Andrzej Misiak

Reading bug reports or feedback forms is one thing, but watching someone actually open your app for the first time and struggle with things you thought were totally obvious is painful.

I sat behind a beta tester last week and watched them bypass our entire onboarding flow using some bizarre workaround I never could have imagined. It made me realize how trapped we get in our own design logic.

When was the last time a user session completely broke your assumptions about your own software?

Let your voice agent actually do things, like check an order or save a lead

A voice agent that can only talk is not enough. Customers want results: "Where is my order?" "Update my address."

I m not an expert researcher, but I use AI daily to pull summaries, market trends, and quick facts for my projects. It s lightning-fast, but it has one terrifying flaw: it sounds just as confident when it s completely wrong.

A few weeks ago, I wrote an article using statistics an AI gave me about a local market growth. It looked solid and cited plausible-sounding sources. Right before publishing, a colleague pointed out that one of the core numbers was entirely fabricated by the model. On the surface, the prose was flawless, so I almost missed it.

It made me wonder if we are trusting AI-generated insights too easily just because the writing sounds professional.

How do you verify facts and data when AI hands them to you?
Do you cross-reference multiple tools, ask it for actual links, or just trust your gut? And has a fact-checked AI response ever turned out to be completely made up for you?