AI大V张咋啦5万粉丝增长秘籍:算法即人脉、警惕AI异化......

marsbitPublished on 2026-03-24Last updated on 2026-03-24

Abstract

AI博主张咋啦分享在X平台快速获取5万粉丝的核心策略:关键在于将平台视为"参与对话的派对"而非单向表演的舞台。她提出四大增长原则:一是精心调教算法推荐,通过主动互动筛选高质量内容;二是拒绝AI批量回复,强调真诚互动能引发算法推荐和大佬关注;三是养成每日发帖习惯,通过公开学习、产品反馈和项目演示提供价值;四是避免过早依赖AI写作,需先建立个人风格。文末指出其成功亦受益于硅谷AI人脉圈和原有小红书粉丝基础,算法本质是社交关系的变现。

作者:zarazhangrui

编译:XinGPT

编者按:张咋啦在来X之前,就是小红书的著名AI博主,自己的定位以哈佛本科和硅谷AI大厂为卖点,做Vibe coding产品类的内容而著称。

以下是翻译全文:“

大多数人在 X 上做不起来,是因为他们的心智模型搞错了。他们把 X 当成一个舞台,觉得自己要走上台去,对着观众发表一场演讲。

正确的心智模型是:X 是一场派对。当你走进一场派对的时候,你不会跑到角落里对着空气自说自话。你会找到一群已经聚在一起的人,听听他们正在聊什么,然后加入他们的对话。

这个心智模型是我所有增长策略的基础:

1. 调教你的信息流

把这个环节理解为"搞清楚该参加哪些派对,以及你想跟哪群人混在一起"。如果你的信息流里全是垃圾内容和标题党,你不会有发帖的灵感,你会参与到错误的对话中去。

在我开始规律发帖之前,我花了大概两周时间刻意调教我的 X 算法。具体来说,每当我看到标题党或者我根本不关心的话题时,我就点击右上角的三个点 -> "不感兴趣"。每当我看到有深度、有价值的内容时,我就通过点赞、转发、回复或收藏来与之互动。随着时间推移,算法会学会把更多高质量的内容推送给你。我现在的 X 信息流里全是关于 AI、新产品发布以及各种有深度的讨论——这些都是我真正想参与的内容,既有启发性又有教育意义。

2. 真诚地回复

不要用 AI 机器人来帮你批量回复。我再说一遍,不要用 AI 机器人来帮你批量回复。

为了回复而回复是没有任何意义的。当我看到自己帖子下面那些明显是 AI 生成的、只是在复述我说过的话的回复时,我实际上觉得被冒犯了。那种感觉极其敷衍、不真诚、而且偷懒。

回到派对的类比上来,回复就像是把头探进一圈人当中,参与他们已有的对话。你得说点真正有意思的东西,这样人们才会注意到你,并且欢迎你加入他们的圈子。

假设你是 A,想通过在 B 的帖子下面回复来和 B 互动——而 B 的粉丝比你多得多。当你真诚且有质量地互动时,B 会回应你——通过给你的回复点赞、回复你的回复,或者反过来关注你。这会告诉算法:"这两个人互相喜欢,让我把更多 A 的内容推送给 B 吧。"然后下次你在自己的时间线上说了什么,就更有可能出现在 B 的时间线上,而且 B 有可能会转发。这就是你增长的方式。

3. 持续发帖,形成条件反射

这是最重要的一条规则。如果你不遵守这条规则,其他所有东西都不重要。我给自己定的目标是每天发 3 条帖子。

这不是时间管理的问题。那些极其忙碌的创始人、CEO 和投资人每天都能发 5 到 10 条帖子。这是一个把门槛降低到足够低、让它变成条件反射的问题。它需要成为你每天已经在做的事情的自然延伸。不要想太多。

那些条件反射是什么呢?以下是几个真正帮助我保持持续输出的习惯:

