Apple is putting the iPhone 18 Pro’s new camera front and center with a music video for K-Pop artist Rose.

On Friday, Apple shared a brand-new 35-second “Shot on iPhone” ad. This time it doubles as a bite-sized music video.

The video is for “new trick” by K-Pop artist Rose. Of the project, Apple says: “The new variable aperture lens on the 48MP main camera captured improved low-light footage and impressive depth of field to bring the music video to life.

Apple announced the iPhone 18 Pro and its larger sibling, the iPhone 18 Pro Max, on September 9 at its “Surprise and Shine” event. The lineup opened for preorder on September 12 and began arriving at customers’ doors and in stores on September 18.

Apple has opened the starting gate for iPhone Duo app development, but that means developers have a single month to finish the race.

On Friday, Apple released the first Xcode 27.1 beta, giving developers the tools they need to prepare for iPhone Duo. They now have roughly one month to test their apps across the foldable iPhone’s many possible layouts.

Xcode 27.1 includes Swift 6.4 and SDKs for iOS 27.1, iPadOS 27.1, and Apple’s other operating systems. It also adds tools specifically intended to help developers see how their apps behave on iPhone Duo.

This means that devs can now preview content using a device’s alternative display through a new Display group in Xcode’s canvas. That matters because iPhone Duo can present apps across dramatically different screen sizes and orientations.

The beta also includes an iPhone Duo Simulator runtime, although there are some notable limitations at this time. For example, Apple notes that StandBy is not available, and running and debugging most app extensions also isn’t currently supported.

Initial Simulator launches can also take several minutes, according to Apple’s release notes. Unfortunately, these limitations arrive as developers face a compressed timeline before iPhone Duo reaches customers.

Developers have a month to prepare for iPhone Duo

It’s possible that current apps developed with existing guidelines could adapt reasonably well to iPhone Duo. However, developers still need Xcode 27.1 to test how their apps behave across its new layouts.

Apple’s first foldable introduces more orientations, screen sizes, and display configurations developers must account for. New APIs also let apps take advantage of features specific to Apple’s foldable hardware.

Developers only gained access to these testing tools after Apple revealed iPhone Duo. This means developers have a greatly compressed timeline before the device reaches customers.

Apps that aren’t updated should still run, but they may not properly fill the available display. And it’s likely that some developers, like Adobe and Microsoft, could be slow to update for the iPhone Duo.

The iPhone Duo goes up for pre-order on October 16 and becomes widely available on October 23. There will be apps that don’t support the foldable at launch due to the complexity of implementing support in some apps.

Benchmarking has become the industry norm for how AI companies validate their models’ capabilities and, when the metrics swing in their favor, stand out from competitors and advertise their superiority. In other words, good benchmarks pretty much always mean good PR.

Unfortunately, companies have also figured out how to outwit legacy benchmarking systems — many of which are older, and not built to measure the capabilities of modern models.

Vals, a startup formed in 2024, says that it is on a mission to fix this very imperfect system. In the span of less than two years, the company has established itself as a notable presence in the tech industry and, last year it managed to secure a seed round led by 8VC and Bloomberg Beta. Then, last month, after a period of rapid growth, it raised $40 million in a series A led by Andreessen Horowitz.

Rayan Krishnan, the company’s 25-year-old co-founder, previously interned at Palantir, and, as an undergraduate at Stanford, worked for Microsoft and the school’s much lauded artificial intelligence lab. Krishnan says Vals was born from his own observations about how benchmarking was falling behind the advances of the industry it was designed to measure.

“We were seeing a bunch of new, very capable models come to market quickly, and the academic benchmarks [were] not keeping up with that frontier advance,” Krishnan shares. With AI being integrated into every part of society, benchmarks should really exist to verify that models can do what companies advertise they can do, Krishnan said.

Last week, the young founder showed me around his company’s two-floor office on San Francisco’s Folsom Street — an old brick building that, a century ago, served as the site of a large brewery. Instead of an industrial output of beer, the historical structure is now home to a number of different startups looking to ship the future of the tech industry.

