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Dianna Russini put credibility of all women in sports at risk
The Athletic's Dianna Russini was photographed holding hands with Patriots coach Mike Vrabel. The photos are problematic for all women in sports.
Morgan Wallen’s Tuscaloosa show is this weekend: Secure last-minute ticket deals before they sell out
Country music star Morgan Wallen will be in Tuscaloosa, Alabama this weekend for the next stop on his Still The Problem North American tour. Concertgoers still looking for tickets can find limited seats available.
'My 600-Lb. Life' Star Dolly Martinez’s Cause of Death Revealed
The former reality star died at age 30 following years of weight-related health problems.
UNC researcher works to make AI more reliable in health care
Researchers at UNC Chapel Hill's Gillings School of Global Public Health are tailoring AI to make it more reliable for solving health problems.
Google’s Latest Pixel Update Is Bad News For Android
Android has a serious problem — and it's now getting worse.
‘Problem solver’ Richard Lamondin bolsters South Florida party support after SD 38 switch
‘His priorities are our priorities.’
Samsung to fix Adaptive Clock glitch in One UI 8.5 update
Adaptive Clock has a glitch on One UI 8.5 devices, and Samsung is working on a fix. The problem has been acknowledged by a community moderator, who promised the addressal with the next Beta software update. It’s already a big day for Samsung users, thanks to One UI 8.5 rollouts. Whether it’s new Beta releases […]
BGHS students earn multiple honors at science competitions - Sent-trib
Bowling Green High School students demonstrated their scientific knowledge, teamwork, and problem-solving skills at two regional science competitions held on March 14, earning multiple top finishes and one student advancing to the state level.
The ‘Annoyance Economy’ Is More Than Just Annoying
A new estimate puts the cost of dealing with robocalls, hidden fees and customer service chatbots that can’t solve most problems at $165 billion.
Mayor Helena Moreno eyes a new City Hall, courthouse: ‘We have waited long enough’
New Orleans Mayor Helena Moreno is taking on a problem that generations of mayors before her have tried — and failed — to solve: The need for a new City Hall.
Yankees news: Boone angry with ABS failures
Yanks worst in baseball at challenges; bottom of lineup a Problem; farewell to Winquest; RIP to an old-timer.
Ex-Tesla president mimicked Domino's pizza ordering to boost car sales
A former Tesla boss said he and Elon Musk spotted a problem with the carmaker's website. They turned to Domino's for inspiration.
How Shadow AI Culture Is Destroying Your Business
Shadow AI isn't a compliance problem. It's a cultural exodus. How institutional control over AI, work, and creativity is driving your best people underground.
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
Brian Cox: ‘We don’t know how powerful AI is going to become – it’s both exciting and potentially a problem’
The physicist, BBC presenter and author on snowflakes, art v science and the time Paul McCartney quizzed him about one of Saturn’s moons
'iPhone Ultra' Will Solve Two Key Problems
Apple reportedly plans to unveil a foldable iPhone in September, and the device will supposedly solve two problems with foldable smartphones. In case you missed it, a leaker on Chinese social media platform Weibo recently claimed that the foldable iPhone will be named "iPhone Ultra". According to Bloomberg's Mark Gurman, the device will have improved screen quality and overall durability compared to competing foldables.
“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.
I found the ‘memory cheat code’ for ChatGPT — and it fixed my worst problem with AI
One simple prompt stopped ChatGPT from “forgetting” everything mid-conversation — and it completely changed how I use it
No refund top customer complaint BBB helps with process of complaints and reviews
For many shoppers in Washington, getting a refund is becoming a bigger headache than the original problem.New data from the Better Business Bureau shows complai
Your Laboratory Does Not Have a Science Problem. It Has a Systems Problem
Most laboratory challenges trace back to fragmented workflows, weak infrastructure, and systems that were never designed for real-world operations
Virginia Beach business addresses car meet concerns
After Virginia Beach Police put out a community notice, addressing complaints of car meetups at a parking lot in the city, one business says what they do isn’t the problem.
