MacBook Neo helped Apple move against the global PC market in the second quarter of 2026, delivering double-digit Mac shipment growth while most leading computer manufacturers sold fewer units than a year earlier.
Preliminary data from IDC estimates that Apple shipped 6.7 million Macs during the quarter, up 10.1% from 6.1 million in Q2 2025. Its worldwide market share rose from 8.5% to 9.9%, placing the company within a fraction of the 10% threshold.
The rest of the industry moved in the opposite direction. Global shipments fell 4.9% year over year to 68.2 million units, ending nine consecutive quarters of growth. A separate Omdia estimate placed the decline at 3.6% and Apple growth at 15.9%, but both research groups identified the same pattern: the Mac gained ground while established Windows PC vendors contracted or remained flat.
IDC directly connected the Apple increase with the arrival of its lowest-priced notebook. MacBook Neo began shipping in March, giving the model its first complete quarter of retail availability between April and June.
MacBook Neo entered the market as an alternative for buyers previously priced out of the Mac lineup.
The 13-inch notebook launched at $599 with an A18 Pro chip, 8GB of unified memory, a Liquid Retina display and up to 16 hours of video playback. It also brought macOS, Apple Intelligence and integration with the iPhone ecosystem into a price range commonly occupied by mainstream Windows laptops and Chromebooks.
That positioning attracted students, families and first-time Mac customers who did not need the performance or display technology of a MacBook Air or MacBook Pro. The four-color aluminum design also gave the entry model a more recognizable identity than the older Mac notebooks that frequently remained on sale through discounts.
IDC had already identified the computer as an unusual source of strength when it revised its notebook forecast earlier in 2026. Demand exceeded initial expectations in several markets, and reported inventory shortages appeared shortly after the March launch.
The effect became much more visible in Q2. MacBook Neo was available for the entire three-month period, allowing Apple to increase production, expand retail distribution and serve customers who could not obtain the computer during its first weeks.
The entry model also widened the Mac lineup without replacing the MacBook Air. Buyers seeking more memory, faster ports, longer wireless web use or additional external-display support could continue moving toward the M5-powered Air, while the less expensive machine handled education, office work, web browsing and communication.
That separation allowed Apple to reach lower price points without reducing the value of its premium notebooks.
Apple remained the fourth-largest traditional PC vendor, but its performance contrasted sharply with the companies above it.
Lenovo retained first place with 16.6 million units and 24.4% market share. Its shipments declined 2.1% from the previous year.
HP experienced the steepest drop among the top five, falling 9% to 13 million units. Dell shipped 9.3 million computers, representing a 5% decline. Asus remained nearly flat at 5 million units, up only 0.2%.
Manufacturers outside the five largest brands collectively suffered a 10.5% contraction, indicating that weaker demand and component inflation placed even more pressure on smaller companies.
The figures measure shipments into distribution channels or directly to end users, not confirmed retail purchases by individual consumers. A manufacturer can therefore record shipment growth while some inventory remains unsold in stores or warehouses.
MacBook Neo demand appears to have been strong enough to support Apple production increases rather than simply filling channels. The company reportedly doubled its annual production target after the initial response, although later quarters will reveal how much of Q2 reflected lasting demand rather than launch-period enthusiasm.
Apple also benefits from operating at a scale that provides leverage with memory, storage and semiconductor suppliers. Smaller PC brands face greater difficulty securing components at competitive prices when shortages intensify.
The Q2 contraction arrived after manufacturers and commercial customers accelerated purchases during late 2025 and early 2026.
Buyers expected memory and storage prices to rise, encouraging some businesses to replace computers earlier than planned. Vendors also increased inventories while components remained available.
That activity supported nine quarters of market growth but borrowed demand from later periods. Once the early purchases were completed, the industry faced a more difficult combination of expensive components, cautious consumers and elevated retail prices.
The memory shortage is closely connected to the AI infrastructure boom. Chipmakers are dedicating more production capacity to high-bandwidth memory used by data center accelerators, limiting the supply of conventional DRAM needed for personal computers.
