Infinite canvas notes in the non-Euclidean Poincaré disk
Hi! This is an infinite canvas note-taking tool where notes are laid out in a non-Euclidean, hyperbolic geometric space. As you drag and navigate through the view, you’ll experience a unique fluid distortion that naturally leverages your brain's spatial memory. I’ve been obsessed with the concept of space in HCI for years. Many modern UI patterns are essentially workarounds for the lack of screen real estate. While researching zoom-based UIs a while back, I stumbled upon old HCI papers that used the Poincaré disk model of the hyperbolic plane to organize data. It elegantly projects an…
In plain words
This is an infinite canvas note-taking application that organizes notes using hyperbolic geometry within a Poincaré disk model. As users navigate and drag through the interface, the non-Euclidean space creates fluid visual distortion designed to enhance spatial memory retention. The tool projects infinite space into a finite disk, keeping all content contextually visible while addressing screen real estate limitations common in traditional note-taking interfaces.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi! This is an infinite canvas note-taking tool where notes are laid out in a non-Euclidean, hyperbolic geometric space. As you drag and navigate through the view, you’ll experience a unique fluid distortion that naturally leverages your brain's spatial memory. I’ve been obsessed with the concept of space in HCI for years. Many modern UI patterns are essentially workarounds for the lack of screen real estate. While researching zoom-based UIs a while back, I stumbled upon old HCI papers that used the Poincaré disk model of the hyperbolic plane to organize data. It elegantly projects an infinite space into a finite disk, keeping everything contextually visible. I wanted to build an experimental app around this concept years ago, but the non-Euclidean math was a significant roadblock. Recently, I decided to give it a shot with the help of LLMs. It turns out that LLMs can handle the mathematical heavy lifting quite well, specifically in designing the coordinate systems and optimization algorithms, provided that you guide them with a solid architectural design. This is still an experimental demo, but I hope it leaves an impression. I’d love to know if you find this paradigm practical for organizing your thoughts.
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