One of the new features of Xcode 16 is called "explicitly built modules". Behind this abstract name is something that makes builds faster and compiler errors more informative. As this is enabled by default for C and Objective-C code, you can experience some of the benefits instantly, but it can also be enabled for Swift code as an experimental feature. In this post, we'll explore how this feature works and the benefits it brings to projects that adopt it.

From traditional brick-and-mortar establishments to sophisticated online platforms, the banking and fintech landscape has drastically evolved. Today, customers expect flawless digital experiences. Any flaw in the experience can cost you their trust, loyalty, and business. Digital banking offers the convenience and accessibility that your customers crave. However, with this transformation comes an increased risk of security breaches, operational failures, and user dissatisfaction.

Thousands of customers have worked with Snowflake to cost-effectively build a secure data foundation as they look to solve a growing variety of business problems with more data. Increasingly customers are looking to expand that powerful foundation to a broader set of data across their enterprise.

Have you ever faced a stubborn ‘403 Forbidden’ or celebrated a ‘200 OK’? HTTP status codes, specifically the “HTTP status code,” are the universal language of web servers, telling you the fate of your online requests. This guide takes you through each HTTP status code, offering insights to resolve and leverage these responses for better web interaction efficiently.

As programmers we know that, despite our best efforts, we’ll never be able to completely eliminate errors from our apps. The sheer complexity of modern apps, not least the reliance on dynamic (often third-party) inputs, means errors are inevitable and error handling (exception handling) is crucial to user experience.

Integrating artificial intelligence (AI) into business operations is no longer optional—it’s necessary. Yet, too often, businesses fail to reap the full rewards. AI can’t produce the results that impress stakeholders and drive tangible results unless you take a strategic approach to its deployment.

In the age of AI, enterprises are increasingly looking to extract value from their data at scale but often find it difficult to establish a scalable data engineering foundation that can process the large amounts of data required to build or improve models. Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems.