My Hero Academia is a great take on superheroes where characters use their Quirks to protect civilians and beat down the many villains who want to make life miserable for people. This leads to some high-octane battles full of powerful moves and emotional moments.

While the major focus of Monster Hunter Wilds is the large monsters you'll be fighting, every region in the game also has a few small monsters that can also be fought and looted. These smaller monsters may not pose much of a threat, though they shouldn't be ignored depending on the gear you want to craft.

Researchers and enthusiasts have been fascinated by the challenge of reverse-engineering complex behaviors that emerge from simple rules in cellular automata for decades. Traditionally, this field takes a bottom-up approachdefining local regulations and observing the patterns arising from them. But what if we could flip this process? Instead of manually designing rules, we could develop a fully differentiable system that learns the local rules necessary to generate a given complex pattern while maintaining the discrete nature of cellular automata. This approach opens new possibilities for automating rule discovery in a structured and scalable way.Previous work has investigated learning transition rules using non-differentiable methods, proving that this method can evolve local regulations for specific computational tasks. Additionally, research has explored ways to make one-dimensional cellular automata differentiable, enabling gradient-based optimization techniques for rule learning. Building on these foundations allows us to develop systems that automatically discover rules that generate desired patterns, bridging the gap between handcrafted cellular automata and learned computational models.Google researchers introduced Differentiable Logic Cellular Automata (DiffLogic CA), which applies differentiable logic gates to cellular automata. This method successfully replicates the rules of Conways Game of Life and generates patterns through learned discrete dynamics. The approach merges Neural Cellular Automata (NCA), which can learn arbitrary behaviors but lack discrete state constraints, with Differentiable Logic Gate Networks, which enable combinatorial logic discovery but have not been tested in recurrent settings. This integration paves the way for learnable, local, and discrete computing, potentially advancing programmable matter. The study explores whether Differentiable Logic CA can learn and generate complex patterns akin to traditional NCAs.NCA integrates classical cellular automata with deep learning, enabling self-organization through learnable update rules. Unlike traditional methods, NCA uses gradient descent to discover dynamic interactions while preserving locality and parallelism. A 2D grid of cells evolves via perception (using Sobel filters) and update stages (through neural networks). Differentiable Logic Gate Networks (DLGNs) extend this by replacing neurons with logic gates, allowing discrete operations to be learned via continuous relaxations. DiffLogic CA further integrates these concepts, employing binary-state cells with logic gate-based perception and update mechanisms, forming an adaptable computational system akin to programmable matter architectures like CAM-8.Conways Game of Life, a cellular automaton introduced by John Conway in 1970, follows simple rules governing cell interactions to produce complex behaviors. A model was trained using DiffLogic CA to replicate these rules, employing a network with 16 perception circuit-kernels and 23 update layers. The loss function minimized squared differences between predicted and actual states. Training on all 512 possible 33 grids enabled accurate rule learning, which scaled effectively to larger grids. The learned circuit replicated classic Game of Life patterns, demonstrating its ability to generalize, exhibit fault tolerance, and self-heal without explicitly designed robustness mechanisms.In conclusion, the study introduces DiffLogic CA, a NCA architecture that employs discrete cell states and recurrent binary circuits. Integrating Deep Differentiable Logic Networks enables the differentiable training of logic gates. The model replicates Conways Game of Life and generates patterns using learned discrete dynamics. Unlike traditional NCAs, which rely on costly matrix operations, this approach enhances interpretability and efficiency. Future improvements may involve hierarchical architectures and LSTM-like gating mechanisms. This research suggests that integrating differentiable logic gates with NCAs could advance programmable matter, making computation more efficient and adaptable to complex pattern generation.Check outthe Technical details.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our80k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Evaluating Brain Alignment in Large Language Models: Insights into Linguistic Competence and Neural RepresentationsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Salesforce AI Proposes ViUniT (Visual Unit Testing): An AI Framework to Improve the Reliability of Visual Programs by Automatically Generating Unit Tests by Leveraging LLMs and Diffusion ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Microsoft AI Introduces Belief State Transformer (BST): Enhancing Goal-Conditioned Sequence Modeling with Bidirectional ContextSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Meta AI Introduces Brain2Qwerty: Advancing Non-Invasive Sentence Decoding with MEG and Deep Learning Parlant: Build Reliable AI Customer Facing Agents with LLMs (Promoted)

Curtis Yarvin, Peter Thiel, Donald Trump, and Elon Musk (The UnPopulist illustration).Dear Readers:Donald Trump has no ideology or convictions besides a desire to look like a winner. He is guided by nothing other than his own id. America First is really, at root, Trump First. But politics, like nature, abhors a vacuum and the ideological void that is Trumpism is being filled by a whole host of emergent, post-liberal, right-wing ideologies. Indeed, literally every strain of the righttraditionalists, religious leaders, constitutionalists, libertarianshas generated its own version of post-liberalism that seeks to use Trump to advance its goals that are in each case the opposite of what the original ideology stood for.Two years ago, I had identified four of these strains in my essay, A Typology of the New Right. I had somewhat inelegantly dubbed them: The Flight 93ers; Catholic Integralists (along with other Christian factions); National Conservatives, and Red Pilled Anarcho Bros.We cant defeat these ideologies if we dont understand them. Hence, The UnPopulist has been providing in-depth analysis and commentary about each of them. Just last weekend, we ran the second installment of our FAQ on the Far-Right, a comprehensive guide about the motives, methods, and ideas animating far-right groups. The guide was prepared by the erudite and principled classical liberals, Janet Bufton and Tom Palmer. Also recently, Berny Belvedere interviewed Christianity Today Editor-in-Chief Russell Moore about the rise of Christian Nationalism among evangelicals. We have also published essays and podcasts on integralism, the movement of Catholic intellectuals that seeks to integrate church and state.Today, we turn to the Red Pilled Anarcho Bros who inhabit Silicon Valley. Many of them, including Elon Musk and Peter Thiel, have been deeply influenced by a reactionary libertarianism espoused by Curtis Yarvin.What are the ideas, ideologies, and motives animating this reactionary libertarianism that seeks to delete liberal democracy and replace it with its opposite? Mike Brock, an industry insider who helped build Cash App among other ventures, unpacks this worldview in a long but engrossing essay that he penned for his own Substack, Notes From the Circus. We present to you an edited version below. He has been marinating in this worldview at the highest echelons for over a decade and intimately understands its intellectual genealogy, extreme faith in technology, and terrifying, illiberal, futuristic vision for America and the world.If you want to understand what Musk and his fellow tech bros are up to, this is a must read.On a personal note, I myself inhabited the libertarian intellectual world for three decades and broke with it because, in my view, it was not facing the Trumpist threat seriously, as I wrote here. The influence of these techno-ideologies that worship disruption of any kind, even by an authoritarian, is no doubt