rhiever

AI agents can make data visualizations now. Ask Manus, Claude, or ChatGPT to chart a dataset and you'll get something back in seconds. The output is almost always... fine. Functional. Generic. The kind of chart that communicates data but doesn't communicate insight. In this post, I experimented with how to make AI agents visualize data better.

In November 2025, AI coding tools went from “halting and clumsy” to surprisingly capable. Suddenly they could produce whole, working apps from minimal instruction, in ways they simply couldn't before.

This experiment makes that shift visible. We gave 22 AI models the same exact prompt to build a working analog clock from scratch, then ran each model five times independently.

Coffee... lots and lots of coffee. :-)

Happy to hear it! I partly made this notebook to convert my coworkers and collaborations to a Pythonic workflow, so this is promising... :-)

Very cool - thank you for pointing me to this!

The native GitHub IPython Notebook viewer also doesn't support within-notebook linking, which can be fairly annoying when you spend an hour putting together a nice table of contents system.

Here's another version that normalizes for births per year: http://rhiever.github.io/baby-name-explorer/

Enjoy!

I originally didn't smooth the data, but it looked way too jagged and crappy for many of the names. Ultimately, I erred on the side of aesthetics and sacrificed the ability to look up exact numbers in the graph. Rest assured, though, if you want the exact numbers you can download the underlying data of every graph with one of the links under the graph.

Thank you for the kind words. :-) Although that app you linked is a bit different - it's showing when names were popular over time. This app goes one step further and calculates how many of those people are still alive and makes a best guess at how old someone is based solely on their first name.

I also made a version similar to the Baby Name Wizard that look solely at name popularity here: http://rhiever.github.io/baby-name-explorer/

And I suppose it's just a matter of aesthetics for which one you prefer in that case.

Cheers!

r

Response: https://twitter.com/randal_olson/status/561539109014241281

If you look in the methods, the experiment has been successfully reproduced twice after the initial trial with distinct cohorts of students.

An R^2 of 0.33 is a fairly decent correlation for a study involving human subjects. It's obviously not perfect, but the authors are not claiming that it's perfect.

Because they tried them, then decided to not continue trying them because they didn't correlate as well and were difficult and expensive to implement.

Check out the paper (Table 3) for significance statements: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2501480

Some of the trends in Part 1 are not statistically significant (e.g., "never attend church" vs. "sometimes attend church"), but I presented them regardless because they have a pretty clear trend. I believe p-values are only presented in comparison to the reference point.

All of the differences highlighted are statistically significant (p < 0.05) in the multivariate model. So there's that. :-)

The downside is that the paper only presents probabilities relative to a reference point, but doesn't give raw probabilities.

That's a great idea! I've heard of researchers doing this kind of analysis to detect "hidden gems," i.e., papers that influenced papers that ended up becoming the big cited paper. The tough part, of course, is that most journals are keen on keeping all of their data behind company walls. Perhaps PLoS would be an exception, though...

Here is a rehost of the web page since it went down: https://i.imgur.com/rABtgo9.jpg

I was very worried until I noticed that. Damn my young adventurous years!

Did you know that they're really only accurate a few days out? Despite the fact that most weather sites provide forecasts a week or two out.

Sure! That's valid. Just didn't want people to get confused. It's happened before. :-)

Different post. :-) That post was about the accuracy/reliability of various weather forecasting services for predicting rain.

This one is about the accuracy of three weather forecasting methods and how far into the future they can predict the temperature.

I think you're right, and in that sense, maybe "accuracy" isn't the best word. "Reliability" might be the better word here.

One plausible theory I've heard is that the early 1980s and the 1990s were a "boom" period for CS, particularly with the dot-com boom in the 1990s. CS saw huge spikes of enrollment during that period, and apparently a larger proportion of women lined up to get a CS degree during the booms.

"Even better" in the sense that there is actually a STEM field that has a slightly majority of women as majors, as compared to the rest that are below a 1:1 ratio. So it helps even out the ratio for STEM fields as a whole a little bit -- but obviously not enough.

Agreed, those charts do a great job of showing what's going on in CS. But it's also useful to look at percentages because college enrollment has gone up severely in the past decade, so the increase in the raw number of women in CS could merely be because there's more people in college in general. Showing the percentages communicates if women have proportionally enrolled more/less in CS.

There have been several theories suggested on /r/dataisbeautiful -- come check it out: http://www.reddit.com/r/dataisbeautiful/comments/27ypq1/coll...

I wish I knew about this earlier! Thanks for explaining it to me though. :-)

If any of the alternative paths were common enough, they would show up in the charts as well. I didn't limit the analysis to a particular set of moves; I simply counted all of the paths present in the data set and showed the most common ones. This is why two variations of the Indian Defence show up in the "White's second move" chart.

I think it'd be interesting to try to combine all possible paths for an opening into a single count, but that would probably be complicated if multiple openings can be reached through the same path. (e.g., which opening would the shared path be assigned to?)

I double-checked my data set to look into this, and you're right. Nearly all of the Pircs in that time period were done by a guy named Moheschunder Bannerjee: https://en.wikipedia.org/wiki/Moheschunder_Bannerjee

He contributed to the development of the Indian Defence.

I just found this research paper on Go: http://biorxiv.org/content/early/2014/05/19/005223