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.

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.

How to use BigQuery vector search to analyze logs and asset metadata stored in BigQuery tables.

Last week, mobile app developers gathered in Cupertino, California, and tuned in across the globe to hear the latest news and future roadmap for Apple at their annual developer conference, WWDC 2024. And Apple did not disappoint.

In today's fast-paced software development environment, the integration of AI into DevOps revolutionizes the way teams approach testing. AI, particularly GenAI , proves to be a game-changer, offering unprecedented efficiency and accuracy in software testing processes. By automating repetitive tasks and providing actionable insights, AI is not only enhancing the quality of software but also accelerating deployment cycles.

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.

Today, we are announcing AWS regions on Koyeb for businesses. The fastest way to build, run, and scale your apps on AWS infrastructure. Over the last months, we've gotten more and more requests from businesses established on AWS to have a way to deploy Koyeb services on AWS infrastructure to: Our platform's core technology is cloud-agnostic and can be operated on top of anything, from high-performance bare metal servers to IaaS providers.

The telecom industry is undergoing an unprecedented transformation. Fueled by tech advancements such as 5G, cloud computing, Internet of Things (IoT) and machine learning (ML), telecoms have the opportunity to reshape and streamline operations and make significant improvements in service delivery, customer experience and network optimization.