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CKD Screening With Mobile Health Units Reveals Disparities
Study investigator, Phillip Levy, MD, discusses the reality of how a zip code can impact CKD risk and treatment options.
El Paso Science Festival returns to Downtown
EL PASO, Texas (KVIA) - Saturday and Sunday, several organizations including La Nube will host the El Paso Science Fest at the El Paso Convention Center, from 10:30 a.m. until 5:00 p.m. According to organizers, attendees can expect over 100 interactive exhibits for visitors to get hands-on experience with launching a spaceship, coding robots, engineering,
Schematik Is ‘Cursor for Hardware.’ Anthropic Wants In
Schematik is a program that aims to help people vibe code for physical devices. Hopefully, it won’t blow anything up.
Texas woman moving to Spain AI codes furniture marketplace
Johanna Abzug tried selling her furniture on Facebook Marketplace, but said buyers were "uncommitted." She decided to vibe code her own platform.
Business: Rewarding Year-rounders, Chatham Business Offers Incentives For Residents, Businesses
Even though the shoulder seasons have become busier in recent years, Lower Cape businesses can still struggle to remain open year-round. Lindsay...
7 AI Tools That Run Your Entire One-Person Business While You Sleep (No Staff, No Code)
These seven tools execute for you — I'll demo each live with prompts so you can build a business that runs even when you're offline.
Can your zip code impact your heart health?
Dr. Deeb Salem from Tufts Medical Center spoke with NewsCenter 5 about the data.
Hoboken zoning overhaul targets business growth
The Hoboken Business Alliance and city launch a zoning code modernization effort to streamline permitting and expand commercial uses.
Hackers are abusing unpatched Windows security flaws to hack into organizations
A security researcher published details of three security vulnerabilities in Windows Defender, and the code used to exploit them. Now, hackers are taking advantage of the vulnerabilities in real life attacks, according to a cybersecurity firm.
Ohio governor race between Ramaswamy, Acton turns ugly
The fight over who'll be the next Ohio governor is getting ugly. And the election is still more than six months away.
OpenAI takes aim at Anthropic with beefed-up Codex that gives it more power over your desktop
OpenAI's agentic coding tool has gotten a major makeover, with a variety of new powers and abilities.
New Codex features include the ability to use your computer in the background
An in-app browser allows visual feedback while building websites and more.
OpenAI’s Codex Mac app adds three key features that go beyond agentic coding
OpenAI is releasing a new version of its Codex desktop app today. The latest Codex update adds three key features...
OpenAI's latest Codex update builds the groundwork for its upcoming super app
OpenAI is pushing out a major update to Codex that significantly expands what that software can do, and offers a glimpse of what OpenAI hopes to build with its upcoming super app.
OpenAI’s big Codex update is a direct shot at Anthropic’s Claude Code
Codex can now use your macOS apps on its own.
Rumour: Red Dead Redemption May Be Getting A Rubbish 'Code-In-A-Box' Release
Out to pasture
4 New Apple Intelligence Features Found in Apple Code, Likely in iOS 27
iOS 27 is likely to introduce at least four new Apple Intelligence features that function within system apps, based on backend code discovered by Nicolás Alvarez and confirmed by MacRumors. First up, Apple is expected to lean more heavily into Visual Intelligence in iOS 27, since the company is reportedly developing AI wearable devices that will leverage the feature. Apple is reportedly working on smart glasses, AirPods with cameras, and a wearable AI pin or pendant.
Browning High School Students to Showcase Mental Health App at National Science Fair
Throughout the school year, Aiyahna Green, Sophia Guerrero-Gobert and Kalani SunRhodes met during their lunch periods, hunching over their phones. The three Browning High School seniors were building an app that could help provide mental health resources for teenagers. The girls represent the Browning High School chapter of Code Girls United, a nonprofit that since […]
Yankees news: Rehab news on Gerrit Cole, Anthony Volpe
Yankees lay out Volpe’s rehab plan; Chivilli promoted; Will ‘Bombers’ bunt more often?
How Dangerous Is Anthropic’s New AI Model? Its Chief Science Officer Explains.
