Run UX testing sessions with three to five users to identify critical usability issues before launch.

Instead of spending too much time on building an AI core product, launch smaller experiments to gather early user feedback before full integration.

Rapid prototyping and user testing are essential; focus on getting a functional prototype out quickly, using tools to create clickable mock-ups or writing throwaway code to test ideas with users, as building a fully functional product before testing can be an expensive mistake.

It's important to quickly build and test ideas. If something doesn't work, it's better to kill it and start again rather than justifying its existence with additional features that may not address user needs.

Start vibe coding by crafting your test cases first, then let the LLM generate the code based on those tests, ensuring quality control.

Instead of integrating every feature individually, focus on using your frontend capabilities to create a more efficient AI agent experience.

In product development, it's crucial to consider the entire system and how different features interact, rather than focusing on individual components in isolation.

Testing ideas in rough form and creating storyboards of users interacting with your product is essential for understanding user experience, as storyboarding helps visualize user interactions, covering discovery, onboarding, habit building, and mastery.

I wouldn't expect AI models to one-shot entire products yet, especially if they're complicated. I prefer to implement features piece by piece and ensure each step works before proceeding.