The idea of AirPods with cameras has already made some people uneasy, even though Apple has not announced such a product yet. Online forums have highlighted growing skepticism around the possibility of camera-equipped earbuds, with concerns ranging from privacy to whether such devices could eventually be restricted in workplaces and other public spaces.

Those concerns may become harder to dismiss after a separate discovery reported by MacRumors offered a more detailed look at how Apple’s camera-equipped AirPods could potentially work.

According to MacRumors, forum member mactracker uncovered a hidden macOS framework called “AccessorySensorManager,” which appears to contain references to sensors capable of collecting visual and environmental information from connected AirPods. The findings do not confirm a final product, but they offer a glimpse into the kind of technology Apple may be testing.

The privacy question arrives before the product does

The prospect of putting cameras into earbuds has already triggered a skeptical response online. Unlike a smartphone or even a pair of smart glasses, earbuds are small, discreet, and often worn for hours at a time, making it far more difficult for people nearby to know when a camera might be active.

That creates an obvious challenge for Apple. Camera-equipped AirPods could make AI assistants considerably more useful by allowing them to understand what a wearer is seeing or provide information about their surroundings without requiring a phone to be pulled from a pocket.

Convenience, however, may not be enough to overcome the social discomfort surrounding always-worn cameras.

Users on Reddit noted concerns that such devices could face restrictions in offices, schools, or other sensitive environments. Similar unease appeared in discussions surrounding the MacRumors report, where readers questioned whether the potential benefits justified adding cameras to a product designed to disappear into everyday life.

MacRumors’ findings show what Apple may be working on

The framework uncovered by mactracker suggests each earbud could contain a camera sensor capable of capturing synchronized RGB images at up to one megapixel, according to MacRumors. The system appears to focus on still-image capture rather than conventional photography or video recording, potentially feeding information into Visual Intelligence and other AI features.

The report also references active and passive capture modes. One could potentially be triggered through Siri, while another may collect lower-resolution environmental information related to nearby speech, sound changes, posture, head movement, and other contextual signals. The code also references a hardware indicator that could visibly signal when image capture is taking place, addressing at least part of the privacy problem.

None of the findings confirm when, or even whether, Apple will release camera-equipped AirPods. MacRumors notes that the code could relate to an earlier design, while Bloomberg has previously reported that Apple is working on a camera-equipped AirPods model that could arrive in 2027.

Apple may have figured out how to make AirPods see. The harder task will be convincing everyone else that they should be comfortable with them looking.

You can keep Instagram from using your activity outside the app to influence the ads you get.

Turn off personalization from other businesses

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Restrict Instagram’s access to your phone and accounts

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Limit what Meta can learn from AI chats

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Indian startups spent years getting consumers accustomed to having groceries and everyday goods delivered within minutes. Now Walmart-owned Flipkart is rapidly closing the gap with those quick-commerce pioneers, as global rival Amazon mounts its own push into instant delivery.

Flipkart Minutes, which debuted in August 2024 as the e-commerce giant’s foray into quick commerce, is now delivering 1.1 million to 1.2 million orders a day, up from about 390,000 to 400,000 in November, people familiar with the matter told TechCrunch. That puts the two-year-old service close to Swiggy’s Instamart, which is delivering about 1.4 million orders a day, according to a person familiar with its operations.

The gap is notable as Flipkart is a relative latecomer to a market whose top ranks have been dominated by Instamart, Blinkit, and Zepto. Food-delivery giant Swiggy launched Instamart in 2020 and Zepto arrived the following year, both during the pandemic, while Blinkit traces its roots to online grocery platform Grofers, founded in 2013. The three have since established themselves as India’s top quick-commerce players.

Blinkit continues to dominate the market with around 3.4 million to 3.6 million daily orders, followed by Zepto at about 2.4 million to 2.6 million, per recent estimates from market research firm Datum Intelligence. Flipkart is now rapidly narrowing the gap with Instamart, the smallest of the three established leaders by order volume.

Instamart still has substantial scale. Earlier this month, Swiggy said the quick commerce service has more than 14 million monthly transacting users and operates over 1,200 dark stores across over 130 cities. The company has also been narrowing Instamart’s contribution-margin losses, with more than 45% of its dark-store network now contribution-margin positive.

