Retail is moving into a new phase where artificial intelligence no longer just advises, but actually acts on it. The rise of agentic AI marks a shift from systems that recommend products or insights to systems that can make decisions and complete tasks on their own within set limits.

In simple terms, agentic AI refers to AI systems that can carry out multi-step actions without constant human prompting.

In simple terms, agentic AI refers to AI systems that can carry out multi-step actions without constant human prompting.

In retail, this includes updating stock levels, adjusting prices, managing supply chain steps, and even completing parts of a customer journey such as product selection or checkout.

The key change is autonomy. AI is moving from being a support tool to becoming an operational actor.

This shift is closely tied to advances in large language models, which now serve as the foundation for more complex AI agents. These systems are increasingly being tested across e-commerce platforms, logistics networks, and marketing operations.

While many applications are still in pilot stages, the direction is consistent across the industry: more tasks are being handed over to automated decision-making systems.

For years, AI in retail has mainly focused on prediction and recommendation. It suggested what customers might buy, or helped businesses forecast demand. Agentic AI goes further by acting on those predictions.

In practical terms, an AI agent can monitor inventory levels across multiple warehouses and trigger restocking when thresholds are reached. It can adjust product listings based on real-time demand signals.

It can also support dynamic pricing, where prices shift in response to supply, demand, and competitor activity.

These systems are being explored in both online and physical retail environments. The goal is to reduce delays between insight and action. Instead of a manager reviewing data and making a decision, the system can execute the response directly, within predefined rules.

Marketing teams are also beginning to use agentic systems to manage campaign performance. AI can test different versions of adverts, adjust targeting, and reallocate budgets based on performance data.

This reduces manual workload and allows faster responses to changing customer behaviour.

One of the most important applications of agentic AI is in supply chain management. Retail supply chains involve many moving parts, including suppliers, warehouses, transport networks and stores. Small delays or forecasting errors can quickly become costly.

Agentic AI systems can track these moving parts in real time. They can identify risks such as low stock, delayed shipments or sudden spikes in demand. In some cases, they can automatically reorder products or reroute logistics flows to reduce disruption.

This type of automation is particularly valuable for global retailers operating across multiple markets. It allows them to respond more quickly to local demand changes and reduce reliance on manual coordination between teams.

However, most retailers are still in early stages of adoption. Current use is typically limited to specific functions rather than full end-to-end autonomy. The focus is on improving accuracy and efficiency while keeping human oversight in place.

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Modern smartphones are often packed with camera lenses, referred to as a camera cluster (or bump). This is why the camera on your phone isn't flat: the lens needs space between the sensor. The more lenses, the more space needed.

But why in the world do we need multiple lenses in our smartphones to begin with?

Well, that's simple. Each lens performs a specific job, such as a main (wide) lens, an ultrawide lens, a telephoto lens, and/or a macro lens. Macro is handy for close-up shots, and telephoto helps capture objects from a distance while taking more of the landscape. You'll use the main lens for other kinds of shots, like portraits. Through software, these photos can be further polished or even combined to create shots through computational photography.

Essentially, smartphones offer a selection of cameras and lenses to give you, the user, the choice of how to take your photos. Your main lens will likely take in the most light for standard use. This is known as aperture, and it's measured on F-stops. The lower the F-stop, the more light your lens lets in, so make sure to check which aperture each lens in your camera phone offers.

Generally, ultrawide lenses offer a slightly higher aperture as less light is taken in to afford a much higher depth of field, so you can shoot large landscapes. As for macro, many manufacturers use the ultrawide to capture macro shots as it can also focus on subjects from a short distance. Camera bumps exist to give us options so our phones can handle any shooting situation.

One of the main features to consider when purchasing a smartphone is the type of camera lenses it offers. Typically, smartphones offer three lenses in the back, though some camera clusters that once offered a dedicated macro lens have switched to a zoomed ultrawide for a similar effect. You can see this on devices from players like OnePlus, which uniquely uses a monochrome lens in its cluster.

Competitors like Samsung and Google also pack an ultrawide that can zoom for macro instead of a dedicated macro lens, ensuring to offer a choice for the best budget and expensive phones for photographers, no matter your brand or shooting preference.

A smartphone's camera quality is seen as a major selling point, and rightfully so, given that 91 percent of phone users regularly use their smartphones for pictures. Cameras are a very commonly used feature.

For comparison, only 80 percent of smartphone users actually use their phones to make calls. So, not only are the cameras built into our phones a normalized feature these days, but they are more important than these gadgets original use. How good these cameras are can easily make or break a phone; that's simply how important the tech is.

So, if you ever wondered why just about every smartphone has a silly-looking camera cluster on the back, there's a very good reason for it. Consumers absolutely demand killer cameras in their smartphones, which is why Google, Apple, and Samsung have all been battling neck and neck for over a decade to offer the very best and most popular smartphone cameras to the public.

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China is now a home to the world's fastest publicly listed supercomputer for the first time since 2017, according to the latest TOP500 ranking released last 23 June.

