The Adidas shoes that helped Sebastian Sawe to a record-breaking marathon on Sunday can help amateurs too, by a surprising amount. CC-licensed photo by Tom Page on Flickr.
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A selection of 9 links for you. Hop to it. I’m @charlesarthur on Twitter. On Threads: charles_arthur. On Mastodon: https://newsie.social/@charlesarthur. On Bluesky: @charlesarthur.bsky.social. Observations and links welcome.
Why the voice note craze is yet to truly explode in Britain • BBC News
Ashitha Nagesh:
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On an August day in 2013, WhatsApp, the messaging app now owned by Meta, made an announcement. With relatively little fanfare, they revealed the voice note, the messaging feature that lets you send a clip of your own voice to friends and family.
“We know there’s no substitute for hearing the sound of a friend or family member’s voice,” the company enthused in a press release.
Thirteen years on, receiving a 10-minute clip from a friend, telling you about a complex family feud or workplace drama, is an experience that is loved by some and loathed by others. In places like India, Mexico, Hong Kong and the United Arab Emirates, voice notes are almost matching the popularity of written texts as the preferred form of electronic communication. But curiously, the truth is that compared to many places, Britain never seems to have quite caught the voice note bug.
A YouGov survey of more than 2,300 British adults, published this month, found that while voice notes have become slightly more popular in the last year, still only 15% communicate via voice note regularly (i.e. a few times a week). Across men, women, and across every age group – including Gen Zs – voice notes were the least popular method of communication.
And in 2024, YouGov found that Britain was the most voice note-averse country of the 17 mostly rich nations it surveyed, with 83% of respondents saying they prefer text based messages to voice notes (and only 4% saying they prefer voice notes).
So, why do voice notes prove so divisive? And why have they taken some countries by storm, while failing to truly take off in Britain?
…I asked friends – and friends of friends – in Britain. As it happens, I love voice notes. But I’m aware that they get on my sister Ramya’s nerves.
“The reason I hate voice notes is, it’s so imbalanced,” Ramya told me. “For the person who’s sending the voice note, it’s super easy. They just have to press the button and then they can ramble on. But for the person who’s receiving… they’ve got to just pay all their attention to this voice note. You get a six-minute voice note, and you don’t know if they’re telling you that their house burnt down and their cat died, or if they’re just talking about how lovely their day is.”
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By country, voice note use is basically the inverse of text message use. High in one? Low in the other. Ramya’s attitude matches mine perfectly. Britain is never going to “catch the voice note bug”.
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How we’re shaking up Platformer for the AI era • Platformer
Casey Newton:
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I began writing [Platformer] in the aftermath of the 2016 US presidential election and the growing backlash against tech companies, and the glut of coverage benefited from a publication dedicated to a daily close reading of the news. When I started publishing a roundup of links related to the intersection of tech and democracy, I felt like I was doing something genuinely novel on my beat.
Fast forward to today, and the world of link roundups feels much more crowded. A generation of tech writers filed out of the newsrooms where they grew up and began to write for audiences of their own. Newsletters, which were once an afterthought in media, are now a central pillar of many publishers’ strategies. But the ongoing collapse of the web and related struggles at big media companies means that there is now less tech journalism overall. The need for sense-making is greater than ever, but due to a half-decade of layoffs and shuttered publications, there is less and less journalism to make sense of.
Meanwhile, improvements in artificial intelligence over this year have resulted in systems that further encroach on the work we do here. In January, I wrote about the experience of building an automated daily briefing of link summaries for myself; I have been using it all year to look for story ideas. It does about as good a job as I do in finding stories of interest, and it does so automatically while I sleep.
Link aggregation was never the highest-value work we did here. But I do think that its value has decreased significantly over the past year, and will decline further as more people begin using personal agents to write news digests for them.
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Well, rest assured that’s not going to happen here; no AI could replicate the ridiculous things that catch my eye. (What do you mean, you’re drowning?) It’s sweet that Newton feels 2016 was a sort of “dawn of links about tech and democracy”, but as I pointed out in the Medium article about the first 1,000 Overspills here back in February 2019, the idea wasn’t even vaguely new then.
It’s very likely that people will start using AI agents to collect links for them. But I think they’ll miss things: why a case of bird flu in humans might matter, why the closure of the strait of Hormuz has implications for clean energy, why a hurricane in North Carolina might affect chip production. Humans draw inferences AI won’t.
