Exploring AI for iOS Development

This book teaches iOS, macOS, and visionOS developers how to integrate AI directly into their apps using Apple's Foundation Models and MLX Swift. Build features like streaming chat interfaces, structured data extraction, and voice synthesis. All running on-device.

In this book, you will learn how to build on-device AI apps for Apple platforms:

Foundation Models for Apple Intelligence

1. Introduction to Foundation Models

Apple's framework for accessing on-device Apple Intelligence models, including guided generation, tool calling, and stateful sessions for iOS 26.0+

2. Supported Languages and Internationalization

Working with Foundation Models across 14 supported locales, handling multilingual conversations, and session management strategies for global apps

3. Getting Started with Sessions

Setting up your first AI session and understanding model availability, error handling and different generation configuration options.

4. Streaming and Snapshots

Building responsive UIs with partial results and understanding how Foundation Models streams complete object snapshots instead of token deltas

5. Generation Options and Sampling Control

Shaping model behavior with temperature, penalties, and other parameters to control output quality and creativity

6. Advanced Chat Patterns

Building production-ready conversation interfaces with context management, memory handling, and graceful error recovery

7. Structured Generation with Schemas

Creating type-safe, structured output directly from AI responses using the @Generable framework

8. Dynamic Generation Schemas

Handling runtime-varying structures without compile-time types for flexible data extraction scenarios

9. Basic Tool Use

Enabling your AI to perform actions and access real-world data with practical examples of working with web search APIs

10. Advanced Tool Patterns

Building sophisticated tool systems for complex AI workflows with parallel execution, sequential chains, and conditional tool selection

11. Safety and Best Practices

Implementing responsible AI features with proper guardrails, user protection, and Apple's safety principles for trustworthy experiences

MLX Swift for Different LLM/VLMs

1. Introduction to MLX

Understanding the fundamentals of the MLX framework and its role in Apple development, including battery life considerations for on-device AI

2. Understanding AI Model Components

The components that make up AI models, including licensing considerations for App Store submissions

3. Loading Models with MLX Swift

How to load various model architectures using MLX Swift, with error handling for network failures and retry strategies

4. Getting Started with MLX Swift

Setting up your development environment and running your first MLX Swift code, with realistic performance expectations for different devices

5. Working with Pre-Trained Models

Using existing open-weights models for different tasks, including memory management and testing strategies for actual devices vs simulators

6. Model Quantization

Techniques to make large AI models smaller and faster for on-device performance, and when to avoid quantization

7. Text Embeddings with MLX Embedders

Using text embedding models to understand semantic meaning of text for search and comparison, with storage for large datasets

8. Customizing Generation Parameters

Fine-tuning parameters like temperature and top-k to control generative model output, with debugging techniques and parameter logging strategies

9. Vision-Language Models

Working with models that understand and describe image content, including image preprocessing best practices and quality considerations

10. MLX Swift Tools

Utilities for MLX Swift development, including integration tips for CI/CD pipelines and automated testing

11. Tool Use with Models

Enabling language models to interact with external tools and APIs, with security considerations and input validation strategies

12. Generative Vision with Image Tool

Working with models that generate and manipulate images based on text instructions, with result evaluation techniques and failure pattern analysis

13. Structured Generation with @Generable

Using Foundation Models' schema system to get type-safe, structured responses from MLX Swift models

14. On-Device TTS with MLX Audio

Building Kokoro and Sesame-based TTS with streaming and raw audio access for natural, human-like speech synthesis

Use the code "STUDENT" for a discount if you are a student.