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SF workers have weeks to claim $240 million in health funds — or the city will pocket it
The city may owe you thousands. It’s a use-it-or-lose-it situation, and the clock is ticking.
Don’t Dismiss the ‘Science of Math’ Movement (Opinion)
Standards of evidence must be applied consistently if ed. policy is to improve student outcomes.
US health insurers advance measures to standardize prior authorization requirements
UnitedHealth and CVS Health said on Friday they have standardized data and submission requirements for more than half of their prior authorizations, as part of an industry effort to reduce delays and paperwork for patients and doctors.
The Cigna Group, Leading Health Plans Advance Work to Simplify Prior Authorization
/PRNewswire/ -- The Cigna Group (NYSE:CI), a global health company, today joined leading health plans in a commitment to standardize electronic prior...
Dueling events at Baylor spotlight growing divide over faith and politics on campus
At Baylor, a Turning Point USA event and the All Are Neighbors gathering reveal deep divides over faith, politics and LGBTQ+ inclusion on campus.
Exclusive: Insurers align pre-treatment review requirements
A standardized approach could reduce the back-and-forth between plans and providers and speed up care.
“Us versus them”: How political propaganda polarises beliefs without providing any news
Political polarisation is often attributed to misinformation, echo chambers, or biased news. But new experimental evidence suggests that merely making a social conflict salient – without providing any information at all – is enough to sharply increase disagreement on both factual and normative questions. This column discusses how politicians and media can ‘artificially’ amplify societal tensions by strategically choosing which conflicts to highlight, and why this mechanism is harder to counter than standard misinformation.
Assassin's Creed Black Flag Resynced Out July 9: Everything You Need to Know
Get all the details on Resynced including release date, PC specs, new content, the different editions available, and more.
All in the numbers: Broncos Lead intern helps local tourist org make data-informed decisions | News
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Entertainment briefs | ‘Dance Visions’ at Cal Poly Humboldt
“The Year to Save the Earth,” a multimedia experience mixing powerful songs and projected images, will be featured May 1 at 7 p.m. at the Humboldt Unitarian Universalist Fellowship, 24 Fellowship Way off Jacoby Creek Road in Bayside. “The Y…
'Catastrophic' | Ohio mental health providers sound alarm over CareSource Medicaid payment clawbacks
CareSource said such adjustments are standard when payment issues are discovered. But many behavioral health providers say the combination of rate cuts and retroactive clawbacks is unprecedented.
Jason Campbell Named to Los Angeles Business Journal’s 2026 Top 100 Lawyers List
Winston partner Jason Campbell was named to the Los Angeles Business Journal’s 2026 Top 100 Lawyers list. This honor recognizes lawyers who not only set the standard for excellence in law but also…
Massive Motorola Razr 2026 leak leaves nothing to the imagination
All the specs of the standard Moto Razr 2026 have leaked ahead of its April 29 launch.
4 things to know about SF’s most dangerous mental health facility
A Standard investigation found that chaos and violence inside the Crestwood Healing Center are traumatizing and abusing patients, with nowhere else to go.
House Ethics Committee Weighs Punishment for Rep. Cherfilus-McCormick
The House Ethics Committee will meet Tuesday to determine what punishment they will recommend for Rep. Sheila Cherfilus-McCormick with Republicans calling for expulsion. The ethics panel found that the Florida Democrat violated 25 House rules and ethical standards. She is also accused of stealing $5 ...
Samsung brings SmartThings integration to IKEA's Matter devices
Samsung just expanded its SmartThings connectivity platform to support a number of IKEA products. These devices, like smart lights, operate via the smart home connectivity standard Matter.
