Google Cloud has expanded its AI infrastructure with new offerings focused on speed and scale. The A3 VMs, based on Nvidia H100 GPUs, are designed for large language models and generative AI training and inference, providing significantly improved performance compared to previous generations. Google is also improving networking infrastructure with the introduction of Cross-Cloud Network platform, allowing easier and more secure connections between Google Cloud and on-premises environments. Furthermore, Google Cloud is enhancing data and storage capabilities with updates to Cloud Storage and Dataproc Spark, boosting data access speeds and enabling faster processing for AI workloads.
That white stuff on your cheese might not be mold! It could be calcium lactate crystals, a harmless byproduct of aging. These crystals, often found on aged cheeses like cheddar, Gouda, and Parmesan, form when lactic acid reacts with calcium in the cheese. They usually appear as small, white, gritty or crunchy spots and indicate a well-aged and flavorful cheese. While they might look unusual, calcium lactate crystals are safe to eat and contribute to the cheese's unique texture and taste. So, before you toss that block of cheese, consider that the "white stuff" might actually be a sign of quality.
Hacker News users discuss the various types of "white stuff" that can appear on cheese, beyond just mold. Several commenters point out that the article fails to mention tyrosine crystals, which are common on aged cheeses and contribute to their flavor. Calcium lactate is also mentioned as another common, harmless crystalline formation. Some users express concern about the author's seeming encouragement to just scrape off the mold and eat the cheese, with several arguing that this is unsafe for certain molds that penetrate deeply. Others note the article conflates "safe" with "harmless", pointing out that even harmless molds might not be palatable. Finally, a few comments offer additional resources for identifying cheese molds and determining their safety.
Werner Vogels argues that while Amazon S3's simplicity was initially a key differentiator and driver of its widespread adoption, maintaining that simplicity in the face of ever-increasing scale and feature requests is an ongoing challenge. He emphasizes that adding features doesn't equate to improving the customer experience and that preserving S3's core simplicity—its fundamental object storage model—is paramount. This involves thoughtful API design, backwards compatibility, and a focus on essential functionality rather than succumbing to the pressure of adding complexity for its own sake. S3's continued success hinges on keeping the service easy to use and understand, even as the underlying technology evolves dramatically.
Hacker News users largely agreed with the premise of the article, emphasizing that S3's simplicity is its greatest strength, while also acknowledging areas where improvements could be made. Several commenters pointed out the hidden complexities of S3, such as eventual consistency and subtle performance gotchas. The discussion also touched on the trade-offs between simplicity and more powerful features, with some arguing that S3's simplicity forces users to build solutions on top of it, leading to more robust architectures. The lack of a true directory structure and efficient renaming operations were also highlighted as pain points. Some users suggested potential improvements like native support for symbolic links or atomic renaming, but the general consensus was that any added features should be carefully considered to avoid compromising S3's core simplicity. A few comments compared S3 to other storage solutions, noting that while some offer more advanced features, none have matched S3's simplicity and ubiquity.
The blog post "IO Devices and Latency" explores the significant impact of I/O operations on overall database performance, emphasizing that optimizing queries alone isn't enough. It breaks down the various types of latency involved in storage systems, from the physical limitations of different storage media (like NVMe drives, SSDs, and HDDs) to the overhead introduced by the operating system and file system layers. The post highlights the performance benefits of using direct I/O, which bypasses the OS page cache, for predictable, low-latency access to data, particularly crucial for database workloads. It also underscores the importance of understanding the characteristics of your storage hardware and software stack to effectively minimize I/O latency and improve database performance.
Hacker News users discussed the challenges of measuring and mitigating I/O latency. Some questioned the blog post's methodology, particularly its reliance on fio and the potential for misleading results due to caching effects. Others offered alternative tools and approaches for benchmarking storage performance, emphasizing the importance of real-world workloads and the limitations of synthetic tests. Several commenters shared their own experiences with storage latency issues and offered practical advice for diagnosing and resolving performance bottlenecks. A recurring theme was the complexity of the storage stack and the need to understand the interplay of various factors, including hardware, drivers, file systems, and application behavior. The discussion also touched on the trade-offs between performance, cost, and complexity when choosing storage solutions.
ParadeDB, a YC S23 startup building a distributed, relational, NewSQL database in Rust, is hiring a Rust Database Engineer. This role involves designing and implementing core database components like query processing, transaction management, and distributed consensus. Ideal candidates have experience building database systems, are proficient in Rust, and possess a strong understanding of distributed systems concepts. They will contribute significantly to the database's architecture and development, working closely with the founding team. The position is remote and offers competitive salary and equity.
