Large Language Models, or LLMs, have become a near-ubiquitous technology in recent years. Promising the ability to generate human-like content with simple and direct prompts, LLMs have been integrated across a diverse array of systems, purposes, and functions, including content generation, image identification and curation, and even heuristics-based performance testing for APIs and other software components.

You may have wrestled with a web application attempting to call an offsite web service, such as an OpenTelemetry Collector, and gotten an odd error with the word CORS in it. Something like: Or, maybe you got a generic thrown error from your fetch statement that states Error: Failed to fetch …and you wondered, “What’s the problem, and how can I fix it?” These kinds of errors are called CORS errors, and they can be a bit confusing.

Microservices are now the architecture of choice for many developers when crafting cloud-native applications. A microservices application is a collection of loosely coupled services that communicate with each other, enhancing collaboration, maintainability, scalability, and deployment. There are several options for enabling this communication between microservices. When it comes to Python, gRPC and REST are two extremely popular directions to go.

Memory leaks happen when a program fails to release memory it no longer needs, and can be a big issue for developers and system administrators alike, as the gradual depletion of available memory often makes for complex troubleshooting and debugging. Given how the consequences of a memory leak can range from decreased system performance to outright crashes, it’s crucial to isolate the root cause of the leak quickly and efficiently.

With systems more interconnected than ever, the ability to communicate quickly and efficiently has become crucial today. This is where gRPC, an open-source framework by Google, comes in to transform the way APIs are designed and utilized. In this blog, we will explore what gRPC is, how it works, how it differs from existing protocols like REST, and the best practices for Optimizing its full potential.

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.

API Monitoring is the process of continuously observing and testing APIs to ensure they perform as expected, maintain uptime, and deliver the desired functionality. This includes tracking metrics such as API availability, uptime, latency, and response times. Whether you’re dealing with a REST API, a web API, or a microservices architecture, it’s important to understand that monitoring is essential for detecting issues before they impact end-users.

As a team, we have spent many years troubleshooting performance problems in production systems. Applications have become so complex that you need a standard methodology to understand performance. Our approach to this problem is called the Golden Signals. By measuring these signals and paying very close attention to these four key metrics, providers can simplify even the most complex systems into an understandable corpus of services and systems.

This enhancement is part of Raygun’s 12 Days of Christmas 2024. Over the next few weeks, we’ll share daily updates on bug fixes and feature improvements inspired by feedback from you, our customers. These are the small but impactful changes you’ve asked for, designed to make Raygun faster and easier to use. Check back tomorrow for the next update and see how we’re leveling up your experience one day at a time! Our special thanks to Gwilym from the U.K.

In the process of developing software, designing and performing testing is a critical aspect of ensuring high software reliability, improving software quality, and deploying strong fit and function. The shift-right testing approach moves testing to later in your production cycle as a way of doing this with more accurate user data and post-production testing practices. Also known as “testing in production,” with shift-right, you test software after it has been deployed.