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The AI Use Case Question Teachers Are Still Asking

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This article is part of the collection: Teaching Tech: Navigating Learning and AI in the Industrial Revolution.


A fourth-grade teacher asked a simple question:

“What can I actually use this for in math?”

This teacher captured the broader moment in education. Over the past several years, schools have been urged to respond to the rapid emergence of generative AI tools such as ChatGPT with limited information and a lot of hype and horror stories. Some have framed the technology as potentially transformative for teaching and learning, while others claim the opposite. Yet in many classrooms, adoption has been slower and more selective than the surrounding hype might suggest.

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That hesitation is often interpreted as resistance to innovation, but conversations with educators suggest a different interpretation. In many cases, teachers behave as experts in most fields do when encountering a new technology, evaluating whether it solves a real problem. When professionals encounter a tool that is widely marketed but still evolving, they ask a basic question: What does this actually help me do better?

For many educators, that question remains unresolved when it comes to classroom instruction, and that’s what our research project aimed to answer: What are teachers experiencing with generative AI in their classrooms?

In fall 2024, EdSurge researchers facilitated discussions between a group of 17 teachers from around the world. We convened a group of third to 12th grade teachers, and some of them designed and delivered their own lesson plans, either teaching with or about AI.

Overall, our participants’ responses reflect a few major themes, with the most prominent sentiment being an air of indifference. In particular, a fourth grade math teacher participant attempted to use generative AI in her instruction. However, before adoption, she asked how AI could help her elementary students learn math. Her question captured what several participants were thinking, aligning with 2024 data from the Pew Research Center that shows educators were split on whether student AI use was more harmful than helpful.

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A Technology Arriving Faster Than Schools Can Unpack

A high school computer science teacher from Georgia describes her fears about generative AI’s widespread push into classrooms:

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One of my biggest fears is actually Arthur C. Clarke’s rule: any sufficiently advanced technology is indistinguishable from magic…we have students, parents, and teachers looking at AI as if it’s magic.

A high school library media specialist from New York described the same tension from a different angle:

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There’s a fear about not being able to keep up with how things progress…the new tools and the impact it has on education.

Schools typically adopt new technologies through deliberate cycles of experimentation, professional development and evaluation. Generative AI has entered classrooms through a different pathway. Consumer tools became available to teachers and students simultaneously, often before schools had developed policies or instructional frameworks for using them.

The result is a situation in which educators encounter the technology while they are still trying to understand its implications.

Where AI Is Already Providing Value

In conversations with teachers, the pattern that appears consistently is a classic user design case. The most immediate use cases for generative AI have little to do with student learning. Instead, an engineering and computer science teacher in New Jersey addressed workload:

I have a running discussion with some of my colleagues about how to use AI to lesson plan. I use it routinely to lesson plan. I don’t really use the lessons, but we have to produce all this stuff for admin that no one reads… AI will just roll it off.

Another teacher described similar experimentation among colleagues:

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It’s really great that so many people have kind of scratched the surface and are using it to support their productivity and efficiency… lesson planning and newsletters and stuff like that.

These examples reflect a pattern seen across many professions: Generative AI is particularly effective at drafting, summarizing and generating text. In contexts where professionals face time pressure and administrative demands, those capabilities can be immediately useful.

Teachers experience those same pressures. Beyond instruction, many juggle grading, lesson planning, parent communication, extracurricular supervision and administrative reporting. In that environment, a chatbot that helps compress routine tasks can feel genuinely helpful.

Recent research, as well as national survey data from RAND’s American Educator Panels, suggests that teachers are adopting generative AI primarily as a productivity tool rather than a core instructional technology, a pattern that mirrors how educators in this study described their own early experimentation.

However, instructional discretion is different from a teacher’s administrative workload.

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The Instructional Use Case Remains Unclear

When teachers consider introducing AI tools to students during class time, the calculations they make change. The relevant question becomes: What student learning problem does this tool solve? Many educators are still trying to answer this question, even after several years of exposure to generative AI in some capacity.

Some teachers are experimenting with AI in limited ways, such as using it as a revision partner in writing. A science teacher from Guam said:

Students write a first draft and then feed it into ChatGPT for a second draft… but I push them not to use it for research.

