Hachette Book Group was approached with what The New York Times claimed was evidence that Shy Girl by Mia Ballard was allegedly AI-generated. Following this, the publisher said its imprint Orbit was removing the book from publication in the US and UK.

The novel follows Gia, a young woman who is “lonely, broke and depressed with a serious case of OCD”. She encounters a mysterious and rich man who, in exchange for her living as his devoted pet, promises to erase all her debts. The novel follows her time in captivity as she becomes increasingly animalistic in nature.

In an email to The New York Times, Ballard said the controversy “has changed my life in many ways and my mental health is at an all-time low”. Ballard has denied personally using AI to write the novel. But she has said that an acquaintance she hired to work on an earlier self-published version incorporated AI tools.

Many people disagree with the use of AI for a host of reasons, from environmental to ethical concerns. But cultivating a climate of distrust around writing and authors is also not necessarily productive, and further pushes AI use into secrecy.

The author now faces a challenging situation, as Hachette withdrawing the book will appear to some to validate the accusations, even if it simply reflects uncertainty.

What happened?

The book was initially self-published in February 2025 before it was bought by Orbit Books, following a growing industry trend to traditionally publish successful self-published or fan-fiction works.

Issues started to arise regarding the novel’s provenance in mid-2025 on Reddit when one user, who claimed they were a book editor, made a post which pointed out several issues with the novel that suggested it was AI-generated.

Their main claim was based on the novel’s repetitive style, something also pointed out by other critical readers. Specifically, they highlighted that almost every noun is preceded by an adjective, actions are frequently described with similes, descriptions come in lists of three, and certain words are overused.

The discussion spread to other platforms such as the BookTok community (TikTok users dedicated to discussing books and publishing), Instagram and YouTube.

There is still no final consensus about how Shy Girl was written and Ballard has removed herself from the public eye and taken her social media accounts offline following the scandal. Hachette told The Independent that they “remain committed to protecting original creative expression and storytelling”. They have made no definitive statement on the claims but did tell the NYT that they conducted a thorough and lengthy review of the text.

How should readers and publishers respond?

Readers and publishers have spent years debating the impact of AI in the abstract but 2026 is the year these debates have become reality.

Stories like Shy Girl and The New York Times’ profile of AI romance author Coral Hart, who boasted of using AI to write and self-publish 200 hundred books across 21 pen names in a recent profile by The New York Times, demonstrate that theoretical disputes did not prepare us to be confronted with the reality of AI.

It’s clear that even the suggestion of AI writing inspires immense disgust in many readers. This means that regardless of the truth (if we ever find it out) Shy Girl and Ballard will likely be tainted by this scandal. Therefore, we must ask whether it is possible for publishing and reading to survive not just AI’s increasing normalisation but also the hostile and suspicious environment its use is creating for writers.

As a researcher of contemporary and digital reading culture, I believe we should cultivate an openness around the use of AI in writing by lobbying publishers to provide this information openly and clearly. This is already starting to happen. The Society of Authors, which is the UK’s largest writers’ trade union, has launched a logo to be used to identify “human authored” books – a step toward empowering consumers to know what they are choosing to support with their money.

Copyright law also needs to reflect AI’s reshaping of the creative field. A work requires a human author to be covered under copyright law in the US, and any doubts about this are potentially a big part of Hachette pulling Shy Girl from publication due to the publisher’s inability to copyright.

This creates a difficult position for the novel and author. The book’s cancellation looks like confirmation of guilt, whereas it may just be doubt. However, UK copyright law does offer protection for computer-generated works. This creates a murky area where AI-generated or assisted works can receive certain legal protections, but not necessarily the same rights as human-authored works.

Under UK law, computer-generated works can qualify for copyright, with authorship attributed to the person who made the necessary arrangements for the work’s creation. However, these works do not benefit from the full range of protections afforded to human authors, particularly moral rights, such as the right to be identified as the author or to object to derogatory treatment of the work.

