Have your musical tastes changed over the years? In celebration of the company’s 20th anniversary, music streaming service Spotify is now allowing people to view and share their listening stats from the day they joined. Spotify 20: Your Party of the Year(s) will include previously hidden data, allowing you to revisit your Spotify history.

The stats include info about your first day on Spotify, the total number of unique songs you’ve listened to, the first song you ever streamed and your all-time most-streamed artist.

You’ll also receive your own personalized All-Time Top Songs Playlist, a collection of your top 120 tracks, with play counts for each song. The feature includes a custom share card, which can be saved, sent to friends or uploaded to Instagram, too. 

The feature became available on Tuesday morning. To find Spotify 20: Your Party of the Year(s), open the Spotify mobile app and search “Spotify 20” or “Party of the Year(s).” You can also visit spotify.com/20 on your mobile device.

Spotify is the most popular music streaming service, with over 700 million users and access to music, podcasts and audiobooks as part of a monthly $13 subscription.  

Potholes are a pesky problem — just ask scooter company Lime, which listed them as an official risk to its business in its IPO filing last week.

History is littered with claims that technology can help solve or blunt the problem of potholes, and still they persist. But as cars become increasingly laden with advanced sensors, they are becoming a tool that can quickly alert cities to potholes and other municipal problems.

Last month, Waymo and Waze announced a pilot program to share pothole data with local governments. Now, fleet management company Samsara says it’s one-upping that idea with its own AI-powered offering that it calls “Ground Intelligence.”

Samsara has spent the last decade giving its customers cameras to mount inside millions of trucks for driver monitoring, theft prevention, and helping with liability claims. The San Francisco-based company has taken all that data and trained its own model that can detect multiple different types of potholes and determine how quickly they are deteriorating.

The idea is that Samsara-equipped trucks are far more prevalent than Waymo’s robotaxi fleet, which currently stands at just around 3,000 vehicles. Even as that number grows, Samsara believes it will be able to collect more data and, crucially, more repeat data from the same locations that show how potholes change over time.

Samsara believes this data will be valuable to cities — the company announced Tuesday that the city of Chicago is already under contract as a customer — and that it will be the first in a series of insights and data points that will be offered in Ground Intelligence. Other potential features include detecting graffiti, broken guardrails, low-hanging power lines, or really “anything that we can observe that has relevance to a city, or also to the private sector,” said Samsara’s vice president of product, Johan Land.

Typically, Land said, cities have to either dispatch workers or sift through hundreds of 311 calls to find these problems. It’s a lot of noise. Samsara’s pitch is that it can deliver the signal, and quickly, because of the sheer number of commercial trucks and vans that already use its cameras.

Ground Intelligence works as a dashboard. It proactively populates warnings on a map of developing potholes and other potential problems. It also allows cities to pull anonymized footage from vehicle cameras to confirm citizen reports of downed street signs, clogged sewers, or other public infrastructure problems.

“That’s the magic here, it takes a process that was reactive and makes it proactive,” Land said. “That means that you don’t just go and fix one pothole. You plan it out: ‘I know where all the potholes are in this area. I go out and I fix one by one, in one sweep.’”

Samsara is also thinking up other ways to leverage this moving municipal surveillance network it has built. On Tuesday, it announced a product called Waste Intelligence, which makes it easier for waste management companies to quickly confirm if their customers’ trash or recycling was picked up. Samsara also announced a “ridership management” offering, which can help alert bus drivers to “unexpected boarding events,” or create a “digital manifest” for school buses.

Thanks to player tracking technology, this is now a quantifiable skill, and, like so many things, Lionel Messi is great at it. Through their research, Bornn and Fernández found that Messi is perhaps one of the best walkers in all of soccer. The Argentine legend is prone to lollygagging, and common conjecture has been that he’s either conserving energy or just can’t be bothered. While this may be part of it, their study demonstrates that Messi’s slow saunters about the pitch short-circuit defenses in unique ways. “That walking behavior is not a detachment from the match but a conscious action to move through empty spaces of value and claim the control of valuable space,” they write. “Messi does this very effectively, placing him near the top of players in terms of space gained during the whole match, despite the lack of active gain.”

In other words, Messi can achieve more on a stroll than most players do with an all-out sprint.