Nonetheless, Flipkart has fueled that growth with an aggressive expansion of its delivery infrastructure. Minutes now operates about 1,020 to 1,050 micro-fulfillment centers — essentially small warehouses located close to customers specially to handle quick deliveries — up from 600 in January and about 340 a year ago, one of the sources told TechCrunch. The company is adding around 100 such facilities a month, the source said, aiming to have 1,500 by the end of 2026.

Flipkart’s advantage goes beyond adding dark stores. The company can tap an enormous pool of existing e-commerce customers it has already spent years and billions of dollars acquiring, giving Minutes a ready audience for faster deliveries, Satish Meena, an adviser at Datum Intelligence, told TechCrunch.

“Flipkart is already a serious player,” Meena said. “Once you open 1,000 dark stores and [are] doing a million orders per day, it’s serious enough.”

Minutes is also seeing customers return and shop more frequently. About 65% to 70% of customers making purchases on the service each month are repeat buyers, while transactions per customer have increased 50% to 60% from a year earlier, people familiar with the matter said.

Those customers are spending an average of about ₹400 to ₹500 (about $4.20–$5.20) per order, with fruits and vegetables, staples, dairy, and meat among the fast-growing categories, the sources said. Flipkart is also expanding its selection of higher-end gourmet products, including organic and artisanal items, as it looks to capture more of customers’ spending on Minutes.

Even as Minutes has expanded, its average delivery time has fallen to about 11 minutes, from 13 minutes a year ago, one of the sources told TechCrunch.

A battle for India’s shoppers

Flipkart’s growth comes as quick commerce takes a bigger role in how Indians shop online, even as broader consumer demand has shown signs of weakness. In a recent report, Bernstein analysts said while the country’s consumption growth softened in July, a shift toward quick commerce and e-commerce continued, with quick-commerce platforms recording healthy growth in monthly active users.

Similar to Flipkart, Amazon is striving to gain its share in the Indian quick-commerce market. The Seattle-based company has been expanding Amazon Now, its quick-commerce service, as it seeks to bring the instant-delivery model to its existing e-commerce customer base.

During CEO Andy Jassy’s visit to India in June, Amazon stated that Now became its fastest-growing business in India, with orders doubling every quarter since launch. The company also laid out plans to take the service to more than 300 cities and set up a network of more than 1,000 micro-fulfilment centers, alongside larger facilities aimed at expanding the range of products it can deliver within minutes.

Amazon, Flipkart, Swiggy, Zepto, and Blinkit parent Eternal did not respond to requests for comment.

The quick commerce expansion is increasingly defensive as well as offensive for both Flipkart and Amazon, Meena told TechCrunch. As consumers grow accustomed to receiving certain purchases almost immediately, the e-commerce giants risk losing those transactions to specialist quick-commerce platforms if they cannot offer comparable speed.

“Can you go back to scheduled delivery now in grocery? No,” Meena said. “You will not go back.”

• The Air Force wants drones spanning hobbyist toys to full stealth fighters
• Fighter-mimicking drones must fly past Mach 1.6 at high altitude
• Bomber-sized drones need matching radar, heat, and physical dimension signatures

The US Air Force is seeking industry proposals for target drones capable of imitating threats from cheap commercial aircraft to stealth fighters.

A recent Request for Information issued by the service sets a deadline of September 28 2026 for companies to submit concepts.

Pop icon Cher’s 1969 album, recorded at the legendary Muscle Shoals Studio, was both a critical success and a commercial failure. Over the years, however, 3614 Jackson Highway has only grown in stature, prime evidence of her artistry and a tasty platter of choice covers arranged with that just-right blend of Southern soulfulness and pop sheen.

That said, finding original copies of this now much sought-after album is not as easy as it used to be, and thus prices can be costly on the collector’s market in top condition. Fortunately, there have been reissues over the years, and in fact, there is a new edition from Rhino’s generally excellent High Fidelity series which you may want to consider, albeit with a bit of caution.