The system, called LineShine and located at the National Supercomputing Centre in the southern city of Shenzhen, displaced the US machine El Capitan from the top spot.

The TOP500 list, published twice a year, ranks the world's most powerful known supercomputers based on a standardized performance benchmark. The latest edition was unveiled at the International Supercomputing Conference (ISC) in Hamburg.

LineShine achieved a performance of 2.198 exaflops, equivalent to more than 2 quintillion calculations per second. El Capitan, located at the Lawrence Livermore National Laboratory in California, recorded 1.809 exaflops.

Supercomputers are used for applications including climate modelling, materials research, industrial development and artificial intelligence, making the rankings a closely watched indicator in the technology competition between China and the United States.

El Capitan, Frontier and Aurora, all based in the US, occupied the next three positions in the ranking. Germany's Jupiter Booster system at the Jülich Research Centre ranked fifth.

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About two weeks after his final appearance hosting Apple's Worldwide Developers' Conference (WDC), outgoing Apple CEO Tim Cook tells the Wall Street Journal the company plans to raise prices on its products. Cook blamed the rising costs of memory chips and storage.

Cook did not say which products would be affected, or when the price hikes would take effect. Apple is expected to release the iPhone 18 series in September, and many expect it to also announce the company's first foldable phone.

"Unfortunately, price increases are unavoidable," Cook told the WSJ. "We're doing our best to mitigate the huge increases that are being passed to us and we've been trying to shield our customers from the increases, but the situation has become unsustainable."

Cook warned during Apple's second-quarter earnings call in May that he expected memory costs to "drive an increasing impact on our business," but without offering specifics. And at least one analyst suggested the memory chip crisis could raise the price of iPhones — but maybe not all models.

Insatiable demand from AI companies has driven the cost of memory and storage chips sky-high, leading to a severe shortage of RAM — a situation dubbed RAMmageddon within the industry.

Apple is far from the only company feeling the squeeze and raising prices as a result. Microsoft raised its Xbox console prices in 2025, and Sony raised the cost of its Playstation consoles in April.

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President Donald Trump said last 18 June that Intel had agreed a to deal with Apple to "design and build" its chips in the U.S.

"America needed its Semiconductor Industry to come back to the U.S.A. We design everything, but we need to BUILD it here, NOW!" Trump wrote in a Truth Social post. "So I decided to help Intel because we need to design and build our Chips right here in America."

"Apple has agreed to work with Intel to design and build its Chips in America," he said.

The president has been a vocal supporter of Intel, striking a deal with the company last August that saw Intel sell a stake of about 10 percent to the U.S. government.

Intel's share price was up more than 10 percent when markets opened Thursday.

Trump's announcements came a few hours after the Wall Street Journal published an interview with Apple CEO Tim Cook, where he said the company would have to raise prices due to memory and storage shortages. "Price increases are unavoidable," Cook said.

Demand from AI companies has driven a sharp increase in the cost of memory and storage, leading to a severe shortage of RAM — known as RAMmageddon within the industry.

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Researchers have developed a soft robotic gripper that can inspect fruit for ripeness and harvest it without causing damage, a capability that could help reduce food waste and improve harvesting efficiency.

The device uses soft silicone and polyurethane fingers equipped with multiple sensors to measure a fruit’s size, shape, color and firmness before deciding whether it is ready to be picked. The system was tested on strawberries and can remove the fruit by twisting its stem rather than cutting it.

The gripper was developed by a team that includes Anand Mishra, assistant professor in the Department of Mechanical, Materials and Aerospace Engineering at West Virginia University. The research began at Cornell University and is continuing at WVU.

The technology addresses a major challenge in agriculture: harvesting delicate fruits at the right stage of ripeness. Fruits such as strawberries and raspberries have a narrow harvest window and can bruise easily during picking, transportation and storage.

"Our gripper’s quick, accurate inspections and harvesting can reduce spoilage of fruits and lower supply chain costs," Mishra said.

"Fruit inspections are critical for harvesting decisions that have traditionally been made by human workers. However, using human workers for harvesting involves challenges such as labor shortages, health concerns and inaccuracies in picking."

Agricultural robots have been proposed as a solution, but many existing systems are designed for controlled greenhouse environments rather than outdoor farms. Traditional robotic grippers can also damage soft fruit because of their rigid structures.

The new system combines tactile and visual sensing in a soft robotic design. Stretchable optical fibers embedded inside each finger act as tactile and curvature sensors, while a miniature camera and distance sensor are mounted in the palm.

The five-fingered gripper can identify a fruit’s shape, stiffness and ripeness while also detecting slippage during handling. Researchers reported that the device can open and close in less than two seconds, lift loads weighing up to one kilogram and achieve nearly 100 percent accuracy in shape prediction.

According to the team, the technology could be particularly useful for fruits that do not show obvious visual signs of ripeness. Avocados are one example, where growers often rely on touch rather than appearance to determine harvest timing.

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