IDC expects little relief before the end of 2027 or early 2028. The research company forecasts an 11.3% decline in worldwide PC shipments across 2026, with the fourth quarter potentially falling 20% from the previous year.
Computer revenue may not decline at the same pace. Manufacturers have raised prices faster than shipments have fallen, allowing them to collect more money from fewer units.
That strategy creates another problem. Retailers and distributors can become trapped with expensive inventory if customers refuse the new prices, forcing promotions or production reductions later.
MacBook Neo initially softened that pressure by giving Apple a new product near the bottom of the notebook market. Its original $599 price made the Mac competitive with midrange Windows machines at the moment many rivals were becoming more expensive.
The strongest Q2 MacBook Neo shipments were recorded while most customers could still buy the computer at its introductory price.
Apple raised the U.S. starting price to $699 in late June as memory and storage costs affected the Mac and iPad lineups. The education price remained at $599, preserving the original offer for eligible students and educators.
A $100 increase changes the comparison with discounted Windows notebooks and places the entry model closer to older MacBook Air configurations available through third-party retailers.
Apple must now determine whether the appeal came primarily from the low launch price or from the combination of macOS, battery life, design and iPhone integration.
The company remains better positioned than many rivals to absorb supply disruption, negotiate long-term component agreements and promote several notebooks at different prices. MacBook Neo also uses an iPhone-class A18 Pro processor rather than an M-series chip, providing another way to manage silicon costs and production capacity.
IDC expects the full PC market to deteriorate during the second half of 2026. Apple will enter that period with almost 10% worldwide share, but the September quarter will be the first to measure MacBook Neo demand almost entirely at the higher $699 retail price.
The post MacBook Neo Defies the Global PC Slump appeared first on AppleMagazine.
Se você procura uma ferramenta completa para ver e editar suas imagens, conheça e veja como instalar o visualizador de imagens XnView MP no Linux via AppImage.
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Today’s 7,600 languages might seem like a lot, but they're just a fraction that survived a vast, ancient extinction.
There's one very stinky reason why Labour's ambitious housebuilding targets are doomed to fail, says Mike Wells: Britain's creaking sewers and drainage systems simply can't cope with all the demands of new homes.
The post There’s One Very Stinky Reason Why Labour’s Housebuilding Targets Are Doomed to Fail appeared first on The Daily Sceptic.
What happens to Reform if, for one reason or another, Nigel Farage exits stage right? In his latest Political Betting column, Mark Littlewood looks at who's waiting in the wings to replace him.
The post Who Next for Reform? appeared first on The Daily Sceptic.
Siri Wolfram Alpha integration helped create one of the defining moments in the early history of consumer artificial intelligence. When Apple introduced Siri with iPhone 4S in October 2011, users were not merely given another form of voice control. They could ask a spoken question and receive a calculated answer rather than a page of search results.
Questions involving mathematics, measurements, dates, nutrition, astronomy and factual comparisons could be routed to Wolfram Alpha, a computational knowledge engine launched in 2009. Siri handled the conversation, while Wolfram Alpha supplied structured information and calculations behind many of the responses.
The experience could feel remarkably advanced for its time. Asking how many days remained before a holiday, comparing the populations of two cities or converting miles into kilometers no longer required opening Safari, entering a query and selecting a website.
That direct path from natural language to a useful answer established an expectation that continues shaping AI products today.
Siri did not begin inside Apple.
Its technological roots extend to research conducted at SRI International, including work associated with the DARPA-funded CALO project. The objective was to develop systems capable of learning, organizing information and helping people complete everyday tasks.
SRI later spun the technology into Siri Inc., founded by a team including Dag Kittlaus, Adam Cheyer and Tom Gruber. The company released a standalone Siri app for iPhone in early 2010.
The original product was designed around actions rather than ordinary web search. A user could ask for a restaurant reservation, request a taxi, check entertainment listings or search for local services. Siri attempted to understand the intent and connect the request with an appropriate online provider.
This approach was ambitious because the assistant needed to coordinate several different systems. Speech had to be converted into text, the request had to be interpreted, context had to be considered and an external service had to return something useful.
