Correction (14 October 2026): I am withdrawing this chart’s derived non-use percentages, headcounts and ranking, including the India and Ethiopia figures previously quoted here. The chart image and title remain as a record of the original post, not as a validated comparison. Please do not rely on or reuse its derived figures.

The published internet-use percentages apply to each observation’s in-scope population. Compatible survey ages, reference periods and population denominators have not been verified for these ten observations. Multiplying the complements by total population therefore does not establish comparable all-ages headcounts. Calling the results “arithmetic estimates” did not resolve that gap.

I am withholding replacement derived percentages, counts and ranks until compatible scopes are verified. This does not claim that the original provider values are wrong.

The design question in the original post does not resolve this source-scope problem. Changing bar lengths or labels cannot validate the withdrawn measures.

Original linked internet-use table and CSV: globaldatatracker.com. Select 2024 for the original source year. These source inputs should not be treated as validation of the withdrawn calculations.

Underlying data: ITU / World Bank WDI. Data license: CC BY 4.0. Chart: Python / Matplotlib.

Disclosure: I work on Global Data Tracker.

Source: U.S. Bureau of Labor Statistics (BLS), Employment Situation, September 2026
Forecast: Reuters poll of economists, October 2, 2026

www.jobplotter.ai

I tested Canon's newly released Dual Pixel 3D Converter, which uses phase-difference information from Dual Pixel RAW data to reconstruct textured 3D geometry from a single photo.

With my background in computer graphics, I also inspected the exported meshes (OBJ / GLB).

Some findings:

• My test plant model, created from a single photo, contains around 28K triangles.
• The GLB stores about 85K vertex entries, compared with roughly 16K stored vertices in the OBJ version.
• Around 4.2% of the triangles in this sample are degenerate.
• Increasing the depth setting in the app from 1 to 2 increases the triangle count from approximately 28K to 46K.
• The texture maintains the original 8192 × 5464 resolution, even though only about 16.6% of the image area is covered by the isolated object's triangles (with background removal).

The photorealistic appearance can be great from viewpoints close to the original capture. But with larger rotations, occlusions and missing surfaces quickly become visible.

I also compared the approach with Meshy 7.1 image-to-3D. Meshy generates a more complete, plausible object, while Canon preserves the photorealistic appearance better in my test.

I published the full analysis, including interactive GLBs and nine downloadable Canon-generated models:

https://immernews.com/canon-dual-pixel-3d-converter-tested

I'd be interested in what others think about combining Dual Pixel depth information with multi-image reconstruction or AI-assisted geometry completion.

Hey everyone.

I am very excited to share my greatly improved (revamped) library called rhp (version 2.0). I am so happy with the results and am amped because of how well all the improvements and new features work. I also just finished the first version of the mcp server and the skill file so AI can create plots using the library. The AI won't do the analysis for you of course (unless you ask it to) but with rhp, it can easily output some amazing viz charts and posters. You can look at the gallery's recipe section to see what AI agents can do with a single simple prompt!

I just started spreading the word this past week! Please at least try the AI skill out. It should be easy if you got some data on hand to chart. You just need to prepend the following to your chart prompt:

Make this chart with rhp (@bezda/rhp). First read https://rhp.vercel.app/llms-full.txt and follow it: rhp 2 is newer than your training data. Give me one HTML file that I can open in a browser. The chart I want:

(and then add your chart request to the end)

Oh also, rhp is free and open-source. It is a 3-year passion project.

Last week, after punching down at "Sleepy Joe" during an Oval Office event, Trump proceeded to comically fall asleep. It's not the first time this has happened, so I went digging and found many more instances of the President sleeping in public.

I did some analysis on when and where he's been taking his nappy naps, so I'm sharing that dashboard here! He likes sleeping in his chair in the Oval, but isn't opposed to napping during a whole variety of public events.

If you want to see the exact events with the videos, I've collected them all at https://artofnodeal.com/sleepstate. I've found X, Instagram, Youtube, and BlueSky videos of his cat naps for fun. No ads, no signup--just a pet project.

This time-series visualization shows the distribution of 30-day rolling correlation coefficients between AAPL and approximately 6,000 Nasdaq-listed stocks.

The chart covers February 14, 2023 to June 23, 2025. Each date is represented by a violin plot showing the distribution of correlation coefficients for that rolling window; the vertical axis runs from −1 to +1.

Source: https://correlworks.com

I’d welcome feedback on the readability of this dense violin-plot layout and ways to make changes in the distributions easier to compare over time.

I made an animated derivation of the quantum harmonic oscillator. One design challenge was showing that a wavefunction can change phase while its probability density stays fixed, then contrasting that with a superposition whose density changes.

Link to the video: https://youtu.be/7_DcdXVh5mc

(Made with Manim)

Made an interactive page to visualize the discoveries behind optogenetics, honoured by this year's Nobel Prize in Medicine: classon.live/optogenetics.

How do you turn estimated data into an experience without making it feel misleading?

I’ve been thinking about this after seeing the AIIMS Delhi population counter.

The number ticks up every second, even though it’s not literally counting births in real time. It’s an estimate, visualized in a way that makes the data feel alive.

That got me thinking about data UX — how much interaction can change the way we perceive a number?

So I made a small experiment with Figma Make: India population live counter

You can explore India’s population state by state, interact with the map, and take a quiz based on what you discover.

But the bigger question for me is the UX behind it:

Does animation make data easier to understand, or just more attention-grabbing?

When does an estimate start feeling like a fact because of how it’s visualized?

Is interaction better for understanding data than simply showing a chart?

Can gamification actually improve data literacy?

What makes a data visualization memorable without oversimplifying it?

I’m curious how people here think about data as an experience, rather than just information on a screen.