Modern cloud-native systems often fall victim to their own scale. A single misconfigured deployment or localized infrastructure degradation can quickly cascade across an entire distributed system, compromising the service for all users simultaneously. When architectural boundaries fail to contain faults, engineering teams face catastrophic service level agreement breaches and prolonged recovery ti
🎓 Contexto acadêmico Universidade de Marília Disciplina: Projeto de Vida e Soft Skils Professor: Gustavo Comassi Autora: Jhenifer Gonçalves Januário Marília - SP | 2026 Com a evolução das aplicações para arquiteturas distribuídas, especialmente com o uso de microserviços, os sistemas deixaram de ser centralizados e passaram a ser compostos por diversos serviços independentes. Cada ser
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
Imagine you run a bustling coffee shop. In the beginning, you take orders, make the coffee, and serve pastries all by yourself. It works perfectly when you have a handful of customers. But as the crowd grows, you become the single point of failure. If you are stuck making a complex latte, the simple drip coffee line grinds to a halt. In software engineering, this "one-person shop" represents a mon
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about