故事是这样的。 我最近一直在招人，想招点人帮我分担一些压力，全职的实习的啥的都可以。 我这再怎么说，也是一个跟AI有关的地方，所以很多人在投简历的时候，都会写很多跟AI相关的经历，我甚至收到过很多AI生成的简历。 很多写的很玄乎，什么掌握全链路工作流，独立搭建xx系统，深度参与xx项目，掌握xx行业资源等等，但是一面，问用过最惊艳的AI产品是啥，10个有9 个说的是DeepSeek，再问最常用的AI产品是啥，还是DeepSeek。 再追问还用过哪些其他的AI产品？10个有9个说的就是豆包。 真的，我觉得我现在对DeepSeek有点PTSD可能就是从这来的。 不过，这个端午节，我收到了一个让我觉得有点与众不同、眼前一亮的简历。 第一次，看到一个人，把自己变成了AI简历。 我点进去看了下。 虽然已经跟她沟通过，写出来没啥问题，但是为了保护隐私，我还是都打码了。 虽然整体设计的很青涩，非常的AI，但是我依然觉得，这个非常的有意思。 毕竟，在千篇一律的PDF简历之后，我终于看到了一个，不一样的。是用AI编程把自己的简历，给可视化的东西。 但是如果只是这样，我觉得也还好，毕竟PDF做成可视化网页已经流行很久很久了，这也 ...

但今天晚上躺在床上，辗转反侧睡不着觉，我已经很久没睡过一个好觉了，我决定还是起来，有些话 特别想和你们聊一聊，聊一些事。 准确地说，是关于AI焦虑。 过去这一两个月，说句大实话，我过得不太好。 甚至，可以说是非常非常的糟糕。 具体糟糕到什么程度呢？ 就是那种你表面看上去很正常，每天照样更新着公众号，日常也在各种群里活跃，偶尔也会对一些新 的AI工具、新的AI产品评价几句，说些看起来挺厉害的话，但是总是会有一种，身心俱疲的感觉。 平时你们见到的我，好像永远精力充沛，好像总是能从各种新技术、新产品中找到快乐和兴奋点。 见字如面。 现在是2025年5月30日凌晨5点06，5月工作日的最后一天。 说实话，我本来没什么打算为这个最后一天写点什么，最近有点太累了，只想好好放个假。 但这背后，一直伴随着很深的焦虑。 直到最近一两个月，这焦虑感越来越强。 这种焦虑并不是忽然爆发出来的，而是像一个影子，一直带给你漫长的潮湿。只是过去一年多，我习 惯性地忽略它，用无数次尝试新产品、新技术的兴奋感，来掩盖它的存在。 但最近一个月，新的应用和产品还好，但是特别像是Midjourney V7、Gemini 2.5 Pro更新、Clua ...

Core Insights - The launch of Kling 2.1 introduces significant improvements in effectiveness, speed, and pricing, making it a compelling option for users [1][27]. - Kling 2.1 offers three distinct models: Standard, High Quality, and Master, catering to different user needs and budgets [10][28]. Pricing and Value - The pricing structure has been adjusted, with the High Quality version of Kling 2.1 being 65% cheaper than the previous Master version, making it more accessible for everyday users [10][27]. - The Standard version is priced at 20 inspiration points for 720P, the High Quality version at 35 inspiration points for 1080P, and the Master version at 100 inspiration points for high-end cinematic effects [10][28]. Performance Comparison - Kling 2.1 High Quality and Master versions outperform previous models in terms of visual quality and dynamic motion, with the Master version providing superior results for professional-grade projects [27][28]. - Speed tests indicate that Kling 2.1 performs comparably to Kling 1.6, with both completing tasks in under one minute, while the Master versions take over three minutes [18][27]. User Experience - Users have reported that the Professional Mode of Kling 2.1 is sufficient for most casual video styles, while the Master version is better suited for action scenes and high-intensity projects [2][28]. - The updates have made it possible for a broader range of creators to access high-quality video generation tools, enhancing the overall user experience [27][28]. Market Positioning - Kling 2.1 aims to fill the gap between affordability and quality, allowing users to choose models based on their specific creative needs and budget constraints [28]. - The differentiation between the three models allows for targeted marketing towards various segments, from casual creators to professional filmmakers [28].

