It’s time we stopped blaming the tools and started asking better questions about how we work, what we value, and how we make space for innovation again.

Summary:  UX in complex, specialized domains requires adapting familiar methods across the design lifecycle to address domain constraints and expert-user needs.

Artificial Intelligence (AI) has moved beyond a niche technology into a crucial business tool that can optimize operations, improve customer experience, and support data-driven decisions.

We’ve gone through glassmorphism, neumorphism, micro-interactions, and parallax scrolling. Some trends look amazing but add nothing. What’s a design trend you wish would just die already?

Despite promising results on synthetic benchmarks (e.g. Vending-Bench, SpreadsheetBench, DSBench), frontier models consistently underperform once they are deployed in complex, real-world situations.

Of all the current debates around AI, one critique has stayed with me: that it’s “flattening the bar.” Tools like ChatGPT, the argument goes, make everyone’s writing sound the same — generic, overly polished, stripped of nuance. The concern is real, and I share it.

Now we’re in the agentic era, and that billing octopus grew some new tentacles just for AI agent billing. Or is it a different octopus? I’m not sure.

For years, it’s been faster to create mockups and prototypes of software than to ship it to production. As a result, software design teams could stay “ahead” of engineering. Now AI coding agents make development 10x faster, flipping the traditional software development process on its head.

From AI assistants to digital platforms, how can we design for rapid mode switching in real life? Reflections about utilitarian and experiential content and why understanding both matters.

Equal-height image layouts seem simple until you try to build one that’s truly responsive. This tutorial walks through my solution using flexbox aspect ratios, the Eleventy Image plugin, and a Nunjucks shortcode.