伊朗最高领袖：要对敌方威胁立即采取有效应对措施
Query-Efficient Domain Knowledge Stealing Against Large Language Models - The Association for the Advancement of Artificial Intelligence
Satellite-Text-Prompted Large Language Model for Photovoltaic Power Forecasting - The Association for the Advancement of Artificial Intelligence
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:
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
What Social Constructs Are (And Are Not)
This post is mostly adapted from a section of another post. I wanted a standalone version, to serve as background for a sequence on the mechanisms of social reality.
You’ve probably heard that things like laws, conventions, norms, cultural associations, status, and so forth are “social constructs”: they’re kinda arbitrary in some sense, an artifact of social reality as opposed to physical reality. This post offers a little more clarity on what social constructs are, and the ways in which they are and are not “arbitrary”.
Example: Vehicle Categories
The vehicles humans build today mostly fall into categories like “minivan” or “truck” (among other categories). These categories cash out in predictions: when I see a vehicle with sliding side doors, I predict that it will have a trunk door that opens overhead, three rows of seats, be driven by someone with kids, etc. On the other hand, when I see a vehicle with a flat bed in the back, I predict that it will not have three rows of seats, will have a back gate which opens downwards, be driven by my cousin, etc. [1]
But humans could choose to make different vehicles, which would change which clusters exist amongst our vehicles. The types of vehicles people make are a matter of social convention - i.e. vehicle types are a social construct. Crucially, this does not mean that vehicle categories are “fake”! Categories like minivan and truck are very real in our world, they have real predictive power, and they have predictive power because humans choose to build vehicles which match those categories.
Two key points:
- Many humans, or a few well-resourced humans, could change which vehicle-clusters exist by changing what vehicles are produced. We can change the social construct by going out and changing things in the real world.
- Changing a social construct is not just about deciding that I personally am going to categorize things differently. If I draw different vehicle-clusters, I will probably just be wrong, in the sense that I make worse predictions about e.g. vehicles with sliding side doors also having three rows of seats.
Here’s the mental picture, in terms of clusters:
This is a classic straightforward case of words pointing to clusters in thingspace. But if people go out and build other kinds of vehicles, it might look like this:
… and if they build enough new vehicles, it can totally change what clusters are present:
Same points as in the previous diagram
Our two key points, in terms of clustering:
- People can totally change what stuff is in the world, in such a way that the “natural categories” (in this case the clusters) change.
- … but that requires going out and changing stuff in the world; it’s not just a matter of me deciding to carve up the same data in different ways.
So even if the clusters can be changed by humans by changing the world, that doesn’t mean that choice of clusters is a totally arbitrary choice of how to view the same data.
Example: Laws
Laws are another classic prototypical example of social constructs.
Like vehicle categories, laws have a lot of predictive power. For instance, if I look up some case law on vehicular manslaughter online, and then I go into any of thousands of different courtrooms in the US, I expect to see proceedings hinge on basically the points described online. Just like the minivan/truck categories provide predictive power insofar as they statistically describe lots of real-world vehicles, laws provide predictive power insofar as they statistically describe lots of real-world actions by police, judges, etc. Even an alien observing humans would find it useful to keep track of the laws, in order to predict how lots of different humans behave.
But just like with the vehicles, humans can collectively decide to adopt different laws. The same two key points apply:
- People can totally change the laws, in such a way that all the predictions summarized by “laws” change.
- … but that requires somehow changing the behaviors of a whole lot of individual police, judges, etc, as well as many written records; it’s not just a matter of me deciding that vehicular manslaughter is fine now.
The General Principle
In general, social constructs are real predictive patterns which are out in the world because humans collectively chose to put those patterns in the world. In principle, humans can collectively choose to put different patterns in the world. And sometimes we do! E.g. laws change, or new vehicle categories are created.
Social constructs are “arbitrary” in the sense that humans a
Regulate the Deployment, Not Just the Model
A who-does-what plan for AI safety, built around Trusted Deployers: certified hosts that keep dangerous models guarded without ending open access.
AI has reached a new level of public interest. That may seem like a silly thing to say, as it’s been of interest for a while. But prior (broadly public) interest was about it as a tool, or about the money to be made or lost, or about an effect on jobs.
The new interest demands policy. It demands action. The new interest is connected with risks I consider real. Being real doesn’t make these risks certain. But being real, even with uncertainty, is reason enough to act.
I’ve written lately about risks, cybersecurity risks, and loss-of-control risks. These tried to explain complex topics, starting from the initial premise. Recommendations follow the explanation. This piece will be a bit different. Its focus is upon what I think we should be doing. You might consider it a policy piece. Policy in a completed form has to be a bit broad. There’s always more than one thing going on. As such, I’ll lay out my outline first, and then offer below the extra detail, background and context that I think is important to understanding each point in that outline.
