Two refinements to the NeighborhoodCoherence judge from part two: pass the metric name straight to ModelJudgeEvaluator to drop the ScoreDimension boilerplate, and shape what the judge actually reads with evaluationTarget and reference.
Code-based evaluators can only measure what code can measure. This companion article goes deep on ModelJudgeEvaluator: choosing a scoring scale, writing scoring levels that actually work, and wiring a model-as-judge into the TripPlanner evaluation from part one.
How to test generative, non-deterministic features with Apple’s Evaluations framework, the five steps of an evaluation, datasets and the Loader protocol, synthetic sample generation, evaluators and metrics, and the compiler gotchas hit along the way.
Cloud APIs put a frontier model behind a single HTTPS call. That convenience is hard to beat, and for most production workloads it remains the right choice. But something has shifted over the last couple of years: the gap between “what a hosted model can do” and “what a model running on your laptop can do” has narrowed enough that local inference is no longer a curiosity. For developers, especially those of us building on Apple Silicon, it has become a serious option. ...
A walkthrough of Anthropic’s new Claude Agents (agent view) inside Claude Code on iOS: how to run a Clean Architecture refactor agent and a unit-testing agent in parallel on isolated Git worktrees, with the full lifecycle and synchronisation pitfalls.
Swift Academy Podcast, Episode 10, Season 2 Beyond the Prompt: Foundation Models as an Architectural Layer in iOS Mohammad Azam on why Apple’s on-device LLM is less an AI feature and more a redefinition of where intelligence lives in your app, @Generable, @Guide, instructions, tools, and adapter-based fine-tuning, viewed through the lens of iOS layered architecture. May 3, 2026 🎧 Swift Academy Podcast · Episode 10, Season 2 · Foundation Models • On-Device LLM • iOS Architecture “Foundation Models is not a chat surface. It is a typed, on-device, structured capability you wire into your architecture, the way you wire in URLSession or SwiftData. Treat it like a chatbot and you will build bad apps. Treat it like a layer and you will build defensible ones.” ...
In Part 1 of this series, we built a minimal LLM inference pipeline on Apple Silicon using MLX Swift. In Part 2, we quantized a model from scratch and saw how 4-bit precision makes billion-parameter models tractable on a phone. This article introduces MLX Embedders, specifically the MLXEmbedders Swift library, and takes a different angle. Instead of generating text, we are going to encode meaning. A user types: “best hikes near a volcano.” A keyword search returns nothing useful. An embedding-based system returns exactly what they need, because it understands what the words mean, not just what they spell. That capability is what embeddings unlock, and it is the foundation of every serious AI feature in production today: semantic search, recommendation systems, RAG pipelines, and clustering. ...
Swift Academy Podcast, Episode 9, Season 2 Cupertino MCP: Local AI Tooling for Swift Developers How the Cupertino MCP server gives AI agents offline access to 302,000+ pages across 307 frameworks, a technical deep-dive into Model Context Protocol, local-first Swift AI development tooling, and the architecture behind a new generation of AI iOS development tools with Mihaela Mihaljević, Senior iOS Architect. April 19, 2026 🎧 Swift Academy Podcast · Episode 9, Season 2 · Model Context Protocol • Swift AI • Local Tooling “Giving an AI agent access to your full Apple documentation stack, offline, locally, without rate limits, is not a convenience. It changes the quality of what the agent can reason about. That’s the real unlock of local-first AI tooling.” ...
Abstract The proliferation of large-scale neural language models has, until recently, been contingent upon access to remote computational infrastructure. The architectural characteristics of Apple Silicon, most notably its unified memory subsystem, present a substantive departure from this dependency. This article examines MLX Swift, a native Swift binding to Apple’s MLX machine learning framework, as a mechanism for deploying quantized Large Language Models (LLMs) directly on consumer Apple hardware. We characterize the layered architecture of the MLX ecosystem, contrast its design philosophy with that of Apple’s Foundation Models API, and present a reference implementation demonstrating the complete inference lifecycle: model acquisition, session initialization, and autoregressive text generation. The discussion is grounded in the computational properties of unified memory and their implications for on-device inference efficiency. ...
Swift Academy Podcast, Episode 8, Season 2 Swift Concurrency Explained Actors, isolation, Sendable types, and the mental model you need to reason correctly about concurrent Swift code, a technical deep-dive with Matt Massicotte, one of the most rigorous voices in the Apple platforms community. March 20, 2026 🎧 Swift Academy Podcast · Episode 8, Season 2 · Swift Concurrency • Actors • Swift 6 “The concurrency model in Swift is not just a new set of APIs. It is a new way of thinking about ownership, boundaries, and correctness. Until you have that mental model, the compiler warnings will feel arbitrary. Once you have it, they feel inevitable.” ...