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我一直在尝试使用NotebookLM，不仅仅是为了总结，还用于对我自己的自动化工作流程进行“红队测试”。我给它提供了一个我构建的n8n研究代理的视频演示，并使用自定义指令将音频概述的主持人塑造成一个特定的人物角色：“厌世、愤世嫉俗的科技评论员。”我的目标是看看模型是否能够仅通过视频流识别出特定的节点逻辑和架构选择（例如手动处理服务器推送事件和强制JSON输出），而不仅仅是总结项目的“要点”。我使用的提示是：“扮演两个厌世、愤世嫉俗的科技评论员……只关注最令人印象深刻的特性……注意‘服务器推送事件’（SSE）配置……承认4层架构是合理的工程设计。”结果：模型成功识别出了我在n8n中实现的特定超时处理逻辑，而标准的总结通常会忽略这一点。如果你提示的是敌意而非同意，这似乎是一种获取架构流程“合成第二意见”的可行方法。输出视频（4分钟）：https://youtu.be/oof9JB3OFO4有没有其他人成功在NotebookLM中强制特定的技术角色？

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I've been experimenting with using NotebookLM not just for summarization, but for "Red Teaming" my own automation workflows.I fed it a video demo of an n8n research agent I built and used Custom Instructions to force the Audio Overview hosts into a specific persona: "Jaded, cynical tech reviewers."My goal was to see if the model could pick up on specific node logic and architectural choices (like manual Server-Sent Events handling and JSON output forcing) from the video feed alone, rather than just summarizing the "gist" of the project.The Prompt I used: "Act as two jaded, cynical tech reviewers... Focus ONLY on the single most impressive feature... Notice the 'Server-Sent Events' (SSE) configuration... Admit that the 4-Layer Architecture is legitimate engineering."The Result: The model successfully identified the specific timeout handling logic I implemented in n8n, which a standard summarization usually misses. It seems like a viable way to get a "synthetic second opinion" on architectural flows if you prompt for hostility rather than agreement.Video of the output (4 min): https://youtu.be/oof9JB3OFO4Has anyone else had success forcing specific technical personas in NotebookLM?

实验：将NotebookLM用作讽刺的代码审查员（通过自定义提示）