Just how fast is the future coming at us, and how worried should we be? Last week, Anthropic announced Claude Mythos, the latest in what has become the leading family of AI models. In a very short time, AI models from Anthropic and other companies have become shockingly powerful at coding. But Mythos stands out because it is not just able to write code that can hack into critical systems, like those that run financial companies, but also to analyze systems and find and string together vulnerabilities in them, allowing Mythos to evade even sophisticated defenses. Anthropic chose to release Mythos to only about 40 leading corporations, so they could analyze the model’s capabilities and prepare countermeasures, in an effort it calls Project Glasswing. The release of Mythos—and the prospect of similarly powerful AIs to follow—has caused tremors at the top levels of Washington and corporate America. Jared Kaplan, co-founder and chief science officer at Anthropic, is at the center of this breakthrough. We sat down with Kaplan so he could walk us through just how powerful Mythos is, what the implications are for issues like our personal privacy, and what comes next. This conversation has been edited for length and clarity. Sean Fischer: For the layman, how is Claude Mythos different—more powerful, more dangerous—from previous models released by Anthropic? Jared Kaplan: I think most people have experienced the fact that our computers get faster and faster over time and over many decades. There’s this very smooth trend where every 18 months, maybe, computer processing power doubles. I think AI is on a trend in which it’s improving maybe 10 times faster than that. A lot of the earliest work that I did in AI was around identifying this scaling trend in AI. Claude Mythos is the latest model from Anthropic, but it’s not different because it’s qualitatively different. It’s really just kind of the culmination of the trend of AI models getting smarter and smarter at all kinds of general capabilities, from reasoning to software engineering to scientific research to knowledge work. For us, it was the first model to demonstrate very elite-level cybersecurity capabilities. It wasn’t because we trained Claude Mythos to focus on cybersecurity, but it was because, as a byproduct of its general intelligence and its general ability with software, it’s particularly good at identifying vulnerabilities in software and how to exploit them.
Weekly Entertainment Report: Music and lively arts, April 16-19, for Southern NH » Nashua Ink Link
Live music and entertainment is the stuff that connects us to one another. Go out and find some!
Apple Pushes Siri Programmers to Adopt AI Coding Tools
Apple plans to send “a significant chunk” of its Siri programmers to a weekslong bootcamp to learn to code using artificial intelligence, The Information
AMD Linux Graphics Driver Introducing "Power Module" To Better Match Windows Behavior
With the set of today's AMDGPU kernel graphics driver Display Core (DC) patches is a rather curious addition with wiring up the Linux code to a 'power module' that looks like it will better match Microsoft Windows behavior with the AMD Radeon driver around display-related power savings features.
Siri Engineers Sent to AI Coding Bootcamp as Apple Prepares to Deliver Siri Overhaul
Apple is sending a large portion of its Siri engineers to a multi-week bootcamp to learn to code using AI, reports The Information. Apple's decision to teach its programmers to better use AI for coding comes just two months before Apple is expected to unveil a smarter, more capable version of Siri at WWDC. While employees attend the coding bootcamp, around 60 members of the Siri development team will stick around to work on Siri, and an additional 60 will evaluate how Siri is performing.
Apple Sends Siri Staffers to Coding ‘Bootcamp’ in Latest Shakeup For Organization
Apple is sending a portion of its Siri programmers back to coding school just two months before the company is expected to unveil a major, AI-powered revamp of the voice assistant, people familiar with the team said. The company plans to send a significant chunk of people working on Siri—a group ...
We tested Anthropic’s redesigned Claude Code desktop app and 'Routines' -- here's what enterprises should know
For the enterprise, the Desktop GUI is likely to become the standard for management and review, while the CLI remains the tool for execution.
Adobe takes Creative Cloud into Claude Code-esque territory
This is a big step in a new strategic direction for Adobe.
To Keep Climate Science Alive, Researchers Are Speaking in Code
Words considered "woke" are vanishing from National Science Foundation proposals. Grist tracked the changes.
3 Central Florida restaurants shut down after health inspections
The Department of Business and Professional Regulation shut down three Central Florida restaurants for health code violations during the week from April 5-11.
Want a Cheat Code for Starting a Business? A New Study of 1,334 Cities Suggests It's Hiding in Plain Sight
I like things that are easy.
Zuckerberg moved his desk and is coding again, says Meta president
"I think he feels so strongly that he has to understand it at that level," Meta's president said about Zuckerberg's work with the AI team.
Where Columbus Blue Jackets' playoff drought ranks among pro sports
The Columbus Blue Jackets missed the NHL playoffs for a sixth straight year. See where their drought ranks in the NHL and North American sports.