The basic ingredients of this new Rhino reissue appear typically idyllic: 180-gram vinyl pressed at Optimal Media, with lacquers cut by Kevin Gray at Coherent Audio from the original master tapes. The label made this tip-on-style glossy laminated cover extra spiffy, adding a silver foil finish beneath Cher’s name. With three Dylan covers, as well as tunes by Dr. John, Stephen Stills, Steve Cropper, Otis Redding, Dan Penn, Spooner Oldham, and Chips Moman, 3614 Jackson Highway has all the ingredients for a terrific listen.

My listening process began well, actually, with the Rhino HiFi edition bettering my original white label promo “Monarch” pressing, the sound appearing wider, bigger, and richer overall.

Unfortunately, on Side 2, I began to hear nasty noises, which turned out to be a rather significant rash of “non-fill.” This production error can happen when the vinyl does not melt well enough to flow into the metal stamper grooves. An imperfect copy results, bearing tiny audible pits where the vinyl didn’t fill the grooves (thus the “non-fill” designation). You can usually easily see these telltale markings, such as in the photo following.

Trying to be fair, I wrote to the label and asked if they could send a replacement, hoping my problem album was a one-off anomaly. So far, Rhino’s High Fidelity series has been exemplary.

Unfortunately, my second copy also had non-fill issues, and this time there was also an audible hairline scratch on one side! This is a very unusual occurrence, in my experience, for records pressed at Optimal. I am not entirely surprised, given the strange, upside-down socioeconomic times our world is enduring these days, where understaffing and overwork are the norm. Someone clearly was rushing the process to meet deadlines.

Stuff happens. I get it. But this doesn’t make it any easier for consumers spending $39.98 on a new record who expect perfection! So, I went ahead with writing this “heads-up” type review. I am not trying to dissuade you from buying Rhino High Fidelity’s reissue of Cher’s 3614 Jackson Highway. If you are not in a rush, you might, however, want to wait for the non-numbered regular edition of this reissue series, which I hope will be a different pressing run that will showcase all the love and care we have come to expect from Rhino and Optimal.

If you do want to order it right away, however, you can go to Rhino’s website to order it for $39.98.

Our Ratings

★★★★★★★★★★ Music

★★★★★★★★★★ Sound

★★★★★★★★★★ Packaging

Mark Smotroff is a deep music enthusiast / collector who has also worked in entertainment oriented marketing communications for decades supporting the likes of DTS, Sega and many others. He reviews vinyl for Analog Planet and has written for Audiophile Review, Sound+Vision, Mix, EQ, etc.  You can learn more about him at LinkedIn. 

Related Reading:

For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments — and in a lot of deployments it’s failing. The companies that end up benefiting from agentic AI won’t necessarily be the ones that have given their agents the most flexibility. They’re the ones who create AI agents with specific responsibilities and make sure they operate within clear rules.

Two numbers tell you almost everything about where agentic AI stands in mid-2026. By Gartner’s own forecast, more than 40% of the agentic AI projects running today won’t survive to see 2028. Not because the models fall short; because of escalating costs, unclear business value, and inadequate risk controls. McKinsey’s 2026 AI Trust Maturity Survey fits right alongside that prediction: Agentic AI deployment is accelerating across every industry, but average responsible-AI maturity sits at just 2.3 out of 4. Only about 30% of organizations have reached a maturity level of three or higher in governance and agentic AI controls specifically.  

Put those two numbers side by side, and the story tells itself. Capability is outrunning control.  

That shift is changing the competitive framing, too. The 2024-to-2025 race was about who could deploy the most autonomous agent the fastest. The 2026-to-2027 race is a trust race.  

It’s not about who can build the most capable agent. It’s about who can get an agent approved for production by risk, legal, and compliance teams, and keep it approved once it’s live. This is a different kind of engineering challenge than most enterprises are prepared for.

Why full autonomy breaks down in production  

Gartner lays out the failure pattern as specific and repeatable. Projects launch with ambitious, broadly autonomous workflows. They hit integration complexity within weeks. Then they stall, with no defensible path to production ROI. Part of the problem is vendor noise. Gartner’s own count puts it starkly: Out of the thousands of products being sold under the ‘agentic AI’ label, only around 130 actually have real autonomous capability behind them. The rest are largely automation or chatbots repackaged for the moment.

