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Best 2-in-1 Laptops (2026): Microsoft, Lenovo, and the iPad
Whether you want a detachable tablet or a laptop screen that spins, these 2-in-1 devices manage to balance being both a tablet and a laptop.
Scientists Finally Crack the Mystery of Water’s Strangest Behavior After Decades of Research
A hidden transition between two forms of liquid water may explain its most puzzling properties.
Hawai‘i Might Just Slay Citizens United — And You Can Help
We have the authority — and the responsibility — to get rid of dark money.
Seniors: Carol Watson, 72
Owner of Carol Watson Greenhouse and Landscaping in LaFayette enjoys her growing business — still puts in six-, seven-day weeks By Mary Beth Roach Carol Watson, the owner of Carol Watson Greenhouse and Landscaping, has been in gardens since she could walk. Literally. She said she learned to walk in her grandfather’s garden, which was next door to her current home and business in LaFayette. Today, at 72, she is running the business that her mother, Claire, opened in 1981 as Watson Greenhouse. Watson worked part-time for her mom until 1990, when she joined her full time. While she may not have had any formal horticultural experience, she said she learned so much about growing and raising plants from her mom, who had an abundance of energy and talent. Now, after 35 years in the business full time, Watson has no plans to retire, although she joked that she’d like a couple of days off a week. On her seven-acre site, complete with four greenhouses, Watson said her favorite parts of the job are working with and educating her customers; helping some of her young customers build terrariums; conducting occasional class field trips; and supporting local art by decorating the shop with an eclectic mix of pottery from at least four area craftspeople. A one-time employee of the former Dey Bros. department store in Syracuse, Watson said she loves merchandising and enjoys displaying unique pieces of art on shelves and even suspending from ceilings. “We’re creative. We also had top quality plants. We got a reputation of quality plants and unusual products,” she said. It’s tending to her customers and her plants, showcasing the unusual products, and hosting special events to benefit the community that keep her motivated to work six to seven days a week. “I just love it. I love taking care of the plants. I’m one of the few people that really enjoys watering,” she said. And while she has cultivated a solid customer base over the years, she remains savvy in her marketing efforts to draw in new customers. For example, she does a gardening segment on CNY Central television; her active Facebook page has thousands of followers; and she hosts a number of unique events and classes that not only introduce her shop to potential new clients, but some benefit nonprofits as well. About 13 years ago, when the local symphony orchestra was struggling, she said she wanted to help. So, partnering with the group, she began offering concerts, called Soirees, featuring the Syracuse Orchestra’s string quartet. Following up on a suggestion from a customer whose brother is a deejay in retirement, specializing in sock hops, she hosted the greenhouse’s first sock hop. Plants got moved back toward the walls to make room for dancing; a caterer was brought in, offering diner-like food; guests came in period outfits; and proceeds went to the Food Bank of CNY. She is excited about doing it again in October. Via her website, she promotes the greenhouse as an “oasis in the snow,” offering visitors the opportunity to pick up some decorating and gift ideas while taking in the floral and citrus scents. And she also caters to her four-legged friends. Over this past winter, she said, she came up with a “brilliant idea,” although at the time she didn’t appreciate just how brilliant it would become. Feeling it too cold outside to take her dog for a walk, she opted to bring it into the greenhouse. And before long, she was inviting people to bring their dogs into the big greenhouse as well. She looks forward to extending that invitation again this upcoming winter season. For Watson and her customers, the greenhouse is always in season.
Spring volleyball: Nebraska tops Iowa State in Sioux Falls
Nebraska volleyball handled business in its first spring match against Iowa State on Saturday, April 11, inside the Sanford Pentagon. Before the match, the two teams agreed to play four sets, as the Huskers won all four (25-17, 25-16, 25-16, 26-24).
“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.
Sisters teen keeping drivers awake with caffeine lollipop business
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Qualcomm's Snapdragon X2 Elite Extreme, Tested: Big 18-Core Energy for Laptops, Incoming!
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2026 NFL Draft rankings: David Bailey tops list of best Edge prospects
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Victor Wembanyama Drops Historic 40-Point Performance Against Mavericks
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CSCU oversight, special ed complaints: CT politics news
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HSU tops list for producing small business owners
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Al Roker stops 'Today' interview with 'Laguna Beach' star over wardrobe malfunction: 'Guys, on live television?'
