Soloham is a yo-yo style where there are two yo-yos off the string. They can be tossed in the air and thrown in patterns. It kind of looks like juggling with yo-yos. Typically, the style was used in a limited way because of difficulty level and scoring in competition, but in 2019, Hajime Miura used Soloham for his full routine.

Hajime had won the year before, and while the routine was a clear crowd-pleaser, he placed seventh. Shocking.. This explainer video shows why.
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Adam Bonica analyzed the age of donors across different groups and politicians. For standard donations, ages tend towards middle age and older with some in between. On the other hand, spamming PACs use the a Nigerian prince strategy.

This is an important question because the answer informs our understanding of the problem at hand and what solutions are viable. Are the donors to these spam PACs just like any other political donors, or are they systematically different? I calculated the age of donors giving to these spam PACs, and the figure below compares their age distribution with that of ActBlue donors who gave directly to presidential campaigns.

What the data reveals is troubling: these spam PACs are part of a system engineered to target and extract money from seniors.

This week we make a solid, straightforward chart more readable and focused. It’s about the small things.

PlanScore uses four measures to define partisan gerrymandering, and they’ve made the data available over time.

Most of our federal and state legislators are elected from districts. Every ten years, state governments redraw district boundaries in a process known as redistricting. PlanScore promotes fairness in the redistricting process. We make it easy for policymakers and advocates to score new district maps and assess whether they’re fair or gerrymandered. We also provide access to the most comprehensive historical dataset of partisan gerrymandering ever assembled.

I think this could be the year that more people learn what gerrymandering is. Only a few thousand more AI-generated memes to go.

For the Washington Post, Drew Harwell reports on the budding industry of AI-generated videos that make no sense but people watch and enjoy anyway.

Their power has spawned a wild cottage industry of AI-video makers, enticed by the possibility of infinite creation for minimal work. Adele, a 20-year-old student in Florida who spoke on the condition that only her first name be used because she fears harassment, told The Washington Post she is taking a break from college to focus on making money from her AI-video accounts. Another creator in Arizona who went viral with an AI airport kangaroo said he made $15,000 in commissions in three months, speaking on the condition of anonymity out of concern over online harassment.

But the flood of financially incentivized “slop” has also given way to a strange new internet, where social media feeds overflow with unsettlingly lifelike imagery and even real videos can appear suspect. Some viral clips now barely rely on humans at all, with AI tools generating not just the imagery but also the ideas.

The novelty will wear off but generative text-to-video tools will also improve in speed and quality. There are curious times ahead.

My young son likes to draw, animate, write stories, and stitch together videos. He likes to make. I tell him to use manual tools so that he knows the process and can develop his imagination. But as things are now, I wonder if and when it’ll be time to introduce other tools.

Hannah Recht is tracking data through the U.S. Census Bureau APIs:

The Census Bureau doesn’t maintain a public changelog or regularly share updates about changes to their sprawling API universe. As a longtime Census data user and package developer I’ve found it nearly impossible to know when data is added or removed. This project attempts to publicly catalog the changes.

We need a government-wide changelog.

You probably have a rough idea of education levels for each audience, but Pew Research ran a survey for a more accurate picture:

There aren’t any surprises in there. I guess I would’ve expected the Washington Post and BBC News (among U.S. adults) to be closer to the New York Times in percentage of college graduates. But maybe that difference would be covered by margin of error.

I would use a vertical line all the way down to show the 36% national average. My eyes see confidence intervals when dots are placed over bars.

For the Pudding, Andrew Aquino, with Russell Samora and Jan Diehm, supplies interactive graphics to show the many ways to cut an onion for an even dice.

An onion has a spherical shape with layers, where the outer most layer has more surface area than the inner most layer. If an onion were just flat like a layer cake, you could cut straight down in a grid pattern and you’d get even cuts. But it’s tricker with a sphere, and you want even cuts for an even cook.

Be confused no more.

Usage of an onion font for titles is also very fitting.

We know that alcohol is not the healthiest beverage to consume. When abused, people can turn into the worst versions of themselves and it makes the body work extra hard to flush out the unhealthy behavior. Kurzgesagt, in signature illustrated style, show how your body reacts to alcohol, how much is too much, and makes suggestions for moderation.
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Jeff Horwitz, for Reuters, tells the story of 76-year-old Thongbue Wongbandue, who grew infatuated with a Meta-made chatbot via Facebook Messenger. He packed his bags for the city, hit his head on they way, and passed a few days after.

The device showed that Bue traveled around two miles, then stopped by a Rutgers University parking lot a little after 9:15 p.m. Linda was about to pick Bue up in her car when the AirTag’s location suddenly updated. It was outside the emergency room of nearby Robert Wood Johnson University Hospital in New Brunswick, where Linda had worked until she retired.

Bue had fallen. He wasn’t breathing when an ambulance arrived. Though doctors were able to restore his pulse 15 minutes later, his wife knew the unforgiving math of oxygen deprivation even before the neurological test results came back.

Bue’s family looked at his phone the next day, they said. The first thing they did was check his call history and texts, finding no clue about the identity of his supposed friend in New York.

Then they opened up Facebook Messenger. At the top of Bue’s inbox, just above his chats with family and friends in Thailand, were messages from an attractive young woman going by the name “Big sis Billie.”

