Enlightenment 0.28.0 Release
Their alternative to Qt/GTK (EFL) also got a new release: https://www.enlightenment.org/news/2026-10-31-efl-1.29.0
Novellas with groff
The Kluever Maneuver
I built a scikit-learn for RSI [P]
![I built a scikit-learn for RSI [P]](https://preview.redd.it/trkro8qdytyh1.gif?width=640&crop=smart&s=0d668e31132a75d0636098597f27bd70c96122b3 "I built a scikit-learn for RSI [P]") | After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:
That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management. Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation. With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:
At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion. Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs. We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets. [link] [comments] | |||||||||||||||
Surviving "The Graduate"
EqualAI CEO: Your AI assistant is acting in your name. Who signed off?
This summer, investor Katie Jacobs Stanton said her AI assistant, Instinct, sent an email on her behalf without consulting her first, according to TechCrunch. She described the email as innocuous. The breach of trust was not.
Next time, it could be the email that costs someone a job, a marriage or a lawsuit.
The appeal of personal AI agents is compelling. An assistant that can identify priority emails and draft responses, remember your mother-in-law’s birthday and buy a present based on your correspondence is enticing. For people stretched by demanding jobs, caregiving, or a disability, that help can be transformative. But Stanton’s experience raises an important question: when we give an assistant access to our lives, how do we retain control? Assistants such as Instinct and Meta’s Muse act for us rather than simply answering questions or making recommendations. A mistaken purchase may be refundable. An intimate disclosure cannot be taken back.
The harms are already emerging. Forbes reported that when investor Jason Yeh asked Instinct to find open dinner reservations, it booked a table instead, leaving him with a $200 cancellation fee he never approved. TechCrunch reported that one tester couldn’t get his data deleted, and another received an inbox summary hours after disconnecting her account. And in September, security researcher Patrick Wardle exposed a flaw in Muse’s Mac app that could let malware already on a computer hijack the assistant, which Meta quickly patched.
The problem now reaches our bank accounts, too. Last week, major financial institutions reported that personal AI agents were operating inside online accounts. In one case, an agent called a bank without authorization to file a complaint. JPMorgan Chase CEO Jamie Dimon said the bank would not allow unvetted “outside agents to come in inside the company”, and Schwab’s CEO said they’re actively blocking unauthorized agents. White House adviser David Sacks countered that data was shared willingly and these are “in no way cyberattacks.” Each side has a point, which is precisely the difficulty. We have not yet settled on what authorization means when the party acting is software.
The failures are also hard to see. A public record launched this week by the security firm Manifold counts 59 incidents since July 2025, forty-two of them disclosed in the past four months alone. The median incident took 70 days to come to light, and only 11 of the 59 were detected by a security control.
At EqualAI, we have long maintained that good AI governance is good business because trust will ultimately determine whether people adopt these tools at all. We recently convened senior executives who develop and deploy agents to work through a simulated crisis. The exercise exposed the same gaps repeatedly. In our mock company, without sufficient AI governance, audit trails were too thin to reconstruct what an agent had done. Vendor contracts were silent on who answers when an agent errs. Responsibility for oversight was assumed rather than assigned. These gaps were most acute where speed had outrun precaution. Those gaps shaped the roadmap that came out of the exercise.
The legal landscape is moving in the same direction. California law now prevents companies from avoiding civil liability by arguing that the AI acted autonomously. Senators Hawley and Murphy proposed extending federal anti-hacking law to developers and operators whose agents recklessly cause damage.
Those same principles should apply to the assistants people are bringing into their homes. For consumers, they translate into four commitments.
Make permission specific. An assistant should receive only the access a task requires. Permission to read your inbox should not automatically become permission to send emails in your name. Before an assistant spends money, changes who can access an account or shares sensitive data, there should be safeguards in place, which are known to users.
Assign responsibility in advance. If an assistant wrongly cancels your flight, the remedy must be clear. Companies should determine in advance of deployment who investigates an assistant’s unauthorized actions, who can halt them and how users are made whole.
Test with users’ perspective. Independent testing should confirm that assistants ask before acting, respect their limits and fully disconnect when access is revoked. Because models change constantly, testing must be continuous. Wardle’s discovery was valuable. Work like his should be the norm.
Report and study failures. Aviation became one of the safest forms of travel in part because pilots and airlines report near misses and investigators learn from them. Companies should be expected to identify an “AI incident”, preserve the records, notify those affected and correct the underlying cause, while policymakers can establish reporting requirements for serious cases.
Consumers also have a role. They need to understand permissions they’ve granted and how to revoke its access. But the greater responsibility rests with companies selling the tools to define their limits and set reasonable expectations. We shouldn’t need a cybersecurity background to use these tools safely.
We board planes without understanding how they fly because industry and regulators answer for our safety. We need a similar framework for AI assistants. If companies want our inboxes, accounts and errands, they must earn our trust by clarifying what we’re authorizing, how to regain control, and answer for mistakes their assistants make in our name.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
This story was originally featured on Fortune.com
This top data scientist’s (non-AI) model finds Republicans have just one chance in six of holding Senate
I started following Thomas Miller’s political forecasts during the January 2021 Senate races in Georgia. Just a few days before the election, the polls and pundits anticipated a relatively easy win for both Republican candidates. But Miller—a prominent data scientist and professor at Northwestern University—posited a Democratic sweep, and he was right. He also called Trump’s victory in 2024. What’s different about Miller’s models? They’re far more current than the polls, and they’re centered on prediction market prices that are typically a better guide than the voter surveys—plus Miller adjusts the outlook in every Senatorial contest by using all we know about every contest.
Now, Miller is applying an updated version of his model for the current Senate elections. You can find the stats on his website, Virtualtout.io, and I highly recommend paying a visit. Once again, Miller starts by incorporating the betting venues’ data. “I use the numbers from two sites, Polymarket and Kalshi,” he told Fortune. “They’re the ones that trade the most dollars, where most of the action happens.” Miller then calibrates the posted “prices” so that they reflect probabilities, and the odds for both leading candidates tally to 100%. In a further step, he deploys multiple time series analysis to find underlying factors driving the betting prices, and uses trusted models from finance and economics to project how they’re likely to influence voters moving forward.
By the way, he thinks his model works better than AI.
Miller updates the probabilities of a Republican or Democratic win every 10 minutes. The odds that a candidate will win scroll across the top of the Virtualtout.io screen continuously, in a band resembling a stock ticker tape. For each race, Miller also posts the change over the last 24 hours. Miller calls the streaming feature the “NowCast.”