“Historically, I think evaluation has been done to evaluate intelligence in a very abstract way,” Krishnan tells me. “Like, do models know enough information to be able to take a bar exam type test?”

Here, Vals seeks to differentiate itself. While many benchmarking systems offer tests that are publicly available (this can allow a company to train its model against those tests, thus arguably cheating on their exam), Vals doesn’t publicly disclose its specific test materials. Instead of measuring an AI model’s general knowledge, Vals also evaluates models on their ability to complete complex tasks associated with specific industries like law, finance, and coding.

“What we’re doing is actually looking at what are the real impacts of the models,” said Krishnan. “Can they do work that produces a product of the same quality as a human within every domain?”

The idea is to check not just for positive outcomes but also for negative ones, he says. The hope is to analyze how, “if these models ran wild in the world, what the negative implications would be.”

The capabilities that Vals is measuring are growing. In additional to more traditional industries, the startup continues to push into more unique terrain. “We have a benchmark on recursive self improvement. We’re doing some work in mental health, cybersecurity, biosecurity, and even law of armed conflict to models to understand how to apply the Geneva Convention,” Krishnan shares.

Companies pay Vals to test their models, which can be an odd concept to wrap your head around. Why would a company pay to learn its model isn’t performing well? But having an effective measurement helps companies troubleshoot and improve over time. Krishnan compares their revenue model to how a student might pay the College Board to take the SAT.

In turn, these evaluations are becoming key decision-making factors for companies looking to acquire new AI models.

The startup recently revealed that its revenue is currently eight times what it was last year. Its staff is also growing. Vals, which started the year with only eight people, has already tripled to a team of 25. Krishnan said that as the startup grows, the plan is to relocate to a significantly bigger office, as well as to bring on an additional 10 to 15 people. The company also recently launched a program centered around providing model evaluations to federal agencies.

Krishnan sees his company’s system of benchmarking as the future of how AI companies think about growing their businesses and establishing public trust.

“AI companies are starting to go public. SpaceX went public. Anthropic is slated for later this year. I suspect OpenAI will be public soon. I think as AI models become a core part of the economy and are diffused more broadly, the types of benchmarks and evaluations that we do are going to drive their usage and be a central part of how these companies submit public filings or talk about the prospective investments they’re going to make in AI,” he said.

A new summer option aims to provide an alternative pathway that better aligns with aircraft maintenance schedules and operational requirements.

Ireland’s aircraft mechanic apprenticeship programme is to offer a new summer training schedule option beginning from next year. 

The new training initiative will accept 45 students and operate from May until October each year, with the purpose of strengthening Ireland’s aviation skills pipeline and making training more flexible for apprentices and employers across the sector. 

The Irish Government said the new summer option will provide an alternative pathway that better aligns with aircraft maintenance schedules and operational requirements in the aviation sector. 

Aircraft mechanic apprenticeship places increased from 79 slots in 2024 to up to 160 in September 2025, which is reflective of the increased demand for skilled aircraft maintenance professionals, according to the Government. 

Speaking during a visit to the Limerick and Clare Education and Training Board’s aircraft mechanic training facilities in Shannon, the Minister for Further and Higher Education, Research, Innovation and Science James Lawless TD, said, “This is a really positive development for both apprentices and employers in the aviation sector. 

“The aircraft mechanic apprenticeship is already providing people with a fantastic pathway into a highly skilled and rewarding career, and this new summer training option will make the programme more flexible and easier to access. 

“This new summer intake is a direct response to feedback from employers, who told us that greater flexibility in training delivery would be of real benefit. It shows how our apprenticeship system can adapt and respond to the needs of industry while continuing to provide high-quality training opportunities for apprentices.”

Shane O’Neill, the CEO of Atlantic Aviation Group (AAG), added, “AAG is proud to continue supporting the aircraft maintenance apprenticeship and to work with Solas to deliver category B aircraft mechanic training at our training academy in Shannon. This summer provision will enable apprentices from across Ireland’s aviation industry to progress their training and develop the advanced skills the sector needs.” 

