Operations | Monitoring | ITSM | DevOps | Cloud

Several years ago—in 2016, to be exact—we decided to host the Checkly platform on Heroku, since it was the hosted cloud offering (at the time) that best aligned with our needs. In particular, we felt that Heroku offered the best PostgreSQL cloud offering available at the time.

When making changes to your production services, it’s important to have a plan for how to detect problems and roll back changes. How many roll out plans would include: “if it breaks, don’t worry, the users will tell us!” But if your monitoring coverage of production services isn’t complete, you’re implicitly relying on your users to tell you when something breaks.

People keep asking us here at Checkly if and how AI can help create solid and maintainable Playwright tests. To answer all these questions, we started by looking at ChatGPT and Claude to conclude that AI tools have the potential to help with test generation but that "normal AI consumer tools" aren't code-focused enough. High-quality results require too complex prompts to be a maintainable solution.

We all implement tools to monitor our nodes and keep our entire cluster up and running. But how often do updates, failures, or errors mean that users suffer outages, even though our status boards look green? As Kubernetes has enabled more complex microservice architecture, the gap between the state of the dashboard, and the health of services for the user, has grown wider.

I'm a bit of an AI skeptic. And even though GitHub Copilot is my daily auto-completion on steroids, I always double-check the code generated by LLMs. If you're using AI for coding, you probably know that the results are sometimes surprisingly good and other times shockingly terrible. Lately, I have seen more and more articles and even docs recommending ChatGPT to generate Playwright tests. Could this be true? Are ChatGPT and friends really that good at generating test code?