Scientists say we’ve been treating Alzheimer’s all wrong
Alzheimer’s isn’t just one problem—it’s a tangled mix of biology, aging, and overall health. That’s why drugs targeting a single factor have fallen short, even as new treatments show modest benefits. Scientists are now pushing toward multi-pronged strategies, from gene editing to brain-cell rejuvenation and gut health interventions. The goal: stop treating Alzheimer’s as one disease and start tackling it as a complex system.
Menopause products are having a hot minute. But doctors urge women to be wary of the marketing surge
Women suffering through the hot flashes, night sweats, mood changes and sleep problems that can come with menopause — all while looking in the mirror and noticing signs of aging — are being bombarded with products.
Off the Grid maker and Game Informer owner Gunzilla Games accused of missing staff salary payments
Current and former staff question the company's stability, alleging months of payroll problems
Rick Scott, Chad Chronister back Sydney Gruters in CD 16 race
'Sydney Gruters is a proven problem solver.'
I swore an oath to the Constitution, not a political party
This solution in search of a problem will come at great cost; closed primaries will cost the state more than $135 million over 10 years, according to the Secretary of State’s estimate.
ESPN hints at $80 million Lamar Jackson problem for Ravens
This is a must-watch story.
Greg Gutfeld: Democrats deemed health and fitness ‘problematic’
‘The Five’ co-hosts analyze HHS Secretary Robert F. Kennedy Jr.’s midterm travel push to boost support for the ‘Make America Healthy Again’ initiative.
Plastic bags infest the Kansas prairie, degrading and threatening our health. We can change course.
Every year, Americans use more than 100 billion plastic bags, and most are used for just 12 minutes. The problem? Those bags don’t disappear.
How sports betting is hooking some young men
Not everyone who uses sports betting apps will get in trouble — but with so many people taking part, even a small percentage represents a big problem.
How sports betting is hooking some young men
Not everyone who uses sports betting apps will get in trouble — but with so many people taking part, even a small percentage represents a big problem.
Southeast Asia’s business leaders want wellness at work—as long as the programs get real results
Successful wellness benefits should avoid portraying the worker as the problem.
Unexplained shoulder pain could signal dangerous health condition, doctor warns
While shoulder pain is often the result of rotator cuff problems, injuries, arthritis, nerve problems, or other orthopedic and muscular issues, it can also signal a dangerous health condition.
Boones Mill business owners claim landlord refused to fix issues with buildings, resulting in their businesses failing
Business owners in Boones Mill say ongoing maintenance problems at a commercial property forced them to shut their doors, raising concerns about landlord responsibilities and tenant protections.
Protect Girls Sports in Maine files motion to intervene in lawsuit over signatures
The lawsuit alleges there were problems with the signature-gathering process.
Leonard Leo Is Still Trying to Crush His (Imaginary) Enemies with Disgusting Amounts of Money
The conservative legal activist thinks “radically woke culture” is the problem with America right now—not the climate-destroying oil and gas industry he lavishly supports.
Rural health care in focus: NC lawmakers raise concerns over shortage of nurses, doctors
There aren’t enough nurses and doctors in North Carolina, state leaders say, a problem that’s particularly acute in rural areas. Meanwhile, cuts to Medicaid and other economic factors threaten rural hospital closures.
Incredible MacBook Neo demand may finish off Apple's A18 Pro stock
The MacBook Neo is a massive success for Apple, with high demand prompting talks between Apple and the supply chain about making more units, and how to solve a possible A18 Pro stock problem.
Apple faces supply chain problem as MacBook Neo demand exceeds expectations
The new MacBook Neo has apparently been a smash hit, and Apple will likely talk up its success at its...
