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Wikipedia Faces a Generational Disconnect Crisis

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Wikipedia celebrates its 25th anniversary this month as the internet’s most reliable knowledge source. Yet behind the celebrations, a troubling pattern has developed: the volunteer community that built this encyclopedia has lately rejected a key innovation designed to serve readers. The same institution founded on the principle of easy and open community collaboration could now be proving unmovable—trapped between the need to adapt and an institutional resistance to change.

Wikipedia’s Digital Sclerosis

Political economist Elinor Ostrom won the 2009 Nobel Prize in economics for studying the ways communities successfully manage shared resources—the “commons.” Wikipedia’s two founders (Jimmy Wales and Larry Sanger) established the internet’s open-source encyclopedia 25 years ago on principles of the commons: its volunteer editors create and enforce policies, resolve disputes, and shape the encyclopedia’s direction.

But building around the commons contains a trade-off, Ostrom’s work found. Communities that make collective decisions tend to develop strong institutional identities. And those identities sometimes spawn reflexively conservative impulses.

Giving users agency over Wikipedia’s rules, as I’ve discovered in some of my own studies of Wikipedia, can lead an institution away ultimately from the needs of those the institution serves.

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Wikipedia’s editors have built the largest collaborative knowledge project in human history. But the governance these editors exercise increasingly resists new generations of innovation.

Paradoxically, Wikipedia’s revolutionarily collaborative structure once put it at the vanguard of innovation on the open internet. But now that same structure may be failing newer generations of readers.

Does Wikipedia’s Format Belong to Readers or Editors?

There’s a generational disconnect today at the heart of Wikipedia’s current struggles. The encyclopedia’s format remains wedded to the information-dense, text-heavy style of Encyclopaedia Britannica—the very model Wikipedia was designed to replace.

A Britannica replacement made sense in 2001. One-quarter of a century ago, the average internet user was older and accustomed to reading long-form content.

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However, teens and twentysomethings today are of a very different demographic and have markedly different media consumption habits compared to Wikipedia’s forebears. Gen Z and Gen Alpha readers are accustomed to TikTok, YouTube, and mobile-first visual media. Their impatience for Wikipedia’s impenetrable walls of text, as any parent of kids of this age knows, arguably threatens the future of the internet’s collaborative knowledge clearinghouse.

The Wikimedia Foundation knows this, too. Research has shown that many readers today greatly value quick overviews of any article, before the reader considers whether to dive into the article’s full text.

So last June, the Foundation launched a modest experiment they called “Simple Article Summaries.” The summaries consisted of AI-generated, simplified text at the top of complex articles. Summaries were clearly labeled as machine-generated and unverified, and they were available only to mobile users who opted in.

Even after all these precautions, however, the volunteer editor community barely gave the experiment time to begin. Editors shut down Simple Article Summaries within a day of its launch.

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The response was fierce. Editors called the experiment a “ghastly idea” and warned of “immediate and irreversible harm” to Wikipedia’s credibility.

Comments in the village pump (a community discussion page) ranged from blunt (“Yuck“) to alarmed, with contributors raising legitimate concerns about AI hallucinations and the erosion of editorial oversight.

Revisiting Wikipedia’s Past Helps Reveal Its Future

Last year’s Simple Summaries storm, and sudden silencing, should be considered in light of historical context. Consider three other flashpoints from Wikipedia’s past:

In 2013, the Foundation launched VisualEditor—a “what you see is what you get” interface meant to make editing easier—as the default for all newcomers. However, the interface often crashed, broke articles, and was so slow that experienced editors fled. After protests erupted, a Wikipedia administrator overrode the Foundation’s rollout, returning VisualEditor to an opt-in feature.

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The following year brought Media Viewer, which changed how images displayed. The community voted to disable it. Then, when an administrator implemented that consensus, a Foundation executive reversed the change and threatened to revoke the admin’s privileges. On the German Wikipedia, the Foundation deployed a new “superprotect” user right to prevent the community from turning Media Viewer off.

Even proposals that technically won majority support met resistance. In 2011, the Foundation held a referendum on an image filter that would let readers voluntarily hide graphic content. Despite 56 percent support, the feature was shelved after the German Wikipedia community voted 86 percent against it.