Shaky political science: using "simulated" elections over real-world elections
[Dear DSOS readers – this article is the latest in our “Shaky Political Science” series. In our 38 page report, “Shaky political ‘science’ misses mark on ranked choice voting,” released in early December, we examined over 40 studies focused on ranked choice voting. We found that many of these studies suffered from puzzling research methodologies, poorly constructed surveys, unrealistic simulated elections, cherry picked data, and faulty analyses that often were contradicted by results from real-world elections (a link to our full paper is here). Here are links to the first three articles in our series: the first one summarizing the overall results of our report, the second one analyzing a study co-authored by University of Minnesota academic Larry Jacobs, which was one of the most error-prone of all the research we reviewed in our report, and a third article showing an admirable example of two political scientists holding two other political scientists accountable for their flawed, sloppy research]. In this fourth article in our “Shaky Political Science” series, we shine a spotlight on seven studies that were web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group (the ERRG is an ad hoc committee headed by New America’s Political Reform Program and joined by the American Enterprise Institute, the Unite America Institute and Stanford University’s Center on Democracy, Development, and the Rule of Law). This research consortium has been led by Lee Drutman, senior fellow with New America’s Political Reform Program. These seven studies are simply not credible from any objective standard about how political “science” is supposed to illuminate our understanding of either ranked choice voting or politics in general. The feature that six of these seven studies have in common is that they all used surveys – artificial, simulated elections -- to attempt to say something cogent or interesting about ranked choice voting (RCV) elections. None of them used real world data from actual RCV elections – either the 1000+ elections in the US or the many thousands of elections from places like Australia, Ireland and elsewhere where RCV has been used for 100 years. While a few of these studies conclude with some mixed positives about RCV, our analysis here is content neutral. The idea that, with a political reform like RCV, you can ask respondents in a mock, artificial election to vote with that method, with only the barest of explanation about how RCV works or its likely impacts (i.e. no spoiler candidates, majority winners, voters liberated to pick their favorite candidates, coalition-building), and then ask them their thoughts/reactions about voting in that system, with fake candidates and no real campaigning, no real exposure to an actual election or actual candidates, rests on some very puzzling assumptions. The findings of these seven studies actually are contradicted by the findings from many other studies based on real-world RCV elections. These New America studies also failed to list the real-world RCV studies in their own citations, instead including those studies that reinforced their conclusions. The seven studies featured in this article were chosen because they form a cohesive cohort which precisely illustrates one of the main points of our research paper, namely that using surveys, i.e. artificial elections, or mathematical/theoretical models, instead of real-world data, is in fact “shaky political science.” Below are summaries of our analysis of five of the seven New America studies. You can read our complete analysis of all seven studies at our paper, “Shaky political ‘science’ misses mark on ranked choice voting.” Choosing to “Vote As Usual” by Andre Blais, Carolina Plescia, and Semra Sevi. New America. (Blais et al., 2021). This study, web-hosted and funded by New America’s Political Reform Program and its Electoral Reform Research Group, used two surveys in which respondents completed ballots for simulated elections using plurality, approval, RCV and point (Score) voting. In both cases the authors provided the respondents with only minimal explanation of each method. For example, the entire explanation for RCV was “For this vote, rank the candidates from your first to your last choice (you do not need to rank them all). This is called the ranked vote.” That’s it. The study did not provide any context or explanation to survey respondents about why she/he might want to rank multiple candidates. Participants were not even told that ballots would be tabulated using an “instant” runoff, not to mention any description of RCVs ability to eliminate the spoiler effect or liberate voters to rank their favorite candidates without fear of helping the “greater evil” candidate to win. That’s like taking an iPhone back in time to 1970 and asking someone if they like it better than their rotary phone without explaining how it works. Their first survey targeted states that held 2020 Super Tuesday Democratic primaries and used the real candidates’ names in the survey; the second survey used fictitious candidates. In other words, they surveyed respondents who had only ever participated in plurality elections, had no history or background with using RCV or any of the other methods, had no clue about its likely impacts, and did not communicate how the alternative methods would be counted, and then asked them: “How satisfied are you with using each system?” Predictably, the winner was plurality elections, the system used for president, Congress, gubernatorial and state