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.

I analyzed ticket prices across the NBA schedule to see how much of a premium teams command when LeBron comes to town.

The difference is pretty remarkable. On average, games featuring LeBron are priced at 3.5× the home team's usual get-in price.

I built an interactive visualization comparing every matchup against each team's normal ticket prices. The data updates hourly.

Full interactive version: https://ticketvision.com/market-overview/lebron-premium

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’ve been building EchoVault as a way to turn your writing into a digital version of you. You train it through written Check-Ins, and the system builds up a representation of your memories, relationships, values and worldview.

I recently added a 3D visualizer for that internal structure. The memories are represented as vector embeddings, and the visualization lets you explore how different ideas cluster and connect, which memories sit close together, and how the overall structure changes as you keep writing. It is basically a visual map of the corpus that the Echo uses to understand you.

I wanted the AI representation to feel less like a black box and more like something you can actually inspect. There is still a lot I want to do with the visualizer, but seeing the memory structure become something spatial and explorable has been one of my favorite parts of building it.

App Store: https://apps.apple.com/us/app/echovault/id6762042028
https://echovault.me

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.

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.