A Perplexity AI executive revealed that Motorola intended to make Perplexity the default search and AI assistant on its phones, but a pre-existing contract with Google prohibited the move. This contract, standard for Android phone manufacturers who want access to Google Mobile Services, requires Google Search to be the default. While Motorola could still pre-install Perplexity, the inability to set it as the primary option significantly hindered its potential for user adoption. This effectively blocks competing AI assistants from gaining a significant foothold on Android devices.
Google has released Gemma, a family of three quantized-aware trained (QAT) models designed to run efficiently on consumer-grade GPUs. These models offer state-of-the-art performance for various tasks including text generation, image captioning, and question answering, while being significantly smaller and faster than previous models. Gemma is available in three sizes – 2B, 7B, and 30B parameters – allowing developers to choose the best balance of performance and resource requirements for their specific use case. By utilizing quantization techniques, Gemma enables powerful AI capabilities on readily available hardware, broadening accessibility for developers and users.
HN commenters generally expressed excitement about the potential of running large language models (LLMs) locally on consumer hardware, praising Google's release of quantized weights for Gemma. Several noted the significance of running a 3B parameter model on a commodity GPU like a 3090. Some questioned the practical utility, citing limitations in context length and performance compared to cloud-based solutions. Others discussed the implications for privacy, the potential for fine-tuning and customization, and the rapidly evolving landscape of open-source LLMs. A few commenters delved into technical details like the choice of quantization methods and the trade-offs between model size and performance. There was also speculation about future developments, including the possibility of running even larger models locally and the integration of these models into everyday applications.
Google has released Gemini 2.5 Flash, a lighter and faster version of their Gemini Pro model optimized for on-device usage. This new model offers improved performance across various tasks, including math, coding, and translation, while being significantly smaller, enabling it to run efficiently on mobile devices like Pixel 8 Pro. Developers can now access Gemini 2.5 Flash through AICore and APIs, allowing them to build AI-powered applications that leverage this enhanced performance directly on users' devices, providing a more responsive and private user experience.
HN commenters generally express cautious optimism about Gemini 2.5 Flash. Several note Google's history of abandoning projects, making them hesitant to invest heavily in the new model. Some highlight the potential of Flash for mobile development due to its smaller size and offline capabilities, contrasting it with the larger, server-dependent nature of Gemini Pro. Others question Google's strategy of releasing multiple Gemini versions, suggesting it might confuse developers. A few commenters compare Flash favorably to other lightweight models like Llama 2, citing its performance and smaller footprint. There's also discussion about the licensing and potential open-sourcing of Gemini, as well as speculation about Google's internal usage of the model within products like Bard.
A federal judge ruled that Google holds a monopoly in the online advertising technology market, echoing the Justice Department's claims in its antitrust lawsuit. The judge found Google's dominance in various aspects of the ad tech ecosystem, including ad buying tools for publishers and advertisers, as well as the ad exchange that connects them, gives the company an unfair advantage and harms competition. This ruling is a significant victory for the government in its effort to rein in Google's power and could potentially lead to structural changes in the company's ad tech business.
Hacker News commenters largely agree with the judge's ruling that Google holds a monopoly in online ad tech. Several highlight the conflict of interest inherent in Google simultaneously owning the dominant ad exchange and representing both buyers and sellers. Some express skepticism that structural separation, as suggested by the Department of Justice, is the right solution, arguing it could stifle innovation and benefit competitors more than consumers. A few point out the irony of the government using antitrust laws to regulate a company built on "free" products, questioning if Google's dominance truly harms consumers. Others discuss the potential impact on ad revenue for publishers and the broader implications for the digital advertising landscape. Several commenters express cynicism about the effectiveness of antitrust actions in the long run, expecting Google to adapt and maintain its substantial market power. A recurring theme is the complexity of the ad tech ecosystem, making it difficult to predict the actual consequences of any intervention.
The article argues that Google is dominating the AI landscape, excelling in research, product integration, and cloud infrastructure. While OpenAI grabbed headlines with ChatGPT, Google possesses a deeper bench of AI talent, foundational models like PaLM 2 and Gemini, and a wider array of applications across search, Android, and cloud services. Its massive data centers and custom-designed TPU chips provide a significant infrastructure advantage, enabling faster training and deployment of increasingly complex models. The author concludes that despite the perceived hype around competitors, Google's breadth and depth in AI position it for long-term leadership.