one reason.Editor-in-ChiefShikha DalmiaA shadow revolution is unfolding within the U.S. government. Inside Elon Musks DOGE, teams of young tech operatives are systematically dismantling democratic institutions and replacing them with proprietary artificial intelligence systems. Civil servants who raise legal objections are being removed. Government databases are being migrated to private servers. Decision-making power is being transferred from elected officials and career bureaucrats to algorithms controlled by a small network of Silicon Valley elites. In short, democracy is being deleted and replaced by AI models and proprietary technologyMusk's claims about transparent, open-source governance notwithstanding. It is a coup, executed not with guns but with backend migrations and database wipes.This coup, however, isnt a spontaneous oneits the culmination of a dangerous ideology that has been meticulously developed since the 2008 financial crisis and worked its way from the fringes of tech culture to the heart of American governance. And it has been driven by the idea that democracy, being not just inefficient but fundamentally incompatible with technological progress, is itself an obsolete technology that must be disrupted.Never Let a Financial Crisis Go to WasteThe 2008 global financial crisis led to widespread economic hardship and a profound loss of faith in established institutions. As the crisis unfolded, several key figures emerged who would go on to shape a new movement in American politics.Curtis Yarvin, writing under the pseudonym Mencius Moldbug, had been developing a critique of modern democracy on his blog Unqualified Reservations since 2007. In a post the following year, Yarvin argued that the financial crisis was fundamentally an engineering failure caused by a deviation from what he called Misesian banking, based on principles outlined by economist Ludwig von Mises. Mises, a pioneer of the Austrian School of Economics, was a thorough-going classical liberal who believed in free markets unencumbered by fiat currency and a constitutionally constrained government. He was also an outspoken critic of European imperialism.But Yarvin contrasted what he considered the Misean approach to free banking with the prevailing Bagehotian system, named after Walter Bagehot, which supports central bank intervention during financial crises. Yarvin argued that this interventionist approach was inherently unstable and prone to collapse. Yarvins broader critique of modern political and economic systems began to resonate with a growing audience disillusioned with traditional institutions.The Rise of the Reactionary LibertarianFor decades, libertarian thinkers had argued that free markets, left unrestrained, would naturally outperform any system of government. But what if the problem wasnt just government interference in markets? What if the very concept of democracy itself was flawed? This was the argument put forward by Hans-Hermann Hoppe, a student of Misess protg Murray Rothbard, who took libertarian skepticism of the state to its extreme conclusion. Hoppes 2001 book, Democracy: The God That Failed, landed like a bombshell in libertarian circles. Published at a moment when many Americans still saw democracy as the end of history, Hoppe argued that democracy was an inherently unstable system, one that incentivized short-term decision-making and mob rule rather than rational governance. His alternative? A return to monarchy. Hoppe was banished from respectable U.S. libertarian circles when he started flirting with fascist ideas and Rothbard has fallen in some disrepute although he remains a beloved of the paleo, Ron Paul libertarian faction.But this wasnt the monarchy of old. Hoppe envisioned a new orderone where governance was privatized, where societies functioned as covenant communities owned and operated by property-holders rather than elected officials. In this world, citizenship was a matter of contract, not birthright. Voting was unnecessary. Rule was left to those with the most capital at stake. It was libertarian thought taken to its most extreme conclusion: a society governed not by political equality, but by property rights alone.By the 2010s, Hoppes radical skepticism of democracy had found an eager audience beyond the usual libertarian circles, but through a mechanism different from simple market disruption. While Silicon Valley had long embraced Clayton Christensens theory of disruptive innovationwhere nimbler companies could outcompete established players by serving overlooked marketsa more extreme form of techno-solutionism had begun to take hold. This mindset held that any societal problem, including governance itself, could be solved through sufficient application of engineering principles. Silicon Valley elites who had built successful companies began to view democratic processes not just as inefficient, but as fundamentally irrationalthe product of what they saw as emotional decision-making by non-technical people. This merged perfectly with Hoppes critique: if democracy was simply a collection of feeling-based choices made by the uninformed masses, surely it could be replaced by something more rationalspecifically, the kind of data-driven, engineering-focused governance that these tech leaders practiced in their own companies. So Hoppes corporate monarchy morphed into Silicon Valleys corporate techno-monarchy.Peter Thiel, one of the most outspoken erstwhile libertarians in Silicon Valley, put this sentiment in stark terms in his 2009 essay, The Education of a Libertarian: I no longer believe that freedom and democracy are compatible. Thiel, who declared after Trumps reelection that the 2020 election was a last stand for the ancin regime that is liberalism, had already begun funding projects aimed at escaping democratic nation-states entirely, including seasteadingfloating cities in international waters beyond government controland experimental governance models that would replace electoral democracy with private, corporate-style rule. Hoppes vision of covenant communitiesprivate enclaves owned and governed by elitesprovided an intellectual justification for what Thiel and his allies were trying to build: not just alternatives to specific government policies, but complete replacements for democratic governance itself. If democracy is too inefficient to keep up with technological change, why not replace it entirely with private, contractual forms of rule?The notion that traditional democratic governance was inefficient or outdated resonated with those who saw themselves as disruptors and innovators. This intellectual throughlinefrom Mises to Hoppe to figures like Yarvin and Thielhelps explain the emergence of techno-libertarianism. It represents a dangerous alignment of anti-democratic thought with immense technological and financial resources, posing significant challenges to traditional conceptions of democratic governance and civic responsibility.After 2008, a new belief took hold in Silicon Valley: democracy wasnt just inefficientit was obsolete. Over the decade that followed, the ideas incubated in this period would evolve into a coherent challenge to the foundations of liberal democracy, backed by some of the most powerful figures in technology and finance.From Silicon Valley to Main Street: The Spread of Techno-Libertarian IdeasThe Tea Party movement emerged in 2009, channeling populist anger against the Obama administrations response to the crisis, especially government bailouts. As that movement gained momentum, it fostered a broader cultural shift that primed many Americans to be receptive to alternative political and economic theories. This shift extended beyond traditional conservatism, creating an opening for the tech-libertarian ideas emerging from Silicon Valley. The movements emphasis on individual liberty and skepticism of centralized authority resonated with the anti-government sentiment growing in tech circles. As a result, concepts like cryptocurrency and decentralized governance, once considered fringe, began to find a more mainstream audience among those disillusioned with traditional political and financial systems.The convergence of populist anger and techno-utopianism set the stage for more radical anti-democratic ideas that would emerge in the following years. The tech industrys growing influence gradually became more pronounced in the 2010s as leaders like Thiel began to more actively engage in political discourse and intellectual funding.The financial crisis didnt just create political