This framework may change following a recent consultation led by the UK government on copyright and artificial intelligence. The consultation has now closed and the government has not yet implemented definitive legislative changes. However, its stated priorities suggest any reforms will aim to balance protecting creators’ rights with supporting innovation, investment and growth in the AI sector.

It’s an undeniably fraught situation, which is continually developing. In the near future we may unfortunately see more authors like Ballard made examples of while, behind the scenes, many more may be using AI undetected.

• Natalie Wall is a PhD in English Literature, University of Liverpool
• This article first appeared in The Conversation

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The extraordinary complexity of the brain’s billions of neurons makes this a very difficult task, of course, even in the era of AI and big data. Until now, whole-brain models have struggled to capture what makes each brain unique.

People’s brains are all wired slightly differently, so everyone has a unique network of neural connections that represents a kind of “brain fingerprint”.

However, most so-called brain twins are currently more like distant cousins. Their performance is barely any closer to the real thing than if the model were using the wiring diagram of a random stranger.

This matters because digital twins are increasingly proposed as tools for testing treatments by computer simulation, before applying them to real people. If these models fail to capture fundamental principles of each patient’s unique brain organisation, their predictions won’t be personalised – and in worst cases could be misleading.

In our latest study, published in Nature Neuroscience, we show that realistic digital brain twins require something that many existing models overlook: competition between the brain’s different systems.

Our findings suggest that without competition, digital twins risk being overly generic, missing out on what makes you “you”.

Excess of cooperation

The human brain is never static. The ebb and flow of its activity can be mapped non-invasively using neuroimaging methods such as functional MRI. A computer model can be built from this, specific to that person and simulating how the regions of their brain interact. This is the idea of the digital twin.

The brain is often described as a highly cooperative system. Yet everyday experiences such as focusing attention or switching between tasks tells us intuitively that brain systems compete for limited resources. Our brains cannot do everything at once, and not all regions can be active together all the time.

Despite this, the vast majority of brain simulations over the past 20 years have not taken these competitive interactions between regions into account. Rather, they have “forced” neighbouring regions to cooperate. This can push the simulated brain into overly synchronised states that are rarely seen in real brains.

In a large comparative study of humans, macaque monkeys and mice, our international team of researchers used non-invasive brain activity recordings to show that the most realistic whole-brain models not only require cooperative interactions within specialised brain circuits, but long-range competitive interactions between different circuits.

To achieve this, we compared two types of brain model: one in which all interactions between brain regions were cooperative, and another in which regions could either excite or suppress each other’s activity. In humans, monkeys and mice, the models that included competitive interactions consistently outperformed cooperative-only models.

Using a large-scale analysis of over 14,000 neuroimaging studies, we found that spontaneous activity in the competitive models more faithfully reflected known cognitive circuits, such as those involved in attention or memory. This suggests competition is crucial for enabling the brain to flexibly activate appropriate combinations of regions – a hallmark of intelligent behaviour.

Visual summary of our study:

We concluded that competitive interactions act as a stabilising force, allowing different brain systems to take turns in shaping the direction of the brain’s ebbs and flows without interference or distraction. This ability to avoid runaway activity may also contribute to the remarkable energy-efficiency of the mammalian brain, which is many orders of magnitude more efficient than modern AI systems.

Crucially, models with competitive interactions were not only more accurate but also more individual-specific. This means they were better at capturing the unique brain fingerprint that distinguishes one person’s brain from another’s.

No longer lost in translation?

The fact that our findings hold across humans and other mammals suggests they reflect fundamental principles of how intelligent systems work. In each case, we found models with competitive interactions generated brain activity patterns that closely resembled those associated with real cognitive processes.

This could have major implications for translational neuroscience. Animal models are routinely used to test treatments before human trials, yet differences between species often limit how well these results translate. Around 90% of treatments for neuropsychiatric disorders are “lost in translation”, failing in human clinical trials after showing promise in animal trials.

