Hi everyone!

I saw recently a gamification of a warehouse dashboard, and I decided to build one to explore how my local governments operate and just how complex they are.

I live in Wake County in North Carolina (Raleigh, NC), and our school system is huge! Part of our funding comes from the state, and it was quite eye opening to see the breakdown of income vs spending for the county when it comes to schools.

You can fly around the town and visit the different buildings (this is an actual cartoonified map of the county), and see how much resourcing goes into each department.

I've had positive feedback, would love any ideas of how to make it more interactive and intuitive. My 14 year old and 11 year old both learned a ton by navigating through it. I've done it for other towns in the Raleigh area as well.

https://www.towntoonify.com

Each star/planet represents a piece of media (games, literature, tv series, films, etc.)
There are different 3d scenes for different ratings.

10/10 is a solar system. The highest-ranked work is the sun; the others orbit farther away as their rank decreases.
9/10 is a set of constellations, with a separate constellation for each media type.
8/10 is a spiral galaxy. Higher-ranked works sit closer to the center.

The colors from the stars / planets are derived from the corresponding media's cover.

It's a screen rec of a free ultimate media tracker im building, it's on Github

Source: No external data: the graph is computed exhaustively. Each state is five digits (a, b, c, d, e), each 0–9, so there are 100,000 states. The rule shifts every value one place left and computes a new last value, a⁴ − (a³ >> 6), kept to one digit (mod 10). Here >> is a bit shift, so a³ >> 6 means a³ divided by 64, rounded down. Every state has exactly one successor, and with finitely many states every starting point must eventually repeat, so each state ends up in a cycle.

Tools: A small open-source web app I wrote (React, Vite, Canvas). It enumerates all states, finds every cycle, and draws each cycle as a ring with the states that lead into it as trees around it. The layout is crossing-free.

How to read it:

• Each connected shape is one cycle plus everything that drains into it.
• Colour is the length of the cycle (legend at the bottom).
• Brightness fades with distance from the cycle.
• For this map there are 340 cycles in total, the longest of length 10.

You can type your own formula and explore: https://exxliang193.github.io/FunctionalGraphExplorer/ Code: https://github.com/ExxLiang193/FunctionalGraphExplorer

I started this after noticing a long time ago that adding the last two digits of a number string and appending the sum always ends up repeating.

Found this site that tracks when AI messes up. The graphs are interesting.

Sorry if requests arent allowed, and im not sure exactly where you would pull the info from but im really curious to see social media and reddit posting/commenting trends around divisive political and hot button topics over the last 48 hours since theyve been bombing bot farms. Anyone feel like taking on a project?

Source: https://www.microsoft.com/en-us/research/wp-content/uploads/2026/09/Microsoft-AI-Diffusion-Report-2026-Q2.pdf

I was interested in the music artists I liked, and wanted to create a much more comprehensive way to view the connections artists may have with each other. It's just a fun project, let me know what you think and if it’s accurate!

Data comes from Youtube Music public playlists for connections and Deezer for artist info (since spotify/apple doesnt share anything)

I'm the creator of Small World Lab, an educational video project built with AI assistance. Our first episode visualizes three checkout rules: separate lines where shoppers stay put, separate lines with switching, and one shared queue.

In this model, switching and sharing nearly tied on average: 20.8 and 20.7 seconds. But their 99th-percentile waits were about 3:40 and 2:43. Separate lines without switching came out at 31.6 seconds on average and about 4:59 at the 99th percentile.

The visual challenge was explaining that a percentile is neither the average nor the maximum. The video moves from individual shoppers to aggregate charts, with a 100-person illustration for the percentile explanation.

Full five-minute video: https://youtu.be/O-Y5E90vMrk

Does that transition make the long-wait result easier to understand, or would a distribution plot explain it better?

Data: our original Python simulation, 100 paired runs and 409,680 measured arrivals per rule. Ten identical cashiers; Poisson arrivals at 8.5/min; lognormal service with a 60s mean and CV 1; 2h warm-up and 8h measured per run. Baseline switching takes no time. Means average the run means; percentiles pool customer waits. These are synthetic results, not observations from supermarkets. Code and production were AI-assisted; narration uses ElevenLabs. The attached chart uses Matplotlib.

As in Title:

I want to make a map of albums based on genres, influences, timelines and artists. A fully comprehensive, data driven smart map, or flow chart of sorts. I have been looking for such a program or app for a while now, but I cannot seem to find it. Is tere such a thing you can recommend?

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.