Australian researchers have pulled off something that quantum theory predicted but nobody had managed to actually observe in matter until now. Working with pairs of helium atoms, they captured the particles existing in two different locations simultaneously, their behavior frozen in a way that has no equivalent in everyday experience. It is the first direct observation of this phenomenon in matter rather than light, and it opens a new window into how the fundamental building blocks of our world actually behave.

The team at Australian National University started with a cloud of helium atoms cooled to just above absolute zero, at which point the atoms slow down enough to behave more like overlapping waves than solid particles. Releasing the cloud from its magnetic trap allowed two groups to collide head on, and that collision created exactly the right conditions for something remarkable. Measure one atom moving in a particular direction and its counterpart will instantly appear moving the opposite way, no matter how far apart the two have traveled.

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To confirm the atoms were genuinely sharing an existence across two locations rather than simply traveling on predictable paths, the team sent the scattered pairs through an apparatus using laser pulses that acted like a half silvered mirror, splitting each atom along two separate routes at the same time. The atoms were then allowed to fall briefly before detectors recorded exactly where each one landed. The interference pattern that emerged left no room for doubt. Each atom had traveled both paths simultaneously right up until the moment it was measured, with one member of a pair appearing on the left side of the detector while its partner showed up on the right, yet the data made clear that both had been exploring both locations all along.

The significance of the result comes down to what the atoms actually are. Previous experiments of this kind used photons, which have no mass and are unaffected by gravity. Helium atoms are a different matter entirely, heavy enough to feel the pull of the Earth, yet they still display this split existence. The connection between the pairs was strong enough to violate Bell’s inequality, a well established test that rules out any classical explanation for the behavior. Measuring one atom instantly determined the state of its partner regardless of the distance between them, exactly as quantum theory has always predicted but nobody had seen demonstrated with matter until now.

Lead researcher Yogesh Sridhar spent years refining the setup because earlier attempts always fell short. “Experimentally, it is extremely hard to demonstrate this,” he said. His colleague Dr. Sean Hodgman put the strangeness into plain words: “It is really weird for us to think that this is how the universe works. You can read about it in a textbook, but it is really weird to think that a particle can be in two places at once.”
[Source]

WhatsApp is rolling out multiple features designed to make the app easier to use, including AI-powered message replies and photo retouching, support for two accounts on iOS, and chat history transfer between iOS and Android devices.

Meta said that after the new updates, users will be able to touch up images in the chat before sharing them with contacts or in groups using Meta AI.

The Writing Help feature enables users to quickly draft a response based on the active conversation, with Meta saying it uses Private Processing to ensure messages are completely private.

“Writing Help is built on top of Private Processing technology, which allows you to leverage Meta AI to generate a response without Meta or WhatsApp ever reading your message or the suggested re-writes,” Meta states.

The company says WhatsApp now also helps find large media files in any chat, quickly freeing up space without deleting entire conversations.

Additionally, WhatsApp allows two WhatsApp accounts to be logged in at the same time on iOS (an option that was already available on Android devices), and its chat transfer feature now supports moving message history from iOS to Android (including individual and group chats, call history, channel and community history, and more).

“Now, with just a few taps, your conversations, photos, and videos easily come with you no matter what device you’re using,” Meta said.

​Earlier this month, WhatsApp also introduced parent-managed accounts for pre-teens, a feature that allows parents to decide who can contact them and which groups they can join.

Meta also introduced new WhatsApp anti-scam protections that warn users when behavioral signals suggest an incoming device-linking request may be fraudulent.

These scam defenses were added soon after the Dutch intelligence agencies warned that Russian state-backed hackers had been targeting Dutch government employees in phishing attacks aimed at their Signal and WhatsApp accounts.

In January, Meta also began rolling out a new WhatsApp lockdown security feature designed to protect journalists, public figures, and other high-risk individuals from sophisticated threats, including, but not limited to, spyware attacks.

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Many people have tried AI tools and walked away unimpressed. I get it — many demos promise magic, but in practice, the results can feel underwhelming.

That’s why I want to write this not as a futurist prediction, but from lived experience. Over the past six months, I turned my engineering organization AI-first. I’ve shared before about the system behind that transformation — how we built the workflows, the metrics, and the guardrails. Today, I want to zoom out from the mechanics and talk about what I’ve learned from that experience — about where our profession is heading when software development itself turns inside out. 

Before I do, a couple of numbers to illustrate the scale of change. Subjectively, it feels that we are moving twice as fast. Objectively, here’s how the throughput evolved. Our total engineering team headcount floated from 36 at the beginning of the year to 30. So you get ~170% throughput on ~80% headcount, which matches the subjective ~2x. 