Hacker News users generally disagreed with the premise that Google is winning on every AI front. Several commenters pointed out that Google's open-sourcing of key technologies, like Transformer models, allowed competitors like OpenAI to build upon their work and surpass them in areas like chatbots and text generation. Others highlighted Meta's contributions to open-source AI and their competitive large language models. The lack of public access to Google's most advanced models was also cited as a reason for skepticism about their supposed dominance, with some suggesting Google's true strength lies in internal tooling and advertising applications rather than publicly demonstrable products. While some acknowledged Google's deep research bench and vast resources, the overall sentiment was that the AI landscape is more competitive than the article suggests, and Google's lead is far from insurmountable.
The author reflects on their time at Google, highlighting both positive and negative aspects. They appreciated the brilliant colleagues, ample resources, and impact of their work, while also acknowledging the bureaucratic processes, internal politics, and feeling of being a small cog in a massive machine. Ultimately, they left Google for a smaller company, seeking greater ownership and a faster pace, but acknowledge the invaluable experience and skills gained during their tenure. They advise current Googlers to proactively seek fulfilling projects and avoid getting bogged down in the corporate structure.
HN commenters largely discuss the author's experience with burnout and Google's culture. Some express skepticism about the "golden handcuffs" narrative, arguing that high compensation should offset long hours if the work is truly enjoyable. Others empathize with the author, sharing similar experiences of burnout and disillusionment within large tech companies. Several commenters note the pervasiveness of performance anxiety and the pressure to constantly prove oneself, even at senior levels. The value of side projects and personal pursuits is also highlighted as a way to maintain a sense of purpose and avoid becoming solely defined by one's job. A few commenters suggest that the author's experience may be specific to certain teams or roles within Google, while others argue that it reflects a broader trend in the tech industry.
Google DeepMind will support Anthropic's Model Card Protocol (MCP) for its Gemini AI model and software development kit (SDK). This move aims to standardize how AI models interact with external data sources and tools, improving transparency and facilitating safer development. By adopting the open standard, Google hopes to make it easier for developers to build and deploy AI applications responsibly, while promoting interoperability between different AI models. This collaboration signifies growing industry interest in standardized practices for AI development.
Hacker News commenters discuss the implications of Google supporting Anthropic's Model Card Protocol (MCP), generally viewing it as a positive move towards standardization and interoperability in the AI model ecosystem. Some express skepticism about Google's commitment to open standards given their past behavior, while others see it as a strategic move to compete with OpenAI. Several commenters highlight the potential benefits of MCP for transparency, safety, and responsible AI development, enabling easier comparison and evaluation of models. The potential for this standardization to foster a more competitive and innovative AI landscape is also discussed, with some suggesting it could lead to a "plug-and-play" future for AI models. A few comments delve into the technical aspects of MCP and its potential limitations, while others focus on the broader implications for the future of AI development.
Google is allowing businesses to run its Gemini AI models on their own infrastructure, addressing data privacy and security concerns. This on-premise offering of Gemini, accessible through Google Cloud's Vertex AI platform, provides companies greater control over their data and model customizations while still leveraging Google's powerful AI capabilities. This move allows clients, particularly in regulated industries like healthcare and finance, to benefit from advanced AI without compromising sensitive information.
Hacker News commenters generally expressed skepticism about Google's announcement of Gemini availability for private data centers. Many doubted the feasibility and affordability for most companies, citing the immense infrastructure and expertise required to run such large models. Some speculated that this offering is primarily targeted at very large enterprises and government agencies with strict data security needs, rather than the average business. Others questioned the true motivation behind the move, suggesting it could be a response to competition or a way for Google to gather more data. Several comments also highlighted the irony of moving large language models "back" to private data centers after the trend of cloud computing. There was also some discussion around the potential benefits for specific use cases requiring low latency and high security, but even these were tempered by concerns about cost and complexity.
