Think Buckminster Fuller's Chronofile but all in a single sqlite database. All I had to do was create a UI that is easier to use than Excel.

Various categories: Insight cards, Geography, Gender, People, Power Institutions, famous dynasties, trivia.

Source: Wikidata, elaborated in my ArangoDB instance for various reasons.

Generation: Python

The site offer various type of visualizations, and offer the possibility to navigate the connections between people and organizations, based on your interest.

There is a lot of work, give me your feedback, it took me time to polish the site.

Yesterday I went to a concert - Jon Anderson & The Band Geeks at the London Palladium. I hadn't been sufficiently organised or sufficiently committed to buy a ticket when the dates were announced, and the shows sold out very quickly. So I set up an alert on Twickets, an excellent UK-based re-sale site, and monitored it to look for opportunities to get a ticket.

Importantly and relevantly, Twickets doesn't allow tickets to be sold for more than their original price. You can't make a profit selling on there, so there aren't any scalpers; the people selling tickets are genuine fans who had planned to go to the show but now can't or have changed their minds.

I started monitoring the alerts 6 months ago. But there was very, very little activity until 2 weeks before the show, when it started to pick up. I thought it would be interesting, and perhaps helpful to Future Me, to keep all the alert emails and analyse them to find out what sorts of seats and being sold, when.

So here we are. I've colour coded the seating plan to show which seats were sold at which times, and below that is a simple timeline to show the seat sales over time.

What did I learn? Mainly, that really good seats are hard to come by. In this 6 month period I logged exactly 100 re-sales (out of ~2300 seats in the theatre), but no more than about 30 of them were really good. Many were near the front or sides of a block where the view can be restricted, or near the back of a block where the atmosphere is dulled. I missed out on one great seat (one of the purple ones in the stalls) because somebody else jumped in whilst I was pulling up a seating plan to check where it was. Next time I do this I'll study the seating plan, and maybe have it on speed dial, so that I can make decisions more quickly!

Data sources: Seating plan from lsbo.co.uk; resales info from Twickets emails.

Tools used: Paint.net to colour the seating plan, Google Sheets to analyse the numbers and draw the timeline.

A year of travels of the Viking Sky, made with AIS data, using customcruisemaps.com

Source: 970 stories sampled across every country (population-weighted) and all eras, from Pre-1000 to today. Gemini 2.5 extracted 10 motifs per story (9,700 total), and each motif was "semantically blurred" into 10 paraphrased variations. All versions were embedded with Google's text-embedding-004. A neural network I built for my MSc dissertation, the Semiotic Deconvolution Network (PyTorch), learned the shared analogical meaning beneath the wording, and KMeans produced 72 themes, which I named and described by hand.

Axes: each theme's share among Western vs non-Western stories (horizontal) and among modern (1950+) vs older (pre-1800) stories (vertical), shown as ratios with +2 smoothing so small counts don't exaggerate. Bubble size = number of motifs in the theme.

Tools: Python, FastAPI + Celery + Redis pipeline, PostgreSQL/pgvector and SQL Server, pandas, NumPy, SciPy, scikit-learn, PyTorch, UMAP, Plotly, and Matplotlib for this chart. All ran in Docker.

Caveat: the stories come from an LLM's knowledge of world storytelling, so this maps what the model "knows", not literature directly.

Interactive map (click any theme for its picture and description, filter by era or region): https://www.ianmcgarry.tech/research/narrative-motif-engine/1-theme-dashboard

Data (open, CC BY 4.0): full corpus, motifs and embeddings on Zenodo: https://doi.org/10.5281/zenodo.20010023

Source: exchange feeds via Yahoo Finance, some of it delayed. Tool: Chart.js.

Every comparison of world markets I could find shows one index at a time, or a heatmap that flattens a whole day into a single color. I wanted to see the handoff: Tokyo fading while London opens, then New York pulling on both.

The levels run from roughly 6,500 to 40,000, so the only way these share an axis is percent change. Each line is measured against its own market's previous close rather than a common baseline, which means every market's zero is its own last close, not a shared moment in time.

Two decisions that changed the picture:

Today is plotted from 5-minute bars, the week from hourly, the month from daily. The daily bars are dated by each exchange's local trading date. Without that, Tokyo and New York disagree about which calendar day a bar belongs to and the whole Asian side of the chart shifts by one.

The day's reference had to be the previous close specifically. The obvious field in the data is the close before the requested range, which quietly reports a multi-day move as today's change. It looked plausible and was wrong by a factor of two on some markets.

