Watch 14-Byte AI "brains" attempt to solve a 2D maze (Its hard)
Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a…
In plain words
This project demonstrates tiny neural networks trained to navigate 2D mazes using only local sensory input. The creator built 14-byte AI models across 46 experimental phases, achieving a 96.5% solve rate on unseen mazes without access to map data or external memory. The project is designed for those interested in neural networks and AI fundamentals, showing how extremely compact models can learn navigation tasks and where they typically fail, such as getting caught in loops.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a loop. The models have no access to coordinates, map-data, or external memory scratches - they must navigate using only immediate local neighbourhood observations. There is a model dropdown and you can see how the model has progressed over each phase, constantly getting smaller and increasing its solve rate. Total trained models number in the thousands - I just expose the winning models from each phase. Overall a fun experiment, with much implementation help from AI agents to scaffold and implement the code (I'm a lazy software dev).
Does the same job
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