Apple researchers introduce SeedLM, a novel approach to drastically compress large language model (LLM) weights. Instead of storing massive parameter sets, SeedLM generates them from a much smaller "seed" using a pseudo-random number generator (PRNG). This seed, along with the PRNG algorithm, effectively encodes the entire model, enabling significant storage savings. While SeedLM models trained from scratch achieve comparable performance to standard models of similar size, adapting pre-trained LLMs to this seed-based framework remains a challenge, resulting in performance degradation when compressing existing models. This research explores the potential for extreme LLM compression, offering a promising direction for more efficient deployment and accessibility of powerful language models.
This blog post explores the architecture and evolution of Darwin, Apple's open-source operating system foundation, and its XNU kernel. It explains how Darwin, built upon the Mach microkernel, incorporates components from BSD and Apple's own I/O Kit. The post details the hybrid kernel approach of XNU, combining the message-passing benefits of a microkernel with the performance advantages of a monolithic kernel. It discusses key XNU subsystems like the process manager, memory manager, file system, and networking stack, highlighting the interplay between Mach and BSD layers. The post also traces Darwin's history, from its NeXTSTEP origins through its evolution into macOS, iOS, watchOS, and tvOS, emphasizing the platform's adaptability and performance.
Hacker News users generally praised the article for its clarity and depth in explaining a complex topic. Several commenters with kernel development experience validated the information presented, noting its accuracy and helpfulness for understanding the evolution of XNU. Some discussion arose around specific architectural choices made by Apple, including the Mach microkernel and its interaction with the BSD environment. One commenter highlighted the performance benefits of the hybrid kernel approach, while others expressed interest in the challenges of maintaining such a system. A few users also pointed out areas where the article could be expanded, such as delving further into I/O Kit details and exploring the security implications of the XNU architecture.
The US National Labor Relations Board (NLRB) has paused two cases against Apple involving alleged retaliation and suppression of union activity. This follows President Biden's appointment of Gwynne Wilcox, a lawyer representing a group accusing Apple of labor violations in one of the cases, to a key NLRB position. To avoid a conflict of interest, the NLRB’s general counsel has withdrawn from the cases until Wilcox is officially confirmed and recuses herself. This delay could impact the timing and outcome of the cases.
HN commenters discuss potential conflicts of interest arising from Gwynne Wilcox's appointment to the NLRB, given her prior involvement in cases against Apple. Some express concern that this appointment could influence future NLRB decisions, potentially favoring unions and hindering Apple's defense against unfair labor practice allegations. Others argue that recusal policies exist to mitigate such conflicts and that Wilcox's expertise is valuable to the board. A few commenters note the broader implications for labor relations and the increasing power of unions, with some suggesting this appointment reflects a pro-union stance by the current administration. The discussion also touches upon the specifics of the Apple cases, including allegations of coercive statements and restrictions on union organizing. Several commenters debate the merits of these allegations and the overall fairness of the NLRB's processes.
Apple's "Cubify Anything" introduces a new approach to 3D object detection within indoor scenes using monocular RGB images. It leverages a pre-trained 2D object detector to identify objects and then fits a cuboid to each detected object by estimating its 3D pose and dimensions. This method, dubbed "cubification," efficiently generates dense 3D models of indoor environments, suitable for applications like augmented reality and scene understanding. The approach simplifies the 3D detection pipeline by directly predicting cuboids instead of complex meshes or point clouds, enabling real-time performance on mobile devices. Importantly, Cubify Anything is designed to work on diverse indoor scenes without requiring specific training data for each scene.
Hacker News users discussed Apple's Cubify research, expressing excitement about its potential applications in AR/VR and robotics. Some questioned the practical use cases given the computational demands, suggesting mobile deployment would be challenging. Several commenters compared it to existing 3D modeling techniques like NeRF, noting Cubify's focus on cuboid representations might offer advantages in certain scenarios, like robot manipulation. There was also interest in the dataset used for training and the possibility of open-sourcing it. Finally, some users expressed skepticism about Apple's history of releasing research code, while others countered that their recent track record had improved.
