El problema real Gestionar infraestructura manualmente sigue siendo uno de los mayores puntos de fricción en equipos DevOps. Cambios no auditados, configuraciones inconsistentes entre ambientes y despliegues manuales generan errores difíciles de rastrear y operaciones poco confiables. La solución moderna es automatizar completamente el ciclo de vida de infraestructura y despliegue utilizando Inf
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
When most developers want to scan their code for security vulnerabilities, they install Semgrep or Snyk and call it a day. I did the opposite. I built one from scratch. Not because the existing tools are bad — they're excellent. But because I'm transitioning from 13 years of software engineering into application security, and I wanted to understand what a SAST tool actually is underneath the hood.
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