These three controversies from Wikipedia’s past reveals how genuine conversations can achieve—after disagreements and controversy—compromise and evolution of Wikipedia’s features and formats. Reflexive vetoes of new experiments, as the Simple Summaries spat highlighted last summer, is not genuine conversation.

Supplementing Wikipedia’s Encyclopedia Britannica-style format with a small component that contains AI summaries is not a simple problem with a cut-and-dry answer. Though neither were VisualEditor or Media Viewer.

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Why did 2025’s Wikipedia crisis result in immediate clampdown, whereas its internal crises between 2011-’14 found more community-based debates involving discussions and plebiscites? Is Wikipedia’s global readership today witnessing the first signs of a dangerous generation gap ?

Wikipedia Needs to Air Its Sustainability Crisis

A still deeper crisis haunts the online encyclopedia: the sustainability of unpaid labor. Wikipedia was built by volunteers who found meaning in collective knowledge creation. That model worked brilliantly when a generation of internet enthusiasts had time, energy, and idealism to spare. But the volunteer base is aging. A 2010 study found the average Wikipedia contributor was in their mid-20s; today, many of those same editors are now in their forties or fifties.

Meanwhile, the tech industry has discovered how to extract billions in value from their work. AI companies train their large language models on Wikipedia’s corpus. The Wikimedia Foundation recently noted it remains one of the highest-quality datasets in the world for AI development. Research confirms that when developers try to omit Wikipedia from training data, their models produce answers that are less accurate, less diverse, and less verifiable.

The irony is stark. AI systems deliver answers derived from Wikipedia without sending users back to the source. Google’s AI Overviews, ChatGPT, and countless other tools have learned from Wikipedia’s volunteer-created content—then present that knowledge in ways that break the virtuous cycle Wikipedia depends on. Fewer readers visit the encyclopedia directly. Fewer visitors become editors. Fewer users donate. The pipeline that sustained Wikipedia for a quarter century is breaking down.

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What Does Wikipedia’s Next 25 Years Look Like?

The Simple Summaries situation arguably risks making the encyclopedia increasingly irrelevant to younger generations of readers. And they’ll be relying on Wikipedia’s information commons for the longest timeframe of any cohort now editing or reading it.

On the other hand, a larger mandate does of course remain at Wikipedia to serve as stewards of the information commons. And wrongly implementing Simple Summaries could fail this ambitious objective. Which would be terrible, too.

All of which, frankly, are what open discussions and sometimes-messy referenda are all about: Not just sudden shutdowns.

Meanwhile, AI systems should credit Wikipedia when drawing on its content, maintaining the transparency that builds public trust. Companies profiting from Wikipedia’s corpus should pay for access through legitimate channels like Wikimedia Enterprise, rather than scraping servers or relying on data dumps that strain infrastructure without contributing to maintenance.

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Perhaps as the AI marketplace matures, there could be room for new large language models trained exclusively on trustworthy Wikimedia data—transparent, verifiable, and free from the pollution of synthetic AI-generated content. Perhaps, too, Creative Commons licenses need updating to account for AI-era realities.

Perhaps Wikipedia itself needs new modalities for creating and sharing knowledge—ones that preserve editorial rigor while meeting audiences where they are.

Wikipedia has survived edit wars, vandalism campaigns, and countless predictions of its demise. It has patiently outlived the skeptics who dismissed it as unreliable. It has proven that strangers can collaborate to build something remarkable.

But Wikipedia cannot survive by refusing to change. Ostrom’s Nobel prize-winning research reminds us that the communities that govern shared resources often grow conservative over time.

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For anyone who cares about the future of reliable information online, Wikipedia’s 25th anniversary is not just a celebration. It is an urgent warning about what happens when the institutions we depend on cannot adapt to the people they are meant to serve.

Dariusz Jemielniak is Vice President of the Polish Academy of Sciences, a Full Professor at Kozminski University in Warsaw, and a faculty associate at the Berkman Klein Center for Internet and Society at Harvard University. He served for a decade on the Wikimedia Foundation Board of Trustees and is the author of Common Knowledge? An Ethnography of Wikipedia (Stanford University Press).

