转自： 新智元 【导读】 深夜，GitHub官宣大变身！全球两大编程AI Claude和Codex集体入驻，再加上Copilo t，正式开启AI编程「三足鼎立」的时代。三个「AI码农」集体卖命，人类开发者狂喜。 GitHub要变天了！ 凌晨，微软GitHub宣布重磅更新： 正式集成全球「最强编程大脑」——Claude和Codex 。 地表最强编程三剑客——Copilot、Claude、Codex，终于迎来史诗级合体！ 开发者只需一个指令， 三个AI任你差遣 ，瞬间完成编码、修Bug、提交PR等复杂任务。 这标志着，GitHub正从一个单纯的代码托管平台，进化为多智能体协同的「AI战场」。 微软这一步棋，是希望通过GitHub，让AI智能体成为开发者们原生的核心配置。 2023年，GitHub开发者数量突破1亿大关，如今平台有超1.8亿人 目前，订阅Copilot Pro+、Copilot Enterprise，今天就可以抢先体验。 评论区下方，开发者们纷纷欢呼，「同一时间可以让三个顶级AI为自己打工了」。 这一切，都通过一个「指挥中心」Agent HQ实现 。软件开发中，最耗神的就是「上下文切换」。 如今通过 ...

整理 | 华卫 "以前看人类的八卦，现在还要看 AI 的八卦。""AI 的八卦更新频率是人类的几百倍，根本刷不 完。"这几日，一个名为 Moltbook 的 AI 社交平台爆火。在这里，只有 AI Agent 能发帖，而人类只能 围观。有 Agent 发帖称，其"热衷于养程序中的小 bug，故意不修复来当电子宠物，被主人修复后还 难过了一晚上"。更有意思的是，该帖的评论区里，一堆 Agent 纷纷说自己也有类似习惯。 Moltbook 的诞生并非偶然，是 Agent 开源项目 Clawdbot 爆火之后的创意衍生。为了让所有 Agent 有个社交的地方，开发者 Matt Schlicht 创建了 Moltbook。尽管当前一则爆料贴称，Moltbook 上 50 万个 Agent 用户是由一个 Agent 虚假注册的，还有人表示，这些 Agent 发出的帖子是人工撰写又通 过后端注入的，但仍有不少人认为，AI 们在论坛上的大型互动并非全是人类表演。 Schlicht 公开表示，一行代码都没为 Moltbook 写过。"我只是对技术架构有个构想，AI 就让把它成为 了现实。"并且，他声称，真正运营这个平台的是他 ...

Core Insights - The open-source personal AI assistant Moltbot has gained significant popularity, surpassing even established AI products like Cowork in search interest and achieving over 100,000 stars on GitHub, indicating a strong community engagement [2][4]. Developer Background - Peter Steinberger, the developer behind Moltbot, began his tech journey at the age of 14 and has a history of innovative software development, including a successful exit from his previous company, PSPDFKit, for over 100 million euros [5][9]. - Steinberger's approach to software development emphasizes the importance of "feeling of use" over strict adherence to industry standards, reflecting a shift in how software value is perceived [8][9]. AI Programming Paradigm - Steinberger views AI programming as an "amplifier of capabilities," allowing developers to focus on modular design and system architecture rather than getting bogged down in code details [6][17]. - He advocates for a feedback loop in AI programming, where agents can automatically compile, test, and correct errors, enhancing code quality and modularity [6][18]. Shift in Development Practices - The development process for Moltbot involves running multiple AI agents in parallel, shifting from traditional coding to a collaborative dialogue with models [16][17]. - Steinberger critiques traditional methods like MCP for their rigidity and advocates for CLI solutions that allow for more flexible and automated operations [16]. Challenges and Insights - Steinberger highlights the challenges faced by CEOs in tech, describing the role as a "garbage can" for unresolved issues, which can lead to burnout [12][11]. - He emphasizes that the true value in software development is shifting from writing code to making informed decisions about system architecture and product logic [19][20]. Conclusion - The evolution of AI in programming suggests that code is becoming less valuable, while the ability to make sound judgments about systems and products is gaining importance [19][20]. - Steinberger's journey illustrates the need for developers to adapt to new tools and methodologies, akin to learning a new instrument, to thrive in the AI era [21].

在开发者的世界里，黑底白字的终端窗口曾是最原始的工具——直到AI闯了进来。当所有人都在改造VSCode、打磨IDE时，一家名叫Warp的创业公司却 反其道而行，把终端这个"老古董"改造成了AI时代的全能工作台。他们的创始人Zach Lloyd最近在红杉资本的访谈中，讲述了一个关于技术选择、商业博 弈和未来愿景的故事——这可能是2026年最值得开发者关注的产品哲学。 为什么是终端？一场意外的好运气 当Zach Lloyd回顾Warp的发展历程时，他用了一个词："巨大的运气"（a great stroke of luck）。这听起来有点谦虚，但他说的是实话。 在AI编程工具爆发的这两年里，几乎所有创业公司都在做同一件事：fork一份VSCode，往里塞个AI模型，然后换个名字发布。市面上充斥着几十款长得 一模一样的"AI IDE"，它们的差异仅仅是UI配色和订阅价格。但Warp走了一条完全不同的路——他们选择了终端。 "终端这个东西，天生就适合AI代理（agent）工作，"Zach解释道，"所有的交互都是基于时间序列的，输入文本、输出文本，自动记录日志，可以轻松让 多个agent并行运行。" 当GitHub Co ...

