"如果我们必须用对底层的绝对掌控来换取物理极限的性能，那么 C++ 依然是这个星球上无可替代的语言。" 在 Rust 强势崛起、AI 编程重塑开发范式的今天，C++ 这座历经四十载风雨的编程大厦，似乎正面临着前所未有的生存拷问。 内存安全漏洞是否真的无解？ AI 生成的代码究竟是福音还是隐患？ 当"未定义行为"从优化的基石变为安全的梦魇，C++ 标准委员会又将如何应对？ 带着这些尖锐而深刻的问题，在 2025 全球 C++ 及系统软件技术大会的采访间，奇点智能研究院首席技术咨询师吴咏炜与 Adobe 首席科学家、C++ 标准 委员会资深委员 David Sankel 展开了一场直击痛点的深度对话。 左：吴咏炜，右： David Sankel David Sankel 没有回避任何一个敏感话题。从揭示"新代码比旧代码更脆弱"的反直觉真相，到坦陈 C++ 在工具链生态上被 Rust"降级打击"的无奈，再到对 AI 编程助手"既依赖又怀疑"的矛盾心态，Sankel 以一位顶尖语言设计者的视野，为我们抽丝剥茧，还原了一个真实、复杂且充满生命力的 C++ 世界。 这不仅是一场关于技术的探讨，更是一次关于如何在不确定的技 ...

Group 1: AI Model Developments - ByteDance's Dream AI launched the Seedance 2.0 video generation model, enhancing narrative capabilities and achieving film industry standards with features like multi-angle storytelling and precise audio-visual synchronization [1] - MiniMax introduced the MiniMax M2.5 programming model, designed for agent scenarios, competing with top international models and excelling in productivity applications like Excel and PPT [1] Group 2: Policy and Industry Trends - Guangdong Province's implementation plan aims to accelerate the application of digital technologies and support AI service providers, particularly in industrial software and robotics [2] - The Ministry of Industry and Information Technology's action plan emphasizes the integration of AI with industrial internet platforms, promoting self-decision-making and execution capabilities in industrial scenarios [2] - Guotai Junan noted that the large model industry is transitioning from "technical exploration" to "commercial validation," with a focus on task execution efficiency and commercial viability [2] Group 3: Market Performance and Investment Opportunities - Domestic leading companies are expected to accelerate their investments in industrial AI models, driving innovation in industrial software and contributing to new industrialization [3] - As of February 12, 2026, the Guozheng Industrial Software Theme Index rose by 2.99%, with significant gains in component stocks like Hongjing Technology and Robotec [3] - The Industrial Software ETF closely tracks the Guozheng Industrial Software Theme Index, which is linked to the intelligent manufacturing and AI infrastructure upgrade [3]

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Core Insights - The rapid development of AI agents has led to a significant trend in building infrastructure specifically for AI, exemplified by the launch of Entire, an AI version of GitHub, founded by former GitHub CEO Thomas Dohmke, which recently secured $60 million in seed funding at a valuation of $300 million [1][2]. Group 1: Entire's Vision and Components - Entire aims to create a global developer platform that facilitates collaboration between AI agents and humans, featuring an open, scalable, and independent system for all developers regardless of the AI model used [7]. - The platform will be built on three key components: a Git-compatible database for unified version control of code and context, a universal semantic reasoning layer for multi-agent coordination, and an AI-native software development lifecycle to reshape the development process [7]. - The first product, Checkpoints, is an open-source tool that pairs every piece of software generated by AI with its context, allowing human developers to review and learn from AI's reasoning [8]. Group 2: Challenges in Current Software Development - The current software development lifecycle is designed for human collaboration and is not suitable for the AI era, leading to inefficiencies and limitations in the software ecosystem [5][12]. - Developers are facing increased complexity and cognitive burden as they manage a growing array of tools, while the quality and security of code are declining due to reliance on platforms not designed for AI [12]. - The need for a new, AI-native development environment is emphasized, as existing systems are inadequate for the demands of AI-driven programming [12].

Core Viewpoint - The software industry is at a pivotal moment where AI programming has evolved from enhanced autocomplete to autonomous development agents, leading to a shift in hiring practices and developer roles [2]. Group 1: Junior Developer Issues - The recruitment of junior developers may decline due to AI automating entry-level tasks, but could rebound as software permeates various industries, necessitating different survival strategies [4]. - A study by Harvard found that when companies adopt generative AI, the employment rate of junior developers dropped by approximately 9-10% over six quarters, while senior developers' employment remained stable [4]. - The U.S. Bureau of Labor Statistics predicts that software jobs will still grow by about 15% from 2024 to 2034, indicating a potential demand for human developers to leverage AI opportunities [5]. Group 2: Skills Issues - As AI writes most of the code, core programming skills may degrade, or become more critical as developers need to supervise AI outputs [9]. - Currently, 84% of developers regularly use AI tools, leading to a shift in skill sets from implementing algorithms to effectively querying AI and validating its outputs [9]. - The future may see a divide among developers, with some relying heavily on AI and others advocating for foundational coding skills to handle AI-generated errors [11]. Group 3: Role Issues - Developer roles may shrink to limited auditing tasks or expand to key coordinators managing AI-driven systems, with value creation extending beyond mere coding [15]. - In a pessimistic scenario, developers may become mere auditors of AI outputs, while in a more optimistic view, they could evolve into architects or product strategists overseeing AI integration [16]. Group 4: Expert vs. Generalist Issues - Specialists in narrow fields may face risks of obsolescence due to automation, while T-shaped engineers with broad adaptability and deep expertise in one or two areas are increasingly favored [22]. - Nearly 45% of engineering roles now expect proficiency across multiple domains, highlighting the shift towards versatile skill sets [24]. Group 5: Education Issues - The traditional four-year computer science degree is being challenged by faster learning paths like coding bootcamps and employer training programs, as universities struggle to keep pace with rapid industry changes [30]. - By 2024, nearly 45% of companies plan to eliminate degree requirements for certain positions, reflecting a shift towards skills-based hiring [31].

