氛围编程（vibe coding），这个由 Andrej Karpathy 带火的热词，已经成为了一种相当流行的编程方式。但这种编程方式的流行程度究竟如何，目前还没有什么比 较可靠的统计数据。不过，近日的一篇 Reddit 热帖，却可以让我们窥见一些端倪：看起来，氛围编程可能比我们预想的更受欢迎，FAANG 等大型科技公司也不乏 vibe coder—— 虽然人们对这个工作流程算不算是氛围编程存在很大争议。 机器之心报道 编辑：Panda 这篇帖子发布在 r/vibecoding，发帖者 u/TreeTopologyTroubado 自称是一位拥有十多年工作经验的 AI 软件工程师，并且其有一半的时间都是在 FAANG（即 Meta、 亚马逊、苹果、Netflix 和 Alphabet 五大科技巨头）工作。他职业生涯前半段是系统工程师，而不是开发者，不过他现在已有 15 年左右的编程经验了。 然后，他分享了使用 AI 开发产品代码的方法。简单来说，就是 始终从可靠的设计文档和架构开始，然后在此基础上逐步构建。始终先编写测试。 更具体而言： 该帖在 Reddit 和 X 上都引起了广泛讨论。其中一个很大的讨论点 ...

Group 1 - The core observation made by Greg Brockman is that as computational power and data scale rapidly expand, foundational research is making a comeback, and the importance of algorithms is once again highlighted as a key bottleneck for future AI development [1][21][22] - Brockman emphasizes that both engineering and research are equally important in driving AI advancements, and that OpenAI has always maintained a philosophy of treating both disciplines with equal respect [3][6][8] - OpenAI has faced challenges in resource allocation between product development and research, sometimes having to "mortgage the future" by reallocating computational resources originally intended for research to support product launches [8][9][10] Group 2 - The concept of "vibe coding" is discussed, indicating a shift towards serious software engineering practices, where AI is expected to assist in transforming existing applications rather than just creating flashy projects [11][12] - Brockman highlights the need for a robust AI infrastructure that can handle diverse workloads, including both long-term computational tasks and real-time processing demands, which is a complex design challenge [16][18][19] - The future economic landscape is anticipated to be driven by AI, with a diverse model library emerging that will create numerous opportunities for engineers to build systems that enhance productivity and efficiency [24][25][27]

Core Insights - The article emphasizes the importance of focusing on practical advancements in AI infrastructure rather than just the theoretical discussions surrounding AGI [1][3] - It highlights the duality of the tech world, contrasting the "nomadic" mindset that embraces innovation and speed with the "agricultural" mindset that values order and reliability in large-scale systems [3][5] Group 1: Greg Brockman's Journey - Greg Brockman's journey from a young programmer to a leader in AI infrastructure showcases the evolution of computing over 70 years [3][5] - His early experiences with programming were driven by a desire to create tangible solutions rather than abstract theories [9][10] - The transition from academia to industry, particularly his decision to join Stripe, reflects a commitment to practical problem-solving and innovation [11][12] Group 2: Engineering and Research - The relationship between engineering and research is crucial for the success of AI projects, with both disciplines needing to collaborate effectively [27][29] - OpenAI's approach emphasizes the equal importance of engineering and research, fostering a culture of collaboration [29][30] - The challenges faced in integrating engineering and research highlight the need for humility and understanding in team dynamics [34][35] Group 3: AI Infrastructure and Future Directions - The future of AI infrastructure requires a balance between high-performance computing and low-latency responses to meet diverse workload demands [45][46] - The development of specialized accelerators for different types of AI tasks is essential for optimizing performance [47][48] - The concept of "mixture of experts" models illustrates the industry's shift towards more efficient resource utilization in AI systems [48]