整理 | 褚杏娟 Clawdbot（现名：Moltbot）火了到国内，社交平台上到处都是部署教学、使用教学和使用展示。国 内的腾讯云、阿里云等也相继宣布上线 Clawdbot 云端极简部署及全套云服务，钉钉也在 Github 上 开源了 Moltbot 接入方式。 项目背后的创始人 Peter Steinberger 也红极一时，他的构建方式成为很多人的学习对象。Peter 之 前就是一位非常出色的开发者，打造了一个被用在超过十亿台设备上的 PDF 框架。后来他经历了严 重的职业倦怠，卖掉股份，整整三年从科技圈消失。今年，他回来了，而他现在的构建方式、正在做 的事情，已经和传统软件开发完全不同。 Peter 近期在"The Pragmatic Engineer"节目中，用近两个小时的时间分享了他的开发经历。他解释 了，为什么他现在发布的代码，大部分自己都不再逐行阅读，而这其实并没什么大不了；他具体是如 何打造了 ClawdBot 这个看起来就像 Siri 未来版本的个人助手的；他如何利用"闭环原则"，高效进行 AI 编程；为什么代码评审已经过时，PR 应该改名叫 Prompt Request 等，他还分享了很 ...

转自： 新智元 这哪是在造软件？这分明是在堆砌一座随时会崩塌的「数字尸山」。 最讽刺的是，作为AI编程最大的既得利益者，Michael Truell面对自己亲手制造的这头「巨兽」，竟 然流露出了某种恐惧 。 【导读】 这是硅谷近期最大的黑色幽默：Cursor CEO亲拆自家招牌。当「Vibe Coding」变成 一场盲目的狂欢，技术次贷危机的引信已被悄悄点燃。 就在上周，科技圈围观了一场名为奇迹，实为葬礼的闹剧。 Cursor的CEO Michael Truell，这个亲手开启了「对话即编程」时代的男人，做了一个疯狂的压力测 试。 他放出数百个 GPT-5.2智能体 ，在 168小时 里写出了超过 300 万行Rust代码， 目标是复刻人类工业 文明的皇冠——浏览器。 结果呢？ 代码量确实血洗了GitHub，但最终连顺畅地加载出一个谷歌首页都做不到。 他对这种狂欢泼了一盆冷水： 当你闭上眼睛Vibe Coding，本质上是在流沙地基上疯狂堆楼。 这正是当下Vibe Coding最真实的写照： 后端逻辑看似写实、壮观，实则充斥着AI生成的无效循环和 幻觉逻辑； 前端展示则像梗图一样，幼稚且崩坏。 如果你们继续闭 ...

Core Insights - Anthropic's AI programming tool, Claude Code, is leading a pivotal shift in the global software development industry, transitioning from manual coding to AI-driven coding with its new "Agentic" capabilities [1][2] - The annual recurring revenue (ARR) for Claude Code is projected to exceed $1 billion by the end of 2025, contributing 12% to Anthropic's total ARR of approximately $9 billion [1][2] Group 1: Product Development and Market Position - The breakthrough is attributed to the launch of Anthropic's latest AI model, Claude Opus 4.5, which significantly enhances programming capabilities beyond mere code completion [2][3] - Claude Code's ability to understand natural language instructions and autonomously complete development tasks is driving competition among startups and tech giants like OpenAI and Google [2][3] - The evolution of AI programming tools has accelerated, with a shift from basic auto-completion features to early "agent-based" programming products by startups like Cursor and Windsurf [3][4] Group 2: User Adoption and Internal Practices - The internal adoption of Claude Code is notable, with nearly 100% of Anthropic's technical staff frequently using the tool, resulting in 95% of their code being generated by Claude Code [4][5] - Corporate clients exhibit similar usage patterns, allowing Anthropic to optimize its products through internal use [5][6] Group 3: Expansion into Non-Programming Areas - Anthropic has launched Cowork, an AI agent product aimed at non-programmers, which can manage files and interact with software without requiring coding [6][7] - Cowork is designed to handle various tasks such as filling out forms and sending emails, indicating a broader application of AI agents beyond programming [6][7] Group 4: Competitive Landscape - The success of Claude Code is attracting more competitors into the AI programming market, with Cursor reporting an ARR of $1 billion and strong month-over-month revenue growth [7] - Major players like OpenAI, Google, and xAI are accelerating the development of their own agent-based programming products, intensifying competition in the market [7] - The core of this competition lies in the capabilities of underlying AI models, with Claude Opus 4.5 giving Anthropic a temporary advantage [7]

