Developer builds Snake game with vision-language model using FeynRL

The project emerged from a desire to demystify the end-to-end process of training large AI models. The developer built a Snake game controller using a vision-language model and published both the project and the FeynRL framework on GitHub. While a vision-language model—an AI that can understand both images and text—is indeed more capable than necessary for controlling a simple game, that overengineering is intentional: it allows the example to showcase the same techniques and workflows used in far more complex real-world systems. The pipeline covers three core stages: preparing training data, training the model, and evaluating its performance. By working through these steps in a visual and interactive context, developers can see how the underlying principles apply to large language models and other production systems. The GitHub repository includes not just the Snake demo but also an examples section designed to help others replicate the approach on their own projects. The developer has framed this as an open invitation: readers are encouraged to build something similar, share feedback, or contribute improvements to the FeynRL project itself.

The Snake project serves as a teaching tool rather than a practical application. The developer explicitly notes that using a vision-language model for Snake is overkill—the real value lies in demonstrating the mechanics. By anchoring the complex process of training large AI models in a simple, visual, and interactive game, the example makes the full machine learning pipeline—data preparation, training, and evaluation—concrete and easier to grasp for people learning how production models like large language models and vision-language models are actually built. The decision to open-source the code and invite community feedback suggests an intent to build a learning resource and collaborative toolkit, rather than to showcase a novel application.

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