But even genuinely agentic systems run into a structural problem that has nothing to do with hype. Autonomy and accountability move in opposite directions.  

An agent capable of independently planning and executing a multi-step task is also an agent whose individual decisions get harder to trace after the fact. Let’s say something breaks a few steps into an autonomous chain. Figuring out why the agent made that decision and who is responsible can be a complicated process, not a simple lookup.

In areas like financial reconciliations, compliance processes, manufacturing quality checks, or clinical documentation, this lack of transparency can be the difference between a manageable mistake and a serious regulatory breach. It’s the reason legal, risk, and compliance teams block agentic projects from reaching production, regardless of how capable the underlying model is.  

Integration complexity keeps showing up as a leading cause of project cancellation. Bolting an autonomous agent onto a legacy workflow takes more than technical connective tissue. The workflow’s existing decision points, approval chains, and audit trails all need to be rebuilt around a system that can now act without waiting for a human. Enterprises that treat this as a pure integration problem, solvable with more engineering hours, tend to be the ones that stall.  

This isn’t a hypothetical risk. McKinsey’s research shows how exposed most enterprises currently are. Across nearly every category of AI risk, from data privacy to intellectual property exposure, the gap between the risks organizations say they’re aware of and the risks they’re actually mitigating remains wide.    

Awareness has surpassed action. This gap is reflected in the businesses that report it as an obstacle to further scaling of agentic AI. Nearly two-thirds now say security and risk issues are the greatest challenge for them, surpassing regulatory uncertainty and technical barriers.    

What governed orchestration actually looks like  

The enterprises that are leading the way are not halting their AI plans. They’re restructuring how autonomy is being distributed within the system. The four patterns that stand out in organizations that are governance-mature are:  

• Narrow-scope agents over general-purpose ones. Decompose end-to-end workflows into single-responsibility agents with tightly bounded mandates. A smaller scope of work results in a smaller scope of failure, and a smaller scope of failure is much easier to audit.  

• Human checkpoints at decision boundaries, before the outcome, not after it. Review agent decisions before high-stakes actions execute, not after the fact. That means checkpoints before sensitive data moves, a transaction posts, or an external system is triggered. McKinsey’s framework calls for real-time, data-driven monitoring built into the agent pipeline itself, with humans retaining final accountability specifically for high-stakes decisions.  

• Decision traceability as a design requirement. A full action log and decision lineage should be available on demand for any agent, any decision. It shouldn’t need to be reconstructed under pressure during an audit. Regulators are pushing the same way. The EU AI Act’s human oversight requirements for high-risk systems are still coming, even though this year’s Digital Omnibus agreement pushed the compliance deadline out to December 2027. Enterprises building agent systems now are effectively building toward that requirement, whether or not it’s technically enforceable yet.  

• Data sovereignty does active governance work, not passive paperwork. Where an agent’s data sits, and who has access to it, decides how contained a failure can be. On-premise or controlled-environment deployment limit the blast radius of a misbehaving agent and simplifies exactly the kind of audit trail regulators and boards are starting to expect.  

The risk runs in both directions, of course. Agentic AI is supposed to cut friction. An agent that needs a human to sign off on every minor task hasn’t cut anything; it’s just automation wearing a manual process as a costume. That quietly undercuts the whole case for building the agent in the first place. The goal isn’t maximum control. It’s calibrated control, concentrated where the cost of an error is actually high.  

Agent deployment is scaling roughly 8x faster than governance maturity is improving.  

A practical framework for evaluating your agent stack  

If enterprise architects are reviewing an existing agent for deployment or are considering deploying an agent, they can begin by asking four questions.  

1. Can you reconstruct, six months from now, exactly why a specific agent took a specific action?  
If the honest answer requires digging through raw logs or guessing, decision lineage isn’t a design feature of the system. It’s an afterthought. And it will show up as a gap in the next audit.  

2. Does every agent in the stack have one clearly bounded responsibility, or is at least one agent authorized to “figure it out” across a broad task?  
Broad, open-ended mandates are exactly where compounding errors and untraceable decisions originate.  

