arXiv is migrating its infrastructure from Cornell University servers to Google Cloud. This move aims to enhance arXiv's long-term sustainability, improve performance and scalability, and leverage Google's expertise in areas like security, storage, and machine learning. The transition will happen in phases, starting with a pilot program. arXiv emphasizes its commitment to remaining open and community-driven, with its operational control staying independent. They are also actively hiring for several roles, including software engineers and system administrators, to support this significant change.
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.
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.
Firebase Studio is a visual development environment built for Firebase, offering a low-code approach to building web and mobile applications. It simplifies backend development with pre-built UI components and integrations for various Firebase services like Authentication, Firestore, Storage, and Cloud Functions. Developers can visually design UI layouts, connect them to data sources, and implement logic without extensive coding. This allows for faster prototyping and development, particularly for frontend developers who may be less familiar with backend complexities. Firebase Studio aims to streamline the entire Firebase development workflow, from building and deploying apps to monitoring performance and user engagement.
HN commenters generally expressed skepticism and disappointment with Firebase Studio. Several pointed out that it seemed like a rebranded version of FlutterFlow, offering little new functionality. Some questioned the value proposition, especially given FlutterFlow's existing presence and the perception of Firebase Studio as a closed-source, vendor-locked solution. Others were critical of the pricing model, considering it expensive compared to alternatives. A few commenters expressed interest in trying it out, but the overall sentiment was one of cautious negativity, with many feeling that it didn't address existing pain points in Firebase development.
BigQuery now supports SQL pipe syntax in public preview. This feature simplifies complex queries by allowing users to chain multiple SQL statements together, passing the results of one statement as input to the next. This improves readability and maintainability, particularly for transformations involving several steps. The pipe operator, |, connects these statements, offering a more streamlined alternative to subqueries and common table expressions (CTEs). This syntax is compatible with various SQL functions and operators, enabling flexible data manipulation within the pipeline.
Hacker News users generally expressed enthusiasm for BigQuery's new pipe syntax, finding it more readable and maintainable than traditional nested queries. Several commenters compared it favorably to dplyr in R and praised its potential for simplifying complex data transformations. Some highlighted the benefits for data scientists and analysts less familiar with SQL intricacies. A few users raised questions about performance implications and debugging, while others wondered about future compatibility with other SQL dialects and the potential for integration with tools like dbt. Overall, the sentiment was positive, with many viewing the pipe syntax as a significant improvement to the BigQuery SQL experience.
Summary of Comments ( 106 )
https://news.ycombinator.com/item?id=43726640
Hacker News users discuss arXiv's move to Google Cloud, expressing concerns about potential vendor lock-in and the implications for long-term data preservation. Some question the cost-effectiveness of the transition, suggesting Cornell's existing infrastructure might have been sufficient with modernization. Others highlight the potential benefits of Google's expertise in scaling and reliability, but emphasize the importance of maintaining open access and avoiding proprietary formats. The need for transparency regarding the terms of the agreement with Google is also a recurring theme, alongside worries about potential censorship or influence from Google on arXiv's content. Several commenters note the irony of a pre-print server initially designed to bypass traditional publishing now relying on a large tech company.
The Hacker News post titled "arXiv moving from Cornell servers to Google Cloud" generated several comments discussing the implications of this transition. Many commenters focused on the potential benefits and drawbacks of moving to a cloud infrastructure.
Several users expressed concerns about Google's potential influence over arXiv's content and operations. One commenter worried about the possibility of Google exerting censorship or prioritizing certain research based on its own interests. Another questioned whether Google might eventually try to monetize arXiv, impacting its open-access nature. The potential for vendor lock-in with Google was also raised as a long-term risk.
On the other hand, some commenters saw the move as a positive step. They argued that Google Cloud's infrastructure could offer improved performance, scalability, and reliability compared to Cornell's existing setup. This could lead to faster download speeds, increased uptime, and better overall user experience. The potential for enhanced search capabilities and integration with other Google services was also mentioned as a potential advantage.
Several comments delved into the technical aspects of the migration. One user with experience in academic computing discussed the challenges of managing a large-scale digital library and suggested that Google's expertise in this area could be beneficial. Another pointed out the potential complexities of migrating the existing data and ensuring seamless operation during the transition.
Some commenters speculated on the reasons behind arXiv's decision, suggesting factors such as cost savings, access to more advanced technology, and the need for specialized expertise that Google could provide.
A few users expressed nostalgia for Cornell's long-standing stewardship of arXiv, while acknowledging the increasing demands and complexities of maintaining the platform in the current technological landscape.
The discussion also touched on broader themes related to the role of large tech companies in academic research and the importance of preserving the open and accessible nature of scientific knowledge. Some users expressed concerns about the increasing concentration of power in the hands of a few large corporations, while others argued that collaboration with such companies could be beneficial for the advancement of science.