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Zip-Drive Emulator Puts Big Disks Back On LPT

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Iomega’s Zip drives filled an interesting niche back in the 1990s. A magnetic disk that was physically floppy-sized, but much larger in capacity– starting at 100 MB, and reaching 750 MB by the end–they never quite had universal appeal, but never really went away until flash memory chased them out of the marketplace in the early 2000s. While not everyone is going to miss them, some of us still think it’s a better form factor than having a USB stick awkwardly protruding from a computer, or microSD cards that are barely large enough to see with the naked eye. [Minh Danh] is one of those who still has affection for Zip drives, and when his parallel port Zip 100 drive started to give up the ghost last year, he decided to do something bold: reverse engineer it, and produce an emulator. First software, and then in hardware.

It’s not the prettiest-ever prototype, but lots of great things start with a mess of wires.

The first was to create a virtual zip drive that could run on a virtual machine and be accessed with DOS or Windows up to XP. The next task was to move that functionality onto a microcontroller to create something like a GoTek floppy emulator for LPT Zip drives that he’s calling the LPT100. Yes, Zip drives were built for APATI, SCSI, FireWire and USB, too, but [Minh]’s was on the parallel port and that’s what he wanted to replace, so the LPT interface is what set out to recreate.

It works, too, though took more guts than was expected– the 8-bit PIC18F4680 he started with just wasn’t up to the task. He moved up to a 32-bit PIC, the PIC32MZ2048EFH144 to be specific, which proved adaquate when testing with his Book 8088, a new DOS PC from 2023. Iomega’s official driver won’t run on an 8088, but the PALMZIP utility does and was able to transfer files, though only at the slow nibble rate due to limitations with the Book8088’s LPT hardware. Watch it in action below.

Alas, moving up to the Pocket386, it seemed the PIC just could not keep up. [Minh] says he’s thinking of moving to the faster Teensy 4.1, which sounds like a good idea. Considering the Teensy can be configured to serve as a drop-in replacement for a 68000, bit-banging the bus at 7.8 MHz, we’d think it should handle anything a parallel port can throw at it.

Thanks to [Minh Danh] for the tip!

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Arizona Charges Kalshi With Illegal Gambling Operation

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Arizona has filed criminal charges against Kalshi, accusing it of operating an illegal gambling business. “Kalshi may brand itself as a ‘prediction market,’ but what it’s actually doing is running an illegal gambling operation and taking bets on Arizona elections, both of which violate Arizona law,” Arizona Attorney General Kris Mayes said in a statement. The case could ultimately head to the Supreme Court to decide whether federal oversight by the Commodity Futures Trading Commission overrides state gambling laws. Bloomberg reports: While state regulators have taken steps to crack down on what they say is unlicensed betting on Kalshi’s site, Arizona appears to be the first state to escalate to criminal charges. The charges cited in the complaint are misdemeanors, which carry less serious penalties than felonies. […] Prediction market exchanges like Kalshi have said they should continue to be regulated by the US Commodity Futures Trading Commission despite opposition from some state officials, who argue the trading should come under state gambling laws.

Arizona’s criminal complaint follows Kalshi’s move last week to block the state’s gaming department from taking enforcement action against the company. “These are the first criminal charges of any kind filed against Kalshi in any court in the United States, but it will likely be the first of several,” said Daniel Wallach, a sports and gaming attorney.

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GlassWorm malware hits 400+ code repos on GitHub, npm, VSCode, OpenVSX

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GlassWorm malware hits 400+ code repos on GitHub, npm, VSCode, OpenVSX

The GlassWorm supply-chain campaign has returned with a new, coordinated attack that targeted hundreds of packages, repositories, and extensions on GitHub, npm, and VSCode/OpenVSX extensions.

Researchers at Aikido, Socket, Step Security, and the OpenSourceMalware community have collectively identified 433 compromised components this month in attacks attributed to GlassWorm.

Evidence of a single threat actor running the GlassWorm campaigns across multiple open-source repositories is provided by the use of the same Solana blockchain address used for command-and-control (C2) activity, identical or functionally similar payloads, and shared infrastructure.

GlassWorm was first observed last October, with attackers using “invisible” Unicode characters to hide malicious code that harvested cryptocurrency wallet data and developer credentials.

