AI guardrails: Non-negotiable safety or overly cautious "slowdowns"?

Identify sources of unnecessary cognitive load and apply strategies to focus on meaningful analysis and exploration.

Depending on the context, we think in different ways

How do we convince product and project people about the importance of testing?

Deciding what really matters can be hard, yet it doesn't have to be that way

AI's impact on quality generalists vs quality specialists

Learn how focusing on user value and trust gives you a clearer, more effective way to test data quality

Hiring managers might not understand the true value of community participation

In the age of AI, for how long should the we test?

Test system resilience by mapping failure paths and running small experiments that reveal what users experience when things fail

Apply a four-dimension framework to assess whether synthetic data can be trusted for performance testing.