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Maine GOP candidate for governor drops
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A year later, L.A.B. Golf’s weird science got bigger … and more personal
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Aviatra Accelerators to host Women-Owned Business Crawl April 25 in Covington
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List of college basketball national players of the year, All-Americans: CBS Sports' archive of award-winners
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France Launches Government Linux Desktop Plan as Windows Exit Begins
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Proto-mammals laid eggs, paleontologists finally confirm
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Russia’s Science Funding Drops Below 1% of GDP, Falling Behind Globally
Russia's R&D spending drops to 0.97% of GDP, falling behind Egypt and Malaysia. Government civil science funding hits a 15-year low.
NVIDIA N1 Gaming SoC Surfaces on Laptop Motherboard with 128 GB LPDDR5X Memory
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Laptops Are About to Get Very Exciting Again... Thanks to Nvidia
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Sports Daily: Royals’ bullpen continues to haunt them; transfer portal dominates offseason basketball news
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I tested three Windows laptops in the MacBook Neo’s price range — there’s no contest
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Conservative Activists Take Aim At Native Hawaiian Health Scholarship
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Hondo Rodeo Fest, 'The Super Bowl of rodeos,' gallops into the Superdome
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NVIDIA’s Upcoming N1 SoC For AI PC Laptops Spotted On Engineering Motherboard: 128 GB Memory, User Claims Launch Expected This Year
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For retiring business owners, employee buyout options can go head-to-head with private equity
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“With Atlanta, we won more than sixty games and really …
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Masters 2026 odds, picks, predictions, props: Expert fading Tyrrell Hatton in best bets at Augusta National
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Secretly Distribution acquires music data and analytics firms Entertainment Intelligence and Babel Ops
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Two supermassive black holes are on a collision course
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Shropshire new children's mental health service to tackle delays
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513 humpback whales gather at massive Caribbean breeding ground
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The results are in: New Edition tops Rock & Roll Hall of Fame fan vote
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ASUS Resorts To Deceptive Practices, Raises The Prices Of Its Snapdragon X2 Elite Laptops Shortly After Reviews Go Live
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Robbinsville's Ella Nelms' perfect game tops Week 7 girls spring sports top performers
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NFL analyst tells the truth about Commanders QB Jayden Daniels
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Moment of Science: New research finds corona discharges glowing on treetops during storms
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Local thrift, pawn shops say rising costs are driving more business
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City’s BLM-era mental health push puts cops in danger after sword-wielding suspect stabs officer: police
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Inside Iowa Politics: Candidate Jim Carlin doesn’t want American troops on the ground in Iran
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Inside Iowa Politics: Candidate Jim Carlin doesn’t want American troops on the ground in Iran
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Is ‘lab-grown’ meat actually safe?
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Kanye West Breaks Silence Amid U.K. Music Festival Backlash
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Trump administration stops enforcing protections for transgender students in several schools
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Florida's Walkable Outdoor Entertainment Oasis Is A Kissimmee Gem With Restaurants, Shops, And A Water Park
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On plant health and embracing new plant-breeding techniques in South Africa
