本文讲述了作者如何从损坏的备份中恢复数据的经历。首先,服务器出现NVMe硬盘I/O超时问题,通过修改GRUB配置解决。随后,VM虚拟机根分区损坏,尝试还原备份失败。借助AI工具GPT的帮助,作者找到了跳过校验强行提取数据的方法,成功挂载RAW磁盘并恢复关键文件。这一过程展示了数据恢复的实用技术,特别是AI在解决技术难题中的应用,对系统管理员和DevOps工程师具有参考价值。
原文链接:V2EX 分享发现
本文讲述了作者如何从损坏的备份中恢复数据的经历。首先,服务器出现NVMe硬盘I/O超时问题,通过修改GRUB配置解决。随后,VM虚拟机根分区损坏,尝试还原备份失败。借助AI工具GPT的帮助,作者找到了跳过校验强行提取数据的方法,成功挂载RAW磁盘并恢复关键文件。这一过程展示了数据恢复的实用技术,特别是AI在解决技术难题中的应用,对系统管理员和DevOps工程师具有参考价值。
原文链接:V2EX 分享发现
This article details the author's experience recovering data from a corrupted backup. First, the server experienced NVMe hard drive I/O timeout issues, which were resolved by modifying the GRUB configuration. Subsequently, the VM virtual machine's root partition was corrupted, and attempts to restore the backup failed. With the help of the AI tool GPT, the author found a method to forcibly extract data by skipping verification, successfully mounting the RAW disk and recovering critical files. This process demonstrates practical data recovery techniques, especially the application of AI in solving technical problems, providing valuable insights for system administrators and DevOps engineers.
Original Link:V2EX Share & Discover
The Pinduoduo platform experienced a severe technical glitch on December 3, 2023, which users exploited to abnormally obtain goods or services. This prompted the platform to implement emergency fixes and compensation measures. The incident exposed significant challenges for e-commerce platforms in security protection and system stability, involving cutting-edge technology topics such as cybersecurity vulnerabilities, user data protection, and risk control. Pinduoduo's rapid response in fixing the vulnerability and compensating affected users highlights how tech companies can handle sudden technical crises. This event holds important reference value for readers interested in technology, AI, and cutting-edge innovations, revealing common issues and improvement directions for the e-commerce industry in the realm of technical security.
Original Link:V2EX Share & Discover
Singapore-based startup Butterfly Effect's AI agent Manus recently announced that its annualized revenue has reached $125 million, a significant increase from $90 million in August. The company's annual recurring revenue has also hit $100 million, just 8 months after launching its paid subscription service in April. Manus was launched in March this year, powered by AI foundation models from companies like Anthropic to drive its agent services. It currently offers subscription plans starting at $17 per month, with the premium tier priced at $167 per month. Butterfly Effect completed a $75 million funding round led by Benchmark in April, reaching a valuation of $500 million, and now has 105 employees across Singapore, Tokyo, and San Francisco. Manus' rapid growth reflects the commercial potential in the AI agent sector.
Original link:V2EX Share & Discover
This article introduces a global server latency testing tool developed based on Contabo speed test API and antigravity technology. The tool uses a reverse ping method, parsing non-existent images within regions and capturing errors to determine latency, solving the technical challenge of browsers being unable to ping directly. The developer has provided a card-based interface design, with different colors intuitively displaying latency status, and has added filtering conditions and internationalization support. This tool can help users test network latency across different regions and VPS providers, serving as a reference for selecting cloud services. The source code has been released on open source platforms, and the developer welcomes community feedback, with plans to expand speed test nodes to more regions.
Original Link:Linux.do
Developers are facing challenges with AI in VSCode extensions that don't follow instructions: despite configuring global AGENTS.md and workspace settings, the AI insists on modifying code rather than just providing suggestions. While setting read-only permissions can temporarily restrict behavior, frequently switching permissions creates additional overhead, affecting development efficiency. This phenomenon highlights the control challenges of AI Agents in practical applications, sparking deeper thinking about AI behavior boundaries and user instruction compliance mechanisms. The article explores real-world cases on how to optimize configurations for precise instruction control while maintaining AI initiative, offering practical advice for developers to enhance the AI-assisted development experience.
Original link:Linux.do
This article describes a technical issue encountered while using Antigravity: when processing long code, the system would disconnect directly without continuing execution, affecting development efficiency. To address this bug, the author proposes a simple and practical solution—respond every hundred lines of code written, which ensures smooth processing. This technique is also applicable in 2API scenarios, effectively avoiding code interruption. This finding comes from a discussion in the Linux community, providing developers with practical debugging and optimization tips that help improve the stability and efficiency of code processing, especially when dealing with complex projects.
Original link:Linux.do
最新评论
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