It does affect your conclusions though.
The choice of null hypothesis in "The DK Effect is Autocorrelation" determined how the random data was generated. The hypothesis is: "nobody has any clue whatsoever how competent they are". The random data was specifically crafted for that hypothesis.
The choice of null hypothesis in this article is: "everyone roughly knows how competent they are". This random data, too, is specifically crafted for the null hypothesis.
So what does this mean? If you pick the a particular null hypothesis then you can try to argue that the DK is a statistical artefact. But it's not, it is an artefact of choosing a particular null hypothesis.
There are valid criticisms of the DK study, though. See this comment for example: https://news.ycombinator.com/item?id=31119196
> the p-value is a strongly nonlinear transformation of data that is interpretable only under the null hypothesis, yet the usual purpose of the p-value in practice is to reject the null. My criticism here is not merely semantic or a clever tongue-twister or a “howler” (as Deborah Mayo would say); it’s real. In settings where the null hypothesis is not a live option, the p-value does not map to anything relevant.
https://statmodeling.stat.columbia.edu/2017/01/07/we-fiddle-...
Anyway, "The DK Effect is Autocorrelation" definitely seems to be both statistically literate, and a good faith criticism of the Dunning-Kruger paper. In light of that, calling it "anti-scientific" seems unfair, since criticism and debate are an important part of science.