legislative elections – in other words, the method they have been using, year after year, all their voting lives. With so little explanation of RCV and its impacts, this study is simply not credible in discovering anything useful about real-world RCV elections. It merely shows that a basic voter education campaign is needed when adopting a new election system. Additionally, the result contradicts many other post-election surveys following real RCV elections, asking opinions from voters who had just participated in a RCV election, that showed a great deal of satisfaction with RCV (see details on that in Studies 22-25 in our paper, as well as the four studies cited in the review for Study 28). Share Other studies on “satisfaction” and “understanding” make similar mistakes, in terms of basing their surveys on inadequate explanation to respondents and lack of education about context. How could any survey ever hope to measure accurately, in essence simulate a real-world election, without actual campaigning candidates, the media writing about those candidates, organizations endorsing candidates and voters being inundated with crucial information? More than anything, what these “studies” based on surveys show is that if you ask voters about something they don’t know very much about, and fail to adequately educate them, their responses will unsurprisingly reflect their confusion. Equally puzzling is that New America’s Electoral Reform Research Group, headed by political scientist Lee Drutman, found this to be a credible enough study that they not only published it but highlighted it in Drutman’s own discussion of research related to ranked choice voting. This pattern was repeated in study after study featured by New America’s Electoral Reform Research Group. For example in Ranked-Choice Voting and Political Expression: Voter Guides Narrow the Gap between Informed and Uninformed Citizens by Cheryl Boudreau, Jonathan Colner, and Scott MacKenzie, the researchers based their analysis on a survey which found that respondents who were asked to vote in a simulated RCV election, with minimal explanation of RCV or how it works, displayed less evidence of being informed about how to use RCV effectively, with the least informed voters ranking fewer candidates. As pointed out under the study above, in a real election voters are informed by a wide variety of methods, including public education by the election administration, by interested political groups, the media, and the candidates themselves who benefit from voters being aware of how to vote in the new system. In Support for Ranked-Choice Voting across Race and Partisanship by Joseph Anthony, David C. Kimball, Jack Santucci and Jamil Scott, the researchers used respondents from different parties and races voting in simulated plurality and RCV elections, who indicated their satisfaction with each voting method. Instead of using real world election results, the authors fell back on the easy, armchair and less reliable academic research methodology of making up mock elections. As with the other studies, little explanation was given of how RCV works or what its implications might be (electing majority winners or eliminating spoilers, for example). Unsurprisingly, this study found that respondents were not very satisfied with RCV. However, when the authors added an explanation that RCV has a tendency to elect more women and people of color, they found that this greater explanation increased the preference for RCV. The more voters were familiar with it, and the more they understood potential positive impacts – surprise! -- the more they were open to switching away from the status quo. Of note, the results from this study are thoroughly contradicted by a number of other studies whose data was obtained from real RCV elections with real voters. Those studies show a great deal of satisfaction with RCV. More “fake election” research An interesting wrinkle on this “research by fake elections” approach, which only served to illustrate how unreliable such “research” can become, was taken in Ranked-Choice Voting, Runoff, and Democracy: Insights from Maine and Other U.S. States by Joseph Cerrone and Cynthia McClintock_,_ also web-hosted and funded by New America’s ERRG. It gets high marks for creativity, but an eye-roll for thinking this could possibly add anything meaningful to our understanding of real world RCV elections. This study tried to determine whether RCV, plurality or two-round runoff elections provided greater voter satisfaction and acceptance of election results. To determine that, the authors conducted a survey asking if voters were satisfied with a fictional newspaper report of an election, with three different versions given to respondents – plurality, runoff and RCV, with variations of Republican/Democrat winners and variations of “come-from-behind” winners,” i.e. candidates not initially in first place but who win the “runoff.” Again, a minimal explanation of RCV or how it works was given, and the survey showed that respondents had the greatest unfamiliarity with RCV than the other methods. The unsurprising result? Respondents were much more satisfied with “come-from-behind” victories in a traditional two-round runoff election than in RCV, even though RCV is also a runoff system but with a single round of counting. Interestingly, the dissatisfaction with RCV disappeared for those who were more familiar with RCV. In fact, among those respondents who were “very familiar” with RCV, they were equally satisfied with RCV, runoff and plurality. Surprise, surprise. Again, what this study