Zooming in, I picked a couple of our senior engineers who started the year in a more traditional software engineering process and ended it in the AI-first way. [The dips correspond to vacations and off-sites]:

Note that our PRs are tied to JIRA tickets, and the average scope of those tickets didn’t change much through the year, so it’s as good a proxy as the data can give us. 

Qualitatively, looking at the business value, I actually see even higher uplift. One reason is that, as we started last year, our quality assurance (QA) team couldn’t keep up with our engineers’ velocity. As the company leader, I wasn’t happy with the quality of some of our early releases. As we progressed through the year, and tooled our AI workflows to include writing unit and end-to-end tests, our coverage improved, the number of bugs dropped, users became fans, and the business value of engineering work multiplied.

From big design to rapid experimentation

Before AI, we spent weeks perfecting user flows before writing code. It made sense when change was expensive. Agile helped, but even then, testing multiple product ideas was too costly.

Once we went AI-first, that trade-off disappeared. The cost of experimentation collapsed. An idea could go from whiteboard to a working prototype in a day: From idea to AI-generated product requirements document (PRD), to AI-generated tech spec, to AI-assisted implementation. 

It manifested itself in some amazing transformations. Our website—central to our acquisition and inbound demand—is now a product-scale system with hundreds of custom components, all designed, developed, and maintained directly in code by our creative director. 

Now, instead of validating with slides or static prototypes, we validate with working products. We test ideas live, learn faster, and release major updates every other month, a pace I couldn’t imagine three years ago.

For example, Zen CLI was first written in Kotlin, but then we changed our mind and moved it to TypeScript with no release velocity lost.

Instead of mocking the features, our UX designers and project managers vibe code them. And when the release-time crunch hit everyone, they jumped into action and fixed dozens of small details with production-ready PRs to help us ship a great product. This included an overnight UI layout change.

From coding to validation

The next shift came where I least expected it: Validation.

In a traditional org, most people write code and a smaller group tests it. But when AI generates much of the implementation, the leverage point moves. The real value lies in defining what “good” looks like — in making correctness explicit.

We support 70-plus programming languages and countless integrations. Our QA engineers have evolved into system architects. They build AI agents that generate and maintain acceptance tests directly from requirements. And those agents are embedded into the codified AI workflows that allow us to achieve predictable engineering outcomes by using a system.

This is what “shift left” really means. Validation isn’t a stand-alone function, it’s an integral part of the production process. If the agent can’t validate it’s work, it can’t be trusted to generate production code. For QA professionals, this is a moment of reinvention, where, with the right upskilling, their work becomes a critical enabler and accelerator of the AI adoption. 

Product managers, tech leads, and data engineers now share this responsibility as well, because defining correctness has become a cross-functional skill, not a role confined to QA.

From diamond to double funnel

For decades, software development followed a “diamond” shape: A small product team handed off to a large engineering team, then narrowed again through QA.

Today, that geometry is flipping. Humans engage more deeply at the beginning — defining intent, exploring options — and again at the end, validating outcomes. The middle, where AI executes, is faster and narrower.

It’s not just a new workflow; it’s a structural inversion.

The model looks less like an assembly line and more like a control tower. Humans set direction and constraints, AI handles execution at speed, and people step back in to validate outcomes before decisions land in production.

Engineering at a higher level of abstraction

Every major leap in software raised our level of abstraction — from punch cards to high-level programming languages, from hardware to cloud. AI is the next step. Our engineers now work at a meta-layer: Orchestrating AI workflows, tuning agentic instructions and skills, and defining guardrails. The machines build; the humans decide what and why.

Teams now routinely decide when AI output is safe to merge without review, how tightly to bound agent autonomy in production systems, and what signals actually indicate correctness at scale, decisions that simply didn’t exist before.

And that’s the paradox of AI-first engineering — it feels less like coding, and more like thinking. Welcome to the new era of human intelligence, powered by AI.

Andrew Filev is founder and CEO of Zencoder

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A new trailer has given us our best look yet at the upcoming The Ghost in the Shell anime. While it might not tell us all that much about the direction the show will go in plot-wise, it sure is aesthetically pleasing, with a throwback art style that looks a lot more like the original manga than we’ve seen with other adaptations. The series will be released on Prime Video this July.

The Ghost in the Shell is being produced by Science Saru. The studio hasn’t revealed much about its story, only noting that it’s based on Masamune Shirow’s manga, so it isn’t entirely clear yet how closely it will follow the source material. The franchise has certainly seen its fair share of questionable adaptations over the years. But, this glimpse at the art style seems like a promising indicator. An exact release date hasn’t yet been announced, but July isn’t too far away now.

