Systems | Development | Analytics | API | Testing

Increasing the test coverage of a backend application is key to ensuring its reliability, functionality, and robustness. Thanks to its intuitive API and agnostic approach to assertions, Mocha has emerged as one of the most popular choices for unit testing in Node.js. In this guide, you'll learn what Mocha is, understand how it works, explore its features, and see it in action with a complete example. Time to become a Mocha unit testing expert!

Agile software development is all about moving fast. Quick iterations, faster releases, constant feedback: it’s a model designed for speed and flexibility. Teams love it because it lets them ship features faster and respond to changes without getting bogged down. But here’s the catch: every time you move fast, there’s a risk you’ll break something. That’s where regression testing comes in.

AI has evolved from simple rule-based systems to models capable of understanding language, generating images, and even assisting in complex decision-making. Yet, most AI systems still operate as a single, standalone entity. But what if AI could work like a team, where each agent brings its own strengths to the table? Multi-agent systems (MAS) make this possible by enabling real-time interaction and coordination among intelligent agents.

You’ve probably interacted with AI more times than you can count—whether it’s getting a movie recommendation, using an AI-powered chatbot, or watching AI-generated content. But have you ever stopped to think about how these AI systems actually work? Not all AI is built the same way, and two key paradigms are emerging as game-changers: Agentic AI and Generative AI.

By default PHPs maximum execution time is set to 30 seconds of CPU time. Since your webserver has a maximum limit of requests it can handle in parallel, this leads to an interesting question or performance and webserver throughput.

In today’s dynamic digital environment, performance testing stands as a critical pillar in ensuring seamless and efficient applications. JMeter, a widely used open-source load testing tool, offers powerful parameterization features that can transform your testing strategy.

We're entering a new era of artificial intelligence (AI), where intelligence isn't just reactive; it's orchestrated. At Agent Taskflow, we're pioneering a new class of systems: multi-agent orchestration platforms. These systems empower teams of AI agents to coordinate, think, reason, and act in concert—just like human teams. But building these systems at scale requires something most AI platforms overlook: real-time, observable, fault-tolerant communication.

For years, data teams have relied on the BigQuery platform to power their analytics and unlock critical business insights. But building, managing, and troubleshooting the data pipelines that feed those insights can be a complex, time-consuming process, requiring specialized expertise and a lot of manual effort. Today, we're excited to announce our vision, a major step forward in simplifying and accelerating data engineering with BigQuery data engineering agent.

Performance testing is essential for ensuring that web applications can handle real-world user loads. A critical part of setting up a reliable test is configuring the ramp-up period correctly in JMeter. Whether you’re a business owner, product owner, developer, or DevOps professional, understanding and configuring this parameter can mean the difference between a test that reflects true performance and one that misses crucial performance problems.

Within the realms of microservices and distributed systems, gRPC has emerged as a cornerstone technology. Its adoption by tech giants like Google, Netflix, and Square underscores its capability to facilitate high-performance, scalable inter-service communication. Built as a modern take on the traditional Remote Procedure Call (RPC) paradigm, gRPC enables services, potentially written in different languages, to communicate efficiently and reliably across networks.