Systems | Development | Analytics | API | Testing

For the past several years, there have been murmurs of an incoming convergence between API management and event management.

Look around yourself. We are swarming in the world of data and AI. From students at school using ChatGPT to complete their assignments to professionals using AI for market research, content creation, or even debugging code, everyone is leveraging the power of large language models (LLMs). Mr. Smith isn’t Googling his tax questions anymore; he’s asking an AI assistant.

The generative AI hype train doesn’t appear to be slowing down, with organizational adoption rising from 33% in 2023 to 78% by the end of 2024. In fact, bigger companies are leading the way in GenAI adoption, with the global AI market projected to grow annually by 36.6% between 2024 and 2030. However, GenAI growth isn’t following a linear path. Organizations are utilizing different AI approaches, depending on their specific use cases.

Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to large language models (LLMs). It’s becoming a foundational layer in many AI-native workflows, especially when working with real-time or continuously updating data sources. We're excited to announce that Choreo now supports the deployment of MCP servers, empowering developers to integrate AI capabilities more efficiently into their applications.

API request logging is essential for secure and efficient APIs. It helps track performance, detect security threats, and meet compliance standards. Here's a quick breakdown of what you need to know: Why It Matters: Logging provides real-time insights, audit trails for investigations, and supports compliance.

The crash-proof app doesn’t exist. It never has, and it probably never will. Because apps can crash for all kinds of reasons, some of them impossible to foresee. No matter how well we build them, crashes are going to happen to our apps. So, as devs, we need to know how to react to a crash when it happens. And in this context, understanding crash reports is crucial. They provide the clues we need to put the pieces together.

Without a good test automation architecture in place, automation turns into a mess: brittle scripts, redundant tests, endless maintenance headaches. This guide breaks down when to build a proper architecture, why it matters more than you think, and how to design something that actually scales with you.

Automated testing with Cypress has revolutionized how developers ensure code quality, but writing comprehensive test suites remains time-consuming and often tedious. Artificial intelligence tools for Cypress testing are emerging as game-changers, dramatically reducing test creation time while maintaining quality. Let’s face it—writing tests can be a real drag. After you’ve spent days coding a beautiful feature, the last thing you want to do is spend another day writing tests for it.

Today, we are excited to announce our partnership with Pruna AI. Pruna AI is the optimization engine built to simplify and accelerate scalable inference. Koyeb offers a serverless cloud platform for teams to deploy ML and AI models on high-performance GPUs, CPUs, and accelerators - globally. By combining Pruna with Koyeb, you can speed up your model optimizations, achieve 5x faster inference speeds, and run them on scalable, high-performance serverless infrastructure.

When evaluating a DevOps testing service, software teams should ask the following questions — it can mean the difference between quickly shipping high-quality code and bottlenecking releases, or having bugs slip into production: In this article, we review five top DevOps testing services, starting with our own, Rainforest QA. We show how we help teams ship high-quality code without constant bottlenecks dragging down the release pipeline.