If you’re scanning the outline and see something you disagree with, I’d suggest you continue on, because the outline is, by design, abbreviated, and not fully descriptive.
This isn’t the only effort of this type. Institute for Progress (IFP) and AI Futures are two well known efforts. What distinguishes this most of all is thinking more deeply about how models are deployed. This is a background element that’s somewhat ignored, because it’s not as novel and is not as visible to users. But it’s absolutely critical. I don’t expect this plan would replace plans from well known organizations, but by illustrating a gap, I hope to inspire improvement amongst those plans.
Who and What
This outline is categorized by who would be expected to do what. In some cases, someone is expected to do it, but there’s also another role, of providing the incentives that improve the expectation they will.
Enforcement is deliberately vague. It depends on jurisdiction, has to fit existing legal frameworks, and will need to be worked out, and negotiated, through the political and legal systems. It would feel like I was putting the cart before the horse to even guess at how that would take shape at national and international levels.
Who does what. Model weights above the threshold reach users only through certified trusted deployers. Independent evaluators, certified by a regulator with legal authority, test the models and audit the deployers.
Layer 1: Infrastructure and Distribution
Trusted deployers
Who are they? A new designation that existing and new organizations can be certified as. Expect major cloud providers (AWS, Azure, Google), AI labs (for self-owned) and neoclouds to join immediately. The plumbing largely already exists, but needs standards and certification.
Revenue model? Charging for compute and operational services as a utility function. Or, an internal unit (for a lab, government, or large organization)
Core Responsibilities
- Develop secure environments to host models that receive an independent AI Deployment Safety Level (AIDSL) rating. Example, but not comprehensive list of requirements:
- Comprehensive monitoring, telemetry, and auditing (including multi-turn agent pattern detection).
- Resistant to external cyberattacks and insider threats.
- Resistant to internal cyberattacks. At minimum red-teamed by deployed model and a second highly capable model.
- Guardrails applied by default to all deployments which prevent access of dangerous capabilities.
- Strict controls for any guardrail exceptions as used for “limited” access deployments. For example, accessing users must be known directly, or an evidence chain validated using only trusted organizations.
- Understand the sensitivity of any model deployed. Never deploy a model in an environment with an AIDSL rating insufficient to the combination of deployment type and model sensitivity.
- Use internal qualification for small custom models and Independent Evaluator data for larger/frontier models.
- Models under training assumed to be at least X% more capable than the best model trained to date at the given scale. If the training scale is unprecedented, increase X by a size factor. (X should be established by independent evaluators)
- Perform or validate Know Your Customer (KYC) verification for all model providers, for Independent Evaluators and anyone with access to deployments with guardrail exceptions.
- Provide access to Independent Evaluators to confirm secure environments meet standards
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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
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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
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Millions of light switches in American homes can’t go smart, and the reason is hiding behind the plate
Smart bulbs are the quick and easy way to bring wirelessly controlled lighting to your home, even if you live in an older house, condo, or apartment. They require constant power and a steady Wi-Fi connection, and flipping the physical switch that controls your light socket temporarily breaks the setup. That's why smart home builders often turn to smart switches instead. Smart switches connect to your smart home through the wiring in your wall, and work with any type of bulb — including incandescent, LED, or halogen bulbs.
Open-Sourcing All Day AI: The Playbook for a 12,000+ Attendee Conference
TL;DR
All Day AI was a free, online, 16-hour marathon event with zero vendor pitches. We pulled it together in about two months, saw 12,313 participants and 140 speakers, and people stayed and watched for an average of more than 6 hours.
An event of this scale doesn’t come together that fast without some serious sprinting, trial and error, and audible-calling. Plenty of people have asked how we did it, so this article open-sources the whole playbook: the goals, the team, the tools, the timeline, and the parts we’d change.
The Why
We built All Day AI for people who were under pressure to put AI to work but struggling to find advice they could trust. The tools and recommendations were changing week to week, and a lot of the conversation still led back to something for sale. We wanted a place where practitioners could explain what they had actually shipped, what had failed, and what another team could reuse.
That need shaped the format. Keeping the event free and online meant people could join from wherever they worked, without a ticket budget, a flight, or a travel approval. Banning vendor pitches kept the focus on useful lessons rather than a sales conversation. Across Build, Lead, Secure, and Ship, the goal was to give people clearer answers and peers they could keep learning with after the day ended.