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The campaign continued with multiple waves and expanded to Microsoft’s official Visual Studio Code marketplace and the OpenVSX registry used by unsupported IDEs, as discovered by Secure Annex’s researcher, John Tuckner.

macOS systems were also targeted, introducing trojanized clients for Trezor and Ledger, and later targeted developers via compromised OpenVSX extensions.

The latest GlassWorm attack wave is far more extensive, though, and spread to:

  • 200 GitHub Python repositories
  • 151 GitHub JS/TS repositories
  • 72 VSCode/OpenVSX extensions
  • 10 npm packages

Initial compromise occurs on GitHub, where accounts are compromised to force-push malicious commits.

Then, malicious packages and extensions are published on npm and VSCode/OpenVSX, featuring obfuscated code (invisible Unicode characters) to evade detection.

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Malicious package on OpenVSX
Malicious package on OpenVSX
Source: Aikido

Across all platforms, the Solana blockchain is queried every five seconds for new instructions. According to Step Security, between November 27, 2025, and March 13, 2026, there were 50 new transactions, mostly to update the payload URL.

The instructions were embedded as memos in the transactions and led to downloading the Node.js runtime and executing a JavaScript-based information stealer. 

GlassWorm attack chain
GlassWorm attack chain
Source: Step Security

The malware targets cryptocurrency wallet data, credentials, and access tokens, SSH keys, and developer environment data.

Analysis of code comments indicates that GlassWorm is orchestrated by Russia-speaking threat actors. Additionally, the malware skips execution if the Russian locale is found on the system. However, this is insufficient data for confident attribution.

Step Security advises developers who install Python packages directly from GitHub or run cloned repositories to check for signs of compromise by searching their codebase for the marker variable “lzcdrtfxyqiplpd,” an indicator of the GlassWorm malware.

Malicious GitHub files
Malicious GitHub files
Source: Step Security

They also recommend inspecting systems for the presence of the ~/init.json file, which is used for persistence, as well as unexpected Node.js installations in the home directory (e.g., ~/node-v22*).

Additionally, developers should look for suspicious i.js files in recently cloned projects and review Git commit histories for anomalies, such as commits where the committer date is significantly newer than the original author date.

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Malware is getting smarter. The Red Report 2026 reveals how new threats use math to detect sandboxes and hide in plain sight.

Download our analysis of 1.1 million malicious samples to uncover the top 10 techniques and see if your security stack is blinded.

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Techdirt Podcast Episode 446: Mike & Karl Talk AI

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from the not-so-opposed dept

There’s a notion that pops up in the comments here on Techdirt that Mike and our writer Karl Bode are deeply opposed in their opinions on AI and engaged in an epic ongoing debate. Alas, the truth is a little less spectacular: while they might have some differences of opinion here and there, they actually agree on most things, and would both prefer to hear (and have) more thoughtful and nuanced discussions about the technology without going to the extremes. By way of demonstration, Karl joins this week’s episode of the podcast for a long conversation with Mike all about AI, its role in our society, the challenges it raises, and where things go from here.

You can also download this episode directly in MP3 format.

Follow the Techdirt Podcast on Soundcloud, subscribe via Apple Podcasts or Spotify, or grab the RSS feed. You can also keep up with all the latest episodes right here on Techdirt.

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Filed Under: ai, artificial intelligence, podcast

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Fascinating Look Back at the Akai PJ-11, an Innovative Mini Stereo with Rotating Speakers from 1984

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Akai PJ-11 Mini Stereo Rotating Speakers 1984
Back in 1984, Akai released the PJ-11, a compact stereo system that brought some fresh ideas to portable audio. Small enough to slip into a bag, it came with two independent speakers connected by cables that carried both power and audio signals, and those speakers could detach from the unit, lock into position at various angles, and be adjusted however the situation called for.



Each speaker could be swiveled precisely into position using 45 degree markers, giving you full control over where the sound was directed. Point them straight ahead for a traditional stereo image, tilt them upward for cleaner vocals, or angle them downward depending on the room. Flip them backward and the left and right channels swap, creating a surprisingly interesting effect in smaller spaces. The whole point was to put the sound where you actually were, rather than just firing it blindly forward the way most systems of the era did.