HN commenters discuss ParadeDB's hiring post, expressing skepticism about the wisdom of choosing Rust for a database due to its complexity and potential performance overhead compared to C++. Some question the value proposition of yet another database, wondering what niche ParadeDB fills that isn't already addressed by existing solutions. Others suggest focusing on a specific problem domain rather than building a general-purpose database. There's also discussion about the startup's name and logo, with some finding them unmemorable or confusing. Finally, a few commenters offer practical advice on hiring, suggesting reaching out to university research groups or specialized job boards.
Backblaze's 12-year hard drive failure rate analysis, visualized through interactive charts, reveals interesting trends. While drive sizes have increased significantly, failure rates haven't followed a clear pattern related to size. Different manufacturers demonstrate varying reliability, with some models showing notably higher or lower failure rates than others. The data allows exploration of failure rates over time, by manufacturer, model, and size, providing valuable insights into drive longevity for large-scale deployments. The visualization highlights the complexity of predicting drive failure and the importance of ongoing monitoring.
Hacker News users discussed the methodology and presentation of the Backblaze data drive statistics. Several commenters questioned the lack of confidence intervals or error bars, making it difficult to draw meaningful conclusions about drive reliability, especially regarding less common models. Others pointed out the potential for selection bias due to Backblaze's specific usage patterns and purchasing decisions. Some suggested alternative visualizations, like Kaplan-Meier survival curves, would be more informative. A few commenters praised the long-term data collection and its value for the community, while also acknowledging its limitations. The visualization itself was generally well-received, with some suggestions for improvements like interactive filtering.
Catalytic computing, a new theoretical framework, aims to overcome the limitations of traditional computing by leveraging the entire storage capacity of a device, such as a hard drive, for computation. Instead of relying on limited working memory, catalytic computing treats the entire memory system as a catalyst, allowing data to transform itself through local interactions within the storage itself. This approach, inspired by chemical catalysts, could drastically expand the complexity and scale of computations possible, potentially enabling the efficient processing of massive datasets that are currently intractable for conventional computers. While still theoretical, catalytic computing represents a fundamental shift in thinking about computation, promising to unlock the untapped potential of existing hardware.
Hacker News users discussed the potential and limitations of catalytic computing. Some expressed skepticism about the practicality and scalability of the approach, questioning the overhead and energy costs involved in repeatedly reading and writing data. Others highlighted the potential benefits, particularly for applications involving massive datasets that don't fit in RAM, drawing parallels to memory mapping and virtual memory. Several commenters pointed out that the concept isn't entirely new, referencing existing techniques like using SSDs as swap space or leveraging database indexing. The discussion also touched upon the specific use cases where catalytic computing might be advantageous, like bioinformatics and large language models, while acknowledging the need for further research and development to overcome current limitations. A few commenters also delved into the theoretical underpinnings of the concept, comparing it to other computational models.
Backblaze's 2024 hard drive stats reveal a continued decline in annualized failure rates (AFR) across most drive models. The overall AFR for 2024 was 0.83%, the lowest ever recorded by Backblaze. Larger capacity drives, particularly 16TB and larger, demonstrated remarkably low failure rates, with some models exhibiting AFRs below 0.5%. While some older drives experienced higher failure rates as expected, the data suggests increasing drive reliability overall. Seagate drives dominated Backblaze's data centers, comprising the majority of drives and continuing to perform reliably. The report highlights the ongoing trend of larger drives becoming more dependable, contributing to the overall improvement in data storage reliability.
Hacker News users discuss Backblaze's 2024 drive stats, focusing on the high failure rates of WDC drives, especially the 16TB and 18TB models. Several commenters question Backblaze's methodology and data interpretation, suggesting their usage case (consumer drives in enterprise settings) skews the results. Others point out the difficulty in comparing different drive models directly due to varying usage and deployment periods. Some highlight the overall decline in drive reliability and express concerns about the industry trend of increasing capacity at the expense of longevity. The discussion also touches on SMART stats, RMA processes, and the potential impact of SMR technology. A few users share their personal experiences with different drive brands, offering anecdotal evidence that contradicts or supports Backblaze's findings.
Reports are surfacing about new Seagate hard drives, predominantly sold through Chinese online marketplaces, exhibiting suspiciously long power-on hours and high usage statistics despite being advertised as new. This suggests potential fraud, where used or refurbished drives are being repackaged and sold as new. While Seagate has acknowledged the issue and is investigating, the extent of the problem remains unclear, with speculation that the drives might originate from cryptocurrency mining operations or other data centers. Buyers are urged to check SMART data upon receiving new Seagate drives to verify their actual usage.