revealed, more than anything, is not only the inadequacy of the explanation for RCV given by the authors of this study, but the failure of the research format itself, thinking that such an artificial mock election could somehow simulate what actually happens in the real world in a real RCV election. Those four studies reviewed above are fitting examples of inadequate and shaky political “science.” Other studies reviewed in our paper from the New America/Electoral Reform Research Group cohort which also used mock elections included: Ranked-Choice Voting is an Acquired Taste by Joseph Anthony and David C. Kimball. New America. (Anthony et al., 2021). Ranked-Choice Voting is No Refuge for Extreme Candidates by Melissa Baker. New America. (Baker, 2021). Also included in this New America cohort is Does Ranked-Choice Voting Reduce Racial Polarization? by Yuki Atsusaka and Theodore Landsman, which actually did use real-world election results data but applied an odd and unconventional methodology to the data which relied on shaky assumptions and non-real-world scenarios that were too divorced from reality. To determine levels of “racially polarized voting,” the researchers imagined “ethnic parties,” by which they mean political parties for each ethnic group, which was especially odd since the election data they were using came from nonpartisan RCV races. But then they made an even odder assumption, namely that since race and ethnic characteristics of studied voters were not available, the authors tried to infer that information based on who voters selected and the racial make-up of voters in each district. This amounted to little more than vague and ambiguous guesswork dressed up with a bunch of mathematical equations used to give it a veneer of scientific appearance. Indeed, it seemed to assume racial polarization in order to prove racial polarization. Based on this questionable methodology, the authors concluded that RCV doesn’t reduce racial polarization. But the premise of this study missed an obvious reality: since RCV incentivizes more candidates to run and can result in more diverse fields of candidates, one would expect more racially-polarized voting in voters’ top rankings, not less, because more voters would have someone on that ballot who represents their racial or ethnic identity and more freedom to vote for their real first choice. By encouraging more diverse candidates and eliminating the spoiler effect, RCV facilitates voting for a favorite candidate of your own race and then selecting a second choice with the best likelihood of winning. The real measure is what voters do with their additional rankings – do they consider other candidates and vote “for the best of the rest”? This study didn’t tell us anything useful about that. But readily available data shows this is common in real RCV elections, and candidates act on this knowledge by engaging with voters outside their racial group. So the authors of this study failed to consider or account for what actually happens in real-world elections, and their conclusions are tainted by this failure. New America has contributed a great deal over the past 2.5 decades to our understanding of many policy areas, including politics and political reform (full disclosure: Steven Hill, one of the authors of this article and of the “Shaky Political Science” research paper and series, is a former senior fellow and former director of New America’s political reform program, 2005-2010). So it was especially disappointing that New America funded and published such a large batch of dubious studies about ranked choice voting. The stated praiseworthy goal of its Electoral Reform Research Group was to inspire new academic research about ranked choice voting that would shed light and help the public better understand the impacts of RCV. Instead, the ERRG research did just the opposite. It produced dubious research that created more confusion and misinformation. In so doing, New America’s political reform program contributed to “shaky political science.” Steven Hill and Paul Haughey Share Upgrade to $5 subscription Thanks for reading DemocracySOS, a reader-supported digital portal for the pro-democracy movement. Subscribe for only $5 per month to receive full benefits and to support our work. Subscribe
WhatsApp Testing Paid 'Plus' Subscription Tier
WhatsApp has started testing a paid subscription tier called WhatsApp Plus, which adds a set of personalization options on top of the standard messaging experience, according to WABetaInfo.
Panasonic Introduces New Compact Standard Prime Lens to its Full Frame
Panasonic Introduces New Compact Standard Prime Lens to its Full Frame
They Built a Legendary Privacy Tool. Now They’re Sworn Enemies
There’s a lot of love all over the world for GrapheneOS, the gold standard of mobile security. There’s very little love between the two guys at the center of its history.
Sports Take: Top 5 Baylor WBB players of all time
From foundational stars to generational icons, Baylor women’s basketball has produced no shortage of greatness over the past 25 years. These five players did more than fill up stat sheets; they helped shape the standard of excellence that defines the program.
Leaker: Apple Downgrading iPhone 18 to Cut Costs
Apple is downgrading the planned specifications of the standard iPhone 18 to cut costs, a leaker claims. In a new post on Weibo, the user known as "Fixed Focus Digital" said that the iPhone 18 features "certain manufacturing downgrades" that bring it more into line with the low-cost iPhone 18e model. The decision is said to be "a cost-cutting measure".