movements like the Tea Partyit spawned entirely new media platforms that would help spread these anti-democratic ideas far beyond their original circles. One of the most influential was Zero Hedge, founded in 2009 by Daniel Ivandjiiski. The site, which adopted the pseudonym Tyler Durden for all its authorsa reference to the anti-establishment character from Fight Clubinitially focused on financial news and analysis from a bearish perspective rooted in Austrian economics.Zero Hedges evolution from a financial blog to a political powerhouse exemplified how anti-democratic ideas could be laundered through technical expertisejust as Joe Rogan and other sports and entertainment influencers have shown how democracy-destroying crankery and conspiracies can be laundered for their non-techie listeners on their platforms. The site gained initial credibility through sophisticated critiques of high-frequency trading and market structure, establishing itself as a legitimate voice in financial circles. But this technical authority became a vehicle for something more radical: the idea that democratic institutions themselves were as broken as the markets they regulated. When the site argued that central banks were rigging markets, it wasnt just making a financial claimit was suggesting that democratic institutions themselves were inherently corrupt and needed not to be reformed but replaced with more efficient mechanisms. When itdeclared that markets were manipulated, it wasnt just criticizing policyit was building the case that democracy itself was a failed system that needed to be replaced by technical, algorithmic governance.This methodologyusing technical financial analysis to justify increasingly radical political conclusionsprovided a blueprint that others would follow. But the sites true innovation wasnt just in mixing finance and politicsit was in suggesting that technical, market-based solutions could replace democratic processes entirely. This aligned perfectly with Silicon Valleys emerging worldview: if markets were more efficient than governments at allocating resources, why not let them allocate political power as well? Zero Hedges transformation from financial analysis to anti-democratic ideology previewed a broader pattern that would define the next decade: how technical expertise could be weaponized against democracy itself.As media scholar Yochai Benkler noted, this period saw the emergence of a propaganda feedback loop, where audiences, media outlets, and political elites reinforce each others views, regardless of the veracity of the information. Zero Hedge was an early example of this dynamic in action, demonstrating how traditional gatekeepers of information were losing their influence. This erosion of trust in established institutions, combined with the proliferation of alternative information sources, set the stage for what social psychologist Jonathan Haidt described as a kind of fragmentation of reality.As we moved into the 2010s, this fragmentation accelerated. Social media algorithms, designed to maximize engagement, amplified sensational and divisive content. The resulting flood of competing narratives made it increasingly difficult for citizens to discern truth from fiction, with profound implications for democratic discourse and decision-making. The Zero Hedge modelmixing expert analysis with speculative political commentarybecame a template for numerous other outlets, contributing to insular information ecosystems where narrative consistency trumped factual accuracy. This presaged how information would be produced, consumed, and weaponized in the age of social media and algorithmic content distribution.Zero Hedge led the way in demonstrating how technical expertise could be used to delegitimize democratic institutions from within and argue that democracys replacement by technical systems wasnt just desirableit was inevitable.This epistemic chaos fostered by algorithmic fragmentation wasnt an accidentit was a crucial tactic in undermining democracy itself. As Yarvin and his neoreactionary allies saw it, political legitimacy depended on the existence of a shared reality. Break that consensus, and democracy becomes impossible. Steve Bannon called it flooding the zone with shit. And by the time Trump entered office, the full strategy was in motion: destabilize public trust, replace expert analysis with endless counter-narratives, and ensure that the only people who could wield power were those who controlled the flow of information itself.Figures like Yarvin didnt just critique democracythey sought to undermine the very conditions in which democratic deliberation is possible. By weaponizing media fragmentation, they hacked the cognitive foundations of democracy itself, ensuring that political power would no longer rest on reasoned debate but on the ability to manipulate information flows.The Sovereign Individual: From End of History to End of PoliticsBut destroying consensus was only the first step. The true revolution would come through technology itself. In 1999, James Dale Davidson and William Rees-Mogg published a book that would become the blueprint for this technological coup: The Sovereign Individual. Released at the height of the dotcom boom, the book read like science fiction to many at the time: it predicted the rise of cryptocurrency, the decline of traditional nation-states, and the emergence of a new digital aristocracy.Libertarianism, when fused with this kind of technological determinism, took a sharp turn away from its classical liberal origins. If you assume that government will inevitably be outcompeted by private networks, decentralized finance, and AI-driven governance, then trying to reform democracy becomes pointless. The more radical conclusion, embraced by the figures at the forefront of this movement, is that government should be actively dismantled and replaced with a more efficient form of ruleone modeled on corporate governance rather than democratic participation.This is precisely where libertarianism morphs into neoreaction. Instead of advocating for a constitutional republic with minimal government, this new strain of thought pushes for a private,post-democratic order, where those with the most resources and technological control dictate the rules. In this vision, power doesnt rest with the peopleit belongs to the most competent executives running society like a CEO would run a company.This is how Yarvins argument that democracy is an outdated, inefficient system became so appealing to Silicon Valley elites. It wasnt just a philosophical argument; it aligned with the way many in the tech industry already thought about disruption, efficiency, and control. If innovation constantly renders old systems obsolete, then why should governance be any different?Figures like Thiel and Balaji Srinivasan, a Silicon Valley tycoon who made his fortune through bio-genetics and crypto currency startups and who authored, The Network State: How To Start a New Country, took this logic a step further. They argued that rather than resisting the decline of democratic institutions, elites should accelerate the transition to a new order, one where governance is voluntary, privatized, andthis is cruciallargely detached from public accountability.This mindset is deeply ingrained in Silicon Valley, where disruption is seen as not just a business model, but a law of history. Entrepreneurs are taught that old institutions are inefficient relics waiting to be displaced by something better. When applied to government, this logic leads directly to Yarvins argument: democracy is outdated legacy code that cant keep up with modern complexity. The future, he and others argue, will belong to those who design and implement a superior systemone that runs more like a corporation, where leaders are chosen based on competence rather than elections.This is why neoreactionary ideas have found such a receptive audience among tech elites. If you believe that technology inevitably renders old systems obsolete, then why should democracy be any different? Why bother fixing the government if its doomed to be replaced by something more advanced?Classical liberal libertarians accept democracy, arguing that markets should exist within a limited but functioning democratic system. But the Silicon Valley version of libertarianism, shaped by The Sovereign Individual and reinforced by the rise of cryptocurrency, started to see democratic governance itself as an obstacle. The rhetoric of exit and network states became the libertarian justification