90% of the time when I explain how the stat cache works in PHP, people are surprised because they expected it to work differently. It was invented to solve a very limited problem when you call several file system related operations on the same file in quick succession. Why should you know how it works? Because sometimes you need to work around the cache with the clearstatcache() function to get PHP code to run without errors.
Machine learning watching generative artificial intelligence (AI) take off feels a little bit like an American Girl doll envying the Barbie movie excitement from afar. What is she, chopped liver? But we can’t forget about machine learning, because it’s the giant that generative AI is standing on. How? Well, machine learning is how generative AI learns. Generative AI takes machine learning a step further by leveraging those learnings to produce something new.
In 2022, financial services firms faced over $8 billion of fines for anti-money laundering (AML) process failures. And for many, things aren’t getting better—false positives keep rising right along with client expectations. Regulations continually increase in number and complexity and criminals are getting ever more sophisticated in their tactics and techniques.
Organizations today are looking to do more with less. The solution for many? Digital transformation. While digital transformation isn’t a new concept, the benefits of boosting efficiency, controlling costs, and delivering better customer experiences are obvious in today’s topsy-turvy economic conditions. Digital transformation often involves making the transition from legacy monoliths to modern cloud native microservices-based architectures.
The data landscape is constantly evolving, and with it come new challenges and opportunities for data teams. While generative AI and large language models (LLMs) seem to be all everyone is talking about, they are just the latest manifestation of a trend that has been evolving over the past several years: organizations tapping into petabyte-scale data volumes and running increasingly massive data pipelines to deliver ever more data analytics projects and AI/ML models.
What device should be used to run the tests? What operating system version, manufacturer and model to use? How to get these devices? These are some of the questions we ask ourselves when planning a mobile application testing strategy. If we consider automating these tests, other questions will surely come to mind.
Front-end testing is a form of black box software testing in that it requires no behind-the-scenes understanding of how a software application works. It’s solely concerned with evaluating the user experience of an app. A front-end test is only effective if it tests both the functionality and visual appearance of an app, where “appearance” includes things like the layout of a page and the size, shape, color, and legibility of visual elements like buttons, form fields, and text.