The article explores a peculiar editing choice in Apple TV+'s Severance. Specifically, it highlights how scenes depicting remote desktop software usage were altered, seemingly to avoid showcasing specific brands or potentially revealing internal Apple practices. Instead of realistic depictions of screen sharing or remote access, the show uses stylized and somewhat nonsensical visuals, which the article suggests might stem from Apple's desire to maintain a controlled image and avoid any unintended associations with its own internal tools or workflows. This meticulous control, while potentially preserving Apple's mystique, ends up creating a slightly distracting and unrealistic portrayal of common workplace technology.
HN commenters discuss the plausibility and implications of the remote editing process depicted in Severance. Some doubt the technical feasibility or efficiency of using remote desktop software for high-end video editing, especially given Apple's own ecosystem. Others suggest it's a commentary on corporate surveillance and control, reflecting real-world trends of employee monitoring. A few commenters highlight the show's satirical nature, arguing that the implausibility is intentional and serves to underscore the dystopian themes. The most compelling comments analyze the remote editing as a metaphor for the detachment and alienation of modern work, where employees are increasingly treated as interchangeable cogs. Several also appreciate the attention to detail in the show's depiction of outdated or quirky software, viewing it as a realistic portrayal of how legacy systems persist in large organizations. A minority of comments focus on the legal and ethical questions raised by the severance procedure itself.
Apple has reorganized its AI leadership, aiming to revitalize Siri and accelerate AI development. John Giannandrea, who oversaw Siri and machine learning, is now focusing solely on a new role leading Apple's broader machine learning strategy. Craig Federighi, Apple's software chief, has taken direct oversight of Siri, indicating a renewed focus on improving the virtual assistant's functionality and integration within Apple's ecosystem. This restructuring suggests Apple is prioritizing advancements in AI and hoping to make Siri more competitive with rivals like Google Assistant and Amazon Alexa.
HN commenters are skeptical of Apple's ability to significantly improve Siri given their past performance and perceived lack of ambition in the AI space. Several point out that Apple's privacy-focused approach, while laudable, might be hindering their AI development compared to competitors who leverage more extensive data collection. Some suggest the reorganization is merely a PR move, while others express hope that new leadership could bring fresh perspective and revitalize Siri. The lack of a clear strategic vision from Apple regarding AI is a recurring concern, with some speculating that they're falling behind in the rapidly evolving generative AI landscape. A few commenters also mention the challenge of attracting and retaining top AI talent in the face of competition from companies like Google and OpenAI.
Apple's imposed limitations hinder the Pebble smartwatch's functionality on iPhones. Features like interactive notifications, sending canned replies, and using the microphone for dictation or voice notes are blocked by Apple's restrictive APIs. While Pebble can display notifications, users can't interact with them directly from the watch, forcing them to pull out their iPhones. This limited integration significantly diminishes the Pebble's usability and convenience for iPhone users, compared to the Apple Watch which enjoys full access to iOS features. The author argues that these restrictions are intentionally imposed by Apple to stifle competition and promote their own smartwatch.
HN commenters largely agree with the author's premise that Apple intentionally crippled Pebble's functionality on iOS. Several users share anecdotes of frustrating limitations, like the inability to reply to messages or use location services effectively. Some point out that Apple's MFi program, while ostensibly about quality control, serves as a gatekeeping mechanism to stifle competition. Others discuss the inherent tension between a closed ecosystem like Apple's and open platforms, noting that Apple prioritizes its own products and services, even if it means a degraded experience for users of third-party devices. A few commenters suggest the limitations are technically unavoidable, but this view is largely dismissed by others who cite examples of better integration on Android. There's also cynicism about Apple's purported security and privacy concerns, with some suggesting these are merely pretexts for anti-competitive behavior.
macOS historically handled null pointer dereferences by trapping them, leading to immediate application crashes. This was achieved by mapping the first page of virtual memory to an inaccessible region. Over time, increasing demands for performance, especially from Java, prompted Apple to introduce "guarded pages" in macOS 10.7 (Lion). This optimization allowed for a small window of usable memory at address zero, improving performance for frequently checked null references but introducing the risk of silent memory corruption if a true null pointer dereference occurred. While efforts were made to mitigate these risks, the behavior shifted again in macOS 12 (Monterey) and later ARM-based systems, where the entire page at zero became usable. This means null pointer dereferences now consistently result in memory corruption, potentially leading to more difficult-to-debug issues.
