Systems | Development | Analytics | API | Testing

Breakpoints are one of the most useful tools we can call on when we’re debugging applications. If you’re not familiar, they allow us to pause execution and examine what the program is doing at that moment. And Android Studio offers a whole bunch of add-ons to supplement its core functionality. In this guide, we’ll show you how Android Studio breakpoints work and how you can maximize their potential in your day-to-day work.

Quality Assurance used to be the responsibility of a single department. But today, the most effective software teams treat it as a shared responsibility, and the results speak for themselves. There’s a quote from one of Ghost Inspector’s customers that highlights this shift: “The victory for us is how Ghost Inspector has changed the face of QA in our company. We are beginning to grow what I believe is a QA culture.

The recent security breach at Vercel, where a$2 million ransom was demanded after the Context AI OAuth breach, is a wake-up call. Vercel continues to be a pillar of the modern web, serving millions of frontend applications to enterprises around the world. A compromise on such a scale has a ripple effect throughout the enterprise ecosystem.The incident points to a particular weak point: a combination of third-party AI integrations and internal system security.

Debugging has always been part of the craft. But in today’s systems — distributed, asynchronous, and increasingly opaque — debugging is no longer just difficult. It’s fragmented. Despite better tooling, more telemetry, and the rise of AI-assisted workflows, many developers still experience the same core frustrations when trying to understand what’s actually happening in production.

I spent years building dashboards that nobody used. Not because they were bad dashboards — they were actually pretty good. Clean visualizations, real-time data, all the metrics leadership said they wanted. But here’s what I learned: the problem was never the dashboard. The problem was the gap between seeing what happened and doing something about it. You look at a dashboard. It doesn’t act.

In a previous blog post in this series, we introduced Kafka Copy Paste (KCP), an open source CLI tool that automates the discovery, provisioning, and data migration steps of moving your Apache Kafka environment to Confluent Cloud. We walked through how KCP and Cluster Linking work together to reduce a process that traditionally took weeks to a matter of hours. At the end of that post, we hinted that automated client migration was coming soon. That day has arrived.

A critical bug is live in production. Your fix is ready. And now your team is staring at a potential multi-day wait for app store review. This is exactly what over-the-air (OTA) updates are designed to solve. Tools like Expo EAS Update, CodePush, Shorebird, Revopush or Stallion make it easy to push updates directly to users’ devices. But OTA updates don't bypass app store rules, they operate within boundaries that both Apple and Google have defined.

Scaling LLM and agentic AI adoption from pilot programs to enterprise-wide deployments is a massive logistical rollout. As AI and agentic usage grow, so does a nagging question for leadership: **Are agents using the right tools to get the job done?** While raw infrastructure metrics might tell you if a server is "up," they fail to tell you if your AI investment is being leveraged.

Teams using TestComplete face a common problem: one small test change can produce a wide set of modified files, and not all of them deserve the same level of scrutiny. The fix is not to review everything equally – it is to classify TestComplete artifacts by risk, then standardize how your team reviews, stages, and merges them. This article outlines this process and offers best practices for using Git effectively with TestComplete projects.

The definition of sustainability is being re-written in the age of AI. Yes, the current discourse that focuses on green IT considerations, including resource efficiency, carbon accounting and water use, is necessary. But it is incomplete. Sustainability in the age of AI implies sustaining the long-term flourishing of people, businesses, societies, and planetary systems together, not just minimizing energy use or carbon.