How I started Inference4j - an open source ML project in java with the help of Claude

Introduction A few weeks ago, I was researching how to run model inference in Java without integrating with an external API or loading a Python sidecar. I stumbled upon ONNX runtime and was immediately hooked. A portable binary execution of a self-contained Neural Network Model. When looking at benchmarks I was even more impressed — ONNX models seem to outperform PyTorch inference in most models I've tested. There's even a nice Java wrapper . However, it still requires users to know about tensor shapes, pre-processing steps, and post-processing steps. Inference4j — Three Lines of Code Inference So I thought, why not make it simple for Java developers and let them approach it from a use-case perspective? Inference4j was born. I'll spare you the details on how it works — I hope the docs make that clear enough. The goal was simple: Make inference a user-driven task. ImageClassifier , TextGenerator , ObjectDetector — these are the contracts users should interface with. Run locally, not as