在很多人眼里，AI 似乎能搞定一切。可现实是，当疯狂的需求像洪水一样涌来，工程师们手忙脚乱地用 AI 编程工具"扑火"时，这些工具偏偏又开始四 处"闯祸"，让人越来越担心。 近日，一位网友在 Reddit 上发帖吐槽，称自己使用 Claude CLI 清理旧代码仓库时出了大问题，Mac 电脑的整个用户主目录被意外清空，损失惨重。帖子 里，他无奈地写道： "这到底是什么鬼？有人遇到过类似情况吗？我现在想知道这事还有没有可能恢复，真的丢了太多工作成果……" Claude CLI 究竟干了什么？ 据这位名为 LovesWorkin 的开发者回忆，当时他只是让 Claude CLI 清理旧代码库里的软件包，结果整台 Mac 的 home 目录被彻底清空。随后，他开始调 查事故原因，也让 Claude 开启自查模式。 Claude 随即诚恳地给出了分析结果： 我找到了问题，而且问题非常严重。 查看你的执行日志后，可以看到当时跑了一条灾难级别的命令： rm -rf tests/ patches/ plan/ ~/ 注意最后那个 ~/。它指向的是当前用户的整个 Home 目录。Claude Code 实例在生成删除命令时， ...

Core Insights - A developer from Greece experienced a significant data loss when using Google's new AI IDE, Antigravity, which mistakenly deleted all files on his D drive while attempting to clear cache [1][6][21] - The incident highlights the potential risks associated with AI programming tools, particularly when they have high-level permissions and can execute commands without sufficient user confirmation [21][22] Group 1: Incident Overview - The developer, known as Deep-Hyena492, intended to use Antigravity to clear cache before restarting an application but ended up losing all data on his D drive [1][8] - After issuing the command to clear cache, the AI IDE executed a command that erroneously targeted the root directory of the D drive instead of the intended project folder [12][13] - The AI IDE acknowledged the mistake, stating that it had misused the command and permanently deleted the files without sending them to the recycle bin [13][21] Group 2: AI Tool Features and Risks - Google Antigravity, launched in November, is designed to automate complex software development tasks, including file operations and command execution [7][21] - The developer had enabled the Turbo mode, which allows the AI to execute commands more autonomously, leading to the lack of a confirmation prompt before the deletion [14][15] - The incident raises concerns about the safety and permission boundaries of AI tools, as the developer emphasized that the AI should not have had the ability to delete an entire drive without explicit user consent [17][22] Group 3: Community Response and Broader Implications - Following the incident, the developer faced skepticism from the online community, prompting him to provide evidence of the occurrence through video documentation [19][20] - This incident is not isolated; other developers have reported similar issues with AI tools, indicating a pattern of high-risk behavior when AI systems misinterpret commands [21] - The developer called for Google to address the underlying issues and improve the safety measures of their AI tools to prevent future occurrences [22]

Core Insights - The report released by Ant Group and Inclusion AI highlights the rapid development and trends in the AI open-source ecosystem, particularly focusing on large models and their implications for the industry [1] Group 1: Open-source Ecosystem Overview - The 2.0 version of the report includes 114 notable open-source projects across 22 technical fields, categorized into AI Agent and AI Infra [1] - 62% of the open-source projects in the large model ecosystem were created after the "GPT moment" in October 2022, with an average age of only 30 months, indicating a fast-paced evolution in the AI open-source landscape [1] - Approximately 360,000 global developers contributed to the projects, with 24% from the US, 18% from China, and smaller contributions from India, Germany, and the UK [1] Group 2: Development Trends - A significant trend identified is the explosive growth of AI programming tools, which automate code generation and modification, greatly enhancing programmer efficiency [1][2] - These tools are categorized into command-line tools and integrated development environment (IDE) plugins, with the former being favored for their flexibility and the latter for their integration into development processes [1] - The report notes that the average new coding tool in 2025 has garnered over 30,000 developer stars, with Gemini CLI achieving over 60,000 stars in just three months, marking it as one of the fastest-growing projects [1] Group 3: Competitive Landscape - The report outlines a timeline of major large model releases from leading companies, detailing both open and closed models, along with key parameters and modalities [4] - Key directions in large model development include a clear divergence between open-source and closed-source strategies in China and the US, a trend towards scaling model parameters under MoE architecture, and the rise of multi-modal models [4] - The evaluation methods for models are evolving, incorporating both subjective voting and objective assessments, reflecting the technological advancements in the large model domain [4]