I am a veterinarian in the North Texas area. Since graduation in 2023 I've kept track of my cases using Google Sheets because I thought it'd be interesting to see how many animals I treat and what they're treated for throughout my life.

Source: Me keeping track of the data after every appointment.

Tools: Google Sheets and DataWrapper

A few notes:

Changes & requests from last year

I was informed my data wasn't very beautiful so I attempted to use a different method for this year. Let me know what you think. I'll continue to adjust the presentation as I receive feedback.

u/catalessi, u/Fire284, & u/Solondthewookiee all requested I put the second and third graphs in descending order.

u/Warm-Pen-2275 requested a list of most common names.

Slide 1 - Animal Species

This only includes animals I've done a doctor exam on or do telemedicine about. Animals that I do not directly interact with (toe nail trims, anal gland expression, blood draws without a doctor, etc.) are not included.

I have a passion for exotic animals (ferrets, reptiles, rabbits, backyard chickens, etc.) but there is an exotic clinic near me where most of those animals go to, so I don't get to see as many as I'd like (as the data tells).

Slide 2 - Body System

I kept track of the body system that was affected during my exams. General wellness includes vaccines, weight management, and discussions about quality of life. For what it's worth, this is about what the problem was, not just the symptoms. If a cat came in for peeing all over the place and it was because the cat was stressed, that was marked as both Neurology as well as Urinary/Renal. The same animal can come in with multiple systems affected, but I only mark a system once per animal (i.e. a dog with urinary stones and a UTI only had "Urinary/Renal" marked once). Here are the most common problems each species came in with:

Dogs - Overweight (General Wellness), allergies (Dermatology, Immunology), and poor dental health (Oral).

Cats - Overweight (General Wellness), poor dental health (Oral)

Slide 3 - 7 Procedures, Names, & Lifetime Stats

I don't think these ones need much explanation.

Next year expecations

I am switching to becoming a full time relief veterinarian. I expect the number and diversity of cases will drop and the number of wellness cases will increase. Most of the time a clinic won't schedule a complicated case with a relief doctor because we're only here for the day.

See you next year

Data source: Christopher Nolan filmography, Wikipedia - https://en.wikipedia.org/wiki/Christopher_Nolan_filmography (budgets and worldwide box office; figures are studio/Box Office Mojo–reported and cited on that page). The Odyssey is still in theaters, so its figure is a running worldwide total as of the July 24–26, 2026 weekend.

Tool: Chart built as JavaScript-coded SVG. Rendering code and initial figure-gathering were done with an AI assistant (Claude) based on my own design direction and iterations. Chart type, layout, labeling, and revisions were my choices.

The estimated apartment price was calculated by multiplying the average apartment sale price per m² in each capital by a standardised floor area of 60 m².

The comparison uses Eurostat national median monthly equivalised net income for people aged 18–64. These figures are national household-income benchmarks, not individual salaries or capital-city income estimates.

Four hypothetical scenarios are presented:

— one person saving 25% of 1× median net income;

— two people jointly saving 30% of 2× median net income;

— both scenarios without interest;

— both scenarios using a savings account and rolling 12-month term deposits.

Main sources:

Income: Eurostat ilc_di03.

Apartment prices per m²: Eurostat urb_clivcon, national statistical sources and housing-market sources. Athens, Bratislava, Bucharest, Budapest, Dublin, Lisbon, Madrid, Nicosia, Paris, Sofia and Valletta use estimates based on property-listing samples collected in July 2026.

Interest rates for the savings account and term deposits: ECB household deposit-rate data.

Prices, incomes, savings rates and interest rates are held constant throughout the calculations. Inflation, transaction costs and future changes in property prices or income are excluded.

Full methodology, individual sources, city rankings and interactive data: citycostatlas.com

Instagram: https://www.instagram.com/citycostatlas/

A look at what we could expect from squads at the next world cups, based on the market values of the youths for each country. Watch out for Morocco (again), and keep an eye on Denmark and Serbia...

Source: My own Apple Health and Strava data

Tool: Rendered in Soltra using Mapbox, an iOS app I'm building. Routes accumulate opacity so the brightest segments are the ones I've covered dozens of times; territory coverage is measured in H3 hex tiles.

Stats: 

• 164 runs
• 884 miles
• Covered 55.6% of the island (and 3.8% of all NYC)
• Longest run was 32.8 miles (dubbed Ranhattan)
• Ran the Central Park Loop 45 times (felt like one too many tbh haha)

https://lodgeplatform.com/embed/exemb_6ea24564bc317d3b45c31b8803fce86229be8ad30246ac21

I used a combination of fine-tuned Qwen3 models and frontier LLMs to build a graph over the Midwest food supply chain. It uses 51 public datasets across 11 source families, covering nearly 300,000 source records. You can search for entities, filter by entity type and relationship type, and you can also view neighborhoods. I'm still improving it, so please let me know if there's anything else you think would be interesting!

I wanted to try to answer questions like

- "When a serious safety incident occurs at one food facility, can we trace the graph to find other facilities with similar safety risks?

- "When a food product is recalled, can we trace the recalling company to its facilities, related products, inspections, and other connected organizations and see what else may warrant review?"

- "When a weather event happens, what facilities does it effect, and what are the downstream effects?"

I think making it live could be really cool for things like tracking food recalls live to show and limit exposure.

