Systems | Development | Analytics | API | Testing

Front-end testing ensures your application looks and behaves as users expect on every device, browser, and interaction. Whether it's clicking a button, filling a form, or navigating pages, front-end tests validate what users actually experience. But manual front-end testing slows teams down. As the interface evolves, so do the tests, and without automation, keeping up is nearly impossible.

Test automation is essential if you want to move fast without breaking things. But here’s the hard truth: not every test is worth automating. And trying to automate everything is how teams burn time, introduce flakiness, and end up maintaining tests that add zero value. So how do you know what test cases to automate? That’s what this guide is for.

AI is transforming software testing by introducing intelligent automation techniques. Unlike traditional scripts that follow static instructions, AI-driven testing uses machine learning, computer vision, and NLP to adapt and make data-driven decisions during testing. This shift offers significant advantages. AI can rapidly analyze large datasets (requirements, code changes, past failures) to identify high-risk areas and prioritize testing efforts.

ClearML v3.25 introduces native support for vector databases within the Hyper-Datasets feature. This release enables users to store and search embeddings directly inside ClearML, opening the door to powerful custom RAG pipelines. In addition, v3.25 includes expanded orchestration metrics, new Application Gateway UI, and a range of UI upgrades to streamline day-to-day operations.

BigQuery ML contribution analysis models, now GA, automate insight generation and output key change drivers from multidimensional data.

Choosing the right relational database management system (RDBMS) is a critical decision for any organization. Two of the most widely adopted options are Microsoft SQL Server and MySQL. Both platforms have evolved rapidly, introducing new features and enhancements that reflect the changing needs of modern data-driven applications. This updated 2025 comparison explores their latest capabilities, performance, security, and suitability for different use cases.

In today’s API-driven world, strong API design is key to building reliable and scalable services. Without structure or standardization, the design phase can quickly spiral into chaos. Common problems like misaligned teams, tightly coupled systems, code duplication, and inconsistent standards begin to snowball. This causes delays, poor customer experiences, and in some cases, long-term technical debt that becomes a serious business risk.

E2E Testing is a method to test a software from the consumer’s perspective. It involves simulating real scenarios, including user interfaces, backend testing. The purpose of E2E testing is to validate the application’s overall behavior, including its functionality, reliability, performance, and security. E2E Testing helps in identifying issues when one or more components interact with each other. It is usually done after integration testing, which tests individual component.

In today’s high-speed world of software delivery, performance engineering can’t afford to be a bottleneck. Teams are shifting left, moving fast, and integrating feedback loops earlier and more often, but performance feedback still too often lags. If your performance testing still hinges on last-mile validations or project-based assessments, you’re not alone. Many teams are struggling with how to evolve their practices from siloed testing to scalable, integrated performance engineering.

We’re excited to announce Ably LiveObjects, launched with a radical vision: to enable any application to work with live, shared data that synchronizes instantly for users everywhere. Shared access to live data is no longer a nice-to-have - it’s becoming a foundational building block of every realtime digital experience.