1. 公开学习

我经常看关于 AI 和创业的视频播客和新闻简报。每当我看到引起共鸣的内容,我就截图并把那句话发到 X 上。重要的是,我会确保 @ 那个说这番话的人。这既是一种尊重原创者的友好姿态,也会让那个人注意到你。他们有可能会转发(人们都喜欢听到关于自己的好话)或者关注你。如果你学到了新东西,就把这些知识分享给全世界。我把 X 当作一本公开的笔记本来用。

2. 公开反馈

作为工作的一部分,我会试用大量新的 AI 产品。每当我遇到一个让我印象深刻的产品或功能,我就在 X 上写一篇公开的夸赞或反馈帖,并确保 @ 他们的官方账号和创始人。创业公司的创始人——尤其是早期阶段的——超级喜欢产品反馈。根据我的经验,如果你作为用户分享了有深度的反馈,90% 的情况下创始人会回复你、转发你、或者私信你。

3. 公开构建

今年我开始玩 vibe coding(氛围编程),我很多最火的帖子都是我用 vibe coding 做出来的产品演示。X 上的人超级喜欢产品演示。所有主要 AI 实验室的开发者关系负责人都在积极关注人们用他们的技术做了什么(我有两个项目被 Google AI Developer 的官方 X 账号转发推荐了)。使用 Screen Studio 之类的工具同时录制你的屏幕和你本人,做 1 到 2 分钟的演示视频。不需要多精致。尤其是当新模型发布的时候,赶紧用它做点东西然后分享出来,哪怕只是一个粗糙的原型。

不要一上来就推销你的产品或公司。对于以上所有这些习惯,我都是在给社区提供价值,而不期待任何回报。人们需要先把你看作一个真诚的、能够提供价值的个体,之后他们才会去关注你未来想说的或想卖的东西。

4. 在形成自己的品味和声音之前,不要用 AI 写作

用 AI 来写东西本身并不是坏事。危险在于,在你还没有形成对什么是好内容的品味之前就用 AI 来写。如果 AI 产出了垃圾内容,你甚至不会意识到那是垃圾。

X 把真实性看得比什么都重要。粗糙胜过精致。这不是关于语法完美或用词华丽。就像你在现实生活中怎么说话,就怎么写。

大量阅读,搞清楚好的内容长什么样。大量发帖,知道自己的声音听起来是什么样的。只有在那之后,才用 AI 来辅助你写作,但要确保写出来的东西听起来确实像你自己。

最终目标不是在 X 上增长本身,而是在增长的同时,依然做真实的自己。”

张咋啦没有告诉你的几点增长秘籍:

1. X的算法对于社交关系权重非常高,而张咋啦在硅谷积累的AI top人脉圈和其之前在大厂平台的经历,让她拥有了非常好的人脉互关网络。

比如她的关注者中包括了像腾讯AI、Minimax、Kimi等非常头部的AI企业号,以及很多AI的创业者和大佬,X的算法本质上也是一种人脉关系的变现。(所以求大佬互关真的有用,大平台也是优势!)

2. 她来运营X之前,在小红书也已经是AI领域的大V,积累了一批种子粉丝,当她开始做X的时候自然有一批已经关注过她的粉丝作为种子用户。

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Related Questions

Q张咋啦认为在X平台上增长粉丝的正确心智模型是什么?

A张咋啦认为正确的心智模型是把X平台看作一场派对,而不是舞台。用户应该像在派对上一样,先倾听他人对话,然后真诚地加入讨论,而不是自说自话。

Q张咋啦提到的调教信息流具体是如何操作的?

A她通过刻意调教X算法来优化信息流:对不感兴趣或标题党内容点击“不感兴趣”,对有深度、有价值的内容通过点赞、转发、回复或收藏进行互动。这样算法会逐渐推送更多高质量内容,帮助用户找到合适的对话圈子。

Q为什么张咋啦强调不要用AI机器人批量回复?

A她认为AI生成的回复显得敷衍、不真诚且偷懒,会冒犯他人。真诚的回复才能有效参与对话,吸引他人注意,并通过算法互动(如点赞、回复)促进粉丝增长。

Q张咋啦提到的“公开学习”和“公开反馈”具体指什么?