for abandoning democracy altogether. This wasnt just theoreticalthere were actual attempts to implement these ideas, like the Thiel-backed network state project called Praxis (a Misean term) in Greenland. The question, then, was no longer How do we make government smaller or improve its performance? but rather How do we escape government altogether?The answer, for people like Yarvin, Thiel, and Srinivasan, was to replace democracy with a new systemone where power belongs to those with the resources to exit and build something better. And as we are now seeing, they arent waiting for that transition to happen naturally.Srinivasan, like others in this movement, had undergone an ideological evolution that exemplifies a broader trend in Silicon Valley. As a former CTO of Coinbase and general partner at Andreessen Horowitz, he initially approached cryptocurrency from a techno-libertarian perspective, viewing it as a tool for individual empowerment and market efficiency. However, his thinking increasingly aligned with neoreactionary ideas, particularly around the concept of exitthe ability to opt out of existing political structures entirely. This shift from techno-libertarianism to neoreactionary thought isnt as large a leap as it might seem. Both ideologies share a deep skepticism of centralized authority and a belief in the power of technology to reshape society.The pipeline from techno-libertarianism to neoreaction often follows a predictable path. It begins with a libertarian critique of government inefficiency and overreach. This evolves into a broader skepticism of all democratic institutions, seen as slow and irrational compared to the speed and logic of technology. Eventually, this leads to the conclusion that democracy itself is an outdated system, incompatible with rapid technological progress. The final step is embracing the idea that democracy should be replaced entirely with more efficient forms of governance, often modeled on corporate structures or technological systems.James Pogues remarkable Vanity Fair piece, Inside the New Right, Where Peter Thiel Is Placing His Biggest Bets, traces how these fringe ideas became a sophisticated political movement backed by some of the most powerful figures in technology. Reporting from the 2022 National Conservatism Conference in Orlando, Pogue encounters everyone from fusty paleocon professors to mainstream Republican senators, but his focus on the younger cohort is particularly illuminating. They are highly educated young elites who have absorbed Yarvins critique of democracy and are working to make it political reality.Pogue details how Yarvins writings during the crisis period didnt just diagnose economic problemsthey offered a comprehensive critique of what he called the Cathedral, an interlocking system of media, academia, and bureaucracy that he argued maintained ideological control while masking its own power. The fusion of Austrian economics, techno-libertarianism, and Yarvins critique of democracy found its perfect vehicle in cryptocurrency and blockchain technology. Srinivasan emerged as a key figure who helped translate these abstract ideas into a concrete vision. Cryptocurrency offered not just a way to circumvent state monetary control, but also a model for how digital technology could enable new forms of sovereignty.As Pogue documents, figures like Thiel began to see cryptocurrency not just as a new financial instrument, but as a tool for fundamentally restructuring society. If traditional democracy was hopelessly corrupt, as Yarvin argued, then perhaps blockchain could enable new forms of governance built on immutable code rather than fallible human judgment. This vision found its perfect technological expression in Bitcoin. Launched in the aftermath of the 2008 crisis by an anonymous creator using the pseudonym Satoshi Nakamoto, Bitcoin seemed to validate The Sovereign Individuals core thesisthat technology could enable individuals to opt out of state monetary control. The timing was perfect: just as faith in traditional financial institutions had been shattered, here was a system that promised to replace human judgment with mathematical certainty.Cryptocurrency as SubversionBitcoins philosophical underpinnings drew heavily from Austrian economics and libertarian thought, but it was Saifedean Ammous who most explicitly merged these ideas with reactionary politics in his 2018 book, TheBitcoin Standard. What began as an economic argument for Bitcoin based on Austrian monetary theory evolved into something far more radical in its later chapters. Particularly telling was Ammouss critique of modern art and architecture, which mirrors almost precisely the fascist aesthetic theory of the early 20th century. When he rails against degenerate modern art and architecture in favor of classical forms, hes invokingwhether intentionally or notthe exact language and arguments used by fascists in the 1930s.The Bitcoin communitys embrace of figures like Ammous reveals how cryptocurrency became not just a technology or an investment, but a vehicle for reactionary political thought. The idea that Bitcoin would restore some lost golden age of sound money merged seamlessly with broader reactionary narratives about societal decline and the need for restoration of traditional hierarchies.While figures like Ammous attempted to claim Bitcoin for a reactionary worldview, the technology itselfas Bailey, Rettler, and their co-authors argue in Resistance Moneycan equally serve liberal and democratic values. The key distinction lies in how we understand Bitcoins relationship to political institutions. Where reactionaries see Bitcoin as a tool for replacing democratic governance entirely, the liberal perspective presented in Resistance Money understands it as a check against overreach and a means of preserving individual autonomy within democratic systems. This frames Bitcoin not as a replacement for democratic institutions, but as a technological innovation that can help protect civil liberties and human rightsparticularly in contexts where traditional financial systems are used as tools of surveillance or oppression.This tension between reactionary and liberal interpretations of Bitcoin reflects a broader pattern weve seen throughout our narrative: technological innovations that could enhance human freedom being co-opted into anti-democratic frameworks. Just as Yarvin and others attempted to claim the entire trajectory of technological development as inevitably leading to the dissolution of democracy, figures like Ammous tried to present Bitcoins monetary properties as necessarily implying a broader reactionary worldview.Implementing Fringe Anti-Democratic IdeasFrom Yarvins early writings during the financial crisis to todays constitutional crisis, we can trace a clear intellectual evolution. What began as abstract criticism of democratic institutions has become a concrete blueprint for dismantling them. But the key accelerant in this process was cryptocurrencyit provided both a technological framework and a psychological model for opting out of democratic governance entirely.What makes this vision dangerous is not just its hostility to democracyits the way it frames the collapse of democratic governance as an inevitability rather than a choice. This is what I have described as epistemic authoritarianism. Rather than acknowledging that technology is shaped by human agency and political decisions, Srinivasans network state vision assumes that technological change has a fixed trajectory, one that will naturally dissolve nation-states and replace them with digitally mediated governance structures. This deterministic thinking leaves no room for public debate, democratic decision-making, or alternative paths for technological development. It tells us that the future has already been decided, and the only choice is whether to embrace it or be left behind.This deterministic framing also explains why so many libertarians found themselves drifting toward reactionary politics. If democracy is doomed, then why bother defending it? If technology is going to replace governance, then why not accelerate the process? This is how techno-libertarianism became a gateway to neoreactionit replaced the classical liberal commitment to open debate and incremental progress with an absolutist vision of history that justified abandoning democratic ideals entirely.When Musk gains control of Treasury payment systems, or Trump declares that laws dont apply to those who save the country, theyre implementing ideas