The major challenge currently confronting South Africa’s agriculture is the spread of foot-and-mouth disease. For many farmers across the country, the most urgent tasks are both securing sufficient vaccines and rolling them out quickly in the coming months, particularly as the beef and dairy industries continue to suffer big losses. There is also pressure on sheep, goats, and other livestock to prevent the disease from spreading. As efforts to get the country through this monumental challenge continue, I believe it is important to sharpen our focus on the broader task at hand: strengthening biosecurity. This covers both animal and plant health. In recent years, we haven’t focused much on plant health, except a few times, such as when we had a goss’s wilt scare in the maize industry, a few cases of citrus black spot, or cases of sclerotinia in sunflower seeds. But the fact that South Africa managed to control the outbreaks of these particular diseases when they occurred a few years ago was due to organized agricultural and government scientific capabilities, and, most importantly, the availability of crop protection or agrochemicals necessary to address some of them. Therefore, as the animal health division in the Department of Agriculture focuses on the pressing challenge of the day, the foot-and-mouth disease, there is an opportunity for the plant health division to assess if they have all the requirements to tackle any outbreak that may occur on fruits, vegetables or crops in future. The challenge with crop diseases is that they can spread quickly and cause much more rapid damage in the field than we typically see with livestock. Therefore, speed is everything in ensuring the appropriate controls are available. I raise this as an important area, particularly given the drastic climate variation we observe across the country. The production conditions in some areas have changed noticeably over time, which may introduce new risks. The other essential part is ensuring that South Africa has access to the best crop protection technology available in the market. Many technology developers worldwide have operations in South Africa. Therefore, the Department of Agriculture, along with the Agricultural Research Council, must continuously interact with technology developers to assess the various crop protection technologies they are releasing globally and determine their suitability and need in South Africa. This will require a proactive approach from the government, unlike the current approach, in which technology developers typically approach regulators with new technologies. The government, recognising the food security risks, must take a proactive approach to ensure that South African farmers have access to the latest crop protection technologies, thereby safeguarding the country’s food security. Beyond crop protection, we must never forget that South Africa’s agriculture is key to ensuring that the country is the most food-secure country at the national level in sub-Saharan Africa. South Africa is also the only African country in the top-40 global agricultural exporters, having more than doubled since 1994 on the back of the adoption of high-yielding seed cultivars. South Africa has been the only African country to adopt a bold approach to embracing science and adopting genetically engineered seeds for grains and oilseeds in the early 2000s. Since then, we have seen crop yields improve noticeably, contributing to the gains we mentioned above. But ensuring that South Africa improves further is not a given. The key lies in South African regulators’ attitudes towards new technologies. We have to maintain a progressive approach and not give in to the politics of non-food and non-scientific voices that shout on the sidelines. Food security is national security, and the government must ensure we progress, leaning on science. Therefore, the registration of high-yielding new genomic techniques is required. South Africa cannot afford to fall behind the EU, the USA, South America, and many of its competitors in global agriculture. The Department of Agriculture’s crop science division must adopt a progress-and-science-embracing approach for the good of the country. Thanks for reading AgriView! Subscribe for free to receive new posts and support my work. Subscribe Share
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Album Review: 6WA by BigXthaPlug & 600 Entertainment
BigXthaPlug spent eight months in Nashville making a country-rap album that debuted at No. 7 on the Billboard 200, put “All the Way” with Bailey Zimmerman at No. 4 on the Hot 100, got him a Rolling Stone cover, and filled arenas with Jelly Roll. And then he came back to Dallas, rounded up four of his signees, and made a gangsta-rap record with N.W.A’s Straight Outta Compton on the cover. 6WA is the second group release from 600 Entertainment, named after the 600 block of Meadowridge Street in Ferris, Texas, where BigX and his people grew up. The whole thing is loaded with West Coast samples. From N.W.A.’s “8Ball,” Eazy-E’s “Real Muthaphuckkin G’s” and “Boyz-N-The-Hood,” Ice-T’s “6 in the Mornin’,” Snoop Dogg’s “Nuthin’ but a ‘G’ Thang” and more. BigX is staking his name on the lineup he built—Rosama, MurdaGang PB, Yung Hood, KevanGotBandz, KaineMusic—and claiming a lineage from 1,400 miles away while insisting Dallas is its own thing. A million dollars in cash, split and handed straight to his mother. That’s the first place BigX goes on “From the Bottom.” He’d probably still be in the streets if he didn’t have his son and daughter. He only started rapping to make life better for Carter. Couldn’t start his car back then, now people get starstruck at Starbucks, and