Designing NotebookLM: A Scalable 3‑Panel UI for AI‑Native Creation

NotebookLM’s design solves tab overwhelm by unifying sources, chat, and outputs within a responsive 3‑panel system grounded in a clear mental model. The interface scales across modes, preserves context at all sizes, and has expanded to support new tools and workflows. Audio Overviews and a ship‑early, iterate‑with‑users process exemplify how AI should be built in, not bolted on, with chat as a dependable anchor.

Key Points

The core problem was “tab overwhelm”; NotebookLM unifies reading, chat, and creation via a clear Inputs → Chat → Outputs mental model.
A flexible 3‑panel architecture (Sources, Chat, Studio/Notes) adapts to user goals with responsive states and multiple layout modes.
The system was designed for scalability, enabling rapid addition of features like flashcards, quizzes, and professional reports without breaking the UI.
Audio Overviews introduced new paradigms (e.g., interrupt) and exemplified shipping early, iterating with users, and building AI in—not bolting it on.
AI‑native UX should be dynamic and context‑aware, with chat as a stable anchor while users transition to richer AI interactions.

Sentiment

The overall sentiment of the Hacker News discussion is predominantly critical and negative regarding NotebookLM's user experience design, its engineering implementation, and the self-congratulatory tone of the article. While many users acknowledge the underlying utility or potential of the product's core concept, they generally feel that the design choices and missing essential features actively hinder its effectiveness and detract from its value.

In Agreement

The core concept of focusing an LLM on user-provided documents for summarization, research, and question-answering is valuable and useful.
Specific features like Audio Overviews (despite quality concerns), source-grounded citations, quizzes, flashcards, and mind maps are appreciated by some users.
NotebookLM is effective for navigating and querying poorly organized documentation (e.g., vendor manuals, TTRPG rulebooks) and for synthesizing information in professional contexts (e.g., pre-sales call transcripts, contract analysis).
One user noted the ease of use on mobile, despite others reporting issues.
There aren't many direct competitors that do 'quite the same thing' as NotebookLM's focus on user-uploaded documents.

Opposed

Both NotebookLM's UI and the accompanying design article are perceived as overdesigned, overengineered, cluttered, and having low information density, which detracts from usability and makes core tasks difficult.
The article is criticized as being self-promotional, a 'promotion package,' and for rationalizing design weaknesses as strengths.
Significant missing features include the inability to save chat histories, poor information export options (e.g., lack of direct Google Docs integration), and issues with chat follow-up questions.
The design suffers from poor scalability, with new features (e.g., 'Artifacts Button Container' with 6 buttons) causing display problems on small screens and obscuring content, necessitating user-created scripts for fixes.
The 'notes' feature is often found confusing or unnecessary, and the distinction between 'my notes' and 'AI notes' is unclear to users.
The generated audio (podcasts/overviews) is criticized for being unnatural, having an off-pace, an occasional 'third voice,' or sometimes discussing irrelevant topics.
The mobile application is noted for lacking functionality and having issues with large downloads, sometimes in inconvenient formats like WAV.
Some users argue that alternative tools like Claude Projects/Code or ChatGPT can perform similar document-focused AI tasks more effectively or with a better user experience.