Findka Essays is a simplified version of the old app. It recommends only essays (instead of any type of content), and you get recommendations through email only, not through a web app. I record which links you click on, and links in future emails get picked based on your click history.
Since the domain (essays) is much more restricted, a more-or-less randomly selected item should still have a decent chance of being a good recommendation (especially since right now I manually curate most of the essays). So I'm hoping that the system will be worth using even before we have lots of data to feed to the algorithm, unlike the old Findka.
Also, I genuinely love essays (reading and writing). I think they are the most valuable form of content, and I think helping good essays spread further would have a big impact on the world. So I'm pretty excited about this pivot.
For those interested, I've written a detailed description of the architecture here. The web app is written in Clojure, using Biff (a web framework I made), and the recommendation algorithm is a ~100 line Python file that uses off-the-shelf k-NN, with some of my own additions for handling exploration and countering popularity bias. See  for details.
I'd be more interested in being sent a selection of recommended classic essays. E.g. by people like Orwell or Montaigne. You could populate it with essays found on project Gutenberg, or from high quality magazines like London Review of Books.
Also the examples are very tech focused. Are you planning to include other areas?
Before today there were about 60 submitted essays; we're up to 117 now. So the recommendations going forward probably won't be dominated by my own submissions, but they will likely still be tech-focused, given the (initial) HN/programmer user base. Eventually (after we have enough users/submissions/click data), Findka should adapt to your preferences quickly. i.e. even if most of the catalog is tech-focused, the algorithm will look at what essays you've submitted and select recommendations from the appropriate niches. But at this early stage, the recommendations will be sampled more-or-less uniformly from the entire catalog.
But at some point, I'll definitely do some A/B tests with content-based filtering.