The New AI Stack

Runtime techniques for flexible AI skills: dynamic dispatch, reflection, hot-reloading, and adaptive behavior. How to build skills that modify their own behavior based on context.

AI code generation quality for SwiftUI evaluated across layout, navigation, data flow, and platform integration. Where SwiftUI shines and where AI still struggles.

Building Mac-native AI applications using Catalyst and AppKit patterns that feel right on macOS. Sidebars, toolbars, split views, and system integration for AI-powered desktop tools.

A style guide is a human PDF; a DESIGN.md is an agent-parseable spec. Here's the head-to-head on format, audience, and which one your AI agent can actually use.

Clean runtime interception patterns for AI agent tools that let you observe, modify, and extend behavior without touching source code. A practical guide to proxy, decorator, and hook strategies.

Modernize desktop applications with AI integration. Architecture patterns for adding AI capabilities to Electron, Tauri, and native apps without full rewrites.

Stitch-style design systems package design tokens and rationale into one agent-readable file. Here's Google's Stitch pattern and why a standard format matters.

Design tokens are named, reusable values for color, type, spacing, and radii. Here's what they are and how AI agents consume them to build on-brand UIs.

A DESIGN.md file is an agent-readable design spec: YAML design tokens plus markdown rationale that teaches AI agents to build on-brand UIs, not generic output.

Network caching patterns for AI-built applications. HTTP caching, CDN strategies, stale-while-revalidate, and cache invalidation patterns every AI skill should implement.

A mental-model shift for 2026: moving from one-shot prompting to closed-loop systems built on feedback, verification, and bounded autonomy.

Closure and callback patterns in AI skill design. Learn how block-based APIs create composable, flexible skills that adapt to different contexts and workflows.