Exploring Unbounded AI Products: Structure, Familiarity, and User Understanding
Join Ben Hilac, founder of Dawn, as he shares insights on building unbounded AI products, drawing from his experiences in designing innovative interfaces, including the Apple Vision Pro.
- 1. Ben Hidalgo is the founder of Dawn, a company that helps businesses build AI products.
- 2. He has a history of working with unbounded products, such as robotics and SpaceX rockets.
- 3. Unbounded products are those that transcend the traditional mouse and monitor interface.
- 4. AI makes products less bounded by allowing for various input modalities like typing, talking, showing images, etc.
- 5. Users often learn about new products through word of mouth, trying what their friends recommend.
- 6. This talk focuses on making good AI products, covering the past, present, and future of unbounded products.
- 7. In the past, most software lived on a screen, with users interacting via typing, swiping, clicking, or tapping.
- 8. Unbounded products like Vision Pro require designers to consider various scenarios, such as user movement or disabilities.
- 9. Three lessons Dawn learned from designing Vision Pro: highlight what matters, establish hierarchy, and leverage familiarity.
- 10. Highlighting what matters quickly helps users understand the main features of a product.
- 11. Hierarchy gives unbounded products shape and purpose, helping users navigate and understand its functionality.
- 12. Familiarity can make unfamiliar products feel more intuitive by using recognizable elements and layouts.
- 13. In the present, AI products must incorporate structure unique to their app, giving it shape and helping users understand its purpose.
- 14. Examples of good AI product design include Claude's use of artifacts and version control, and ChatGPT's project-based memory feature.
- 15. Spreadsheets are a familiar structure that can help make agents more understandable to users.
- 16. Using examples, presets, and templates can help users quickly grasp an app's purpose and avoid the blank canvas problem.
- 17. The future of interfaces will likely involve less prompt engineering, as seen with generative images and adjustable text tones.
- 18. Sparse autoencoders may offer a promising path towards providing numerous presets for users to avoid being reductive.
- 19. Ranked presets can be personalized, searchable, and even invoked through natural language.
- 20. Developer-defined personalization allows for fine-tuning per user based on their preferences and needs.
- 21. The future of AI products will shift from evals to analytics, focusing on understanding and meeting users' unique needs.
Source: AI Engineer via YouTube
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