AI tools are now embedded across development workflows, but the accountability model hasn't fundamentally changed. Engineers have always been responsible for what ships, regardless of the tools they use. When compilers became more sophisticated, we didn't stop holding developers accountable for their output. AI is another abstraction layer. What's changing is not responsibility, but visibility. AI is exposing gaps that already existed — in validation, testing, and ownership — but were less obvious in more manual workflows ...

AI-powered systems are no longer experimental. They sit at the center of cloud-native applications, making real-time decisions that affect customers, revenue, and risk. Yet many organizations still secure these systems the same way they did a decade ago, by focusing on infrastructure and perimeter controls while assuming the "intelligence layer" will behave as expected. That assumption is becoming increasingly dangerous ...

Over the past year, "vibe coding" has emerged as one of the most talked-about shifts in modern software development ... The bottom line is that the excitement around vibe coding sometimes obscures a harder truth. On its own, it's not enough. Sustainable, scalable, enterprise-grade software still depends on human expertise in architecture, validation, security, and product sense ...

As we look back on 2025, it's clear that this was the year AI moved beyond simple code assistance and started reshaping how engineering teams design, orchestrate, and deliver software. This shift is already having a profound effect across the industry and reshaping roles, especially for senior developers ...

We're in a moment of rapid transformation in how software developers approach their work. According to our Dev Barometer Q3 2025 findings, 65% of developers say they're worried about falling behind on AI skills, and they're taking matters into their own hands. They're saving, on average, over seven hours a week thanks to AI tools, and most are reinvesting that time into learning. They're not waiting for permission or a better timing to learn. They're teaching themselves new skills, diving into prompt engineering (44%), AI/ML specialization (45%), and learning how to use AI to boost productivity across the board ...

Application Performance Management (APM) and Observability are two of the most important tools in the ITOps, DevOps and development toolboxes. Yet there seems to be confusion about them. What is the difference between APM and Observability? Does each offer different capabilities or serve different use cases? Do you need both, or is one enough? These are the questions this epic 12-part APMdigest series will attempt to answer over the next few weeks ...

Cloud computing has transformed how we build and scale software, but it has also quietly introduced one of the most persistent challenges in modern IT: cost visibility and control ... So why, after more than a decade of cloud adoption, are cloud costs still spiraling out of control? The answer lies not in tooling but in culture ...