Ask the people who work deep inside soccer’s analytical engine rooms about how their work affects the way they view the game, and you’ll get some illuminating responses. “I watch in a strange way,” Bornn says. “I tend to watch with an eye toward what the tactical system could be, or whether the data that’s being collected is miscapturing what’s going on, or that the data might capture the core components but our models will miss what’s going on. It has kind of ruined sports for me.”

Sarah Rudd tends to agree. “It’s a little exhausting watching every game so analytically,” she says. “It’s hard to turn off that part of your brain, but you still want to be a fan and you want to enjoy.” Rudd got into soccer analytics so early, she essentially had to invent it from scratch. After graduating from Columbia University, she spent a few years living in Chile, where she fell further in love with her favorite sport. She fondly recalls squinting at her small, standard-definition television set to watch broadcasts of matches from Argentina. “You had to really know the teams,” she says. “If you weren’t really familiar with the teams, you couldn’t figure out who players were. It’s hard to read the numbers, and you couldn’t really see their faces.”

Rudd and her boyfriend at the time invented a game based on this challenge. “We would turn on the TV, and if Boca [Juniors] was playing, it was how quickly can you spot Carlos Tevez. Not because of his face but because he had this really weird running style. It was like, ‘Ope! There he is.’” Built like a fire hydrant, the stout, pugnacious Tevez was a rabid delivery robot programmed to kill on the pitch. Just thinking about it makes Rudd wistful: “What a player.”

Of her time in South America, Rudd recalls, “It made me want to work in football even more.” She took a gig doing data mining and machine learning for Microsoft in Seattle but continued to search for entry points into the sports industry. “A friend of mine suggested that I do an MBA program and then see if I could get a job at Nike or Adidas in their football business unit.” In 2011 she caught wind of a contest being held by sports analytics company StatDNA. “They were doing a research competition where they gave you a dataset,” she says, noting that, until that point “there was practically nothing” of the sort that had been collected for soccer.

Using a spreadsheet of rudimentary player-location data, Rudd set out to devise a method for analyzing an individual’s performance in more complex ways than simple goals and assists. “There wasn’t a ton of direction,” she recalls. “I think just from watching the game I was interested in evaluating how much value are people adding with every action that they do. Not necessarily trying to evaluate alternatives but being able to somewhat quantify, like, that was a dangerous giveaway, or it’s stupid to take a shot from there, that sort of thing.” To accomplish this she used Markov chains, a statistical tool that helps determine the likelihood of something happening within a system based on its current state.

First introduced in 1906, Markov chains represent a departure from the principle of absolute independence, a core tenant of probability theory seen in things like roulette wheels where each spin offers a fresh experiment with repeated odds. The chains are a way to examine ongoing scenarios where each starting point presents a different opportunity for the future. In the magazine American Scientist, Brian Hayes uses the board game Monopoly as an example:

Rolling the dice determines how many steps your token advances around the board, but where you land at the end of a move obviously depends on where you begin. From different starting points, the same number of steps could take you to the Boardwalk or put you in jail. The probabilities of future events depend on the current state of the system. The events are linked, one to the next; they form a Markov chain.

The chains were invented by and named for Andrey Markov, an ornery Russian mathematician who, according to Hayes’ reporting, stopped attending meetings at the Academy of Sciences in Saint Petersburg late in his career because he claimed he didn’t have proper shoes. When the school sent him a pair of new boots, he said they were “stupidly stitched,” thus proving that his current state (pissed off) contributed to the likelihood of his return (zero).

The roots of Markov’s discovery sprouted from a dispute over the law of large numbers and free will. He long believed the universe was a series of events whose interconnectedness can be understood through mathematics. He refined this idea by condensing the text of the Alexander Pushkin novel in verse Eugene Onegin into one long sequence of letters suitable for mathematical analysis. In doing this, he discovered that stable patterns of double vowels and double consonants appeared throughout the work. Taking a large sample from the beginning of the text, he was able to determine that letter distribution didn’t adhere to the principle of independence, demonstrating that even something as beautiful and fluid as poetry was prisoner to the cold deductive properties of mathematics. He published his first paper on the subject in 1906 and formally presented his findings in 1913, one year after his request to be excommunicated from the Russian Orthodox Church.