U.S. Delivers Maternal Health Equipment Across Madagascar - U.S. Embassy in Madagascar
U.S. Delivers Maternal Health Equipment Across Madagascar U.S. Chargé d’Affaires Stephanie Arnold today provided critical maternal and newborn health equipment that will reach healthcare facilities across Madagascar, demonstrating how U.S. foreign assistance builds a safer, healthier world by helping recipient countries build self-reliant health systems in line with the America First Global Health Strategy. Under President Trump’s America First foreign assistance policy and the America First Global Health Policy, U.S. support serves as a tool of strategic engagement – not global charity. Every U.S. taxpayer dollar is subject to rigorous oversight to ensure measurable results, maximum efficiency, and accountability, with zero tolerance for fraud, waste, or abuse. Through the bilateral Global Health Memorandum of Understanding with Madagascar’s Ministry of Public Health, both governments agreed on shared priorities and responsibilities, including a strong focus on maternal and newborn health and Madagascar’s increasing co-investment in its own health system. Madagascar continues to face major challenges in maternal and newborn health, including high maternal mortality rates and significant gaps in access to quality delivery care. In rural regions, many women still give birth at home or in facilities without adequate infection-prevention supplies. Through the U.S.-funded Momentum Country and Global Leadership Project, the United States is providing essential equipment to healthcare facilities nationwide: calibrated reusable drapes for infection prevention in approximately 1,000 community health centers (CSBs), single-use calibrated drapes for 35 hospitals, and management tools for health workers to improve data collection and care coordination. “U.S. foreign assistance makes America safer by preventing instability and strengthening health systems that can detect and contain infectious diseases before they cross borders,” said Chargé d’Affaires Arnold during the handover ceremony. “Our support is temporary and targeted, designed to help Madagascar build greater self-reliance and reduce long-term dependence on U.S. taxpayer resources.” This U.S. support will help standardize safe delivery practices, strengthen infection prevention, and improve data management systems. Used effectively, these materials will contribute directly to reducing maternal and newborn deaths, especially in rural and hard-to-reach areas where access to quality delivery care remains limited. From major hospitals to remote health centers, U.S.-funded equipment is already delivering results. The nationwide scope of this support ensures that women and newborns across Madagascar benefit from improved infection prevention and safer delivery practices. This targeted assistance helps save lives today while supporting Madagascar’s transition toward a self-sufficient health system, ensuring that today’s U.S. support leads to Madagascar’s future independence from foreign assistance.
To protest rising taxes, Russia’s small businesses take their ‘flash mobs’ online
With the costs of the war in Ukraine growing, the Kremlin has been raising taxes on and rolling back exemptions for Russian small businesses.
Review | ‘You, Me & Tuscany’ is a fresh spin on old ideas
The film is setting a new standard for new-age rom-coms.
Boosting the berry business
Blueberry growers recover from the blues and strawberries have their saviors after a blistery—by Florida standards—winter. Add other growing challenges and Southwest Florida’s berry businesses need a boost. Still, it’s peak season for blueberries in the area and residents can enjoy the healthy treat as the crop losses mostly affect commercial buyers, local growers said.
Advancing epidemic and pandemic preparedness through research readiness: SEAR Unity Studies Network Sites chart the way forward in 2026
On 25 March 2026, WHO Regional Office for the South‑East Asia Region (SEARO) convened the first meeting of the SEAR Chapter of the Unity Studies Network Meeting in 2026 to take stock of progress and plan next steps for strengthening epidemic and pandemic preparedness across the Region. The meeting focused on reviewing the ongoing adaptation of selected, standard Unity Study methodologies, sharing field implementation experiences from 2025, and identifying key priorities to guide collaborative work in 2026.Held virtually, the meeting brought together representatives from six Unity Studies network sites across five countries (Bangladesh, India, Nepal, Sri Lanka, and Thailand) as well as participants from all three levels of WHO.The meeting took place against the backdrop of ongoing regional efforts in South‑East Asia to strengthen early investigations and studies for epidemic and pandemic preparedness, with a focus on pandemic influenza and other emerging respiratory pathogens with epidemic and pandemic potential. In this context, and as part of preparedness for the rapid generation of evidence, the WHO Health Emergencies Programme in SEAR (WHE/SEAR) encourages Unity Studies sites to adapt prioritized, standardized Unity Study protocols to their national contexts. This approach supports the generation of timely and reliable evidence to inform public health decision‑making during epidemics and pandemics.These efforts are supported through a combination of experience sharing, participation