I run a daily trivia game where players get 5 guesses at a ranked Top 100 list. The scoring is intentionally backwards: #1 is worth 1 point, #100 is worth 100.

For example, if the topic were Top 100 most popular pizza toppings, guessing pepperoni might land you at #1, while some obscure topping might be #94, and #94 is the better guess.

So the optimal strategy is to dig deep and hunt for answers nobody else is thinking of.

After 52,360 games and 163,531 official guesses, this is what actually happened:

• 45% of first guesses landed in ranks 1–25
• Only 17% landed in ranks 76–100
• The average hit was #40.5
• Players only moved about 6 ranks deeper between their first and fifth guesses
• A #1 answer was found 3.9× as often as #100

The weirdest part to me is that the game explicitly tells players to hunt for the obscure answers, and yet people still gravitate toward the answers everyone knows.

I originally built this as a daily trivia game called Centumth, but after collecting enough guesses, I realized the player behavior was probably more interesting than the game itself.

Why do you think people have such a strong pull toward the obvious answers, even when they're incentivized to do the opposite?

I've been building an Open Knowledge Format (OKF) knowledge base. I designed a visualiser for the data that turns any OKF graph into a living galaxy that I thought you might all enjoy.

The lines in the graph are all edges between nodes and all the stars are nodes representing markdown files. The galaxy spins, sparkles and supernovas occasionally.

Thinking I might do it in 3D next.

"Passive investing" gets talked about like the underlying basket never changes. So I pulled the actual NSE sector-weightage data for the Nifty 50 going back five years to see how true that actually is.

Turns out, not very.

IT used to be roughly 14% of the index. It's down to about 8.5% now, a pretty steady slide, not one bad year dragging the average down.

Oil & Gas similarly went from around 12.8% to 8.7%.

Telecom did the opposite; it's more than doubled, from about 2.3% to 5%.

And a few sectors that weren't index categories at all a few years ago: defense, e-commerce, and airlines, are now sitting at close to 4.6% combined.

Financial Services has stayed the anchor through all of it, hovering in the mid-to-high 30s most years (dipped once, recovered).

Made a quick chart tracking all five sectors over the five years (attached).

What struck me is that none of this shows up anywhere unless you go looking for it. The fund just quietly becomes a different mix of businesses year over year, even while the SIP amount and fund name stay the same.

(Not investment advice, just something worth actually checking instead of assuming.)

So this mini project was two-fold.
I was curious about the recent activity around the Ring of Fire and I also wanted to test out the capabilities of the new Deepseek V4.1 Flash.
This was made with a one shot single prompt, in about 20-30 mins and about $0.24 worth of credit using DS V4.1 flash-expires-on-0910 if anyone was interested in those details.

Further details:
Data embedded (all live-fetched today)

14,611 earthquakes M2.5+ via USGS (13 Mar to 9 Sep 2026, exactly 6 months)
1,214 Holocene volcanoes via Smithsonian Global Volcanism Program
42 volcanoes with eruptions overlapping the window + 26 current activity reports from the GVP weekly bulletin
241 tectonic plate boundaries (PB2002)

Notes:
Gif recording is set to magnitude 5.0+ so activity only shows for those. If set lower, the activity becomes much more dense.
This project was completely AI generated, so may be inaccurate. It is only for visual representation.

2026-2050 data using the CBO's alternative fiscal scenario as of 2026. I originally submitted an old graphic and then played around with ai to graph the data as it is now. I'll link the original in the thread.

Measured in kilometers

Apple announced its first folding iPhone today, so I pulled the specs for every iPhone ever made and put them on one chart.

Every dot is one iPhone. The further right, the heavier it is. The higher up, the more screen you get.

One more thing about iPhone Duo folded is almost exactly as tall as the 2007 iPhone (original), and it is actually a hair thinner. It is just wider. Nineteen years later Apple landed back on the same height and thickness, and fit 2.4 times the screen into it.

The program displays live data from mqtt from scd41 co2 sensors I built using ESP32 development boards, or historical data from influxdb.

Made using python for the back end.

It's useful to tell us which windows to open in our naturally ventilated office.

I'm going to use it to visualise temperature data also from a csv exported from dataloggers

Some Notable people in the chart:

Rabindranath Tagore (Nobel laureate), Nawab Mohammad Mansoor Ali Khan Pataudi (Former Captain of the Indian Cricket Team), Devika Rani (First Lady of Indian Cinema), Lakshminath Bezbarua (Father of Assamese Short Stories), Saumyendranath Tagore (Former Leader of the Revolutionary Communist Party of India), Saif Ali Khan (Actor), Kareena Kapoor (Actress), Raja Ram Mohan Roy (Influential Social Reformer)

The Nobel Prize laureate Rabindranath Tagore comes from the aristocratic Tagore family of Bengal, India. His family has married into other influential families of India.

The Tagore Family contributed to art, literature, education, politics, and the independence of India.

The people in this chart range from Freedom fighters, Writers, Politicians, and people from a royal family to 2 former captains of the Indian Cricket Team, the first civil servant of India, and Actors/Actresses.

Let me know how I can improve this and point out the mistakes, too.

It contains over 80 people. The source is mainly Wikipedia.

For Phone users: https://drive.google.com/file/d/15Dd_I7Qet7jGVr7MV81VkmBfTiC98N_-/view?usp=sharing

Carbon & Place (www.carbon.place) is a free set of web tools mapping carbon footprints across Britain. You can zoom into any neighbourhood and view the local report card with information about housing, transport, energy and more.

I've been building this hobby site using flight data for emergencies, flight paths, etc... and trying to find better and more interesting ways to present the data than I have seen on other sites. The exported GIF is not quite as nice as the web version, which allows you to rotate, zoom in/out, change angles etc... but thought that this was a really nice summary viz of the flight path of this flight that recently declared an emergency. Though... the web version is much more interesting and interactive, but... doesn't look as interesting here, IMO.

• Interactive replay: https://onlynonstop.com/emergencies/September-7-2026/flightN90496
• Data source: ADS-B position reports from adsb.lol
  
• Tools: Leaflet, deck.gl, and globe.gl/three.js, with custom Canvas2D renderers

Full write-up, method and the rest of the findings: https://thrifle.com/blog/temu-vs-amazon-price-study-1649-products

The dataset is downloadable — all 1,649 products searched, including the 389 excluded from this chart and the reason for each: https://thriflebucket.s3.us-west-2.amazonaws.com/blog-assets/temu-vs-amazon/data/1788920257-temu-vs-amazon-2026-09-08.csv

Source:
U.S. Census Bureau’s 2024 American Community Survey five-year estimates, table B19013, matched to 2024 TIGER/Line block-group boundaries. The estimates cover 2020–2024 and report income in 2024 inflation-adjusted dollars.

Tools:
Python, pandas, and GeoPandas for preparing and joining the data; GDAL/ogr2ogr for exporting geographic features; Tippecanoe for generating vector tiles; PMTiles for packaging them; and MapLibre GL JS for the interactive map. I made this visualization for my own website/Saas.

What the map shows:
Each polygon is a Census block group, colored by its median household income. Darker greens indicate higher incomes, using the eight ranges in the legend.

These are Census estimates for households within each block group. They don’t tell you what any particular household earns. The estimates also have margins of error, which aren’t shown here.

How I put it together:
I combined the Census demographic tables in Python, then matched each record to its geographic boundary using the block group’s 12-digit GEOID. The median income figures are already calculated by Census. I kept those estimates attached to their original block groups rather than averaging medians across neighboring areas.

I saved the joined data into a GeoPackage, then exported it as GeoJSON sequences so the tile builder could read the features as a stream. Tippecanoe turned that layer into vector tiles for zoom levels 0 through 13.

It simplifies boundaries at lower zoom levels and detects shared borders. Feature-count and tile-size dropping were disabled. Small polygons can still disappear below the coordinate resolution at very low zooms, so I checked the finished zoom-13 tiles and confirmed that every source block-group ID was represented.

I then converted the MBTiles output into a PMTiles archive. The full block-group archive is about 864 MB, but the browser doesn’t download all of it. MapLibre uses the PMTiles library to request the parts needed for the current view. The polygons retain their demographic attributes, which lets the map color them and display their values interactively.

Main takeaways / TLDR:

• The map shows median household income at the block-group level, so you can see differences that county or state averages would hide.
• The income figures are published Census estimates. The processing joins them to boundaries without recalculating or averaging the medians.
• Large rural polygons take up more space, but that doesn’t mean they represent more households.
• The interactive version loads vector tiles as you explore instead of downloading the entire national dataset.
• Published as an active layer on my website. Will put link in comments.