Sara Morgan

For a long time, using clean mock data during API integration felt fast and smooth. The problem was that edge-case bugs always snuck into production whenever real data contained missing fields, null values, or unexpected formatting.

Three weeks ago, I completely changed my approach. Now, before I write a single line of frontend code, I make AI generate worst-case API payloads missing fields, broken timestamps, and oversized strings.

Last Friday, testing against these broken mocks helped me spot a critical UI crash in under five minutes locally. Under my old workflow, this bad data issue would have quietly slipped through and wasted hours during QA.

Are you still using clean mock data during development, or do you test against bad payloads first?

When we first launched, we built a comprehensive onboarding flow to capture every detail. But our trial drop-off rate was brutal.

Watching user session recordings made us realize that people weren't leaving because they lacked interest they were giving up because we asked for 10 minutes of configuration before showing a single outcome. They wanted immediate relief, not an admin task.

For founders managing products with heavy initial setup: how do you deliver that first aha moment before requiring full integration?

I want to share something counterintuitive that took me two years to figure out.

Like most founders, I was obsessed with user happiness. Did they like the product? Were they satisfied? Did they leave a good review? I'd send NPS surveys, add "how are you feeling?" prompts, respond to every bit of feedback with gratitude.

But churn stayed stubbornly high. People weren't unhappy they just weren't using the product. They'd sign up, poke around, and drift away.

Then I accidentally noticed something. The users who stuck around weren't the ones who said they loved the product. They were the ones who had figured out the one specific thing the product was actually good at. They weren't confused anymore. They had clicked with the core value.