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Akai PJ-11 Mini Stereo Rotating Speakers 1984
Battery power came from four C-cells tucked inside each speaker, meaning the full system ran on eight batteries when you were out and about. That added some noticeable weight, but it also gave the speakers a reassuringly solid feel in your hands. Back at home a separate power adapter plugged into the rear of the main unit, sliding into place on a dedicated rail to keep everything sitting level and stable on a shelf or table. Pull the adapter and speakers off and the whole thing becomes a genuinely compact grab and go setup with no extra bulk to worry about.

Akai PJ-11 Mini Stereo Rotating Speakers 1984
The front panel features four sliders that allow you to make rapid adjustments on the fly. On the left is your overall level, and the following three are a super simple graphic equalizer that allows you to shape the bass, midrange, and treble with a twiddle. One of the buttons opens the cassette door, but the mechanism itself is turned upside-down, so you may have to squint to figure out what’s what, especially if some of the labels on the controls appear a little strange as a result. There’s a separate metal tape playback lever, and the built-in mono microphone can easily record speech or ambient sound, automatically adjusting the settings so you don’t have to.

Akai PJ-11 Mini Stereo Rotating Speakers 1984
Tuning in is handle by a four-band radio component with AM, FM, and shortwave reception that can pull in distant broadcasts when conditions are favorable. The FM side is very sensitive, and it includes a mono mode and a beat-cut filter to reduce interference. There’s a 3.5 millimeter connection on the front panel that allows you to connect signals from external players or recorders, allowing you to play them via the speakers or record them directly into the cassette without having to look for hidden ports.

Akai PJ-11 Mini Stereo Rotating Speakers 1984
Many users were caught off guard by the PJ-11’s unusually full and rich sound despite its small size. Voices came through clearly on angled up speakers, and the overall balance was pleasing, rather than harsh and tinny like some other radios. During high solar activity years, you could pick up shortwave broadcasts loud enough to fill a room, and cassettes had a pleasant warmth that kept the listener listening in for longer than you’d expect from a budget model priced around one fifty at introduction in 1984.

Akai PJ-11 Mini Stereo Rotating Speakers 1984
Akai only produced a small number of PJ-11s before going on to larger models such as the PJ-33, which is probably why they are so hard to come by now. You had all these convenient features, such as detachable speakers that spun around, a front-facing aux in, and the ability to run on batteries. All of this combined to create a design that felt refreshingly practical for ordinary listeners in 1984, and forty years later, it retains a certain attractiveness because it solved simple difficulties in a way that appears to have been completely forgotten these days

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Researchers disclose vulnerabilities in IP KVMs from four manufacturers

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Researchers are warning about the risks posed by a low-cost device that can give insiders and hackers unusually broad powers in compromising networks.

The devices, which typically sell for $30 to $100, are known as IP KVMs. Administrators often use them to remotely access machines on networks. The devices, not much bigger than a deck of cards, allow the machines to be accessed at the BIOS/UEFI level, the firmware that runs before the loading of the operating system.

This provides power and convenience to admins, but in the wrong hands, the capabilities can often torpedo what might otherwise be a secure network. Risks are posed when the devices—which are exposed to the Internet—are deployed with weak security configurations or surreptitiously connected to by insiders. Firmware vulnerabilities also leave them open to remote takeover.

No exotic zero-days here

On Tuesday, researchers from security firm Eclypsium disclosed a total of nine vulnerabilities in IP KVMs from four manufacturers. The most severe flaws allow unauthenticated hackers to gain root access or run malicious code on them.

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“These are not exotic zero-days requiring months of reverse engineering,” Eclypsium researchers Paul Asadoorian and Reynaldo Vasquez Garcia wrote. “These are fundamental security controls that any networked device should implement. Input validation. Authentication. Cryptographic verification. Rate limiting. We are looking at the same class of failures that plagued early IoT devices a decade ago, but now on a device class that provides the equivalent of physical access to everything it connects to.”

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Kalshi’s legal troubles pile up, as Arizona files first ever criminal charges over ‘illegal gambling business’

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Arizona Attorney General Kris Mayes has filed criminal charges against prediction market platform Kalshi, for allegedly operating an illegal gambling business in the state without a license and for election wagering.

