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
This article is an AI-assisted translation of a Japanese technical article. In April 2026, Amazon Bedrock AgentCore added a new capability called Optimization, which takes real agent traces and proposes prompt improvements based on them. https://aws.amazon.com/about-aws/whats-new/2026/05/bedrock-agentcore-optimization-preview/ In this article, I apply AgentCore Optimization to a Strands Agents-as-
DynamoDB Global Tables replicate data across regions in seconds, but replication is still asynchronous. That means a simple read from a replica region can occasionally return stale data, which is acceptable in most application as the user doesn’t require the latest available data all the time, but in some systems, stale reads can break important processes and stability of a platform. So the questi
Most AWS security setups focus heavily on inbound traffic. But outbound is often left open. Security Groups. NACLs. Maybe WAF. But outbound traffic often gets far less attention — and that’s where problems begin. Every outbound request starts with a DNS query. Before your application connects anywhere, it first resolves a domain name. That step is easy to ignore, but it’s where a lot of risk begin
TL;DR: I built the same browser agent twice — once with 500 lines of Python, once with 7 lines of JSON. The second one took 5 minutes. The agent harness layer is becoming the real competitive advantage, not the model. Last month, I built a browser automation agent. Playwright. Custom orchestration. Login handlers. Error retries. Session management. React-aware form filling. Anti-detection scripts.
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
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