Hacker News users discussed the nuances of null pointer dereferences on macOS and other systems. Some highlighted that the behavior described (where dereferencing a NULL pointer doesn't always crash) isn't unique to macOS and stems from virtual memory page zero being unmapped. Others pointed out the security implications, particularly in the kernel, where such behavior could be exploited. Several commenters mentioned the trade-off between debugging ease (catching null pointer dereferences early) and performance (the overhead of checking for null every time). The history of this design choice and its evolution in different macOS versions was also a topic of conversation, along with comparisons to other operating systems' handling of null pointers. One commenter noted the irony of Apple moving away from this behavior, as it was initially designed to make things less crashy. The utility of tools like scribble
for catching such errors was also mentioned.
SheepShaver is a free and open-source emulator that allows you to run classic PowerPC Mac OS versions (from 7.5.2 up to 9.0.4) on modern macOS, Windows, and Linux systems. It requires a ROM image from a compatible Mac model to function and offers good performance for many older Mac applications and games. While support for newer macOS versions relies on community patches, SheepShaver remains a viable option for revisiting classic Mac software.
Commenters on Hacker News express nostalgia for classic Mac OS and discuss their experiences using SheepShaver. Some highlight its speed and compatibility, even on low-powered hardware like the Raspberry Pi. Others reminisce about specific games and software that ran well on the emulator. A few users mention the limitations of emulating older systems and suggest alternative emulators like Basilisk II for 68k Macs. Some discuss the legal gray area of ROM acquisition, essential for running SheepShaver. The thread also touches on the challenges of preserving old software and hardware, as well as the ongoing interest in retro computing.
A hidden recovery partition, dating back to 1994 and designed for Apple's Quadra 630, has been discovered. This partition, previously unknown, contains a mini operating system and tools intended to help users recover their system in case of corruption. Located on a small sliver of the hard drive, it was found by a user who meticulously examined disk images and realized its purpose. This "mini OS" is bootable and provides utilities for repairing directory damage and other system issues, offering a fascinating glimpse into early Apple recovery methods.
Hacker News users discuss the practicality and historical significance of the discovered System 7.5 recovery partition. Several commenters note the small size of the partition (20MB) reflects the much smaller software of the time, with one pointing out a full System 7.5 install was only 50MB. Others delve into the technical aspects, discussing how the partition was hidden and the methods used to access it. Some express skepticism about the "lost" nature of the partition, suggesting it was simply undocumented rather than unknown. The overall sentiment is one of mild interest in this piece of Apple history, with a touch of nostalgia and some technical curiosity about retro computing.
Apple is reportedly planning to add support for encrypted Rich Communication Services (RCS) messaging between iPhones and Android devices. This means messages, photos, and videos sent between the two platforms will be end-to-end encrypted, providing significantly more privacy and security than the current SMS/MMS system. While no official timeline has been given, the implementation appears to be dependent on Google updating its Messages app to support encryption for group chats. This move would finally bring a modern, secure messaging experience to cross-platform communication, replacing the outdated SMS standard.
Hacker News commenters generally expressed skepticism about Apple's purported move towards supporting encrypted RCS messaging. Several doubted Apple's sincerity, suggesting it's a PR move to deflect criticism about iMessage lock-in, rather than a genuine commitment to interoperability. Some pointed out that Apple benefits from the "green bubble" effect, which pressures users to stay within the Apple ecosystem. Others questioned the technical details of Apple's implementation, highlighting the complexities of key management and potential vulnerabilities. A few commenters welcomed the move, though with reservations, hoping it's a genuine step toward better cross-platform messaging. Overall, the sentiment leaned towards cautious pessimism, with many anticipating further "Apple-style" limitations and caveats in their RCS implementation.
Pressure is mounting on the UK Parliament's Intelligence and Security Committee (ISC) to hold its hearing on Apple's data privacy practices in public. The ISC plans to examine claims made in a recent report that Apple's data extraction policies could compromise national security and aid authoritarian regimes. Privacy advocates and legal experts argue a public hearing is essential for transparency and accountability, especially given the significant implications for user privacy. The ISC typically operates in secrecy, but critics contend this case warrants an open session due to the broad public interest and potential impact of its findings.
HN commenters largely agree that Apple's argument for a closed-door hearing regarding data privacy doesn't hold water. Several highlight the irony of Apple's public stance on privacy conflicting with their desire for secrecy in this legal proceeding. Some express skepticism about the sincerity of Apple's privacy concerns, suggesting it's more about competitive advantage. A few commenters suggest the closed hearing might be justified due to legitimate technical details or competitive sensitivities, but this view is in the minority. Others point out the inherent conflict between national security and individual privacy, noting that this case touches upon that tension. A few express cynicism about government overreach in general.