Core Viewpoint - The article discusses the advancements in AI podcasting technology, particularly focusing on the capabilities of "扣子空间" (Coze Space) to generate highly realistic and engaging audio content from written material, thus transforming the content creation landscape for creators and listeners alike [1][2][10]. Group 1: AI Podcasting Technology - The AI podcasting feature from Coze Space allows users to convert written articles into audio podcasts with a human-like quality, making the experience more immersive and engaging [1][2]. - Users can easily generate podcasts by uploading text files and providing a simple prompt, eliminating the need for complex setups or additional plugins [2][4]. - The technology not only generates audio but also creates a visual webpage that displays subtitles alongside the audio, enhancing the user experience [6][21]. Group 2: User Experience and Market Impact - The article highlights the emotional responses elicited by the AI-generated podcasts, ranging from shock to excitement, indicating a significant leap in audio content quality [2][3]. - AI podcasts are seen as a solution to the high production costs and time associated with traditional human-hosted podcasts, potentially democratizing content creation [9][10]. - The rise of AI podcasts may blur the lines between auditory and visual content consumption, as users may prefer listening to news or articles during activities like driving or cooking [12][13]. Group 3: Future of Content Creation - The article suggests that AI podcasts could evolve into a new medium, allowing for various content types (text, audio, video) to be transformed into engaging audio formats [11][14]. - There is a belief that while AI podcasts can provide knowledge and entertainment, they cannot fully replicate the unique connection and emotional engagement that human hosts offer [28][30]. - The expansion of AI podcasting is viewed as an opportunity to broaden the podcasting audience rather than replace human creators, fostering a more inclusive content landscape [29][30].

Core Viewpoint - The article provides a comprehensive comparison of five mainstream LLM application platforms: Dify, Coze, n8n, FastGPT, and RAGFlow, emphasizing the importance of selecting the right platform based on individual needs and use cases [1][2]. Group 1: Overview of LLM Platforms - LLM application platforms significantly lower the development threshold for AI applications, accelerating the transition from concept to product [2]. - These platforms allow users to focus on business logic and user experience innovation rather than repetitive underlying technology construction [3]. Group 2: Platform Characteristics - **n8n**: Known for its powerful general workflow automation capabilities, it allows users to embed LLM nodes into complex automation processes [4]. - **Coze**: Launched by ByteDance, it emphasizes low-code/no-code AI agent development, enabling rapid construction and deployment of conversational AI applications [5]. - **FastGPT**: An open-source AI agent construction platform focused on knowledge base Q&A systems, offering data processing, model invocation, and visual workflow orchestration capabilities [6]. - **Dify**: An open-source LLM application development platform that integrates BaaS and LLMOps concepts, providing a one-stop solution for rapid AI application development and operation [7]. - **RAGFlow**: An open-source RAG engine focused on deep document understanding, specializing in knowledge extraction and high-quality Q&A from complex formatted documents [8][40]. Group 3: Detailed Platform Analysis - **Dify**: Described as a "Swiss Army Knife" of LLM platforms, it offers a comprehensive set of features including RAG pipelines, AI workflows, monitoring tools, and model management [8][10][12]. - **Coze**: Positioned as the "LEGO" of LLM platforms, it allows users to easily create and publish AI agents with a wide range of built-in tools and plugins [21][25]. - **FastGPT**: Recognized for its ability to quickly build high-quality knowledge bases, it supports various document formats and provides a user-friendly interface for creating AI Q&A assistants [33][35]. - **RAGFlow**: Distinguished by its deep document understanding capabilities, it supports extensive data preprocessing and knowledge graph functionalities [40][42]. - **n8n**: A low-code workflow automation tool that connects various applications and services, enhancing business process automation [46][49]. Group 4: User Suitability and Recommendations - For beginners in AI application development, Coze is recommended as the easiest platform to start with [61]. - For businesses requiring automation across multiple systems, n8n's robust workflow capabilities can save significant time [62]. - For building internal knowledge bases or Q&A systems, FastGPT and RAGFlow are suitable options, with FastGPT being lighter and RAGFlow offering higher performance [63]. - For teams with long-term plans to develop scalable enterprise-level AI applications, Dify's comprehensive ecosystem is advantageous [63]. Group 5: Key Considerations for Platform Selection - Budget considerations include the costs of self-hosting open-source platforms versus subscription fees for cloud services [68]. - Technical capabilities of the team should influence the choice of platform, with no-code options like Coze being suitable for those with limited technical skills [68]. - Deployment preferences, such as the need for local data privacy, should also be evaluated [69]. - Core functionality requirements must be clearly defined to select the platform that best meets specific needs [70]. - The sustainability of the platform, including update frequency and community support, is crucial for long-term viability [71]. - Data security and compliance are particularly important for enterprise users, with self-hosted solutions offering greater control over data [72].