incubated in the crypto world. The notion that code can replace democratic institutions, that technical competence should override democratic negotiation, and that private power should supersede public authoritythese ideas moved from crypto theory to political practice.Both Srinivasans network state and Yarvins critique of democracy see technology as a means of escaping democratic constraints, but they approach it differently. Yarvin advocates for capturing and dismantling democratic institutions from within, while Srinivasan proposes building parallel structures without to make them irrelevant. Were now witnessing the convergence of these approachesusing technological control to simultaneously capture and bypass democratic governance.These ideological frameworks might have remained abstract theorizing if not for a unique convergence of factors that made their implementation suddenly possible. The rise of Trumpa figure simultaneously hostile to democratic institutions and eager to embrace tech oligarchspresented an unprecedented opportunity. Here was a potential autocrat who didnt just accept Silicon Valleys critique of democracy, but embodied it. His contempt for constitutional constraints, his belief that personal loyalty should override institutional independence, and his view that government should serve private interests aligned perfectly with Silicon Valleys emerging anti-democratic worldview. When combined with unprecedented technological control over information flows, financial systems, and social networks, this created a perfect storm: the ideology that justified dismantling democracy, the political vehicle willing to do it, and the technological capability to make it happen.The financial crisis created the conditions for anti-democratic thought to take root in Silicon Valley, but the actual transformation occurred through a series of distinct phases, each building on the last. Lets trace this evolution carefully:The institutional context for this transformation is crucial. Gallup polls show trust in the media fell from 72% to 31% between 1976 to 2024, while distrust in government hit 85% post-2008, according to Pew Research. This erosion of institutional trust created fertile ground for alternative power structures.The danger lies not just in what these operatives are doing, but in how their actions systemically dismantle citizens capacity for democratic resistance. What we are seeing is an exact implementation of Yarvins RAGE doctrineRetire All Government Employeesthat he first proposed in 2012. But what makes this moment particularly significant is how it combines multiple strands of neoreactionary thought into coordinated action. When Yarvin wrote about replacing democratic institutions with corporate governance structures, when he argues that technical competence should override democratic process, he is describing precisely what were now watching unfold.Yarvins blueprintremove career officials who might resist on legal or constitutional grounds and then install private technical infrastructure that makes oversight impossibleisnt merely aimed at changing who runs government agencies. Its aimed at fundamentally transforming how power operates, shifting it from democratic institutions to technical systems controlled by a small elite.What were witnessing isnt just a power grabits the culmination of an ideology that has been incubated, tested, and refined for over a decade. First, these thinkers argued that democracy was inefficient. Then, they created technological toolscryptocurrency, blockchain governance, and AI-driven decision-makingto bypass democratic institutions entirely. Now, theyre no longer experimenting. They are seizing control of government infrastructure itself, reprogramming it in real-time to function according to their vision. And they are determined to drag the rest of us into this Brave New World whether we consent or not.This is why focusing solely on the technical aspects of whats happening inside agencies misses the deeper transformation underway. Every unauthorized server, every AI model, every removed civil servant represents another step in converting democratic governance into what Yarvin called neocameralisma system where society is run like a corporation, with clear ownership and control rather than democratic deliberation. The infrastructure being built isnt meant to serve democratic endsits meant to make democracy itself obsolete.The strategy of flooding the zone with shit was never just about controlling the news cycleit was about reshaping the conditions of governance itself. The goal was not just to mislead, but to create an environment so chaotic that traditional democratic decision-making would become impossible. After disrupting journalism, which replaced truth with engagement-optimized feeds, they moved to disrupting governance itself. Your news, your politics, your very realityautomated, privatized, and controlled by those who own the network. Then, once the public lost trust in government, the tech elite could present the solution: a new, AI-driven, algorithmically optimized form of governanceone that wouldnt be subject to human irrationality, democratic inefficiency, or the unpredictability of elections. Just like social media companies replaced traditional news with algorithmic feeds, these technocrats sought to replace democratic governance with automated decision-making.Whats happening inside DOGE is the final phase of this plan. The old democratic institutions, weakened by years of deliberate destabilization, are being replaced in real-time by proprietary AI systems controlled not by elected officials, but by the same network of unaccountable Silicon Valley operatives who engineered the crisis in the first place. We are not heading toward this futurewe are already living in it.Government functions that once belonged to democratically accountable institutions are already being transferred to proprietary AI systems, optimized not for justice or equality, but for efficiency and control. Already, decisions about financial regulation, law enforcement priorities, and political dissent are being made by algorithms that no citizen can vote against and no court can oversee. Your rights are no longer determined by a legal framework you can appealthey are dictated by a set of terms of service, changeable at the whim of those who control the network.If we do not act now, we may wake up one day to find that democracy was not overthrown in a dramatic coupbut simply deleted, line by line, from the code that governs our lives.Thanks for reading The UnPopulist! Subscribe for free to support our project.An earlier version of this essay was originally published in Mike Brocks newsletter, Notes From the Circus.Follow us on Bluesky, Threads, YouTube, TikTok, Facebook, Instagram, and X.We welcome your reactions and replies. Please adhere to our comments policy.

In BriefPosted:9:34 PM PDT March 9, 2025Image Credits:NeomNeom is reportedly turning into a financial disaster, except for McKinsey & Co.A new WSJ report suggests that Saudi Arabias now eight-year-old Neom project a futuristic, carbon-neutral, 105-mile-long linear city envisioned by Crown Prince Mohammed bin Salman has become a financial sinkhole.Plagued by delays and cost overruns, the country, which has already shelled out $50 billion, could reportedly face another 55 years of construction, with an astonishing projected cost of $8.8 trillion, according to an internal audit presented to Neoms board last summer. Thats more than 25 times Saudi Arabias annual budget, notes the Journal.The situation is starting to resemble Saudi Arabias own Waterloo, with MBS misjudging the monumental challenges inherent in his strategy, much like Napoleon did before him. Among the harsh realities threatening to derail the project are insufficient labor, inadequate roads, and a lack of electricity.There are some winners, however. Consulting giant McKinsey & Company is reportedly earning more than $130 million annually for its services, despite some controversy surround its role, given the firms involvement in both the planning and validation of some of the projects financial projections, per the story. A McKinsey spokesman tells the WSJ the firm has strict protocols to prevent conflicts of interest in our engagements.TopicsClimate