The author argues that Apple products, despite their walled-garden reputation, function as "exclaves" – territories politically separate from the main country/OS but economically and culturally tied to it. While seemingly restrictive, this model allows Apple to maintain tight control over hardware and software quality, ensuring a consistent user experience. This control, combined with deep integration across devices, fosters a sense of premium quality and reliability, which justifies higher prices and builds brand loyalty. This exclave strategy, while limiting interoperability with other platforms, strengthens Apple's ecosystem and ultimately benefits users within it through a streamlined and unified experience.
Hacker News users discuss the concept of "Apple Exclaves" where Apple services are tightly integrated into non-Apple hardware. Several commenters point out the irony of Apple, known for its "walled garden" approach, now extending its services to other platforms. Some speculate this is a strategic move to broaden their user base and increase service revenue, while others are concerned about the potential for vendor lock-in and the compromise of user privacy. The discussion also explores the implications for competing platforms and whether this approach will ultimately benefit or harm consumers. A few commenters question the author's premise, arguing that these integrations are simply standard business practices, not a novel strategy. The idea that Apple might be intentionally creating a hardware-agnostic service layer to further cement its market dominance is a recurring theme.
1984 saw the rise of networked filesystems like NFS, which offered performance comparable to local filesystems, and the introduction of the Andrew File System (AFS), designed for large-scale distributed environments with client-side caching and whole-file serving. Research focused on improving performance and reliability, with log-structured filesystems like LFS emerging to optimize write operations. Additionally, the standardization of file systems continued, with work on the ISO 9660 standard for CD-ROMs solidifying the format's widespread adoption. This year highlighted the increasing importance of networking and the evolving demands placed upon file systems for both performance and portability.
The Hacker News comments discuss the blog post's focus on the early days of networked filesystems, particularly NFS. Several commenters share their own experiences with early NFS, highlighting its initial slow performance and eventual improvements. Some discuss the influence of Sun Microsystems and the rise of distributed systems. Others delve into technical details like caching, consistency models, and the challenges of implementing distributed locks. A few comments compare NFS to other contemporary filesystems and contemplate the enduring relevance of some of the challenges faced in the 1980s. There's a general appreciation for the historical perspective offered by the blog post.
The blog post urges Apple to implement disappearing messages in iMessage, arguing it's a crucial privacy feature already offered by competitors like Signal and WhatsApp. The author emphasizes that ephemerality is essential for protecting user privacy against device seizure, data breaches, and unwanted surveillance, citing real-world scenarios where sensitive information shared via iMessage has been exposed. They highlight the inherent risk of permanent message storage and propose that Apple offer user-configurable expiration times, similar to existing self-destructing media features. This would empower users to control the lifespan of their messages and minimize the potential for misuse or unintended exposure.
Hacker News users generally supported the idea of ephemeral messages in iMessage, citing privacy benefits and the existing precedent set by other messaging platforms. Some commenters raised concerns about the potential for misuse, particularly regarding evidence preservation in legal cases or investigations. Others discussed technical implementation details, questioning the reliability and security of such a feature, and suggesting potential solutions like server-side deletion or client-side cryptography. A few pointed out Apple's historical resistance to features perceived as hindering law enforcement access to data, speculating that this might be a factor in the absence of ephemeral messaging in iMessage. Finally, some questioned the effectiveness of disappearing messages given the possibility of screenshots and screen recordings.
The UK's National Cyber Security Centre (NCSC), along with GCHQ, quietly removed official advice recommending the use of Apple's device encryption for protecting sensitive information. While no official explanation was given, the change coincides with the UK government's ongoing push for legislation enabling access to encrypted communications, suggesting a conflict between promoting security best practices and pursuing surveillance capabilities. This removal raises concerns about the government's commitment to strong encryption and the potential chilling effect on individuals and organizations relying on such advice for data protection.
