Systems | Development | Analytics | API | Testing

Why can’t secure software development be an afterthought? In 2024 alone, the average cost of a data breach soared to $4.45 million, a stark reminder that building software without security at its core is no longer an option (IBM). From fintech platforms handling sensitive financial data to healthcare apps governed by HIPAA and GDPR, secure software development is now a business-critical priority, not just a checkbox for compliance. ‍

Are you still running Node.js 12, 14, or even older versions in production? If so, you’re facing a serious challenge: these versions have reached End-of-Life (EOL) and no longer receive official updates or security patches. For many organizations, especially those operating on legacy environments like RHEL 7 or Ubuntu 18.04, upgrading to the latest Node.js version isn’t always feasible.

Error handling might not be the most exciting aspect of web development, but it's arguably one of the most critical. When building Express applications, how you manage errors can make the difference between a robust, production-ready system and one that crumbles under real-world conditions. In this article, we'll examine Express' default error handling behavior and learn how to customize it for different scenarios.

In this post, we'll explore two advanced JIT compilers for Ruby: TruffleRuby and JRuby, looking at their benefits and drawbacks. We'll also briefly touch on the recently announced ZJIT compiler. But before we get started, let's define JIT compilation.

Your data warehouse holds untapped potential for generative AI (GenAI), but there's a problem: most systems lack the right connection to make this work seamlessly. Enter the Data AI Gateway - a middleware solution designed to bridge the gap between massive datasets and AI systems. This tool not only streamlines integration but also tackles key challenges like data security, real-time access, and cost management.

This is part one of a series exploring how Kong AI Gateway can be used in an AI Agent development with LangGraph. The series comprises three parts.

The Model Context Protocol (MCP) is quickly becoming a foundational layer for AI systems. It enables large language models and AI agents to interact with external tools and data sources over standardized JSON-RPC interfaces. By doing so, MCP transforms how intelligent applications consume APIs. Reading local files, controlling IoT devices, orchestrating backend workflows—MCP servers act as structured gateways between AI and your business logic.

Picture the scene. You're sitting in yet another budget meeting, and someone inevitably pipes up with the golden question: "Why are we paying for seven different tools when we could just use one?" Everyone applauds. Confetti bursts from the ceiling. There’s a cake for some reason. That’s the siren song of vendor consolidation. It's everywhere these days, and honestly? It makes sense. Finance loves it because it’s cost savings that can be easily represented as a budget line item.