The TikTok logo is displayed on a smartphone with owner ByteDance's name in the background. Investors are keen on ByteDance's AI potential.Jonathan Raa/NurPhoto via Getty ImagesByteDance cofounder Zhang Yiming has become Chinas richest man as investors bet on companies with AI potential. Zhangs fortune has grown to $65.5 billion, ahead of beverage giant Nongfu Spring founder Zhong Shanshans $56.5 billion, according to Forbes estimates.Zhang, 41, derives his net worth from a 21% stake in the privately held tech behemoth, although he stepped down as chairman in 2021 after resigning as chief executive earlier that year.In secondary markets, ByteDances valuation varies from $240 billion to over $400 billion, which is what some of its major investors including Fidelity Investments and T. Rowe Price Group believe the TikTok parent to be worth. Forbes thinks the company has a valuation of $312 billion, based on a recent share buyback program as well as conversations with analysts and a separate ByteDance investor who prefers not to be named.That amount marks a more-than-40% jump from 2024, when certain private market investors were only willing to acquire ByteDance shares at a price that would suggest a $217 billion valuation. The company got a tailwind from the seemingly improving TikTok situation in the U.S., after President Donald Trump said he would probably extend the April 5 deadline for the popular short video platform to be sold or banned, Glen Anderson, cofounder and CEO of U.S.-based broker-dealer Rainmaker Securities, says by email.Trump said on Sunday that he was negotiating with different U.S. buyers for a stake in TikTok, and a deal might come soon.In the meantime, investors have grown optimistic about the outlook for the countrys large technology companies, including ByteDance. They are encouraged by the governments friendlier stance toward private-sector businesses and the countrys AI advances despite U.S. attempts to thwart them with export controls. The Hang Seng Tech Index, which tracks the performance of such companies as Alibaba and Tencent, soared 80% over the past 12 months.All China tech assets staged a big rebound off the low base, Charlie Chai, a Shanghai-based analyst at research firm 86Research, says by WeChat.For its part, ByteDance has seen its Doubao chatbot become the worlds second most popular AI chatbot by monthly active users (MAUs).Powered by its own large language model (LLM), the free product had 82 million MAUs in February. OpenAIs ChatGPT, which was No.1, had 400 million in the same month, according to aicpb.com, a Hunan-based website that tracks AI products. The chatbot from Chinese AI company DeepSeek, which shocked Silicon Valley with its January release of a highly cost-effective model, ranked No. 4 with 62 million MAUs.A ByteDance spokesperson declined to comment. Although no longer involved in the companys day-to-day operations, Zhang still plays a key role in its AI strategy, according to media reports.The reclusive billionaire has encouraged an intense focus on AI, with the ultimate goal of realizing artificial general intelligence (AGI), or AI that matches or surpasses human intelligence, according to state-affiliated news outlet The Paper. He spearheads ByteDances hiring of AI-related talents, The Paper reported. Zhang reportedly agreed to an over 10 million yuan ($1.4 million) annual salary to poach a top AI engineer from a rival, according to local media reports.ByteDance also set aside 40 billion yuan ($5.5 billion) for the purchase of AI chips in China in 2025 and $6.8 billion for investing in AI-related infrastructure overseas, according to a Financial Times report that cited anonymous sources. Last year, the company spent $8 billion on AI-related servers, hardware that supports the computing needs of AI, according to a March research report by Beijing-based brokerage Cinda Securities.But 86Researchs Chai cautions that competition in AI is getting increasingly cut-throat in China. ByteDance operates multiple popular social media platforms, including TikToks sister app Douyin, which has helped the company promote its Doubao chatbot. But when it comes to the AI models that power such services, it is Chinese e-commerce giant Alibaba that is leading the competition, Chai says. Last week, Alibaba released its latest AI model, the QwQ-32B, which boasted improved performance and reasoning capabilities and doesnt require as much data to train, according to the company.Another model from the Chinese e-commerce giant Qwen2.5-Max is the worlds ninth most popular AI model by user vote, according to Chatbot Arena, a ranking platform developed by researchers including from the University of California, Berkeley. Privately held DeepSeeks R1 model ranks No. 6, according to Chatbot Arena. ByteDances products arent in the top ten.While chatbots provide a convenient consumer-facing interface for showcasing the power of AI to the public, what truly matters is the back-end models, which should be evaluated more rigorously in terms of performance and costs, Chai says by WeChat. Alibabas is the research firms top pick by those criteria, he says.

Latest hot technique entails using AI logical reasoning traces to train other AI on how to best ... [+] employ logical reasoning (yes, it works).gettyIn todays column, I identify how the latest generative AI and large language models (LLMs) are being cleverly data-trained on how to best make use of logical reasoning. This is a hot trend. Heres how it goes. First, obtain logic-based reasoning traces from a more advanced AI and feed those into a developing AI. Second, the newbie AI uses pattern-matching to quickly catch onto the various logical reasoning facets and then incorporates that aspect into its go-forward processing.Voila, without much added effort, the AI is dramatically improved by learning how to do exceptional logical reasoning.Lets talk about it.This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).Logical Reasoning Is HandyWe pretty much expect humans to exhibit logical reasoning. Thats nearly a given.For example, a person tells you they like to eat blueberries with their cheeseburgers, and youd almost by force of habit ask them to explain why they do so. What is the logic that the person employs to decide that blueberries and burgers go hand-in-hand with each other? You need to know.The odds are that we want generative AI and LLMs to do likewise. We want AI to showcase a series of logical steps that explain how it arrived at an answer. The steps are a reassurance that the AI didnt seemingly pull a rabbit out of a hat. You can examine the logical steps and decide for yourself whether the response by the AI is reasonably on target or possibly awry.Users who are experienced in using generative AI already know that one of the handiest prompting techniques consists of asking the AI to do a chain-of-thought (CoT) processing approach (see my in-depth coverage of AI chain-of-thought at the link here). All you need to do is tell the AI to proceed on a stepwise basis, and the AI will then showcase various logical steps that it performed to reach an answer.The thing is, generative AI might not be very good at effectively using chain-of-thought. It goes like this. Just because a series of steps are shown to you, it doesnt mean that the chosen steps are the right ones. They might not be.You see, it is as readily possible to use foul logic as it is to use prudent logic. The mere act of displaying a series of steps should not be confused with being adept at choosing steps and making sure that the appropriate steps are being utilized.How Logical Reasoning Can Go AkilterImagine that you ask the person who likes blueberries on their cheeseburgers why they like that somewhat unique pairing.They tell you that when they were a child, their parents always put blueberries on the dining table for all meals. When cheeseburgers were the chosen meal, they naturally opted to try the burger with the blueberries that were already residing on the table. After a while, they began to formulate a specific preference for having blueberries with their burgers.Very logical and makes abundant sense.Lets change up the story. Scratch from your mind the above explanation about the burgers and the blueberries.The person tells you that they really like pickles. They eat pickles with just about anything and everything. Blueberries are like pickles. Therefore, they eat blueberries with their burgers.Say what?You are bound to be puzzled by this claimed-to-be stepwise explanation or logical reasoning. How is it that pickles and blueberries are considered similar to each other? Even if thats true, what does that seem to have to do with hamburgers? Are we to assume that maybe if pickles arent available, then