Retrieval-augmented generation (RAG) is gaining traction, and for good reason. As businesses and AI experts search for more intelligent ways to process information, RAG combines the best of both worlds, i.e., the vast knowledge of retrieval systems and the creative power of generation models. But what exactly is RAG, and why is everyone talking about it?

As a follow-up to our recent Kong Insomnia v10 release that shipped with an unlimited and free Collection Runner and one week after announcing our official SCIM support for automated user provisioning, we're now excited to announce Insomnia v10.1. This release ships with the general availability of the CLI to use the Collection Runner with CI/CD automation, a new in-app invite wizard that simplifies working with collaborators, and many other improvements.

Testing is a critical aspect of software development, as it ensures your application works as intended and meets quality standards. In Node.js, testing is essential for the early detection of bugs in public endpoints. However, there are many challenges associated with testing in Node. External dependencies, asynchronous operations, and several possible input scenarios make writing tests a daunting task.

What do you think of when you think of generative AI? Generating photos, animations, and videos? Coding and solving math problems? Writing content and brainstorming with a chatbot? These have all driven plenty of excitement around AI, but there’s so much more to it than that! From an enterprise perspective, Generative AI’s impact on intelligent document processing technology is remarkable.

Producing high-quality content on a consistent basis is no small feat. Even seasoned content teams struggle to handle the pressure of churning out work that resonates, converts and ranks (especially with Google’s slew of algorithm updates ). At companies with smaller in-house marketing teams without specialized content professionals? That pressure can feel…crushing.

In today’s fast-paced world, operational efficiency is paramount for businesses striving to stay competitive. The ability to manage time and resources effectively directly impacts overall performance. Our recent tech paper on – ‘Maximizing Efficiency by Real-Time Detection of Beacon Check-In Delays’, highlights how real-time detection technologies can revolutionize industries by identifying and mitigating inefficiencies caused by delays in beacon check-ins.

In an era dominated by mobile apps, desktop applications remain vital across numerous industries. From gaming to enterprise solutions, tracking desktop app performance, user behavior, and feedback is crucial. While mobile tracking is a well-established practice, companies also need to pay more attention to the importance of desktop tracking. Despite the importance of desktop applications, many analytics platforms remain heavily focused on mobile tracking.

Lambda functions in Python are a powerful way to create small, anonymous functions on the fly. These functions are typically used for short, simple operations where the overhead of a full function definition would be unnecessary. While traditional functions are defined using the def keyword, Lambda functions are defined using the lambda keyword and are directly integrated into lines of code. In particular, they are often used as arguments for built-in functions.

If you’re at all familiar with APIs, you know that REST APIs are the main API used, particularly when it comes to microservices and their applications. gRPC is a high-performance, binary, and strongly-typed protocol using HTTP/2, while REST is a simpler, text-based, and stateless protocol using HTTP with JSON/XML.

Some of the popular AI tools people and corporations are using now include ChatGPT, Google Gemini, and Microsoft Copilot. This has resulted in higher usage and adoption of this technology and this has caused some worry among people, particularly in terms of employment. However, for software testers, these changes should be seen as a chance to improve rather than a threat.