he throws the whole field away in one sentence: “How you think we competin’? You on the same bag, I just made me some millions off country.” Goyard bags off a Nashville payday, and he wants you to know exactly how far he’s come from everybody else. “Safe to Say” does something similar but looser. It’s a straight Ice Cube “It Was a Good Day” flip, and BigX nails it. Wakes up, checks both phones, nobody’s locked up, kid accounts look good. Gets pulled over, only catches a warning. Pulls up on the squad to liquor, ladies, and haze. Rosama takes the second verse and matches him. Wakes up to his girl cleaning his cuticles, twins playing peekaboo, pays off his car, calls a cousin, sees the opps and they don’t trip. Lawyer calls, he beat his case. Rosama is the strongest voice in the 600 camp after BigX, and “Life of a Gangster” is why. Third person, riding low, blacked-out windows, snub-nose revolver. A character who chose sticking people up over selling dope. “Quick eraser like a number-two pencil.” He never breaks character, and his delivery drops lower and slower than anything he does on the group cuts, like he’s telling you something at a cookout that he probably shouldn’t. Then on “Long Live Fre$h” he’s a completely different rapper. “Short-bus diamonds, these bitches retarded,” wide-body Durango he can’t even park. That kind of range matters on a group record. MurdaGang PB’s best verse is on the title track. Feds at his door at 6 AM, cameras busted, flushing dope, ram truck putting everybody on the floor. Yung Hood on “The Hottest” just lays out four reasons to keep hustling. Who wants to be broke, women cost money, you need bail, and home has to be straight. It’s funnier and more honest than most rappers get when they’re trying to explain why they sell drugs. KevanGotBandz on “Ain’t Never Slowin’ Down” talks about his father getting caught on the dope while he’s sitting on $300K somewhere in the hills, which is a specific kind of guilt that doesn’t usually make it onto crew tapes. KaineMusic, the newest signee, takes the back half of “Who Dunnit” and introduces herself in third person—“Took flight just to show these hoes how to perform”—but it’s too short to know if she can hold a full song. The flex tracks fill the middle of the LP and mostly run together. “I Go” is Rosama and PB trading brands, guns, and food punchlines. Rosama’s “anemic, my iron tucked low in this shirt” is a slick gun bar, PB fires back with “.223 with a clip ‘bout as big as a toddler” and “I go Burger King, stay with that whopper.” Quick, funny sometimes, but it’s all shopping lists. “Dopeman” is a chant. “6ixer Party” brings Snoop Dogg, who does exactly what you’d expect Snoop Dogg to do. BigX contributes the night’s wildest overshare: “She say she brought a friend too, well, I’m tryna put sausage on her dental.” These songs move fast and don’t overstay, but three or four of them could trade verses and nobody would catch it. Midway through, The D.O.C. shows up for a spoken-word interlude. Tracy Curry. West Dallas native, former N.W.A affiliate, co-writer on a lot of the records getting sampled on this very album. He thanks BigX for the tribute and then gets direct: “This your time, this DFW time. You need to do it like we do it down here.” Leave bread crumbs so everybody behind you can eat. Keep God in your life. It’s fatherly and generous, and it does raise the question of whether 6WA follows that advice. The album borrows heavy. The cover art, the Eazy-E samples, “Amerikkka’s Most Wanted” opening with a direct “Boyz-N-The-Hood” lift, Yung Hood rapping “Flag on my left side, Crip until I die.” But the stuff that actually stays with you is Dallas. BigX talking about Pleasant Grove, Rosama calling himself a country boy, PB recounting a raid that could’ve been last Tuesday. The record smells most like itself when it smells like Ferris, Texas, and least like Compton. BigX on “The Hottest” says his father was the first person he knew who had chickens. Not the way rappers usually wave at the drug trade. He means his actual father, his actual family. On “600 Degrees,” Rosama compares his chopper’s stutter to a speech disorder, PB claims he still has the block on his back even after leaving the city. BigX closes that one saying he used to spin and smack, and now the plaques are platinum. Thirty-two minutes was right for this. Nobody overstays, Tony Coles and Rance and Charley Cooks keep the production punchy, and BigX gives his signees real room instead of crowding every song. 6WA doesn’t reinvent anything. Five rappers, good beats, short clock. Most of what they say you’ve heard before. Some of it—the million to his mother, the cuticles and peekaboo, the four reasons to hustle—you won’t forget. Solid (★★★½☆) Favorite Track(s): “Safe to Say,” “From the Bottom,” “The Hottest” Subscribe
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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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Johnson said Wembanyama’s sore ankle, though minor, is …
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“Honestly I was super surprised”
“Honestly I was super surprised”
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