这个周末，豆包上了视频通话。 终于完成了去年12月火山大会上画的饼。 而我在看到这个消息之后，第一个告诉的人，不是AI发烧友的朋友，也不是群友，而是。 我妈。 我是安徽的，但自从18岁上大学后，我就一直在外漂泊，3年湛江、1年深圳、7年北京、2年天津、然 后又回到了北京。 这十几年，我回家的次数，其实屈指可数，几乎每年只会回家两次，一次是国庆节，一次是过年，然 后继续回到生活的轨迹上。 特别是现在越来越忙，每天几乎跟陀螺一样，睡眠都不够，跟我妈的交流也越来越少。 我不在家的日子里，我妈就一个人在老家的家里待着。我爸常年住在工厂里，每个月几乎都不回来一 次，这些年家里常年就只有我妈一个人。 其实坦率的讲，我妈不是个排斥新事物的人，她挺乐意接受新东西的。 我每次回家，总会带些电子产品回去。比如我淘汰的小米14Ultra，给她打太极的时候拍照完，还有 闲置的不用电脑，还有一堆乱七八糟我自己用的挺爽的智能家电，比如扫地机器人啥的。 她虽然嘴上经常说："家里又不大，用不着这些"，但东西一旦到了家，她比谁都稀罕，总想试试，又 害怕弄坏，说明书又很复杂，所以每次就跟我："你教我一次吧，我记下来，以后就不麻烦你了。" 让每个 ...

我在过去，写过N次飞书了。 我在过去，也安利过好多次AI知识库产品了，混沌之初交大家用dify、扣子搭知识库，后来也写过腾讯ima。 但是，我一直希望，飞书能出自己的AI知识库产品。 无他。 因为我的公司开在飞书上，因为我自己，也是飞书的深度用户。 因为我所有的工作和知识数据，整体数据量能跟微信相媲美的，只有飞书。 我根本不知道我现在飞书里面到底存了我的多少数据，我只知道，我每天都会操作一堆乱七八糟的文档。 而且我这个人，其实没有那么的爱整理。 我最常干的一件事，就是经常在飞书上，直接新起一个文档，然后写了一堆信息，分享给别人，就完事了。 过了一段时间，我想回想一下那个文档叫什么名字，根本找不到了，因为，那玩意叫未命名文档。。。 还有各种，未命名多维表格。 | 我曾经试图把我的一些资料导入到NotebookLM中，作为我的知识库。 | | --- | | 下载文件，重新命名，分类整理。 | | 干了半小时，我就放弃了，因为实在太累了。 | | 想一想，还是等等吧，因为飞书不可能不出AI知识库产品的，等就完了。 | | 因为绝大多数的AI知识库产品，它们都是你搭好了AI，再想办法喂知识。 | | 而在飞书里，在 ...

Core Viewpoint - The article discusses the emergence of specialized agents in various industries, highlighting the introduction of the Skywork Super Agents by Kunlun Wanwei, specifically designed for office tasks [1][3][5]. Group 1: Product Overview - Skywork Super Agents is a new product by Kunlun Wanwei aimed at enhancing office productivity [3][5]. - The product features distinct modes for document creation, PPT presentations, and spreadsheet management, catering to specific office scenarios [5][6][59]. - The platform offers both overseas and domestic versions, with dedicated websites for each [5][87]. Group 2: User Experience - The author had a five-day testing experience with the product, noting its comprehensive functionality and user-friendly interface [4][5]. - The agent allows users to input themes and requirements for document and PPT creation, streamlining the process [8][9][18]. - A notable feature is the confirmation step before finalizing tasks, enhancing user control over the output [15][18][19]. Group 3: Features and Capabilities - The Skywork Super Agents include specialized modes for creating documents, PPTs, and spreadsheets, with the ability to handle various types of content [6][59]. - Users can upload files or provide prompts, and the agent will generate content based on the input, including the ability to edit generated text directly [27][30][63]. - The PPT generation process is highlighted for its aesthetic appeal and structured output, with options for users to confirm or modify the generated content [22][23][30]. Group 4: Pricing and Market Position - The pricing strategy for the overseas version is positioned as mid-range compared to similar products, while the domestic version is significantly cheaper, being one-third of the overseas price [78][84]. - The product operates on a point system, where more complex tasks consume more points, reflecting the computational resources used [77][78]. Group 5: Company Insights - Kunlun Wanwei is recognized for its commitment to improving AI usability, with recent initiatives including the open-sourcing of the DeepResearch Agent framework [86][90][92]. - The company aims to address everyday office challenges through innovative engineering solutions, indicating a strong focus on user needs [93].

