We're all learning how to ship more side projects. If you're "in the bubble" it can feel like everyone is repo-maxxing. Shipping weekly. Spinning up agents to scaffold full apps overnight. New OSS dropped every Friday. The reality I see with most developers is much more normal: They have six or seven repos sitting in various states of half-attention. A side project from last year that still gets a
I have a confession: I used react-i18next for years and genuinely never questioned it. It worked. It was everywhere. Every project I joined during my internships at DNB had it set up. You install it, you configure it, you wrap your app in a provider, and you ship. Done. But then I started building more things on my own, projects where I got to choose the stack from scratch, and I started noticing
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
Exemplo mínimo de uso com Bun (baseado na documentação oficial) Aviso: Este exemplo é puramente acadêmico, baseado na documentação oficial do Next.js. Para um ambiente de produção real, ajustes adicionais de segurança, performance e monitoramento são necessários. 1 - Ajustar o next.config.ts para "Standalone": import type { NextConfig } from "next"; const nextConfig: NextConfig = { output: "
We Rewrote Our Angular 18 App in React 20 and Increased Developer Velocity by 40% Last quarter, our engineering team made the bold call to rewrite our 3-year-old Angular 18 production application in React 20. After 6 months of development, we cut over to the new stack with zero downtime, and the results have exceeded our expectations: we’ve measured a 40% increase in developer velocity, alongsid
Building a Full-Stack Habit Tracker with Claude Code - Part 2: Polish, Testing & Deployment Taking the habit tracker from MVP to production-ready with categories, analytics, comprehensive testing, and Vercel deployment In [Part 1], we built a fully functional habit tracker MVP in about 6-8 hours using Claude Code as our AI pair programmer. We had: ✅ Basic CRUD operations for habits ✅ Date-based
The "Unsharable" Dashboard Problem Imagine this common B2B SaaS scenario: An executive opens your analytics dashboard. They spend three minutes configuring the data—they filter the status to "Active," set the date range to "Last 30 Days," sort the table by "Highest Revenue," and navigate to Page 4. They copy the URL and Slack it to their team lead. The team lead clicks the link, but instead of see