Operations | Monitoring | ITSM | DevOps | Cloud

Staging environments are often looked at as the testing ground ahead of the “real” production environment. The idea is simple – build a duplicate of your production environment, run your tests, and ship with confidence. But the reality of using staging in the real world as part of a holistic API testing strategy is rarely that clean. No matter how meticulously you mirror production services, staging always falls a little short.

Docker layer caching (DLC) is a powerful technique that can significantly accelerate your CI/CD pipelines. By reusing unchanged image layers across builds, DLC not only cuts down on build times but also reduces cloud costs and boosts developer productivity. In this article, we’ll break down how Docker layer caching works, how to implement it effectively, and how to combine it with ephemeral environments for maximum impact.

The Uptrace team is thrilled to announce the release of v2.0—our biggest update yet! This release represents a complete reimagining of how observability data should be stored, queried, and managed. With multi-project support, revolutionary JSON-based storage, powerful data transformations, and a host of developer-friendly features, Uptrace v2.0 is designed to scale with your growing infrastructure needs.

Key takeaways In the fast-moving world of CI/CD pipelines, microservice architectures, and container orchestration, software changes rapidly. What exists in a codebase today might be gone next week. At this scale and speed, it’s impossible for development teams to manually track every line of code and every new piece of functionality.

In the world of enterprise IT, “support” can mean many things. For some, it’s a safety net – insurance for the day something breaks. For others, it’s the difference between a minor hiccup and a full-scale outage. At Canonical, it means a simple, comprehensive subscription that takes care of everything, so that everything you build works the way you want it to, for all the people who love to use it.

Over and over, we’ve seen that teams who invest in adding rich, relevant context to their telemetry end up debugging faster and collaborating more effectively during incidents. Getting meaningful context added can feel like a big cross-team project, but some of the highest-leverage improvements don’t require app code changes or coordination across services.

When I started using Playwright, there was a single command that blew me away. I immediately became (and still am) a huge Playwright Codegen fanboy. Playwright's codegen command opens up a browser window, and whatever you do in this window will be recorded. Navigating URLs, clicking links, and filling out form elements—the Playwright inspector records all your actions and generates a Playwright test for you. Magic!

You’ve instrumented your LangChain app, and traces are now flowing into Last9. Now the issues are visible: API costs are crossing $200/day, average response times exceed 3 seconds, and performance degrades under 100 concurrent users. A single tool call adds over 2 seconds. Bloated context windows are pushing up token usage, wasting $50/day. Here’s how to use trace data to identify and fix these inefficiencies, systematically and at scale.

Adding Grafana dashboards directly into your app lets users see monitoring data without switching tabs or tools. Using an iframe to embed Grafana does work, but it brings along some tricky authentication and security issues that aren’t always obvious at first. In this blog, we’ll go over the practical ways to embed Grafana dashboards from easy public snapshots to secure, private dashboards that need authentication.

Over the past few years, Platform Engineering has taken off as more and more as enterprise organisations adopt the practice of creating a centralised, self-service interface for developers to access the tools they need in order for them to do the job they were meant to do: build amazing software. At the heart of every Golden Path lies the ability to reliably produce, store, and consume build artifacts, from container images to internal libraries.