Others are designing lessons where the technology itself becomes the subject of inquiry. A high school special education teacher in New York shared how she removes the veil from the magic of chatbots.

We purposely trained [a chatbot] wrong, so students could understand the data is only as good as how and who trains it.

Learning science research suggests that students benefit most when technology supports reflection and revision, rather than replacing the productive struggle of critical thinking and problem solving, a principle that many teachers in this study have applied. In these cases, AI becomes a tool that students analyze and critique. The participants do not attribute AI as a source of authoritative knowledge.

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AI Literacy as a Practical Classroom Entry Point

Many teachers see the most promising instructional opportunity in AI literacy, as it may feel most appropriate to teach students about the tools they’re hearing about and encountering daily. International guidance from the United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organisation for Economic Co-operation and Development (OECD) increasingly frames AI literacy as a foundational skill for students, encouraging schools to help young people understand how algorithmic systems generate information, rather than incorporating AI tools into everyday classroom tasks.

Students already live in environments shaped by algorithmically designed systems, from social media feeds to recommendation engines. Generative AI introduces another layer to that ecosystem.

An elementary teacher from New York state describes focusing on helping students understand how these systems produce information and where they fail:

For me it starts with literacy — [teaching] students how to prompt, and then how to fact-check the information that’s generated to make sure there’s no bias in it.

A middle school teacher from New York uses simple analogies to illustrate how machine learning systems work:

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We used an exercise about making the best peanut butter and jelly sandwich. The ingredients were the dataset, the procedure was the algorithm, and the output depended on how it was designed.

These lessons treat AI less as a productivity tool and more as a window into how digital systems generate knowledge.

Hallucinations, Bias and the Question of Trust

Teachers also raised consistent concerns about the reliability of generative AI outputs. An elementary library media specialist from New York said:

You ask ChatGPT to write a paper on something and it makes something up totally imaginary.

To illustrate the risks, some educators point to real-world examples. A high school French teacher shared:

I tried ChatGPT. I think it’s very useful if you know your content very well. IIf you don’t know your content, it’s hard to tell whether or not it’s accurate.

Others connect these issues to broader discussions about algorithmic bias, explaining why they fear that students will become reliant on these tools. A high school computer science teacher in New Jersey shares her concerns about the increased use of AI by students. She works at a school with large populations of African American, Latino and Black newcomer families from African and Caribbean countries:

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When we talk about bias, we look at hiring data and incarceration data… and facial recognition systems where error rates vary depending on who the system is trying to recognize.

In these contexts, AI becomes less a tool for answering questions and more a case study of how technological systems shape information.

The “Air of Indifference”

Taken together, these conversations reveal a stance that is not often captured in public discussions of AI in schools. What initially appeared to be an insignificant factor in keeping teachers interested in robust discussions about AI turned out to be a prominent theme aligned with both existing and emerging research.

By and large, teachers are not rejecting the technology. But they are also not reorganizing their classrooms around AI.

Instead, many are adopting a posture that might be described as pragmatic indifference:

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“I use it for lesson planning… but I don’t really use the lessons.”

“I push students not to use it for research.”

In other words, teachers are using AI where it clearly saves time while maintaining boundaries around core learning tasks. This posture reflects professional judgment, rather than resistance to inevitable technological innovation.

Schools exist partly to create conditions in which students practice complex cognitive work, such as deep reading, methodical writing, reasoning through problems and evaluating evidence. If a tool primarily reduces the need to perform that work, teachers have reason to question whether it advances or undermines learning.

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And that brings us back to the fourth-grade teacher’s question: What can I use this for with fourth-grade math?

If the instructional use case for AI remains unclear, what should students be learning instead?

That question leads to a deeper conversation about the kinds of skills that remain valuable even as technologies change.

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Windows devs rerolled old code to save precious bytes

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OS PLaTFORMS

There was a time when Microsoft cared about every KB

Microsoft’s latest Windows update might or might not have improved performance for the company’s flagship operating system, but there was a time when its engineers cared about performance. A lot. 

