After years of designing and building digital products, I’m sharing 10 UX lessons that challenged my assumptions about minimalism, AI, familiarity, and more.
Brand archetypes can define your personality, but they don’t tell you what to say. The imagined speaker technique turns an abstract archetype into a real voice you can write through.
Finding the right UX research participants is hard, and AI has made verifying them even harder. Here’s how to recruit real users, screen for quality, and use synthetic participants without compromising your research.
Navigation menus aren’t always the fastest way forward. Explore how search, personalization, AI, and conversational interfaces are changing how users find what they need, and why traditional navigation still matters.
Not every enterprise product needs to be intuitive. Complex workflows often require complexity, and trying to eliminate it can make products less powerful for experienced users. Explore why enterprise UX should balance intuitiveness with learnability, efficiency, and long-term mastery.
Open-source Figma alternatives are becoming viable for teams seeking self-hosting, open file formats, offline access, flexible AI integrations, and greater control over their design workflows. Compare Penpot, OpenPencil, Quant UX, and Open Design to see where open-source tools stand against Figma.
I used to leave design reviews with a stack of subjective edits. Then I learned to tell the story behind my work and rework dropped fast.
Learn what human-computer interaction is, discover its principles, and take a look at interaction design in HCI and its applications here.
Skipping prototypes can leave developers guessing and important UX problems undiscovered. Here’s how to prototype with purpose, choose the right level of fidelity, and use AI-assisted tools without losing sight of what you actually need to validate.
Where AI appears in your interface can shape how users discover, trust, and adopt it. Learn the strengths of common AI UI placement patterns and how to choose the right one for your product.
AI is messy, variable, and tough to capture in traditional requirements. Prompt sets, structured evals, and clear acceptance criteria help teams define, test, and refine how AI should behave.
Vibe coding lets you describe an app in plain language and watch AI build it for you. Here’s how I created two micro-apps — a form-filling extension and a X trend finder —that cut my daily workload in half.
