shrynk is not doing any compression itself, it's using ML to do a "meta" approach: it computes features on your data like how many rows, how many columns, how much duplication etc, and then predicts which existing compression algorithm (out of the available compressions) will be best given your requirements/preferences in terms of size, write time and read time.
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How does it compare to context mixing algorithms such as https://en.wikipedia.org/wiki/ZPAQ ?
shrynk is not doing any compression itself, it's using ML to do a "meta" approach: it computes features on your data like how many rows, how many columns, how much duplication etc, and then predicts which existing compression algorithm (out of the available compressions) will be best given your requirements/preferences in terms of size, write time and read time.