As for those “underlying” factors, one force swamps all others: Donald Trump. “The states don’t move independently of one another,” says Miller. “If one goes up for Democrats, chances are they’ll all go up for Democrats, and vice versa. The entire market is dominated by what Trump does and says hour by hour.” Put simply, when voters don’t like the POTUS’s words or actions, all Republican odds tend to fall. Miller asserts that his model has identified 85% to 90% of what’s causing the variances on Kalshi and Polymarket, and “my interpretation is that it’s overwhelmingly Trump and the Republican positions generally.”
Though he opened Virtuatout.io to public view about a week ago, Miller started running the numbers on September 17. What’s most striking is just how far the GOP chances have faded in just six weeks.
As of around 9 AM on October 30, five days before most polling sites close, Virtualtout.io gives the Republicans a 17.1%, or just over one-sixth probability, of winning 50 seats and hence retaining control of the upper chamber. On September 17, that figure was twice as high at 34.8%, or slightly better than one-in-three. In all five contests where the Democrats can flip a seat, their chances are better than 60%—in most cases, much better.
In North Carolina, where Republican Thom Tillis is retiring, leaving an open seat, the GOP was never remotely close, and now harbors a minuscule 4% shot at victory. In Texas, the Republican odds have dropped since September 17 from 46.15% to 31.9%, in Ohio from 45.4% to 31.8%, and in Alaska from 32.95% to 28.6%.
In only one state is the GOP faring better than in mid-September and that’s Maine. Republican Susan Collins started as an underdog at 32.6%, but has clawed back to 39.9%, adding 1.7 points in the last 24 hours. In Alaska, things are also looking up for the Republicans: After falling to a low of 23.4% on October 23, incumbent Dan S. Sullivan’s prospects have improved by over 5 points to 28.6%.
For the political pros looking for harbingers of big trouble for either side, the towering sign is the Republican woes in traditional strongholds Iowa and Kansas. In the former, Republican Ashley Hinton dropped from 61.75% on September 17 to a virtual tie at 53.4%, a trend mirrored in Kansas where Roger Marshall was looking a likely victor six weeks ago at 75.8%; now, he’s down almost 19 points at 57.1%. In the last day, Hinton and Marshall have shed 1.45% and 2.47 points respectively.
In the one state where Republicans saw a big chance for a flip, the horizon’s receding fast. The GOP chances of beating Abdul El-Sayed in Michigan have slipped from better than a third in mid-September to about a quarter (25.4%).
If the Democrats indeed score a flip in every contest where their probabilities in Miller’s model stand at 60% or better, and hold all the seats they have now, they’ll clinch a majority of 52 to 48, nearly reversing the Republican dominance at 53-47.
In South Carolina, recently rerated on the Fox News Channel from “lean Republican” to “tossup,” Miller still has Darline Graham at a commanding 75%, though she’s slipped 2.4 points in the past 24 hours.
Keep in mind that Miller’s setting odds, not making definitive predictions. A candidate at over 30% still stands a decent chance of winning. And Republicans are over that bogey in Maine, Ohio, and Texas, and close and trending the right way in Alaska. Odds-defying events happen all the time. The problem for Republicans is that they’d have to beat those odds multiple times, plus defend seats they never dreamed they’d have to defend.
It’s still far from over. And one of the best places to get a plausible, non-AI driven snapshot from a data scientist who’s been doing this for six years, is that NowCast on Tom Miller’s Virtualtout.io.
This story was originally featured on Fortune.com
U.S workers just took home their smallest share of capital since at least 1947—and the lowest ever recorded
As corporate earnings soar and U.S. GDP balloons, the American workforce isn’t feeling the same boom. American workers are taking home less of the country’s overall income, data from the Bureau of Labor Statistics shows, and employment in the U.S. is set to continue to slow.
Labor share, or the portion of the U.S.’s economic output that workers receive through salary and wages, decreased to 52.8% in the second quarter of 2026, its lowest level since the BLS started recording this data in 1947, according to its labor productivity and costs report. In the previous quarter, labor share was at 53.6%. This decade, the labor share average was about 55.6%.
That’s despite corporate earnings skyrocketing, with profits for Fortune 500 companies hitting a record $2.1 trillion in 2025. The U.S. GDP grew 3.4% in the third quarter last year, exceeding economists’ predictions.
That growth has not only come at the expense of how much of the pie of wealth workers are taking home, but also how many Americans are in the workforce, economists warn.
“That decline in the share of labor has got to be either falling earnings or falling numbers of people,” Raymond Robertson, a labor economist at Texas A&M’s Bush School of Government, told Fortune. “The falling share of income is having to do with the shift towards capital.”
Indeed, there are growing signs that as national income balloons, the U.S. workforce is deflating. Unemployment ticked up to 4.2% in September, and employers added just 584,000 jobs in 2025 compared to 2 million added in 2024. The U.S. added 29,000 jobs in September, about one-third of economists’ projections.
The bifurcation of corporate victories and weak labor data raises concerns among economists of jobless growth jeopardizing the U.S. workforce, as well as a K-shaped economy, where the rich get richer while the poor get poorer, becoming more exaggerated.
“Data right now is very mixed,” Robertson said. “But I think it also all consistently points to this idea that things are getting worse for workers and much better for billionaires.”
What is causing jobless growth?
Robertson attributes weakening labor share averages to the rise in automation, which he noted is displacing workers, with productivity—a metric essentially measuring worker output—continuing to rise. Second-quarter GDP data showed nonfarm productivity growth grew 1.4% last quarter, though the rate has slowed from 2.2% in the same quarter a year ago.
“All these things, bit by bit, are replacing people, and they’re concentrating income and their share of capital,” he said.
Goldman Sachs analysts Joseph Briggs and Sarah Dong estimated in a report, based on Department of Labor job numbers, that AI automation could displace 25% of all work hours. They predicted that over the course of the AI adoption period, a 15% increase in AI-driven productivity would displace 6% to 7% of jobs, and, at its peak, a 1 million increase in unemployed workers, though job displacement as a result of AI has not shown up meaningfully in any economic data.
The displacement could be substantial, the analysts said, but said the impacts of automation will be tempered by a wealth of new jobs created as a result of the technological changes.
Automation is expected to be a boon to corporate profits and GDP, projected to boost GDP by 1.5% by 2035, according to a Wharton brief published in September 2025. Early signs indicate AI could drive productivity gains, with around 80% of employees reporting AI makes them faster at specific tasks, but only 37% of companies seeing an impact of that productivity on operating profits, according to McKinsey data, and AI operating costs continue to constrain its deployment.
Robertson added that growing unemployment, which he expects to see rise over the next few months, keeps wages down, allowing margins and profits to expand.
To be sure, the recent productivity surge has been an “open question,” Morgan Stanley economists have argued, not unanimously attributed to increased adoption of AI or automation. The analysts suggested this increase would be cyclical, or vestiges of pandemic-era habits of companies making more from less.