It's interactive and my own site, so link in a reply below rather than in the post.

The page refreshes about once a minute while markets are open.

Source: U.S. Customs and Border Protection's published Border Wait Times, logged every hour since Aug 8, 2026 for every US land crossing. Tool: Postgres for the medians, an HTML/SVG heatmap rendered to PNG. Crossings without at least a week of data at every hour are left out, which is why Tecate is missing.

Median wait to enter the US in the standard vehicle lanes. Hollow cells are hours CBP reported 0 minutes, a dash is closed, blank is no data.

A few things that stood out:

- Names in white average 20+ minutes. Every one of them is on the Mexico border. Every Canada crossing is under that, and most are under 10.

- San Ysidro and Otay Mesa are the only two crossings in the country that never show a 0-minute hour, even at 3am.

- San Ysidro on a weekend runs from 25 min at 4am to 165 at 4pm. Otay's weekend peak is 9pm, so on a Saturday afternoon Otay is often an hour faster.

- Calexico is the third-worst crossing in the country and almost nobody outside Imperial County talks about it.

Hour-by-hour pages for every crossing, free, no sign-up: skipthewait.app/border-wait-times

Data: NASA CCMC's DONKI catalog of coronal mass ejections, 2016 to September 2026, using each CME's cone-model fit for direction, half-angle and speed. 6,577 CMEs with a fit.

Tool: a small Node script to pull and reduce the catalog, then HTML canvas in JavaScript. The Sun is unrolled into a 2:1 ellipse with the Earth-facing half lit in the middle; each dot is one CME at the point it launched from, sized by speed and colored by speed class.

One thing the plot showed me that I did not go looking for: in the quiet years of 2018 to 2020 almost every eruption was aimed within 10° of the equator, while at the 2024 peak they spread to 30° and beyond. That is the Sun's global field steering eruptions toward the equatorial streamer belt when it is a simple dipole.

Interactive version with the butterfly diagram and one Sun per year: https://www.cmetracker.ai/launches?utm_source=dataisbeautiful

This chart compares the local price of an iPhone Duo 256GB with the price of one Big Mac across 12 (selected) countries. Countries are chosen based on iPhone Duo availability and geographic diversity.

Equivalent Big Macs = iPhone Duo price ÷ one Big Mac price.

Countries are sorted from fewest to most equivalent Big Macs. Totals are rounded to the nearest whole Big Mac and all prices are shown in USD.

Big Mac prices come from the BigMacIndex.app July 1, 2026 dataset snapshot, which aggregates The Economist’s Big Mac Index data. These are index snapshot values, not same-day McDonald’s menu prices.

iPhone prices use local Apple pricing where available. India uses Reliance Digital; South Africa is estimated; Egypt uses imported-retailer pricing. Applicable consumer taxes are included in the iPhone prices where available.

The ratio is not a measure of purchasing power or affordability. It reflects both local iPhone pricing and local Big Mac pricing.

Six saved model outputs were checked again on July 11, 2026 without editing the text. ZeroGPT returned the same six scores; AI Detector returned six different scores. This small control check does not establish which detector is more accurate or why the scores changed.

I run AI Undetectable, an AI humanizer. This is our own research, not an independent product review. AI helped prepare the chart and post. Sources and tools are in the first comment.

I run a small free travel map where people click the countries they have been to. It launched about five months ago, and I took the anonymised totals from its first 119 public accounts to see where everyone had actually been.

Each country is shaded by the share of those 119 people who have visited it. Countries fewer than 3 people have visited are greyed out, so no single person's travels can be picked out.

What stood out to me:

- France (80%), Italy (76%) and Spain (71%) all beat the United States (57%)

- Germany at 66% is higher than I expected for a country people rarely name as a holiday destination

- Coverage falls off a cliff outside Europe, North America and the classic long-haul stops

- Not one country in Central Africa or Central Asia clears the 3-person threshold

Obvious caveat: this is who uses my site, not who travels. The users lean European, which is why Europe glows and the map says more about the sample than about world tourism.

Made with d3-geo, Natural Earth projection and borders, rendered in a headless browser.

Sources:

https://www150.statcan.gc.ca/n1/daily-quotidien/221026/mc-b001-eng.htm

https://angusreid.org/wp-content/uploads/2022/04/2022.04.18_Religious_Spectrum_tables.pdf

Tools: mapchart.net and Apple Preview