in the global Unity Studies network webinars, capacity‑building of the national public health workforce, and regional collaboration that promotes inter‑country, inter‑agency, and inter‑ministerial action. In parallel, sites are encouraged to explore diversified funding sources to help sustain research preparedness over the long term.Discussions highlighted the importance of WHO’s catalytic financial support through the Pandemic Influenza Preparedness (PIP) Partnership Contributions (PIP‑PC). When combined with national and institutional resources, this support enables countries and sites to maintain continuity of activities at a core level and remain prepared to respond rapidly and effectively to future epidemics and pandemics.Regional and Global Updates: Opening the session, Dr Pushpa Ranjan Wijesinghe, Programme Area Manager, Pandemic and Epidemic Management (PEM) Unit, WHO Health Emergencies Programme, highlighted the progress made in operationalizing the Unity Studies Network across the Region and acknowledged the collaborative efforts of WHO at all three levels, together with country unity studies sites. He emphasized the availability of limited but catalytic funding through the Pandemic Influenza Preparedness (PIP) Partnership Contribution (PIP‑PC) workplan at SEARO to sustain network activities in 2026, while encouraging sites to leverage additional funding sources to expand implementation.Representing SEARO, Dr Ashok Basnet, consultant, Pandemic Influenza Preparedness reported that, at the regional level, the Unity Studies Network currently includes six sites across five countries: Bangladesh, India, Nepal, Sri Lanka, and Thailand. He noted that in 2025, catalytic funding supported protocol adaptation, training, and pilot implementation. In addition, a regional Unity Studies symposium held during the Global Public Health Summit in Sri Lanka strengthened and advocated collaboration between the Unity Studies site, national decision‑making forum, relevant technical units of the ministry of health, WHO and other partners.The symposium also highlighted the role of early investigations and studies as a key component of a multi‑source surveillance system needed at country level to generate a broad range of evidence. It underscored the critical role of country Unity Studies sites and similar research entities in generating evidence to support public health decision‑making during epidemics and pandemics.Participants of the WHO GISRS Unity Studies Sites Network- SEAR Chapter Meeting 2026 (Photo credit- PEM/WHE/SEARO). SEARO also updated participants on ongoing efforts to develop a regional Unity Studies webpage and to promote participation in global knowledge‑sharing platforms, including webinars and the WHO Community of Practice. Priorities for 2026 include supporting the implementation of Unity protocols, strengthening collaboration and experience sharing across sites, documenting and publishing findings, integrating Unity Studies activities as a component of multi-source national surveillance systems, and building workforce capacity.Dr Nickle Boddington of the Global Influenza Programme (GIP) at WHO Headquarters, which coordinates the Unity Studies Network globally, provided updates on global Unity Studies activities, noting that the network has expanded to 16 sites worldwide. New and updated protocols, including rapid clinical severity assessment tools and standardized data platforms, are being developed to support implementation. The global Community of Practice has also been launched to facilitate technical exchange, and continued support will be provided to sites for protocol adaptation, ethical approvals, and readiness for early investigation during outbreaks. Updates from Unity Studies sites in SEAR in 2025Unity Studies sites shared progress and experiences in implementing protocols across diverse country contexts. Sites initiated activities in 2025 using catalytic funding from SEARO, complemented by the mobilization of internal national resources. The Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh, reported the adoption and piloting of investigation protocols on non‑seasonal influenza and emerging respiratory diseases during recent avian influenza outbreaks, demonstrating a multi‑source funding approach involving government, WHO and One Health partners.The Hamdard Institute of Medical Sciences and Research (HIMSR), New Delhi, India, highlighted the piloting of household transmission investigation studies, the expansion of the national Unity Studies network, and the contribution of study findings to global evidence through published outputs. The All-India Institute of Medical Sciences (AIIMS), New Delhi, India, shared experiences from large‑scale seroprevalence studies conducted during the COVID‑19 pandemic, recent efforts to strengthen national Unity network sites through training activities, and plans to initiate multi‑centre studies on respiratory viruses.Similarly, Prince of Songkla University, Thailand, highlighted its experience in developing integrated surveillance and diagnostic protocols for multiple respiratory pathogens, enhancing readiness for targeted investigations of emerging threats. The University of Sri Jayewardenepura, Sri Lanka, reported ongoing engagement with the Ministry of Health and exploration of additional funding sources to initiate studies. Meanwhile, the National Public Health Laboratory, Nepal, provided updates on