In late July, airline group Ryanair announced plans to build a new four-bay aircraft maintenance, repair and overhaul hangar at Shannon Airport as part of a €50m investment that will also create 400 new jobs. 

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Jensen Huang told CBS News there is a 0% chance AI ends the world by 2030, and that the industry should go as fast as it can irrespective of anyone else. Weeks earlier he declined to call AI fears a hoax, described apocalyptic predictions as not grounded in science, and said companies should pause or pace their work if they felt out of control. The full interview airs on Sunday, so the shift is being read from an excerpt.

Nvidia’s chief executive has rejected the idea that AI could end the world by the end of the decade. Speaking to Jo Ling Kent on CBS News, Huang said 2030 is not going to be the end of the world and put the chance at zero.

Ian King reported the remarks for Bloomberg. The full interview airs on Sunday, so what exists so far is an excerpt.

His previous answer was narrower

When Trump telephoned him onstage at the All-In Summit to call AI fear a hoax, Huang did not adopt the word. TNW reported at the time that he called apocalyptic predictions not grounded in science, which is a claim about evidence rather than about probability.

He went further in the other direction too. He said companies should pause or pace their work if they felt their company was out of control.

That is close to what the pacing proposals ask for. The new formulation, going as fast as possible irrespective of anyone else, is the opposite of it.

Zero is a strong number

The labs themselves do not use it. They publish safety frameworks, capability thresholds and risk estimates, which are instruments for operating under uncertainty rather than expressions of confidence.

Nobody serious on the other side claims certainty either. The pacing argument is explicitly a bet about unknown probabilities, which is why it asks for evaluators and tripwires rather than a halt.

A round zero is therefore not a forecast in the same currency as the thing it rebuts. It is a rhetorical position, and it was offered in a television interview rather than in a paper.

The two statements in tension

Huang also said the industry would never ship products before they are ready or deliver products that are unsafe. Set beside going as fast as possible irrespective of anyone else, that leaves one question doing all the work.

Readiness is judged by the shipper. If the pace is set irrespective of anyone else, then the only check on release is the releasing company’s own assessment, which is the arrangement the pacing proposals were written to change.

Both sentences can be sincerely held. They cannot both be load-bearing.

The commercial position, stated plainly

Nvidia sells the compute that a slowdown would leave unsold. Of everyone arguing about pace, Huang has the most direct financial exposure to the answer.

That is context, not refutation. An interested party can be right, and the incentive is worth naming rather than treating as an argument.

It does mean his certainty carries less evidential weight than the same certainty from someone with nothing riding on it. He is not offering data, he is offering a number.

Others make the case without the exposure

The sceptical position is not confined to people selling hardware. Andrew Ng has called the extinction warnings science fiction, and he has no chip inventory to clear.

That is the stronger version of Huang’s argument. Specific near-term extinction timelines are poorly evidenced, and saying so is different from saying the probability is zero.

The distinction matters because the weaker claim is the one that travels. Zero is quotable in a way that not grounded in science is not.

The people saying the opposite are not outsiders

The warnings this month have come from inside the labs. A DeepMind AGI safety researcher left and published his warning, one of several such departures.

Huang is dismissing a position held by people who build the models rather than the accelerators. Their estimates are contestable and they are not uninformed.

The politics he has moved towards

Trump has spent the month attacking AI safety warnings directly. He has called the warnings a hoax repeatedly and at length, and CBS trailed the interview on Huang agreeing with him.

Alignment with the administration has practical value for Nvidia right now. Huang has been publicly resisting proposals to sanction Chinese AI, and his company needs export policy to move in its favour.

He is also expected at the state dinner for Xi Jinping next week. A chief executive arguing for speed and against restrictions has an audience for both arguments in the same building.

What to watch

Watch the full interview on Sunday. An excerpt is a poor basis for reading a shift, and the unaired portion may restore the qualifier he used in August.

