The stdlib inclusion angle is what makes this interesting to me beyond the compression-as-classifier debate.

Before 3.14, doing this required pip installing pyzstd or python-zstandard, which meant an extra dependency and a C extension build step. Having zstd in the stdlib means you can do dictionary-based compression tricks in any Python 3.14 environment without worrying about whether the deployment target has build tools or whether the package version matches across your team.

That matters more than it sounds for reproducibility. The gzip/zlib approach kenschutte mentions has been in stdlib forever, but zstd dictionaries are meaningfully better at learning domain-specific patterns from small training corpora. The difference between 41 and 54 bytes in the taco example is small, but on real classification tasks with thousands of categories the gap compounds.

Python 3.14 quietly shipped a lot of practical stuff like this that gets overshadowed by the free-threading and JIT headlines. The ZSTD module, t-strings for template injection safety, and deferred annotation evaluation are all things that change how you write everyday code, not just how the runtime performs.