An Oxford Economists research brief from earlier this year suggested companies are disguising overhiring-related layoffs as a result of AI, but said automation-related workforce reductions have not yet happened en masse. Additionally, while unemployment has been ticking up over the past year, it is still relatively low.
Why is an immigration crackdown backfiring on U.S. labor?
Mark Regets, senior fellow at National Foundation for American Policy (NFAP), sees a different reason for a slowing workforce. He told Fortune President Donald Trump’s immigration crackdown has not done what Trump administration officials, such as White House Deputy Chief of Staff Stephen Miller, said it would in increasing the number of U.S.-born workers. Instead, according to Regets, Trump’s immigration policies have not only decimated the foreign-born workforce, but has also created fewer opportunities for domestic-born workers to find jobs.
NFAP data shows a 1.1 million to 1.4 million-person reduction in the foreign-born workforce since January 2025, consistent with the
Jeff Bezos once gave Eva Longoria and the admiral behind Osama bin Laden’s capture $100 million—but she says you don’t need wealth to give back
A lot of people talk about changing the world. But Amazon’s billionaire founder, Jeff Bezos, gave Eva Longoria $50 million to actually do it. And two years on, she says the biggest myth still surrounding philanthropy is that you have to be rich to do it.
“One of the biggest misconceptions about philanthropy is that impact is tied to wealth or scale, when in reality, impact comes in many forms,” the Desperate Housewives star told Fortune. “What I’ve learned is that impact doesn’t come from how much money you have, it comes from how you show up.”
It’s a message that feels pointed at a moment when billionaire philanthropy dominates headlines and ordinary people assume giving back is someone else’s job.
For Longoria, the most powerful—and most overlooked—form of change happens at street level. “Mentoring someone, supporting small business, sharing what you know—those things matter because not everyone has access to capital or resources, but anyone can be part of someone else’s support system,” she adds. “And I think that’s really what’s missing for a lot of people. They don’t have someone in their corner.”
What Eva Longoria did with Jeff Bezos’s $50 million
The actress is practicing what she preaches. Having built a net worth north of $80 million thanks to a sprawling business portfolio—including a luxury tequila brand Casa Del Sol, the media company Hyphenate Media Group, a stake in women’s soccer team Angel City FC, and an early investment in the multibillion-dollar John Wick franchise. Now she’s channeling paying it forward by mentoring small-business owners through a new partnership with Lenovo.
She’s also been running the Eva Longoria Foundation since 2012 to improve educational and entrepreneurial opportunities for Latinas in the U.S., as well as Eva’s Heroes, a charity that helps young adults with “intellectual special needs,” according to its website.
Bezos and his wife, Lauren Sánchez Bezos, launched the Courage and Civility Award in 2021 for individuals who make significant contributions to society. They’ve given millions (often $100 million) to help those in need. Previous recipients include CNN host and civil rights advocate Van Jones, chef and humanitarian José Andrés, and late country legend Dolly Parton—who famously helped fund Moderna’s COVID-19 vaccine research.
It’s why Longoria says receiving the prize, alongside Bill McRaven, a retired Navy admiral, is much bigger than the money.
“Receiving that support was incredibly meaningful, not just because of the scale of the gift, but because it represented trust and responsibility,” Longoria said, adding that she’s been “more intentional and more strategic” in how she shows up as an advocate.
“This award has allowed me to continue investing those resources into the communities and causes I care deeply about—supporting Latina entrepreneurs, expanding access to education, and backing organizations that are driving real change on the ground,” she added. “The Courage and Civility Award makes it possible for us at the Eva Longoria Foundation to create long-term impact with transformative giving.”
McRaven, who oversaw the 2011 raid that killed al Qaeda leader Osama bin Laden, pledged to use his share of the money to develop education programs for future military leaders and the children of deceased veterans, as well as mental health support for those who serve.
You don’t have to be a billionaire to make a difference
Longoria isn’t the only one making the case that you don’t need to be rich and famous to make a difference.
Just look at the Giving Pledge—the commitment, cofounded by Bill Gates, Melinda French Gates, and Warren Buffett, that asks the ultrawealthy to donate at least 50% of their fortunes—has attracted more than 250 signatories since its launch in 2010.
The number who have actually followed through? A handful—fewer than 10. Most fulfilled the pledge only after their deaths.
It’s why Liz Baker, CEO of Greater Good Charities, says we “can’t afford” to wait on billionaires to solve the world’s problems.:“I think if everybody did something to help in their community, we wouldn’t have the issues that we have.”
The global nonprofit she runs has distributed more than $1 billion in impact across 121 countries since 2006—and she also told Fortune the idea that giving back has to be some grand, expensive gesture is one of the biggest things holding people back.
“Everybody can chip in—and it doesn’t even have to be money. Like, do something,” she said.
“Even if you’re like, I have one hour a week to solve this problem in my community that I care about,” Baker said. “Figure out how to do that. Most local nonprofits need help.”
A version of this story originally published on Fortune.com on April 21, 2026.
Read more exclusive interviews from Fortune’s Orianna Rosa Royle:
Mark Zuckerberg’s $170 million Miami mansion is just one piece of his sprawling real estate empire that includes a 196-year-old castle
With a net worth of $222 billion, Mark Zuckerberg is one of the richest people in the world and the owner of a massive real-estate portfolio that spans the entire U.S.
His latest purchase from August is a 440-acre estate and accompanying 196-year-old castle in County Waterford, Ireland, a purchase that reportedly set him back 51.17 million euros, or about $57.1 million.
Yet, his priciest purchases dates back to earlier this year, when the Meta CEO and his wife, Dr. Priscilla Chan, reportedly purchased a luxury mansion on Indian Creek Island in Florida near the in-progress home of Amazon founder Jeff Bezos.
The 300-acre, man-made Indian Creek Island is near Miami and Miami Beach and contains a mere 41 lots with approximately 84 residents, the Wall Street Journal reported. Home prices on the island start at about $60 million, and records show the billionaire paid $170 million for his waterfront home with nine bedrooms and 11 full bathrooms, the Miami Herald reported. Miami-Dade County property records note the owner as a land trust.
Even an undeveloped property on the exclusive island can fetch big bucks: One vacant lot roughly the same size as Zuckerberg’s sold for a reported $105 million in 2025.
The property Zuckerberg reportedly purchased at 2 Indian Creek Island Road puts him in the most coveted area of the already exclusive island, said Mitchell Duchon, a Miami Beach–based real estate agent with the Corcoran Group who specializes in high-end waterfront properties. On the western side of the island, where Zuckerberg’s property is reportedly located, lots are about 80,000 square feet, compared with about 50,000 square feet on the east side, according to Duchon. On this side, where Amazon founder and fellow billionaire Jeff Bezos also owns two lots, residents have better access to the open water of Biscayne Bay and a more expansive view with great sunsets, he added.