“Any attempt to simulate probable events based on vast amounts of data—the weather, a Google search, the behavior of liquids—relies on Markov’s idea,” states an article in the Harvard Gazette. Sarah Rudd, who studied computer and environmental science as an undergrad at Columbia University and worked on Microsoft’s Bing search engine, added soccer to this list. Her paper “A Framework for Tactical Analysis and Individual Offensive Production Assessment in Soccer Using Markov Chains” placed players into one of 39 “states,” depending on things like location and ball possession to calculate the likelihood of what would happen next.

Rudd’s work was impressive enough to win her both the competition and a job with StatDNA. When the company was acquired by Premier League giant Arsenal the following year, Rudd suddenly found herself in London working for her favorite team and introducing the backroom staff to her advanced research. She spent nearly a decade at the club and became the head of analytics before leaving in 2021 to start her own firm with her husband.

“One of our jobs is to be the calm voice of reason,” Rudd says. “This is one of the things I like about consulting versus working for a club. You can be a little bit emotionally detached. You can be a little bit calmer. Because when you’re at the training ground every day, emotions are high. It’s a really stressful environment. There’s a lot at stake.”

In an interview with The Athletic, Rudd says she started her own firm, in part, “to figure out football.” I ask her what this would look like, and she concedes that “it’s really hard,” almost to the point of being self-defeating. “One of the difficult things about analytics in football is that there are so many different ways to win. There are so many trade-offs. I think somebody described it as trying to cover yourself with a blanket that’s too short. If you press really high, that’s going to come at the expense of something else. There are a few things we know that really help you win, but there’s still a whole lot where you could be just as effective doing something else.”

No matter how much research is done, soccer maintains its severe allergy to simple answers. Even something as fundamental as whether you want your team to have the ball or not is up for debate at the highest levels. As Dutch legend Johan Cruyff argued, a “footballer has to have the ball at his feet.”

Diametrically opposed to this philosophy is José Mourinho, one of the most successful managers of the 21st century. The rakish Portuguese gadfly opined that “whoever has the ball has fear,” preferring that his teams lie in wait and capitalize on opponents’ mistakes like the humans in The War of the Worlds who hunkered down until the Martians caught a sniffle and died.

Where else can such drastically conflicting worldviews have equal footing but in the poorly designed experiment that is soccer? “For so long it was, like, if only we have really wide-scale access to tracking data, that will solve all of our problems,” Rudd tells me. “And then we got it and, nope, we still have lots of problems.”

Reflecting on her 2011 paper using Markov chains, Sarah Rudd can’t help but poke holes in the research that made her a pioneer of the movement. “At the time I wrote that paper, I wasn’t looking at it nearly as analytically as I do now,” she says. “I think there were definitely a lot of decisions that I would have done differently, particularly, how you break down the field.” Rudd divided the pitch into equal boxes, dividing the expanse of open grass into a grid of easy-to-track cells. It was order from chaos manufactured out of misguided desperation. “Now we know that how the pitch operates isn’t necessarily linear or in neat little squares,” Rudd says. “There are certain zones where things happen for a number of reasons that don’t quite align with those pitch markings.” These areas of congestion are nebulous and reactive to tactical trends, such as defenses funneling play out wide or pressing high when out of possession, strategies informed by the work of people like Bornn and Rudd, analysts who are pulling at the proverbial blanket in offices unseen from public view.

“I’m not a huge fan of jumping straight to pragmatism if that’s not what’s required,” Rudd tells me. “We have to remember that we’re in the entertainment industry. It’s got to be fun.”

Excerpted from How to Watch Soccer Like a Genius: What Architects, Stuntwomen, Paleoanthropologists, and Computer Scientists Reveal About the World’s Game. Copyright © 2026 by Nick Greene. Used with permission of the publisher, Abrams Books. All rights reserved.

Farrah was fed up with her vagina.

For the past two years, the 29-year-old dancer from Ohio had been dealing with severe pelvic pain and vaginal odor. “It was like 8/10, horrible core pain,” she says. “I couldn’t lie down. I couldn’t even work an office job. It was bad.”

When she visited doctors, she told them what she thought the culprit was: an allergic reaction to soy oil in a vat of water she’d swam in during a pirate-themed dinner theater performance. But they didn’t believe her. “They attempted to fix it with antibiotics,” she says. “And they just did nothing.”