A“公开学习”是指分享学习AI和创业内容时的共鸣点,截图发帖并@原创者,既尊重对方也可能获得转发或关注。“公开反馈”是指试用AI产品后发布深度夸赞或反馈帖,并@官方账号和创始人,容易获得回复和互动。

Q张咋啦的成功除了策略外,还有哪些隐藏优势?

A她拥有硅谷AI顶级人脉圈和之前在大厂平台的经历,关注者中包含头部AI企业号和创业者,X算法会加权这些人脉关系。此外,她在小红书已是AI大V,有种子粉丝基础,迁移到X时自然拥有初始用户群。

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How Does SPERO,$$s$ Work? SPERO,$$s$ employs a multi-faceted framework that distinguishes it from conventional cryptocurrency projects. Here are some of the key features that underline its uniqueness and innovation: Decentralised Governance: SPERO,$$s$ integrates decentralised governance models, empowering users to participate actively in decision-making processes regarding the project’s future. This approach fosters a sense of ownership and accountability among community members. Token Utility: SPERO,$$s$ utilises its own cryptocurrency token, designed to serve various functions within the ecosystem. These tokens enable transactions, rewards, and the facilitation of services offered on the platform, enhancing overall engagement and utility. Layered Architecture: The technical architecture of SPERO,$$s$ supports modularity and scalability, allowing for seamless integration of additional features and applications as the project evolves. This adaptability is paramount for sustaining relevance in the ever-changing crypto landscape. Community Engagement: The project emphasises community-driven initiatives, employing mechanisms that incentivise collaboration and feedback. By nurturing a strong community, SPERO,$$s$ can better address user needs and adapt to market trends. Focus on Inclusion: By offering low transaction fees and user-friendly interfaces, SPERO,$$s$ aims to attract a diverse user base, including individuals who may not previously have engaged in the crypto space. This commitment to inclusion aligns with its overarching mission of empowerment through accessibility. Timeline of SPERO,$$s$ Understanding a project's history provides crucial insights into its development trajectory and milestones. Below is a suggested timeline mapping significant events in the evolution of SPERO,$$s$: Conceptualisation and Ideation Phase: The initial ideas forming the basis of SPERO,$$s$ were conceived, aligning closely with the principles of decentralisation and community focus within the blockchain industry. Launch of Project Whitepaper: Following the conceptual phase, a comprehensive whitepaper detailing the vision, goals, and technological infrastructure of SPERO,$$s$ was released to garner community interest and feedback. Community Building and Early Engagements: Active outreach efforts were made to build a community of early adopters and potential investors, facilitating discussions around the project’s goals and garnering support. Token Generation Event: SPERO,$$s$ conducted a token generation event (TGE) to distribute its native tokens to early supporters and establish initial liquidity within the ecosystem. Launch of Initial dApp: The first decentralised application (dApp) associated with SPERO,$$s$ went live, allowing users to engage with the platform's core functionalities. Ongoing Development and Partnerships: Continuous updates and enhancements to the project's offerings, including strategic partnerships with other players in the blockchain space, have shaped SPERO,$$s$ into a competitive and evolving player in the crypto market. Conclusion SPERO,$$s$ stands as a testament to the potential of web3 and cryptocurrency to revolutionise financial systems and empower individuals. With a commitment to decentralised governance, community engagement, and innovatively designed functionalities, it paves the way toward a more inclusive financial landscape. As with any investment in the rapidly evolving crypto space, potential investors and users are encouraged to research thoroughly and engage thoughtfully with the ongoing developments within SPERO,$$s$. The project showcases the innovative spirit of the crypto industry, inviting further exploration into its myriad possibilities. While the journey of SPERO,$$s$ is still unfolding, its foundational principles may indeed influence the future of how we interact with technology, finance, and each other in interconnected digital ecosystems.

156 Total ViewsPublished 2024.12.17Updated 2024.12.17

What is $S$

What is AGENT S

Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

824 Total ViewsPublished 2025.01.14Updated 2025.01.14

What is AGENT S

Discussions

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