Apple acquired Siri in April 2010. Steve Jobs had personally recognized the potential of moving beyond a collection of apps toward a conversational layer capable of reaching them on behalf of the user.
When the assistant returned as an integrated feature of iPhone 4S, Apple had narrowed some aspects of the original startup vision while making the technology accessible to a much larger audience.
Siri could send messages, create reminders, place calls, manage calendar events, check weather conditions and answer questions. Apple initially labeled the feature as a beta, an unusual decision for a prominent part of a new iPhone launch.
The beta label acknowledged that natural-language interaction remained unpredictable. Accents, ambiguous phrasing, network conditions and external data providers could all influence whether a request succeeded.
Siri Wolfram Alpha became especially memorable because it showed the difference between retrieving information and computing an answer.
A conventional search engine could locate pages containing a formula. Wolfram Alpha could apply that formula to the values supplied by the user. It could interpret units, compare quantities, calculate probabilities and organize factual data into a structured response.
That distinction allowed Siri to appear more knowledgeable than a voice interface simply reading search results aloud.
A request such as “What is 15% of $86?” could return the calculation directly. A question about the distance between planets could produce a value without requiring the user to inspect several websites. Wolfram Alpha could also answer unusually specific requests, including information about aircraft flying overhead when the necessary location and aviation data were available.
The system was not a large language model. It did not generate open-ended prose in the way current AI assistants do. Wolfram Alpha relied on curated data, symbolic computation, algorithms and formal representations of knowledge.
That structure provided an advantage that remains relevant: many answers could be calculated and traced through defined information rather than improvised statistically.
Early Siri combined several specialized capabilities instead of depending on one system to do everything. One provider might supply restaurant information, another could handle maps and Wolfram Alpha could process computational questions.
The assistant acted as the conversational coordinator.
This architecture made Siri seem like one intelligence even though the response could come from a network of separate services. Modern AI platforms have returned to a similar idea through tools, agents and external data connections. A language model may manage the conversation while calling a calculator, calendar, search engine or business system when a task requires reliable external action.
Siri demonstrated that model on a consumer smartphone more than a decade earlier.
The Wolfram Alpha partnership influenced how people began judging digital assistants.
Before Siri, voice features on phones were commonly associated with rigid commands such as calling a contact or selecting music. Siri invited users to speak more naturally and attempt questions that would previously have been typed into a browser.
The results were inconsistent, but successful interactions changed perceptions immediately. The iPhone appeared capable of understanding intent, reaching into a knowledge system and returning a concise answer through one conversational interface.
That apparent simplicity concealed substantial infrastructure. Speech recognition, language interpretation, location, personal context and external databases all had to work together quickly enough to preserve the feeling of a conversation.
Wolfram Alpha also gave Apple access to expertise that would have taken years to reproduce internally. Stephen Wolfram had spent decades building Mathematica and computational systems before launching the public knowledge engine.
Following the Siri announcement, Wolfram described the integration as a characteristically direct Steve Jobs decision: users wanted access to knowledge and actions without unnecessary intermediate steps.
That idea became more influential than any individual answer Siri provided.
The assistant did not need to show how many services had participated. It needed to understand the request and return the result.
The first version of Siri could feel futuristic one moment and frustrating the next.
Some questions reached Wolfram Alpha correctly, while similar wording could trigger a web search or an unrelated response. Siri occasionally struggled to determine whether the user wanted a calculation, a local result or an action inside the phone.
Wolfram Alpha responses were also better suited to some subjects than others. Mathematical and scientific questions matched its computational structure, while casual conversation, current events and subjective requests required different sources.
Network dependence created another weakness. Early Siri processing relied heavily on remote servers, so delays or outages could make a basic request fail even when the iPhone itself was functioning normally.
Those limitations foreshadowed the central challenge surrounding modern assistants. A convincing interface encourages people to ask almost anything, but no single technical system performs equally well across every type of request.