The 20-count complaint, filed in Maricopa County court on Tuesday, accuses the company of engaging in unlicensed gambling activities, claiming that the site “accepted bets from Arizona residents on a wide range of events,” including state elections, a practice that is illegal in Arizona. The complaint charged Kalshi with four counts of election wagering for accepting bets from Arizona residents on the 2028 presidential race, the 2026 Arizona gubernatorial race, the 2026 Arizona Republican gubernatorial primary, and the 2026 Arizona Secretary of State race.

This is the first time a state has pursued such charges against the company, according to the Arizona Mirror, and marks a significant escalation in the battle between states and the prediction market industry.

“Kalshi may brand itself as a ‘prediction market,’ but what it’s actually doing is running an illegal gambling operation and taking bets on Arizona elections, both of which violate Arizona law,” Attorney General Mayes said in a statement. “No company gets to decide for itself which laws to follow.”

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It’s worth noting that the charges are technically misdemeanors. They follow a small surge of cease-and-desist letters, lawsuits, and other official actions from states over Kalshi’s activities, in which numerous officials have complained that the company is skirting state gambling laws.

Conversely, prediction sites like Kalshi have argued that they are not in violation of state law because they are subject to federal regulation via the Commodity Futures Trading Commission.

Kalshi may be getting attacked left, right, and center, but the Kalshi has also taken its own, often preemptive, legal action.

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Kalshi sued Arizona’s Department of Gaming in federal court on March 12. The company’s lawsuit argued that Arizona’s regulatory attempts were intruding “into the federal government’s exclusive authority to regulate derivatives trading on exchanges.” Kalshi also recently sued Iowa and Utah on similar grounds.

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Mayes’ office argues the company is merely trying to avoid accountability.

“Kalshi is making a habit of suing states rather than following their laws. In the last three weeks alone, the company has filed lawsuits against Iowa and Utah, and now Arizona,” Mayes said in a statement. “Rather than work within the legal frameworks that states like Arizona have established, Kalshi is running to federal court to try to avoid accountability.”

Elisabeth Diana, Kalshi’s head of communications, called the Arizona criminal charges “seriously flawed” and a matter of “gamesmanship” related to the company’s own litigation against the state.

“Four days after Kalshi filed suit in federal court, these charges were filed to circumvent federal court and short-circuit the normal judicial process,” Diana said. “They attempt to prevent federal courts from evaluating the case based on the merits – whether Kalshi is subject to exclusive federal jurisdiction. These charges are meritless, and we look forward to fighting them in court.”

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Federal officials have signaled that they’re on the prediction industry’s side, setting up a potential regulatory showdown between states and the federal bureaucracy. Mike Selig, chair of the Commodity Futures Trading Commission, recently published an op-ed in the Wall Street Journal in which he accused state governments of having “waged legal attacks on the CFTC’s authority to regulate” such sites. Selig also claimed that his agency would no longer “sit idly by while overzealous state governments” undermined the agency’s “exclusive jurisdiction” over the industry.

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Shoe-Sized Dolphin Robot Swims Straight Into Oil Spills and Pulls Them Clean Out

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Bio Dolphin Robot RMIT Oil Spills
Researchers at RMIT University in Australia built a small robot shaped like a dolphin. About the size of a sneaker, the machine glides across the surface of polluted water and gathers oil with a pump mounted at the front. A filter inside separates the oil from everything else, sending only the slick into an onboard tank while the water flows away untouched.


Bio Dolphin Robot RMIT Oil Spills
The filter draws its clever design from sea urchins. Microscopic spikes coat the sponge-like surface, too small to see without an electron microscope. Those spikes hold pockets of air that push water aside so it beads up and rolls off. Oil, on the other hand, spreads across the spikes and soaks in right away. The coating mixes oleic acid-treated barium carbonate with thin sheets of reduced graphene oxide. No fluorine or silane chemicals go into it, which keeps the whole setup safer for the environment than many older filters.


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Lab tests put the robot through its paces using blue kerosene as a stand-in for real oil. It collected about two milliliters every minute, and the liquid that ended up in the tank measured more than 95 percent pure. The filter never clogged or soaked up water. One full battery charge keeps the machine running for roughly 15 minutes. The same material can absorb between 15 and 65 times its own weight in oil, then release most of it when squeezed and return to work with over 97 percent of its original performance intact. Salt water does not corrode it, and stray contaminants rinse away easily.