Veteran Microsoft engineer Raymond Chen on Monday hearked back to that time by telling another war story from the glory days of Windows, when a team was working on an x86-32 emulator for an unnamed processor (though it isn’t particularly difficult to identify potential candidates).

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The emulator used binary translation – native code was generated for the original x86-32 code. Chen explained, “This offered a significant performance improvement over emulation via interpreter. You can imagine that x86-32 is just a bytecode, and the emulator is a JIT compiler.”

The team came across a function that needed to allocate 64 KB of memory. Simple enough stuff – check that there is enough memory available, subtract 65536 from the stack pointer, and then initialize the memory in a loop.

Use the comments to correct me, but this sounds like loop rolling, where repetitive code gets condensed into a loop.

However, it appeared that a compiler had … optimized … the code “by unrolling the loop into 65,536 individual ‘write byte to memory’ instructions, each 4 bytes long.”

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Perhaps a bit quicker, but goodness – quite the memory hog. “All in all,” wrote Chen, “it took this program 256 kilobytes of code to initialize 64 kilobytes of data.”

Almost like a glimpse into a future where operating systems don’t appear to give two hoots about efficient use of storage. What would that look like?

As for the engineers working on the CPU emulator, Chen said, “This offended the team so much that they added special code to the translator to detect this horrible function and replace it with the equivalent tight loop.”

It would be interesting to know what that same team would make of the internals of some Windows binaries today, but it is heartening to know that, at one point, engineers cared about memory efficiency enough to reroll something. Sure, there might, just might, have been a performance hit, but spitting out 256 KB of code just to initialize 64 KB of data?

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Naughty. Very naughty. The much younger version of this hack, optimizing the heck out of code to fit within the confines of computers from yesteryear, would have been horrified. ®

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AI and Brain-Computer Interface Allow Speechless ALS Patient To Work a Full-Time Job

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UC Davis researchers say an implanted brain-computer interface has allowed Casey Harrell, an ALS patient who cannot speak, to synthesize sentences from brain activity with 99% accuracy in controlled tests and about 92% accuracy in everyday use. The Register reports that the system has remained usable at home since 2023, helping Harrell communicate naturally, control a computer, and return to full-time work without researchers needing to supervise each session. The Register reports: A team of scientists from the University of California, Davis, published a paper Monday detailing a years-long study of a brain computer interface (BCI) system implanted in a patient with amyotrophic lateral sclerosis (ALS, also known as Lou Gehrig’s disease), which destroys motor neurons and causes loss of motor control and eventual paralysis. According to the team, their patient, Casey Harrell, has been living with BCI implants since 2023 that are still working today, giving him the ability not only to control a computer cursor with his thoughts, but also to speak. […] Davis neurosurgeon David Brandman, co-principal investigator and co-senior author of the paper published Monday, as well as the surgeon who placed Harrell’s implant, described the results his team published as the crossing of a threshold in BCI technology: Not only has Harrell’s implant been working well with daily use since 2023, but it’s also incredibly accurate.

In controlled tests, the system managed to synthesize sentences from Harrell’s brain activity with 99 percent accuracy; outside of the lab in daily use, Harrell still assessed it as being accurate 92 percent of the time. “The key thing to me is that it’s enabling everyday communication for a guy who wants to talk but can’t,” Brandman told The Register in an interview. “Despite being paralyzed [Harrell] has gone back to work full time and has meaningful conversations with his daughter who’s never heard the sound of his voice.”

Prior work in the BCI space, Brandman told us, has either required researchers to be in a patient’s home whenever they’re using the tech, or for the patient to come to the researchers. That’s not the case here, with the system allowing Harrell’s home care team to hook him up to the system themselves, enabling him to use the device for more than 3,800 hours in the past few years. Based on the time the study was filed (It published Monday but went into peer review in July 2025) that would mean Harrell was using the device for more than five hours a day, on average. “It is a life that is more full of dynamic action and with friends and family, with colleagues, and it is something that allows me to communicate more in my natural way of communicating than any other technology that I have experienced,” Harrell told UC Davis via his BCI system.

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OpenAI Claims Fake Social Media Accounts Make Americans Hate Data Centers

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OpenAI has revealed details of fake social media campaigns designed to spread disinformation about data center projects, among others. The company says that as a result of the findings, some China-linked ChatGPT accounts have been banned. 

