I don't want to sound overly dismissive, but it's pretty clear reading this that the author has not worked on recommendation or personalization systems recently.
The high level description of how he believes TikTok works is probably accurate (I don't work there so I can only speculate), but it's not unique to TikTok. Also most of the details are probably not accurate.
Some examples:
> For its algorithm to become as effective as it has, TikTok became its own source of training data.
^This is true of nearly every recommendation system.
> If you click into a text post by someone on Facebook but don’t comment or like ... That negative sentiment is difficult to capture
You can do the same thing with comments that he claims TT does with video (dwell time threshold feature)
> As you scroll up and past many stories, the algorithm can’t “see” which story your eyes rest on.
I don't work at Twitter, but I doubt that matters very much.
Also regarding "negative sentiment" feedback from users. TikTok does not have a UI tool to say "I don't like this", but other apps do. For example, you can ask Instagram to "show me fewer posts like this".
I agree with the claim that the "one thing at a time" UI is nice for the user in a lot of ways. But as a person who builds these things, I don't think this makes much of a difference for machine learning.