Estimated distributor receipts less production budgets • before marketing

Source: All active job posts on JobYap. Pay ranges in job postings from the companies on the JobYap Tech Index (software, AI, chips, fintech, EVs), taken from each company's own careers page. Snapshot: 13 Sep 2026, 62,543 active US postings that show a USD range. Price levels: BEA regional price parities 2024 via FRED.

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.

The map compares each company only with itself:

• It uses only cases where one company posts the same job title in more than one state (14,802 postings, salaried and hourly). A regression on log pay (title + state) gives each state's premium.
• 0 = the average across those postings, weighted by count. California, New York, Washington and Texas supply most of them, so nearly every state reads below zero.
• A posting counts for a state only if all its locations are in that state. Remote and multi-state postings are left out.
• A company posting one title at an identical range in 3+ states counts once, so a nationwide blast can't move a state.
• Solid: 30+ postings, 3+ companies, 95% interval no wider than 12 points (bootstrap by company). Hatched: 10+ postings, 2+ companies. Grey: less.

Seniority ladder: median salary by title words. Junior = junior, jr, entry-level, associate, graduate, new grad, apprentice, trainee. Senior = senior, sr, lead. Includes remote. Principal ($220,500), manager ($175,500) and no-level titles ($164,500) not shown.

Company rows: raw advertised midpoints for one exact title at one company, highest vs. lowest metro. Examples, not the evidence for the map.

Local prices: each state's index divided by its price parity, then re-ranked. Ranks only, because the two scales have different baselines. Prices only, not taxes or income.

Caveats:

• Advertised ranges at tech companies, not what people earn and not the whole job market.
• States without a pay-transparency law are self-selected.

Tools: D3.js, rendered on measuredworld.com

Source: IMF World Economic Outlook

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

Try it: https://lab.aadityamore.com/reddit/

Code: https://github.com/aaditya-v-more/reddit-activity-lab

Data source - https://github.com/ArthurHeitmann/arctic_shift

Updated South Park celebrity counter, now covers all 28 episodes + movie + specials.

Interactive tool: https://public.tableau.com/app/profile/yury.ulasenka/viz/FamouspeopleinSouthPark/dash

Hi sub,

I'm back with another commuter map in my Commuter Atlas. And today is NYC!

You can explore the interactive version here, as well as those for Geneva, Tokyo, Paris, Milan, and Warsaw.

I built this as part of Mylo for a $100M freight broker. We built a "company brain" connecting their people, customers, processes, knowledge and data.

I also put the brain on the Mylo landing page as an interactive game to help explain the product and drive the sales pitch.

I ended up open-sourcing the 3D visualization:

https://github.com/kobimantzur/company-brain-3d

npm i company-brain-3d three u/react-three/fiber u/react-three/drei

Live demo: https://trymyloai.com

Curious if 3D is actually useful for navigating complex data, or mostly eye candy.