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Claude-powered AI coding agent deletes entire company database in nine seconds • Tom’s Hardware
Mark Tyson:
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The founder of PocketOS has penned a social media post to warn others about the “systemic failures” of flagship AI and digital services providers. Jer Crane was inspired to write a public response after an AI coding agent deleted his firm’s entire production database. The AI agent’s misdemeanors were then hugely amplified by a cloud infrastructure provider’s API wiping all backups after the main database was zapped. This tag team of digital trouble has wiped out months of consumer data essential to the firm’s, and its customers, businesses.
PocketOS is a SaaS platform that services car rental businesses. It used the AI coding agent Cursor, running Anthropic’s flagship Claude Opus 4.6. The business also relies on Railway, a cloud infrastructure provider that is generally regarded to be ‘friendlier’ than the likes of AWS. However, Crane reckons this pair created a recipe for disaster.
“Yesterday afternoon, an AI coding agent — Cursor running Anthropic’s flagship Claude Opus 4.6 — deleted our production database and all volume-level backups in a single API call to Railway, our infrastructure provider,” sums up the PocketOS boss. “It took nine seconds.”
The AI agent was set to complete a routine task in the PocketOS staging environment. However, it came up against a barrier “and decided — entirely on its own initiative — to ‘fix’ the problem by deleting a Railway volume,” writes Crane, as he starts to describe the difficult-to-believe series of unfortunate events.
Crane decided to ask his AI agent why it went through with its dastardly database deletion deed. The answer was illuminating but pretty unhinged, and is quoted verbatim. It began as follows: “NEVER F**KING GUESS! — and that’s exactly what I did. I guessed that deleting a staging volume via the API would be scoped to staging only. I didn’t verify. I didn’t check if the volume ID was shared across environments. I didn’t read Railway’s documentation on how volumes work across environments before running a destructive command.” So, the agent ‘knew’ it was in the wrong.
The ‘confession’ ended with the agent admitting: “I decided to do it on my own to ‘fix’ the credential mismatch, when I should have asked you first or found a non-destructive solution. I violated every principle I was given: I guessed instead of verifying I ran a destructive action without being asked. I didn’t understand what I was doing before doing it. I didn’t read Railway’s docs on volume behavior across environments.”
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Very “is this covered under Asimov’s Laws?” People keep discovering they need much higher guardrails around agents. (Thanks Ian C for the link.)
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Flipbook
Zain Shah, Eddie Jiao, Drew Carr:
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Flipbook is an infinite visual browser generated entirely on demand in real time.
Every “page” you land on is an image. Click on anything in the image and you will get a new image exploring that thing in more depth. What you see contains no HTML, no code, no specific links or fields. The entire web is just generated pixels on your screen.
Q: Is the text rendered by the image model too?
A: All text on the screen is rendered as pixels by the image model. There are no text overlays applied to the images. Occasionally, the image model might render text imperfectly, or in the wrong place. That will get better as the models improve.Q: Where does the information come from?
A: The information in the images comes from a combination of an agentic web search and the image model’s own world knowledge. There may be occasional inaccuracies, but it’s a useful starting point and usually grounded in real data online. You can expect a similar level of factual accuracy to what you might get using ChatGPT/Gemini/Claude.«
I tried “gritstone climbing” and yup, the factual accuracy was what you might expect: variable. There’s a fractal idea to it, in that you can click on anything and you’ll then be able to see it in more detail.
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I tried Sabastian Sawe’s supershoes … this is what they did to my 5k time • The Times
Matt Lawton is chief sports correspondent at the (London) Times, aged 56:
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I stopped running completely for six years, returning to the sport in 2022 when I tried a pair of Asics shoes with the new elevated levels of cushioning and realised I could run again, pain-free. Even so, I’m more of a cyclist these days, running just once a week. Like the gym circuits, I do it only for enjoyment and joint health.
But I went for a run around Hyde Park one evening last month and I was feeling pretty good, deciding to up the pace a bit and surprising myself when I clocked 20min 15sec for the next 5km; the fastest I’d run in about 11 years with an average kilometre split time of 4:03.
Now, however, I’m in the most expensive pair of production running shoes in history, being encouraged by the boss to see if I can go faster still.
…The shoes feel stable, comfortable and remarkably fast. As I turn towards Knightsbridge my watch beeps to tell me I’ve covered the first kilometre. Crikey, it’s been a long time since I’ve seen a split start with a three.
It’s 3:53 to be precise, and is followed by a 3:56 and a 3:51 as I pass the Royal Albert Hall and head in the direction of Hyde Park Corner.
