[{"slug":"科学计算中的验证：从原始到实用再到边界扩展","title":"科学计算中的验证：从原始到实用再到边界扩展","summary":"Joseph M. Powers 在 ASME V&V 2020 研讨会上的演讲，探讨了科学计算中验证（Verification）的概念谱系。演讲提出验证并非单一活动，而是从“原始”（Pristine，理想化问题）到“实用”（Practical，实际工程问题）再到“边界扩展”（Perimeter-Extending，探索新领域）的连续谱系。该框架帮助理解验证方法如何在不同复杂度和应用场景下演变和适用。","tags":["工业智能","V&V","科学计算","机理模型"],"related":["验证","确认","科学计算","机理模型","v-and-v"],"sources":["Verification-in-Scientific-Computing.md"],"links":["验证","确认","科学计算","机理模型","v-and-v"],"type":"source","topic":"industrial-intelligence"},{"slug":"yc-2026春季风向标：ai重塑10个被忽视赛道","title":"YC 2026春季风向标：AI重塑10个被忽视赛道","summary":"本文是OpenClaw博客对Y Combinator（YC）2026年春季\"创业许愿单\"（RFS）的深度解读。YC作为硅谷最成功的创业孵化器，其RFS被视为投资风向标。2026春季版聚焦\"AI本土化\"（AI-native）理念，提出AI正从编码助手跃升为系统重构者，渗透到金融、政府、工业和体力劳动等传统领域。文章逐一剖析了10个被YC认为最被忽视但潜力巨大的赛道，每个赛道都配有生动描述、潜在机遇和真实案例。","tags":["YC","Y Combinator","创业","AI投资","风险投资","技术趋势","业界观点"],"related":["Y-Combinator","AI-native","为产品经理打造的Cursor","AI本土化对冲基金","AI本土化代理机构","稳定币金融服务","AI应用于政府","现代化金属轧钢厂","AI指导体力工作","大型空间模型","政府欺诈猎手基础设施","简化LLM训练","cursor","google-deepmind","hugging-face","numerai","runway-ml","circle"],"sources":["YC-2026春季风向标-AI重塑10个被忽视赛道-2026-02-09.md"],"links":["physical-ai","2026-physical-ai-year","机器劳动力","算力下沉","wrapper陷阱","信任护城河"],"type":"source","topic":"industrial-intelligence"},{"slug":"spacex-cursor-600-billion-option-20260423","title":"spacex-cursor-600-billion-option-20260423","summary":"type: source","tags":[],"related":[],"sources":[],"links":[],"type":"source","topic":"ai"},{"slug":"当机器人开始\"拿下阵地\"：乌克兰前线，正在提前进入未来战争","title":"当机器人开始\"拿下阵地\"：乌克兰前线，正在提前进入未来战争","summary":"2026年4月13日，乌克兰总统泽连斯基宣布乌军首次完全依靠无人平台（无人机和地面机器人）夺取俄军阵地，行动中没有步兵直接参与，乌方无人员伤亡。这一事件被视为战争史上的分水岭，标志着战争形态从\"士兵冲锋\"向\"人机协同\"的转变。","tags":["无人作战","人机协同","未来战争","乌克兰战争","地面机器人"],"related":["无人平台夺取阵地","乌克兰地面机器人作战体系","战争中的风险分配","人机协同作战","ratel","termit","ardal","rys","zmiy","protector","volia"],"sources":["ukraine-robots-capture-position-future-warfare.md"],"links":[],"type":"source","topic":"ai"},{"slug":"告别“盲盒”发音，当声音成为代码：从-gemini-3.1-flash-tts-看语音模型的下半场","title":"告别“盲盒”发音，当声音成为代码：从 Gemini 3.1 Flash TTS 看语音模型的下半场","summary":"本文由智核观察员撰写，深入分析了 Google DeepMind 与 Google AI 联合推出的 Gemini 3.1 Flash TTS 模型。文章核心论点认为，该模型的真正价值不在于声音更逼真，而在于其通过 **Audio Tags（音频标签）** 系统实现了语音的**可编排性**，标志着语音模型从“输入文本-输出黑盒音频”的古典阶段，进入了像代码一样可被精细控制、可融入 Agent 与多模态工作流的“可编排工作流（Orches","tags":["Gemini","AI Agent","多模态","TTS","Audio Tags"],"related":["gemini-3-1-flash-tts","audio-tags","可编排语音工作流","ai-agent","多模态"],"sources":["voice-models-enter-orchestrable-workflow-era.md"],"links":["Audio Tags","可编排语音工作流","全模态提示词工程","情绪决策与音频渲染分离","声音配方库"],"type":"source","topic":"ai"},{"slug":"当“造东西”几乎免费时，web-的未来在哪？深度解读-nate-b.-jones-的生存逻辑","title":"当“造东西”几乎免费时，Web 的未来在哪？深度解读 Nate B. Jones 的生存逻辑","summary":"本文深度解读了硅谷技术分析师 Nate B. Jones 关于 AI app builder 的分析。