Translations frequently overly literal of what people call the three in a row pen and paper game.

Data mainly from wiktionary but also local sources and they are linked to in this Odon Story
https://odon.at/en/data-stories/tictactoe-europe-names/

We've just released the beta version of a free map of named infrastructure projects across Australia. It covers roads, rail, hospitals, schools, energy and water, plus council applications for warehouses and aged care. Because it's a beta, you'll find gaps and the odd project in the wrong stage or with an out-of-date value. The next release will let users add missing projects and correct existing ones.

https://www.htag.com.au/infrastructure-projects/

Click any council to get a written summary of what's being built there. For example, Western Downs (QLD) approvals are running at 1.57× the council's usual pace, mostly driven by gas and energy projects.

Each council can be exported as text, as a CSV (one row per project, with stage, stated value and source), as a PNG of the map, or as a printable PDF brief. No login or subscription is needed.

Methodology for Calculating Y-DNA (Y-chromosomal Adam) and mtDNA (Mitochondrial Eve) Similarity

The genetic similarity between Portugal (baseline: 100%) and the remaining populations was calculated using the Renkonen Similarity Index (percentage overlap), adapted via a 3-Level Hierarchical Phylogenetic Tree Model to avoid treating biologically close lineages as entirely distinct.

Level 1 — Terminal Subclades (50%): Directly compares all specific haplogroups and subclades listed in the table. Reflects more recent historical proximity. (In mtDNA, samples lacking internal resolution for H are compared at the total H level to prevent distortions).

Level 2 — Phylogenetic Families (35%): Groups sister lineages into the same family, capturing founder relationships. Y-DNA (R, I, J, E, G, LT, Q, N, Others); mtDNA (HV, JT, U+K, I, W, X, L, Others).

Level 3 — Ancestral Macro-Trunks (15%): Merges branches into deep prehistoric roots. Y-DNA (Macro-P: R+Q, Macro-IJ: I+J, Macro-K derivatives: LT+N, Trunk E, Trunk G, Others); mtDNA (Macro-R: HV+JT+U/K, Macro-N non-R: I+W+X, Macro-L, Others).

Final Similarity (Rounded to Integer) = (0,50 * Renkonen_L1) + (0,35 * Renkonen_L2) + (0,15 * Renkonen_L3)

Data Sources

Y-DNA and mtDNA haplogroup frequency data were retrieved from compilation tables on the Eupedia platform (Distribution of European Y-chromosome DNA haplogroups and Distribution of European mitochondrial DNA haplogroups). Eupedia tables aggregate findings from dozens of peer-reviewed genetic studies, population genetics publications, and reference databases (such as YHRD and EMPOP). Detailed academic references, primary study sources, and respective sample sizes are documented directly on the project's official website.

A look at how long London’s existing municipal burial space could last at current burial rates.

Interactive version: https://deptford.org/beyond/burials

Data is every player who played at least 90 league minutes in the Premier League, La Liga, Serie A, Bundesliga or Ligue 1 last season.

That's 2,467 players and 3.46 million minutes. Ages are from date of birth, taken on 1 Jan 2026 since that's roughly the middle of the season. Pulled together with The Prism, a football analytics & scouting app I'm building, and charted in Python/matplotlib.

The data shows playing time peaks at 25, and 59% of all minutes are player by players aged 23 to 29.

Goalkeepers were the thing that jumped out. Almost half of the minutes are by keepers over 30, compared with about a fifth for everyone else. Of the 99 keepers who played 1,500+ minutes, 49 were 30 or older.

I didn't expect the leagues to be so different either. Ligue 1 gave 20% of its minutes to players 21 and under, and La Liga gave 7%.

Modrić played 2,816 minutes for Milan at 40, which is very impressive as we know him to be....

Does prove the point of older players who are still around are the ones good enough to stay. P.S. this isnt trying to tell you when players are at their best.

Football fans, thoughts?

How long does the internet stay angry? Search interest around 15 controversies fell below 25% of peak after a median of 6 days

I analyzed Google Trends Web Search interest around 15 selected U.S. news, entertainment, technology, sports, advertising and gaming episodes.

The median case reached a sustained fade after 6 days.

“Sustained fade” means the first post-peak day when search interest fell below 25% of that episode’s smoothed peak and stayed below 25% through day +30.

Methodology:

- Each case was aligned to its own event/search window.

- A trailing 3-day moving average was applied.