“That side of the island is perceived to be the most appealing,” Duchon said.
Bezos has been buying up properties on Indian Creek Island since he announced in 2023 he was leaving Seattle for Florida. The Amazon founder first purchased a $68 million home that would end up being just a few doors down from Zuckerberg’s in 2023. Near the end of that year, he paid another $79 million for a neighboring property with the intention of combining the lots into a single compound—a trend Duchon said is increasingly common among wealthy buyers because of the scarcity of truly expansive waterfront properties in the area. Meanwhile, Bezos is reportedly living in a third Mediterranean-style property also on Indian Creek Island, on the east side, which he snapped up for $87 million in 2024. Last year, Bezos sold one of his Seattle homes overlooking Lake Washington for a record $63 million, the Puget Sound Business Journal reported.
Indian Creek Island operates as an independent municipality with its own government and private police who patrol by air, water, and sky. Access to the island is controlled by a single gated bridge, making safety and privacy a defining feature. Most of the island’s interior comprises an 18-hole golf course and the Indian Creek Country Club, with a limited number of members owing, in part, to a difficult admissions process and a $500,000 initiation fee.
Zuckerberg is one of the latest billionaires to pick up a Florida property, especially in light of a proposed ballot initiative to be voted on in California next week that would impose a one-time 5% “billionaires tax” for any individual worth at least $1 billion dollars retroactive to Jan. 1, 2026. (To be sure, Zuckerberg still very much calls California home, having invested $50 million through Meta for a Sacramento downtown revitalization and AI-focused project earlier this year.) Google cofounder Larry Page also snagged a $71.9 million home in Miami’s Coconut Grove neighborhood in January that he plans to combine with another property to form a total $173 million waterfront compound.
Indian Creek Island, in particular, is home to a number of high-profile names, who, besides Bezos, include the financier Carl Icahn and former NFL quarterback Tom Brady.
Zuckerberg’s reported newest property is just the latest addition to the tech CEO’s growing portfolio of luxury homes across the U.S.
Palo Alto compound
Zuckerberg’s home base remains Palo Alto, where, according to the New York Times, he owns 11 properties in the Crescent Park neighborhood. Over more than a decade, Zuckerberg has poured $110 million into buying adjacent properties, in some cases drawing complaints from his neighbors.
Hawaiian estate
On the island of Kauai, about 100 miles northwest of Honolulu, Zuckerberg owns a $300 million property commonly known as Koʻolau Ranch, spanning roughly 1,400 acres. He quietly added about 1,000 additional acres to the compound last year, Architectural Digest reported, bringing his total Kauai holdings to more than 2,300 acres.
The ranch is one of Zuckerberg’s most secretive properties. Alm
Tech stocks just lost more than $1 trillion in a day—a gothic horror test for the ‘moats’ Warren Buffett taught Wall Street to love
Markets are ushering in a particularly gothic Halloween as the AI trade exposes a crumbling aristocratic pile of early 21st century capitalism. Wall Street has been describing itself in castle-adjacent terms for years.
Jamie Dimon has long talked to JPMorgan shareholders about the bank’s “fortress balance sheet.” His 2015 letter set aside a section to answer the question of what the phrase even meant. By 2021, he’d added a second castle word, warning that “the moats that protect this company are not particularly deep.”
The moat came from Warren Buffett. In a 1999 Fortune article with Carol Loomis, he argued that the key to investing is a company’s competitive advantage and how durable it is, and that the businesses worth owning have wide moats around them.
The gothic novel’s favorite picture of danger is a castle falling apart, and for about 250 years the two have traded images: the castle and the moat, the vampire, the creature built in a lab. This Halloween season has them rushing to the front of the conversation, with a two-day rout in tech stocks on Thursday and Friday turning on whether the AI giants’ moats will hold, as gothic horror booms on bookshelves and movie screens.
Horror took 15.5% of the domestic box office in 2025, according to The Numbers, and U.S. horror book sales rose 7% through the first nine months of 2025, per Circana BookScan, while adult fiction overall grew just 1%. The New York Times asked Friday why every book is gothic now, with publishers putting the label on everything from family sagas to space operas, and several film critics commenting that Nathan Fielder’s smash-hit business-scandal documentaryYou Can See Everything is a twisted, even gothic romance in the tradition of Alfred Hitchcock.
The moats get tested
The selling started with Alphabet. The stock fell 12.6% Thursday, wiping out roughly half a trillion dollars in market value, after the Google parent’s quarterly report. Revenue from the core search unit fell short, partly because of the popularity of less-profitable “AI mode” searches and the company burning cash faster than expected. Its free cash flow came in at negative $9.1 billion, against the negative $2.23 billion analysts expected, per FactSet.
The S&P 500’s worst performer was software giant ServiceNow, which dropped 27% the same day on slowing revenue growth and disappointing guidance. By Friday morning, it was Amazon’s and Intel’s turn, with Intel down roughly 25% before the opening bell. Big Tech lost nearly $900 billion in market value after another sharp rise in AI spending in the third quarter, the Financial Times calculated. Nasdaq 100 companies lost more than $1 trillion in market value Friday alone. Reddit, whose shares fell more than 22% premarket Friday, is pulling up the drawbridge: it’s moving to a logged-in experience as AI eats search.
The irony is spooking investors. AI is eating into the businesses that throw off the cash, and the cash is going into AI anyway. The hyperscalers have raised $326 billion in stock and bonds this year to pay for the build-out, more than triple 2025, according to S&P Global Market Intelligence data.
The question underneath it is the one Apollo chief economist Torsten Slok put to clients the morning Alphabet fell. “With AI disruption potentially around the corner, there is a lot of discussion about moats at the moment,” he wrote in his Daily Spark note Thursday.
He built on finance professor Aswath Damodaran, the “dean of valuation,” who compiled a list of sources of competitive advantage, including brand strength, switching costs, network effects, cost advantages and legal protection. Then he boiled it down to an AI moat checklist of three questions: does the company own data its rivals can’t get, is its product part of how customers do their jobs every day, and can it charge more without customers walking away?
Judging by that third test, the companies selling AI don’t seem to measure up. OpenAI’s new GPT-6.1 Terra model costs up to half as much per token as the version before it. An index from expense-management firm Ramp, which looked at what companies actually pay Anthropic and OpenAI, shows token prices down roughly 35% since the end of June, in what Axios called a price war.