The Hard Part
The first version of our website went up in August, and the event was slated to go live on October 22. That gave us roughly two months to hit our admittedly aggressive goals:
- More than 10,000 registrants
- More than 120 practitioner-led sessions
- 4 tracks: Build, Lead, Secure, and Ship
- A surviving Discord community
What made those numbers hard was that we were starting from zero. There was no existing All Day AI community, no speakers signed up, no website, no event platform, and no social presence. Every registrant and every speaker had to come from something we built or someone we reached in those two months.
If you’ve run an online event with multiple speakers, you know that even one track can be a logistical nightmare. We knew 120+ speakers across 4 tracks could get ugly, fast, so most of what follows is about how we kept it from getting ugly: a committed team, a few deliberate bets, and a lot of help from AI agents.
The People
The most important part of the ADAI stack was the people. The core organizing team had more than 20 people across event co-founders, moderators, hackathon judges, ushers, and marketers, and they worked quickly and unselfishly through the build-up and especially on the day of. The team brought experience from organizations including Anaconda, Matejczyk Group, Carnegie Mellon University’s Software Engineering Institute, Techstrong Group, ACI Worldwide, and Community Hackathons, produced by Crowdbyte Solutions.
This was a community event, so all of it fell outside everyone’s day job. Nobody was paid to make All Day AI happen, and the fact that it happened anyway is a credit to how much the team believed in the project. They deserve not only credit but admiration.
Three Bets That Paid Off
Before we picked a single tool, we made a few decisions about what kind of event this would be. Those decisions shaped everything else, and in hindsight they did more for our numbers than any piece of software.
- No vendor pitches, ever. Most conferences exist with at least a subtle motivation of money changing hands. People can sense that, and when they just want to learn, it’s a turnoff. So we banned pitches from the jump. That gave us a simple promise to make to attendees and speakers alike: you’re here to learn and to teach, not to be sold to.
- Look like nothing else. Our goal for the website was to get visitors to understand right away that this wasn’t a normal tech conference. Our hallmark “Rockin’ Rooster” came out of a ~3 day back-and-forth with the team, where other ideas like “Rockin’ Grandma” were rejected in its favor. We felt the rooster represented something new, loud, and disruptive, in a good way. I used a free online SVG converter to turn the original crude cartoon into a scalable logo.We carried the same idea into the speaker pages. Every headshot got a uniform background and photo effect inspired by vintage concert posters. I worked out the look in ChatGPT, then built a small app in Kilo Code that applied it programmatically through the OpenAI image API, so every new speaker matched the rest without manual editing. It wasn’t long before people recognized the rooster before they even knew what the event was about, which is proof the branding landed.
- Trust the community to spread the word. This was our biggest growth lever. On a random weekday in September, I had the idea to ask my Dot (an AI agent I’ll explain below) to reach out to 3,000 tech groups, hackathons, and events around the world: anyone who might have a shred of interest in All Day AI. I proposed a free deal. Tell your community about our event, and I’ll put your logo, link, and CTA on our website. Hundreds responded. Based on rough estimates of community sizes, I would safely attribute 20-30% of our registrations to that one message. It worked because the offer cost nobody anything, and because a free, pitch-free event is something community organizers are happy to share.
The Stack
With those bets in place, the job became execution, and the tools we chose were all about saving the team’s time for the work only people could do.
Platform
We wanted to spend as little time and money as possible on event infrastructure, so we looked for a platform that could natively handle 5 stages, registration forms, communications, and recording. We landed on Zuddl, which was modular enough to meet our requirements without us stitching together separate tools.
Website
Version 1 was built in Kilo Code for VS Code using GPT 5.6 and cost about $5. It was stored in GitHub, deployed to Vercel, and used Cloudflare for domain management. Because the speaker lineup and partner list changed constantly, the site had to change constantly too. The team and I made PRs as the event evolved, and I approved and merged them.
Operations
Here’s where we had to get crafty. We wanted to attract top speakers across the AI industry, and quality speakers want a clear point of contact with a name and a quick response time. A shared, generic email address wasn’t going to cut it, so we used mine.
That solved the trust problem and created a volume problem: one person fielding 140 speakers’ worth of email. To manage it, I used a series of AI agents with a human-in-the-loop editorial process.
- OpenAI Dots. My Dot was the operating system for speaker communicatio
Microduck, the Robot Pet That Imitates Life: Can We Really Grow Attached to a Machine?
Imagine a small robotic duck walking around your home, reacting to its surroundings, and even picking up small objects with its beak. When it simulates sadness, it lowers its head and makes soft, melancholic sounds. When it expresses happiness, it produces higher-pitched, lively sounds accompanied by body movements. Its name is Microduck.
Standing just 25 centimeters tall and weighing less than 800 grams, it illustrates how advances in artificial intelligence and robotics are making increasingly sophisticated interactions between humans and machines possible.