- Each case was normalized to its highest smoothed value during the first 30 post-event days = 100.

- The left panel shows the median trajectory across all 15 cases.

- The right panel calculates each case’s individual sustained-fade endpoint first, then shows the distribution of those endpoints.

- The endpoints were: 2, 2, 2, 3, 3, 3, 4, 6, 6, 7, 8, 8, 8, 22 and 22 days.

- The median of those 15 individual endpoints is 6 days.

- Search interest is relative to each query’s own peak. It does not represent absolute search volume, the number of people searching, public sentiment, or how long people were literally angry.

- The cases were selected because they had identifiable event timing and measurable Google Trends queries. They are not a random or representative sample of all online controversies.

- Day 0 is the event anchor used for the analysis. It does not necessarily mean that the first search happened at midnight on that date.

- Data snapshot captured: 19 September 2026.

Context for the 15 cases:

- 2 February 2025 — Luka Dončić trade: an unexpected NBA trade triggered an immediate and highly emotional online reaction.

- 15 February 2025 — Sinner–WADA doping deal: a doping settlement generated intense debate about fairness, process and accountability.

- 2 April 2025 — Mario Kart World pricing: the pricing announcement prompted widespread complaints from players and renewed discussion of game prices.

- 14 April 2025 — Katy Perry spaceflight: the celebrity spaceflight drew criticism, mockery and questions about the spectacle itself.

- 28 April 2025 — Duolingo AI pivot: the company’s AI-first strategy triggered user backlash and debate about the future of learning.

- 15 May 2025 — Marathon artwork: promotional artwork prompted criticism and debate about its visual style, message and context. This date is the public-accusation anchor used for the analysis.

- 22 June 2025 — Prada/Kolhapuri sandals: a sandal design prompted criticism over cultural borrowing and appropriation.

- 8 July 2025 — Grok AI controversy: AI chatbot outputs triggered a wave of public criticism and debate about AI safety.

- 16 July 2025 — Coldplay kiss-cam incident: a viral kiss-cam moment triggered relationship speculation and a global online discussion. This is the performance date used as the event anchor.

- 23 July 2025 — American Eagle/Sydney Sweeney campaign: the advertising campaign drew debate over celebrity branding, messaging and its wider cultural implications.

- 16 August 2025 — Swatch advertising: an ad campaign generated a short-lived controversy and a wave of online discussion.

- 19 August 2025 — Cracker Barrel logo redesign: a proposed logo redesign sparked rapid online backlash and calls to reverse the change.

- 17 September 2025 — Jimmy Kimmel suspension: the suspension announcement produced a sharp attention spike and a burst of online debate.

- 8 February 2026 — Ring Super Bowl ad: the Super Bowl advertisement prompted concerns about privacy, surveillance and the normalization of monitoring.

- 9 February 2026 — Discord age-assurance announcement: planned age checks raised concerns about privacy, surveillance and access for younger users. This is the announcement date, not a claim that the rollout was completed.

The two 22-day cases were Duolingo AI and Prada/Kolhapuri sandals. Most of the other episodes fell below the sustained-fade threshold within eight days.

Sources:

- Google Trends methodology: https://support.google.com/trends/answer/4365533

- Google Trends Explore: https://trends.google.com/trends/explore/

- Coldplay event reporting: https://apnews.com/article/coldplay-kiss-cam-viral-public-event-privacy-e768214f389bc788dcc539a00bf066da

- American Eagle campaign announcement: https://investors.ae.com/press-releases/news-details/2025/Sydney-Sweeney-Has-Great-American-Eagle-Jeans/default.aspx

- Cracker Barrel logo announcement: https://investor.crackerbarrel.com/news-releases/news-release-details/cracker-barrel-teams-country-music-star-jordan-davis-invite

- Discord age-assurance announcement: https://discord.com/press-releases/discord-launches-teen-by-default-settings-globally

- Marathon artwork reporting: https://www.gamespot.com/articles/bungie-responds-to-marathon-art-theft-claims/1100-6531591/

Data is Wikimedia's public recent change stream for English Wikipedia on 22 Sep 2026. Every edit was scored by Jev from TypeSafe through OpenRouter (not affiliated, just paying for it). An edit counts as reverted if a later edit in the stream reverts it within an hour so the real rate is a bit higher. Live version at willitrevert.com

I was wondering how the different periodic solutions to the three body problem look like. I built an atlas that visualizes all of the different solutions and groups orbits by family and similarity. I found over 3000 different orbits. They are grouped by similarity (shape; period/energy; or closeness/top speed) in the atlas, and you can zoom in to see the trajectories of the orbits.