Combining brain imaging data from human patients with whole-brain modelling could radically change this. A framework that works across species would provide a powerful bridge between basic research and clinical application.

If someone needs intervention in the brain, for example due to epilepsy or a tumour, their digital twin could be used to explore how the patient’s brain activity would change when stimulated with different levels of drugs or electrical impulses. This might significantly improve on existing trial-and-error approaches with real patients, and thus provide better treatments.

The general principles of brain organisation across species also offer a path for understanding how to shape the next generation of artificial intelligence. In the not-too-distant future, we may be able to construct digital twins that are more faithful in reproducing the salient features of the human brain – and potentially, AI models that are more faithful to the human mind.

• Andrea Luppi is a Senior Research Associate, Department of Psychiatry, University of Oxford
• Gustavo Deco is a Professor of Computational Neuroscience, Center for Brain and Cognition, Universitat Pompeu Fabra
• Morten L. Kringelbach is a Professor of Neuroscience, University of Oxford
• This article first appeared in The Conversation

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Diesel stays losing

If the CEF’s daily predictive fuel price snapshot captured on 1 April is accurate (and we’re sorry to say that it is), then petrol drivers could be facing yet another R6+ increase. Diesel drivers, once again, get the worst end of the deal, with early predictions currently setting them up for a R14+ per-litre price hike. It’s really that bad.

Of course, there’s still time to turn this ship around. The Central Energy Fund (CEF) tracks those factors that influence the price of fuel on behalf of the Department of Mineral Resources and Energy (DMRE) before it officially adjusts the country’s fuel pumps. This typically takes place on the first Wednesday of the new month.

In this case, that’d be 6 May 2026, leaving a little over a month for things to sort themselves out. It’s unlikely, but we can dream, can’t we? The CEF specifically tracks things like the price of oil globally, as well as the average Rand/US Dollar exchange rate over any given month. Neither of these is looking so hot right now, really.

With the closure of the Strait of Hormuz, oil reserves are slowly drying up all over the globe. Prices are fluctuating wildly, leading to the rough (in two senses of the word) estimates provided by the CEF below. Check back with Stuff regularly to get an idea of how South Africa’s petrol and diesel prices are shaping up this May.

Here are the petrol and diesel price predictions (so far) for May 2026:

• Petrol 93: increase of 593 cents per litre (R5.93)
• Petrol 95: increase of 630 cents per litre (R6.30)
• Diesel 0.05%: increase of 1,485 cents per litre (R14.85)
• Diesel 0.005%: increase of 1,491 cents per litre (R14.91)
• Illuminating Paraffin: increase of 1,395 cents per litre (R13.95)

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NASA has just reset its Artemis program, marking a clear strategic shift: Space exploration is moving away from a race to achieve milestones and toward a system built on repeated operations, a sustained presence and lunar infrastructure that could become part of the technology networks we rely on here on Earth.

That shift is reflected in newly announced plans to invest billions of dollars in building a long-term lunar base, with habitats, power systems and surface infrastructure designed to support ongoing human activity. The message? Humans have already normalised travel to space. The next step is normalising living beyond Earth.

Artemis is NASA’s plan to return people to the Moon with the goal of staying. Unlike the short Apollo missions of the 1960s and 1970s, it consists of increasingly complex missions: flying around the Moon, landing on its surface and eventually establishing a base near the lunar south pole. The program aims to create a reliable way for humans to live and work there, develop technologies useful on Earth and prepare for the journey to Mars.

Rather than moving straight from the upcoming Artemis II crewed lunar flyby to a surface landing, the new road map adds an intermediate mission in 2027. Astronauts will test docking, life-support systems and communications with commercial lunar landers from SpaceX and Blue Origin, but in low Earth orbit, the region roughly 100 to 1,200 miles (160 to 2,000 kilometres above Earth’s surface, where rescue remains possible.