continued coordination with national stakeholders to initiate implementation, despite delays related to the country context.Across sites, shared experiences underscored the importance of protocol adaptation, multisectoral collaboration, and sustainable funding to operationalize Unity Studies and generate evidence for public health action.Discussions and Way ForwardThe meeting reinforced key priorities for 2026, including strengthening unity studies protocol implementation, enhancing collaboration, knowledge sharing, integrating research as a component of national multi-source surveillance systems, and ensuring that generated evidence informs public health decision‑making. Participants also emphasized the importance of documentation, visibility, and leveraging multiple funding sources, and agreed on the following action points.Unity Studies Sites and WHO Country Offices:Advance adoption and implementation of at least one Unity protocol, continuing activities initiated in 2025 with catalytic funding, supplemented with other sources of funding where feasible.Document site-specific experiences, outcomes, and lessons learned; participate in regional and global knowledge-sharing platforms including WHO webinars and the Community of Practice.Work closely with ministries of health to establish unified national platforms for translating generated evidence into national public health decision-making. WHO Country Offices may facilitate technical support and complementary, synergistic funding opportunities where feasible. WHO Regional OfficeCoordinate with WHO Country Offices to support implementation of core unity studies activities by network sites through catalytic funding and provide technical guidance and virtual engagement opportunities throughout 2026.Facilitate the participation of all Unity Studies sites in the regional meeting on Unity Studies implementation in October 2026, to be held back‑to‑back with the Annual SEAR Regional PIP‑PC Meeting; develop and host a SEAR Unity Studies website; and mobilize support from academic institutions within the SEAR Unity Studies Network to support the development of regional manuscripts that showcase Unity Studies activities to scientific audiences and promote knowledge‑sharing.Explore complementary funding opportunities and partnerships to sustain and expand network activities across the Region.WHO HeadquartersProvide global technical guidance, updated protocols, and toolkits to support the implementation of Unity Studies in SEAR.Facilitate engagement of SEAR sites in global webinars, the Community of Practice, and opportunities to showcase their work.Support joint publications, inclusion of SEAR experiences in global outputs, and identification of additional funding and collaborative opportunities to strengthen Unity Studies activities.
Lurie’s Asia trip targets tourism rebound, new cultural exchanges with San Francisco
Daniel Lurie kicked off his delegation to Shanghai by signing a partnership between the SF Conservatory of Music and its counterpart 6,000 miles away.
How a little-known founder is trying to change Calif. politics — to the tune of $1 billion
You may not have heard of Maria Davidson, but tech investors like Joe Lonsdale and Garry Tan trust her with their political ambitions.
Ogden Raptors add new broadcast voice with familiar pedigree
The Ogden Raptors are adding a new voice to the broadcast booth for the 2026 season. The team announced the hire of Arden Louchheim as an additional announcer for play by play and color commentary. Louchheim is finishing up both her sports media degree and her women’s golf career at the University of Nebraska but […]
S.F. measles case highlights risk for babies too young for vaccine
Even parents following standard measles vaccination guidance, which recommends the first shot at 12 to 15 months, may find their infants exposed to the disease as it resurges globally, experts say.
WA police lag on required deescalation, mental health training
Washington law enforcement officers are way behind completing training required under a marquee police accountability law.
Galaxy S27 may deliver insanely fast storage speed with UFS 5.0
Samsung is rumored to equip at least some models of the Galaxy S27 series next year with the faster UFS 5.0 standard storage chips.
Earth Day marks grand opening of new, dedicated space for WMU’s IDEAS Collaboratory | News
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The Corvette ZR1X Had 1,250 HP And AWD At The ‘Ring. The Mustang GTD Competition Had Neither, And Won Anyway
The GTD Competition beats the standard GTD's previous 'Ring time by more than 11 seconds, and the Corvette's by eight
Controversial leader of powerful SF political group steps down
His six-year tenure as executive director was marked by controversy and major influence on local politics.
Protecting kids from deceptive and unsubstantiated health claims: the FTC’s case against TruHeight
Parents and kids (like all consumers) need clear, truthful information to make informed decisions — especially when those decisions affect their children’s health and physical safety. With that in mind, the FTC is working to protect kids and teens from deceptive health claims. Take the Agency’s recent case: the FTC charged Vanilla Chip LLC, which does business as TruHeight, with using unsubstantiated health claims and fake or incentivized reviews to market its supplements to kids, teenagers, and caregivers.
