Everyday digital feminism refers to the ordinary, often pragmatic ways women articulate lived experiences and cultivate solidarity in online spaces. In China, such practices flourish on RedNote through discussions under hashtags like ''women's growth''. Recently, DeepSeek-generated content has been taken up as a new voice in these conversations. Given widely recognized gender biases in LLMs, this raises critical concerns about how LLMs interact with everyday feminist practices. Through an analysis of 430 RedNote posts, 139 shared DeepSeek responses, and 3211 comments, we found that users predominantly welcomed DeepSeek's advice. Yet feminist critical discourse analysis revealed that these responses primarily encouraged women to self-optimize and pursue achievements within prevailing norms rather than challenge them. By interpreting this case, we discuss the opportunities and risks that LLMs introduce for everyday feminism as a pathway toward women's empowerment, and offer design implications for leveraging LLMs to better support such practices.

翻译：日常数字女性主义指的是女性在网络空间中通过日常化、通常务实的方式表达生活经验并建立团结的实践。在中国，此类实践在RedNote平台上通过“女性成长”等话题标签下的讨论而蓬勃发展。近期，由DeepSeek生成的内容已成为这些对话中的新兴声音。鉴于大语言模型中普遍存在的性别偏见，这引发了关于大语言模型如何与日常女性主义实践互动的关键关切。通过对430篇RedNote帖子、139条共享的DeepSeek回复及3211条评论的分析，我们发现用户普遍欢迎DeepSeek的建议。然而，女性主义批判话语分析揭示，这些回复主要鼓励女性在主流规范内进行自我优化并追求成就，而非挑战这些规范。通过解读这一案例，我们讨论了大语言模型为作为女性赋权途径的日常女性主义带来的机遇与风险，并为利用大语言模型更好地支持此类实践提供了设计启示。