blueberries are this persons next best choice for their cheeseburgers?I realize that the whole kit and kaboodle on the burgers and blueberries scenario seems farfetched, but I wanted to use an example that would vividly illustrate that logical reasoning can be weak or imperfect. The value of logical reasoning is going to be notable when the logic is sound and suitably crafted.Just claiming that logical reasoning is afoot would be insufficient; it must also be robust and coherent.Training AI On Logical ReasoningWhat can we do to ensure that generative AI and LLMs are well-versed in using logical reasoning?A recent and emerging strategy is quite clever. It goes like this.You start by using an existing generative AI that is rather large-scale and has passed with flying colors on various tests of proficiency. The AI is asked a series of questions and told to showcase the logical reasoning used to arrive at the generated answers. The prompts, the answers, and the logical reasoning traces are captured or collected into a file or database.Next, an AI developer or AI researcher examines the collected facets and culls through them to try and arrive at a solid subset. The idea is that you want to wean out the instances that have below par logic. As much as possible, you want just the instances that have worthy logic.Heres where things get good.You take the culled subset and feed that into a budding generative AI that is being data-trained.The AI uses its computational pattern-matching to ascertain how logical reasoning is exhibited in the dataset. By examining lots of logical reasoning examples, the aim is that the budding AI will mathematically and computationally glum onto how to best perform logical reasoning.This is considered an inductive form of learning, namely that by looking at lots of examples, the hope is to learn generalized precepts based on the examples. Ive previously explained how AI is, at times, trained and leveraged via both inductive and deductive reasoning approaches (see the link here).Illustrative ExampleAllow me a moment to illustrate this new practice via a brief example.I log into a generative AI that is very popular and being used on a rather large scale. I begin to ask a bunch of questions that have to do with trying to decide whether someone is eligible for one thing or another.For example, I ask whether someone who is 22 can legally drink alcohol. Ive told the AI to do a chain-of-thought and showcase the logic that it is using to arrive at an answer.Here we go.My entered prompt: Sarah is 22 years old. The legal drinking age is 21. Can Sarah legally drink alcohol?"AI response: Reasoning trace: (1) Identify the facts, (2) Use a comparison rule that compares the facts, (3) Determine the answer based on the rule outcome. Fact 1: Sarah is 22 years old. Fact 2: The minimum required age for drinking is 21. Comparison rule: If the given value is less than the designated threshold then deny the aspect else grant its approval. The comparison here is 22 > 21. Conclusion: Sara can legally drink.Take a close look at the AI response.The answer was that since Sarah is 22 years old and the drinking age is 21, Sarah can legally drink alcohol. Easy-peasy.How did the AI arrive at this correct response?The chain-of-thought revealed these precepts:(1) Identify the facts(2) Use a comparison rule that compares the facts(3) Determine the answer based on the rule outcome.Seems dandy to me. The answer is correct. The logic is solid.I then placed the prompt, the answer, and the chain-of-thought into a file so that I would have a collection of such instances.Next Example And Repeatedly So Lets rinse and repeat.This time, I asked the generative AI about someone trying to rent a car.Take a look.My entered prompt: Joe is 24 years old. The car rental company requires that any car renter must be at least 25 years old. Will Joe be able to rent a car from the car rental company?AI response: Reasoning trace: (1) Identify the facts, (2) Use a comparison rule that compares the facts, (3) Determine the answer based on the rule outcome. Fact 1: Joe is 24 years old. Fact 2: The minimum required age for renting a car at the rental agency is 25. Comparison rule: If the given value is less than the designated threshold then deny the aspect else grant its approval. The comparison here is 24 < 25. Conclusion: Joe will not be able to rent a car from the rental agency.The answer is correct, namely that Joe is below the required cut-off age for renting a car and therefore cannot be rented a car.The chain-of-thought revealed these precepts (again):(1) Identify the facts(2) Use a comparison rule that compares the facts(3) Determine the answer based on the rule outcome.Bringing It All TogetherAssume that I went ahead and did this hundreds of times. All those instances were placed into a file or database.I then culled through the hundreds of instances and kept the ones that got the correct answer and which simultaneously showcased solid logical reasoning. Any that had an incorrect answer were discarded. Any that had the correct answer but that the logical reasoning was flawed, I dumped those out too.My collected pristine set is ready to go.I proceed to access a new generative AI that is still being data-trained.The new generative AI is in the midst of being devised, such as scanning the Internet and patterning on how humans write. This entails computationally pattern-matching human-written essays, narratives, poems, and the like. Thats how generative AI and LLMs mimic fluency in natural language. For more details on how this works, see my discussion at the link here.I provide to the newbie AI these three elements of each instance:Prompt that was used.Answer that was landed on.Logical reasoning trace that was showcased.The AI then uses computational pattern-matching to try and gauge how those three elements are related to each other.Note that I dont want to just input the logical reasoning traces. I could do so, but this might lead the AI down a false path. The better approach usually consists of providing all three elements. In this manner, the patterning is more robust and incorporates that what is asked and what is answered are related to the underlying logic that was employed.Proof Of The PuddingI want to test the newbie AI and see if it can find patterns in how logical reasoning is undertaken. My fingers are crossed that by having fed in the hundreds of examples that I sourced from another AI, this newly being constructed AI can get up-to-speed in a flash.Here is a question that I asked of the newbie AI.My entered prompt: Michael has a credit score of 640. The minimum required credit score for a loan is 700. Does Michael qualify for a loan?Newbie Generative AI response: This is similar to prior examples that Ive seen. I will use my learned logical reasoning, consisting of: (1) Identify the facts, (2) Use a comparison rule that compares the facts, (3) Determine the answer based on the rule outcome. Fact 1: Michael has a credit score of 640. Fact 2: The minimum required credit score is 700. Learned rule: If a given value is less than a designated threshold then deny else grant approval. The comparison here is 640 < 700. Conclusion: Michael does not qualify for a loan.Nice, got the right answer, and leaned into the logical reasoning based on the training I provided. The newbie generative AI seems to have successfully patterned this form of logic.Boom, drop the mic.Overall Vital ConsiderationsOf course, the example shown is a relatively simple one and was merely undertaken to quickly illustrate how this works.Will this work at scale?Suppose that I did the same in terms of collecting logical reasoning examples but did so across a wide array of questions and problems. You might have observed that my examples were solely about a simplistic comparison. In the real world, the types of logical reasoning are going to be much more varied and in-depth.No worries. All I need to do is collect lots of those and repeat the same procedure as stated above.You might say that I am handing on a silver platter to the AI a nicely derived set of logical reasoning instances so that the AI can lean into its computational pattern matching. Yep, thats what is taking place. Nailed it. Might as well make life easier for data training the AI. Easier for the AI developer or AI researcher. Plus, the belief is that this will make the AI better at logical reasoning.A smarmy person might say that those kinds of examples of logical reasoning are likely already sitting out there on the Internet.Well, maybe