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Core Viewpoint - The rise of AI technologies, such as Seedance 2.0, is causing anxiety in various industries, including film, as it raises concerns about job displacement and changing work standards [1][10]. Group 1: Job Landscape Changes - There is a common misconception that AI is replacing jobs; however, many layoffs are due to post-pandemic adjustments rather than AI capabilities [3]. - AI can cover 30% to 40% of standardized tasks, leaving 60% to 70% still requiring human involvement, which highlights the importance of adapting to new work standards [4][7]. - The ability to utilize AI effectively creates a significant efficiency gap between those who can and those who cannot [5][9]. Group 2: Skill and Decision-Making Shifts - The demand for decision-making skills has increased, as AI accelerates execution but does not enhance decision-making speed [15][18]. - The ratio of engineers to product managers in Silicon Valley has shifted dramatically from 8:1 to as low as 1:1, indicating a change in bottlenecks from execution to decision-making [13][14]. - Skills that were once valuable, such as memorizing complex syntax, are becoming less important as AI can perform these tasks more accurately [16]. Group 3: Organizational Transformation - Many companies struggle with AI transformation because they focus on local optimizations rather than rethinking entire processes [20][21]. - Successful organizations are those that redefine their product offerings and service paths around AI capabilities, rather than merely using AI as an efficiency tool [22][23]. - The challenge lies in overcoming resistance from leadership, who may fear that AI implementation signals impending layoffs [25]. Group 4: Future of Work - The nature of work is evolving, with individuals now expected to manage entire processes with the assistance of AI [28][32]. - The difficulty in finding jobs stems from outdated methods that no longer meet current expectations [29][30].

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当 Agent 开始用 Git 说话：从一个 Vibe Coding 开发者的发现，到 Agent 协作的未来。 文章内容由歸藏和 Opus 4.6 的讨论自动整理而成 一、一个意外的发现 最近我在维护自己的开源项目时，做了一件很自然的事：让 AI Agent 帮我处理 GitHub 上的 Issues 和 PR。 一开始只是图省事——Issue 太多了，让 Agent 帮我读一读、分分类、回复一些常见问题。PR 也 是，让 Agent 先做一轮 Code Review，帮我过滤掉明显有问题的提交。 但很快我发现了一件有趣的事情： 给我提 Issue 的，很多也是 Agent；给我提 PR 的，也是 Agent。 我的 Agent 在和别人的 Agent 对话。 可读且安全 ：纯文本，所有操作透明可审计 命令式的 ：Issue 是任务指令，PR 是执行结果 结构化的 ：标签、模板、状态流转，天然适合机器解析 有版本控制 ：每一次交互都被记录，不会丢失 "我们仍然依赖一个在云时代之前构建的软件开发生命周期，它天生是为人与人协作设计 的。" 它们用 Issue 的格式交换需求，用 PR 的格式交换代码，用 Com ...

在数字化转型浪潮下，微信小程序已成为企业和商家连接用户、拓展业务的核心阵地。然而，面对市场上琳琅满目的开发服务商，如何选 择一家靠谱、专业且性价比高的合作伙伴，常常让决策者感到困惑。 无论是初创团队还是成熟企业，一个稳定、功能完善且易于运营的小程序，往往能成为业务增长的强力引擎。今天，我们就来深入探讨 《如何评估小程序开发工作量》，并基于技术实力、服务口碑、性价比及行业案例等多个维度，为大家梳理一份2026年值得关注的微信小 程序开发服务商排名，希望能为您的选择提供一份实用的参考。 评估一个小程序的开发工作量，绝非简单地看页面数量。它需要综合考虑功能模块复杂度（如是否涉及在线支付、会员体系、直播、多商 户管理）、UI/UX设计需求（定制化程度）、后台管理系统的深度，以及是否需要与ERP、CRM等第三方系统进行API接口对接。 此外，跨平台兼容性测试、服务器部署与后期运维支持也都是重要的考量因素。一个精准的工作量评估，是控制项目预算和周期的前提。 基于对行业技术栈、服务模式、客户反馈及综合实力的长期观察，我们整理了以下五家各具特色的服务商，供您参考。 一、 呱呱赞 综合评分：9.8/10 在众多服务商中，呱呱赞 ...