The first landing near the lunar south pole is now targeted for 2028. This timeline may sound delayed, but in reality, it has been deliberately reset to prioritise building reliable systems that can operate long into the future over speed.

As a professor of air and space law, I’ve been watching these developments closely. The United States is still in a race – particularly with China – but it is choosing to compete on its own terms. Rather than chasing the fastest possible landing, NASA is focused on building a system that can support repeated missions and a lasting human presence.

From sprint to system

The original Artemis plan aimed to leap quickly from test flights to a crewed landing while simultaneously developing new rockets, spacecraft and landing systems. That approach carried risk. Artemis I, an uncrewed mission, flew successfully in 2022. After a few delays, Artemis II is now nearing launch, with windows planned for early April 2026. But the further jump to a safe and reliable landing remains significant.

NASA’s new road map slows the transition deliberately. Instead of stand-alone milestones, NASA is now building a sequence of repeatable steps to gain hands-on experience.

This change includes a substantial new investment, with a multiphase plan for a lunar base with habitats, power systems and the surface infrastructure needed for a long-term human presence on the Moon. Consistent launch cadence and repeatable operations are how teams develop the expertise needed for safe, reliable spaceflight and eventually for travelling to Mars.

This shift is reflected in the decision to pause the planned lunar Gateway station, a small space station intended to orbit the Moon, and prioritise infrastructure on the lunar surface itself, where astronauts will live, work and build over time.

The new changes also emphasise a shifting role for commercial companies. SpaceX’s and Blue Origin’s lunar landers are integrated into the mission architecture.

The 2027 test mission, for example, will practice docking between crewed spacecraft and new commercial lunar landers in low Earth orbit. NASA is coordinating a network of public and private partners rather than running a single government-run Apollo-like program.

This method spreads risk across partners, lowers costs and speeds development, though success now depends on multiple players working reliably together.

Law follows activity

NASA’s road map is not just about lowering technical risk. It is also about shaping the future environment of lunar activity.

International space law, including the 1967 Outer Space Treaty, sets out broad principles to guide space activities, like avoiding harmful interference with others’ activities. But those rules only gain real meaning through repeated, coordinated activity, especially on the lunar surface, where desirable landing sites are limited.

Countries and companies that maintain a sustained presence on the Moon will shape the practical expectations everyone will share while living and working on the Moon. One-off demonstrations, like lunar landings, don’t shape lunar activity like continued operations would.

Why this matters – even if you never go to space

It would be easy to see these changes as purely technical, but they are not. The structure of a space program shapes what technologies are developed, how industries grow and which countries influence how space is used. Technologies developed for sustained lunar activity, including life-support systems, energy storage and advanced communications, have found applications on Earth, from medicine to disaster response.

There are economic effects as well. The Artemis program supports jobs across the United States and among its international partners. It helps build industries that extend far beyond NASA itself.

And there is a strategic dimension. As more countries and companies operate in space, the question is no longer just who arrives first, but who helps define how activity is carried out. Over time, that presence will likely become part of the infrastructure that supports daily life on Earth.

Communications, navigation, supply chains and scientific data already depend on space-based systems. As activity expands to the Moon, facilities there, from energy systems to communications relay systems that transmit data and signals back to Earth, will become integrated into those networks. What is built on the Moon will not sit apart from life on Earth, but increasingly function as an extension of it.

The Moon is becoming a place where infrastructure, industry and rules and expectations for how humans operate there are already beginning to take shape. NASA’s updated plan signals that the United States intends to be present there consistently.

The updates to the Artemis program are a statement about how the United States intends to engage in the next phase of space exploration. Rather than pursuing a single dramatic landing, the U.S. is committing to the steady, repeatable work of building a lasting foothold on the Moon, and redefining humanity’s relationship with space itself.

