Systems | Development | Analytics | API | Testing

AI testing careers are shifting in ways that most people in QA are not fully prepared for, and the changes are creating opportunities that did not exist even a few years ago.

Hi everyone, my name is Bailey Ahrens, and I’m a marketing intern at Speedscale! I just returned a few days ago from KubeCon + CloudNativeCon North America 2025. While it was only my second trade show, it felt like a huge step forward in my confidence, skills, and future direction. My first conference (API World) was all about stepping outside my comfort zone. KubeCon was where I started leaning into that confidence and finding my place in the tech community.

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.

As modern applications grow more complex and API-driven, maintaining test stability becomes increasingly challenging. Many QA teams struggle to manage scattered test scripts and inconsistent environments, leading to inefficiency and missed defects. The real solution lies in adopting a structured automation framework that brings order, scalability, and speed to the testing process. In an API-first setup, it helps teams validate functionality continuously and collaboratively.

The SmartBear community is made up of curious, collaborative builders. They’re developers, testers, creators, and innovators who every day are experimenting with new technologies, customizing workflows, and solving complex problems.

AI systems change faster than traditional QA models can react, which means quality risks now emerge in real time rather than at release.

When building modern React applications that need to interact with existing enterprise infrastructure, organizations often face several challenges.

Enterprise data infrastructure is reaching an inflection point. The traditional approach — building for predictability, optimizing for stability — no longer matches how organizations actually operate. Companies are inundated with data that now comes from and lives everywhere: on-premises systems, multiple clouds, edge environments, and partner and industry ecosystems. Workloads shift daily.

Salesforce has now completed its acquisition of Informatica, in a deal positioned as a way to strengthen Salesforce’s AI data foundation. If you are an Informatica customer, that headline is not the end of the story. It is the start of a long, complicated chapter. Informatica is no longer an independent data company. It is now one piece of Salesforce’s AI CRM story. That has real consequences for your data strategy, your AI roadmap, and your room to manoeuvre.

The close of 2025 is near, and that also means a new version of PHP is about to be released: 8.5! There has already been some discussion regarding the latest features and modifications affecting developers, for example on Laravel News, PHP.Watch or the Zend Blog. In this post we are highlighting just the performance, debugging, and operations-related changes in PHP 8.5 that you will not find in the posts listed above. Several of these changes were even contributed by Tideways employees.