Systems | Development | Analytics | API | Testing

Black Friday is when eCommerce sites face their toughest critics: deal-hunting shoppers with zero patience for glitches. Were you aware that 47 of visitors anticipate a website to finish loading within 2 seconds? Even more concerning, half of the visitors will leave a sluggish website and switch to a competitor. One crash during busy times can lead to significant financial loss and permanent harm to your brand's image.

Business leaders are worried that despite their fervent enthusiasm for building innovative solutions, they haven’t been able to explore even half of the possibilities that digital has to offer. This anxiety has made CXOs desperate to associate with keywords like Generative AI, multi-cloud, SaaS, and more. However, accommodating these big technologies in a rush often comes at the cost of quality.

AI is becoming a part of our everyday lives, and in the software testing industry, it is starting to show its impact as well. Traditional testing methods can often happen at a later stage of the development life cycle, which may present challenges for meeting the demands of modern software delivery. This is where shift-left testing comes to shine. This testing methodology has become one of the most used strategies for delivering high-quality software without missing bug findings along the way.

App Center, a popular CI/CD platform from Microsoft, announced it will close its doors on March 31st 2025. This has left many mobile developers and teams looking for an alternative. Probably the most affected is the React Native community who have relied on App Center because of its OTA updates with CodePush.

JSON, which stands for JavaScript Object Notation, is a lightweight data-interchange format that offers a way to store and exchange data that is simple to read and write. It can be viewed as a format for organizing data so that different programs can understand it. It is frequently used for transmitting data in web applications between a server and a client. JSON logs are log entries that are formatted in JSON.

Today’s software testing trends show the growing demand for more efficient and automated API testing. Manual testing is not only time-intensive for internal testing teams, it can also lead to poor customer experiences. When manual testing processes cannot proactively discover issues, your customers may inevitably be the ones finding them. Many of the current test automation solutions today focus on the UI, while most API-level testing is still done manually.

In today’s digital age, data has evolved from being a mere byproduct of business processes to becoming the cornerstone of strategic decision-making. Yet, for many organizations, unlocking the full potential of their data remains a significant challenge. Traditional data analytics models often create bottlenecks, relying heavily on overextended IT departments to provide insights, which delays decision-making and limits agility.

In the previous part of this series, we discovered how to create persistent connections in Rack in theory, but now we'll put what we learned into practice. The web has two formalized specifications for communication over a persistent connection: server-sent events (SSEs) and WebSockets. WebSockets are widely used and highly popular, but SSEs are far less well-known. Let's explore them first.

Unit tests are essential for increasing the test coverage of a backend application, ensuring its reliability, functionality, and robustness. Jest has become one of the most popular solutions when unit testing in Node, due to its intuitive API, zero-configuration philosophy, and flexible approach to code transpilation. In this guide, you will learn more about Jest, explore its features, and see it in action through a complete example. Become a Jest unit testing expert!

PostgreSQL's EXPLAIN command is a powerful tool that provides insights into how a query will be executed. It helps developers and database administrators identify potential performance issues in their queries. In this blog, we will explore what EXPLAIN is, when to use it, how to interpret its output, and techniques for optimizing queries based on the insights it provides.