Systems | Development | Analytics | API | Testing

In today's digital-first world, usability testing has emerged as a crucial tool for ensuring that products function as their users expect. Testlio September 20th, 2024 Discover Why Skipping Usability Testing Isn't Ideal Usability testing is a research process in which real users are observed while interacting with a product to identify usability problems. The aim is to make products as intuitive and user-friendly as possible, ensuring a smooth, frictionless experience.

In the landscape of API development, strict adherence to established standards is crucial for sustaining consistency, ensuring reliability, and enabling seamless compatibility across evolving environments. With the release of Katalon Studio 9.7, we are excited to introduce OpenAPI schema compliance validation. This significant enhancement extends our schema compliance testing capabilities to include OpenAPI and Swagger specifications.

Today, we are excited to unveil a suite of innovations that will transform your testing processes and elevate the quality of your software. From harnessing the power of AI for automated regression testing with TrueTest to expanding the capabilities of TestOps and redefining test automation with Katalon Studio 10, these new releases are designed to help you stay ahead in the ever-evolving world of software testing. Let's dive in!

Imagine this: You’ve just rolled out a small update to your application, but suddenly a core feature stops working. Sound familiar? That’s where automated regression testing comes to the rescue. It's like having a diligent, tireless assistant that constantly checks your software for unintended side effects every time you make a change. Let’s dive into how automated regression testing can transform your software development process, saving you time, money, and sleepless nights.

It’s the final day of Koyeb's Launch Week Round 2, and today we’re announcing the public preview of Volumes! Volumes on Koyeb are blazing-fast NVMe SSD you can use to persist data across deployments. After announcing Volumes in technical preview a few months ago, we are now opening the preview to all users on the Starter plan! Offering high throughput and low latency, Volumes open the door to a wide range of new workloads and use cases to handle the state of your applications.

It’s no secret that artificial intelligence (AI) is revolutionizing the way companies operate with its ability to sift through mountains of data and make accurate predictions at record speed. But with great power comes great responsibility. As AI systems are more regularly incorporated into business, it’s critical that data sources are both accurate and secure to prevent error.

Multimodal AI is a critical player in the new wave of artificial intelligence. By combining different types of data like text, images, and audio, multimodal AI creates more intuitive and versatile AI systems that can more closely mimic human decision-making.

In JavaScript, errors are only detected during runtime. Therefore, it is impossible to get compile-time errors when building JavaScript applications. There are several reasons an app might encounter a JavaScript exception: invalid input, server error, syntax errors, or errors in the application logic. In this article, we will dig into exception handling in JavaScript so that when your app encounters errors, it affects users as little as possible.

Databricks is lighting the way for organizations to thrive in an increasingly AI-driven world. The Databricks Platform is built on lakehouse architecture, empowering organizations to break down existing data silos, store enterprise data in a single centralized repository with unified data governance powered by Unity Catalog, and make the data available to a variety of user groups to support diverse analytics use cases.

Artificial Intelligence (AI) is transforming industries, enhancing decision-making processes, and creating new opportunities across various domains. However, selecting the right type of AI for your specific needs can be challenging. Broadly, AI can be categorized into predictive (sometimes known as discriminative or traditional) AI and generative AI, each serving different purposes.