HN commenters discuss the UK government's removal of advice recommending Apple's encryption, speculating on the reasons. Some suggest it's due to Apple's upcoming changes to client-side scanning (now abandoned), fearing it weakens end-to-end encryption. Others point to the Online Safety Bill, which could mandate scanning of encrypted messages, making previous recommendations untenable. A few posit the change is related to legal challenges or simply outdated advice, with Apple no longer being the sole provider of strong encryption. The overall sentiment expresses concern and distrust towards the government's motives, with many suspecting a push towards weakening encryption for surveillance purposes. Some also criticize the lack of transparency surrounding the change.
Apple is challenging a UK court order demanding they create a "backdoor" into an encrypted iPhone belonging to a suspected terrorist. They argue that complying would compromise the security of all their devices and set a dangerous precedent globally, potentially forcing them to create similar backdoors for other governments. Apple claims the Investigatory Powers Act, under which the order was issued, doesn't authorize such demands and violates their human rights. They're seeking judicial review of the order, arguing existing tools are sufficient for the investigation.
HN commenters are largely skeptical of Apple's claims, pointing out that Apple already complies with lawful intercept requests in other countries and questioning whether this case is truly about a "backdoor" or simply about the scope and process of existing surveillance capabilities. Some suspect Apple is using this lawsuit as a PR move to bolster its privacy image, especially given the lack of technical details provided. Others suggest Apple is trying to establish legal precedent to push back against increasing government surveillance overreach. A few commenters express concern over the UK's Investigatory Powers Act and its implications for privacy and security. Several highlight the inherent conflict between national security and individual privacy, with no easy answers in sight. There's also discussion about the technical feasibility and potential risks of implementing such a system, including the possibility of it being exploited by malicious actors.
The MacBook Air with the M2 chip boasts all-day battery life and impressive performance in a thin, fanless design. Available in four finishes, it features a stunning 13.6-inch Liquid Retina display, a 1080p FaceTime HD camera, and a powerful 8-core CPU. The M2 chip also allows for fast graphics performance, ideal for gaming and demanding applications. Configurations offer up to 24GB of unified memory and up to 2TB of SSD storage. It also includes MagSafe charging, two Thunderbolt ports, and a headphone jack.
HN commenters generally praise the new MacBook Air M4, particularly its performance and battery life. Several note the significant performance increase over the M1 and Intel-based predecessors, with some claiming it's the best value laptop on the market. A few express disappointment about the lack of a higher refresh rate display and the return of the MagSafe charging port, viewing the latter as taking up a valuable Thunderbolt port. Others question the need for the notch, though some defend it as unobtrusive. Price is a recurring theme, with many acknowledging its premium but arguing it's justified given the performance and build quality. There's also discussion around the base model's SSD performance being slower than the M1, attributed to using a single NAND chip instead of two. Despite these minor criticisms, the overall sentiment is highly positive.
Apple announced the new Mac Studio, claiming it's their most powerful Mac yet. It's powered by the M2 Max chip, offering significant performance boosts over the previous generation for demanding workflows like video editing and 3D rendering. The Mac Studio also features extensive connectivity options, including HDMI, Thunderbolt 4, and 10Gb Ethernet. It's designed for professional users who need a compact yet incredibly powerful desktop machine.
HN commenters generally expressed excitement but also skepticism about Apple's "most powerful" claim. Several questioned the value proposition, noting the high price and limited upgradeability compared to building a similarly powerful PC. Some debated the target audience, suggesting it was aimed at professionals needing specific macOS software or those prioritizing a polished ecosystem over raw performance. The lack of GPU upgrades and the potential for thermal throttling were also discussed. Several users expressed interest in benchmarks comparing the M4 Max to competing hardware, while others pointed out the quiet operation as a key advantage. Some comments lamented the loss of user-serviceability and upgradability that characterized older Macs.
Apple announced the M3 Ultra, its most powerful chip yet. Built using a second-generation 3nm process, the M3 Ultra boasts up to 32 high-performance CPU cores, up to 80 graphics cores, and a Neural Engine capable of 32 trillion operations per second. This new SoC offers a substantial performance leap over the M2 Ultra, with up to 20% faster CPU performance and up to 30% faster GPU performance. The M3 Ultra also supports up to 192GB of unified memory, enabling professionals to work with massive datasets and complex workflows. The chip is available in new Mac Studio and Mac Pro configurations.