Google Cloud's Immersive Stream for XR and other AI technologies are powering Sphere's upcoming "The Wizard of Oz" experience. This interactive exhibit lets visitors step into the world of Oz through a custom-built spherical stage with 100 million pixels of projected video, spatial audio, and interactive elements. AI played a crucial role in creating the experience, from generating realistic environments and populating them with detailed characters to enabling real-time interactions like affecting the weather within the virtual world. This combination of technology and storytelling aims to offer a uniquely immersive and personalized journey down the yellow brick road.
HN commenters were largely unimpressed with Google's "Wizard of Oz" tech demo. Several pointed out the irony of using an army of humans to create the illusion of advanced AI, calling it a glorified Mechanical Turk setup. Some questioned the long-term viability and scalability of this approach, especially given the high labor costs. Others criticized the lack of genuine innovation, suggesting that the underlying technology isn't significantly different from existing chatbot frameworks. A few expressed mild interest in the potential applications, but the overall sentiment was skepticism about the project's significance and Google's marketing spin.
Google has introduced the Agent2Agent (A2A) protocol, a new open standard designed to enable interoperability between software agents. A2A allows agents from different developers to communicate and collaborate, regardless of their underlying architecture or programming language. It defines a common language and set of functionalities for agents to discover each other, negotiate tasks, and exchange information securely. This framework aims to foster a more interconnected and collaborative agent ecosystem, facilitating tasks like scheduling meetings, booking travel, and managing data across various platforms. Ultimately, A2A seeks to empower developers to build more capable and helpful agents that can seamlessly integrate into users' lives.
HN commenters are generally skeptical of Google's A2A protocol. Several express concerns about Google's history of abandoning projects, creating walled gardens, and potentially using this as a data grab. Some doubt the technical feasibility or usefulness of the protocol, pointing to existing interoperability solutions and the difficulty of achieving true agent autonomy. Others question the motivation behind open-sourcing it now, speculating it might be a defensive move against competing standards or a way to gain control of the agent ecosystem. A few are cautiously optimistic, hoping it fosters genuine interoperability, but remain wary of Google's involvement. Overall, the sentiment is one of cautious pessimism, with many believing that true agent interoperability requires a more decentralized and open approach than Google is likely to provide.
Google has announced Ironwood, its latest TPU (Tensor Processing Unit) specifically designed for inference workloads. Focusing on cost-effectiveness and ease of use, Ironwood offers a simpler, more accessible architecture than its predecessors for running large language models (LLMs) and generative AI applications. It provides substantial performance improvements over previous generation TPUs and integrates tightly with Google Cloud's Vertex AI platform, streamlining development and deployment. This new TPU aims to democratize access to cutting-edge AI acceleration hardware, enabling a wider range of developers to build and deploy powerful AI solutions.
HN commenters generally express skepticism about Google's claims regarding Ironwood's performance and cost-effectiveness. Several doubt the "10x better perf/watt" claim, citing the lack of specific benchmarks and comparing it to previous TPU generations that also promised significant improvements but didn't always deliver. Some also question the long-term viability of Google's TPU strategy, suggesting that Nvidia's more open ecosystem and software maturity give them a significant advantage. A few commenters point out Google's history of abandoning hardware projects, making them hesitant to invest in the TPU ecosystem. Finally, some express interest in the technical details, wishing for more in-depth information beyond the high-level marketing blog post.
Google's Gemini robotics models are built by combining Gemini's large language models with visual and robotic data. This approach allows the robots to understand and respond to complex, natural language instructions. The training process uses diverse datasets, including simulation, videos, and real-world robot interactions, enabling the models to learn a wide range of skills and adapt to new environments. Through imitation and reinforcement learning, the robots can generalize their learning to perform unseen tasks, exhibit complex behaviors, and even demonstrate emergent reasoning abilities, paving the way for more capable and adaptable robots in the future.
Hacker News commenters generally express skepticism about Google's claims regarding Gemini's robotic capabilities. Several point out the lack of quantifiable metrics and the heavy reliance on carefully curated demos, suggesting a gap between the marketing and the actual achievable performance. Some question the novelty, arguing that the underlying techniques are not groundbreaking and have been explored elsewhere. Others discuss the challenges of real-world deployment, citing issues like robustness, safety, and the difficulty of generalizing to diverse environments. A few commenters express cautious optimism, acknowledging the potential of the technology but emphasizing the need for more concrete evidence before drawing firm conclusions. Some also raise concerns about the ethical implications of advanced robotics and the potential for job displacement.