Investigators identified two “clusters” of ChatGPT accounts that they believed originated in China and accessed the platform through a firewall to circumvent ChatGPT restrictions in the country. One of these clusters is referred to by OpenAI as Data Center Bandwagon. This group used ChatGPT to create social media posts claiming that domestic electricity prices in the US were rising due to demand from AI data centers. As well as this disinformation campaign, this group also used social media posts to target overseas Chinese dissidents. This content targeted dissidents like Li Ying (often called Teacher Li), which added to the evidence that the cluster was Chinese-based. 

The second cluster of accounts changed the narrative from data centers to “technology and tariffs”. This cluster posted on suspected fake X accounts and concentrated on the US/China technological competition. The accounts used English language posts and cartoons to spread misinformation about tariffs, AI, and rare earths. The “bad actors” also created posts claiming that America is seeking global technological dominance. This group also posted Chinese-language posts that attacked the US, Israel, and Chinese dissidents. 

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As noted by OpenAI in its June 2026 threat report, there is a certain irony in this: American AI models are creating content that attacks American AI infrastructure. 

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How the data center disinformation campaign worked

Data centers in the US already have a bad rap, a point underscored by a recent Gallup poll finding that more Americans would oppose building a data center near them than a nuclear power plant. 

It’s perhaps just as well, then, that the OpenAI investigation concluded the fake Chinese campaigns gained little traction. According to the company, the campaign ranked as a Category One on the Breakout Scale. The Breakout Scale is a method of measuring the effectiveness of disinformation campaigns. Category One is the least effective and refers to campaigns that remained isolated on a single platform. Indeed, OpenAI reported that most of the posts on X received little or no engagement.

As an example of the type of content the fake accounts produced, the company cites a set of cartoons generated by the ChatGPT platform. These were based on genuine reporting from a regional newspaper and covered a power grid operator’s auction prices and how rising demand from data centers was driving electricity prices up for domestic customers. These cartoons were posted on suspected fake X accounts and used genuine links to actual news stories to add substance to the claims. 

Other tactics included using ChatGPT to doctor existing marketing images to support the narrative that the American public is effectively paying for AI data centers. As a side note on the topic, one American company is launching a scheme that can pay your electricity bill if you put a mini data center in your yard

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Why it matters

Despite its “Category One” ranking, the company still flags the campaign as strategically important. OpenAI argues that the bigger picture is what the campaign illustrates about ongoing foreign interference and the narratives they’re attempting to push. 

In its report, the company states that, “Both clusters attempted to connect US technology policies and industries to everyday economic anxieties and geopolitical instability.” In other words, these posts are designed to sow mistrust among the broader American public — mistrust that targets US institutions, technology companies, and the government.

OpenAI claims this is the first time it has seen such action against AI data centers by Chinese-linked accounts. It also stated that the accounts used in the “Data Center Bandwagon” cartoons were linked to a Chinese Government contractor. 

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However, although it’s the first time OpenAI has detected such disinformation claims, it isn’t the first instance of Chinese misuse of the platform. In another reported case earlier in 2026, the company suspended the account of a user linked to Chinese law enforcement agencies. The account was being used to attempt a covert influence operation against the Japanese Prime Minister, but the safeguards built into the ChatGPT model prevented it from proceeding. 

The data center campaign may have had little direct effect, but it does demonstrate the double-edged sword nature of the technology and how it can be used to heighten tensions in a time when many states are trying hard to delay building AI data centers, and one farmer turned down $15 million to keep an AI data center out of his backyard.

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The White House App Will Reportedly Be Auto-Installed On Homeland Security Staff’s Devices

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The administration sent out a notice to DHS personnel on June 16, Politico said.

The White House app is reportedly coming to all devices managed by the Department of Homeland Security, whether the user wants to download it or not. According to Politico, an email went out to all Homeland Security personnel on June 16, telling them the app will be automatically installed on all government devices. 

it’s not quite clear whether that means it will eventually be loaded onto all federal agencies’ phones in the future, with Homeland Security being one of the first. But the email reportedly described the app as “a convenient way to access official White House communications, including announcements, executive actions, speeches, livestreams, videos and other updates.”

