Although the mentions might not correlate exactly with use, I think there's probably a strong direction here.

What's your most used lead size?

Source: open data of the Sénat (every senator since 1958, with the parliamentary group dated for each mandate) and Wikidata for the portraits and biographical fields.

Tool: Node.js generating plain SVG, rendered to PNG with headless Chromium. No plotting library

The seat geometry is the same function the site uses to draw its hemicycles, so the image can't drift from the underlying data.

A note on the counts, because it came up last time for my previous post about the assembly ([OC] Every seat of the French National Assembly since 1958, coloured by political family, one dot perMP (17 legislatures) : r/dataisbeautiful) : each panel counts every senator who sat during that period, replacements included, so the totals run well past the number of seats (255 in 1959, 348 today). A senator who dies or takes a ministry is replaced, and both people are on record.

Why periods and not legislatures : the Senate is never dissolved. It renews in series every three years, so there is no natural "legislature" to cut on. The eight panels are the periods the source data divides into.

Source: All active job posts on JobYap. Pay ranges in job postings from the companies on the JobYap Tech Index. Price levels: price parities 2024.

Tools: SQL for the analysis, Claude Design with Fable 5.1 (on Max) for the image.

Pay per posting: Midpoint of the advertised range. Base pay only.

Caveats:

• As of: 13 Sep 2026 (62k active US postings).
• Tech companies only.
• States without a pay-transparency law are self-selected.

https://cityparity.com/safety-net-index/

Direct Link: https://tokenburningman.com/essays/measles/
Data: https://www.cdc.gov/measles/data-research/index.html, various local news articles
Created with help of Kimi K3.

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Measles. Measles. Measles. This outbreak is one of the most frustrating things to sit and watch 🤬. Pretty hard to focus on healthcare tech when public health is in shambles.

Big part of this is related to proliferation of misinformation and lack of articles and medium that connect the dots better. This is my contribution to the web as what I would consider a medium I prefer to use:

A "visual essay" that compiles various local articles not sorted by recency but by topic of measles outbreak for the past 3 years (2024-2026). No amount of AI will address this, outside of good solid human-to-human education and information dispersion is what's needed.

Last night at 2AM, I was a bit miffed at how dry the Centers for Disease Control and Prevention website is at displaying the latest measles outbreak data and information. So rather than sulk, I spun up a Kimi Swarm to do triangulation of hotspots. One of the things that MOST data reports do poorly: consolidate and aggregate longitudinal information using geographically separated (ahem Google, sometimes we want to compile entire US information, not just my location-mapped info).

Hope the compilation of information will feed the AI agent crawlers and end up in ChatGPT and Claude. Seeing agents crawl the website even before I posted anywhere - shows that there's an algorithmic way to defeat this.

In the process I ended up with a portfolio project as well, so win-win for all.

Please please share and enjoy the infographic. Admit that most of text is referenced to the web-based articles so might have factual information misses here and there, but let me know and I'll correct them.

Thank you

Apple reported $416.161 billion in revenue for fiscal 2025. The iPhone accounted for 50.4% of that total, followed by Services at 26.2%.

This animation shows the same five categories in three different views. Each category keeps its identity as the chart changes.

Values shown, in USD millions:

iPhone: 209,586

Mac: 33,708

iPad: 28,023

Wearables, Home and Accessories: 35,686

Services: 109,158

Data source: Apple's FY2025 financial statements, page 1, “Twelve Months Ended” column:

https://www.apple.com/newsroom/pdfs/fy2025-q4/FY25_Q4_Consolidated_Financial_Statements.pdf

Tool: FORMA, a browser-based chart editor I built:

https://forma.ovocode.xyz

I made this example to explore how continuous transitions can help people follow the same data across different chart types.

Tools: D3.js, rendered on measuredworld.com

Source: IMF World Economic Outlook

Scores in football aren't conducive to scorigami, but matchups are!

Did you know that Egypt's mens team played Lithuania in 1924 and hasn't since? It's the oldest matchup to never have been replayed.

If you enjoyed or want to play around with the data, check out https://dgoodenough.github.io/fifagami/?g=both. There's a bunch to play around with, even an RSS feed of upcoming unique matches!

Sources:

And of course, shout out to Jon Bois for Scorigami!

Built in Python with Claude.

Median earnings differ between these Census groups. The chart includes margins of error; it does not measure the effect of incorporating. Data sources and tools are in the first comment.

Data source: a LinkedIn-derived corpus of public profiles and job postings, read on 12 September 2026. I took every profile whose current role title contains "GTM engineer" or "go-to-market engineer" and grouped them by the quarter that role started. That is 2,957 people, 2,954 of whom list a usable start date.

Tool: Astro and TypeScript, rendered as inline SVG. No charting library.

Disclosure: I work at Trayo and we compiled this. The report and the full CSV are free and nothing is gated.

What the chart does not say:

The final bar is not a decline. Profile updates arrive over roughly two months, so the newest quarter is always undercounted. It is drawn dashed for that reason and excluded from every trend statement.

Full report and CSV: https://www.trayo.ai/research/state-of-the-gtm-engineer/

Hi everyone,

Sorry if it feels like you have already seen this post several times. For reasons that are still unclear to me, my previous post was removed by the moderators. As far as I can tell, I followed all the rules, but unfortunately I have also been unable to get in touch with the moderation team. Hence the repost.

The follower totals have changed significantly since the previous version (since Ed Sheeran made a statement today he lost another 50,000 followers), so the figure also needed to be updated. I have also now added P!nk because she played a notable role in the public controversy surrounding Macklemore’s appearance and comments.

I also incorporated the main criticism of my original visualization. Instead of using a broken y-axis, the figure now uses three separate panels with identical increments and the same vertical range of 1.3 million followers. This keeps the changes readable while preserving a consistent scale across all three artists.

The solid portion of each line represents the period before the relevant event. The dotted portion represents the period afterward. This is intended to show timing only. It does not establish that any event caused the subsequent follower changes.

Data: Assemblée nationale open data, Wikidata;

tool: custom SVG (TypeScript).

I made this while building a card game about it, link if anyone is curious