How vibe coding app Anything is rebuilding after getting booted from the App Store twice
Anything is planning to launch a desktop companion app to aid mobile app development after its App Store removal.
VA announces intent to reorganize the Veterans Health Administration
The Department of Veterans Affairs is restructuring the Veterans Health Administration (VHA) to eliminate administrative hurdles and grant local hospital directors greater autonomy, the VA said in an announcement on Dec. 15, 2025. The shift aims to modernize the system, ensuring veterans receive consistent, high-quality care regardless of the VA facility they visit. The VA said this isn’t a reduction in force or an attempt to reduce VHA staffing levels. The department said the goal is to improve veteran health outcomes and ensure policies are applied consistently throughout all facilities. As part of the reorganization, the VHA Central Office will have responsibility for setting policy goals and conducting financial management, oversight and compliance, and operation centers and Veterans Integrated Service Networks (VISNs) will take policy direction from the VHA Central Office to develop operational, quality and performance standards. The changes aim to provide clearer guidance for delivering health care throughout the VA. For DAV’s statement on VHA reorganization, scan the code or visit qrs.ly/q9h7shd. For DAV’s statement on VHA reorganization, visit dav.org/learn-more/news/2025/dav-statement-on-vha-reorganization/.
The Pixel 10 is getting a major security boost you'll never actually see
Google is bringing memory-safe Rust code to the Pixel 10 modem to protect users from remote hacking and memory-safety vulnerabilities.
Scientists Discover Bizarre Crocodile Relative That Walked on Two Legs
A strange crocodile relative that may have shifted from four legs to two is shedding light on convergent evolution and the hidden diversity of Triassic ecosystems.
Google shoehorned Rust into Pixel 10 modem to make legacy code safer
Cellular modems are complex black boxes of legacy code, but Google is making them safer with Rust.
Scientists decode the dynamic heart of elephant grass chromosomes
Centromeres act as the control centers of chromosomes, ensuring accurate genetic inheritance during cell division, yet they remain among the least understood genomic regions due to their repetitive structure.
I use Claude, Gemini, and ChatGPT every day—here's the only one you should pay for
The battle of the bots.
Opinion: Science is recognizing the protective effects of religion
A new study suggests that religion and spirituality could be a component in preventing and recovering from addiction
The Linux kernel now allows AI-written code, but you're on the hook for it
Your AI-generated code is still your code.
Female students, leaders gather for Business of Her conference
More than 200 students and other female leaders gatehred at McKenna Hall for the Business of Her conference.The day was full of panels, breakout sessions, insp
ON THE JOB: “What is the Best Business Advice You’ve Ever Received?
Interviews by Deborah Jeanne Sergeant Sound advice can contribute to your success. We recently asked area business leaders, what was their best advice they’d ever received. Here’s what they said: “The best advice I ever received was from my then –boss, Gerry Church, Fulton plant manager at Miller Brewing Co. He told me that whenever I wanted to discuss a business problem or issue with him, I should also be prepared to offer possible solutions. It’s the difference between being proactive or reactive. It’s also the difference between being a leader or follower. That has served me well in any business relationship I have had since then.” — Stephen Chirello, Owner, Steve Chirello Advertising, Fulton “Someone will always do it for less; they will also take the most effort to service or satisfy and leave you for the next lower price.” — John M. Henry, Owner, Speedway Press, Mitchell Printing, Phoenix Press, Oswego “I’m going to answer this question by turning it around and giving my own advice that is, ‘Always treat every customer as if they were your only customer!’ We are celebrating our 50th anniversary in business this year. We started our business on March 25, 1976. My advice works.”” — Nick Chervinsky, President, Brewerton Pharmacy, Brewerton “’You need to toot your own horn, my grandfather, Howard D. Dice, told me.” — Beth Ann Dice, Women’s active aging and cancer recovery coach, Mission Movement Wellness Studio & Breast Cancer Recovery Studio, Fulton “In the micro enterprise class, they taught, ‘Get a good lawyer, accountant and bank.’ That was over 30 years ago and I still have those three today. I chose good people I could grow with.” — Tony Pauldine, Owner, Anthony M. Pauldine General Contractors Inc., Oswego “From my grandfather; ‘Listen to everyone! You never know where you will get the solution you need.’” — Kevin Dean, Executive director, Oswego County Opportunities, Fulton “‘Preparation is the mother of success.’ My dad always drilled that into my head growing up, and it has helped me avoid a lot of snags in both my personal and professional life. Thanks, Dad!” — Bekkah Frisch, Owner and president, iHeart Holdings, Inc., Fulton “If you don’t take a risk and try, you will never know.” — Justin Taber, Owner, Torbitt’s Service Center, Oswego “Be ready to take a loss.” — Cody Seymour, Manager, Damiano’s Eatery, Mexico “I’ve received so much good business advice over the years. When people are giving you advice, always be curious rather than judgmental. Follow your gut. Doing the right thing is never the wrong thing.” — Dana Decker, Principal owner/broker, Berkshire Hathaway Home Services, Oswego ’Do it and figure it out later.’ It hasn’t failed me yet. Too many people take time to think about doing this and this and this and then the opportunity is over with. If it’s somewhat reasonable — if it’s not something outrageous — do it and figure it out later.” — Jeremy Allen, Licensed real estate salesperson, Acropolis Realty Group, Syracuse “’Try to meet the needs of the customer. Treat them like you’d like to be treated.’ It’s like the Golden Rule says.” — Douglas Dickson, Physician and associate at CNY Foot Surgery and Podiatry Care, East Syracuse “The best piece of business advice I’ve ever received applies to life in general: always be honest. Never overstate your capabilities and always be willing to ask questions. The more specific you can be in any given topic will always lead to a better outcome.” — Tom Fernandez, President, Woodbine Hospitality, Syracuse “As a business owner, the advice would be ‘Don’t overextend yourself. Especially in agriculture, expect issues to come up.’ If something is going right, it’s not true agriculture. This last season, we scaled very hard to field another crew. We got more drones, crew and generators based on projections. This last season hit the ag community hard with a very wet spring. We did 15,000 to 20,000 acres for fungicide and the same for cover crop seeding in 2024. We scaled to 25,000 to 30,000 for 2025. We ended up doing 2,000 for fungicide and 3,000 for cover crop seeding. That’s why that advice was good.” — Rick Jordan, Operations manager, CNY Drove Services, Clinton “’Keep your head down, mind your own business and do the best for your clients that you can do.’ It has a lot to do with my success in working for Galloway Century 21.” — Ed Fayette, New York state licensed real estate agent, Century 21 Galloway, Oswego “’Expand in all ways, always.’ It just keeps you going and keeps you growing.” — Stephen Baier, Licensed real estate person, The Real Estate Agency & Bouck Commercial, Auburn “The best advice is, ‘Your biggest asset you won’t see on the books because it comes from your employees.’” — David Flickinger, Owner, Edible Arrangements, Syracuse “Don’t take things personally when you’re working with the public.” — Leeann Desimone, Manager, Annie’s Hallmark Shop, Auburn “‘Follow up.’ The customer knows that you care about what you are doing and that their opinion counts. Sometimes they might be on the fence about whether they want to use your company. If they’re asking your opinion on things, they feel you are concerned about what you’re doing. They like to be kept in the loop if they’re spending money with you.” — Dorothy Casper, Saslesperson, SweetWoods Memorials, Phoenix “The best business advice I received is ‘Never be the smartest person in the room. Surround yourself with people who are smarter than you.’ That’s how you learn.” — Debbie Bilello, Owner, Virtual Office Solutions, Central Square “’You need to grow your business but you can’t grow too big too fast.’ That’s what we’ve done at Pinnacle Maintenance. We’ve tried to grow but not too fast. Along those same lines, there’s another good piece of advice, ‘Don’t take on more than you can handle because it only leads to a bad name for your business and a bad experience for your customers.’ It goes along the same lines as growing your business slowly and carefully.” — David Wines, Owner, Pinnacle Maintenance, Central Square “Always be on time and do what you say you are going to do.’ I’ve done very well following that advice. Most of my work is referral and I don’t really do any advertising so I have a pretty good customer base. It’s mostly word-of-mouth from satisfied customers.” — Robert J. Fleming, Owner, Bob Fleming Windows & Doors, Brewerton “’Concentrate on your finances, always pay your taxes, and form a team you can depend on. Try to help the community you’re in as much as you can.’ We do all of these all the time here. We give and give and give to the community and the community gives back to us always.” — Susan Bonnet, Co-owner, Bonnet Sales and Service, Central Square “Keep smiling. It brings a positive attitude to the workplace.” — Karin Lindsley, Vice president, DTS Truck Service, Syracuse “‘Customer service above all.’ Following this advice has served us very well.’ — Guy Fox, Owner, Fox’s Discount Parts, West Monroe “The best thing to do is to make sure you’re taking in more money than you’re spending. I wish I could say I’m a good businessman but I have been in business since 1981. I’m not a bean counter but work well with my hands and its’s served me well all these years and I am still in business.” — Matt McGill, Owner, Matt McGill Collision, Brewerton
$10,000 bounty on the implementation of rollback netcode for Ultimate Marvel vs. Capcom 3 placed by Maximilian Dood
It's been nearly 15 years since Ultimate Marvel vs. Capcom 3 was released on November 15, 2011. Funnily enough, Ultimate Marvel vs. Capcom 3 was released about nine months after Marvel vs. Capcom 3 (February 15, 2011), which spawned ...