3. Are human checkpoints placed at defined decision boundaries, or only as a final review after the agent has already acted?  
A review after the fact catches consequences. A checkpoint before the fact prevents them.  

4. If an agent were compromised or malfunctioning right now, how much data and how many downstream systems could it touch before anyone noticed?  
This is where data sovereignty and access scoping stop being compliance line items and start functioning as containment strategy.  

These are all questions that don’t need to slow down the adoption of agentic AI. They need direction on how and where autonomy is of value to their organization and how to open up to exposure. This suggests building the orchestration layer on that separation, rather than adding governance after a production incident forces the question.  

The real competitive advantage  

Gartner’s 40% cancellation forecast isn’t really a warning about AI capability. It’s a forecast about organizational discipline. Right now, agentic AI sits at what Gartner defines as the “peak of inflated expectations,” and there’s a fairly straightforward explanation. Enterprises spent 2024 and 2025 optimizing almost entirely for autonomy. Now they’re paying down the governance debt that approach accumulated.  

The winning position by 2027 won’t belong to whoever deployed the most autonomous agents fastest. It will belong to whoever built agent systems trustworthy enough that risk, compliance, and legal teams stopped being the bottleneck. The architecture answered their questions before anyone had to ask them.  

That’s a different design brief than most agentic AI roadmaps were written against. It’s about making scoped autonomy, checkpointed decisions, full traceability, and data sovereignty integral to the architecture from the start, not add-ons after a pilot project has been a success.

Midhula Mariyam Jeevan is a content writer specializing in AI, enterprise technology, software engineering, and SEO.

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LG Energy Solution says the unsolved problem in solid-state batteries is making large cells at scale, and expects the technology to reach smartphones roughly a decade before electric vehicles. Europe’s first solid-state gigafactory broke ground in France in February.

The company that has just opened America’s newest battery plant does not think solid-state cells are close for cars. “The problem with solid state is large-scale production,” said Robert Lee, North America president of LG Energy Solution, at a media roundtable at its new Lansing factory.

The difficulty scales with the cell. Small formats reach good energy density, Lee said, but “if you’re making very large form factors, most companies are struggling.”

His ordering of the market is the blunt part. Solid-state will appear in smartphones “probably a decade before you would see it in EVs,” with specialised applications arriving before either cars or grid storage.

Europe is building on the opposite assumption. ProLogium broke ground in February on a 12 GWh solid-state plant in Dunkirk, and a Dutch startup won public money to develop a factory of its own.

European carmakers are further along in testing than in production. Mercedes ran a prototype EQS almost 750 miles on cells from Factorial Energy, BMW has put a Solid Power pack in an i7, and Stellantis is testing a pack in a Dodge Charger Daytona.

Two of those three are semi-solid rather than fully solid. That distinction is precisely the gap Lee is describing, and it runs through most of the promised launch dates in this field.

China is moving faster on paper. BYD, CATL, Geely and others are targeting pilot production of solid-state cells in 2027, with wider deployment hoped for by the end of the decade.

LG’s answer is to improve what already works. It is developing lithium manganese rich cells with General Motors that use far less nickel and cobalt, promising more than 400 miles of range from 2028 at roughly the cost of cheaper lithium iron phosphate packs.

It is also hedging across chemistries. The company is preparing 46-series cylindrical cells at its Arizona plant and running a sodium-ion pilot for storage, in a market where CATL commercialised sodium first.

For Europe the timing question matters more than the technology. The continent lost its own champion when Northvolt collapsed, and the plants now being built here belong to Taiwanese, Korean and Chinese companies.

Which makes Lee’s caution worth reading twice. If large-format solid-state cells are a decade out, Europe’s route back into batteries runs through incremental lithium-ion improvements it does not currently manufacture at scale.

• Army AI agents now perform specialized cyber work under human supervision
• Human commanders still decide which operational risks machines can handle
• AI agents train for cyber roles using standards similar to personnel

The US Army is training AI agents to work alongside human forces in defined cyber roles, assigning specialized responsibilities within operational units.

According to Army Cyber Command (ARCYBER), these systems are being prepared to perform technical duties alongside personnel, while humans retain responsibility for decisions involving risk.