Data is no longer treated as a byproduct of business operations and has become one of the most valuable organizational assets. Every interaction on a banking application, e-commerce platform, hospital system, logistics network or social media service generates data continuously. As organizations increasingly adopt digital workflows, cloud platforms, machine learning systems and real-time applicati
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
In modern data-driven organizations, managing and analyzing data efficiently is critical. OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are both integral parts of data management, but they have different functionalities. Understanding how they differ, and how they complement each other is essential for anyone working with data systems. Online Transaction Processing (
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.
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
The setup I was building an AI article generator. Four phases: Research — pull live SERP data, extract entities, score competitors Brief — Claude Sonnet writes a structured brief from the SERP Draft — section-by-section drafting in a fixed persona voice Polish — adversarial review pass that flags AI tells End-to-end: 6–8 minutes. Each phase: 60–180 seconds. Multiple LLM calls per phase. Token st