Gov. Sanders announces appointments - Talk Business & Politics
Gov. Sarah Sanders announced Thursday (April 16) the following appointments to state boards and commissions. Arkansas Commission on Law Enforcement Standards and Training Jeffery Hagar, Sherwood, to the Arkansas Commission...
Ionic Studios Signs Electric Entertainment's ELECTRIC NOW/ ELECTRIC NOW EN ESPANOL FAST/ AVOD APP, Featuring Leverage, The Librarians, and The Ark to Its CTV Portfolio
Ionic Studios, the CTV studio for the open ecosystem, today announced a strategic partnership with Electric Entertainment, the Los Angeles-based production and distribution company, founded by Dean Devlin. Under the agreement, Electric's content will join Ionic serving as brand-level representative across CTV and FAST—merchandising the studio's streaming platforms, ENOW and ENOW EN ESPANOL to advertisers, packaging it into audience-defined offerings, and delivering the buying standards that medi
Scientists Just Confirmed One of the Greatest Mysteries of Our Universe. Now What?
Observations don’t match the predictions of the standard model—and it’s not just a fluke.
Scientists Just Confirmed One of the Greatest Mysteries of Our Universe. Now What?
Observations don’t match the predictions of the standard model—and it’s not just a fluke.
ChatGPT’s new ‘Thinking’ mode just hit a 94% reasoning score — 7 prompts it can solve that standard AI can’t
OpenAI's latest flagship model, GPT-5.4, introduces an "Extended Thinking" toggle — here is how to use its 94% reasoning power to solve "impossible" tasks
Indigenous Leadership for Health: Virtual Preparatory Webinar to UNPFII 2026
English - French - Portuguese - Spanish - Russian Indigenous Peoples have the right to the highest attainable standard of physical and mental health. WHO invites you to join Indigenous Leadership for Health, a virtual preparatory webinar to the 25th Session of the UN Permanent Forum on Indigenous Issues (UNPFII 2026), to be held on Thursday, 16 April 2026, from 13:30 to 16:00 CEST, via Zoom. Interpretation will be available in English, French, Portuguese, Russian, and Spanish.This event brings together Indigenous leaders working on health to help shape the forthcoming WHO Global Plan of Action on the Health of Indigenous Peoples (GPoA). It is co-organized by WHO, the Government of Brazil, the Government of the Philippines, and the Johns Hopkins Center for Indigenous Health.The webinar will be divided into two parts, each with a corresponding objective:Part I – Contents of the draft Global Plan of Action. Part I's objective is to provide an update on the process for development of the Global Plan of Action on the Health of Indigenous Peoples, to share important lessons learnt from previous work and inputs received thus far, and to invite participants to send written inputs to the Plan. The linkages to the health topics to be discussed at the forthcoming UNPFII will also be highlighted.Part II – Panel on Indigenous Leadership for Health. Part II's objective is to highlight Indigenous leadership, knowledge systems, and community-driven approaches that contribute to improving the health and well-being of Indigenous Peoples, through the sharing of select country and regional examples.Registration is free and open to all. Register hereWe encourage all Indigenous organizations, communities, partners, and individuals to join us and contribute to this important global process.
State lawmakers question DPI officials over transparency concerns
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Aegon to sell UK business for 2 billion pounds to Standard Life
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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/.
Trump touts 'gold standard science.' What does that actually mean?
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Apple Testing Four Smart Glasses Styles Made of High-End Materials
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Ryan Day reveals Ohio State's extreme recruiting standards
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“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.
The Rogue Prince Of Persia Physical Release Now Available On Switch 1 & 2
Including standard and Immortal Editions
Interview: Rob McClure on the Unsettling Reality of SPARE PARTS – “This Isn’t Science Fiction. It’s Just Science.”
“Experimenting on yourself is insane. An experiment on a single person, with no controls, tells you nothing. It is incapable of predicting anything.” Or… is it? Such is the premise of Spare Parts, the darkly comedic and deeply twisted new drama currently shaking up Theatre Row through April 30th — and subverting our standards of what …Read more
YouTube Premium is getting pricier
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Penn researchers use AI to surface unreported GLP-1 side effects in Reddit posts
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a e iran-war military-and-defense politics washington-news general-news u-s-news entertainment world-news | Newsradio 1140 WRVA
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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
H.264 streaming license fees were raised to as much as $4.5 million, open standards now look even more attractive
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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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