The author argues that Google's search quality has declined due to a prioritization of advertising revenue and its own products over relevant results. This manifests in excessive ads, low-quality content from SEO-driven websites, and a tendency to push users towards Google services like Maps and Flights, even when external options might be superior. The post criticizes the cluttered and information-poor nature of modern search results pages, lamenting the loss of a cleaner, more direct search experience that prioritized genuine user needs over Google's business interests. This degradation, the author claims, is driving users away from Google Search and towards alternatives.
HN commenters largely agree with the author's premise that Google search quality has declined. Many attribute this to increased ads, irrelevant results, and a focus on Google's own products. Several commenters shared anecdotes of needing to use specific search operators or alternative search engines like DuckDuckGo or Bing to find desired information. Some suggest the decline is due to Google's dominant market share, arguing they lack the incentive to improve. A few pushed back, attributing perceived declines to changes in user search habits or the increasing complexity of the internet. Several commenters also discussed the bloat of Google's other services, particularly Maps.
Google is shifting internal Android development to a private model, similar to how it develops other products. While Android will remain open source, the day-to-day development process will no longer be publicly visible. Google claims this change will improve efficiency and security. The company insists this won't affect the open-source nature of Android, promising continued AOSP releases and collaboration with external partners. They anticipate no changes to the public bug tracker, release schedules, or the overall openness of the platform itself.
Hacker News users largely expressed skepticism and concern over Google's shift towards internal Android development. Many questioned whether "open source releases" would truly remain open if Google's internal development diverged significantly, leading to a de facto closed-source model similar to iOS. Some worried about potential stagnation of the platform, with fewer external contributions and slower innovation. Others saw it as a natural progression for a maturing platform, focusing on stability and polish over rapid feature additions. A few commenters pointed out the potential benefits, such as improved security and consistency through tighter control. The prevailing sentiment, however, was cautious pessimism about the long-term implications for Android's openness and community involvement.
Starting next week, Google will significantly reduce public access to the Android Open Source Project (AOSP) development process. Key parts of the next Android release's development, including platform changes and internal testing, will occur in private. While the source code will eventually be released publicly as usual, the day-to-day development and decision-making will be hidden from the public eye. This shift aims to improve efficiency and reduce early leaks of information about upcoming Android features. Google emphasizes that AOSP will remain open source, and they intend to enhance opportunities for external contributions through other avenues like quarterly platform releases and pre-release program expansions.
Hacker News commenters express concern over Google's move to develop Android AOSP primarily behind closed doors. Several suggest this signals a shift towards prioritizing Pixel features and potentially neglecting the broader Android ecosystem. Some worry this will stifle innovation and community contributions, leading to a more fragmented and less open Android experience. Others speculate this is a cost-cutting measure or a response to security concerns. A few commenters downplay the impact, believing open-source contributions were already minimal and Google's commitment to open source remains, albeit with a different approach. The discussion also touches upon the potential impact on custom ROM development and the future of AOSP's openness.
Google's Gemini 2.5 significantly improves multimodal reasoning and coding capabilities compared to its predecessor. Key advancements include enhanced understanding and generation of complex multi-turn dialogues, stronger problem-solving across various domains like math and physics, and more efficient handling of long contexts. Gemini 2.5 also features improved coding proficiency, enabling it to generate, debug, and explain code in multiple programming languages more effectively. These advancements are powered by a new architecture and training methodologies emphasizing improved memory and knowledge retrieval, leading to more insightful and comprehensive responses.
HN commenters are generally skeptical of Google's claims about Gemini 2.5. Several point out the lack of concrete examples and benchmarks, dismissing the blog post as marketing fluff. Some express concern over the focus on multimodal capabilities without addressing fundamental issues like reasoning and bias. Others question the feasibility of the claimed improvements in efficiency, suggesting Google is prioritizing marketing over substance. A few commenters offer more neutral perspectives, acknowledging the potential of multimodal models while waiting for more rigorous evaluations. The overall sentiment is one of cautious pessimism, with many calling for more transparency and less hype.