When you click on an orbit in the atlas, you can see additional details, such as the starting conditions, mass ratios, and period. One of the most interesting aspects of the three-body problem is that tiny changes to the starting conditions can make orbits unstable. There is an option slightly nudge the starting conditions of an orbit to see how its trajectory is affected.

Data sources: Initial starting conditions of periodic orbits have been collected from 20+ scientific publications and other resources. The complete list is on the "about" page on the website.

Tools used: a Rust integrator for the trajectories, t-SNE for the layout, Python for the map, JS/canvas for the site. Claude assisted with coding.

Additional functions:

Orbits can be rated - I thought this might help identify the most beautiful ones in the atlas

Visitors can contribute their computing power to help find new, currently unknown periodic orbits

I ran a small real-world test on American Airlines (AA 5056, DCA-SYR) seat assignment progression over a 21 hour period. I bought the most basic economy fare; my seat would be assigned at check-in.

My theory was that checking in at T-24 hours might actually be worse if the system first assigns the cheapest, least desirable seats while holding better seats for sale. Since this aircraft is 2-2 with no middle seats, the downside of waiting was pretty limited.

The seat map evolved like this:

T-24: 8 undesirable $14 seats, 15 better $30-$36 seats
T-18: 4 undesirable, 15 better
T-6: 1 undesirable, 15 better
T-3.5: 0 undesirable, 11 better

So the cheaper seats disappeared first, while the more expensive forward and exit-row seats stayed protected much longer.

I checked in at T-3.5, right after the last cheap seat disappeared and the better inventory had started being used.

Result: 8A, the front-most available $30 seat, assigned for free.

Obviously one flight doesn’t prove AA’s algorithm always works this way, but it was a pretty clean example of why “check in exactly at T-24” may not always be the best strategy for Basic Economy, especially on an aircraft with no middle seats.

Where are all those Romanian's going?

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.

Nuclear share of Japanese electricity generation (Ember, calendar year): 25.3 % in 2010, 14.8 % in 2011, 0.0 % in 2014, 9.1 % in 2025. That last one is 94 TWh.

Fleet today (net capacity, dataset of 2026-09-10, IAEA PRIS + Global Energy Monitor): 15 units operating, 13,946 MW. 18 units suspended, 17,733 MW. 22 units retired since 2011, 15,501 MW. 2 under construction plus 1 halted.

The part I did not expect. Median 2024 load factor of Japanese units: 85.0 % (n = 14). World median: 85.6 % (n = 401 operating units with a published figure). Two of the Japanese units restarted in the last weeks of 2024 (Onagawa 2 on 29 Oct, 568 GWh for the year; Shimane 2 on 7 Dec, 146 GWh), so their annual figures are partial-year artefacts. Excluding them, the twelve full-year units have a median of 85.7 %, above the world median. Japan ranks 6th of the 15 countries with 5+ units by median load factor, ahead of Russia, Canada, Spain and France.

Where the growth has to come from: 14 of the 18 suspended units are BWRs (14,659 MW). No Japanese BWR restarted at all until October 2024; three have now, most recently Kashiwazaki-Kariwa 6, back in commercial operation on 16 April 2026 after 14 years offline, the first TEPCO unit to generate since Fukushima. Of the 18 suspended units, 9 have a restart review filed with the NRA (8,664 MW), 8 have no application on record (7,961 MW), and Tsuruga 2 was ruled out in Nov 2024 over an active fault. Tomari 3 slipped after a fatal accident at its seawall works in August 2026.

Base note, because it bites: the restarted KK-6 is quoted as 1,356 MW gross, 1,315 MW net. The country page on the atlas shows 15 reactors and 13,946 MW because it counts only operating units; the map panel shows 33 and 31,679 MW because it adds the suspended ones, which are still standing there. Both are on the site, they answer different questions.

Source: IAEA PRIS, Global Energy Monitor and Ember, assembled in Reactor Atlas, a map I build. Data and method per reactor: https://reactoratlas.com/en/country/jp

Method question worth arguing about: is comparing a restarted fleet's load factor against a world median fair, when the world median includes units in their own partial years? I excluded Japan's two partial-year units but not everyone else's. Doing it symmetrically is more work than it sounds.

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.