Your attention span can begin recovering its natural depth and duration within 48 hours of eliminating all notifications from your digital devices. The constant interruptions from phones, computers, and smart devices have trained your brain to expect distraction every few minutes, creating a fragmented attention pattern that makes sustained focus nearly impossible. When you remove these artificial interruptions, your brain quickly begins rebuilding the neural pathways necessary for deep, concentrated thinking that modern life has systematically eroded.
In our second Release in 2025, we’re introducing additional performance insights to help you identify and resolve even more Bottlenecks faster and more effectively. Detailed information is now integrated in our Weekly Report, namely the number and types of the observations discovered in your application. We’re eager to make it easier than ever to stay on top of optimizations.
Today, we’re excited to unveil Kong AI Gateway 3.10! This release introduces new functionality to enhance AI governance, reduce LLM hallucinations, and accelerate developer productivity when working with AI. Read on to learn more.
It is hard to imagine a time not long ago where AI has not been front and centre of our everyday news, let alone in the software engineering world? The advent of LLMs coupled with the existing compute power catapulted the use of AI in our everyday lives and in particular so in the life of a software engineer. This article breaks down some of the use cases of AI in software engineering and suggests a path to investigate the key question: Did we actually become more productive?
Software development is a demanding field that requires precision, efficiency, and collaboration. Developers juggle various tasks, including writing code, debugging, testing, version control, and deployment. Without the right set of tools, inefficiencies can creep in, slowing down progress and reducing code quality.
As advanced analytics and AI continue to drive enterprise strategy, leaders are tasked with building flexible, resilient data pipelines that accelerate trusted insights. AI pioneer Andrew Ng recently underscored that robust data engineering is foundational to the success of data-centric AI—a strategy that prioritizes data quality over model complexity.
As we close out 2024, developer productivity and happiness continue to be a focus for many organizations. Platform engineering can play a pivotal role in shaping the developer experience. With the growing complexity of distributed systems and the ever-pressing need for faster delivery cycles, platform engineers are uniquely positioned to enable developers to focus on what they do best: shipping high-quality code.
Developer productivity is not just about writing code faster—it’s about creating efficient, self-service environments that minimize friction and reduce the time from development to deployment. In the context of Kubernetes clusters, enhancing productivity means leveraging the right tools and workflows to streamline development, reduce errors, and speed up feature releases. This guide explores the key strategies and tools that can significantly boost productivity in Kubernetes environments.
Engineering teams need a collaborative tool purpose-built for the complicated and multi-layered system architectures that underpin most companies’ operations.