• Michelle L.D. Hanlon is a Professor of Air and Space Law, University of Mississippi
• This article first appeared in The Conversation

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Legs or not, the autonomous droid has already gone to work. Specifically, at a UK-based automotive factory for a company called Martur Fompak. They install car seats, for the most part. The HMND 01 spent some time on the company’s factory floor, where it functioned as a robotic factory worker proof-of-concept.

HMND 01? Where’s CLRKSN 02?

Judging by the video released by Humanoid, it performed well enough. Slower than an actual human would have, but since the wheeled robot doesn’t have to sleep, it’ll outperform us all eventually. The robot’s design is definitely more industrial than most, though we’d expect something with a 300kg weight and four-hour runtime to manage more than fifteen kilograms at a time.

The key thing with the HMND 01’s operations here was autonomy. It accepted orders directly from Martur Fompak’s software and carried out tasks without human intervention. The bot functioned on a live factory floor during the January/February 2026 trial, with most of its tasks involving packing items onto a trolley. It found and delivered those items on its own, however.

The robot’s range of sensors helped. It packs several RGB and depth cameras in its skull. Six-degrees-of-freedom sensors in its arms and AI software (helpfully powered by Nvidia) handled much of the rest. It’s unlikely to turn up in your own workplace soon, unless some enterprising IT fellow opts to approach Humanoid for early access. Presumably, that’s when the price tag will be shared with those who can afford it.

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But as the UK’s chief medical officer recently warned, that may not be wise when it comes to medical decisions. In a recent study, colleagues and I tested how well large language model (LLM) chatbots help the public deal with common health problems. The results were striking.

The chatbots we tested were not ready to act as doctors. A common response to studies like this is that AI moves faster than academic publishing. By the time a paper appears, the models tested may already have been updated. But studies using newer versions of these systems for patient triage suggest the same problems remain.

We gave participants brief descriptions of common medical situations. They were randomly assigned either to use one of three widely available chatbots or to rely on whatever sources they would normally use at home. After interacting with the chatbot, we asked two questions: What condition might explain the symptoms? And where should they seek help?

People who used chatbots were less likely to identify the correct condition than those who didn’t. They were also no better at determining the right place to seek care than the control group. In other words, interacting with a chatbot did not help people make better health decisions.

Strong knowledge, weak outcomes

This does not mean the models lack medical knowledge because LLMs can pass medical licensing exams with ease. When we removed the human element and gave the same scenarios directly to the chatbots, their performance improved dramatically. Without human involvement, the models identified relevant conditions in the vast majority of cases and often suggested appropriate levels of care.

So why did the results deteriorate when people actually used the systems? When we looked at the conversations, the problems emerged. Chatbots frequently mentioned the relevant diagnosis somewhere in the conversation, yet participants did not always notice or remember it when summarising their final answer.

In other cases, users provided incomplete information or the chatbot misinterpreted key details. The issue was not simply a failure of medical knowledge – it was a failure of communication between human and machine.

The study shows that policymakers need information about the real-world performance of technology before introducing it into high-stakes settings such as frontline healthcare. Our findings highlight an important limitation of many current evaluations of AI in medicine. Language models often perform extremely well on structured exam questions or simulated “model-to-model” interactions.

But real-world use is much messier. Patients describe symptoms in a vague or incomplete way and can misunderstand explanations. They ask questions in unpredictable sequences. A system that performs impressively on benchmarks may behave very differently once real people begin interacting with it.

It also underscores a broader point about clinical care. As a GP, my job involves far more than recalling facts. Medicine is often described as an art rather than a science. A consultation isn’t simply about identifying the correct diagnosis. It involves interpreting a patient’s story, exploring uncertainty and negotiating decisions.

Medical educators have long recognised this complexity. For decades, future doctors were taught using the Calgary–Cambridge model. This meant building a rapport with the patient, gathering information through careful questioning, understanding the patient’s concerns and expectations, explaining findings clearly and agreeing a shared plan for management.