Driven by the sudden success of OpenAI's ChatGPT, Google embarked on a two-year internal overhaul to accelerate its AI development. This involved merging DeepMind with Google Brain, prioritizing large language models, and streamlining decision-making. The result is Gemini, Google's new flagship AI model, which the company claims surpasses GPT-4 in certain capabilities. The reorganization involved significant internal friction and a rapid shift in priorities, highlighting the intense pressure Google felt to catch up in the generative AI race. Despite the challenges, Google believes Gemini represents a significant step forward and positions them to compete effectively in the rapidly evolving AI landscape.
HN commenters discuss Google's struggle to catch OpenAI, attributing it to organizational bloat and risk aversion. Several suggest Google's internal processes stifled innovation, contrasting it with OpenAI's more agile approach. Some argue Google's vast resources and talent pool should have given them an advantage, but bureaucracy and a focus on incremental improvements rather than groundbreaking research held them back. The discussion also touches on Gemini's potential, with some expressing skepticism about its ability to truly surpass GPT-4, while others are cautiously optimistic. A few comments point out the article's reliance on anonymous sources, questioning its objectivity.
Google has agreed to acquire cybersecurity startup Wiz for a reported $32 billion. This deal, expected to close in 2025, marks a significant investment by Google in cloud security and will bolster its Google Cloud Platform offerings. Wiz specializes in agentless cloud security, offering vulnerability assessment and other protective measures. The acquisition price tag represents a substantial premium over Wiz's previous valuation, highlighting the growing importance of cloud security in the tech industry.
Hacker News users discuss the high acquisition price of Wiz, especially considering its relatively short existence and the current market downturn. Some speculate about the strategic value Google sees in Wiz, suggesting it might be related to cloud security competition with Microsoft, or a desire to bolster Google Cloud Platform's security offerings. Others question the due diligence process, wondering if Google overpaid. A few commenters note the significant payout for Wiz's founders and investors, and contemplate the broader implications for the cybersecurity market and startup valuations. There's also skepticism about the reported valuation, with some suggesting it might be inflated.
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.
Google DeepMind has introduced Gemini Robotics, a new system that combines Gemini's large language model capabilities with robotic control. This allows robots to understand and execute complex instructions given in natural language, moving beyond pre-programmed behaviors. Gemini provides high-level understanding and planning, while a smaller, specialized model handles low-level control in real-time. The system is designed to be adaptable across various robot types and environments, learning new skills more efficiently and generalizing its knowledge. Initial testing shows improved performance in complex tasks, opening up possibilities for more sophisticated and helpful robots in diverse settings.
HN commenters express cautious optimism about Gemini's robotics advancements. Several highlight the impressive nature of the multimodal training, enabling robots to learn from diverse data sources like YouTube videos. Some question the real-world applicability, pointing to the highly controlled lab environments and the gap between demonstrated tasks and complex, unstructured real-world scenarios. Others raise concerns about safety and the potential for misuse of such technology. A recurring theme is the difficulty of bridging the "sim-to-real" gap, with skepticism about whether these advancements will translate to robust and reliable performance in practical applications. A few commenters mention the limited information provided and the lack of open-sourcing, hindering a thorough evaluation of Gemini's capabilities.
The Department of Justice is reportedly still pushing for Google to sell off parts of its Chrome business, even as it prepares its main antitrust lawsuit against the company for trial. Sources say the DOJ believes Google's dominance in online advertising is partly due to its control over Chrome and that divesting the browser, or portions of it, is a necessary remedy. This potential divestiture could include parts of Chrome's ad tech business and potentially even the browser itself, a significantly more aggressive move than previously reported. While the DOJ's primary focus remains its existing ad tech lawsuit, pressure for a Chrome divestiture continues behind the scenes.
HN commenters are largely skeptical of the DOJ's potential antitrust suit against Google regarding Chrome. Many believe it's a misguided effort, arguing that Chrome is free, open-source (Chromium), and faces robust competition from other browsers like Firefox and Safari. Some suggest the DOJ should focus on more pressing antitrust issues, like Google's dominance in search advertising and its potential abuse of Android. A few commenters discuss the potential implications of such a divestiture, including the possibility of a fork of Chrome or the browser becoming part of another large company. Some express concern about the potential negative impact on user privacy. Several commenters also point out the irony of the government potentially mandating Google divest from a free product.
