Ruby on Rails is the most popular web framework in the Ruby ecosystem and has a large user base, ranging from freelancers to large established companies. With an active user community and wide-ranging documentation, it can be used to build everything from simple applications to complex web platforms. That said, a new contestant is taking on Rails’ dominance for the full-stack Ruby framework title: Hanami.

Kong Insomnia 9.0 is now generally available for download. It ships with many new features and improvements — over one hundred improvements in total! This new version of Kong Insomnia also makes it much easier to migrate from other products with a more reliable “import” experience, while improving the speed of using the product and making it more customizable. Let’s dive into all the notable features.

We're excited to introduce WSO2 API Platform for Kubernetes (WSO2 APK) 1.1, a key update to our Kubernetes-native API management solution. This release leverages the strengths of Kubernetes to offer high-performance and cloud-scale API management, ensuring automation, reliability, and improved developer experiences. Traditional API management solutions aren't built for Kubernetes environments, leading to issues with performance, scalability, and manual configuration.

In the ever-evolving web design and development world, having the right tools at your disposal can make all the difference. One such tool that has been making waves in the industry is InVision. With over a million users worldwide, InVision has become a go-to platform for web professionals looking to streamline their design process and create stunning, interactive prototypes.

BrowserStack and Sauce Labs are industry-leading, cloud-based web and mobile testing platforms for developers. Both platforms offer quality features to streamline and simplify testing across browsers and devices. If you’re choosing between BrowserStack and Sauce Labs, looking at the basic features won’t cut it. You need to dig deeper and identify where the two vendors differ.

Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. As researchers, we have grappled with the constraints of efficiently training and inferencing LLMs for years.

New research shows overconfidence about AI could lead to a higher risk of failure and costly mistakes amounting to hundreds of millions of dollars.

A 2021 Educase report shows that 75% of surveyed institutions have started or are planning extensive digital transformation strategies to improve their data utilization. The importance of a strategic approach to data utilization in this context cannot be overstated. Effective data management frameworks ensure that data across the institution is accurate, consistent, and used in alignment with internal policies and external regulations.