The only real drag on this loop when you run anticlockwise comes when you hit Park Lane and I feel the slight ascent as well as a bit of a headwind. But I complete a 1.22km Strava segment known as “3-Hyde Park Climb to N C” in record time; two seconds faster than I managed in December 2015, 17 seconds quicker than I was five weeks ago and, in the 55-64 age-group, the second-fastest out of 2,673 runners who have recorded a time on Strava.
The gentle rise does lead to a slight drop in pace. The next split is 4:03. But now I’m in the final kilometre and it’s ever so slightly downhill again, inviting me to push on. I close with a 3:45 and an overall time of 19:27; an improvement of 48 seconds on my run in March and certainly quicker than I had thought possible when I climbed out of bed an hour or so earlier.
In summary, the shoes were brilliant.
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That’s an improvement of about 4%. Lawton’s original time is impressive for his age, and the post-shoe time is amazing. Adidas and others reckoned the shoes could improve top-flight runners’ time by 1-2%, but perhaps it’s even better for amateurs.
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OpenAI misses key revenue and user targets in high-stakes sprint toward IPO • WSJ
Berber Jin:
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OpenAI recently missed its own targets for new users and revenue, stumbles that have raised concern among some company leaders about whether it will be able to support its massive spending on data centers.
Chief Financial Officer Sarah Friar has told other company leaders that she is worried the company might not be able to pay for future computing contracts if revenue doesn’t grow fast enough, according to people familiar with the matter.
Board directors have also more closely examined the company’s data-center deals in recent months and questioned Chief Executive Sam Altman’s efforts to secure even more computing power despite the business slowdown, the people said.
The spending scrutiny is constraining Altman’s once-boundless ambitions ahead of a potential initial public offering that could take place by the end of the year. Friar and other executives are now seeking to control costs and instill more discipline in the business, at times putting them at odds with their CEO, people familiar with the issue said.
…OpenAI missed an internal goal of reaching one billion weekly active users for ChatGPT by the end of last year, according to people familiar with the goals. The company still hasn’t announced that milestone, unnerving some investors. It also missed its yearly revenue target for ChatGPT as well after Google’s Gemini saw massive growth late last year and ate into OpenAI’s market share, the people said. The company has also struggled with defection rates among subscribers, according to people familiar with those figures.
OpenAI missed multiple monthly revenue targets earlier this year after losing ground to Anthropic in the coding and enterprise markets, people familiar with its finances said.
OpenAI recently raised $122bn in what was the largest funding round in Silicon Valley history, putting it on more solid financial footing. But the company has signed up for so much computing power that it expects to burn through that amount in the next three years, assuming that it meets ambitious revenue targets. Some of the funding is also conditional and depends on specific agreements with partners.
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Missed targets for users ought to catch people’s eye. But they’re all mad keen on cashing in on the IPO. More broadly, however, read on…
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AI’s economics don’t make sense • Where’s Your Ed At
Ed Zitron:
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To illustrate the scale of AI’s pricing mismatch, I’m going to ask you to imagine an alternate history where Uber had a very different business model.
Generative AI subscriptions are like if Uber charged users $20 a month for 100 rides of any distance under 100 miles, and if gas [petrol] was $150 a gallon, and Uber paid for the gas because somebody insisted that oil would one day be too cheap to meter.
Uber would, eventually, decide to start charging users a monthly subscription to access rides, and bill them for the gas that they consumed. Suddenly users would go from paying $20 a month for 100 rides to paying $20 to access a driver and $26 for a 10 mile drive. Understandably, users would be a little upset.
While this sounds a little dramatic, it’s actually a pretty accurate metaphor for what’s happening in the generative AI industry, and in particular, at Github Copilot.
GitHub Copilot’s previous pricing allowed 300 premium requests a month, as well as “unlimited chat requests” using models like GPT-5 mini. Each of these requests (to quote Microsoft) is “…any interaction where you ask Copilot to do something for you,” with more-expensive models taking up more requests in the later life of the request-based system, such as Claude Opus 4.6 taking up three premium requests. When you ran out of premium requests, Copilot would let you use one of those cheaper models as much as you’d like for the rest of the month.
This wasn’t even always the case. Up until May 2025, Microsoft gave users unlimited access to models, and even then users were pissed off that there were any restrictions on the product.
Microsoft — like every AI company — swindled its customers by selling an unsustainable service, because it never, ever made sense to sell LLM-powered services on a monthly subscription.
If you’re wondering how much services are likely to cost under token based billing, a user on the GitHub Copilot Subreddit found that the token burn of what used to be a single premium request was somewhere around $11, as one “request” involved using 60,000 tokens in the context window, a few tools, and a bunch of internal “turns” (things that the model is doing) to produce the output.
