Back in August, SmartBear acquired Stoplight, a leader in API design and documentation. Since this acquisition, our focus has been on seamlessly integrating Stoplight's open-source tools—Spectral, Elements, and Prism—into SwaggerHub. Our goal is to simplify complex API development tasks and address the common challenges encountered in API development.

Grape is a popular Ruby framework for building RESTful APIs. Exception handling plays a crucial role in ensuring the stability and reliability of any application, including those made with Grape. This article will explore the basics of Grape exception handling, including customizing exceptions. We'll also touch on some best practices, and how to integrate your app with AppSignal for enhanced error monitoring and management. Let's get started!

The topic of testing AI and ensuring its responsibility, safety, and security has never been more urgent. Controversy and incidents of AI misuse have increased 26-fold since 2021, highlighting growing concerns. As users quickly find out, AI tools are not infallible; they can make mistakes, display overconfidence, and lack critical questioning. The reality of the market is that AI is prone to error. This is exactly why testing AI is crucial. But how do we test AI?

Today Snowflake is launching and open-sourcing with an Apache 2.0 license the Snowflake Arctic embed family of models. Based on the Massive Text Embedding Benchmark (MTEB) Retrieval Leaderboard, the largest Arctic embed model with only 334 million parameters is the only one to surpass average retrieval performance of 55.9, a feat only less practical to deploy models with over 1 billion parameters are able to achieve.