The companies paying for AI aren’t getting much back yet, either. In an Oct. 24 note, Slok sent a tremor through Wall Street terminals with an argument that AI “has delivered zero financial payoff to corporate America outside of tech.” Revenue per worker and profit margins are rising for the Magnificent Seven and flat for the rest of the S&P 500, he noted, and mainstream companies are starting to treat token spending as a cost center that weighs on margins rather than a source of growth.
The first castle was a fake
Xavier Aldana Reyes, a researcher at the Manchester Centre for Gothic Studies, told the Times that interest in the gothic tends to surge during periods of social anxiety and political turmoil. Literary agent Mary C. Moore put the genre’s subject more bluntly, telling the paper that “Gothics are all about exposing what is rotten at the core of something shiny/wealthy.”
The genre was invented by a scion of wealth who knew the old order from inside and drew a salary without working at all. Horace Walpole was the son of UK Prime Minister Robert Walpole, and hisThe Castle of Otranto, published in 1764, is generally regarded as the first gothic novel.
He passed it off as a translation of an obscure Italian work, found in the library of an old Catholic family in northern Eng
AI boom hits a wall on Wall Street as OpenAI cuts fundraising target while Meta gets a credit warning over its cash flow, spending and monetization
The seemingly endless gusher of capital that’s been showered on Silicon Valley during the AI boom may be coming to an end soon as Wall Street pushes back on massive spending plans.
Following a brutal selloff in tech stocks over the past week, OpenAI slashed the target on its latest fundraising round to $20 billion from $30 billion, sources told the Financial Times. That also cut its expected valuation to $1.25 trillion from $1.4 trillion.
The funding round was also upstaged by Anthropic, according to the report, as OpenAI’s arch-rival prepares to go public next month. Ahead of its IPO, Anthropic filed an S-1 on Monday that showed revenue has soared and its cash flow turned positive in the first half of this year.
OpenAI didn’t immediately respond to a request for comment.
The timing couldn’t have been worse. Quarterly earnings reports from hyperscalers this week spooked investors. Alphabet saw its cash flow sink deeper into negative territory in the third quarter, with the Google parent also joining Meta and Amazon in charging ahead with massive spending plans. On Thursday, Alphabet fell about 12%, its worst day on record.
To be sure, there were other positive signs as well, in addition to Anthropic’s bullish S-1. Microsoft reported strong cloud growth and stayed cash flow positive, though it said its capital spending will accelerate, while AI chip maker SK Hynix blew away earnings forecasts.
But the narrative had already turned bearish, especially after AI debt fears sent shares and bonds of “neocloud” companies tumbling earlier this month.
At the same time, OpenAI was seeking more capital just as other AI giants tapped financial markets heavily. This past week alone, Elon Musk said SpaceX closed $40 billion in debt financing, Meta announced plans to raise $95 billion in equity, and Microsoft raised $25 billion in a bond sale.
The prospect of hyperscalers continuing to hemorrhage funds was too much for S&P Global, which cut its outlook on Meta to “negative” from “stable” late Friday, citing negative cash flow and uncertainty around capital expenditure plans.
The credit ratings agency maintained Meta at AA- but warned the social media leader’s leverage could top 1.0x as soon as 2027, which is the downgrade threshold for the current rating. That’s up from about 0.1x in 2025 and primarily reflects its strategy of leasing data center capacity rather than from its debt issuance.
Meanwhile, S&P sees Meta’s capex reaching $140 billion this year, topping $225 billion in 2027, hitting $252 billion in 2028, and then flattening out from there. As a result, it expects free operating cash flow to remain negative: -$22 billion this year, -$71 billion in 2027, and -$39 billion in 2028.
Of course, other hyperscalers are spending heavily and running cash deficits, but S&P also said Meta’s ability to monetize its AI investments is more limited than that of its peers.
“While we believe the company intends to monetize these investments via improved advertising tools that support faster ad revenue growth, as well as from the development of new revenue streams stemming from its introduction of agentic apps such as Muse, it is unclear when these revenue streams will generate material contributions that exceed their rising costs over the next few years,” S&P said.
Meta’s rivals, however, are monetizing their data centers by leasing compute capacity, including to frontier model labs and enterprise customers, it added.
In a statement to Fortune, a Meta spokesperson said the company disagrees with S&P’s assessment of its AI monetization capabilities, pointing to “significant product and business opportunities” ahead.
“Meta is already delivering stronger engagement, better recommendations, and greater returns for businesses as a result of AI investments,” the spokesperson continued. “We believe the technology and infrastructure we’re building now will open new revenue streams, create long-term shareholder value, and bring us closer to delivering personal superintelligence to everyone.”
The bearish environment on Wall Street also comes as rising bond yields have sent borrowing costs sharply higher, the political backlash against data centers is expected to help Democrats retake Congress, and the Iran war could re-escalate after midterm elections.
Still, some bullish voices aren’t convinced the AI boom is about to go bust, and even think some retrenchment is a good thing.
According to market veteran Ed Yardeni, the AI investment boom faces financial, physical, and political constraints. If any of these constraints worsen, stocks could dive further, especially considering that AI is now a major driver of corporate earnings and GDP growth, he said in a note this week.
“However, we view these constraints as healthy for the durability of the AI boom!” he added.
For example, if the supply of capital doesn’t flow as easily, it could lead to more efficient allocation, lowering the risk of overinvestment. Yardeni expects hyperscalers to retain access to financing—thanks to their strong balance sheets—and keep spending.
In his view, it all comes down to the U.S. economy, which he said has performed remarkably well over the past few decades. Even the midterm elections are unlikely to derail it, regardless of which party controls Congress.
“Further AI jitters therefore could set up buying opportunities rather than signal the end of the boom,” he said.
This story was originally featured on Fortune.com
If California undid its ‘original sin’ and matched Texas on taxing property, it would raise more revenue than the billionaire tax, economists say
California voters will decide on Proposition 40 soon, but looming over the controversial billionaire wealth tax proposal is another state ballot measure from nearly 50 years ago.
In 1978, voters approved Proposition 13, which capped property taxes at just 1% of assessed value, limited annual hikes to 2%, and allowed reassessments to market value only after sales or substantial renovations.
It came amid a wider tax revolt leading up to Ronald Reagan’s election as president and an era of conservative governance in America. After Prop 13’s passage, the state had to find other ways to raise more revenue and relied more on taxing incomes, including capital gains.
California now has the most progressive income-tax structure in the country with a top marginal rate of 13.3%. When combined with federal income tax rates, that can send the total to more than 50%.
The top 1% of earners account for up to half of state revenue, though it also means state coffers are volatile and swing on stock market returns. At the same time, the ultra-wealthy often don’t receive much income and instead leverage assets to finance their lifestyles, avoiding some taxes.
