modern gcd

Demystify explicitly built modules

Explore how builds are changing in Xcode 16 with explicitly built modules. Discover how modules are used to build your code, how explicitly built modules improve transparency in compilation tasks, and how you can optimize your build by sharing modules across targets.

What’s new in Swift

Join us for an update on Swift. We’ll briefly go through a history of Swift over the past decade, and show you how the community has grown through workgroups, expanded the package ecosystem, and increased platform support. We’ll introduce you to a new language mode that achieves data-race safety by default, and a language subset that lets you run Swift on highly constrained systems. We’ll also explore some language updates including noncopyable types, typed throws, and improved C++ interoperability.

Deploy machine learning and AI models on-device with Core ML

Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We’ll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.

Analyze heap memory

Dive into the basis for your app’s dynamic memory: the heap! Explore how to use Instruments and Xcode to measure, analyze, and fix common heap issues. We’ll also cover some techniques and best practices for diagnosing transient growth, persistent growth, and leaks in your app.

Explore Swift performance

Discover how Swift balances abstraction and performance. Learn what elements of performance to consider and how the Swift optimizer affects them. Explore the different features of Swift and how they’re implemented to further understand the tradeoffs available that can impact performance.

Support real-time ML inference on the CPU

Discover how you can use BNNSGraph to accelerate the execution of your machine learning model on the CPU. We will show you how to use BNNSGraph to compile and execute a machine learning model on the CPU and share how it provides real-time guarantees such as no runtime memory allocation and single-threaded running for audio or signal processing models.