Systems | Development | Analytics | API | Testing

Exceptions are inevitable. It’s how we deal with them that matters. An effective exception handling regime is the difference between an app that only works in sandbox and one that can adapt and scale in the real world. JavaScript can throw up all kinds of weird and wonderful exceptions, because it runs in inherently unpredictable environments. So we’ve put together this guide to give you a clear, repeatable plan for handling them.

Jenkins has earned its place at the center of enterprise CI/CD. For organizations building backend services, orchestrating multi-stage deployments, and managing complex polyglot pipelines, Jenkins delivers the flexibility and control that engineering teams depend on. Ripping it out isn’t a conversation most organizations want to have - nor should it be. But mobile is different.

If you’re a mobile developer running builds on Jenkins, you already know the drill: a flaky agent goes down on a Friday afternoon, your Xcode version is three months behind, and the DevOps engineer who set the whole thing up left six months ago. The builds ship eventually - but at what cost? Jenkins is a powerful, battle-tested automation server. For teams building web backends or managing complex polyglot pipelines, it earns its place.

AI is reshaping how software gets built, tested, and delivered. For quality engineering teams, AI agents promise extraordinary acceleration by automating analysis, executing tests, generating assets, and orchestrating tasks across the SDLC. But when enterprises begin experimenting at scale, new challenges appear. Where are these agents running? What exactly are they doing? Who approves their decisions? How do we govern them safely?

Queues for Kafka is now in General Availability (GA) on Confluent Cloud and is coming soon to Confluent Platform, coinciding with the Apache Kafka 4.2 release. This milestone brings production-ready queue semantics and elastic consumer scaling natively to Kafka through KIP-932, enabling organizations to consolidate their messaging infrastructures while gaining elastic consumer scaling and per-message processing controls. Get started.

‍ Enterprise payment systems are at a breaking point: rising volumes, tighter margins, and ever-more sophisticated fraud are pushing traditional automation to its limits. The AI-enabled payments market was valued at $38.36 billion in 2024 and is projected to grow over the next decade. As firms seek smarter, real-time decisioning and risk control, highlighting how indispensable AI has become in payment stacks today. -

We’ve all been there. When engineering teams evaluate AI-powered QA tools, the same questions come up again and again. Some are rooted in genuine technical curiosity. Others stem from experiences with earlier-generation tools that earned a healthy dose of skepticism. After hundreds of these conversations, I’ve identified the seven most common misconceptions. Contents Toggle.