Current generative AI can produce more natural language and handle a wider range of topics. It can also create confident errors when factual grounding is weak. The computational approach represented by Wolfram Alpha remains valuable precisely because some questions require verified data and exact calculation rather than fluent speculation.
The story behind the first Siri steps is therefore not only about an old iPhone feature. It shows that a powerful assistant needs more than a conversational personality. It requires access to dependable tools, structured knowledge and the ability to recognize when another system should provide the answer.
Siri Wolfram Alpha offered that lesson in 2011, long before AI assistants became capable of writing essays, interpreting screens and acting across apps.
The post Siri Wolfram Alpha Defined the First AI Assistant appeared first on AppleMagazine.
One task awaiting new energy secretary Miatta Fahnbulleh is devising a plan to avoid a crisis by the end of the decade
Welcome to the energy secretary job, Miatta Fahnbulleh. You have a politically tricky decision to make on whether to approve production at the Jackdaw gasfield and the Rosebank oilfield. But here’s another one that can’t wait much longer: what is the broader plan to ensure Britain avoids a gas supply crisis in 2030?
By way of reminder, one of the more alarming reports published by the energy department under Ed Miliband was the one that warned of “an emerging risk” to gas supplies at the turn of the decade if an important piece of kit – an import terminal, say, or the critical 725-mile Langeled pipeline from Norway – were to be out of the action during a severe cold snap.
Continue reading...
© Photograph: PA Images/Alamy
© Photograph: PA Images/Alamy
© Photograph: PA Images/Alamy
SpamSieve 3.3.1 is an update of my Mac e-mail spam filter that improves the filtering accuracy and includes various optimizations, fixes, and updates for macOS 27. Some interesting engineering issues were:
I’m still supporting macOS 10.13, but the app can now build with the macOS 12 deployment target in preparation for Xcode 27. The way Swift handles calling deprecated APIs remains annoying. You need workarounds like protocols to suppress a warning in a particular region of code. Often, I am updating to a newer API, but I want to continue using the old one on earlier versions of macOS. Right now I am heavily testing on macOS 26, so I would like to only deploy the new code there. Sometimes the initial versions of APIs are buggy, and I don’t want to have to retest every feature on every version. But Swift doesn’t like me to gate the calls to macOS 26 and later if the new API was actually introduced in, say, macOS 11.
The most risky update was probably switching to the Swift API for Scanner. This is tricky because the current scan position changes from being based on UTF-16 indexes to String.Index. And in some cases my code was intentionally scanning into the middle of a combining character. I was really glad to have extensive tests here.
Recent versions of Tahoe introduced a Core Data bug that broke save recovery (when invalid Unicode can’t be written to the database and we have to fix it up first). When the error comes in, SpamSieve looks under the NSDetailedErrorsKey key, but the value of this key is "NSDetailedErrorsKey" while the actual error info is under the key "NSDetailedErrors".
Database corruption combined with a Core Data bug and two Swift behaviors were causing crashes. The root problem, I think, is that in this situation Core Data neglects to set the error pointer for -[NSManagedObjectContext countForFetchRequest:error:]. Normally, Swift would detect this and throw a generic nilError. However, this method signals errors by returning NSNotFound instead of nil. It’s annotated as swift_error(nonnull_error), which I think means that Swift doesn’t even look at the return value, only the (unset) error. So my code was receiving NSNotFound on the non-error path, treating it as a real count, and doing some math with it, which (by default in Swift) leads to a crash if there’s overflow. Now my code checks for NSNotFound and uses Int.addingReportingOverflow().
I reimplemented Core Data batch fetching because of two problems with the fetchBatchSize option. First, it logs spurious errors when fetching from multiple stores at once. Second, it leads to high memory use. It will load objects in batches, but it never releases the earlier batches. You can manually refault objects that you no longer need, but it will still hold onto the full object shells, which can add up. My implementation only holds onto IDs, and it can fetch the IDs using a different context than the objects to push more work to background threads.
I’m now using String.makeContiguousUTF8() in various places to avoid slow iteration of bridged strings.