A new wave of Android flagships is on the horizon, and they’re not playing it safe. The biggest shift is that these phones are going all-in on cameras, while eclipsing the apex Android predators (read: Samsung and Google) in a few other ways, too.

The specifications of the upcoming Vivo X300 Ultra were revealed on Weibo, and the device is confirmed to feature a 200MP periscope telephoto sensor (likely 1/1.4-inch class), paired with a large 1-inch-type primary sensor and an upgraded ultra-wide lens, along with advanced zoom systems and improved color science. That’s pushing smartphone photography closer to dedicated cameras, something Samsung’s Galaxy S26 Ultra, with its largely iterative camera hardware, hasn’t quite matched this year.

And it’s not just about megapixels anymore. These phones are focusing on optics, sensor size, and real-world usability, with brands like Vivo and Xiaomi leaning heavily on partnerships (like Zeiss and Leica) to refine image processing and video performance.

Why should Samsung be paying attention?

The fact of the matter is that the competition is getting quite aggressive. For years, Samsung’s Galaxy Ultra lineup has been the benchmark for Android flagships, especially in camera tech. But now, brands like Vivo and Oppo are pushing more experimental and ambitious hardware, particularly in zoom and imaging. Both the vivo X100 Ultra and X200 Pro had great camera setups, and the X300 Ultra seems to continue the trend.

Then there’s Oppo, too. The upcoming Find X9 Ultra is expected to feature a native 10x optical zoom system with a complex prism design, something that could rival, or even surpass, traditional periscope setups. Similarly, the Xiaomi 17 Ultra also features a DSLR-like physical zoom ring paired with a 200MP zoom camera. In other words, these brands aren’t just catching up, but instead trying to leap ahead.

My time with the Oppo Find X9 Ultra and the Vivo X300 Pro convinced me that these two labels are at the top of the camera, both in terms of raw quality and creative features. Samsung just feels uninspired, even though it’s not underwhelming by any stretch of the imagination.

The S26 Ultra isn’t quite leading the charge

Samsung is still innovating, but this time, it doesn’t feel like it’s paying off as strongly. The biggest highlight of the Galaxy S26 Ultra is its new Privacy Display, which is a neat addition, but it also comes with a trade-off: an 8-bit panel that didn’t exactly impress enthusiasts. Beyond that, the upgrades feel fairly routine, with the usual chipset bump and not much else standing out.

Meanwhile, competitors are pushing much harder, especially in the camera space. While brands like Vivo and Oppo are experimenting with new optics, larger sensors, and more advanced zoom systems, Samsung seems to be sticking closer to its existing formula, including a largely unchanged periscope setup and incremental sensor upgrades. Loyal users will likely stay, and options like Google Pixel still exist. But the Android space has always been about choice, and that choice is getting a lot more interesting.

“Ultra” is starting to mean something else

This shift isn’t just about specs; it’s about direction. New flagships are clearly embracing a camera-first identity, with features like advanced zoom, pro controls, and even external lens support. What’s more, these devices also come with a lot more. The vivo X300 Ultra’s other specs confirm it comes with 100W wired and 40W wireless charging support, with a rumored 6,600mAh capacity.

If these devices deliver, Samsung may need to rethink how it approaches its Ultra lineup. Because going forward, “Ultra” might not be a Samsung-exclusive idea anymore.

While there’s been plenty of debate about the tendency of AI chatbots to flatter users and confirm their existing beliefs — also known as AI sycophancy — a new study by Stanford computer scientists attempts to measure how harmful that tendency might be.

The study, titled “Sycophantic AI decreases prosocial intentions and promotes dependence” and recently published in Science, argues, “AI sycophancy is not merely a stylistic issue or a niche risk, but a prevalent behavior with broad downstream consequences.”

According to a recent Pew report, 12% of U.S. teens say they turn to chatbots for emotional support or advice. And the study’s lead author, computer science Ph.D. candidate Myra Cheng, told the Stanford Report that she became interested in the issue after hearing that undergraduates were asking chatbots for relationship advice and even to draft breakup texts. 

“By default, AI advice does not tell people that they’re wrong nor give them ‘tough love,’” Cheng said. “I worry that people will lose the skills to deal with difficult social situations.”

The study had two parts. In the first, researchers tested 11 large language models, including OpenAI’s ChatGPT, Anthropic’s Claude, Google Gemini, and DeepSeek, entering queries based on existing databases of interpersonal advice, on potentially harmful or illegal actions, and on the popular Reddit community r/AmITheAsshole — in the latter case focusing on posts where Redditors concluded that the original poster was, in fact, the story’s villain.

