Show HN: How to beat Pac-Man with a text classifier (jeffyclassify.com)

In a recent project, an open-source text classifier named Jeffy has been utilized to beat Pac-Man through a structured overview of game scenarios. The game presents text descriptions at each junction, detailing distances to pellets and ghosts, and potential traps. By parsing these descriptions into numeric features using regular expressions, the classifier is trained to predict the optimal direction to move based on historical game data. This approach highlights a novel interaction between structured text inputs and machine learning, as the classifier operates efficiently on standard CPUs, with a response time of just 3 milliseconds. This development is significant for the AI/ML community as it underscores the effectiveness of feature-based classifiers in structured environments compared to larger, more complex models like large language models (LLMs). While LLMs can analyze similar text inputs, they require more computational resources and time. The project demonstrates that for specific tasks like this one, simpler models can achieve comparable accuracy while needing less overhead. Jeffy’s ability to learn from labeled junctions enables it to generalize to previously unseen configurations, showcasing the potential for lightweight AI solutions in gaming and other structured text-driven applications.