Our interview process includes giving candidates messy data sets to analyze, assessing their ability to identify and address underlying assumptions in the data.

There's a risk of overcomplicating prompts by trying to anticipate every possible issue the model might face, which can lead to ineffective results.

Many founders fail to read the guidance provided in the YC application, leading to common mistakes that could be avoided.

Founders often don't understand what their analytics numbers represent, so it's crucial to know your data before relying on it.

Many founders mistakenly believe they know what their customers want without validating their assumptions through user feedback.

When making decisions, sometimes you have to trust your gut over the data, especially when you understand the end user's needs.

A significant source of noise in the hiring process comes from companies being inconsistent in their interview questions, leading to a gut call that can misrepresent candidates' abilities.

Common mistakes in prototyping include overbuilding and not engaging with users early enough to gather feedback.

AI systems struggle to accurately verify information, which can lead to significant issues in critical fields like medicine, where incorrect diagnoses can have serious consequences.