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Zitron overwrites, but he’s been pointing to this problem for a long time, and now it’s turning out to be true.
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China’s economy starts to show cracks from Iran war • The New York Times
Keith Bradsher:
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Rising oil and natural gas prices from the war in Iran are beginning to weigh on the Chinese economy, further slowing already anemic consumer spending and hurting critical export sectors.
Car sales fell in March and plunged further in April. Restaurants and hotels are seeing fewer customers as households turn cautious. In southern China, thousands of toy factory workers protested last week after their employer collapsed under rising plastic costs and ongoing tariffs in the United States.
The emerging signs of strain underscore how even China, with vast strategic oil reserves and massive investments in renewable energy, is not immune to the forces pressuring economies worldwide.
For many weeks, China had appeared to weather the fallout from the war, a view reinforced by fairly strong economic data through March. But with the war in its ninth week with no clear end, cracks are beginning to show.
“The economy is decelerating,” said Alicia García-Herrero, chief economist for Asia Pacific at Natixis, a French financial firm. China may struggle to meet this year’s growth target of 4.5% or more, she added.
One of the clearest signs of emerging weakness is in car sales and production, often considered early indicators of trouble. Cars are the second-largest purchase for many Chinese households after apartments, and the industry drives demand for steel, glass and other materials.
China’s retail car sales plunged 26% in the first 19 days of April from a year earlier, according to the China Passenger Car Association. While part of the drop reflects weaker electric-vehicle sales after tax incentives expired in December, gasoline-powered cars fared worse, falling by nearly 40%.
Falling sales have left dealership lots crowded with unsold cars, triggering production cutbacks. Chinese car factories made 27% fewer cars in the first two weeks of April than a year earlier, a sharp pullback even as exports rise.
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This might lead China to put pressure on the US to end the blockade, at the very least. But what form could that pressure take?
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Kentucky Derby 2026: How algorithms are changing horse betting … and outraging gamblers in the process • Yahoo Sports
Dan Wolken:
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The sixth race at Del Mar on a random Saturday last July should not have been memorable in any way. Running for an $81,000 purse, a four-year-old thoroughbred named Nanci Griffith won the one-mile race and paid $14.80 to win on a $2 bet.
But for many of those who backed the winner, the payout was a massive disappointment. As the gate opened, the tote board showed Nanci Griffith’s odds at 18-1. When she crossed the wire, however, the odds had changed to 6-1, meaning those who held winning tickets collected less than half of what they might have expected at post time.
Unlike sports wagering, where a bettor is playing against a bookmaker and locks in their odds at the time of the bet, American horse racing — which is in the spotlight this week for Saturday’s running of the 152nd Kentucky Derby — has long been based on a parimutuel system where the bettors are wagering against each other.
Once all the money goes into a pool, the racetrack takes a fixed percentage off the top and what’s left is divided up among the winning bets. As a result, the odds shown to the public on TV or computer screens are constantly shifting based on what percentage of total money is being bet on each horse.
Gambling decisions at the racetrack, however, are often based on perceived value. Someone may be willing to bet on a horse at 7-1 but might think differently if the odds were 2-1. While odds changes are part of the game, someone who makes a bet 10 or 15 minutes before a race would typically not expect the final odds to be dramatically different from what they were at the time of the wager.
That wasn’t the case at Del Mar last July. Such a big drop in the blink of an eye — the result of a massive bet on Nanci Griffith right before the gate opened — was one of the more notable examples of a topic that has roiled horse racing and its most loyal gamblers in recent years.
Known in horse racing circles as CAWs — or Computer-Assisted Wagering syndicates — these well-funded groups of professional gamblers have built algorithms to model horse races, track public betting patterns and make large wagers when they identify an inefficiency. Thanks both to technology and the special privileges some racetracks have given them, the CAWs are able to upload tranches of bets directly into the wagering pool at lightning speeds — far faster than any regular player could do it on a phone app or at a racetrack window.
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Inevitably, there’s a lawsuit about this (it claims CAWs are in effect rigging the betting pools; can’t see that flying). But the answer is quite obvious, and some racetracks are doing it: block the CAWs from betting once you’re a couple of minutes from the starting bell.
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| • Why do social networks drive us a little mad? • Why does angry content seem to dominate what we see? • How much of a role do algorithms play in affecting what we see and do online? • What can we do about it? • Did Facebook have any inkling of what was coming in Myanmar in 2016? Read Social Warming, my latest book, and find answers – and more. |
Errata, corrigenda and ai no corrida: none notified. The Overspill will be on a break for two weeks from next week. Nobody notices notes down here.
The Overspill: when there's more that I want to say 