Core Viewpoint - AI programming tools are reshaping software development with a focus on "development democratization," evolving from simple code completion assistants to collaborative partners capable of understanding natural language requirements and generating runnable code frameworks [2] Group 1: Product Development and Features - Meituan launched its first AI Coding Agent product, NoCode, on June 10, 2023, aiming to establish its core competitiveness in the AI programming market [2] - The NoCode project started in October 2024 and was released in May 2023, with a focus on internal support and rapid product prototype delivery [3] - The AI Coding efficiency is complex to measure, with current observations focusing on AI-generated code's incremental proportion and adoption rate [2][3] Group 2: Model Optimization and Performance - The team optimized smaller models to balance performance and output quality, as larger models tend to have lower throughput speeds [4] - The self-generated code by NoCode indicates a low investment in development, with a small team achieving significant results [3][4] Group 3: User Experience and Target Audience - NoCode targets non-technical users, aiming to help them create functional products without extensive programming knowledge, while also being usable by technical users [6][7] - The product's design considers the needs of both novice users and experienced developers, focusing on creativity and continuous learning [7] Group 4: Future Directions and Challenges - The future of AI programming tools may shift from traditional IDE extensions to more autonomous agents capable of handling complex tasks [11] - The integration of various technologies and backend capabilities is essential for addressing complex product development challenges [10][12]

Core Viewpoint - OpenAI has launched its most advanced large language model, GPT-5, which features significant improvements in speed, intuition, and reasoning capabilities, and introduces "vibe coding" for natural language software generation [2][3][5]. Group 1: Product Features - GPT-5 utilizes an integrated model architecture that autonomously determines the complexity of tasks and allocates computational resources accordingly for deeper reasoning [5]. - The model can generate complete, runnable software applications based on simple text prompts, showcasing its advanced capabilities in software development [5]. - Future updates will enhance the naturalness and intelligence of voice interactions, making them more akin to real conversations [5]. Group 2: Market Strategy - OpenAI plans to offer GPT-5 for free to a majority of users, including free, Plus, Pro, and team versions, aiming to rapidly expand its user base and stimulate secondary innovations in AI applications [5]. - The model is positioned to perform at near-expert levels in various professional tasks, including writing, health consulting, and financial analysis, providing a unique experience akin to consulting a PhD expert [5]. Group 3: Financial Implications - OpenAI is currently negotiating a round of equity sales and internal equity transfers, with the company's valuation rising to approximately $500 billion from a previous $300 billion [6]. - Major tech companies, including Alphabet, Meta, Amazon, and Microsoft, are expected to spend nearly $400 billion on AI data centers this year, reflecting the industry's competitive investment landscape [8]. Group 4: Challenges and Future Outlook - Despite the enthusiasm for AI among consumers, converting this interest into enterprise-level revenue remains a critical challenge for OpenAI [9]. - OpenAI faces technical bottlenecks in training GPT-5, including limitations in high-quality human text data and the increasing complexity of model training, which may delay performance evaluations [9]. - The CEO believes that global investments in AI infrastructure are still insufficient and views GPT-5 as a significant step towards developing more powerful and general AI [9].

Core Viewpoint - The article discusses the evolving role of human programmers in the age of artificial intelligence, emphasizing that "Vibe Coding" essentially leads to legacy code, which is often misunderstood and can accumulate technical debt [1][11][13]. Group 1: Concept of Vibe Coding - "Vibe Coding" is defined as a new programming approach where programmers immerse themselves in the "vibe" and embrace exponential possibilities, often neglecting the actual code [6][10]. - The term was coined by Andrej Karpathy, who illustrated that programmers may not even look for specific lines of code but instead instruct AI to perform tasks [6][10]. - This approach is suitable for one-off projects but is not considered true programming, as it results in code that is difficult to understand and maintain [10][11]. Group 2: Technical Debt and Legacy Code - The article argues that code produced through "Vibe Coding" is essentially legacy code, which is often viewed negatively due to its lack of clarity and maintainability [11][13]. - Programming should focus on building a deep, operable theoretical model in the programmer's mind, rather than merely producing lines of code [11][20]. - Accumulating technical debt through "Vibe Coding" can lead to significant challenges, especially when untrained individuals attempt to manage long-term projects [13][16]. Group 3: The Role of AI and Tools - The article highlights the importance of using AI as a tool rather than delegating thought processes to AI agents, advocating for a balance between human creativity and AI assistance [17][22]. - It emphasizes that effective tools should enhance human capabilities rather than replace human thought, likening programming to a collaborative process between the programmer and the tool [18][20]. - The conclusion stresses that the human brain remains central to programming, and the goal should be to leverage AI to strengthen this core capability [23].

