Systems | Development | Analytics | API | Testing

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Postman Load Test Tutorial

Postman is highly popular in the testing tools space for verifying API requests. While its use for general API testing is widely adopted, conducting load testing with Postman is not as straightforward. In this post, we assume that you have some experience working with Postman and are familiar with the fundamentals of creating and sending requests. If you’re new to Postman, there are numerous resources available in the Postman Learning Center.

One Workflow to Rule Them All

Let’s say you’re leading a company that receives thousands of documents daily. These documents come in various formats like Excel, PDFs, CSVs, and more. And they differ in terms of layout. Before you can analyze the data, your team spends hours sorting, cleaning, and preparing these documents. Most of their time is spent preparing the documents for integration into business systems. Then, a colleague shares how intelligent document processing helped him save time and boost productivity.

The Fall and Rise of Embedded Plugins: The Trend Away from IFrames

IFrames were used, and are still used, in many embed frameworks. After much experimentation, including many unsuccessful platforms, iframes have proven themselves to be a simple and powerful way to let partners customize your experience. However, their limitations are also known: Consequently, platform teams have tested and continue to test alternatives. Certain platforms now exclude iframes entirely, while others combine iframes with non-iframe alternatives.

RAG: An X-Ray for Your Data

Retrieval Augmented Generation (RAG) is an intelligent assistant that helps you find exactly what you’re looking for in a pile of medical records. Like an X-ray shows you hidden details inside the body, RAG helps you quickly extract precise information from complex data. RAG provides instant, accurate answers—often visualized in charts or summaries that require analysts to produce manually. RAG combines two AI capabilities—retrieval systems and generative models.

Why Automation Visual Testing: The Future of Software QA

Within the domain of software quality assurance, functionality and user experience hold a paramount position, and visual testing becomes an indispensable protector of the digital environment. The complexity of modern apps is always growing, thus it's critical to guarantee pixel-perfect precision and smooth visual consistency across all platforms and devices.

How AIOps is Transforming the Future of IT Operations?

Sam Altman, the key visionary behind the popular adaption of Gen AI and essentially the father of ChatGPT, deemed it “unthinkable” to have products and services without AI integration in the future. I’m sure that among other beliefs that inspired him to make such claims, the need for intelligence beyond efficiency in the modern digital ecosystem was a key one. It only makes sense to rethink IT management in this context and replace our traditional methods with the benefits of AIOps.

Boosting Release Confidence: Scalable and Secure Testing to Prepare for the Holidays

Delivering high-quality digital experiences during the holiday season is crucial, with 30% of annual revenue on the line. Developers need scalable, secure, and compliant infrastructure to ensure smooth releases and easy software testing. However, over 60% of developers admit to releasing untested code to meet deadlines, risking bugs and jeopardizing customer experience and revenue. Listen to the discussion about how to mitigate risk, catch bugs earlier, and test securely at scale in preparation for a busy holiday season.

Avoiding Automation Testing Pitfalls with High-Quality Test Code

So in today’s world of software development, where things happen so fast, test automation is no longer an option but a necessity. Thus, it helps developers shift their attention to creativity and develop new features rather than being bogged down by monotonous microtasks. But here’s the catch: automation isn’t an effortless solution. If not handled properly, it can create more issues than solutions—poor-quality test code, unreliable (flaky) tests, and holes in test coverage.