The Register reports that Google collects and transmits Android user data, including hardware identifiers and location, to its servers even before a user opens any apps or completes device setup. This pre-setup data collection involves several Google services and occurs during the initial boot process, transmitting information like IMEI, hardware serial number, SIM serial number, and nearby Wi-Fi access point details. While Google claims this data is crucial for essential services like fraud prevention and software updates, the article raises privacy concerns, particularly because users are not informed of this data collection nor given the opportunity to opt out. This behavior raises questions about the balance between user privacy and Google's data collection practices.
HN commenters discuss the implications of Google's data collection on Android even before app usage. Some highlight the irony of Google's privacy claims contrasted with their extensive tracking. Several express resignation, suggesting this behavior is expected from Google and other large tech companies. One commenter mentions a study showing Google collecting data even when location services are disabled, and another points to the difficulty of truly opting out of this tracking without significant technical knowledge. The discussion also touches upon the limitations of using alternative Android ROMs or de-Googled phones, acknowledging their usability compromises. There's a general sense of pessimism about the ability of users to control their data in the Android ecosystem.
In 2008, amidst controversy surrounding its initial Chrome End User License Agreement (EULA), Google clarified that the license only applied to Chrome itself, not to user-generated content created using Chrome. Matt Cutts explained that the broad language in the original EULA was standard boilerplate, intended for protecting Google's intellectual property within the browser, not claiming ownership over user data. The company quickly revised the EULA to eliminate ambiguity and explicitly state that Google claims no rights to user content created with Chrome. This addressed concerns about Google overreaching and reassured users that their work remained their own.
HN commenters in 2023 discuss Matt Cutts' 2008 blog post clarifying Google's Chrome license agreement. Several express skepticism of Google, pointing out that the license has changed since the post and that Google's data collection practices are extensive regardless. Some commenters suggest the original concern arose from a misunderstanding of legalese surrounding granting a license to use software versus a license to user-created content. Others mention that granting a license to "sync" data is distinct from other usage and requires its own scrutiny. A few commenters reflect on the relative naivety of concerns about data privacy in 2008 compared to the present day, where such concerns are much more widespread. The discussion ultimately highlights the evolution of public perception regarding online privacy and the persistent distrust of large tech companies like Google.
Newsweek reports that Google Calendar has stopped automatically displaying certain US cultural events like Pride Month, Black History Month, and Holocaust Remembrance Day in the main calendar view for some users. While these events are still accessible within other calendar layers, like the "Interesting Calendars" section, the change has sparked concern and frustration among users who relied on the prominent reminders. Google has not officially commented on the reason for the removal or whether it is a temporary glitch or a permanent change.
HN commenters were largely skeptical of the Newsweek article, pointing out that the events still appeared on their calendars and suggesting user error or a temporary glitch as more likely explanations than intentional removal. Several suggested checking calendar settings, specifically "Browse interesting calendars" under "Other calendars," to ensure the specialized calendars are enabled. Some questioned Newsweek's journalistic integrity and the sensationalist framing of the headline. A few commenters expressed general frustration with Google's frequent, unannounced changes to their products and services. There was also discussion about the effectiveness and potential annoyance of these awareness calendars, with some finding them useful reminders while others viewing them as intrusive or performative.
A recent study reveals that CAPTCHAs are essentially a profitable tracking system disguised as a security measure. While ostensibly designed to differentiate bots from humans, CAPTCHAs allow companies like Google to collect vast amounts of user data for targeted advertising and other purposes. This system has cost users a staggering amount of time—an estimated 819 billion hours globally—and has generated nearly $1 trillion in revenue, primarily for Google. The study argues that the actual security benefits of CAPTCHAs are minimal compared to the immense profits generated from the user data they collect. This raises concerns about the balance between online security and user privacy, suggesting CAPTCHAs function more as a data harvesting tool than an effective bot deterrent.
Hacker News users generally agree with the premise that CAPTCHAs are exploitative. Several point out the irony of Google using them for training AI while simultaneously claiming they prevent bots. Some highlight the accessibility issues CAPTCHAs create, particularly for disabled users. Others discuss alternatives, such as Cloudflare's Turnstile, and the privacy implications of different solutions. The increasing difficulty and frequency of CAPTCHAs are also criticized, with some speculating it's a deliberate tactic to push users towards paid "captcha-free" services. Several commenters express frustration with the current state of CAPTCHAs and the lack of viable alternatives.