HN commenters generally express excitement, but with caveats. Many praise the performance gains, particularly for video editing and other professional workloads. Some express concern about the price, questioning the value proposition for average users. Several discuss the continued lack of upgradability and repairability in Macs, with some arguing that this limits the lifespan and ultimate value of the machines. Others point out the increasing reliance on cloud services and subscription models that accompany Apple's hardware. A few commenters express skepticism about the claimed performance figures, awaiting independent benchmarks. There's also some discussion of the potential impact on competing hardware manufacturers, particularly Intel and AMD.
Eliseo Martelli's blog post argues that Apple's software quality has declined, despite its premium hardware. He points to increased bugs, regressions, and a lack of polish in recent macOS and iOS releases as evidence. Martelli contends that this decline stems from factors like rapid feature iteration, prioritizing marketing over engineering rigor, and a potential shift in internal culture. He ultimately calls on Apple to refocus on its historical commitment to quality and user experience.
HN commenters largely agree with the author's premise that Apple's software quality has declined. Several point to specific examples like bugs in macOS Ventura and iOS, regressions in previously stable features, and a perceived lack of polish. Some attribute the decline to Apple's increasing focus on services and new hardware at the expense of refining existing software. Others suggest rapid feature additions and a larger codebase contribute to the problem. A few dissenters argue the issues are overblown or limited to specific areas, while others claim that software quality is cyclical and Apple will eventually address the problems. Some suggest the move to universal silicon has exacerbated the problems, while others point to the increasing complexity of software as a whole. A few comments mention specific frustrations like poor keyboard shortcuts and confusing UI/UX choices.
This blog post offers a collection of macOS tips and tricks to enhance productivity and user experience. It covers various aspects of the operating system, from basic shortcuts like quickly hiding all other applications (⌘⌥H) to more advanced techniques involving the terminal and shell scripting. The post highlights features such as using the Preview app for quick image edits, leveraging Quick Look for file previews and actions, customizing the Dock and menu bar, and employing keyboard shortcuts for various tasks. It also emphasizes the power of the Terminal for automating actions and managing system settings, and recommends several useful third-party applications to further improve workflow.
HN users generally praised the macOS tips listed in the article, finding them useful and well-organized. Several commenters highlighted specific tips they appreciated, such as using keyboard shortcuts for moving windows between monitors, the "Say" command for text-to-speech, and the ability to paste rich text into plain text fields with a modified paste command. Some users shared additional tips of their own, including using Automator for repetitive tasks and leveraging specific terminal commands. A few questioned the necessity of some of the listed "tricks," suggesting they are standard macOS features. Overall, the discussion revolved around the practicality of the tips and expanding upon the list with further macOS productivity enhancements.
A new Safari extension allows users to set ChatGPT as their default search engine. The extension intercepts search queries entered in the Safari address bar and redirects them to ChatGPT, providing a conversational AI-powered search experience directly within the browser. This offers an alternative to traditional search engines, leveraging ChatGPT's ability to synthesize information and respond in natural language.
Hacker News users discussed the practicality and privacy implications of using a ChatGPT extension as a default search engine. Several questioned the value proposition, arguing that search engines are better suited for information retrieval while ChatGPT excels at generating text. Privacy concerns were raised regarding sending every search query to OpenAI. Some commenters expressed interest in using ChatGPT for specific use cases, like code generation or creative writing prompts, but not as a general search replacement. Others highlighted potential benefits, like more conversational search results and the possibility of bypassing paywalled content using ChatGPT's summarization abilities. The potential for bias and manipulation in ChatGPT's responses was also mentioned.
Apple announced a plan to invest over $500 billion in the US economy over the next four years. This builds on the $430 billion contributed over the previous five years and includes direct spending with US suppliers, data center expansions, capital expenditures in US manufacturing, and investments in American jobs and innovation. The company highlights key areas like 5G innovation and silicon engineering, as well as supporting emerging technologies. Apple's commitment extends beyond its own operations to include investments in next-generation manufacturing and renewable energy projects across the country.
Hacker News commenters generally expressed skepticism about Apple's announced $500B investment. Several pointed out that this is not new spending, but a continuation of existing trends, repackaged as a large number for PR purposes. Some questioned the actual impact of this spending, suggesting much of it will go towards stock buybacks and dividends rather than job creation or meaningful technological advancement. Others discussed the potential influence of government incentives and tax breaks on Apple's decision. A few commenters highlighted Apple's reliance on Asian manufacturing, arguing that true investment in the US would involve more domestic production. Overall, the sentiment leaned towards viewing the announcement as primarily a public relations move rather than a substantial shift in Apple's business strategy.
