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Alex Cornell is a design director at Meta, where he leads design for Muse. In this episode, he explains how progressive disclosure makes Muse approachable for casual users without limiting technical ones, why its main chat is designed as a single persistent relationship, and how generative UI can feel personal without constantly changing the interface. He also shares how he uses Muse to turn years of Instagram saves into a shopping catalog, analyze his golf and chess games, and transform a 20-year-old blog into a daily podcast.
**AI agents should be designed for conversations with people, not interactions with software.**Alex’s guiding question for Muse was “How would this work if you were talking to another human?” That principle shaped everything from removing traditional message bubbles to placing the agent at the top of the screen instead of inside a standard chat window.**Progressive disclosure is one of the most useful principles in AI product design.**Muse represents the same activity at three levels: a simple emoji for casual users, a task list for anyone who wants more detail, and raw logs for engineers. Each person gets the transparency they need without adding clutter for everyone else.**Visible chain-of-thought is often a workaround for latency, not a valuable feature.**Many AI products display reasoning steps to reassure users while a slow model works. Muse is fast enough to operate quietly, showing that agents do not need to perform their thinking when they can simply deliver the result.**Generative UI works best when the structure stays fixed and the content changes.**Muse’s avatar, feed items, and idea cards are deliberately designed surfaces. The dynamic elements are the agent’s status emoji, personalized suggestions, and small animations, allowing the experience to feel alive without rebuilding the interface every session.**Instagram saves may be one of the most overlooked data sources for consumer AI.**Alex connected years of saved posts to Muse, asked it to organize them, and turned the results into shopping recommendations styled like a SkyMall catalog. It gave forgotten personal data a practical new use without requiring technical setup.**Designing non-deterministic systems requires a different relationship with craft.**Alex has spent his career controlling exactly how interfaces appeared, but Muse looks different for every person and cannot guarantee pixel-perfect outputs. Learning to judge the results by usefulness rather than visual precision has been one of the most liberating shifts in his career.**Muse’s single persistent chat is a meaningful product bet.**Instead of dividing every interaction into separate threads, it treats the main conversation as an ongoing relationship with the agent. Side chats handle scoped tasks, but the central thread preserves continuous context in a way Alex believes the industry has not fully explored.**Polite prompting may be an effective strategy, not merely good manners.**Alex responds to frustrating results with calm, specific feedback rather than anger. He’s also seen agents change their responses when users express frustration more sharply. His conclusion is that clear, respectful direction may produce better results.
Alex Cornell’s 3 Meta Muse Workflows for Shopping, Hobbies, and Archives: https://www.chatprd.ai/how-i-ai/alex-cornell-meta-muse-workflows
↳ How to Create a Personalized Shopping Catalog from Your Instagram Saves Using AI: https://www.chatprd.ai/how-i-ai/workflows/meta-muse-instagram-shopping-catalog
↳ How to Use AI for Hobby Improvement and Data Analysis for Golf & Chess: https://www.chatprd.ai/how-i-ai/workflows/meta-muse-golf-chess-analysis
↳ How to Revive an Old Blog or Digital Archive as a Personal AI Podcast: https://www.chatprd.ai/how-i-ai/workflows/meta-muse-blog-archive-podcast
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
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