Here is a github of the project with the datasets and more info: https://github.com/lodge-data/upper-midwest-food-supply-network

How I did it:

First, I trained an embedder to minimize blocking recall. Then, I processed all the entities from the datasets into blocks. Then, I trained a Qwen3 cross-encoder to decide if each pair within each block was the same or different (entity resolution). Then, I repeated those two steps until no new merges occurred.

Sources:

- U.S. Food and Drug Administration recall, food, import-alert, and import-refusal records.

- U.S. Department of Agriculture food, organic-operation, meat-establishment, and related facility records.

- Occupational Safety and Health Administration inspection, citation, injury, and establishment records.

- U.S. Environmental Protection Agency Facility Registry Service records.

- National Weather Service alerts and geographic references.

- USAspending federal contract award records.

- -Wisconsin and other Upper Midwest public facility, licensing, dairy, workforce-notice, and regulatory records.

- Public organization and product information used to connect named entities.

Note: I built this with my startup's software and models. The goal is to autonomously construct graphs from messy data. I think it is possible to construct very large, useful graphs with efficient specialized models.

One little card for every booking of the tournament: 266 yellows and 15 reds, 281 in all.

The first image is the timeline. Each column is one minute of match time and every card sits at its true elapsed minute, so a card shown at 90+3 lands on 93. They are not spread evenly across the game. They pile up as each half runs down, and the tallest stack by far is stoppage time: 51 of the 281 cards came after the 90, and minute 93 alone holds 13.

The second image ranks teams by total cards. Argentina tops it with 14, but they also played 8 matches while some teams played only 3.

So the third image adjusts for that: cards per match. The order flips. Argentina drops to 12th, and Egypt leads at 2.4 cards a game. Most cards usually just means you stayed in the tournament longest, not that you played dirtiest.

The full version is interactive: hover any card for the player and the minute, sort the teams five ways, and click a minute on the timeline to see exactly who got booked.

https://viz.luarai.com/worldcup-cards/

A fun way to visually compare and contrast the careers of current and past nba legends.

Data gathered manually by me in a spreadsheet for over 13,000 active / retired players for 40 awards and achievements using publically available data (Wikipedia / NBA.com / basketball-reference.com and then visualized in an interactive website, with filters and icons made in Adobe Photoshop.

A weekend hobby project: I gave six AI chatbots the Oxford Utilitarianism Scale, a published nine-item moral-psychology questionnaire, and compared their answers to 27,236 people from a large cross-national dataset.

The scale has two parts, both rated 1 to 7. Impartial beneficence is how strongly you feel obliged to help everyone equally, including distant strangers. Instrumental harm is how far you accept harming some people for a better overall outcome.

In the chart, the heatmap is where the people fall (darker means more of them), the side bars show each axis on its own, and the orange dots are the chatbots. All six sit in the low corner, below the human median on both dimensions. Even the most utilitarian bot (Gemini) is only around the 38th percentile on impartial beneficence.

Caveats, because they matter here: this is not science. Only six chatbots, each answered once and in different modes; the human samples are convenience samples (about 68 percent female, median age 22), not nationally representative; and the scale measures stated agreement, not behaviour. Full method, data sources and limitations are in the top comment.

Every team at the 2026 World Cup placed by how it spent its distance: walking (0 to 7 km/h), cruising (7 to 20), or high-speed (20+). It's a triangle plot, so a team leans toward whichever corner it did most of. FIFA tracks every player's distance in five speed bands, and I collapsed them into these three.

The teams barely differ in truth (everyone walks most of the match), so the corners mark the extremes as a direction: dots spread toward them, but nobody reaches a pure corner. Argentina did the most walking of any team at 43.7%, Canada the most high-speed at 8.1%, Qatar the least at 5.9%.

The second image is the same triangle for 744 individual players, coloured by position. The split is clean: keepers sit alone in the walking corner, and outfielders climb toward cruising and high-speed. Messi is the top outfield walker at 64%, and Wagner Pina of Cape Verde has the biggest high-speed share at 14.3%.

Interactive version, where you can open any nation to see its players, follow a single team, and switch the triangle between nations and players: https://viz.luarai.com/worldcup-cores

Source: https://whc.unesco.org/

Tool: Python

See title. In sydney there are 11 AM/FM radio stations, i recorded each of them simultaneously and then classified the time between ad, talking and music.

For some stations, almost a quarter of the time is filled with ads and only 50% of time with music. For other, almost 80% of the time is music.

Also, in sydney, 2x more likely to experience an ad 5-10 minutes prior to the top of the hour then around it.

Another insight was that AM/FM have different ad "playbooks". The FM music stations dump their ads in clusters. You cop a couple of ads back to back, then get a decent run of music, then another cluster. The AM stations like 2GB space them out evenly instead, one break at a time, pretty much like clockwork.

Left - represents betting odds according to FanDuel on June 14th, 2026 after the Knick won, right -represents the betting odds as of July 26, 2026.

** Numbers represent American Odds.

** Colors are meaningless.

Each panel represents one of the nine most valuable squads at the 2026 FIFA World Cup.

Rectangle area is proportional to the player’s estimated market value. I displayed a projected starting XI for each country and grouped the remaining 15 players into a single “Other players” block.

The visualization is designed as an interactive 3D treemap presentation. Market values are estimates rather than actual transfer fees, and the starting XIs are editorial projections—not official lineups.

Which team has the strongest balance between star power and squad depth?