Hacker News users discuss potential explanations for unexpectedly high reported runtime hours on seemingly new Seagate hard drives. Some suggest these drives are refurbished units falsely marketed as new, with inflated SMART data to disguise their prior use. Others propose the issue stems from quality control problems leading to extended testing periods at the factory, or even the use of drives in cryptocurrency mining operations before being sold as new. Several users share personal anecdotes of encountering similar issues with Seagate drives, reinforcing suspicion about the company's practices. Skepticism also arises about the reliability of SMART data as an indicator of true drive usage, with some arguing it can be manipulated. Some users suggest buying hard drives from more reputable retailers or considering alternative brands to avoid potential issues.
German consumers are reporting that Seagate hard drives advertised and sold as new were actually refurbished drives with heavy prior usage. Some drives reportedly logged tens of thousands of power-on hours and possessed SMART data indicating significant wear, including reallocated sectors and high spin-retry counts. This affects several models, including IronWolf and Exos enterprise-grade drives purchased through various retailers. While Seagate has initiated replacements for some affected customers, the extent of the issue and the company's official response remain unclear. Concerns persist regarding the potential for widespread resale of used drives as new, raising questions about Seagate's quality control and refurbishment practices.
Hacker News commenters express skepticism and concern over the report of Seagate allegedly selling used hard drives as new in Germany. Several users doubt the veracity of the claims, suggesting the reported drive hours could be a SMART reporting error or a misunderstanding. Others point out the potential for refurbished drives to be sold unknowingly, highlighting the difficulty in distinguishing between genuinely new and refurbished drives. Some commenters call for more evidence, suggesting analysis of the drive's physical condition or firmware versions. A few users share anecdotes of similar experiences with Seagate drives failing prematurely. The overall sentiment is one of caution towards Seagate, with some users recommending alternative brands.
Dan Luu's "Working with Files Is Hard" explores the surprising complexity of file I/O. While seemingly simple, file operations are fraught with subtle difficulties stemming from the interplay of operating systems, filesystems, programming languages, and hardware. The post dissects various common pitfalls, including partial writes, renaming and moving files across devices, unexpected caching behaviors, and the challenges of ensuring data integrity in the face of interruptions. Ultimately, the article highlights the importance of understanding these complexities and employing robust strategies, such as atomic operations and careful error handling, to build reliable file-handling code.
HN commenters largely agree with the premise that file handling is surprisingly complex. Many shared anecdotes reinforcing the difficulties encountered with different file systems, character encodings, and path manipulation. Some highlighted the problems of hidden characters causing issues, the challenges of cross-platform compatibility (especially Windows vs. *nix), and the subtle bugs that can arise from incorrect assumptions about file sizes or atomicity. A few pointed out the relative simplicity of dealing with files in Plan 9, and others mentioned more modern approaches like using memory-mapped files or higher-level libraries to abstract away some of the complexity. The lack of libraries to handle text files reliably across platforms was a recurring theme. A top comment emphasizes how corner cases, like filenames containing newlines or other special characters, are often overlooked until they cause real-world problems.
The blog post argues that file systems, particularly hierarchical ones, are a form of hypermedia that predates the web. It highlights how directories act like web pages, containing links (files and subdirectories) that can lead to other content or executable programs. This linking structure, combined with metadata like file types and modification dates, allows for navigation and information retrieval similar to browsing the web. The post further suggests that the web's hypermedia capabilities essentially replicate and expand upon the fundamental principles already present in file systems, emphasizing a deeper connection between these two technologies than commonly recognized.
Hacker News users largely praised the article for its clear explanation of file systems as a foundational hypermedia system. Several commenters highlighted the elegance and simplicity of this concept, often overlooked in the modern web's complexity. Some discussed the potential of leveraging file system principles for improved web experiences, like decentralized systems or simpler content management. A few pointed out limitations, such as the lack of inherent versioning in basic file systems and the challenges of metadata handling. The discussion also touched on related concepts like Plan 9 and the semantic web, contrasting their approaches to linking and information organization with the basic file system model. Several users reminisced about early computing experiences and the directness of navigating files and folders, suggesting a potential return to such simplicity.
The author migrated away from Bcachefs due to persistent performance issues and instability despite extensive troubleshooting. While initially impressed with Bcachefs's features, they experienced slowdowns, freezes, and data corruption, especially under memory pressure. Attempts to identify and fix the problems through kernel debugging and communication with the developers were unsuccessful, leaving the author with no choice but to switch back to ZFS. Although acknowledging Bcachefs's potential, the author concludes it's not currently production-ready for their workload.
