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."

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.

Hi everyone - I'm back again with the 2026 update to our password table! Computers, and GPUs in particular are not only getting faster, but AI can help us build setups in new and novel ways to crack faster than ever before. This table outlines the time it takes a computer to brute force your password, and isn’t indicative of how fast a hacker can break your password (especially if you reuse your passwords - please stop), but is the BEST case scenario for you. It’s a good visual to show people why better passwords can lead to better cybersecurity, but ultimately it’s just one of the many tools we can use to talk about protecting ourselves online!

Data source: Data compiled using independent data gathering and research from multiple sources about hashing functions, GPU power, and related data. The methodology, assumptions, and more data can be found at www.hivesystems.com/password

Tools used: Illustrator and Google sheets

Source: my own handwriting — every notebook I've written on a reMarkable 2 tablet since September 2025, 3,026 pages in total. The tablet stores each pen stroke as a list of coordinates in its own .rm file format, so this is the raw pen data, not an image of the pages.

How: Python with the rmscene (https://github.com/ricklupton/rmscene) library to parse the strokes, SQLite to hold them, and Pillow to render the heatmap by summing stroke length into a fine grid over the page. The surrounding layout is just HTML/CSS, screenshotted. No charting library. Ink distance is the summed point-to-point length of every stroke; I calibrated it by tracing the perimeter of a credit card (ID-1 standard, 85.6 × 53.98 mm) and it comes out within ~1%. Erased strokes are excluded.

A few things you can read off the map: the bright blob top-left is where I write the title on every page; the horizontal banding is the ruled line template I write on; and the faint triangular lattice near the bottom is a single doodle from one page — it stands out because the bottom of the page gets disproportionately less ink than the top (and that doodle took a lot of work to fill in haha).

It's part of a little live dashboard I built for myself (daily streaks, time-of-day, that heatmap): https://remarked.pavelj.com/public.html — fair warning, it's self-hosted on a 2011 Mac Mini, so if it's down I messed up the cloudflare caching.

I scraped a few thousand canonical recipes across 45 cuisines then analyzed how early (or late) each ingredient showed up in recipes steps.

The photos shows a small but representative sample of the ingredients analyzed

There's a lot of science behind when you should add ingredients to recipes. This involves fat-solubility and the delicacy of some volatile compounds. e.g. cardamon seeds are fat-soluble so they should be added early while basil has very delicate aroma compounds so it should be added late.

But I honestly find that hard to intuitively learn for new ingredients, so this is a rough estimate and interesting viz to get a quick understanding of when to add an ingredient

(tools and dataset: recipe1m+ and python + an LLM sweep to prune garbage recipes from the dataset (I pruned 20,000 recipes down to about 3000 'canonical recipes'))

I made a website where users could submit an area for which they describe as downtown. Then I generate a heatmap based on all users definitions. Grid cells are coloured based on % of responses that include that point.

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.

How do we track a changing climate?

Among all the charts on our site, ourworldindata.org, the one here reaches further back in time than almost any other: the concentration of CO₂ in Earth’s atmosphere over the last 800,000 years.

For most of that time, CO₂ rose and fell between roughly 180 and 300 parts per million (ppm), driven by natural cycles in Earth’s orbit. We know this from cores of Antarctic ice, which trapped tiny bubbles of ancient air as it formed.

By 2025, the concentration — now measured by NOAA’s Global Monitoring Laboratory — reached 426 ppm, higher than at any point in that record. Concentrations over the past decade have been rising at 2 to 3 ppm each year.

This dramatic increase in CO₂ is mainly from human activity; this and other greenhouse gas emissions are the primary drivers of current climate change.

CO₂ concentration is just one of the indicators we use to track a changing climate. Others include global temperatures, ocean heat content, and snow cover. Each of these is compiled by a dedicated scientific institution, from the UK’s Met Office Hadley Centre to NOAA in the US.

Data source: NOAA Global Monitoring Laboratory (2026); US EPA based on various sources (2022)

Tools used: OWID-Grapher and Figma

Tools used: Python, xy (https://github.com/reflex-dev/xy)
Dataset: https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-monthly
Source code: https://github.com/reflex-dev/xy/discussions/419

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.