So Farrah (who requested we withhold her full name to speak freely about health matters) started Googling her symptoms. That’s how she stumbled on Neueve, a vaginal health company that provides supplements, suppositories, and at-home vaginal microbiome testing kits.

She ordered a test from the company for $150, and it came back with a diagnosis: aerobic vaginitis (AV), a bacterial infection caused by an overgrowth of E. coli or streptococcus. She ordered supplements the company recommended, and she says the pain abated almost immediately. “I was just so glad to actually know what was wrong,” she says.

Farrah is one of a growing number of women who have used at-home tests to self-diagnose issues with the vaginal microbiome—an ecosystem of bacteria growing inside the vagina; the presence of “good” bacteria correlates with lower risk of STIs and other types of infections, according to numerous studies. The industry got a shoutout when the Silicon Valley entrepreneur Bryan Johnson recently posted on X that he had just given oral sex to his girlfriend, Kate Tolo, then followed up with a screengrab of her TinyHealth vaginal microbiome report. He proclaimed that she scored “100/100” and that hers was in the “top 1% of all vaginas” due to the dominance of Lactobacillus crispatus, a type of “good” bacteria found in the vagina.

Johnson’s thread garnered widespread mockery, with many questioning why Johnson would publicly quantify his partner’s vaginal health in such a fashion. But it also received replies from women online who are tracking their own vaginal microbiomes to treat their bacterial infections, to boost fertility, or just out of interest. Some even posted their results.

The market for at-home vaginal microbiome tests is growing—TinyHealth, the startup Tolo used, claims vaginal health testing sales spiked 2,000 percent within the first 48 hours of Johnson’s post—and similar companies include Juno Bio, which partners with Neueve; the UK-based Daye, and Evvy. But some experts believe there’s not yet enough research to support the long-term validity of such tests. None of the at-home kits on the market are approved by the FDA. There are also questions as to whether they empower women to take their health care into their own hands or simply create more anxiety for them.

Twenty-eight-year-old Samantha (she also requested a pseudonym given the sensitive nature of this topic) developed an interest in vaginal microbiome testing after experiencing a bout of bacterial vaginosis, or BV. She ordered a testing kit from Evvy upon the recommendation of the Facebook group Beyond BV, which offers support for women with recurring vaginal infections, and where they often post their own results.

Samantha found her test results useful, but she also noticed a distinct strain of paranoia within the group. For instance, when many women receive their results, they tend to focus on whether they have enough Lactobacillus crispatus, or “good” bacteria, in the vagina. “I’ll read posts where women are freaking out if they have like 97 percent crispatus and then they’ll retest and they’ll have like 60 percent and be really disappointed and scared,” she says. The opposite also holds true. “Women will post about having 100 percent crispatus and other women in the comments will just be like, ‘Oh, I’m so jealous, I’m having so many issues, I hope to be you one day.’”

If you use Signal, there’s something important you should know. The encrypted messaging app has rolled out a set of new in-app safety measures designed to protect you from phishing and social engineering attacks.

This isn’t out of nowhere either. Back in March, Signal confirmed that its platform had been targeted by phishing attacks aimed specifically at government officials and journalists. It seems that these new changes are a direct response to that.

What are the new safety changes in Signal app?

The most notable addition is a “name not verified” notice that now appears on profiles. This is important because Signal cannot actually verify the names users display on their profiles. Anyone can claim to be anyone since the profile name is set by individual users.

Signal has also introduced an extra confirmation step when you receive a message request. The idea is to only accept requests from people you actually know and trust. This is quite similar to how WhatsApp handles chats from unknown numbers, where you get the option to accept or cancel when it comes from an unknown number.

The app now also surfaces more detailed safety guidance directly in the interface. You’ll see reminders not to respond to chats claiming to be from Signal, since Signal will never reach out to ask for your PIN, registration code, or recovery key. If someone is asking for any of those things, it’s a scam.

The app also highlights vague messages – designed to lure a reply, suspicious web links, and any chat pushing financial tips – as red flags to watch for.

Why this matters?

Social engineering is one of the most common ways people get compromised online. It doesn’t require a technical hack. It just requires tricking you into handing over the right information.

Scammers impersonating Signal itself is a particularly sneaky tactic because it exploits the trust people place in the app. Signal has confirmed more changes are on the way, so this is just the beginning of a broader push to make the platform safer for everyone.