There was a regression where some menu code could crash on macOS 10.13. -[NSMenu setItemArray:] is only available on macOS 10.14 and later, whereas the getter is always available. With old Objective-C, the two methods could be annotated with separate availabilities. The Objective-C property syntax doesn’t allow this, so there’s no way for the compiler to flag calls where the setter is missing.
The AppleScript support works much better with large data sets. The key, I think, is to never return an array to Cocoa for a scripting property/element. First, if you give it a lazy array it will sometimes try to access all the elements even if it only needs a few. And, second, AppleScript will run out of memory if the array is too large. By using indexed accessors, you can maintain laziness and only convert one object to a descriptor at a time. By caching a batch-fetching collection, you can limit the number of fetches and respond quickly when AppleScript requests individual objects.
Previously:
In macOS 28 and later, the Mac OS Extended file system format will be supported only for volumes (disks and other storage devices) that aren’t encrypted. For future macOS compatibility, either decrypt or reformat any encrypted Mac OS Extended volumes.
I think Apple removed the user interface for creating such volumes way back in Big Sur. It’s not really surprising that they’re going away, although only offering a year of warnings seems rather aggressive. Many people delay updating to major OS versions or wait until they get a new Mac. There will be users that switch to a Mac that can only run macOS 28 and get rid of their old one before realizing that some drive they haven’t connected in a while won’t mount.
The other surprising part is that Apple’s options—reformat or decrypt—are not great. During the initial transition to APFS, macOS would transparently convert an encrypted HFS+ boot volume to encrypted APFS without rewriting any data. This functionality is nominally still available via the diskutil apfs convert command, but Apple hasn’t mentioned this or built an automatic upgrader GUI.
[Decrypting] doesn’t apply to encrypted Time Machine backup disks.
It’s obvious why. The old Time Machine format with hard links is totally different from the new one based on APFS snapshots. But this means that you’ll either need to buy a new Time Machine drive for use with macOS 28 or delete your backup history. Even if you keep the old drive, you’ll need an older boot drive or an old Mac in order to be able to access it.
Previously:
Update (2026-07-24): Marcus Mendes:
Apple has now revised the page to add a third option, allowing users to convert an encrypted HFS+ volume directly to encrypted APFS without decrypting it first.
[…]
The company has also removed its previous instructions for decrypting the volume first, converting it to APFS without erasing it, and then optionally re-encrypting it.
Ignore Apple’s guidance and do not use Convert to APFS in Disk Utility for disk images.
The first encrypted HFS+ disk image I tried to convert that way failed and left me with an unusable disk image. If I didn’t have backups I’d have lost its contents.
It didn’t corrupt the disk images that I tried, but it didn’t work, either, instead failing with an error about expecting a GPT partitioning scheme.
Update (2026-07-28): Howard Oakley:
As encrypted disk images have never used CoreStorage, withdrawal of support for that has no impact on the continued use of any form of encrypted disk image, including those using HFS+ as their virtual file system.
This is a good point. Apple’s support page refers to “encrypted Mac OS Extended disks,” but the developer release notes refer to CoreStorage specifically. So this shouldn’t affect most encrypted disk images, which show up in Finder as unencrypted after mounting. The encryption is at the disk image level rather than at the file system level. However, I’m not sure that all encrypted disk images are safe. Presumably, a device image, which directly copies the blocks rather than the files, would still use CoreStorage.
Apple’s challenge against the EU’s designation of its App Stores and iOS platform as “gatekeepers” was dismissed by Europe’s top court on Wednesday, reports Reuters.
[…]
The company also disputed the classification of iMessage as a number-independent interpersonal communications service, or NIICS, which would subject the app to EU telecoms rules. But the General Court said Apple’s actions regarding the iMessage service are inadmissible.
Back in 2023, Apple acknowledged that it qualified under the then-new definition of being what the EU called a gatekeeper service. But key to that is the size of the operation, and since 2024, Apple has been claiming it runs five smaller App Stores rather than one large one.
[…]
“Irrespective of the devices in question,” ruled the judges, “those stores have the same purpose, namely to connect app developers with end users in order to facilitate the distribution of software applications.”