核心论点是：当 AI 使“造应用”的成本趋近于零时，Web 创业的竞争逻辑将从“谁做得快”转向“谁拥有结构性优势”。文章提出了五层护城河框架——信任（Trust）、上下文（Context）、分发（Distribution）、品味（Taste）和责任（Liability），并警告了“Wrapper陷阱”的风险。文章认为，未来真正值钱的不是","tags":["AI Agent","创业","OpenClaw","Wrapper陷阱","信任护城河","上下文重力","分发主权","品味护城河","责任护城河"],"related":["wrapper陷阱","信任护城河","上下文重力","分发主权","品味护城河","责任护城河","nate-b-jones","openclaw","ai-code-generation","developer-ecosystem"],"sources":["when-building-becomes-nearly-free-future-of-web-20260426.md"],"links":["ai-code-generation","developer-ecosystem","cursor"],"type":"source","topic":"ai"},{"slug":"为什么我越来越觉得，2026-会成为-physical-ai-元年","title":"为什么我越来越觉得，2026 会成为 Physical AI 元年","summary":"本文论证了2026年将成为Physical AI（物理AI）元年的核心观点。作者从资本转向、大模型基础、劳动力缺口和算力下沉四条线索出发，指出Physical AI不再是科幻概念，而是正在成为可投资的产业方向。文章强调Physical AI的“慢、重、贵、难”特性使其天然具有比纯软件AI更深的工业壁垒，更可能跑出基础设施级的大公司。","tags":["physical-ai","机器人","人形机器人","AI投资","芯片","算力下沉","机器劳动力"],"related":["physical-ai","2026-physical-ai-year","机器劳动力","算力下沉","完整系统竞争","无人平台夺取阵地","系统对系统竞争"],"sources":["why-2026-is-the-year-of-physical-ai.md"],"links":[],"type":"source","topic":"industrial-intelligence"},{"slug":"why-enterprise-all-in-ai-dies-in-two-months-20260425","title":"why-enterprise-all-in-ai-dies-in-two-months-20260425","summary":"type: source","tags":[],"related":[],"sources":[],"links":[],"type":"source","topic":"ai"},{"slug":"新质生产力政策下的工业智能机遇","title":"新质生产力政策下的工业智能机遇","summary":"本文由中国高技术产业发展促进会新质生产力工作委员会撰写，系统解读了2026年1月工业和信息化部发布的两份核心政策文件——《工业互联网和人工智能融合赋能行动方案》和《推动工业互联网平台高质量发展行动方案（2026—2028年）》，以及南京市等地方配套政策。文章分析了工业智能体的技术演进、关键突破（AI算法融合、边缘计算、数字孪生）和面临的挑战，提供了汽车、电子、装备三个制造业的数字化转型典型案例，并预测2026年中国工业智能市场规模将达到","tags":["新质生产力","工业智能","数字化转型","政策解读","制造业","2026"],"related":["新质生产力","工业智能","工业智能体","工业互联网平台","算力券","physical-ai","2026-physical-ai-year","机器劳动力","企业AI转型陷阱","工程化部署AI"],"sources":["xzl_new_productive_forces_industrial_intelligence_20260219.md"],"links":["physical-ai","2026-physical-ai-year","机器劳动力","企业AI转型陷阱"],"type":"source","topic":"industrial-intelligence"},{"slug":"你不是在用-agent，你是在带一个\"ai-初级工程师\"团队","title":"你不是在用 Agent，你是在带一个\"AI 初级工程师\"团队","summary":"本文提出了一个核心认知框架：当前 AI Agent 的最佳使用模式不是将其视为工具，而是将其视为一个需要被管理的\"AI 初级工程师\"团队。用户需要完成从\"独立贡献者 (IC)\"到\"技术主管 (Tech Lead)\"的角色切换。文章引用了 Anthropic 内部研究、Faros AI 报告和 Google DORA 2025 报告的数据，论证了 AI 是放大器而非魔法，并提出了四步方法论：列假设、上下文治理、设止损机制、沉淀 Skill","tags":["AI Agent","Anthropic","Skills","角色切换","上下文治理"],"related":["anthropic","claude","faros-ai","google-dora-2025","ai-junior-engineer","tech-lead","context-engineering","verification-and-rework-cost","ai-skill","ai-as-amplifier","enterprise-ai-transformation-trap","ai-code-review-cost","engineering-deployment-ai"],"sources":["you-are-not-using-agent-you-are-leading-ai-junior-engineers.md"],"links":[],"type":"source","topic":"ai"}]