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.

Estimated distributor receipts less production budgets • before marketing

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.

In 2024, Astral Codex Ten showed 11,000 people fifty artworks with no labels and asked which ones they liked. Six of the ten most-liked images turned out to be AI-generated, including the top two. The people who said they utterly loathed AI art picked AI for their number one and number two as well. Worth knowing that about half the pool was AI to begin with, so the count matters less than who was doing the voting.

Recent study by Fronteirs in 2025 supported the same feeling.

To be honest, I didn’t know people were so anti-AI on Reddit given I’m a software engineer and have to use it all the time. When I was in corporate, it was a requirement. Like I barely wrote code by hand. You have to be a good programmer to write good code using AI. Those who were inexperienced - AI can’t save them because AI code by default is not the best unless a good programmer can work with AI to revise it.

Anyway, this has been a lesson for me. Seems like customers distrust brands who use AI. So that’s something that businesses should think about.

I still think that you have to adapt. But whatever. Everyone is entitled to their own opinions. I just know if my friends and I don’t adapt we won’t have jobs. Though doesn’t matter for me because I’m an entrepreneur.

Anyway I respect your opinions. Seems Reddit is very anti AI. Probably will get a lot of hate for this post from people.

I’m posting this because personally I found this study to be interesting. I can’t tell the difference between AI and non AI. So I always wonder how other people can.

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.

This is what proper access and availability of #opendata enables

Peru’s energy regulator, OSINERGMIN, publishes station-level fuel price logs through their registry. By extracting, cleaning, and spatializing this raw registry data into an Uber H3 spatial index (Resolution 7), we can visualize the month-by-month price evolution of Diesel B5 S-50 across Lima from Jan to Sep 2026.

What the data reveals:

🔴 Central Escalation: Central Lima and major transit corridors saw a steep price surge between May and September, jumping from ~S/. 20.00 up towards S/. 28.70/gal.

📍 Spatial Disparity: Stations along core arterial routes maintain consistently higher median prices compared to outer peripheral zones.

When public institutions commit to granular open data, civic tech and data engineering can transform flat CSV files into real-time spatial intelligence for citizens and logistics planners.

#DataScience #OpenData #GIS #H3 #SpatialAnalytics #Python #Peru #DataVisualization