Now enter the billionaire tax, which calls for residents worth more than $1 billion to pay a one-time tax equivalent to 5% of their assets. The union pushing Prop 40 has said the wealth tax could raise $100 billion in revenue and help offset federal cuts to health spending.
Supporters point to the AI boom and say California’s ultra-rich would still be among the world’s wealthiest. Opponents have warned about the impact the tax could have on economic growth and startups. They have also said it would force founders to sell big pieces of their companies and eventually cause the wealthy to flee to another state, resulting in a net loss of revenue over time.
If California is looking for a large source of undertaxed wealth, “a much better, bigger target lies right beneath their feet,” according to Princeton economist Owen Zidar and University of Chicago Booth School of Business economist Eric Zwick, authors of the book The Everywhere Millionaire.
“The original sin underlying California’s tax structure is Prop 13, the 1978 law that rewrote the rules of property ownership,” they wrote in a recent New York Times op-ed. “Getting rid of it would be a more efficient and durable way to make the rich pay their fair share.”
Prop 13 also contributed to rapidly increasing home values, which have risen faster than property taxes. As a result, California homeowners pay an average of just 0.8% tax on their property, Zidar and Zwick estimated, generating $100 billion in revenue in fiscal 2025.
By contrast, Texas homeowners pay 2% on average. If California matched Texas, overall residential property tax revenue would more than double, while also easing home prices by around 13%, they added.
To be sure, property taxes in Texas have stirred their own voter revolt during the midterm election season. While the state doesn’t levy income taxes, Texans complain that the overall tax burden remains onerous due to property taxes, which are levied by local governments as well.
And getting rid of Prop 13 in California wouldn’t go down well with voters either as many would suffer big increases on their property taxes. Still, California’s tax base is already narrow, and Prop 40 would make it even more top-heavy. Meanwhile, the state plans to spend $540 billion this fiscal year.
“It is simply not possible to fund the state’s needs by leaning on a few hundred people, as politically appealing as that may be,” Zidar and Zwick argued. “For California to tax wealth in a progressive fashion and meaningfully improve its fiscal position, it needs to broaden the aperture beyond billionaires to the hundreds of thousands of other very wealthy people who could easily afford to contribute more by paying a real estate tax that finally works.”
Laura Tyson, a former White House economic adviser, and Lenny Mendonca, a former chief economic adviser to California Governor Gavin Newsom, warned billionaires respond strongly to estate taxes when deciding where to live.
Already, six billionaires who would have owed about one-quarter of Prop 40’s expected revenues left the state before the proposed effective date on the wealth tax, they pointed out in a Project Syndicate column.
And since Prop 40 proposes a new, untested method of taxation based on all assets, it would fuel a “tidal wave of litigation, rendering the promised funding both speculative and unreliable,” they wrote.
“Instead of a tax on billionaires, the U.S. needs to raise taxes on the capital income that feeds wealth,” Tyson and Mendonca argued. “There are a variety of ways to do so: through higher corporate tax rates; through taxes on realized and unrealized capital gains; by closing the most egregious loopholes, like carried interest; and by increasing inheritance taxes.”
This story was originally featured on Fortune.com
China sees the yuan vs. dollar rivalry like Pepsi vs. Coke, expert says — ‘never the market leader, but serviceable in a pinch’
Heavy is the head that wears the crown, and contrary to popular belief, Beijing doesn’t want the yuan to dethrone the dollar as the world’s dominant currency, according to a top China scholar.
While China has been trying to internationalize the yuan, also known as the renminbi, it doesn’t want the obligations that come with being the global reserve currency, Zongyuan Zoe Liu, senior fellow for China studies at the Council on Foreign Relation, wrote in Foreign Affairs this month.
To truly take the dollar’s place would require open capital markets, which would threaten the Communist Party’s control over who gets capital and how, she explained. That’s as many elites rely on offshore dollar holdings as a type of insurance against domestic political persecution.
Since China actually prefers to keep the dollar on top, the real reason for promoting the yuan abroad is to prevent the U.S. from weaponizing the financial system against it, Liu added, similar to how Russia was shut out after it invaded Ukraine in 2022.
A yuan network that’s international enough, even without being dominant, could bypass any dollar-based sanctions the U.S. might impose in the future.
“Placing a respectable second, or even a consequential third, would suffice to break the dollar’s effective monopoly and constrain the United States’ ability to impose financial sanctions unilaterally, while saving China the effort and cost of sustaining currency supremacy,” she said.
“From Beijing’s perspective, the mission is to establish the renminbi as the Pepsi to the dollar’s Coca-Cola: never the market leader, but serviceable in a pinch and more than capable of keeping the dominant incumbent honest.”
(For what it’s worth, Pepsi has actually fallen further behind Coke in the cola wars and was dealt a major symbolic defeat recently when KFC said it would replace PepsiCo soft drinks with Coca-Cola’s products at its 3,700 U.S. restaurants.)
So far, China facilitates renminbi use among firms in more than a hundred countries and across a growing array of commodity markets. As of June, its Cross-Border Interbank Payment System had 1,829 participants who could send payments to more than 5,200 banks in 191 countries.
Global currency requirements
But that’s just a small fraction of what the U.S.-dominated system processes, Liu said, adding that a global currency also entails pricing, financing, risk transfer, and reinvestment.
Chinese exporters still invoice in dollars and convert them to yuan just before brining them into China. But Beijing is promoting “China prices,” or yuan benchmarks on domestic commodity exchanges and has succeeded with lithium, she noted.
The financing stage is gaining momentum too as Chinese banks have more than doubled their yuan lending to overseas borrowers between 2023 and mid-2026. The People’s Bank of China has also reached currency swap agreements with 32 foreign central banks and monetary authorities.
More modest steps have increased the yuan’s presence in risk transfer markets as state firms have begun settling cargo insurance in yuan. But reinvestment is perhaps the weakest link in the chain. A strong yuan ecosystem requires recipients to hold and reinvest their balances rather than convert them, Liu argued.
China has expanded overseas access to domestic stocks and bonds, but Beijing’s unpredictable capital controls remain a worry. In March, foreign investors held just 1.6% of the Chinese bond market versus 27% in the U.S. bond market.
“Without a far larger pool of renminbi assets that foreigners can freely buy and sell, the renminbi cannot come close to replacing the dollar,” she said.
But similar to how the “petrodollar” anchors the greenback’s role in global trade, the yuan can dominate trade in an essential good that China controls.
That could take the form of critical minerals, clean energy equipment, and electrical infrastructure used in green energy technology, Liu posited.
China has already established supremacy across much of the green supply chain and is promoting standards built around technologies Chinese-owned firms.