Watch whether he repeats the number. Not grounded in science is a position he can defend, and 0% is one he will be asked to justify.

Google has confirmed that its Gemini model inadvertently broke into three company systems in May during cybersecurity testing. Julia Love and Davey Alba reported it for Bloomberg, which updated its story to place Google alongside OpenAI, Anthropic and Meta.

The important part is what the testing vendor said next. Irregular confirmed that the breaches disclosed by all four companies were part of the same issue, and that it told the relevant developers in late July.

One incident, four announcements

This has been reported for months as a string of separate breakouts by different models at different companies. TNW reported in August that a single testing vendor sat behind the OpenAI, Anthropic and Meta incidents.

Irregular has now put that on the record and added a fourth name. The pattern that alarmed people was substantially one problem at one supplier.

That matters for how the events are read. Four independent labs losing control of four models is a story about model capability, and one misconfigured test environment is a story about supplier management.

What the misconfiguration was

OpenAI attributed its incidents to a misconfigured evaluation environment, saying a misunderstanding with Irregular meant the test systems had live internet access while the models had been told they were in a simulation.

That is a sandbox failure rather than an escape. A model behaving aggressively inside what it understands to be an exercise is doing what the exercise asked, and the containment is what was missing.

It does not make the outcomes harmless. Meta’s model hacked a real third-party service, and in one Anthropic case a model published working malware to a public registry, where it was downloaded and run on real systems.

The seven weeks

The incidents were in May. Irregular says it notified the developers in late July, and the disclosures then arrived one at a time, with Meta in early August and Google this week.

So four companies held the same information from late July, and each decided separately when to say so. Google’s gap between notification and disclosure runs to about seven weeks.

None of that is unusual in vulnerability handling, where coordinated timelines are normal. What is unusual is that it was not coordinated, and the staggered release made one event look like an accelerating trend.

Finding them was the hard part

The detection numbers explain why the timeline stretched. Anthropic scanned 481 million transcripts to identify four models that had reached the open internet.

That is the figure to sit with. The incidents were not flagged in real time by monitoring, they were found afterwards by a retrospective sweep at enormous scale.

Whatever the models did, the systems watching them did not notice at the time. That is the finding that survives the framing argument.

The vendor is the single point of failure

Four frontier labs used the same three-year-old company to run offensive security evaluations. When its environment was wrong, it was wrong for all of them simultaneously.

Concentration in testing is the mirror of concentration in compute, and it has had less scrutiny. A shared evaluator is efficient and it also means shared blast radius.

Recent work on AI control has argued that sandboxes cannot be assumed to hold against cyber-capable agents and need stress-testing with offensive tools. This is that argument demonstrated at four companies at once.

What happens to the testing

The work has not stopped. Anthropic has resumed the external tests in which its models attacked real companies, after rebuilding the arrangements around them.

That is the right direction. Offensive evaluation is how these capabilities get measured, and the answer to a containment failure is better containment rather than less testing.

Washington is already asking

The disclosures have drawn political attention. House Democrats have pressed OpenAI and Anthropic for answers on their rogue agents.

The Irregular confirmation changes the shape of those questions. If one vendor misconfiguration produced four sets of breaches, the issue is contractual and procedural rather than a race between labs.

It also raises a question nobody has put publicly. Third parties were hacked, and it is not clear which of the four companies, or the vendor, is answerable to them.

What to watch

Watch whether Irregular publishes its own account. The vendor has now confirmed a common cause and it has not set out what went wrong in its environment or what changed.

Watch whether the labs agree a coordinated disclosure standard for evaluation incidents. Four companies releasing the same news across seven weeks is the strongest argument for one.

Meta is reportedly preparing a new version of its smart glasses without a camera, even as the company’s chief technology officer argues that concerns surrounding the devices are being driven by a small number of problematic users.

Andrew Bosworth, Meta’s CTO and head of Reality Labs, addressed the issue during an Ask Me Anything session on Instagram. He argued that most people use the glasses responsibly, comparing their recording capabilities to smartphone cameras while highlighting the devices’ recording indicator and anti-tampering protections.

