Every week, another breathless headline declares software engineering dead. Another AI demo shows a chatbot building a full-stack app in 90 seconds. Another LinkedIn thought leader posts a funeral wreath emoji next to the words "traditional coding." And every week, I watch senior engineers at real companies quietly doing something that looks nothing like those demos. They're not typing code line b
很多团队的网络监控并不算差。 链路可用率有、接口带宽有、CPU 和内存有、异常告警也接进了企业微信、飞书和短信。但真正出了事，复盘时还是会出现同一句话：当时知道出问题了，但没有把现场留住。 这就是为什么越来越多团队开始关注网络回溯分析系统。 它解决的不是“能不能看到告警”这个初级问题，而是更关键的两个问题： 告警发生时，能不能快速还原到底是哪一段流量、哪一条路径、哪一种会话出了问题 事故结束后，能不能基于证据复盘，而不是靠聊天记录和印象拼凑过程 对云上和混合云场景来说，这件事尤其重要。因为链路更长、设备更多、路径更动态，很多故障不是“持续坏”，而是短时抖动、瞬时拥塞、路径切换、策略误命中。如果没有回溯能力，排障就很容易沦为赛后猜谜。 这篇文章不讲空洞概念，直接从一线运维视角拆清楚：云上网络回溯分析系统到底该怎么建，应该覆盖哪些能力，落地时最容易踩哪些坑。 先说结论： 传统监控擅长发现“异常
State of Software Engineering in 2026: A Reality Check Beyond the AI Hype Three and a half years ago, Matt Welsh, PhD and former Google engineer, published "The End of Programming" in Communications of the ACM and declared that classical computer science was over. The meteor had hit. Engineers were the dinosaurs. The state of software engineering in 2026, he implied, would look nothing like what
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