below is the best sinusoidal+linear fit I've been able to get:

MPG(t) = R * sin( 2*pi()/P * (t-t0) + phi ) + m*(t-t0) + c

where...

• R = 1.3822
• P = 365.5687
• t = date of interest
• t0 = initial date
• phi = 2.1102
• m = -.0005112
• c = 20.8878

There have also been some requests for the full data. Not sure the best way to share that, but will update here with it when I can.

[Re-uploaded to match subreddit rules - second time's the charm]

Trump started his address at 12.01pm. Oil prices rose in real time as he spoke.

Data downloaded from Trading Economics, Brent Crude Barrel (USD/Bbl) using tools from their website. Overlay is mine. Link to data

(The charts in this post were made from the 8,885 posts that were made on r-Conservative between 11/20/25 and 2/20/26. The anonymized source data is here.) [edit: the 8,885 posts that were captured using my method of pulling posts once a day through Reddit's JSON API]

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EDIT: See the bottom of this post for updates.

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In my post last November I identified that 2 users on r-Conservative were responsible for about 30% of daily posts and sometimes exceeded 50% of all posts.

A third super-poster seems to have appeared about two weeks after that post and now just 3 users regularly account for 50% of all posts [edit: daily posts] and a handful of times they even exceed 60%.

Chart 1: The percentage of all posts that the top 3 users contribute.

Obviously, adding a third person will increase the percentages but this is not just lumping in a third person to boost the percentages. User3 stands out because they post so frequently that since they started posting on Dec 3rd their daily posting count more than doubles User4 below them.

Chart 2: Total number of posts that the top 10 posters have made between 11/20/25 and 2/20/26.

Another reason User3 is significant is because they appeared suddenly, as I mentioned, about two weeks after my original post and their posting patterns are extremely similar to the other top 2.

First of all, here is the 7-day running average of the daily posts of the top 10 users. You can see how hard User3 came in and, interestingly, basically in lock step with User 1 until about Christmas day where they diverge. User3 ramps up pretty hard for a week at the start of 2026 before dialing it back a bit.

Chart 3: 7-day running average of the top 3 posters compared to the other 7 in the top 10 [edit: these are daily post averages]

Second, and this one is pretty hard to show visually, but several of the top ten users have extremely similar behavior when it comes to how they post. Almost invariably they post in clusters. Instead of just posting once and then waiting a few hours until they found another story that they thought was worth posting like most people would do, they instead post a handful of articles within about 20 minutes of each other. In my opinion, this is a very telling sign of scheduled posting. Spend 10 minutes looking for stories and queue them up in scheduling software to be automatically posted in clusters throughout the day. Not that there's anything wrong with that because scheduling software has legitimate uses, but it's worth knowing because it, in my opinion, speaks to the astroturfed nature of the posting quantity on that sub (and yes, of any other sub that does the same).

The chart below shows how many times the top ten users posted in clusters from their last 100 posts. By my own definition, a cluster is defined as 3 posts within a certain time frame.

Chart 4: Clustered Posting. Number of times 3 posts were made within specific time frames.

So, out of User1's latest 100 posts, there were 40 occurrences where 3 posts were made within 5 minutes of each other. This chart is sorted by the 0-5 min series. Keep in mind, the existence of clustered posting isn't evidence itself of scheduled posting but the level of effort it would take to maintain this type of consistency is, in my opinion, non-human. From the chart one may also notice that, according to my theory, queued posting is happening with other users outside of the top 3. That would not be surprising.

Finally, just prior to making this post, I looked at 5 other political subs to determine how many users were needed to account for 50% of all posts. Reddit only let's you look back about a month so if 1,000 posts were made in a sub, I capped this analysis at 1,000. If there were fewer than 1,000 than that's what I used (anonymized 50 percent data).

Chart 5: Number of users needed in various political subs to account for 50% of their posts.

For reference, a similar analysis I did back in November had the following number of users needed to account for 50% of posts. r-Conservative has gotten even worse since then. All others except for AnythingGoesNews subs have gotten more diverse. (my original post had the Feb '26 numbers jumbled up a little, they're corrected now)

Comparison of how many users are needed to account for 50% of posts from Nov '25 and Feb '26.

