Python 3.14's new `compression.zstd` module introduces a powerful way to perform text classification through compression. Developed from Facebook's Zstandard algorithm, Zstd supports incremental compression, allowing data to be processed in chunks while maintaining an internal state. This feature addresses a significant limitation of traditional algorithms like gzip and LZW, which don't provide an incremental API. As a result, Zstd enables more efficient classification by allowing classifiers to update without needing to recompress all training data. The author describes a method where text classification is achieved by comparing the size of compressed outputs from different compressors trained on distinct classes. When a new document is received, the algorithm rebuilds the compressor using the current data for that class, which is stored in a buffer. This process is efficient, taking just tens of microseconds, thus making frequent updates feasible. Key parameters such as window size, compression level, and rebuild frequency can be adjusted to optimize performance based on the specific use case. The implementation of the `ZstdClassifier` class is straightforward, focusing on managing buffers for each class and rebuilding compressors as new data arrives. The author also provides a benchmark using the 20 Newsgroups dataset, which demonstrates the classifier's learning ability and efficiency. Overall, this approach streamlines text classification without the complexities of traditional machine learning techniques, relying instead on the inherent capabilities of the compression algorithm.