All these processes rely on human connection, tailored communication, clarification, gentle probing, judgement shaped by context and trust. These qualities cannot easily be reduced to pattern recognition.

A different role for AI

Yet the lesson from our study is not that AI has no place in healthcare. Far from it. The key is understanding what these systems are currently good at and where their limitations lie.

One useful way to think about today’s chatbots is that they function more like secretaries than physicians. They are remarkably effective at organising information, summarising text and structuring complex documents. These are the kinds of tasks where language models are already proving useful within healthcare systems, for example in drafting clinical notes, summarising patient records or generating referral letters.

The promise of AI in medicine remains real, but its role is likely to be more supportive than revolutionary in the near term. Chatbots should not be expected to act as the front door to healthcare. They are not ready to diagnose conditions or direct patients to the right level of care.

Artificial intelligence may be able to pass medical exams. But just as passing a theory test doesn’t make you a competent driver, practising medicine involves far more than answering questions correctly. It requires judgement, empathy and the ability to navigate the complexity that sits behind every clinical encounter. For now, at least, that requires people rather than bots.

• Rebecca Payne is a Clinical Senior Lecturer, Bangor University; University of Oxford
• This article first appeared in The Conversation

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Apple has sold some 3 billion iPhones since it launched in 2007, according to Counterpoint Research, bringing in an estimated $2.3 trillion in revenue.

More than anything, this large touchscreen device enabled the mobile world we find ourselves in. Perhaps “super-charged it” is more apt. Android’s rise to be the dominant mobile operating system was built on the early successes of Steve Jobs’ vision to have something so easy to use that it had just one button.

It’s easy to forget how revolutionary that alone was.

But what is a smartphone if not a smaller personal computer than the big beige boxes that first appeared on desks in the 1970s and 1980s? The Macintosh changed computing as much as the iPhone changed the mobile space.

Read More: Joburg’s Education Town goes high-tech

The first Mac incorporated existing technologies and fused them into a consumer-friendly package, including its then-innovative graphical user interface (GUI) and mouse. These were both pilfered from Xerox’s famous X lab, where Jobs saw them in action and realised they were being underutilised.

The iPhone was launched in 2007 when Nokia still sold two out of three feature phones. It used the available touchscreens and miniaturised hard drives, amongst other tech, and combined them into something so user-friendly it upended both the mobile and overall computing industries.

Smartphones are the predominant computer in the world now – all effectively copied from Jobs’ original design with Jony Ive.

And the newest Apple release has impressed us mightily at Stuff Towers.

We reckon the MacBook Neo is a game-changer that will introduce cheaper MacBooks to a broader audience. The Windows computer makers will be watching with keen interest as this slicker, more colourful, and decidedly Apple laptop introduces a whole new category of budget users to the Apple ecosystem.

Happy birthday, Apple.

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Of course, until Sony confirms it, take your console leaks with a pinch of salt. But KeplerL2 is a known quantity, if one with a patchy track record. Only six months ago, he claimed that the PS6 was locked in for 2027. That may still technically be true, but it’s still worth scrutinising his PlayStation rumours more thoroughly.

Sony’s getting back in the handheld game

Multiple reports reckon the project is known internally as ‘Canis’, so its existence seems all but assured. KeplerL2, chatting on NeoGaf, claims that Canis is “massively ahead” of the Xbox Series S (the puny one) in terms of rasterisation, path tracing, and ray tracing capabilities. The forum was discussing some ‘new’ leaked specs.

Those are courtesy of YouTuber Moore’s Law is Dead (MLD), whose record is similarly spotty when it comes to Sony. These stem from a leaked AMD presentation, reportedly captured in 2023. That alone casts doubt on the veracity of the claims, but there you go. MLD points to Canis featuring a monolithic 3nm die equipped with four Zen 6C cores.  That’s in addition to the console’s supposed 12-20 RDNA 5 Compute Units (between 1.6 and 2.2GHz).

