I can't tell if this post is presenting a trivial idea as a novel one or not. Typical compression algorithms indeed are not good at compressing a short text, but only because they have no prior knowledge about the input distribution. There are several ways to feed that knowledge to existing algorithms (e.g. zstandard custom dictionaries).

The "Type-Based Compression Algorithm" presented in the post does not do any kind of distribution modelling. It simply assumes that every possible input is uniformly distributed, hence incompressible, and that's obviously not true. The post gives an example of Turkish car number plates; its first two digits indicate the province code, so you will see certain two-digit prefixes more frequently. TBCA can't see this distribution.

TBCA can also try to limit the range of first two digits for the better coding, but it will break with a new province code. Typical compression algorithms might be inefficient for those inputs but can surely handle them. This is perhaps the biggest drawback of TBCA: a wrong assumption of the input data results in a hard failure (unless you have an escape code). It is not the same kind of algorithm you can compare with general compression algorithms. It is just a clever database schema that gives some compression without those general compression algorithms.