Operations | Monitoring | ITSM | DevOps | Cloud

API security testing is software testing performed on APIs. It is meant to identify vulnerabilities in API endpoint communication and access. In modern software development, API security is a crucial aspect that cannot be ignored. API security testing can now be automated in CI/CD, enabling early detection of vulnerabilities, maintaining security standards without slowing down development, and reducing human errors.

Losing a customer to a competitor can be costly; customer retention is vital for business success and growth. Businesses must anticipate when and why a customer might leave, so they can implement measures to retain them. One solution might be to build a system that predicts churn. But can it be done? Using machine learning (ML) techniques to analyze customer service interactions can provide valuable insight into customer sentiment.

Frequently in support conversations and posts on Playwright forums, a problem has come up that’s a little bit hard to describe, but comes down to synchronous testing: developers writing a series of Playwright tests that operate on the assumption that one of the tests will either run first or run last, and perform the function of a setup and cleanup script.

AI platforms have experienced several service interruptions over the past few months. We’ve all seen the memes fly when ChatGPT, Gemini or Perplexity go down. They’re funny at first, but then reality hits: if you rely on AI tools for work or business, these outages can grind your day to a halt.

I first started using AI coding assistants in early 2021, with an invite code from a friend who worked on the original GitHub Copilot team. Back then, the workflow was just single-line tab completion, but you could also guide code generation with comments and it’d try its best to implement what you want. Fast forward to 2025. There’s now a wide range of coding assistants that are packed with features.

AI/ML development is getting a lot of attention as organizations rush to bring AI services into their business applications. While emerging MLOps practices are designed to make developing AI applications easier, the complexity and fragmentation of available MLOps tools often complicates the work of Data Scientists and ML Engineers, and lessens trust in what’s being delivered.

Building dashboards one by one in Grafana can quickly become tedious. Clicking through the UI for every change isn’t exactly efficient. There’s a better way. The Grafana API lets you automate repetitive tasks and extend Grafana’s capabilities beyond the UI. If you're new to monitoring or managing a complex observability setup, understanding the API can make your workflow more efficient and scalable.