Dr. Ataur Rahman, who leads the project at RMIT’s School of Engineering, described the thinking behind the build. Oil spills bring heavy costs to nature and to economies everywhere. The team wanted a device that deploys fast, steers with precision, and reaches places too dangerous for crews on boats. PhD researcher Surya Kanta Ghadei, who developed the filter material, shared what drove his part of the work. Growing up in India, he watched spills harm marine life, especially turtles. That memory pushed him to find a way for responders to act quicker and shield wildlife from harm.

Bio Dolphin Robot RMIT Oil Spills
Right now the robot answers to a Wi-Fi remote. A larger version, closer to the actual size of a dolphin, sits in the plans. Its exact scale will depend on the pump and the tank it carries. In that future form the machine will run without anyone steering it. It will vacuum oil from the surface, head back to a base station to empty the tank and recharge, then return to the spill and start again. The cycle keeps going until the area clears.

Bio Dolphin Robot RMIT Oil Spills
Engineers see clear advantages over systems that simply float in place and wait for oil to drift their way. This robot moves through the slick on its own, collecting as it goes. The filter stays dry and ready for repeated use, so crews avoid the constant swaps and messy disposal that older setups demand. Next steps include scaling up the filter area, strengthening the pump, running field trials, and checking long-term durability in open water.
[Source]

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This wild iPhone 17 Pro case features a touchscreen for 48MP selfies

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The new Center Stage selfie camera is one of the best features of Apple’s iPhone 17 series — but why settle for 18MP snaps when 48MP selfies are possible?

That’s the question posed by Kickstarter case brand Dockcase, whose latest offering, the Selfix case, adds a touchscreen to the back of your iPhone 17 Pro for seamless, main camera-quality selfies.

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Gamers React With Overwhelming Disgust To DLSS 5’s Generative AI Glow-Ups

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Kyle Orland writes via Ars Technica: Since deep-learning super-sampling (DLSS) launched on 2018’s RTX 2080 cards, gamers have been generally bullish on the technology as a way to effectively use machine-learning upscaling techniques to increase resolutions or juice frame rates in games. With yesterday’s tease of the upcoming DLSS 5, though, Nvidia has crossed a line from mere upscaling into complete lighting and texture overhauls influenced by “generative AI.” The result is a bland, uncanny gloss that has received an instant and overwhelmingly negative reaction from large swaths of gamers and the industry at large.

While previous DLSS releases rendered upscaled frames or created entirely new ones to smooth out gaps, Nvidia calls DLSS 5 — which it plans to launch in Autumn — “a real-time neural rendering model” that can “deliver a new level of photoreal computer graphics previously only achieved in Hollywood visual effects.” Nvidia CEO Jensen Huang said explicitly that the technology melds “generative AI” with “handcrafted rendering” for “a dramatic leap in visual realism while preserving the control artists need for creative expression.”

Unlike existing generative video models, which Nvidia notes are “difficult to precisely control and often lack predictability,” DLSS 5 uses a game’s internal color and motion vectors “to infuse the scene with photoreal lighting and materials that are anchored to source 3D content and consistent from frame to frame.” That underlying game data helps the system “understand complex scene semantics such as characters, hair, fabric and translucent skin, along with environmental lighting conditions like front-lit, back-lit or overcast,” the company says. Nvidia’s announcement video and detailed Digital Foundry breakdown can be found at their respective links.

“Reactions have compared the effect to air-brushed pornography, ‘yassified, looks-maxed freaks,’ or those uncanny, unavoidable Evony ads,” writes Orland. “Others have noted how DLSS 5 seems to mangle the intended art direction by dampening shadows in favor of a homogenized look.”

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Thomas Was Alone developer Mike Bithell said the technology seems designed “for when you absolutely, positively, don’t want any art direction in your gaming experience.”

Gunfire Games Senior Concept Artist Jeff Talbot added that “in every shot the art direction was taken away for the senseless addition of ‘details.’ Each DLSS 5 shot looked worse and had less character than the original. This is just a garbage AI Filter.”

DLSS 5’s “AI dogshit is actually depressing,” said New Blood Interactive founder and CEO Dave Oshry, adding that future generations “won’t even know this looks ‘bad’ or ‘wrong’ because to them it’ll be normal.”

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