It's essential to test web applications to ensure reliability, functionality, and a good user experience. That's why robust testing frameworks have become so important for web developers. Among the plethora of available tools, Cypress and Playwright have emerged as two of the most popular choices for automating end-to-end testing. In this Cypress versus Playwright comparison guide, we'll explore the strengths and weaknesses of the two tools, their features, and their differences. Cypress or Playwright?

We're thrilled to announce the general availability of Kong Gateway Open Source (OSS) 3.7. This latest version features cutting-edge AI Gateway updates with new streaming plugins, as well as performance and security improvements. Let’s dive into the enhancements and explore the tangible benefits that these features bring to your organization.

The payments industry is evolving rapidly, fueled by technological advancements, changing consumer behaviors, and a growing appetite for real-time transactions. As this transformation unfolds, new standards have been introduced to ensure the payments ecosystem's safety, security, and efficiency.

Quality Assurance (QA) teams must run extensive tests to ensure that a website or application performs as intended before release. However, rather than manually running these tests, which can hinder productivity, modern teams are using QA automation. By automating QA testing workflows, teams can rapidly execute these tests using standardized processes, frameworks, and software. Developers can quickly fix any errors and deliver high-quality code so that product teams can speed up release times.

Amazon OpenSearch is a popular fully managed analytics engine that makes it easier for customers to do interactive log analytics, real-time application monitoring, and semantic and keyword searches. It can also be used as a vector engine that helps organizations build and augment GenAI applications without managing infrastructure (we’ll talk about this in future blogs). Additionally, the service provides a reliable, scalable infrastructure designed to handle massive data volumes.

When it comes to developing cross-platform apps, Flutter is one of the top choices that comes to developers' minds. But, what’s the reason behind it? Thanks to the innovative cross-platform development capabilities offered by the Flutter framework, creating native applications for both Android and iOS platforms has become remarkably simple.

While controversial in functional programming, dependency injection can be a useful pattern in Elixir for managing dependencies and improving testability. In this, the first part of a two-part series, we will cover the basic concepts, core principles, and types of dependency injection. We'll explore its benefits in terms of modularity, testability, and maintainability. Then, we will look into a specific scenario where dependency injection can be beneficial, in this case, testing.

Imagine transforming raw business data into actionable insights with minimal effort. This is the value proposition of automated analytics, a form of data analytics fast becoming more accessible among modern business intelligence (BI) and analytics software solution vendors. Independent software vendors (ISVs) and enterprise organizations at the cusp of investing in analytics struggle with manual data processes that are time-consuming and prone to errors.

Django is one of the leading Python frameworks used to create full-stack web applications. In this comprehensive guide, you will explore the intricacies of testing and debugging within the Django framework, focusing on advanced methodologies and essential tools. Beginning with the fundamentals of Django unit and integration testing, you will delve into advanced techniques such as mocking, testing middleware, and profiling for optimal performance.

Despite the transformative potential of AI, many financial planning and analysis (FP&A) teams are hesitating, waiting for this emerging technology to mature before investing. According to a recent Gartner report, a staggering 61% of finance organizations haven’t yet adopted AI. Finance has always been considered risk averse, so it is perhaps unsurprising to see that AI adoption in finance significantly lags other departments.