Thanks to your comment. I persuaded my friend who purchased an M3 Air 24GB recently and we got 200$ back (Remuneration for price drop valid for 14 days after the date of DELIVERY) where we live

I was looking for something like Noteplan as well. The subscription model and the price was a deterrent to me and I went with Agenda [0]

[0] https://agenda.com/

This sounds a lot like the experimental project Jacquard [0] from Ink & Switch.

[0] https://www.inkandswitch.com/jacquard/notebook/

End to end encryption is not really a pledge. That is expected of companies like such. Nevertheless, their promise to not sell any data is interesting. If they don’t sell data (which cannot be sold anyways for an E2EE system) I wonder why they collect so much data related to one’s identity as disclosed by them in the App Store Page? Is the behaviour of journaling then becomes a data point to be sold by these companies? Makes you wonder. And as mentioned in their privacy policy page, they are also not except from disclosing information the the US Govt if mandated by a warrant.

E2EE is not enabled by default on their cloud sync journals.

The statement that this can be implemented with a quantum algorithm is a bit ambiguous. If you look in detail, the problem is only formulated on the quantum computer while the optimization routine which essential solves the problem is left to a classical computer. There are some notions of quantum gradients. But I wouldn’t know how it applies to such problems

Still missing RSS feed

The problem with this approach is determining the what k is for the k-means. But again, we could use the “elbow” technique to determine what’s the optimal k and then start grouping them together. I wonder if there are any automatic sophisticated clustering algorithms?

Hierarchical and DBSCAN don’t require upfront knowledge about the number of clusters.