Apple disagreed with the decision but didn’t say yet if it would appeal. “We firmly believe the DMA’s mandate goes beyond what is lawful and proportionate, threatening to erode decades of privacy and security protections we’ve built and leaving our users vulnerable to new risks,” an Apple spokesperson said in a statement to multiple outlets. “We will continue advocating for the innovation and privacy our European customers deserve.”
Apple still has two cases pending with EU courts. The first is a challenge to the EU Commission’s decision last year forcing Apple to open iOS to third-party developers, and the second is an appeal against the €500 million fine imposed in April last year for anti-steering violations.
Previously:
The USB device drivers report that they are destroying the “Rocket XTRM Q” device due to a “link change interrupt”
APFS filesystem drivers report a “dangling mount”, failures to finish transactions before unmount, and dozens of “media is not present” errors.[…]
storagekit collects a report of the “disappeared” devices and notices that one of them was mounted. This is ultimately what leads to the “Disk Not Ejected Properly” notification.
Unfortunately, nothing in here explains what caused the “link change interrupt”; all we know is that the USB device drivers detected an interruption.
Not discussed here, perhaps because there’s little we can do about it, is the role of changes to macOS. I’ve documented problems in the past of hard drives, which used to work perfectly, suddenly start spontaneously unmounting with every backup after updating to a new OS version. The Sierra to Big Sur period was particularly bad, as I recall. (There are also some hard drive enclosures, and cheap SSDs, that seem to be problematic to matter the OS version, but I don’t continue to use those.) Sometime around Monterey or Ventura, the spontaneous unmounts almost completely disappeared for me, though Ventura introduced other mounting/unmounting problems. Last week, I finally updated to Tahoe, and I’m sad to say that spontaneous unmounts are back. Almost every night, one of my Samsung SSDs—connected to a Thunderbolt dock—gets reported as improperly ejected. Sometimes it remounts by itself; other times I have to unplug and replug it.
Previously:
Update (2026-07-09): Rosyna Keller:
I keep getting these errors too. It only started when I connected the external SSD to a OWC Thunderbolt dock. It never happened with direct connect.
My Thunderbolt dock is from Anker.
Update (2026-07-10): Matt:
I also just stared getting these a bunch. Mine is connected to a CalDigit Thunderbolt dock.
I have to wonder if something changed in a recent update (15.7/26.4?)
macOS 27 Golden Gate Beta 3 Release Notes:
Apple Intelligence for Home requires an iCloud+ subscription starting at 2TB.
Via Rajesh Pandey:
As part of the upgrade, the Home app uses AI to generate summaries of motion alerts from compatible HomeKit Secure Video cameras.
[…]
Apple likely requires the 2TB iCloud+ tier because HomeKit Secure Video already relies on iCloud+ storage tiers. The 2TB plan supports a larger number of cameras, and AI-generated video descriptions would add to Apple’s cloud processing costs.
That makes sense, but it seems odd because the iCloud tiers are named based on storage amounts, yet the feature isn’t tied to how much space you’re actually using. A lower iCloud plan with tons of free space doesn’t get the features, yet a nearly full 2 TB plan does. There are already restrictions where supporting more cameras requires higher plans, but that is at least quasi related to storage.
I reluctantly upgraded to a 2 TB family plan within the last year or so. We don’t store many files in iCloud, messages are set to auto-delete after a year, and I manually offload photos older than a year (except for hearted favorites). It’s hard to believe given how I remember the original 5 GB free tier once being sufficient, but staying under 200 GB became untenable without a lot of extra work. It doesn’t help that Live Photos sometimes gets inadvertently turned on, so that we’re storing all these little videos without realizing it.
Previously:
ASIF takes a lot of inspiration from existing virtual disk formats. Practically, that means it’s another sparse virtual disk format, and functions very similar to sparse VMDK, VHDX or QCOW2 files (for the uninitiated, it allow you to store a large disk, or file, in a smaller, “sparse” manner).
Shortly before the release of macOS Tahoe (late 2025), I thought it’d be a fun exercise to try and write a parser for ASIF files.
Previously:
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