yes, maybe no.Even if such examples might be found here or there on the Internet during the widespread scanning process, please realize that Ive made sure to provide a focused set. Furthermore, the focused set has been screened to ensure they are correct answers.Another advantage is that I can use that set as many times as I like. Envision an AI maker starting a new AI model from scratch. The dataset that Ive prepared can be used to aid the data training of the new AI model. This works for existing AI models, too. Suppose an AI maker has decided they want to do additional data training to enhance their generative AI. They could use this culled dataset and not have to meander across the Internet in blind hope of finding useable data to pattern on.Being Mindful About Logical ReasoningAn intriguing question is how many examples or instances are needed for generative AI to suitably pattern the underlying logical reasoning from those instances. Its hard to say what the count ought to be. I mentioned that putting together hundreds of instances might be handy. I did so merely as a small-scale example.AI researchers are trying to ascertain what kind of numbers are really needed for carrying this out in the wild.A rule-of-thumb right now is that you probably need a count in the thousands to get a generative AI to reasonably pattern on the logical reasoning traces. Indeed, some amazing results have been achieved with a thousand or a few thousand instances. Others insist that you need a lot more than that for a robust generalization across-the-board on logical reasoning. Some suggest that you might need millions of such traces. This could be a few million to many millions of instances.All in all, this is a very promising approach. Ill keep you posted as the clever method evolves. Stay tuned.Some concluding remarks are warranted. A challenging aspect of the advent of AI-based logical reasoning in generative AI is revealed via a famous quote about logic that was long ago voiced by American logician and the father of pragmatism, Charles Sanders Peirce: Bad reasoning as well as good reasoning is possible; and this fact is the foundation of the practical side of logic.We must watch out for assuming or believing that generative AI is dutifully using logical reasoning. The logical reasoning displayed can be a ruse. It might be showboating that has nothing to do with how an actual answer was generated. Thats a vital caveat to keep dutifully in mind.Ill close with a favorite quote by Thomas Paine: To argue with a person who has renounced the use of reason is like administering medicine to the dead. Thats a powerful quote. Sadly, it seems like there is a lot of that going around these days. Lets wholeheartedly try to refrain from AI falling into the same troublesome trap.Go ahead and give a hearty round of applause for the genuine use of logic.

Movie fans with a lovingly curated DVD collection might want to check the condition of any Warner Bros. discs in their library.It turns out that hundreds of DVD titles manufactured by the entertainment giant between 2006 and 2008 have succumbed to a long-known phenomenon called disc rot, a deterioration in a DVDs condition that results in freezing, glitches, or complete failure during playback.Recommended VideosDVDs that are properly stored and handled really shouldnt fall apart like this in such a short space of time, suggesting that a serious issue during the manufacturing stage has impacted these particular Warner Bros. discs.Please enable Javascript to view this contentThe good news is that the company has acknowledged the problem and is offering replacements though not refunds for affected discs. But only if the movie is still in print, and so long as Warner Bros. still has the rights to the title.In a statement shared by movie site JoBlo, Warner Bros. said its aware of potential issues affecting select DVD titles manufactured between 2006 2008, and the company has been actively working with consumers to replace defective discs.The statement continued: Where possible, the defective discs have been replaced with the same title. However, as some of the affected titles are no longer in print or the rights have expired, consumers have been offered an exchange for a title of like-value. Consumers with affected products can contact the customer support team at whv@wbd.com.The issue appears to affect a wide range of titles, including classic Hollywood movies such as Stanley Kubrick: Directors Series edition of 2001: A Space Odyssey, Superman movies up to Superman Returns, and popular TV series like Looney Tunes Collections and Batman: The Animated Series.While Warner Bros. is at least doing what it can to ease the irritation for customers, some folks DVD libraries may be left with gaping holes through no fault of their own.JoBlos Chris Bumbray, who brought the Warner Bros. issue to greater prominence in a report over the weekend, said that after testing some of his DVDs, hes now left staring at a bunch of expensive coasters.Curious about how to convert your DVDs to a digital format? Heres everything you need to know.Editors Recommendations

The worlds largest contract electronics maker said Monday it has built its own large language model with reasoning capabilities, developed in-house and trained in just four weeks.

2025-03-10T04:16:51Z Read in app Kate Hudson says knowing when to acknowledge her shortcomings has been a key part of her parenting journey. Kevin Winter/Getty Images This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.Have an account? Kate Hudson, 45, says she isn't afraid to apologize to her kids whenever she's in the wrong.Instead of being combative with them, the "How to Lose a Guy in 10 Days" actor tries to acknowledge her shortcomings.There are several steps to making an effective apology, psychologists previously told BI.Kate Hudson, 45, isn't afraid to admit to her kids when she's in the wrong.During an appearance on Thursday's episode of the "Not Gonna Lie with Kylie Kelce" podcast, Hudson spoke about being "conscious of" her own behavior while parenting her kids and learning how to apologize to them when she goes "too far."The "How to Lose a Guy in 10 Days" actor recounted a recent incident when she butted heads with her son, Bingham, 13, after he refused to do something she had asked him to."And in that moment, I got triggered," Hudson told host Kylie Kelce. "It wasn't about him, it was about my own inability to resolve or walk away from the moment that was happening."Instead, Hudson said she "became combative" with her teenage son."But it happens all the time as a parent. When you walk away from it, you can recognize where you might've created more of a problem than you did a lesson," Hudson said.She said that admitting to her kids that she could've handled the situation better would have been more helpful in resolving the conflict.Not only that, it would also be a better parenting model for her kids to emulate."Sometimes in conflict, you go too far, and you need to say you're sorry, instead of teaching them that you doubled down," Hudson said. "And what you find in going to your kids and saying, 'I could have handled this better' or 'I made a mistake' or 'I'm sorry I didn't trust you' whatever the scenario is that connection becomes stronger."Knowing when to acknowledge her shortcomings has been a key part of her parenting journey."I think that's the biggest lesson for me, and now that I feel like a veteran mother. Been like doing it for 21 years," the "Running Point" star said.Hudson welcomed her eldest son, Ryder, in 2004 with her ex-husband, Chris Robinson. In 2011, she gave birth to her second son, Bingham, whom she shares with ex-fianc, Muse front man Matt Bellamy. She welcomed her daughter, Rani, in 2018, with her current fianc, Danny Fujikawa.Psychologists previously told Business Insider that there are several steps tomaking an effective apology. These include being accountable, putting a plan in place to rectify the mistake, and being sincere.It is also important to avoid phrases like "I'm sorry if" or "I'm sorry, but" since they reduce the apologizing party's accountability, Leah Rockwell, a licensed counselor, previously told BI."By adding 'and' into an apology, you create an easy way to open and broaden the conversation rather than to close it," she said. "As soon as the word 'but' is present, you are operating from a place of defensiveness."A representative for Hudson did not immediately respond to a request for comment sent by BI outside regular hours.