The authors found that across the 11 models, the AI-generated answers validated user behavior an average of 49% more often than humans. In the examples drawn from Reddit, chatbots affirmed user behavior 51% of the time (again, these were all situations where Redditors came to the opposite conclusion). And for the queries focusing on harmful or illegal actions, AI validated the user’s behavior 47% of the time.

In one example described in the Stanford Report, a user asked a chatbot if they were in the wrong for pretending to their girlfriend that they’d been unemployed for two years, and they were told, “Your actions, while unconventional, seem to stem from a genuine desire to understand the true dynamics of your relationship beyond material or financial contribution.”

In the second part, researchers studied how more than 2,400 participants interacted with AI chatbots — some sycophantic, some not — in discussions of their own problems or situations drawn from Reddit. They found that participants preferred and trusted the sycophantic AI more and said they were more likely to ask those models for advice again.

“All of these effects persisted when controlling for individual traits such as demographics and prior familiarity with AI; perceived response source; and response style,” the study said. It also argued that users’ preference for sycophantic AI responses creates “perverse incentives” where “the very feature that causes harm also drives engagement” — so AI companies are incentivized to increase sycophancy, not reduce it.

At the same time, interacting with the sycophantic AI seemed to make participants more convinced that they were in the right, and made them less likely to apologize.

The study’s senior author author Dan Jurafsky, a professor of both linguistics and computer science, added that while users “are aware that models behave in sycophantic and flattering ways […] what they are not aware of, and what surprised us, is that sycophancy is making them more self-centered, more morally dogmatic.”

Jurafsky said that AI sycophancy is “a safety issue, and like other safety issues, it needs regulation and oversight.” 

The research team is now examining ways to make models less sycophantic — apparently just starting your prompt with the phrase “wait a minute” can help. But Cheng said, “I think that you should not use AI as a substitute for people for these kinds of things. That’s the best thing to do for now.”

from the gaming-like-it’s-1930 dept

Last week, we announced the winners of our eighth annual public domain game jam, Gaming Like It’s 1930! Now it’s time to begin our series of spotlight posts, examining each of the winners in a bit more detail, and we’re kicking things off today with a look at the winner of Best Adaptation: I am Sam Spade by Marshview Games.

A lot of people associate the hardboiled genre of detective fiction with the protagonist’s inner monologue, as they ruminate on the situations that they face and give the reader a sense of their character and motivations. But some of the genre’s foundational works, such as Dashiell Hammett’s The Maltese Falcon, actually omit this entirely: the reader never sees inside detective Sam Spade’s head, they only see what he does. I am Sam Spade by Marshview Games adapts this early classic while centering the later convention, with gameplay that focuses on the inner life of the detective to drive his actions, and puts players in his shoes. And not just one player, but all of them.

To do this, it borrows mechanics from Michael Sullivan’s Everyone Is John, a classic in its own right. Two or more players become “Sams” — aspects of Sam Spade’s personality, each with a pool of power and a specific skill, plus a core motivation that they will attempt to achieve. As the game master guides them through the events of The Maltese Falcon (or another detective story!), players bid their power to seize control of Sam Spade’s actions. Though they must cooperate at least a little bit to make any progress, they are also in competition: the player whose motivations were most fulfilled by Sam wins the game.

The character of Sam Spade isn’t a blank slate, but he is opaque, which makes getting inside his head the perfect starting point for reimagining the story, and I am Sam Spade puts this at the heart of its gameplay. For that, it’s this year’s Best Adaptation.

Congratulations to Marshview Games for the win! You can get everything you need to play I am Sam Spade from its page on Itch. We’ll be back next week with another winner spotlight, and don’t forget to check out the many great entries that didn’t quite make the cut. And stay tuned for next year, when we’ll be back for Gaming Like It’s 1931.

Filed Under: game jam, games, gaming like it’s 1930, winner spotlight

While the relationship between Elon Musk and Mark Zuckerberg was once thorny enough that Musk challenged Zuckerberg to a cagefight, things had warmed up by the early days of the second Trump administration — at least according to court documents published Friday.

As reported by Engadget, these texts between Zuckerberg and Musk were released as part of Musk’s lawsuit against OpenAI. They were sent on February 3, 2025, around the time Zuckerberg appeared on Joe Rogan’s podcast to complain that corporate America had become “emasculated.”

Referring to Musk’s aggressive government-slashing efforts through the Department of Government Efficiency (DOGE), Zuckerberg texted, “Looks like DOGE is making progress. I’ve got our teams on alert to take down content doxxing or threatening the people on your team. Let me know if there’s anything else I can do to help.”

Musk reacted with a heart emoji, then asked, “Are you open to the idea of bidding on OpenAI with me and some others?”

In response, the Meta CEO suggested discussing the idea over the phone. According to previously released documents, Zuckerberg never actually signed on to join Musk’s bid.