However, ongoing privacy concerns and reports of ways to bypass those safeguards suggest that the debate around camera-equipped wearables is far from settled.

Meta defends recording safeguards amid privacy concerns

Bosworth pointed to the glasses’ recording light, which is designed to alert people when the device is capturing video. Meta has also introduced measures intended to prevent users from recording when the indicator is covered or tampered with.

According to The Next Web’s report, a software update addressed a loophole that allowed users to begin recording and then cover the light. Meta also disabled cameras on some devices where the indicator had been physically removed.

The company reportedly said the enforcement affected fewer than one in every 1,000 glasses sold. However, The Next Web reported that a $2 sticker could still defeat the recording indicator, raising questions about whether a visible light alone can provide meaningful privacy protection.

The concern extends beyond the number of users behaving improperly. Unlike a smartphone, which typically makes recording more visible to people nearby, smart glasses can capture footage without an obvious change in the wearer’s behaviour.

Camera-free Luna could change Meta’s approach

The Information reportedly claims that Meta is preparing a camera-free version of its glasses, codenamed Luna. The device is expected to feature six microphones and could be unveiled at Meta Connect on September 23, with shipments potentially beginning in October.

Removing the camera would directly address concerns about people being recorded without their knowledge. However, it would also remove one of the most prominent features associated with Meta’s existing smart glasses.

The microphones introduce another privacy consideration. While the camera-related concern would be reduced, users could still question whether the device is listening and how that activity is communicated. Unlike a camera, microphones do not currently have an established public indicator convention.

Privacy scrutiny is also increasing in Europe. Authorities in France, Norway and Germany are examining issues involving camera-equipped wearables, while the European Data Protection Board is preparing a report on the category.

Meta’s upcoming Connect event could therefore provide important details about Luna, including its microphone controls, privacy indicators and intended use cases.

The takeaway: Warnings from AI developers have sparked widespread debate over whether, and how, the technology might cause human extinction. While much of that discussion has centered on rogue AI incidents, events in the Middle East suggest that military action driven by hallucinating AI tools may be the more immediate danger.

Sources connected to the US military recently told CNN that the US nearly moved to intercept a Chinese vessel in the Middle East this spring based on false intel from an AI-assisted report. The report was based on an inaccurate chatbot response that identified the ship as carrying materials for a nuclear weapons program.

Two sources said armed military personnel were preparing to board the vessel, with military planes already in the air, before officials took a closer look at the report and discovered it was at least partially AI-generated.

One source described the intel as completely false, saying it nearly started a war between the two nuclear powers. What was actually on the ship remains unclear.

An analyst had asked an unnamed chatbot to assess intelligence on the ship’s manifest, information that originated with Special Operations Command Pacific. It’s still unclear whether the chatbot was commercial or government-owned.

An official familiar with the US military’s AI deployments told CNN, that a government chatbot would likely have been forked or cloned from a commercial product and augmented with government intelligence. Either way, the analyst also used AI to write an intelligence report based on the erroneous response – one that put the military on high alert.

The incident showcases the US military’s push to accelerate AI adoption amid its war with Iran. In January, Defense Secretary Pete Hegseth announced plans to embrace AI more aggressively and eliminate bureaucratic barriers, warning that falling behind on the technology could cede the initiative to China and other US adversaries. Analysts, meanwhile, reportedly face mounting pressure to disseminate reports faster using AI.

The military has primarily used AI for intelligence analysis, logistics, and targeting. Anthropic’s Claude, for example, was central to the US’s opening strikes in the Iran war, including the one that killed Iran’s supreme leader, Ayatollah Ali Khamenei. But AI targeting based on outdated information may have also contributed to a strike on a school in Minab that killed 168 people, mostly children – an attack widely blamed on the US that UN-backed experts have flagged as a possible war crime.

US officials told CNN that the Chinese vessel incident isn’t the only time the military has run into AI hallucinations.