Apple announced a plan to invest $430 billion in the US economy over five years, creating 20,000 new jobs. This investment will focus on American-made components for its products, including a new line of AI servers. The company also highlighted its commitment to renewable energy and its growing investments in silicon engineering, 5G innovation, and manufacturing.
Hacker News users discuss Apple's announcement with skepticism. Several question the feasibility of Apple producing their own AI servers at scale, given their lack of experience in this area and the existing dominance of Nvidia. Commenters also point out the vagueness of the announcement, lacking concrete details on the types of jobs created or the specific AI applications Apple intends to pursue. The large $500 billion figure is also met with suspicion, with some speculating it includes existing R&D spending repackaged for a press release. Finally, some express cynicism about the announcement being driven by political motivations related to onshoring and subsidies, rather than genuine technological advancement.
Apple has removed its iCloud Advanced Data Protection feature, which offers end-to-end encryption for almost all iCloud data, from its beta software in the UK. This follows reported concerns from the UK's National Cyber Security Centre (NCSC) that the enhanced security measures would hinder law enforcement's ability to access data for investigations. Apple maintains that the feature will be available to UK users eventually, but hasn't provided a clear timeline for its reintroduction. While the feature remains available in other countries, this move raises questions about the balance between privacy and government access to data.
HN commenters largely agree that Apple's decision to pull its child safety features, specifically the client-side scanning of photos, is a positive outcome. Some believe Apple was pressured by the UK government's proposed changes to the Investigatory Powers Act, which would compel companies to disable security features if deemed a national security risk. Others suggest Apple abandoned the plan due to widespread criticism and technical challenges. A few express disappointment, feeling the feature had potential if implemented carefully, and worry about the implications for future child safety initiatives. The prevalence of false positives and the potential for governments to abuse the system were cited as major concerns. Some skepticism towards the UK government's motivations is also evident.
A US federal judge invalidated a key patent held by Omni MedSci related to non-invasive blood glucose monitoring. This ruling potentially clears a significant obstacle for companies like Apple, who are reportedly developing similar technology for devices like the Apple Watch. The invalidated patent covered a method of using light to measure glucose levels, a technique believed to be central to Apple's rumored efforts. This decision could accelerate the development and release of non-invasive blood glucose monitoring technology for consumer wearables.
Hacker News commenters discuss the implications of the patent invalidation, with some skeptical about Apple's ability to deliver a reliable non-invasive blood glucose monitor soon. Several point out that regulatory hurdles remain a significant challenge, regardless of patent issues. Others note that the invalidation doesn't automatically clear the way for Apple, as other patents and technical challenges still exist. Some express hope for the technology's potential to improve diabetes management, while others highlight the difficulties of accurate non-invasive glucose monitoring. A few commenters also discuss the specifics of the patent and the legal reasoning behind its invalidation.
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HN commenters are generally skeptical of the iPhone 16e's value proposition. Several express disappointment that it uses the older A16 Bionic chip rather than the A17, questioning the "powerful" claim in the press release. Some see it as a cynical move by Apple to segment the market and push users towards the more expensive standard iPhone 16. The price point is also a source of contention, with many feeling it's overpriced for the offered specifications, especially compared to competing Android devices. A few commenters, however, appreciate Apple offering a smaller, more affordable option, acknowledging that not everyone needs the latest processor. The lack of a USB-C port is also criticized.
The blog post "Biases in Apple's Image Playground" reveals significant biases in Apple's image suggestion feature within Swift Playgrounds. The author demonstrates how, when prompted with various incomplete code snippets, the Playground consistently suggests images reinforcing stereotypical gender roles and Western-centric beauty standards. For example, code related to cooking predominantly suggests images of women, while code involving technology favors images of men. Similarly, searches for "person," "face," or "human" yield primarily images of white individuals. The post argues that these biases, likely stemming from the datasets used to train the image suggestion model, perpetuate harmful stereotypes and highlight the need for greater diversity and ethical considerations in AI development.