The blog post argues that Carbon, while presented as a new language, is functionally more of a dialect or a sustained, large-scale fork of C++. It shares so much of C++'s syntax, semantics, and tooling that it blurs the line between a distinct language and a significantly evolved version of existing C++. This close relationship makes migration easier, but also raises questions about whether the benefits of a 'new' language outweigh the costs of maintaining another C++-like ecosystem, especially given ongoing modernization efforts within C++ itself. The author suggests that Carbon is less a revolution and more of a strategic response to the inertia surrounding large C++ codebases, offering a cleaner starting point while retaining substantial compatibility.
Hacker News commenters largely agree with the author's premise that Carbon, despite Google's marketing, isn't yet a fully realized language. Several point out the lack of a stable ABI and the dependence on constantly evolving C++ tooling as major roadblocks. Some highlight the ambiguity around its governance model, questioning whether it will truly be community-driven or remain under Google's control. The most compelling comments delve into the practical implications of this, expressing skepticism about adopting a language with such a precarious foundation and predicting a long road ahead before Carbon reaches production readiness for substantial projects. Others counter that this is expected for a young language and that Carbon's potential merits are worth the wait, citing its modern features and interoperability with C++. A few commenters express disappointment or frustration with the slow pace of Carbon's development, contrasting it with other language projects.
Google altered its Super Bowl ad for its Bard AI chatbot after it provided inaccurate information in a demo. The ad showcased Bard's ability to simplify complex topics, but it incorrectly stated the James Webb Space Telescope took the very first pictures of a planet outside our solar system. Google corrected the error before airing the ad, highlighting the ongoing challenges of ensuring accuracy in AI chatbots, even in highly publicized marketing campaigns.
Hacker News commenters generally expressed skepticism about Google's Bard AI and the implications of the ad's factual errors. Several pointed out the irony of needing to edit an ad showcasing AI's capabilities because the AI itself got the facts wrong. Some questioned the ethics of heavily promoting a technology that's clearly still flawed, especially given Google's vast influence. Others debated the significance of the errors, with some suggesting they were minor while others argued they highlighted deeper issues with the technology's reliability. A few commenters also discussed the pressure Google is under from competitors like Bing and the potential for AI chatbots to confidently hallucinate incorrect information. A recurring theme was the difficulty of balancing the hype around AI with the reality of its current limitations.
Pixel 4a owners who haven't updated their phones are now stuck with a buggy December 2022 battery update as Google has removed older firmware versions from its servers. This means users can no longer downgrade to escape the battery drain and random shutdown issues introduced by the update. While Google has acknowledged the problem and promised a fix, there's no ETA, leaving affected users with no immediate solution. Essentially, Pixel 4a owners are forced to endure the battery problems until Google releases the corrected update.
HN commenters generally express frustration and disappointment with Google's handling of the Pixel 4a battery issue. Several users report experiencing the battery drain problem after the update, with some claiming significantly reduced battery life. Some criticize Google's lack of communication and the removal of older firmware, making it impossible to revert to a working version. Others discuss potential workarounds, including custom ROMs like LineageOS, but acknowledge the risks and technical knowledge required. A few commenters mention the declining quality control of Pixel phones and question Google's commitment to supporting older devices. The overall sentiment is negative, with many expressing regret over purchasing a Pixel phone and a loss of trust in Google's hardware division.
Google has open-sourced the Pebble OS, including firmware, apps, developer tools, and watchfaces. This release, dubbed "Pebble.js," allows developers and enthusiasts to explore and tinker with the code that powered these iconic smartwatches. The repository provides access to the entire Pebble software ecosystem, enabling potential revival or adaptation of the platform for other devices and purposes. While official support from Google is limited, the open-source nature of the project invites community contributions and future development.
