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The industry is rapidly moving towards deeper AI integration than ever before. What was once simply focused on chatbots or recommendation engines has pivoted significantly to AI systems communicating with other AI systems. These AI tools are leveraging multi-agent workflows to accomplish complex tasks that traditional systems have struggled with. Innovation without validation is a liability. Any developer worth their salt will know that these systems require ample testability and validation.

Zendesk is a goldmine of customer insights, but extracting that value from its usage is not simple. Teams trying to export data from Zendesk often run into paywall restrictions, API rate limits, and third-party tools that promise simplicity but falter at scale. For organizations integrating support data into BI platforms, migrating systems, or automating reporting pipelines, these challenges stall analytics and strategy.

Kubernetes, also known as K8s, is an open-source, portable, and scalable container orchestration platform. With K8s, you can reliably manage distributed systems for your applications, enabling declarative configuration and automatic deployment. Yet, K8s can be resource-intensive and costly, with a rather steep learning curve. But in 2019, a lighter, faster, and potentially more cost-effective alternative appeared: K3s. Still, K3s is not a magic wand that works for all Kubernetes deployments.

Open source software is everywhere. Research shows that around 97% of codebases contain open source software, and it’s clear to see why. It’s always magical to realize that there are thousands of free-to-use, ready-built programs and code repositories that solve problems you’d otherwise need to spend weeks building the solutions for from scratch. However, like with all software, you still need to ensure that your software supply chain is secure and safe to consume.

The Linux Foundation’s latest report, Open source as Europe’s strategic advantage: trends, barriers, and priorities for the European open source community amid regulatory and geopolitical shifts, provides key insights into how European enterprises are using open source software (OSS), as well as the barriers towards further development of open source in the continent.

If you’re struggling to contain cloud costs in this suddenly volatile AI-fixated environment, it might be time to consider FinOps as an exercise in granular allocation and unit economics, with a focus on outcome.

When a payment service runs across 12 pods — each serving different customer segments — and an authentication layer spans three namespaces, performance issues can originate in both the application code and the orchestration layer. The challenge is linking request-level performance data with what’s happening inside the cluster: container CPU limits, pod scheduling decisions, and node-level events.

We’re excited to announce step metrics – a new capability coming to Bitbucket Pipelines to help you better manage and optimise your CI/CD workflows. Ever wondered what’s happening under the hood during your pipeline runs? Step metrics provide a window into the resource usage of your build and service containers. More specifically, step metrics let you monitor CPU and memory usage for each build and service container in your pipeline steps.

Elasticsearch is a distributed search and analytics engine that powers everything from log management platforms to e-commerce search bars. It excels at indexing and retrieving large volumes of data quickly, but like any complex system it can slow down under heavy load or inefficient queries.