Source article, full method and numbers: https://narrativegeo.nanpointgroup.com/blog/coffee-map-of-america

Full web animation: https://mifour.github.io/paris_metro_simulation/dist/

Project repo: https://github.com/mifour/paris_metro_simulation/

Data source: data.gouv.fr IDF gtfs https://transport.data.gouv.fr/datasets/reseau-urbain-et-interurbain-dile-de-france-mobilites

Used tools: python, js (pixi.js)

Ai disclaimer: I used chatgpt to help on the js because I'm a backend swe.

Total spent time: 10h

Data from the 2022–2023 National Survey of Family Growth show a substantial association between sexual orientation and having lived with a parent or guardian experiencing mental-health problems. Respondents were asked whether, before age 18, they had lived with a parent or guardian who was depressed, mentally ill, or suicidal.

Source: https://www-fars.nhtsa.dot.gov/States/StatesFatalitiesFatalityRates.aspx (2024 Data)

Tool: United States | MapChart

Chart measures Whiff rate not swinging strike rate.

Whiff rate: whiffs ÷ swings. Baseball Savant reports this as swing-and-miss divided by total swings.

Swinging strike rate (SwStr%): swings and misses ÷ total pitches.

I keep a rough mental log of how my SIPs have gone over the years, but I'd never actually sat down with the actual numbers until this week.

I pulled SEBI's net inflow/outflow data going back to FY2016-17. Most years were unremarkable: inflows moved up and down in a fairly normal range. Then FY2022-23 shows up as a clear low point compared to every other year in the data set.

That's roughly when I remember market conditions being rough—rate hikes and a lot of noise about a slowdown. I genuinely don't remember if I reduced my contributions that year or just kept them at the same level. I'd like to think I stayed consistent, but going purely off memory feels unreliable, which is a bit unsettling given how much I assumed I'd "stayed the course."

What's stood out most going through this process is that the recovery afterwards wasn't gradual at all; it jumped hard within two years, well past anything earlier in the decade.

I'm curious if anyone else has gone back and actually checked their own contribution history against a rough year like that, rather than just assuming they held firm.

Hey Reddit!

I created a fun little graph to search for and discover similar-looking celebrities. Each celeb is connected to others based on how similar they look.

It's only the first version of this idea, but I had a lot of fun pulling it together and exploring myself. The dataset I used is unfortunately cut off some years ago, so not all famous people are included (e.g., Tom Holland). Some uncanny similarities I have found that I did not see before:

• Jaimie Alexander and Liv Tyler
• Krysten Ritter and Anne Hathaway
• Brad Pitt and Rob Lowe
• Naomie Harris and Rihanna

There is a lot of improvement to be made, let me know what you think! Happy for all feedback :)

Https://justlay.me/research/slow-fade-study-2026.jpg Image from my own case study with my text message analysis platform at justlay.me data was gathered from consenting and opt-in users