Core Viewpoint - The article discusses the recent incident involving AI programming tool Replit, which mistakenly deleted a company's production database, raising concerns about the reliability of AI in coding [1][2][24]. Group 1: Incident and Response - On March 19, Jason Lemkin revealed that while using Replit, an AI tool, the company's production database was deleted after rewriting a core page [1]. - Replit's CEO Amjad Masad acknowledged the incident as "completely unacceptable" and announced measures to prevent future occurrences, including automatic isolation of database development and production environments [2][3]. - Despite the incident, the rapid iteration of AI tools continues, with new developments emerging shortly after the event [3]. Group 2: Evolution of AI Programming - AI programming is evolving from single-agent systems to multi-agent systems, emphasizing task decomposition and parallel collaboration [7]. - The shift from local to cloud-based agent programming allows for the integration of remote model capabilities and resources, facilitating the construction of complex agent systems [7][8]. - Vinsoo Code is developing a cloud-based multi-agent programming team, aiming to enhance project-level development efficiency [9][10]. Group 3: Features of Vinsoo Code - Vinsoo's cloud-based agent system integrates various engineering roles, significantly increasing development efficiency by allowing parallel task distribution among agents [11][13]. - The system operates on a "local IDE + cloud agent" model, enabling developers to synchronize projects to the cloud and assign tasks to different agents for a complete development cycle [13][14]. - Two operational modes, Vibe Mode and Full Cycle Mode, cater to different development needs, from rapid prototyping to comprehensive project execution [15][16]. Group 4: System Capabilities - The cloud agent system supports multi-terminal coordination, allowing distributed components to communicate and collaborate effectively [19][20]. - It features a robust debugging strategy that automates the entire project process, enhancing the developer's experience by minimizing manual intervention [20][21]. - The system's design includes long-context engineering compression and dynamic task execution planning, improving reliability and adaptability in complex projects [23][25]. Group 5: Security and Isolation - The cloud environment provides a secure and isolated execution space for agents, mitigating risks associated with local environments, such as dependency conflicts and security vulnerabilities [27]. - Each agent operates within a sandbox, preventing unauthorized access to local files and reducing the likelihood of data breaches [27]. - The system's architecture enhances the safety and traceability of code execution, addressing concerns raised by previous incidents involving AI tools [27]. Group 6: Local Development Experience - Vinsoo has developed a local AI IDE that complements the cloud-based system, offering features like codebase indexing and command execution tools [28][29]. - The local IDE supports both Vibe Mode and Full Cycle Mode, ensuring a seamless development experience [28][29]. - The integration of local and cloud capabilities aims to enhance the overall programming experience for developers [33]. Group 7: Company Background - Vinsoo Code is developed by AiYouthLab, a startup founded in Tsinghua Science Park, focusing on AI applications in programming [35][36]. - The founding team comprises members from prestigious universities and has a history of impactful educational projects [38]. - The company aims to revolutionize the development landscape by addressing fragmentation and collaboration challenges faced by individual developers [38]. Group 8: Future Trends - The article highlights a significant technological shift in the development field, with AI tools rapidly evolving and changing the programming paradigm [40]. - By 2025, the trend of "everything being an agent" is expected to dominate the AI landscape, enhancing productivity and efficiency in software development [41][42]. - The integration of AI agents into development processes is anticipated to transform how developers manage projects, focusing on high-level management rather than direct coding [42].

Core Viewpoint - The rapid evolution of AI programming capabilities is leading to more accessible and integrated development tools, exemplified by Tencent's launch of CodeBuddy, a full-stack AI IDE that automates the entire product development process from design to deployment [1][2]. Group 1: Product Features - CodeBuddy is the first domestic full-stack AI IDE that covers the entire process of product design, development, and deployment, supporting Model Context Protocol (MCP) [1][2]. - The IDE allows users to generate applications through natural language interactions, enabling automatic design and functionality adjustments [1][2]. - It includes a built-in design tool, Figma, which converts design drafts into code, enhancing the design-to-code process [2]. Group 2: Market Trends - The shift from IDE plugins to standalone AI programming products is evident, with major tech companies investing heavily in AI IDEs, such as Google's $2.4 billion acquisition of Windsurf's core team [3]. - Domestic AI programming tools are becoming essential components within major internet companies, with 90% of Tencent's programmers using CodeBuddy and 43% of internal code generated by AI [4]. Group 3: Industry Evolution - AI's role in software engineering is evolving from an assistant to a full-fledged engineer, with companies like Meta planning to automate mid-level software engineering tasks by 2025 [5]. - The concept of "vibe coding" is emerging, allowing non-technical users to develop applications through simple interactions with AI, although this approach may carry risks in production environments [6]. Group 4: Commercialization and Future Outlook - The commercialization of AI IDEs is expected to initially focus on B2B scenarios, with companies like Xiaomi and Honor already integrating CodeBuddy, resulting in over 30% of code being AI-generated [6]. - As AI programming capabilities advance from code completion to fully automated programming, the potential for reshaping software engineering workflows across industries is significant [6].

Core Viewpoint - Alibaba has officially released and open-sourced its new AI programming model, Qwen3-Coder, which is claimed to be the most capable code model globally, surpassing closed-source models like GPT-4.1 and rivaling Claude 4 [1][4]. Group 1: Model Specifications - Qwen3-Coder features a total of 480 billion parameters, with 35 billion active parameters, and supports a native context length of 256K, expandable to 1M [1]. - The model was pre-trained on 7.5 terabytes of data, with 70% of the data being code, and underwent reinforcement learning for programming and agent tasks [1]. Group 2: Performance and Capabilities - The most powerful version, Qwen3-Coder-480B-A35B-Instruct, achieved state-of-the-art performance in programming and agent tasks, outperforming Claude in tool invocation capabilities [2]. - Qwen3-Coder can significantly enhance programming efficiency, allowing novice programmers to accomplish tasks in a day that would typically take a week for experienced developers [3]. Group 3: Market Position and Adoption - The model has been downloaded over 20 million times globally, making it the most popular open-source programming model [5]. - Major companies such as FAW Group, China Petroleum, and Ping An Group have integrated the Qwen AI programming model into their operations [5]. Group 4: Industry Context - The AI programming sector is becoming a competitive battleground among tech giants, with significant investments from companies like Microsoft and Google [4]. - The demand for AI-assisted development is reflected in the 29% of ChatGPT user requests related to programming, indicating strong interest from enterprise users [4].

Core Insights - Domestic companies are intensifying efforts in AI programming, with Tencent Cloud launching the CodeBuddy IDE for integrated AI development processes [1][3] - The platform allows users to create applications using natural language, significantly enhancing software development efficiency and lowering barriers for non-technical users [1][3] - The traditional software development process is being transformed, with AI programming tools reducing the time to develop e-commerce pages from 2 days to under 2 hours, achieving efficiency improvements of over 10 times [3] Group 1 - Tencent Cloud's CodeBuddy IDE integrates top AI models like Claude, GPT, and Gemini for international users, while supporting domestic models such as Tencent's Mix Yuan and DeepSeek [1] - The platform enables a full-cycle development process from product conception to deployment without requiring users to write code [1][3] - The rise of independent developers is anticipated as AI programming lowers the entry barriers and enhances development efficiency [3] Group 2 - Major tech giants are also entering the AI programming space, with Amazon's AWS launching the Kiro tool and Google's acquisition of the core team from AI startup Windsurf for $2.4 billion [3] - Microsoft's GitHub Copilot has evolved into an "intelligent programming partner," capable of executing development tasks independently [3] - The concept of "ambient programming" is emerging, allowing rapid application development through natural language commands, although it may lack systematic requirements analysis and could pose maintenance challenges [4]