Hacker News commenters largely agree with the author's premise that Apple's Image Playground exhibits biases, particularly around gender and race. Several commenters point out the inherent difficulty in training AI models without bias due to the biased datasets they are trained on. Some suggest that the small size and specialized nature of the Playground model might exacerbate these issues. A compelling argument arises around the tradeoff between "correctness" and usefulness. One commenter argues that forcing the model to produce statistically "accurate" outputs might limit its creative potential, suggesting that Playground is designed for artistic exploration rather than factual representation. Others point out the difficulty in defining "correctness" itself, given societal biases. The ethics of AI training and the responsibility of companies like Apple to address these biases are recurring themes in the discussion.
A UK watchdog is investigating Apple's compliance with its own App Tracking Transparency (ATT) framework, questioning why Apple's first-party apps seem exempt from the same stringent data collection rules imposed on third-party developers. The Competition and Markets Authority (CMA) is particularly scrutinizing how Apple gathers and uses user data within its own apps, given that it doesn't require user permission via the ATT pop-up prompts like third-party apps must. The probe aims to determine if this apparent double standard gives Apple an unfair competitive advantage in the advertising and app markets, potentially breaching competition law.
HN commenters largely agree that Apple's behavior is hypocritical, applying stricter tracking rules to third-party apps while seemingly exempting its own. Some suggest this is classic regulatory capture, where Apple leverages its gatekeeper status to stifle competition. Others point out the difficulty of proving Apple's data collection is for personalized ads, as Apple claims it's for "personalized experiences." A few commenters argue Apple's first-party data usage is less problematic because the data isn't shared externally, while others counter that the distinction is irrelevant from a privacy perspective. The lack of transparency around Apple's data collection practices fuels suspicion. A common sentiment is that Apple's privacy stance is more about marketing than genuine user protection. Some users also highlight the inherent conflict of interest in Apple acting as both platform owner and app developer.
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https://news.ycombinator.com/item?id=43599967
HN commenters discuss Apple's SeedLM, focusing on its novelty and potential impact. Some express skepticism about the claimed compression ratios, questioning the practicality and performance trade-offs. Others highlight the intriguing possibility of evolving or optimizing these "seeds," potentially enabling faster model adaptation and personalized LLMs. Several commenters draw parallels to older techniques like PCA and word embeddings, while others speculate about the implications for model security and intellectual property. The limited training data used is also a point of discussion, with some wondering how SeedLM would perform with a larger, more diverse dataset. A few users express excitement about the potential for smaller, more efficient models running on personal devices.
The Hacker News thread for "SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators" contains several interesting comments discussing the feasibility, implications, and potential flaws of the proposed approach.
Several commenters express skepticism about the practical applicability of SeedLM. One points out that the claim of compressing a 7B parameter model into a 100KB seed is misleading, as training requires an enormous amount of compute, negating the storage savings. They argue this makes it less of a compression technique and more of a novel training method. Another user expands on this by questioning the efficiency of the pseudo-random generator (PRG) computation itself. If the PRG is computationally expensive, retrieving the weights could become a bottleneck, outweighing the benefits of the reduced storage size.
A related thread of discussion revolves around the nature of the PRG and the seed. Commenters debate whether the seed truly encapsulates all the information of the model or if it relies on implicit biases within the PRG's algorithm. One comment suggests the PRG itself might be encoding a significant portion of the model's "knowledge," making the seed more of a pointer than a compressed representation. This leads to speculation about the possibility of reverse-engineering the PRG to understand the learned information.
Some users delve into the potential consequences for model security and intellectual property. They suggest that if SeedLM becomes practical, it could simplify the process of stealing or copying models, as only the small seed would need to be exfiltrated. This raises concerns about protecting proprietary models and controlling their distribution.
Another commenter brings up the potential connection to biological systems, wondering if something akin to SeedLM might be happening in the human brain, where a relatively small amount of genetic information gives rise to complex neural structures.
Finally, a few comments address the experimental setup and results. One commenter questions the choice of tasks used to evaluate SeedLM, suggesting they might be too simple to adequately assess the capabilities of the compressed model. Another points out the lack of comparison with existing compression techniques, making it difficult to judge the relative effectiveness of SeedLM.
Overall, the comments reflect a mixture of intrigue and skepticism about the proposed SeedLM approach. While acknowledging the novelty of the idea, many users raise critical questions about its practical viability, computational cost, and potential security implications. The discussion highlights the need for further research to fully understand the potential and limitations of compressing large language models into pseudo-random generator seeds.