In the fast-paced world of continuous integration and deployment (CI/CD), managing sensitive information like API keys, tokens, and credentials—collectively known as secrets—is not just a best practice; it's a critical foundation for security and efficiency. GitHub Actions provides a robust framework for automating workflows, but a common friction point for many development teams, particularly tho
The Challenge of Scalable Secrets Management in GitHub Actions For development teams scaling beyond a handful of repositories, managing environment-specific variables and secrets in GitHub Actions can quickly become a significant bottleneck. The manual duplication of configurations across multiple repos, especially when dealing with distinct environments like development, staging, and production
I got tired of the same three-step content publish loop: write draft → open CMS → paste, format, re-paste, fight the rich-text editor, click publish. Repeat for every environment — staging, then production. For one article, fine. For a team publishing 20+ pieces a month? That workflow is a quiet tax on everyone's time. So I wired up a pipeline that cuts the loop entirely. You commit a .md file to
Most teams I have worked with have one auth test in their suite. It looks like this: test('valid token verifies', () => { const token = signSync({ sub: 'user-1', aud: 'api://backend' }, secret); const result = verify(token, options); expect(result.valid).toBe(true); }); That test is fine. It is also a smoke test, not a regression suite. It catches the case where verification is completely b
E aí, gurizada! De uns tempos pra cá, eu percebi um burburinho enorme em torno de uma ferramenta que tem chamado a atenção, e não é por menos: o OpenClaw. Eu, que vivo mergulhado nesse universo de IA e automação, gravei um vídeo recentemente, que está lá no meu canal, assista no YouTube, justamente pra desmistificar essa parada. E hoje, vim aqui no Dev.to pra gente conversar um pouco mais sobre o
Uma skill ruim gera código ruim em escala. Uma skill boa gera código bom em escala. A diferença entre as duas não está na ferramenta, está em como a skill foi construída. Quando uma skill é criada sem contexto suficiente, a IA passa a alucinar sistematicamente: gera código tecnicamente válido, mas semanticamente errado. E faz isso toda vez que a skill é chamada, para todo mundo que a usa. Percebi
The blog you're reading right now was built in a single conversation with Claude Code, Anthropic's CLI, in about 30 minutes. No all-nighter, no purchased template, no WordPress. One working session in the terminal. Here's exactly how it went — real code, real commands, and what almost went wrong. My portfolio web-developpeur.com does its job: showcasing my background and projects. But a static sit