Perhaps we want to time a quiz or put pressure on a survey. Maybe we are just trying to making a dramatic countdown after a user has done something great like successfully booking a concert ticket.

Well-designed interfaces don’t just happen. Great product experiences are the result of great people, tools, and of course, processes.

This article delves into the effects of these changes, exploring how they influence content creators’ strategies and YouTube’s overall user engagement.

It’s that time again to check out some tools, libraries, and frameworks for our web developer friends. This month’s collection has a bunch of goodies, from cool JavaScript libraries to handy tools for GitHub releases, and even a DIY version of Heroku you can host yourself.

It’s sad to say, but many have come to see research as being expendable. If budgets or timelines are tight, research tends to be one of the first things to go. Instead of investing in research, some product managers rely on designers or—worse—their own opinion to make the “right” choices for users based on their experience or accepted best practices.

Welcome to the second (and final) instalment of the UX research methods playbook. Hopefully splitting it makes the content less cumbersome and easier to use.

Familiarity enables the transfer of experience between similar products or services, allowing us to be productive without first learning how a system works. When we encounter familiar interface patterns and conventions, we intuitively understand them based on previous experience. This rule of thumb is the basis of Jakob’s Law and helps guide design decisions.

UX as presented to the general public | Learn typography concepts through interactive exercises | Use AI for personal tasks, for example supermarket shopping | Growth expected in UX jobs in 2024 | Where should UX report within a big company

Curated and handpicked by the best for the best. Discover your next mobile design idea here.

There are two objections that (much like GPT outputs) are so common they are themselves practically commodities, that inevitably crop up in defense of AI “design.” AI skeptics will say: We will only use the LLM as a starting point, or to bounce off ideas, and verify its work. And AI boosters will say: Our model won’t have this problem, it will be uniquely high-quality because we will train it…