The government officially launched the White House app back in March, promising live streams of presidential addresses, press briefings, latest events and articles that praise the Trump administration. It also gives users access to consolidated feeds from the government’s official social media accounts, and apparently, a list of the current cost of common grocery items. 

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While the administration has yet to confirm this, Government Executive reported in May that it was planning to automatically install the White House app on government employees’ work phones. The rollout will span “all government-furnished mobile phones in the executive branch,” the internal memo the publication saw reportedly said. At the time, the app was already slated for installation on all Federal Aviation Administration devices, GovExec said. As Gizmodo notes, a former government IT executive told GovExec that it was a “cause for alarm,” as any app installed on government-issued devices can “potentially create backdoor access to government networks behind the firewall.”

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Stop Killing Games Fails To Secure EU Law Despite 1.3 Million Signatures

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The European Commission has declined (PDF) to propose a law requiring publishers to keep discontinued video games playable, despite the Stop Killing Games initiative collecting nearly 1.3 million verified signatures. Instead, it plans to develop a voluntary industry code covering end-of-life transparency and preservation. Dextero reports: The Commission’s full communication said a legal obligation to keep games playable, as requested by the initiative, “would not be proportionate.” It cited concerns about intellectual property rights, confidential business information, publisher costs, and potential cybersecurity or safety risks once games are no longer supported. The code of conduct could include more transparent storefront labeling about possible game discontinuation, along with more partnerships between publishers and cultural heritage institutions to preserve games. However, it would not legally require publishers to provide offline patches, private server tools, or other methods for players to continue accessing games after official support ends. The Commission also argued that existing EU consumer law already provides some safeguards, including requirements around transparency, contract duration, termination conditions, and possible refunds if a shutdown conflicts with the agreement or a consumer’s reasonable expectations.

[…] Despite the setback, Stop Killing Games has said it is not ending its push for legislation. In a response posted after the Commission’s decision, the official Stop Killing Games account said the outcome was “not unexpected” and claimed the campaign had already prepared for the result. The group said it is now pushing for members of the European Parliament to amend Stop Killing Games into the Digital Fairness Act instead. “We can move on without the Commission and their non-decision,” the group said, referencing earlier comments from Accursed Farms creator Ross Scott.

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West Antarctica Is Missing Way Too Much Ice

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Antarctica’s west coast is missing an area of winter sea ice the size of France, sparking concerns for threatened penguins other marine life and global sea levels.

One expert said the loss of ice in the Bellingshausen Sea was “depressing” and the failure of ice to form could have intensified a heatwave over the continent’s peninsular last week that saw daytime temperatures peak at 15.4 degrees Celsius which is more than 20 degrees Celsius above average.

It’s winter in Antarctica, when sea ice expands rapidly around the continent peaking in September.

But satellite observations showed the Bellingshausen Sea—on the west side of the Antarctic peninsular and which by June would usually be covered by ice—was almost completely ice free.

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Scientists said the region was missing about 650,000 square kilometers (250,000 square miles) of sea ice, compared with the average between 1991 and 2020. That is an area about the size of France and almost tenfold the size of Tasmania.

“I’m concerned. It’s depressing,” said Dr Will Hobbs, an Antarctic sea ice expert at the University of Tasmania with the Australian Antarctic Program Partnership.

“It is remarkable that we are in June, and there is no sea ice there.”

He said this was the third time in four years that sea ice had been very low in the region. “I don’t think we will see sea ice there any more. It’s done,” he said.

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He said the loss of sea ice was likely linked to changes in the ocean and scientists were trying to understand if global heating was a factor.

He said the region was important for krill—a critical part of the food web for species in the region. Krill would usually be hiding from predators under the ice in winter, where they graze on algae.

On June 10 there was about 11.4 m square kilometers of sea ice around the entire continent compared to a long-term average for that date of 12.6 m square km.

Dr. Phil Reid, who monitors Antarctic conditions at Australia’s Bureau of Meteorology, said the Bellingshausen Sea had seen “incredible coastal exposure” in winter and summer in recent years.

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He said just to the area’s west were the Pine Island and Thwaites glaciers—the continent’s major contributors to ice loss and sea level rise.

