speed-focus-mode VS No-Distractions-Full-Screen

Compare speed-focus-mode vs No-Distractions-Full-Screen and see what are their differences.

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speed-focus-mode No-Distractions-Full-Screen
28 9
30 18
- -
0.0 0.0
10 months ago about 1 year ago
Python Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

speed-focus-mode

Posts with mentions or reviews of speed-focus-mode. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-23.

No-Distractions-Full-Screen

Posts with mentions or reviews of No-Distractions-Full-Screen. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-20.

What are some alternatives?

When comparing speed-focus-mode and No-Distractions-Full-Screen you can also consider the following projects:

anki_straight_reward - Escape Ease Hell!

Anki-Android - AnkiDroid: Anki flashcards on Android. Your secret trick to achieve superhuman information retention.

No-Distractions-Full-Screen - Anki addon for distraction-free reviews

autocards - Accelerating learning through machine-generated flashcards.

review-heatmap - Anki add-on to help you keep track of your review activity

anki-delay-siblings

progress-bar - Progress Bar add-on for Anki

syntax-highlighting - A fork of Tiago Barroso's Syntax Highlighting add-on with support for Anki 2.1

carden - Flashcards with spaced repetition and gamification 🌱

AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity