Some recent fun data from my government auction platform: the distribution of winning bids across >100k listings from the last 6 months or so. X axis is a log scale of bidding price, and Y axis is the number of listings sold at that price.

People love to bid (and win!) things for $10 - you can get a lot of stuff for that much, like a classroom full of desks and furniture. The expensive part is transporting it all.

The median listing is a 1997 Ford Ranger that went for $210 - vehicles are by far the most popular thing that people bid on, at a wide range of prices and conditions.

There's a link below to the interactive chart and a bit more context on the data - you can filter by category to see how the curve changes for things like vehicles or Real Estate.

Source: The Govauctions.app auction database, including listings from GSA, GovDeals, PublicSurplus and more.

Tools: SVG and Javascript

Interactive version of the chart, where you can sort by category and see listings at a given bid range: https://govauctions.app/research/what-the-government-sells-for

Last year, 4 out of every 10 grown ups in America didn't finish a single book. But a small group, about 1 in 10 people, read 25 or more books. That small group read so much that they probably account for about half of all the books read in the whole country. So basically, a few people are doing almost all the reading, while most people are barely reading at all.

EDIT - This includes audio books. The survey question was "Have you read or listened to ..."?

Also a graph for another day - most people who did read, read Mystery/Crime.

posted months ago my crying stats from the first quarter of the year and i mentioned in that that i was tracking the data because i was planning on getting medicated over the summer and someone mentioned they wanted a before and after so here that is lol. no need to worry any longer, i am fixed!

source: my life

tools for visualization: canva (slides 1-2) + i tracked the data in my calendar app (slides 3-whatever, since people seemed to enjoy seeing my reasons last time)

Own Work. Sources at the Bottom.

Own Work. Sources: *Norris et al. (2018), PubMed Central. (https://pubmed.ncbi.nlm.nih.gov/30537949/) for Colombia/Mexico/Peru* Borda et al. (2024), ScienceDirect. (https://www.sciencedirect.com/science/article/pii/S2666979X24003215) for Chile/Peru/Brazil/Uruguay/Mexico/Guatemala/Costa Rica/Dom Rep *Ruiz-Linares et al. (2014), PubMed Central. (https://pmc.ncbi.nlm.nih.gov/articles/PMC4177621/) for Colombia/Mexico/Peru/Brazil *Horimoto et al. (2021), KI Reports (https://www.kireports.org/article/S2468-0249(21)00593-3/fulltext) for central america *Homburger et at. (2015), PubMed Central (https://pubmed.ncbi.nlm.nih.gov/26636962/) for South America *Moreno-Estrada et al. (2013), PubMed. (https://pubmed.ncbi.nlm.nih.gov/24244192/) for The Caribbean. *Johnson et al. (2011), PubMed (https://pmc.ncbi.nlm.nih.gov/articles/PMC3240599/) for Mexico *Suarez-Curtz. (2010), ResearchGate (https://www.researchgate.net/publication/51563335_Pharmacogenetics_in_the_Brazilian_Population) for Brazil *Eyheramendy et at. (2015), Nature Communications (https://www.nature.com/articles/ncomms7472) for Chile *Avena et al. (2012) ResearchGate (https://www.nature.com/articles/ncomms7472) for Argentina *Asgari et al. (2020), ResearchGate. (https://www.nature.com/articles/ncomms7472) for Peru *Ibarra et al. (2014), Plos Journals (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087202) for Colombia *Söchtig et al. (2015), ResearchGate (https://www.researchgate.net/publication/273703459_Genomic_insights_on_the_ethno-history_of_the_Maya_and_the_'Ladinos'_from_Guatemala) for Guatemala *Castro-Pérez et al. (2016), ISPUB (https://ispub.com/IJBA/9/1/44045) for Panama

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.

Public champion tier lists, all 11 rank filters x 5 roles, patch 16.17.1, snapshotted 2026-08-29. 2,765 champion/role/rank entries.

Tools: I built a lobby advisor ( https://shouldidodge.lol ) and this came out of calibrating it. C# to collect the daily snapshot, Python + NumPy for the simulation, matplotlib for the chart.

Rank Position by Score, 1-32

Score Headline index. Draft z-score and undrafted z-score mixed 79 / 21, re-standardized, displayed as 50 + 16z. 50 = league average, higher is better

Draft The draft-only score, same scale

Surplus/Pick Career wAV above slot expectation per pick, standardized within draft class, restated in AV-equivalent units. +6.7 ≈ each pick returned 6.7 AV more than its slot should yield

Star% Share of picks finishing in the top 10% of their own draft class by career wAV

Bust% Share of rounds 1-3 picks finishing ≥0.5 class SD below slot expectation. Lower is better (shading inverted)

Best pick Highest career-wAV draft pick of the window (year and overall pick)

Top-Third% Share of picks finishing in the top 33% of their own draft class

Day 3 Surplus Same surplus measure, rounds 4-7 only the late-round scouting signal

All-Pro First-team All-Pro selections by the team's picks (PFR's field; includes specialists). Displayed, not scored

Pro Bowl Pro Bowl selections by the team's picks. Displayed, not scored

Kept% dr_av ÷ w_av share of the career value a team drafted that was produced for that team. Retention, not selection. Displayed, not scored

2026 picks Picks made in the April 2026 draft. Capital only no games played, so ungraded

[OC] What programming languages are programming languages actually built with? An interactive lineage graph

I’ve been building an interactive graph that maps implementation relationships between programming languages, compilers, and runtimes.

For example:

• Go’s compiler moved from C to Go around Go 1.5
• modern Rust uses staged self-hosting
• CPython is primarily implemented in C
• HotSpot is largely C++
• javac is implemented in Java
• V8 is primarily C++

I wanted to visualize something a little different from the usual “which language influenced which language?” trees.

The graph focuses on implementation lineage:

compiler_written_in
runtime_written_in
bootstrap_written_in

Each relationship has a time range, confidence score, and evidence source rather than treating every historical relationship as equally certain.

Interactive version:
https://www.languagelineage.org/explore

I’m expanding the dataset now. I’d especially like feedback on questionable relationships, missing historical transitions, or better primary sources.

I was consistently suicidal for most of my life, but I turned that around a few years ago. This chart shows my approximate, self-assessed mood data on a scale of 1 (Very Upset) to 5 (Very Calm). I defined these values in the Journey.cloud mobile app from May 2018, when I started recording the data, to August 2026. I assembled the chart in Excel and Photopea.com.

The trendline shows the annual average. I also preferred to show the noise because I found the variability interesting. Further, I chose calmness over happiness as the positive emotion, as I've learned being calm is more sustainable than trying to be happy all the time.

Below is more about what it took for me to change.

--

I'm proud to share that, after many years of self-loathing and self-hatred, I finally learned to love myself.

What does that mean, "love myself"? First, I don't hate myself! I appreciate what I do well instead of saying "Yeah, but…" to any self-affirmation. Second, I have self-confidence. I trust my capacity to create change or to accept what I can't. Third, I hold space for second-tries. This enables me to take initiative and express ideas with far less anxiety. Fourth, I feel more in control of my personality, my emotions, and my story.

I can now pursue what I value because of who I am, not in spite of it.

What helped me? I narrowed my list down to five major factors. (Disclaimer: What worked for me may not work for everyone.)

1. Positive reasons to live (i.e., something to hope for)
2. People who cared for me when I couldn't care for myself, e.g., my wife and many therapists
3. Mindfulness, Internal Family Systems Therapy (IFS), and Acceptance and Commitment Therapy (ACT)
4. Finding the right medications for me—and sticking with them
5. Most importantly, I had to commit to take care of myself. Sliding a noose around my neck in 2019 was the moment of clarity I needed to change.

I’ve been doing relatively well despite life’s ups and downs. That’s because I now have the coping skills to treat myself humanely. To be clear: what I went through can happen again. I’m not invulnerable to suicidal thinking, and I never will be. My only solution is to stay alert and meet it when it comes.

I could go on, but I'm in such a better place now. Putting this chart together helped me visualize that. All of my hard work to heal myself has been worth it. It can be for you, too.

I still have every phone I’ve ever owned, and wanted to explore my message history with my parents over my life.

The last decade was easy - iMessage database and WhatsApp exports, but beyond this got more difficult. I ended up building a rig to *quickly* photograph every message on my older phones, and used OCR to read the message content, sender, and date. Took a full day, which was probably longer than it would have taken to just manually transcribe them…

There were gaps in the timeline where storage had run out on older phones, or in one case where I’d performed a factory reset, so I used a model to predict message patterns during these gaps (validated using a train-test split).

This chart shows messages exchanged per month with each parent, shading is the model’s 95% confidence interval, and the phones along the bottom show what I was using at the time. It’s one of the many charts that came out of this project. I also looked at message length, response time, and sentiment.

I’ll leave a link to a GitHub repo with the project code in the comments.

I wanted to make something that could show which party has the most voters in each county in Maryland, but could also provide a little printout of county stats as you select them. So, if you click the dropdown under the map (located under some overview cards, defaults to "Statewide") it'll give you a summary based on the data it has on hand.

This is what it pastes if you select Baltimore City, as an example:

Baltimore City has 408,115 active registered voters. Democrats account for 298,445 voters (73.13%), Republicans account for 29,388 (7.20%), and 80,282 voters (19.67%) are registered with a third party or are unaffiliated. Democrats hold a registration edge of 269,057 voters, or 65.93 percentage points. Kamala Harris won Baltimore City in the 2024 presidential election with 195,109 votes, 84.55% of the 230,754 votes cast in the race.

Statewide rankings:

• Registered voters: 5th-largest electorate in Maryland

• Democratic share: 1st-highest among Maryland's 24 jurisdictions

• Republican share: 23rd-highest among Maryland's 24 jurisdictions

• Third-party/unaffiliated share: 23rd-highest among Maryland's 24 jurisdictions

• Registration margin: 2nd-most Democratic among Maryland's 24 jurisdictions

Neighbor comparison:

Baltimore City borders Anne Arundel County and Baltimore County. By voter registration, all of its Maryland neighbors have Democratic registration advantages. Among those neighbors, Baltimore County is closest to Baltimore City in Democratic-Republican registration balance. Kamala Harris won Baltimore City, Anne Arundel County, and Baltimore County in the 2024 presidential election.

Interactive live dashboard.

Key insights from August 2026 (69,365 new passenger cars):

• Pure Electric (BEV) share: 13.6% (9,450 units)

• Electrified total (BEV + PHEV): 27.6%

• Hybrid Gasoline (HEV): 43.4%

• Pure Gasoline: 18.2% | Diesel: 3.8% | Gas (LPG/CNG): 4.6%

Top EV models in August:

1. Kia EV3 (433 un.)

2. BYD Atto 2 (373 un.)

3. BYD Dolphin Surf (343 un.)

4. Leapmotor B10 (332 un.)

5. Citroën ë-C3 (302 un.)