“AI polls” are fake polls
A few weeks after Donald Trump’s second presidential win, I took the train up from London (where I was living at the time) to Oxford to attend a conference on polls and forecasts of the 2024 election. Most of the attendees were pollsters or academics, but I also watched presentations from Aaru and Electric Twin, two companies that do what is interchangeably called synthetic sampling, silicon sampling, or synthetic audiences. Stripped of startup jargon, that means they use large language models (LLMs) to simulate responses to public opinion polls by having AI agents take on the role of survey respondents. I had already heard of Aaru thanks to some articles with eye-catching headlines like “No people, no problem: AI chatbots predict elections better than humans” in the months leading up to Election Day. The founders were making some big, some might even say far-fetched claims, such as: “within two years, we will simulate the entire globe — from the way crops are grown in Ukraine to how that impacts production of oil in Iraq, trade through the strait of Malacca, and elections for the mayor of Baltimore.” When Semafor asked Aaru’s cofounders — Cameron Fink and Ned Koh — about my boss, they said “we respect all those who came before us.” Nate (as he so often does) shared his thoughts on Twitter: Fink and Koh were relatively good-natured about this back-and-forth when we spoke at Oxford. They even offered to mail me one of the t-shirts featuring Nate’s quote they apparently had made. I never took them up on the offer, which I now somewhat regret. These synthetic sampling companies fell off my radar for a while, but they do still exist. In fact, Aaru recently received a $1 billion valuation. Is what they’re doing anywhere close to the most important frontier in AI development? Not by a longshot, especially when Anthropic just developed a model so adept at exploiting software vulnerabilities that it’s only being released to 40 companies. Still, silicon sampling is increasingly finding its way into public polling. Axios reported in March that “a majority of people trust their own doctors and nurses” based on findings from Aaru — without mentioning that the “people” in that sentence were actually LLMs. Around the same time, the Public Sentiment Institute “boosted” their online sample of 373 real survey respondents with 114 AI agents.1 (Spoiler alert: even the co-founder of Electric Twin doesn’t think that’s a particularly defensible approach.) Polling companies like Qualtrics and Ipsos are also developing synthetic data panels. So, what should we make of these … “polls”? Let’s get one thing out of the way: whatever they are, they’re not polls in the way that term is usually defined. Subscribe You can’t replace polls with AI On one hand, using LLMs to essentially make up fake survey respondents sort of sounds like the dumbest idea ever, one that will at best imperfectly replicate real polls while introducing all sorts of biases. On the other hand, with LLMs improving at a remarkable, perhaps even alarming rate, maybe that means I’m a dinosaur at the ripe old age of 24 because I still want to rely on polls that talk to actual people. I’m not going to argue that synthetic samples are completely useless. In fact, as I’ll return to later, there is evidence that some techniques can replicate topline survey results quickly and cheaply. But the marketing from certain companies can be slightly optimistic. “No traditional poll will exist by the time the next general election occurs,” said Fink in 2024. We’re just 206 days away from the midterms, and based on the fact that I still have to collect a bunch of polls every day, I’d say he should have run that prediction by a sample of AI agents before the interview.2 To see why synthetic samples can’t replace polls, here’s a quick primer on how they work. The simplest version of these models involves taking a LLM (like ChatGPT or Claude), giving it a demographic profile (e.g., a white, college-educated woman who lives in Utah and makes $70k a year), and then asking it to respond to a survey question. You repeat that process a few thousand times using different demographic profiles and end up with a sample of synthetic survey responses. The actual models used by private companies are more sophisticated than this, usually because they incorporate more hypothesized demographic characteristics for each agent and provide them with extra information. Aaru, for example, feeds agents a diet of news and information they’d be likely to consume, while Electric Twin incorporates their customers’ proprietary data about the audience they’re trying to replicate. The way Ben Warner, the co-founder of Electric Twin, explained it to me was “we have a large amount of data on […] for instance, 5,000 people. Can we make an accurate prediction of how they would respond to another question?” Still, it should be obvious why synthetic samples can’t replace polls. Polling is fundamentally a data collection process. We might use surveys to make predictions by feeding them into election forecasts, but the main purpose of a poll isn’t prediction, it’s gathering new data about what people think and how they feel. Silicon