Read More: Sony reportedly got Gran Turismo 7 running on the Switch 2, just for the fun of it

A 12-bus sits up with LPDDR5X 7500 RAM, apparently. As for the bits your dad might understand, the console is believed to be PS4/PS5 backwards compatible, with microSD and SSD storage support, as well as haptic feedback, a touchscreen, and dual microphones. A single USB-C port offers charging and video output. KeplerL2 continued, claiming that it’ll bring about PSSR 3, which itself will outperform DLSS 4.5.

If that’s still true all these months later, and KeplerL2 isn’t wrong, Sony’s got a winner on its hands. The Japanese gaming outfit has allowed Nintendo to control the handheld market for too long, even allowing Microsoft an foot in the door. More competition in the space is always a good thing, even if it ends up costing an arm and a leg.

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That may take a while, though. The RS 5 PHEV has been announced for Europe so far, with a starting price of R2.08 million (€106,200) for the sedan model and R2.11 million (€107,850) for the RS 5 Avant. It’ll ship to that region this year, but South African availability isn’t known.

Ride the lightning RS 5

Since it’s an Audi, you’re likely to pay attention to the engine first. The German company is sticking a 375kW 2.9-litre V6 twin-turbo inside the RS 5, augmenting it with a 130kW electric motor. That’ll give drivers 470kW (even if the math isn’t mathing) of output. Running purely on the electric motor renders 80km of range. Plus, stealth capabilities.

The front end is designed with airflow in mind, while a selection of RS-branded additions has this vehicle ready for the track. That’s where you’re supposed to drive it like a maniac. RS sport exhausts and suspension, the latter featuring twin-valve shocks, and RS-spec steering should help drivers make their corners. An eight-speed hybrid gearbox, rear aerodynamic diffuser, and “a completely new rear transaxle with electro-mechanical torque vectoring – a world first in a production model” should also help.

Audi’s new PHEV will take drivers up to 285km/h, but they’ll have to pay for it. The increased top speed comes as part of the company’s Sport package, which also adds custom front and rear bumpers and “two-tone diamond-cut phantom black 21-inch wheels with matte accents.” The package also spruces up the interior with unique “Serpentine green and brass’ stitching. Exterior elements include customised paint jobs, including a new metallic Bedford green (whatever that is) finish. RS ceramic brakes and bronze calipers are also included. Obviously, you’re supposed to opt for the greener hues for the best visual effect.

The RS 5’s interior sports one of Audi’s 14.5in MMI touch displays, optimised for track days. It can, according to the company, “analyze and store sector times” — preferably not while you’re taking laps.

Audi has loads more detail on the interplay between its electric and traditional motors at its official announcement.

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Melon Mobile, a local mobile virtual network operator (MVNO), has teamed up with Airalo, an international eSIM provider. This gives tourists in SA another way to access mobile services, including data, voice, and SMS services, without the need to buy a physical SIM card or an expensive roaming plan.

Who wants a South African eSIM?

“Melon has always been about simplifying connectivity and putting the customer first. By teaming up with Airalo, we’re giving international visitors something truly game-changing: seamless digital connectivity that works instantly, without limits, anywhere in South Africa,” said Calvin Collett, CEO of Melon Digital.

To make use of this partnership, download the Airalo app (Android, iOS) and pick South Africa as your destination before you actually get here. You should see the options to get a data-only eSIM or one that supports data, calls, and texts. While both list ‘RSA CellC’ as the provider, that’s likely a bug. We’ve reached out to confirm.

Next, you’ll need to verify your identity. Once that’s done, you should be able to purchase your chosen package. After you’ve paid, you should receive an email with instructions on how to install the eSIM on your supported device — you’ll want to make sure Airalo recognises your device as supported.

If you’ve done all the right things, when you touch down in SA, you should be automatically connected via Melon Mobile, which uses MTN’s infrastructure. Top-ups are available when you run out.

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