一位学音乐、零技术基础的Meta产品经理，在一次旅行中偶然被Claude唤醒了「超能力」！他利用AI重构工作流，2天竟能干完一个团队几周 的活。 不会用AI的人，终将被那些善用AI的人取代！ 在AI浪潮下，这句话曾像警钟一样敲打着无数人的神经。 但如今，这句话却演绎出了一个全新的版本： 当他看到演示视频中，有人仅用自然语言就构建出完整的应用程序时，一种电流般的战栗感瞬间击穿了他。 「嗨，你现在拥有超能力了。」 他甚至没来得及解开行李箱，就冲向了电脑。 那一刻他意识到：那堵横亘在他与创造力之间的高墙，被推倒了。 Zevi的故事绝非孤例。 AI正在把构建未来的权力，交还给每一个有想法的普通人。 主持人Lenny（左）与Meta产品经理Zevi Arnovitz（右） 在最新的《Lenny's Podcast》中，Zevi分享了自己的这段有趣经历。 借助Cursor和Claude，他不仅治愈了对代码的恐惧，更重新定义了产品经理的边界。 AI不会替代你，而会为你推倒那道挡在你与梦想之间的技术高墙。 Meta产品经理Zevi Arnovitz就是一个很好的例子。 高中搞音乐，大学读心理，Zevi的履历上写满了「非技术」 ...

Core Insights - The article highlights the innovative approach of Zevi Arnovitz, a product manager at Meta, who successfully developed a profitable AI tool called StudyMate without prior coding experience, by leveraging AI as a collaborative team rather than just a tool [1][2][3]. Group 1: Initial Steps - Zevi's first step was to establish a dedicated dialogue with an AI, assigning it the role of CTO, which allowed for a collaborative environment where the AI could challenge his ideas and provide technical insights [5][6][11]. - The focus for non-technical individuals is on learning to collaborate with AI, emphasizing communication skills over technical knowledge [7][8][12]. Group 2: Building an AI Team - After establishing a dialogue, Zevi expanded from using a single AI to creating a team of AIs, each with specific roles and responsibilities, allowing for a more efficient development process [13][14][19]. - This division of labor among AIs was crucial, as each model had its strengths and weaknesses, enabling them to focus on their areas of expertise [15][16][18]. Group 3: Product Development Process - The development of StudyMate involved a structured process from idea generation to product launch, including defining tasks, analyzing technical risks, and conducting thorough testing [22][24][26][29]. - Zevi emphasized the importance of iterative feedback and documentation throughout the development cycle, allowing for continuous improvement and learning [30][31][32]. Group 4: Replicable Workflow - Zevi's approach resulted in a standardized workflow that can be replicated by others, consisting of clear steps for engaging with AI, executing tasks, and reviewing outcomes [35][36][38]. - The workflow encourages a systematic method for transforming ideas into actionable tasks, ensuring that each stage of development is documented and can be refined over time [41][42].