That’s a fair question... I wrote the implementation and experiments myself. I did use an LLM to refine and structure the README for clarity, but the design, benchmarking, and validation are my own... By (production ready), I mean the system has been validated beyond just accuracy metrics. It has been benchmarked against GBMs and linear models under the same settings for both regression and classification, with competitive results. I’ve also measured batch and single-query latency, including p95 inference time, and tested memory usage under CPU only constraints. It’s been scale-tested into the low millions of samples on limited RAM, with stable behavior across multiple runs and consistent accuracy. And it’s not yet deployed in a live environment this post is partly to gather feedback.. but the claim is based on reproducibility, API stability, deterministic inference, and performance validation. If you think there are additional criteria I should meet before calling it production-ready, I’d genuinely appreciate the feedback..