Floating ice shelves in front of the glaciers could break up faster if protective sea ice is absent for longer periods, he said, and this could then speed up the loss of ice from the glaciers, pushing up global sea levels in the future.

The Bellingshausen Sea’s coastline was the site of tragedy in late 2022 when thousands of emperor penguin chicks died during a “catastrophic breeding failure” in four colonies.

That event contributed to UN advisers pushing the species up two categories to “endangered” on its international threatened species list earlier this year.

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Dr. Peter Fretwell, a scientist at the British Antarctic Survey who has been documenting the penguin’s decline, said the current loss of sea ice in the region was “a serious problem for penguins, especially emperors.”

“Sea ice is forming too late and breaking up too early. It leads to reduced breeding success and longer trips to molting grounds.”

Adelie penguin numbers were also falling and crabeater seals were being forced to migrate in summer to find stable ice, he said.

This month the Antarctic peninsular witnessed an extreme temperature spike over several days. Hobbs said while “nobody has done the numbers” it was reasonable to suggest the heat wave was “made worse by the lack of sea ice.”

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Sea ice would usually help to cool any warmer airflow entering the region from the north, he said.

Officials at Argentina’s national weather service, Servicio Meteorológico Nacional, said the country’s Esperanza base at the peninsular’s northeastern tip had experienced an “extreme temperature event” that peaked on June 5 and 6.

Maximum temperatures of 15.4 degrees Celsius and 13.4 degrees Celsius, respectively, were recorded at a period when average daily maximums were minus 6.2 degrees Celsius. The previous June temperature record at the base of 13.3 degrees Celsius was set on June 12, 1998.

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Windows update leaves third-party Office document launches in limbo

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PERSONAL TECH

Microsoft won the OLE vs OpenDoc wars. Now it’s saying OLE dependencies don’t matter

Microsoft’s June Windows update has upset some third-party applications that use Object Linking and Embedding (OLE) automation to open or control Office apps, leaving users with failed document launches and, in some cases, no error message to explain what went wrong.

According to Microsoft, “reports indicate that this issue may affect applications such as CCH Engagement, Workpaper Manager, dental software (such as Dentrix and Softdent), and Zotero; other similar applications might also be impacted.”

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The workaround is to “open the application or document directly instead of launching it from the affected third-party application.”

Microsoft was quick to point out that this wasn’t its problem. The third parties concerned are “independent of Microsoft.”

“We make no warranty, implied or otherwise, about the performance or reliability of these products.”

That would be fair enough were it not for the fact that these third parties are relying on Windows plumbing that has been around since the 1990s, and abruptly breaking or changing something in a Windows release doesn’t give those vendors much time to deal with the problem.

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OLE allows one application to control another – for example, firing up a Word document or Excel spreadsheet from an accounting application. When it works properly, users don’t need to switch between applications. The process should be seamless.

If opening the file directly, which somewhat defeats the point of OLE, doesn’t help, ordinary users will have to wait for a fix in “a future Windows update.” There is a mitigation for affected devices within organizations, though obtaining it requires contacting Microsoft support for business customers.

Veteran techies may find this mess ironic, given that in the 1990s Microsoft went all-in on OLE and ultimately saw off the rival OpenDoc tech backed by Apple and IBM.

The issue is the first that Microsoft has acknowledged in the patch, although the company’s forums are full of users complaining about other difficulties, including OneDrive and BitLocker problems. ®

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Doroni H1-X Debuts at Soul of the Sky, is a Personal Flying Vehicle That Actually Wants to Live in Your Garage

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Doroni H1-X Personal Flying Vehicle eVTOL
Photo credit: Doroni Aerospace
Doroni Aerospace has spent the better part of a decade moving from early garage experiments to a finished design it believes regular people could operate. The H1-X sits at the center of that effort. It is a two-seat electric vertical takeoff and landing aircraft built first for personal use rather than fleet service or air taxi routes.



