Systems | Development | Analytics | API | Testing

Software systems are becoming more complex and interconnected every day, and as a result, effective testing is more important than ever.

Libuv is one of the most crucial components behind Node.js, yet many developers aren't fully aware of its role in enabling asynchronous I/O operations. As a high-performance, cross-platform support library, Libuv is at the heart of event-driven programming in Node.js, but its impact extends far beyond that. Santiago Gimeno is a Libuv maintainer and Senior Software Architect at NodeSource.

Article four in the “Quality Engineering for CEOs” series brings you an exploration of the role of software quality in organizational confidence.

In our previous post, we covered the basics of setting up and using Dialyzer in your Elixir project. Now, let's dive deeper into advanced type specifications, complex warning patterns, and troubleshooting techniques that will help you make the most of Dialyzer.

Generally, in today’s fast-moving development cycle, software reliability and system performance rely on successful code testing. Programmers depend on code tester tools to accomplish pre-deployment code debugging, validation, and optimization. The correct selection of code testing tools brings substantial workflow improvements to developers working with web development, software engineering, or mobile applications.

How data lakes provide the flexible, scalable solution companies need to unlock their data’s potential to power AI workloads.

Node.js is designed to be asynchronous and non-blocking, making it highly efficient for handling multiple operations at once. Unlike traditional multi-threaded architectures, Node.js operates on a single-threaded event loop, meaning it executes JavaScript code in a single thread but can still handle multiple tasks concurrently. This is achieved through asynchronous I/O and event-driven programming, allowing Node.js to remain lightweight and performant even under heavy workloads.

MCP is a new way to integrate LLMs and AI agents with third-party data sources and APIs. It significantly improves how we build tool integrations by eliminating duplicated code and providing a centralized interface for multiple agents to access shared tools. Today, we’re excited to announce the release of Kong’s MCP Server for the Kong Konnect platform. This empowers customers to integrate AI agents and query LLMs to discover APIs, services, and traffic analytics in real time.

Worldwide, over 150,000 companies rely on Salesforce for business-critical operations such as sales, marketing, or customer service. Given its critical role in business success, ensuring that Salesforce applications function correctly is not just a best practice — it’s a necessity. However, Salesforce environments are highly dynamic. Frequent updates, extensive customizations, and numerous third-party integrations mean that changes can introduce unexpected issues.

Large language models (LLMs) have redefined artificial intelligence (AI), pushing the boundaries of natural language processing (NLP) and enabling machines to understand, generate, and manipulate human-like text. From chatbots and content creation to legal and medical applications, LLMs are transforming industries at an unprecedented pace. But what makes these models so powerful? How do they work? And what challenges do they pose?