One of its most curious features is its voice. Every Microduck has a unique sonic identity generated from its processor's serial number. As a result, no two units produce exactly the same sounds. It may seem like a minor detail, but it makes each robot recognizable, almost as if it had its own personality. Through different sounds and postures, Microduck can also simulate emotional states such as sadness and happiness.
Of course, the robot does not actually experience these emotions: it reproduces their outward expressions. Yet, the effect can be surprisingly convincing to anyone watching. There is also another interesting aspect: social behavior. When two or more Microducks come together, they can interact and, when appropriately configured, even perform a kind of chorus. Their behavior resembles that of animals that live in groups and communicate with one another.
But behind this playful appearance lies an enormous technological challenge. Microduck's software is open source, allowing developers to study how it works, modify it, and create new behaviors. Moreover, thanks to a digital twin and reinforcement learning, the robot can be trained in a virtual environment.
There, it can experiment with thousands of movements, fall, correct its mistakes, and try again. Cloud computing makes it possible to run large numbers of experiments in parallel, something that would require considerable time, money, and resources in the physical world. Once learned, these behaviors can be transferred to the real robot, an approach commonly known as sim-to-real transfer. This is how it can acquire unusual skills, such as learning to move on skates.
Another major challenge is efficiency. Microduck is equipped with fifteen motors, sensors, and a compact computing system. A neural network helps control its movements, continuously adjusting its balance and position. All of this must run on a small battery, which provides limited autonomy. Nature has been dealing with similar challenges for millions of years: animals perceive their environment, react, and move using energy obtained from food.
We tend to take these abilities for granted, yet reproducing them in machines remains extraordinarily difficult. Consider the complexity of developing increasingly autonomous robots capable of understanding instructions, reasoning about what to do, and translating decisions into precise physical movements. Combining artificial intelligence, perception, and physical action in small, affordable devices with limited energy consumption is one of the major technological challenges of the coming years.
This brings us to an interesting question: why did Microduck's developers choose these particular characteristics? Its distinctive voice, simulated emotions, interactions with other robots, and amusing movements seem directly inspired by the animal world. The goal is probably not just to make the product appealing, but also to encourage an emotional connection with its users.
A small robot like Microduck could anticipate a new generation of artificial pets, somewhere between intelligent toys and future household assistants.
It could keep us company, entertain us, and learn new behaviors through our interactions. It would also offer several practical advantages: it does not make a mess, does not need to be taken outside, does not get sick, and never requires a visit to the veterinarian. If we leave home for a week, we can simply turn it off and find it exactly where we left it when we return.
But a real pet is something profoundly different. A dog or a cat does not simply simulate emotions through a computer program. Animals are living beings, with instincts and biological needs, capable of experiencing pleasure, pain, and fear. They explore, seek companionship, and spontaneously react to what happens around them. They build relationships through experience, learning, and living together. The bond between humans and animals also develops through everyday care, shared habits, and trust built over time.
Above all, having a pet means accepting responsibility. An animal can become sick, grow old, and suffer. It needs us even when we are tired, busy, or would rather be doing something else. A robot, on the other hand, may offer many of the enjoyable aspects of companionship without requiring the same sacrifices. And perhaps that is precisely one of the reasons why devices like Microduck could become successful.
Yet there is a fundamental difference: simulating life is not the same as living it. An animal is not merely a collection of needs and responsibilities. It gives us affection, companionship, and moments of happiness. It gets to know us, recognizes our habits, and develops a relationship that changes over time.
Caring for it, facing difficulties together, and even knowing that one day we will have to say goodbye are all part of that relationship. We cannot simply turn off an animal when it becomes inconvenient, because it is not an object at our disposal. It is a living being with whom we share part of our existence.
But our relationship with an animal is only one part of its life. Every living organism belongs to a much larger cycle, in which different forms of life are interconnected and contribute to the balance of nature. Millions of years of evolution have shaped organisms capable of performing extraordinarily complex functions, often using surprisingly small amounts of energy and resources.
A robot can imitate an animal's voice, movements, and even its emotional expressions, but it does not participate in this biological cycle as a living organism. However sophisticated it becomes, it remains a collection of circuits, motors, and silicon chips.
Microduck is nevertheless an interesting technological achievement, showing how rapidly robotics is evolving, partly thanks to advances in artificial intelligence. I am convinced that similar devices will become successful and occupy an increasingly important place in our homes.
Yet, I must admit that this prospect also raises some concerns, particularly about the relationships we may develop with them. Perhaps the most interesting question is not how closely robots will come to resemble animals, but how willing we will be to grow attached to something that appears alive without actually being alive.