dmlc/xgboost ? reverse-engineered prompt

Build me a fast machine learning library like XGBoost for training gradient boosted tree models. I want data scientists to be able to use it to solve prediction problems quickly, both on a single laptop and on very large distributed data.

It should feel flexible and portable. Please support the core library in C++ and make it usable from Python, R, Java, Scala, and C++ if possible. Users should be able to train a model, run predictions, save and load models, and use it for common classification and regression tasks.

Make performance a priority with parallel training, and design it so the same approach can run in bigger environments like Spark, Dask, Hadoop, Kubernetes, or similar distributed setups. Add clear demos, tests, and documentation so someone can install it and try a small example right away.

Look up current docs online if you need to, but keep the first version practical and working.