#service-bindings

#service-bindings

The dynamic type hints feature in Module Federation 2.0 dramatically streamlines the development process by automatically generating and loading types from remote modules, eliminating the need for shared type packages.

If Ingress is the Legacy Path, then the Gateway API is the modern highway. In this guide, I will walk you through a complete migration demonstrating how to swap out your old Ingress controllers for Envoy Gateway. We won't just move traffic; we'll leverage Envoy's power to implement seamless request mirroring and more robust, path-based routing that was previously hidden behind complex annotations.

Events are essential inputs to modern front-end systems. But when we mistake reactions for architecture, complexity quietly multiplies. Over time, many front-end architectures have come to resemble chains of reactions rather than models of structure. The result is systems that are expressive, but increasingly difficult to reason about.

In order to use agents or in order to use AI in IT operations, all of your systems need to be interconnected and what interconnects all of your systems is an automation platform. Interconnecting systems is only a piece of the puzzle though. There is also some well-founded concern about the autonomous AI systems we are moving towards. AI agents may make decisions and inferences, but enterprises remain hesitant to allow direct execution on production systems.

Building APIs is so simple. Caveat, it's not. Actually, working with tools with no security, you've got a consumer and an API service, you can pretty much get that up and running on your laptop in two or three minutes with some modern frameworks. Then, authentication and authorization comes in. You need a way to model this.

The integration of AI-enhanced microservices within the SAFe 5.0 framework presents a novel approach to achieving scalability in enterprise solutions. This article explores how AI can serve as a lean portfolio ally to enhance value stream performance, reduce noise, and automate tasks such as financial forecasting and risk management. The cross-industry application of AI, from automotive predictive maintenance to healthcare, demonstrates its potential to redefine processes and improve outcomes.

For years, reliability discussions have focused on uptime and whether a service met its internal SLO. However, as systems become more distributed, reliant on complex internet stacks, and integrated with AI, this binary perspective is no longer sufficient. Reliability now encompasses digital experience, speed, and business impact. For the second year in a row, The SRE Report highlights this shift.

Almost a quarter of those surveyed said they had experienced a container-related security incident in the past year. The bottleneck is rarely in detecting vulnerabilities, but mainly in what happens next. Weeks or months can pass between the discovery of a problem and the actual implementation of a solution. During that period, applications continued to run with known risks, making organizations vulnerable, reports The Register.

An observability control plane isn't just a dashboard. It's the operational authority system. It defines alert rules, routing, ownership, escalation policy, and notification endpoints. When that layer is wrong, the impact is immediate. The wrong team gets paged. The right team never hears about the incident. Your service level indicators look clean while production burns.

Over the past decade, software development has undergone a massive transformation due to continuous innovations in tools, processors and novel architectures. In the past, most applications were monoliths and then shifted to microservices, and now we find ourselves embracing composability - a paradigm that prioritizes modular, reusable, and flexible software design. Instead of writing separate, tightly coupled applications, developers now compose software using reusable business capabilities that can be plugged into multiple projects. This enables greater scalability, maintainability, and collaboration across teams and organizations. At the heart of this movement is Bit Harmony, a framework designed to make composability a first-class citizen in modern web development.

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

The Harness Resilience Testing platform extends the scope of the tests provided to include application load and disaster recovery (DR) testing tools that will enable DevOps teams to further streamline workflows.

While building apps I learned that writing code is only half the journey - getting it deployed, updated, and running reliably is also just as important if not more. When I started deploying my apps to the cloud, I realized how many manual steps it took to get the app running. That's when I discovered CI/CD and GitOps tools that automate everything from testing to deployment, so developers can focus on writing code instead of wasting time on manually deploying each time.

Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.

Over the past few years, I've reviewed thousands of APIs across startups, enterprises and global platforms. Almost all shipped OpenAPI documents. On paper, they should be well-defined and interoperable. In practice, most fail when consumed predictably by AI systems. They were designed for human readers, not machines that need to reason, plan and safely execute actions. When APIs are ambiguous, inconsistent or structurally unreliable, AI systems struggle or fail outright.

Steve Yegge thinks he has the answer. The veteran engineer - 40+ years at Amazon, Google and Sourcegraph - spent the second half of 2025 building Gas Town, an open-source orchestration system that coordinates 20 to 30 Claude Code instances working in parallel on the same codebase. He describes it as "Kubernetes for AI coding agents." The comparison isn't just marketing. It's architecturally accurate.

When I manage infrastructure for major events (whether it is the Olympics, a Premier League match or a season finale) I am dealing with a "thundering herd" problem that few systems ever face. Millions of users log in, browse and hit "play" within the same three-minute window. But this challenge isn't unique to media. It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude?

The main advantage of going the Multi-Cloud way is that organizations can "put their eggs in different baskets" and be more versatile in their approach to how they do things. For example, they can mix it up and opt for a cloud-based Platform-as-a-Service (PaaS) solution when it comes to the database, while going the Software-as-a-Service (SaaS) route for their application endeavors.