The H1-X’s tandem wings measure nearly 18 feet across, but its eight ducted fans do the majority of the heavy lifting when it takes off and lands. Once it reaches cruising speed, two additional ducted fans at the back keep it moving forward. The carbon fiber design reduces the empty weight to approximately 1,850 pounds while still allowing it to carry a 500-pound payload. It’s nearly six feet tall, 16 to 18 feet long, depending on who’s measuring, yet small enough to fit in most conventional two-car garages without having to fold up.

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However, having the fans tucked away changes everything. Nothing spinning madly in the open means you’re not taking a low pass over the garden or driveway and colliding with plants, a fence, or people. The ducts guide the thrust downward, which decreases noise. Once up to speed, the wings begin to generate lift on their own, the vertical fans slow down, and the range and battery life improve dramatically because you don’t have to stay in a continual hover state like some other designs.

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Doroni Aerospace H1-X Personal Flying Vehicle eVTOL
Short hops in urban and suburban settings are crucial for performance. Its top speed is 120 mph, and cruises at 95 mph. It currently has a range of roughly 60 miles, but the manufacturer believes that after the batteries are sorted out, it will reach 100 miles. Its battery can be charged to 80% capacity in 25 minutes using one of those electric car chargers, giving you approximately 45 minutes of flight time. It won’t be straining up into the stratosphere, but rather 500 to 1,500 feet above land.

Doroni Aerospace H1-X Personal Flying Vehicle eVTOL
You sit side by side with the pilot behind a large clear canopy that allows you to see all around. The interior resembles a modern car rather than a traditional aircraft cockpit, with a single joystick controlling all flight controls, including roll, pitch, and yaw. And then there’s the self-stabilizing software, which helps eliminate all of the minor changes required to keep it stable. Taking off and landing is as simple as tapping one or two buttons.

Doroni Aerospace H1-X Personal Flying Vehicle eVTOL
The real change, however, is in the software, which is powered by what Doroni calls SOUL AI. The central screen displays all of the important information, including navigation, battery, time to destination, altitude, speed, nearest charging sites, and weather, on one screen. There are sensors all around the place that monitor 360 degrees. Radar, LiDAR, and cameras work together to detect obstructions and keep you on track, even if you mistakenly let go of the stick. They want to make the pilot more of a guide, a navigator, rather than a hands-on controller.

Doroni Aerospace H1-X Personal Flying Vehicle eVTOL
Safety is a key consideration throughout the design process, as the ducted fans eliminate one of the classic risks, the propeller. In the event of an emergency, a ballistic parachute provides an alternative means of safely landing. The safety features include redundant motors in a few ducts and continuous sensor monitoring to keep an eye on things at all times. The Doroni team is aiming for certification as a Light Sport Aircraft under the FAA’s new MOSAIC standards, which happen to match the H1-X’s size and characteristics. The company’s previous test vehicles, such as the H1-X, received special airworthiness certificates and successfully completed manned test flights.

Doroni Aerospace H1-X Personal Flying Vehicle eVTOL
Doron Merdinger launched the company in 2016, and they recently unveiled a full-scale model of the H1-X at their annual ‘Soul of the Sky’ event in Florida. People were able to go inside, check out the interface, and even test out a flight simulator. We’ve already received pre-orders for several hundred units, indicating that lots of individuals are eager to become involved. Pricing is projected to be in the $350,000 to $400,000 region, with deliveries set to begin in 2028 once the certification process is completed.

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Up-Close with Walter, the Workshop Sentry That Looks Like a Camera and Responds Like a Robot

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Walter Workshop Sentry Robot Camera Security
John Boss needed reliable oversight for a workshop packed with projects still under wraps. Standard internet cameras record events after the fact and offer little in the moment. He chose a different route and built Walter, a workshop sentry meets security robot, instead.



Walter hangs hung from the ceiling on a unique motorized mount, which at first view resembles a high-end pan-tilt security camera. The camera module, lights, and sensors sit in the tiny black shell, but a second look changes everything, as the inclusion of a full-fledged Nerf blaster gives the whole thing personality.

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A geared lazy Susan bearing controls the unit’s movement, allowing it to swivel smoothly in a full circle. Stepper motors control the pan and tilt movements through a pulley and gearing arrangement. A slip ring keeps the cables from twisting or becoming tangled as the joint rotates, while limit switches keep everything under control in either direction. Overall, the movement is relatively rapid and precise.

