The cost incident that started this Three weeks after we put our chatbot into production, I opened the OpenAI billing dashboard on a Monday morning and stopped breathing for a second. One session — not one user, one session — had burned through roughly four times the daily budget for the entire app. Over a single afternoon. The session wasn't malicious. It was a test account someone forgot to lo
Just wrapped up the core setup for my e-commerce API (Impextech): Auth, Products, and Users. Everything is running on Node.js, Express, and TypeScript. Instead of just getting it to work, I spent this week focusing on security, keeping the code clean, and fixing some annoyances in my dev environment. Here’s a breakdown of what I built and a few "gotchas" I learned along the way. I split everythin
An opinionated list of Python frameworks, libraries, tools, and resources
A LinkedIn recruiter pitched me a remote "Software Engineer at a DEX" project this week. Reasonable comp range, tech stack squarely in my wheelhouse. After a couple of friendly exchanges, she asked me to "review the codebase before the technical interview" and sent me a GitHub repo link plus a Calendly invite for the call. The repo was malware. It didn't get me, but it's something developers shoul
Multi-tenancy is the economic engine of SaaS. Sharing infrastructure across customers reduces cost and simplifies operations. But it introduces a risk that can end your business overnight: tenant data leakage. When one customer can see another customer's data — even accidentally — the consequences are severe. Regulatory fines, contract termination, public disclosure requirements, and irreparable t
TL;DR: I built ChessDada — a free multiplayer chess platform inspired by old Yahoo Chess. No signup, no download, just instant browser-based chess. Built with Node.js, Socket.IO, and chess.js. Modern chess sites are bloated. Chess.com forces you through signup. Lichess defaults to account creation. The "5-second click and play" experience that made Yahoo Chess legendary in the 2000s is essentially
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
From Prompt to Production: AYW Workflow Case Study How we built a production-ready customer support chatbot in 6 hours (with full understanding, security review, and audit trails). Build a customer support bot that can: Handle 500+ concurrent users Integrate with Zendesk ticketing Support English + Spanish Maintain audit logs for SOC2 compliance Deploy on AWS with auto-scaling Traditional estim