Life & fun · February 23, 2026
isometric.nyc/snow
Hey HN, As NYC wakes up to the largest blizzard in years, I’d like to share [isometric.nyc/snow](http://isometric.nyc/snow) - a wintry mix of the original isometric pixel map of NYC . This work builds on the original isometric.nyc ideas but aims to increasingly automate the process of generating the tiled images. I’ve written up a follow-up addendum at [cannoneyed.com/projects/isometric-nyc\#snow](http://cannoneyed.com/projects/isometric-nyc#snow) that describes some of this work, but the tl;dr is: * ensuring that training data and…
In plain words
isometric.nyc/snow presents an isometric pixel art map of New York City rendered in snowy conditions. Building on the original isometric.nyc project, it uses automated image generation techniques to create the tiled visualizations. The creator employed machine learning approaches including color normalization, pattern augmentation for water features, and distributed inference to improve consistency and speed in generating the winter-themed map.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, As NYC wakes up to the largest blizzard in years, I’d like to share [isometric.nyc/snow](http://isometric.nyc/snow) - a wintry mix of the original isometric pixel map of NYC . This work builds on the original isometric.nyc ideas but aims to increasingly automate the process of generating the tiled images. I’ve written up a follow-up addendum at [cannoneyed.com/projects/isometric-nyc\#snow](http://cannoneyed.com/projects/isometric-nyc#snow) that describes some of this work, but the tl;dr is: * ensuring that training data and generations are color balanced / normalized goes a long way for consistency (duh) * augmenting water with a crosshatch pattern allowed the model to learn what it was instead of hallucinating (interesting!) * dramatically increasing inference throughput on [modal.com](http://modal.com) meant I could generate 10s of thousands of tiles in a few hours at very little cost, allowing me to experiment much more rapidly This project continues to be a lot of fun, but I’m now mostly focusing on the agentic workflows that power this kind of ambitious generation at scale. Can’t wait to share more soon.
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