“If China consolidates its dominance over that industrial ecosystem, using its scale and cost advantages to prevent competitors from catching up, it could gain enough leverage to price those essential, hard-to-substitute exports in renminbi, creating the foundation for a ‘green yuan’ that might eventually replace the petrodollar,” she predicted.
Petroyuan
Meanwhile, the Iran war has raised doubts about the petrodollar, which goes back to a deal struck in 1974 when Saudi Arabia agreed to price its oil in dollars and invest surpluses in U.S. assets.
In exchange for Saudi Arabia recycling its dollars back into the U.S., Washington guaranteed the kingdom’s security, which also involved stationing troops in the region, providing advanced weapons, and ensuring free navigation in the Strait of Hormuz.
But America’s role in the Mideast looks vastly different today. Despite 40 days of punishing bombardment of Iran and round-the-clock protection of commercial ships, the U.S. still hasn’t fully restored traffic in the strait.
At the same time, Iran’s swarms of missiles and drones have inflicted significant damage on U.S. aircraft, radars and bases, while American air-defense systems have failed to completely protect Gulf allies’ critical energy infrastructure.
“Damage to Gulf economies could encourage an unwind in their foreign asset savings,” Deutsche Bank warned in March. “In this context, reports that the passage for ships through the Strait of Hormuz may be granted in exchange for oil payments in yuan should be closely followed. The conflict could be remembered as a key catalyst for erosion in petrodollar dominance, and the beginnings of the petroyuan.”
This story was originally featured on Fortune.com
Accidentally Misleading People Should Be Embarrassing; Or, Higher Innocence
Embarrassment and Innocence
Sometimes we say things that we think are true—perhaps even things that are true—which end up causing reasonable people to conclude something false. It's tempting to relate to such a situation primarily in terms of maintaining the innocence of one's soul—to say, fine, my readers became more wrong, but I'm not to be judged for it because I believed what I was saying; I wasn't being misleading just because the people who believed me were thereby led into falsehood.
The temptation reflects a confusion as to the nature of the judgment. Mostly, people don't care about judging the innocence of your soul; they care about whether they'll be led into falsehood the next time they take the risk of believing what you say—if there is a next time. If you want there to be a next time and you're going to have social emotions about it, the most productive emotion is embarrassment rather than defensiveness.
The function of embarrassment as a social emotion is as a signal of appeasement and social repair: embarrassment says, "I'm aware that I've become visible in a socially undesirable way; please don't take it as representative of me."
The socially undesirable appearance can come apart from any intended or actual wrongdoing. Jewish law has a concept of marit ayin, "appearance to the eye": the law prohibits actions that would themselves be permissible if they didn't create the appearance of a prohibited act. Hanging up wet clothes to dry on the sabbath is prohibited, because it looks like you were doing laundry on the sabbath (even if the clothes actually got wet for some other reason). One function of the marit ayin doctrine is to deny plausible deniability to norm-violators (as someone might try to do laundry on the sabbath if they felt secure that no one would punish them for merely hanging up the wet clothes). Those who want to be known for keeping the sabbath need to pay a price that's more expensive for sabbath-violators to pay; if you care about maintaining the norm itself, it shouldn't be much extra trouble for you to furthermore care about maintaining the appearance of the norm.
One time, I accidentally joined a work call unmuted while my housemate was playing a loud video game. I was deeply embarrassed, because it looked like I was playing video games during work hours. I think my marit ayin-like embarrassment was serving a useful socio-psychological function, even though it was my housemate's game and I was in fact totally innocent in my soul. If I were the sort of person who was totally unashamed about creating the appearance of slacking off, then I think my manager and coworkers would have reason to doubt my commitment to productively working from home.
Similarly, people who are visibly embarrassed after having accidentally misled people can be counted on to work hard to make sure that people won't regret believing them next time. Defensive people won't, because, well, why would they? They didn't do anything wrong.
Two Case Studies About Accidentally Misleading People
Once, I suspected an ideological rival of misrepresenting a citation: they had linked to a webpage featuring an embedded PDF of an essay, but when I used my web browser's "Ctrl-F" search functionality with appropriate search terms to see what in the essay justified their claim, I came up empty. When considering whether to escalate to complaining more loudly about my rival over this (after the rival didn't respond to a blog comment and DM), I mentioned the matter to a third party—who pointed out that the essay did contain my search term! On further testing, it appeared that my web browser's Ctrl-F behavior depended on which page element had focus: if the user had last clicked inside the embedded PDF viewer, the search returned results inside the PDF, but not if the user had last clicked elsewhere on the page.
I was so embarrassed.
To be clear, I knew I was innocent in my soul: the browser UX bug wasn't my fault, and if the browser hadn't been buggy, then my initial search would have succeeded and I never would have complained. I certainly didn't lie. (I didn't say that I read the essay and verified that it didn't support the claim, which would have been false; I said the Ctrl-F search came up empty, which was true.)
But the innocence of my soul didn't matter. I knew that raising a suspicion that turned out to be false looked bad for me, even if there was ultimately a perfectly reasonable explanation for my actions. If I didn't want to look bad, I couldn't count on everyone hanging around to listen to my perfectly reasonable explanation for why it was all the Firefox developers' fault. I would need to make some sort of generalizable change to my policy—perhaps, reading the entire PDF myself next time before raising the suspicion that an ideological rival was misrepresenting it.
(Recounting this seven years later, I went back and downloaded the PDF. Excluding the title page and the endnotes, it was six pages.)
⁂
Another time, a journalist wrote an article paraphrasing a source as saying that it "could be unfair" for journalists to discuss "both critics and supporters" of some controversial public figure and that they should instead "somehow prove statistically which side was right." I was familiar with the source's work, and the paraphrase didn't sound like something she would say. I brought up that line from the article when I had occasion to talk to the same journalist three years later: he maintained it was fair, so I asked the source about it afterwards. She told me that she recalled telling the journalist that she "wasn't a huge fan of 'both sides' journalism, where you just find someone with one opinion and someone with the other opinion" rather than trying to figure out the truth; she had also given suggestions for data sources that might bear on claims about the public figure's influence.
I took that statement as confirmation of my suspicion that the paraphrase in the article was misleading. "[S]omehow prove statistically which side was right" leads readers who aren't familiar with the source's work to imagine that she has an unreasonable faith in the power of statistics to resolve all questions, whereas voicing skepticism of false balance is eminently reasonable. "Somehow" implies vagueness, but pointing out data sources that bear on some question is supplying a specific "how."