The Hacker News comments express excitement about Google open-sourcing the Pebble OS, with many reminiscing about their fondness for the now-defunct smartwatches. Several commenters anticipate tinkering with the newly released code and exploring potential uses, like repurposing it for other wearables or integrating it with existing projects. Some discuss the technical aspects of the OS and speculate about the motivations behind Google's decision, suggesting it could be a move to preserve Pebble's legacy, foster community development, or potentially even lay the groundwork for future wearable projects. A few commenters express a degree of disappointment that the release doesn't include all aspects of the Pebble ecosystem, such as the mobile apps or cloud services. There's also a recurring theme of gratitude towards Google for making the source code available, acknowledging the significance of this move for the Pebble community and wearable technology enthusiasts.
A phishing attack leveraged Google's URL shortener, g.co, to mask malicious links. The attacker sent emails appearing to be from a legitimate source, containing a g.co shortened link. This short link redirected to a fake Google login page designed to steal user credentials. Because the initial link displayed g.co, it bypassed suspicion and instilled a false sense of security, making the phishing attempt more effective. The post highlights the danger of trusting shortened URLs, even those from seemingly reputable services, and emphasizes the importance of carefully inspecting links before clicking.
HN users discuss a sophisticated phishing attack using g.co shortened URLs. Several express concern about Google's seeming inaction on the issue, despite reports. Some suggest solutions like automatically blocking known malicious short URLs or requiring explicit user confirmation before redirecting. Others question the practicality of such solutions given the vast scale of Google's services. The vulnerability of URL shorteners in general is highlighted, with some suggesting they should be avoided entirely due to the inherent security risks. The discussion also touches upon the user's role in security, advocating for caution and skepticism when encountering shortened URLs. Some users mention being successfully targeted by this attack, and the frustration of banks accepting screenshots of g.co links as proof of payment. The conversation emphasizes the ongoing tension between user convenience and security, and the difficulty of completely mitigating phishing risks.
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https://news.ycombinator.com/item?id=43776512
Hacker News users discuss the implications of Google allegedly blocking Motorola from setting Perplexity as the default assistant. Some express skepticism about the claims, suggesting Perplexity might be exaggerating the situation for publicity. Others point out the potential antitrust implications, comparing it to Microsoft's bundling of Internet Explorer with Windows. A recurring theme is the difficulty of competing with Google given their control over Android and the default search settings. Several commenters suggest Google's behavior is unsurprising, given their dominant market position and the threat posed by alternative AI assistants. Some see this as a reason to support open-source alternatives to Android. There's also discussion about the potential benefits for consumers if they had more choice in AI assistants.
The Hacker News comments on the Bloomberg article about Motorola being contractually blocked from setting Perplexity as the default assistant are quite extensive and offer diverse perspectives. Several commenters express skepticism about the claims made by Perplexity's executive, suggesting that it could be a publicity stunt to gain attention. They question why Motorola would even consider switching to a lesser-known assistant like Perplexity when Google Assistant is so deeply integrated into the Android ecosystem.
Some commenters delve into the potential antitrust implications of Google's actions, arguing that preventing Motorola from setting a different default assistant reinforces Google's dominance in the search and mobile markets. They draw parallels with past antitrust cases against Microsoft and speculate whether this could lead to further scrutiny of Google's practices.
A few technical commenters discuss the challenges of switching default assistants on Android, highlighting the tight integration of Google Assistant and the potential difficulties for users if a different assistant were to be implemented. They also raise concerns about the privacy implications of using alternative assistants and the potential for data sharing with lesser-known companies.
Several commenters express a desire for more competition in the assistant market, believing that Google's dominance stifles innovation. They see Perplexity's attempt to become the default assistant, even if unsuccessful, as a positive sign for the future.
Some commenters question the strategic decisions of both Motorola and Perplexity. They wonder why Motorola would enter into such a restrictive contract with Google in the first place, and why Perplexity would target Motorola, given the known contractual limitations.
A recurring theme throughout the comments is the perception of Google as a monopolistic force in the tech industry. Commenters express frustration with Google's perceived control over the Android ecosystem and its tendency to prioritize its own services over competitors.
Finally, some comments focus on the technical aspects of the Perplexity assistant itself, comparing its features and capabilities to Google Assistant and other competitors. They discuss the potential benefits of alternative assistants, such as improved privacy and more specialized functionalities.
Overall, the comments paint a picture of a complex situation with significant implications for the future of the mobile assistant market. They highlight the challenges faced by smaller companies trying to compete with tech giants like Google, and the ongoing debate about antitrust and consumer choice in the digital age.