sampling, on the other hand, produces no new data. It’s simply a model: you input LLM training data, demographic prompts, and a bunch of other information, and it spits out a prediction for what a poll would say. We love models here, but models aren’t polls. That difference is an important philosophical sticking point for most pollsters I talk to. “I think politics should stay away from [synthetic sampling], because we’re trying to […] represent the voice of the people,” said Natalie Jackson, a vice president at GQR Insights. Democratic pollster John Hagner told me: “I think I’m just incredibly skeptical of this idea. I don’t think it’s research. At that point, you’re asking the machine to tell you what you already believe.” Hagner has seen some presentations of early synthetic sampling experiments, but so far, “if it’s being used in a campaign, people are keeping it incredibly quiet.”3 But Eli, I hear you saying, aren’t polls themselves increasingly governed by modeling decisions? Indeed they are: pollsters’ choices on which sampling method to use, how to define their likely voter models, and how to weight their samples can and do lead to dramatic differences in the results they publish. Aaru even referenced these limitations in the methodology statement included with that maternal mortality “poll” — although I’m using the term “methodology statement” loosely here, because it doesn’t really explain how the model works at all. We can ignore the (frankly preposterous) implication that synthetic sampling isn’t subject to a separate set of biases. The important point is that there’s still a meaningful difference between using weighting and other statistical techniques on actual polling data and using a model to predict what a poll would say. The latter is far closer to election forecasts or techniques like MRP — potentially useful models, but not a replacement for polls.4 To be fair, other synthetic sampling companies are perfectly happy with the distinction between polls and models. Warner compared polling and synthetic sampling to different tools in a toolbox. “The mistake I think we make is we think that these new tools should either work in exactly the same way or somehow replace these old tools,” he said. “Rather than thinking of it as, okay, so we’ve always had the hammer, we’ve always had the screwdriver, now we’ve got a saw. But don’t use a saw to try [to] do the job of a hammer.” A quick comment from Nate Eli didn’t ask me for a comment — rather rude of him, don’t you think? But since I’m editing this story, I figured I’d add a few quick thoughts rather than putting words in his mouth. Beyond the frequently misleading marketing, what bothers me about the AI “poll” hype is that as AI tools make statistical inference cheaper and/or better (note that these are not synonyms) that actually increases the comparative value of collecting original data. You might be able to train a model to make a reasonable estimate of what some hard-to-reach poll respondent would say — say, a young Black man who voted for Trump. (Such a person checks a number of boxes for a voter who is usually hard to reach in surveys.) Indeed, this is closely related to what models like the Silver Bulletin forecast already do. They essentially smooth out the kinks in noisy survey data by making inferences based on past voting patterns or national polls or surveys of other states. But you don’t actually know what these voters think unless you’re reaching them directly. If there’s a shift in opinion among this subgroup, you’re not going to detect it. So if I were running a campaign, I’d invest more in going the extra mile to find a representative sample of those voters. And then I’d hire some smart quants — or Claude? — to figure out the implications for campaign strategy based on proprietary data that my competitors didn’t have access to. -Nate Silver Are these models any good? If synthetic surveys are just a new type of model, the next obvious question is whether the models are at least accurate. The answer very much depends on who you ask. On one end of the spectrum, you have the maximalist argument that synthetic sampling is more accurate than actual polls. “It’s an incredibly challenging problem to go to someone and say ‘hey, we’re going to be more accurate at predicting human behavior than you, even when you talk to your customers directly’,” Koh recently told CNBC. In his view, synthetic sampling isn’t a saw to polling’s hammer, it’s “magic.” There’s certainly evidence that synthetic samples can replicate certain survey toplines. But if Aaru does have any examples of their approach outperforming the polls, they’re keeping those to themselves.5 Aaru’s 2024 election model, for example, had Kamala Harris leading in Michigan, Nevada, Pennsylvania, and Wisconsin on November 4th. And although they’ve since taken down their forecast page, they gave Harris a 50.5 percent chance of winning the race on November 2nd.6 After the election, Fink told Semafor he was happy enough with those results because they were “within margin of error,” a term that is completely meaningless when applied to a “sample” of AI agents. And of course, Aaru says their models have improved since 2024, so supposedly