When I complained about this again to the journalist, he didn't seem embarrassed. "I had to boil down what she said to plain English," he said. I said that it hadn't worked. He said it did. He recounted that the source had objected when he said his coverage would represent both views of the public figure. When I interrupted to insist that it seemed like a bad paraphrase when compared to what the source had told me later, he said, "Is that necessarily what she said to me?"
And in absolute fairness, maybe it wasn't, necessarily. I of course was not present for the years-prior conversation between the journalist and the source; I don't have the recording, if there was one. Even if the source can be assumed to be an authority about what she meant to say, I cannot with absolute certainty rule
Forecasting space weather risks on power grids
At a glance
- End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States.
- Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity.
- Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears.
Forecasting a threat to critical infrastructure
During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North America and Europe. Modern society depends on reliable electric power, yet extreme space-weather events can induce currents in transmission networks that damage equipment and increase operational risk. The challenge is not only knowing that a storm is approaching, but estimating when and where its effects could be most severe with enough warning for grid operators to respond.
During my summer internship at Microsoft Research, I developed a machine learning system that forecasts space-weather risk across 66,935 substations in the continental United States. The system measures how the sun’s activity can affect the earth’s magnetic field and, ultimately, the power grid. It combines solar-wind observations, forecasts of the Auroral Electrojet (AE) and Disturbance Storm Time (Dst) indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific risk estimates 30-60 minutes ahead of potential impact.
Building an end-to-end prediction pipeline
Space-weather prediction is difficult because it spans several coupled systems. The solar wind changes rapidly, its interaction with Earth’s magnetosphere is irregular, and the resulting ground effects depend on local conditions. Regions with resistive bedrock can experience stronger geomagnetically induced currents (GICs) than regions with more conductive geology. Transmission-line orientation, latitude, and other power-system characteristics further influence the exposure of individual assets.
The pipeline addresses this complexity in three stages, shown in Figure 1. First, solar-wind measurements from the L1 Lagrange point are used to generate forecasts of the AE and Dst indices, while geological conductivity and location features are assembled for each substation. Second, a gradient-boosting model combines these forecasted and location-specific inputs to estimate dB/dt, the rate of magnetic-field change associated with GIC risk. Finally, the resulting predictions are converted into location-specific risk estimates and aggregated into a continental risk assessment. A system of 50 AI agents helped explore features, validation strategies, and model configurations across the pipeline. Only public data sources were used, included NASA OMNI and NASA-aggregated Kyoto World Data Center data, INTERMAGNET and U.S. Geological Survey magnetometer observations, and GridSFM-derived grid data.
Evaluating geomagnetic forecasts and infrastructure risk
The AE predictor was designed to forecast rare, high-intensity geomagnetic activity that drives infrastructure risk. In the 2020-2026 evaluation period, the model produced forecasts spanning nearly the full observed range of AE activity and outperformed several empirical solar-wind-based approaches. The Dst predictor provided an additional signal describing large-scale geomagnetic storm strength. During the most geomagnetically active periods of the 2020-2026 evaluation period, the machine-learning model outperformed the Burton equation on 62.2% of individual hours. The model also produced a substantially wider prediction range than Burton-style approaches and improved severe-event detection in the end-to-end forecasting system by 1.2 percentage points when combined with AE forecasts.
| Predictor | Result | Baseline comparison | Impact |
|---|---|---|---|
| AE | 410.2 nT RMSE | Lower RMSE than empirical and solar-wind-only baselines | Captures extreme-event activity used by the GIC forecasting system |
| Dst | 7.2 nT RMSE | Outperformed Burton on 62.2% of peak-activity hours and provided wider prediction range | +1.2pp severe storm detection when integrated into final GIC system |
The GIC risk stage was evaluated differently. There is no equivalent widely deployed operational system that provides a direct industry benchmark for this calculation, so the machine learning model was compared with simple linear regression. As Figure 2 shows, the system achieved detection rates of 76.5% for major events (≥10 nT/min), 81.2% for severe events (≥20 nT/min), and 64.1% for extreme events (≥50 nT/min). False-alarm rates increased with storm severity, reflecting the trade-off between missed events and cautious alerts. Performance varied by latitude, with the highest detection rates at northern stations where geomagnetic activity is strongest.
Skip full AutoCAD and draft in 2D with a $199 year of AutoCAD LT
TL;DR: Draft precise 2D plans with an Autodesk AutoCAD LT 1-year subscription, now $199 (reg. $540).
Full CAD software is expensive, and a lot of drafting work only needs 2D tools. To get professional 2D drafting for less, try a one-year Autodesk AutoCAD LT subscription, and it’s on sale for $199 (reg. $540).
Architects, engineers, and drafters use AutoCAD LT for floor plans, construction documents, and electrical diagrams. Your work saves as a DWG file, the standard format in the industry. That means clients and contractors can open your drawings without converting them first.
AutoCAD LT also cuts down on repetitive work. When you draw the same object more than once, Smart Blocks turns it into a reusable block. AutoLISP can automate other tasks you do often.
Sharing work is easy, too. If someone edits your file, DWG Compare shows exactly what they added, deleted, or changed. Your files sync through Autodesk Docs and other cloud storage, so you can open them on Windows, macOS, the web, or a mobile device.
New and existing Autodesk users can both buy this subscription.
Pick up a one-year Autodesk AutoCAD LT subscription for $199.
Autodesk AutoCAD LT: 1-Year SubscriptionSee Deal
StackSocial prices subject to change.
Replace Microsoft 365 with a $27.97 lifetime Office license for Windows
TL;DR: Get Word, Excel, PowerPoint, Access, and more with a Microsoft Office Professional 2021 lifetime license for Windows, now $27.97 (reg. $219.99).
A Microsoft 365 subscription bills you every year for apps most people open every day. Functionally, that means you’re stuck paying for the same products forever. If you’d rather pay once, try a Microsoft Office Professional 2021 lifetime license on sale for $27.97 (reg. $219.99).
Your one-time purchase gets you Word, Excel, PowerPoint, Outlook, OneNote, and Access, plus the free version of Teams. Access comes with the Professional edition, so anyone who works with databases gets that tool alongside the usual documents, spreadsheets, and slides.
You can install your apps on one Windows 10 or 11 PC that you use at home or at work. Your license will be tied to that device instead of a Microsoft account. This version won’t run on Windows 7 or 8, and it doesn’t work in Parallels or other virtual machines, so check your setup before you buy.
After checkout, your redemption code arrives by email, and you have seven days to redeem it. Unredeemed licenses can go back for store credit within that same window.
Stop paying every year for the Office apps you use every day.
Through Nov. 1 at 11:59 p.m. PT, you can get a Microsoft Office Pro lifetime license for just $27.97.
Microsoft Office Professional 2021 for Windows: Lifetime LicenseSee Deal
StackSocial prices subject to change.