Qualcomm Snapdragon X2 Elite Extreme Analysis, Benchmarks & Efficiency - Serious rival for Apple and a problem for AMD & Intel
Notebookcheck review/analysis of the Qualcomm Snapdragon X2 Elite & X2 Elite Extreme mobile ARM processors.
UNE and Maine Public Health Association announce innovative workforce training partnership
Starting in spring 2026, students across multiple programs, including public health, nutrition, marine science, climate change leadership, medicine, and business, will work together with MPHA to solve real-world problems, learn how to advocate for change, and connect with mentors already working in the field. With UNE's Institute of Public and Planetary Health now MPHA’s largest academic member, the partnership will leverage UNE’s new Institute of Public and Planetary Health to provide critical transdisciplinary public health workforce training for UNE students.
Small businesses are ‘doing fine.’ That’s a problem for the U.S. Chamber of Commerce
Small business owners are feeling uneasy about the Iran war. They're doing just fine, but that's not good considering all of the tax incentives created to help them do much better.
Social Science Has a Replication Problem
And that isn’t necessarily a bad thing, if it inspires self-correction.
The brain might not create consciousness after all
Is consciousness something the brain produces, or is it woven into the fabric of reality itself? Renowned neuroscientist Christof Koch is challenging long-held scientific assumptions by confronting the “hard problem” of consciousness — why and how subjective experience exists at all. He highlights growing tensions between neuroscience, physics, and unexplained phenomena like near-death experiences and sudden moments of clarity before death.
Shelby County Clerk Wanda Halbert ouster resumes after court denies appeal
Also, the County Commission has new rules for disruptive members and two Tennessee lawmakers want to fix a Medicare problem for rural hospitals.
Elementary Science Olympiad exposes Alabama students to high-level science competition
Nearly 250 elementary students gathered at Auburn University for the Elementary Science Olympiad, a day of hands-on competition and discovery. Through fast-paced challenges and collaborative problem-solving, students experienced what it means to think and work like scientists.
FDA warns 2 supplements sold at gas stations pose serious health risks
A new alert from the Food and Drug Administration (FDA) warns that consumers should avoid two supplement brands that could pose serious health problems.
Omnicom’s Analytics Chief: For AI, Guardrails will Set Business Outcomes Free
The data has never been more abundant. The problem is what to do with it. Three-quarters of U.S. buy-side leaders now believe traditional ad measurement approaches are underp...
Is it okay to eat standing up? A doctor weighs in.
That grab-and-go lunch might be the real problem.
Orioles news: Early problems in need of solutions
It was supposed to be better than this and so far it isn’t.
I'm unable to switch from Microsoft to Google ecosystem because of this single app
Google's ecosystem has one giant problem
The billion-dollar bet that turned insurance into entertainment
Four companies in one of America’s least glamorous industries solved one of its hardest business problems—by stopping talking about their product entirely.
The billion-dollar bet that turned insurance into entertainment
Four companies in one of America’s least glamorous industries solved one of its hardest business problems—by stopping talking about their product entirely.
The billion-dollar bet that turned insurance into entertainment
Four companies in one of America’s least glamorous industries solved one of its hardest business problems—by stopping talking about their product entirely.
Microplastics have been found everywhere. That's a problem for research
Microplastics scientists are finding the tiny particles everywhere, even in their own research laboratories. So how do they know if what they're finding is in the sample or contamination from plastic fibers floating in the air?
Recap: Bruins let points slip away again, lose to Tampa
Mo’ Florida Men, mo’ problems.
Rewards before awards: The benefits of science competitions
Orlando Science Center inspires students from kindergarten to 12th grade to pursue STEM careers with a variety of science competitions. Young people grow stronger in critical thinking, problem-solving, public speaking, organization and time management skills, among others, when they participate. OSC science competitions help students develop these essential life skills that allow them to be
When legal sports betting surges, so do Americans' financial problems
As online betting has grown in popularity, a new report from the New York Federal Reserve builds on the troubling link between legal sports wagering and financial health.
Dover School District doing away with health insurance provider
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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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