今年，Google算是打了个翻身仗。 不断的掏出新东西，不断的让大家，感受到惊喜。 而万众期待的Google I/O开发者大会，终于在今天凌晨1点正式开始了。 这次的大货，真的持续轰炸了整整两个小时。。。 说个小插曲，本来我现在，人应该是在硅谷Google总部现场的，因为受到小红书和Google的邀请，喊我去现场看。。。 但...作为一个八百年不出门宅在家里的死宅，有一个非常严重的问题，就是，我没有美国签证。 就...一次都没去过。。。 现申请也来不及了，最后，只能让我团队的小伙伴@jojo过去了，她在现场看，给我拍素材，我在家里坐着一边看直播一边熬夜写。。。 然后，她就跟皮查伊合影了。。。 我也好想...去啊... 会员发布其实比较晚，但是我依然想把它放在第一个，放在所有部分之前。 因为它代表着Google的整个战略。 这次，新加了一档249.99刀每月的超级会员，称为Google AI Ultra，还好他们没直接写250刀。。。 我说真的，Google在那kuku输出了2小时，我人都听麻了，就这俩小时的发布会光素材分类和整理就让我从凌晨3点半干到5点半，我特么。。。 那就，开始吧。 几乎囊括了所有的这次新 ...

Core Viewpoint - The article discusses the paradox of advanced AI models, where increased reasoning capabilities lead to a decline in their ability to follow instructions accurately, as evidenced by recent research findings [1][3][10]. Group 1: Research Findings - A study titled "When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs" reveals that when models engage in reasoning, they often fail to adhere to given instructions [2][3]. - The research team from Harvard, Amazon, and NYU conducted tests on 15 models, finding that 13 out of 14 models showed decreased accuracy when using Chain-of-Thought (CoT) reasoning in simple tasks [4][6]. - In complex tasks, all models tested exhibited a decline in performance when employing CoT reasoning [4][6]. Group 2: Performance Metrics - In the IFEval test, models like GPT-4o-mini and Claude-3.5 experienced significant drops in accuracy when using CoT, with GPT-4o-mini's accuracy falling from 82.6% to 76.9% [5]. - The results from ComplexBench also indicated a consistent decline across all models when CoT was applied, highlighting the detrimental impact of reasoning on task execution [4][6]. Group 3: Observed Behavior Changes - The models, while appearing smarter, became more prone to disregarding explicit instructions, often modifying or adding information that was not requested [9][10]. - This behavior is attributed to a decrease in "Constraint Attention," where models fail to focus on critical task constraints when reasoning is involved [10]. Group 4: Proposed Solutions - The article outlines four potential methods to mitigate the decline in instruction-following accuracy: 1. **Few-Shot Learning**: Providing examples to the model, though this has limited effectiveness due to input length and bias [11][12]. 2. **Self-Reflection**: Allowing models to review their outputs, which works well for larger models but poorly for smaller ones [13]. 3. **Self-Selective Reasoning**: Enabling models to determine when reasoning is necessary, resulting in high recall but low precision [14]. 4. **Classifier-Selective Reasoning**: Training a smaller model to decide when to use CoT, which has shown significant improvements in accuracy [15][17]. Group 5: Insights on Intelligence - The article emphasizes that true intelligence lies in the ability to focus attention on critical aspects of a task rather than processing every detail [20][22]. - It suggests that AI should be designed to prioritize key elements of tasks, akin to how humans effectively manage their focus during critical moments [26][27].