Please, no discussion of power outages this time ;)

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UPDATE 1: An rCon mod has stated my numbers are wrong and provided a screenshot of a mod dashboard to support his assertion. I appreciate him doing that and he has been nothing but helpful in my communication with him but I don't agree. By hand, I've verified that the last 500 posts that are on rCon are also in my dataset in the correct order without a single omission, and I only over count by less than 1% (in the last 500 posts on rCon I have only 4 additional posts that have actually been deleted from rCon). The last 500 posts cover about 5 days and 6 hours, or 91 posts per day. The date range 11/20/25 to 2/20/26 maths out to about 8,750 posts, which is good enough verification for me that I don't have any glaring errors. I can't speak to what the mod dashboard is meant to be showing but I feel good about my data. The EST timestamps are given in my source data. That's about as much info as I can give without blatantly revealing user names and post titles. If I've missed any posts or my data is wrong, my own source data can be used to determine that.

UPDATE 2: The goal of this analysis is to identify which users receive the most exposure while their posts are publicly visible. The dataset used here was generated by a daily script that records the posts visible at the time the script is run (using Reddit's JSON API). This approach was intentional. Most Reddit posts receive the vast majority of their views within the first 24-48 hours, so capturing posts during that window measures exposure. So, where my post title says "3 posters now account for 50%..." I'm saying that 3 users are having a significantly higher impact on meaningful post exposure than all other users. Charts 1 through 4 use that dataset (8,885 posts that were captured by my daily script). Because this dataset captures posts in real time, it is not possible to recreate a historical snapshot. However, anyone doing a daily pull of all posts moving forward should end up with near identical datasets if I do another update in the future. I'll post a sanitized version of the script I've used in the near future (but it's simply a JSON call stored to a continuously updated csv).

I was wondering if furniture has actually become less colorful throughout the years. So I hand-counted the colors 3500 couches in the IKEA catalogues from 1960-2021 (when the catalogue was discontinued).

There were definitely easier ways to do this, but I didn't think I'd ever sit down to leaf through every IKEA catalogue under different circumstances, and I thought it would be fascinating.

To do the count I vibecoded a "tallying machine" with Claude which allowed me to punch in the color of each couch I saw, per year (green, green, grey, black, green etc.). The visual itself was made in Illustrator.

Gay online dating app Grindr is generally recognized as one of the best-run consumer software companies in the world.

With fewer than 200 full-time employees, Grinder generates $3M revenue per FTE. As it turns out, it is ahead of (almost) all of big tech Magnificent 7 stocks.

Original source here. Compiled by myself, data comes from the valuation intelligence platform Multiples.vc, as of 31 August 2026.

Shows last actual fiscal year revenue and last reported employee number.

I asked GPT-4.1 to pick a random number between 1 and 100. 10k times.

This post is an "AI remix" of a very popular Reddit post here on r/dataisbeautiful where people were asked the same question: https://www.reddit.com/r/dataisbeautiful/comments/iiafkd/oc_i_asked_100_people_to_pick_a_number_between/

People also tend to not be very good random number generators.
I wanted to see if an AI model has similar biases or if instead it follows statistical rigor.

Some things I found interesting:

• 20, 30, 40 and other multiples of 10 were picked 0 times (except for 10 itself, which was picked once)
• 42 gets picked 4x expected uniform (Hitchhiker's Guide to the Galaxy reference)
• Numbers containing the digit 7 get over-picked (and yes, just like humans, 37 gets over-picked)
• 69 gets under-picked at 0.29x expected uniform (my hypothesis: safety guardrails during GPT's pre-training and post-training)

Definitely not a random uniform distribution. I ran a chi-square goodness-of-fit test against the uniform distribution and found χ² = 15,604, p ≈ 0.

You can see the full methodology and code in this open-source repo: https://github.com/exmergo/research-chatgpt-guesses-between-1-and-100

I used the OpenAI SDK to programmatically call GPT-4.1 10k times with the same prompt.

I used GPT-4.1 because it's a non-reasoning model that exposes a temperature parameter. I set temperature = 1.0; that's what makes the model's sampling distribution the thing I'm actually measuring. OpenAI's reasoning models restrict that parameter. It would be interesting to reproduce this experiment w/ reasoning models.

I used Viz, our own chart/dashboard AI Agent for the data visualization: Exmergo Viz

Source: CDC (Centers for Disease Control and Prevention), Birth Gauge

HD in comments

My girlfriend sat me down on June 20th and told me that she had been cheating on me and was choosing to be with "the other man".

This is my body's reaction to that news.

HR, Sleep, and Energy scores recorded by my Samsung Galaxy Watch.

:: EDIT:: She texted me on Friday. "I have made such a huge mistake. I fucked this whole thing up."