HN commenters generally express disappointment with Bcachefs's lack of mainline inclusion in the kernel, viewing it as a significant barrier to adoption and a potential sign of deeper issues. Some suggest the lengthy development process and stalled upstreaming might indicate fundamental flaws or maintainability problems within the filesystem itself. Several commenters express a preference for established filesystems like ZFS and btrfs, despite their own imperfections, due to their maturity and broader community support. Others question the wisdom of investing time in a filesystem unlikely to become a standard, citing concerns about future development and maintenance. While acknowledging Bcachefs's technically intriguing features, the consensus leans toward caution and skepticism about its long-term viability. A few offer more neutral perspectives, suggesting the author's experience might not be universally applicable and hoping for the project's eventual success.
This spreadsheet documents a personal file system designed to mitigate data loss at home. It outlines a tiered backup strategy using various methods and media, including cloud storage (Google Drive, Backblaze), local network drives (NAS), and external hard drives. The system emphasizes redundancy by storing multiple copies of important data in different locations, and incorporates a structured approach to file organization and a regular backup schedule. The author categorizes their data by importance and sensitivity, employing different strategies for each category, reflecting a focus on preserving critical data in the event of various failure scenarios, from accidental deletion to hardware malfunction or even house fire.
Several commenters on Hacker News expressed skepticism about the practicality and necessity of the "Home Loss File System" presented in the linked Google Doc. Some questioned the complexity introduced by the system, suggesting simpler solutions like cloud backups or RAID would be more effective and less prone to user error. Others pointed out potential vulnerabilities related to security and data integrity, especially concerning the proposed encryption method and the reliance on physical media exchange. A few commenters questioned the overall value proposition, arguing that the risk of complete home loss, while real, might be better mitigated through insurance rather than a complex custom file system. The discussion also touched on potential improvements to the system, such as using existing decentralized storage solutions and more robust encryption algorithms.
Obsidian-textgrams is a plugin that allows users to create and embed ASCII diagrams directly within their Obsidian notes. It leverages code blocks and a custom renderer to display the diagrams, offering features like syntax highlighting and the ability to store diagram source code within the note itself. This provides a convenient way to visualize information using simple text-based graphics within the Obsidian environment, eliminating the need for external image files or complex drawing tools.
HN users generally expressed interest in the Obsidian Textgrams plugin, praising its lightweight approach compared to alternatives like Excalidraw or Mermaid. Some suggested improvements, including the ability to embed rendered diagrams as images for compatibility with other Markdown editors, and better text alignment within shapes. One commenter highlighted the usefulness for quickly mocking up system designs or diagrams, while another appreciated its simplicity for note-taking. The discussion also touched upon alternative tools like PlantUML and Graphviz, but the consensus leaned towards appreciating Textgrams' minimalist and fast rendering capabilities within Obsidian. A few users expressed interest in seeing support for more complex shapes and connections.
Summary of Comments ( 68 )
https://news.ycombinator.com/item?id=43639642
HN commenters are skeptical of Google's "AI hypercomputer" announcement, viewing it more as a marketing push than a substantial technical advancement. They question the vagueness of the term "hypercomputer" and the lack of concrete details on its architecture and capabilities. Several point out that Google is simply catching up to existing offerings from competitors like AWS and Azure in terms of interconnected GPUs and high-speed networking. Others express cynicism about Google's track record of abandoning cloud projects. There's also discussion about the actual cost-effectiveness and accessibility of such infrastructure for smaller research teams, with doubts raised about whether the benefits will trickle down beyond large, well-funded organizations.
The Hacker News post titled "Google Cloud Rapid Storage" linking to a Google Cloud blog post about AI supercomputers has a modest number of comments, focusing on a few key themes. No one directly discusses "Rapid Storage" which is curious given the HN post title. Instead, they discuss the overall strategy and implications of Google's AI infrastructure investments.
Several commenters express skepticism about Google's ability to compete effectively with NVIDIA in the AI hardware space. One commenter points out Google's history of entering and exiting markets, suggesting that their commitment to AI hardware may not be long-term. They question whether Google has the necessary focus and expertise to challenge NVIDIA's dominance. This sentiment is echoed by another commenter who highlights the challenges Google faces in catching up to NVIDIA's established ecosystem and software stack.
Another discussion thread revolves around the closed nature of Google's AI infrastructure. Commenters contrast this with the more open approach of other players in the market, arguing that a closed ecosystem limits innovation and collaboration. They suggest that Google's strategy might hinder the broader adoption of their AI technology.
The high cost of using Google's AI infrastructure is also mentioned. One commenter questions the affordability of these advanced resources, suggesting that they are primarily accessible to large corporations and research institutions, potentially leaving smaller players at a disadvantage.
Finally, some commenters express interest in the technical details of Google's AI supercomputer, particularly the networking technology and the performance of their custom TPU chips. However, the comments lack in-depth technical analysis, primarily focusing on high-level strategic considerations and market dynamics. There is a desire for more information, but the comments remain at a relatively surface level in terms of technical specifics.