now they’d be more accurate than the polls? Still, their stronger argument is on cost: “We are significantly faster and cheaper than traditional polling, and still more accurate,” said Fink. The first two claims are undeniably true, but the third brings us to the opposite end of the spectrum. Both Jackson and Hagner are skeptical that these models are reliable for anything beyond replicating common survey toplines. “I just […] don’t think the machines are what we want when we’re looking for nuanced views. My example on this is people in Arizona and Nevada in 2024 who voted for Trump and voted for expanding abortion in their states on ballot initiatives,” said Jackson. Hagner identified another issue. Maybe the synthetic respondents, like sycophantic LLMs, are inhuman in one important way: they’re too nice. “The reports that have come through at the meetings that I’ve been at are that the early experiments on this, they cannot get respondents to be as racist or sexist or, frankly, as negative as human respondents,” he said. Academic research mostly agrees on this point. While there are some papers that show promising results when using LLMs to replicate polling data, most show that LLMs suffer from various quirks like producing too few “don’t know” responses and can seriously overpredict the favorability of politicians like Donald Trump and Kamala Harris. They also seem to struggle with too little variation between demographic subgroups, so the difference in predicted opinion between Democrats and Republicans, for example, is too small. When I asked Warner about these studies, his response to these papers was that just because academics can’t get synthetic sampling to work doesn’t mean that the technique doesn’t work in general. “Actually, the argument is, okay, yours does not [work]. That does not mean […] for this complex set of machinery, which uses a lot of investment, a lot of time, a lot of money, you can’t get it to work.” Cards on the table, I’m somewhat sympathetic to this argument because academics aren’t exactly great at making election forecasts. Usually, the people with skin in the game are the most accurate. Warner’s argument is that the approach Electric Twin takes — which includes, for example, making multiple predictions for each synthetic respondent using different models and prompts and subsequently averaging those to get a final prediction in a sort of ensemble forecast — produces better results than the simpler academic models. Warner shared a comparison between his method and the method from a recent academic paper with me, and Electric Twin was indeed able to get more accurate replication. But even still, he acknowledged that synthetic sampling “is not a crystal ball.” “If you asked me, do I think using other data sources will be more accurate than asking somebody who they will vote for, I would probably say no. But if you asked me ‘would your system be useful for our turnout modeling today?’ I would say yes.” For better or worse, it looks like the method is already getting more popular in the market research world. Most of the clients Aaru touts these days are businesses like EY and McDonald’s. And AI will probably begin to pop up in other parts of the political polling process. Pollsters are already using it to code open-ended survey responses, and some firms, like YouGov, are testing using LLMs to ask survey respondents questions. More worryingly, one danger to actual polls is that AI agents can be used to infiltrate online surveys. Most online polls use various checks to prevent that from happening, but there’s conflicting evidence on how effective those filters are and how prevalent AI agents currently are in online panels. If those agents ever become impossible to detect, it might spell the end of online polling, but the solution isn’t to replace all of your respondents with ChatGPT. Silver Bulletin is a reader-supported publication. To receive new posts and support our work, consider becoming a subscriber. Subscribe 1 That particular poll obviously doesn’t meet Silver Bulletin standards for aggregation. But we exclude all Public Sentiment Institute polls from our averages because we classify them as an amateur polling firm. 2 You could argue that Fink meant the next presidential election, but (a) I’m also confident we’ll still have real polls in 2028 and (b) in that case he should have asked an LLM to define “general election.” 3 Quick caveat: that’s reporting from a Democratic pollster. It’s possible that Republicans are more willing to use AI in political campaigns. 4 Indeed, Silver Bulletin does not include “polls” produced by MRP in our forecasts or averages, and we think it’s extremely misleading when their practitioners describe them in a way that suggests original data had been collected among a large number of states or Congressional districts. 5 A recent report from Aaru and EY did show two examples of a synthetic estimate being closer than a survey to a real-world benchmark — but I’d take those findings with a grain of salt because the report reads more like an ad and didn’t involve any sort of prediction being made ahead of time. 6 For comparison, our odds for Harris on the same day were 48.2 percent.
