Swift, AI, and product design notes from Stephen Dixon.
Exploring Swift development, AI engineering, and product design with real-world insights on building smarter apps.
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Building AI Features in SwiftUI the Right Way
Over the past year, a lot of developers have experimented with adding AI to their apps. A chat interface here. A “Generate” button there. Maybe a summarisation feature bolted onto an existing screen. Technically, this works. The model responds. The feature demos well. But the deeper you go, the more
AI-Native UX: What Most Apps Are Still Getting Wrong
Over the last year, I’ve started noticing something subtle but important. A lot of apps claim to be “AI-powered,” but very few feel genuinely AI-native. At first glance, it’s hard to articulate the difference. The features look impressive. There’s a model involved. Text is being
From Prompt Engineering to Context Engineering
There was a moment — not long ago — when prompt engineering felt like the future. Threads went viral. Templates were shared. People built entire workflows around carefully crafted paragraphs sent to GPT. And to be fair — it worked. For demos. For experiments. For one-off interactions. But if you’ve tried
Designing My Life OS: How I’m Using My iPhone, Habits, and Systems to Shape 2026
In late 2025, I found myself caught in a loop. I was building apps designed to reduce friction, help people track mindfully, and support healthier habits — but my own environment wasn’t serving me. My phone was noisy. My routines were reactive. And I felt like I was living slightly
Everything That Happened at OpenAI DevDay 2025 (And Why It Matters to You)
OpenAI just wrapped their biggest event of the year — and it wasn’t just about AI. It was about software. About how we build it, how fast we build it, and what’s now possible when language models aren’t just assistants — they’re part of your stack. If you
Context Blocks: How I Structure AI Inputs That Actually Work
If you’ve read any of my recent posts, you know I’ve been deep in the weeds with Model Context Protocol (MCP), foundation models, and AI-native app design. There’s a pattern I keep coming back to — not because it’s trendy, but because it works. I call
Designing Context On-Device: What Foundation Models Mean for AI-Native Apps
For the past few months, I’ve been diving deep into MCP (Model Context Protocol) — shaping inputs, modularising prompts, and designing smarter ways to talk to models like GPT-4. I even wrote a post about it: Designing Context. It was a personal reckoning: prompting alone won’t scale. You
Designing Context: The Craft Behind Smarter AI Inputs
Prompting doesn’t scale. Not for real products. If you’ve ever tried building something AI-native — not just a demo, but an actual app people use — you’ve likely hit the wall where prompting alone isn’t enough. That’s where context design comes in. This post isn’t
MCP: The Missing Layer Between Your App and the AI
Last year, building with AI meant prompt engineering. This year? It’s all about context engineering. There’s a quiet but powerful shift underway in how we design intelligent features. Not just what the model can do — but what it knows before you even ask. That’s where MCP comes
Getting to Grips with MCP: My Early Learnings (and Why You Should Care)
When I first heard about “MCP” in the context of AI, I shrugged it off. Model Context Protocol? Sounds like a cool acronym. No clue what it actually meant. A few days later, I’d read the docs, run a handful of tests, and started thinking differently about how I