Remember when Facebook was about connecting people to real people? Those were the days.

AI companies like to say that they are close to or reached a level of “intelligence” in their tools that it’s like having a PhD assistant in your pocket. Claus Wilke argues that classification is misguided.

Now, presumably AI models have the required tenacity for a PhD (as long as somebody pays for the token budget), and I just said exceptional intelligence is not required. So what’s keeping current AI models from PhD-level performance? In my opinion, it’s the ability to actually reason, to introspect and self-reflect, and to develop and update over time an accurate mental model of their research topic. And most importantly, since PhD-level research occurs at the edge of human knowledge, it’s the ability to deal with a situation and set of facts that few people have encountered or written about.

In practical terms, I’m pretty sure most people do not want a PhD-level assistant. They want immediate answers. They want a diligent intern.

A PhD assistant is going to answer your question with more questions, and then five to seven years later, you will finally get an “answer” that might be correct but you won’t know for sure because there will be a lot of uncertainty attached. However, the good news is that you might be able to explore more with further research and future directions. You will have to do that on your own though or find another PhD assistant, because the original assistant has since moved on to a different interest.

Because no one is perfect, even those striving for PhD-level intelligence.

Wplace uses a world map as a canvas. Zoom in to where you live and paint with pixels, or just gander at what others’ masterpieces (given technical difficulties, it seems you can only look for now).

If this looks familiar, Wplace is based on r/place from 2017, when Reddit released a canvas that anyone could draw a pixel on every few minutes. Wplace is like that but in a geographic space and no battles for pixel space.

In what now seems like a tale as old as time, a man grew convinced that he had untapped mathematical genius, with the help of ChatGPT. But 90,000 words later, it seems that might not be the case. For the New York Times, Kashmir Hill and Dylan Freedman evaluated Allan Brooks’ very long chat.

This is going to keep happening, and it’s probably going to get worse until people realize that the chatbot is not thinking. It’s a product of statistical convergence. The “delusions” are computer errors. Please stop pretending the chatbots are people.

Molly Smith reports for Bloomberg on the appointing of EJ Antoni to head the BLS. Antoni has been vocal about recent estimate revisions.

Antoni came on Bannon’s podcast shortly after the latest jobs report was released, where he was asked if there was a “MAGA Republican” in charge of BLS. Antoni responded, “No, unfortunately.”

Antoni added that the absence of a Trump pick running the agency is “part of the reason why we continue to have all of these different data problems.” He contributed to the Project 2025 policy rubric, which, in part, called for maximizing hiring of political appointees at the Labor Department, which oversees BLS.

Antoni is calling for an audit of the full statistical process and more transparency. Interesting.

Many are discovering that the Bureau of Labor Statistics updates past estimates, but some have been misled to believe that the recent correction to jobs numbers was politically motivated. For the New York Times, Ben Casselman, with graphics by Keith Collins and Christine Zhang, explains why updates to the data are a regular thing.

There is a fundamental tension inherent in all economic data: accuracy vs. timeliness.

Policymakers, investors and businesses want information as quickly as possible so it can inform their decisions. But the most complete data is often based on tax returns, Social Security filings or other records that aren’t available until months or years later.

Revisions are the imperfect solution to this problem. Statistical agencies release preliminary estimates of job growth, inflation, gross domestic product and other measures, then revise them as more complete data becomes available.

Reduced funding, agency layoffs, and uncertainty in the economy don’t help with the accuracy.

The stores use Flock cameras to collect license plate data from cars entering parking lots. Law enforcement is tapping the data as a source for their growing surveillance systems. Jason Koebler for 404 Media reports.

“What we’re learning is that two of the country’s most popular home improvement stores are contributing to the massive surveillance dragnet coordinated by Flock Safety,” Dave Maass, director of investigations at the Electronic Frontier Foundation, told 404 Media. “Do customers know that these stores are collecting their data and sharing indiscriminately? Probably not. Have these companies given thought about how this data might put their customers in danger, whether it’s cops stalking their exes or aggressive ICE agents targeting yard workers? Probably not. If these companies want customers to feel safe in their homes, then they should make sure they’re also safe where they buy their supplies.”

Maybe this doesn’t affect you directly now, but on our current path, it will eventually.

During the DOGE-fueled federal firings, which seems like a lifetime ago already, the National Weather Service lost about 550 employees. They received permission recently to hire 450, because it is necessary. For CNN, Andrew Freedman reports.

The announcement was also met with frustration over the people the agency lost in the failed attempt at government savings.

“How much time/money is it going to cost to train a bunch of new people when we had already-trained people in place?” asked another NOAA official, who requested anonymity because they were not authorized to talk to the media. It is possible that some of the new hires will have been previously trained employees who were let go in the DOGE cuts.

OpenAI introduced GPT-5 in a livestream, and they used a set of seemingly straightforward charts for benchmarks. The point was to show the improved performance of GPT-5 over previous models. However, the labels do not remotely match the bar heights.

The bar for 69.1% is the same height as the one for 30.8% when the former should be more than twice the height of the latter. The bar for 52.8% is taller than the one for 69.1%. It’s off.
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