Shift-left is a great solution… in an ideal world. The reality is, developers don’t have time to add testing to their plates. Rushing through both development and testing is a recipe for bugs to pop up down the road.

We don’t write tests for next week. We write tests for 6 months from now.

To shift or not to shift – that is the question. If you’ve been around the software development world lately, you’ve likely heard of shift-left – the practice of integrating testing, security, and operations early in the software development lifecycle to detect issues early. This approach is meant to be a win-win-win – saving time, money, and headaches. But is it as great in practice as it sounds in theory? In this episode, we debate the real-world benefits of shift-left and get to the heart of the issue: testing well.

Front-end software developers often own small pieces of the User Interface, and are frequently challenged by changes introduced elsewhere in the project. A slight change somewhere on one web page can cause a shift or misalignment elsewhere. With so many teams working together, it’s important to get fast feedback about changes to the layout or appearance of an app. Here's how Sauce Labs can do just that.

Back in the 2000s, code lived in one execution thread. Database queries, user interactions, and data pipelines were all managed by the same processes. The problem? Everything was interconnected and interdependent. Fixing one thing might break another. Releases were all or nothing. We call this monolith hell. Your ticket out? Microservices.

Back in the 2000s, code lived in one execution thread. Database queries, user interactions, and data pipelines were all managed by the same processes. The problem? Everything was interconnected and interdependent. Fixing one thing might break another. Releases were all or nothing. We call this monolith hell. Your ticket out? Microservices.

Back in the 2000s, code lived in one execution thread. Database queries, user interactions, and data pipelines were all managed by the same processes. The problem? Everything was interconnected and interdependent. Fixing one thing might break another. Releases were all or nothing. We call this monolith hell. Your ticket out? Microservices.

Back in the 2000s, code lived in one execution thread. Database queries, user interactions, and data pipelines were all managed by the same processes. The problem? Everything was interconnected and interdependent. Fixing one thing might break another. Releases were all or nothing. We call this monolith hell. Your ticket out? Microservices.

Back in the 2000s, code lived in one execution thread. Database queries, user interactions, and data pipelines were all managed by the same processes. The problem? Everything was interconnected and interdependent. Fixing one thing might break another. Releases were all or nothing. We call this monolith hell. Your ticket out? Microservices.

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.