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Walter Workshop Sentry Robot Camera Security
The Raspberry Pi 5 serves as the Walter’s brain, processing all of the video from the USB camera, running computer vision procedures to detect and track movement, managing network connections, and making major decisions. Meanwhile, an Arduino Nano clone serves as the motor control system, accepting direct orders from the Pi to ensure responsiveness while the Pi works on the vision and logic. An Android tablet combined with a gamepad allows you to control from anywhere while also providing a low-latency visual stream.

Walter Workshop Sentry Robot Camera Security
When Walter is left to its own devices, it operates in Sentry mode. Motion detection comes in and can even give you notifications, after which computer vision takes over and tracks whatever caused the movement. The camera and mount maintain focus on the subject without requiring human intervention at any point. Voice recognition adds an additional layer of control; a microphone listens for orders or a spoken password to activate or disable the system, and a small USB speaker allows Walter to respond, give a challenge, or send you an alert.

Walter Workshop Sentry Robot Camera Security
The defensive options are a little more intriguing, as you get two high-intensity LED floodlights that can either flood the area or generate some very blinding glare. A pair of lasers provides a couple of brilliant points of light that are useful for aiming and visual emphasis. Then there’s the Nerf blaster, which can shoot darts under human or automatic control, and the noise alone is enough to deter even the most determined unwanted visitor.

Walter Workshop Sentry Robot Camera Security
Walter is the real deal, as it watches the room like a standard camera, but also moves, speaks, shines a light, and generally participates when necessary. When not in use, the ceiling mount hides it, and the pan-tilt mechanics and software allow it to cover the entire space. For a maker space with work that isn’t quite ready for public viewing, the system provides an excellent blend of surveillance and active deterrence that seems like it’s on your side rather than simply sitting there looking technological.
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OpenAI burned $3.7bn in the first quarter, The Information reports

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OpenAI burned through $3.7bn in the first three months of 2026, more than half its revenue of $5.7bn over the same period, according to The Information, which cited documents the company shared with shareholders.

Both numbers tripled from a year earlier, a symmetry that captures the company’s peculiar position: growing faster than almost any business in history, and spending faster still.

The tripling is the figure worth pausing on. Revenue of $5.7bn in a single quarter would be the envy of nearly any technology company; revenue that grew threefold year on year is rarer still. The trouble is that the cost of producing it grew at the same rate.

Scaling has not yet bought OpenAI the operating leverage that usually rewards a company this size, because the thing it sells, frontier-model inference, gets more expensive to deliver as more people use it.

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The balance sheet looks, on its face, reassuring. OpenAI held more than $73bn in cash and marketable securities at the end of the quarter, up from $40bn at the end of December.

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That jump reflects a large funding round announced at the end of March rather than money thrown off by the business, a distinction that matters when the quarterly burn is measured in billions. The cushion is real; it is also, in part, freshly raised.

That round was the one that closed at an $852bn valuation, a figure large enough that some of the company’s own investors have questioned it against the underlying revenue. The Q1 figures do not settle that argument so much as supply ammunition to both sides: the bulls point to the revenue curve, the bears to the burn beneath it.

OpenAI has also said it filed confidentially for a US initial public offering that could come as early as September and value the company at up to $1tn.

A flotation at that level would be among the largest in history, and it would put the kind of quarterly numbers reported this week in front of public-market investors who tend to ask harder questions about the path to profit than late-stage private backers do. The company has moved quickly on the filing as rivals race to list.

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The pattern is not new for OpenAI, which has spent its way through every previous stage of its growth on the bet that scale eventually pays. The company has spoken about spending on the order of tens of billions in a single year on compute, research and infrastructure, and has indicated it does not expect to turn a profit until the end of the decade.

The Q1 burn fits that trajectory rather than departing from it. The novelty is the size of the numbers and the proximity of a public listing that will expose them to a different class of scrutiny.

None of the figures in the report came from OpenAI directly, and the company did not comment publicly on the specifics. What the numbers describe, if accurate, is a business operating at a scale and a loss that are both expanding in lockstep, ahead of a listing that will ask whether the second can ever stop chasing the first.

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