This article explores how Python 3.14's zstd module enables efficient text classification through incremental compression. It outlines a method where text is classified based on the size of compressed output from different class-specific compressors, demonstrating improved speed and accuracy over traditional methods.

• Python 3.14's zstd module supports incremental compression, letting classifiers update on new data in tens of microseconds instead of recompressing everything from scratch.
• Classification works by feeding a new document to per-class compressors and picking the class whose compressed output size increases least.
• Tunable parameters (window size, compression level, rebuild frequency) let you trade off speed against accuracy for a given use case.
• Benchmarked on 20 Newsgroups, the compression-based classifier shows competitive learning ability while avoiding traditional ML pipeline complexity.