How AI used Adobe Lightroom like a human, only faster
OpenAI's Codex AI coding service worked out how to use Adobe Lightroom like a human, denoising 50 photos without an API or plugin.
Cody Canada to receive Restless Spirit Award at Stillwater's Gypsy Cafe
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MAGA Is Winning Its War Against U.S. Science
Source: NBER With all the other terrible news right now, you may not have noticed that Donald Trump is in the process of killing American science. OK, that’s an exaggeration — but not that much of an exaggeration. The Trump administration’s latest budget proposal calls for a gigantic increase in military spending combined with severe cuts to social programs. But as the chart above shows, it also calls for debilitating reductions in research funding. Furthermore, Trump appointees have already been strangling science by sharply reducing the rate at which research grants are approved. Here, for example, is the number of new grants approved by the National Science Foundation: Source: Nature Large numbers of existing grants have also been frozen or terminated, especially in the study of infectious diseases. Add to this a sharp drop in visas issued to foreign students, who often play a direct role in research and who help support academic departments that do research: Put all of this together, and much U.S. scientific research is set to come to a screeching halt — not a few years from now, but over the course of the next year or two. This new assault on U.S. science is taking place at a time when the role of American science in the world has already been greatly eroded. The chart below, based on research recently reported by the National Bureau of Economic Research, measures national strength in science by the share of publications in highly ranked journals. In the 1990s the United States had more such publications than the rest of the world combined. Since then we’ve dropped into third place — well behind China and slightly below the European Union. And this was before the Trump administration’s attack on science had time to take its full effect. Source Some erosion in U.S. scientific preeminence was inevitable given China’s growing sophistication and wealth. But we’ve also fallen behind Europe, even though everyone says that Europe is lagging economically and technologically. Claims about Europe’s underperformance are, in fact, dubious if one looks hard at the data. But it’s still striking to see America lagging. What’s going on? There are presumably multiple factors behind America’s scientific lag. But even before Trump II the growing hostility of the U.S. right to science surely had some negative effect. And since the rise of MAGA G.O.P. attitudes toward science in general have become overwhelmingly hostile. This is true even for the Republican rank and file: And while I haven’t been able to find good survey data, it’s obvious that the anti-science turn has been even more pronounced — and began earlier — among the Republican political elite. Chris Mooney published The Republican War on Science in 2005, and even then was describing a longstanding trend. Why have Republicans turned so anti-science? Part of the answer is that they believe that scientists don’t support them. And they’re right! A study of who scientists give money to shows that only a small percentage gave money to Republicans even 20 years ago, and that almost none of them donate to Republicans now: Social scientists have always been strongly pro-Democratic, while there used to be a significant number of physical, “hard” scientists supporting the GOP. But these days physicists are almost as uniformly Democratic, or at least non-Republican, as sociologists. Why are there almost no Republican scientists? It’s not a mystery. GOP political orthodoxy includes positions that are at odds with the scientific consensus on multiple issues, ranging from the validity of the theory of evolution, to the reality of climate change, to the efficacy and safety of vaccines. In each case the scientific consensus is solidly grounded in evidence. But even before the rise of MAGA the U.S. right was increasingly hostile to evidence-based policymaking — especially, of course, where the evidence is unfavorable to fossil fuel interests or quack medicine, both financial mainstays of right-wing politics. So scientists don’t support Republicans, and the feeling is mutual. Today’s Republican Party doesn’t like science or scientists. It doesn’t like having its preconceived views challenged by appeals to evidence. It knows that very few scientists are on its side electorally. In general, it sees scientific research as a threat to its grasp on political power. Add in MAGA’s combination of rabid anti-intellectualism and allergy to any hint of criticism, and one has the makings of a drastic anti-science turn in policy. “Ignorance is strength” might was well be an official MAGA motto. And as I said, we aren’t talking about something that will happen over the course of multiple years: The U.S. scientific enterprise is threatened with severe damage, even collapse, over just the next year. There are many reasons to find this prospect horrifying: Think of all the beneficial advances, affecting almost every part of life, that won’t happen because U.S. science — still crucial to the world — has been eviscerated. But think, also, of America’s international standing. Can a nation that has forfeited its role as a leader, or even a contender, in global science, still be a Great Power? No. MUSICAL CODA
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Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
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