Learn to debug Foundation Models sessions using Instruments in Xcode, focusing on latency, prompts, and tool workflows.
Guide to using Foundation Models in SwiftUI for image analysis, extracting structured data from photos in iOS apps.
Guide on choosing between on-device and Private Cloud Compute foundation models in Swift for iOS apps.
Guide to evaluating and securing Apple Intelligence features using Swift Testing and the Evaluations framework before shipping.
Apple's WWDC26 introduces Dynamic Profiles for building agentic Swift apps with AI, simplifying session orchestration and mode switching.
A developer shares WWDC26 wishes, including Foundation Model image input and custom lazy layouts for SwiftUI.
Guide to adding tool calling to Foundation Models sessions in Swift, with examples using SwiftUI and a reading-list app.
Learn to stream Foundation Models responses into SwiftUI for real-time UI updates using AsyncSequence and @Generable types.
Apple confirms a multi-year AI partnership with Google to use Gemini models and cloud tech for Apple Foundation Models and future Apple Intelligence features.
Explores the application of Graph Neural Networks, embeddings, and foundation models to spatial data science, with practical examples in R.
How to build on-device search suggestions using Apple's Foundation Models framework for SwiftUI apps.
Analysis of the rising prominence of Chinese AI labs like DeepSeek and Kimi in the global AI landscape and their rapid technological advancements.
A technical guide on implementing real-time streaming for AI-generated content using Apple's Foundation Models and the Streaming API.
A technical guide on using Foundation Models for structured content generation in Swift, including code examples for generating typed AI responses.
A guide on using Apple's new Foundation Models framework to build AI features in apps, including code examples for model availability and session management.
Summarizes key challenges and methods for evaluating open-ended responses from large language models and foundation models, based on Chip Huyen's book.
A summary and discussion of Chapter 1 of Chip Huyen's book, exploring the definition of AI Engineering, its distinction from ML, and the AI Engineering stack.
Explores AI agents, their capabilities, and frameworks for development, focusing on tools, planning, and evaluation.
Introduces CARTE, a foundation model for tabular data, explaining its architecture, pretraining on knowledge graphs, and results.
An analysis of 900 popular open-source AI tools, categorizing them into infrastructure, model development, and application layers.