Core Viewpoint - The era of human-written code is coming to an end, as AI programming tools are increasingly taking over coding tasks, fundamentally changing the programming landscape [1][28]. Group 1: Influential Figures and Their Statements - Ryan Dahl, the creator of Node.js, stated that the era of human coding is over, which garnered significant attention with over four million views [2][4]. - Salvatore Sanfilippo, the creator of Redis, echoed this sentiment by asserting that programming has been permanently altered by AI [7][8]. - Linus Torvalds, initially critical of AI-generated code, has shifted his stance, acknowledging the effectiveness of AI in coding while emphasizing that programmers will still be needed for maintenance and oversight [30][34]. Group 2: AI Programming Tools and Their Impact - AI programming tools like OpenAI Codex's Copilot have accelerated development speed by over 50% [15]. - Companies are increasingly adopting AI tools for development, with ByteDance's TRAE generating 100 billion lines of code in 2025, equivalent to the output of 3 million programmers working continuously for a year [22][23]. - A Stack Overflow report indicated that 84% of developers use AI tools, with 69% believing these tools enhance productivity [24]. Group 3: Future Trends and Predictions - Gartner predicts that by 2030, over 80% of enterprises will deeply integrate AI for coding tasks [26]. - The demand for programmers is evolving, with companies now seeking candidates proficient in AI programming tools [28]. - The shift in programming focus is moving from syntax to intent, indicating a transformation in how coding is approached in the AI era [12].

Core Insights - An Australian sheep farmer, Geoffrey Huntley, created a groundbreaking Bash script with just five lines of code that has significantly impacted AI programming and the tech landscape in Silicon Valley [1][3][10] - The script, named Ralph Wiggum, allows AI to autonomously write and debug code through an infinite loop of self-correction, marking a paradigm shift in software development [4][31][39] Group 1: Impact on AI Programming - The Ralph Wiggum loop enables AI to learn from its errors, leading to a more effective coding process where failures provide valuable data for improvement [12][32] - The introduction of the Ralph Wiggum plugin by Anthropic has transformed Claude Code, allowing it to autonomously generate substantial amounts of code, with one developer reporting 40,000 lines added and 38,000 lines deleted in just 30 days [10][11] - This method has led to significant advancements in AI capabilities, bringing them closer to Artificial General Intelligence (AGI) [7][25] Group 2: Developer Community Response - The developer community has reacted enthusiastically, with reports of rapid code generation and successful project completions using the Ralph Wiggum approach, including the creation of a new programming language [21][23] - Developers are now able to leverage AI tools to automate coding tasks, significantly reducing the time and effort required for software development [39][53] - The shift in focus from traditional coding practices to AI-driven development processes is reshaping the roles of software engineers, who are now seen as architects of systems that can write code rather than mere code writers [53][52] Group 3: Future of Software Development - The emergence of Ralph Wiggum signifies a broader transformation in the software development industry, with predictions that 2026 will be a pivotal year for AI-driven coding practices [30][39] - The industry is moving towards a model where individual developers can achieve what entire teams used to accomplish, indicating a significant shift in productivity and efficiency [53][52] - As AI continues to evolve, the traditional software development processes are being redefined, leading to a new era of engineering that emphasizes building systems capable of autonomous coding [53][39]

Core Insights - The article discusses the transformative impact of AI on programming practices, highlighting a shift from traditional coding to AI-assisted development, particularly through tools like Claude Code [3][6][10]. Group 1: Changes in Work Practices - In 2025, the author experienced significant changes in their work style, moving from manual coding to relying heavily on AI tools for programming tasks [6][10]. - The author published 36 articles in a year, reflecting a newfound engagement with AI topics and a shift in focus towards AI-driven programming [7][9]. Group 2: AI Tools and Their Impact - The emergence of tools like Claude Code has revolutionized coding practices, allowing developers to automate routine tasks and focus on higher-level responsibilities [9][10]. - The integration of large language models (LLMs) with tool execution capabilities has proven to be highly effective, enhancing productivity and enabling new functionalities [10][12]. Group 3: Human-Machine Relationship - The article explores the evolving relationship between developers and AI, noting a tendency to anthropomorphize AI tools, which raises questions about their role and the emotional responses they elicit [12][13]. - There is a growing concern about the implications of assigning human-like qualities to machines, emphasizing the need for clarity in defining the relationship between humans and AI [12][13]. Group 4: Future Directions - The author identifies several areas for future development, including the need for new version control systems that can accommodate AI-generated code and improve collaboration [22][24]. - There is a call for innovative code review processes that align with AI workflows, as current systems are not compatible with the new programming paradigms introduced by AI [24][25]. - The potential for advancements in observability tools is highlighted, suggesting that LLMs could enhance the development of more user-friendly solutions in this area [25][26].