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13 commits

Author SHA1 Message Date
Robert Schütz d440ffab7d mnemosyne: 2.3.2 -> 2.6 2018-02-11 11:49:54 +01:00
Samuel Dionne-Riel 7b97c8c0c8 treewide: homepage+src updates (found by repology, #33263) 2018-01-05 20:42:46 +01:00
Laverne Schrock f56fa4824a mnemosyne: move generated locale files to correct location
It feels like a bit of a hack to move them after the install rather
than generating them in the correct spot in the first place, but this
fixes #12763.
2017-02-15 00:50:05 +01:00
Vladimír Čunát 6eeea6effd Python: more evaluation fixups. 2016-10-14 00:03:12 +02:00
Frederik Rietdijk 6842a24b21 Remove top-level pyqt4
See #11567.
2016-08-14 12:38:01 +02:00
Frederik Rietdijk 4d06bf70f4 buildPythonApplication: use new function for Python applications 2016-02-19 13:16:41 +01:00
Domen Kožar 84a559393b mnemosyne: add webob dep (fixes #11746) 2015-12-17 09:30:02 +01:00
Peter Simons 7e45327e9a mnemosyne: update to version 2.3.2 2015-03-10 15:39:28 +01:00
Pascal Wittmann 8df0e0b151 Fixed many descriptions 2014-11-11 14:36:34 +01:00
vi b5acc84ff3 Mnemosyne: provide capacity to override dependencies at the fidelity of a Python package. 2014-03-26 00:37:14 +08:00
vi ed63dcb7c7 Have Mnemosyne take pkgs, rather than pythonPackages as argument. 2014-03-26 00:05:34 +08:00
vi 5149f278aa Patch *all* references to sys.exec_prefix. 2014-03-25 03:28:19 +08:00
vi 344279495a Add Mnemosyne 2.2.1.
The Mnemosyne Project has two aspects:

  * It's a free flash-card tool which optimizes your learning process.
  * It's a research project into the nature of long-term memory.

We strive to provide a clear, uncluttered piece of software, easy to use
and to understand for newbies, but still infinitely customisable through
plugins and scripts for power users.

Mnemosyne uses a sophisticated algorithm to schedule the best time for
a card to come up for review. Difficult cards that you tend to forget
quickly will be scheduled more often, while Mnemosyne won't waste your
time on things you remember well.

If you want, anonymous statistics on your learning process can be
uploaded to a central server for analysis. This data will be valuable to
study the behaviour of our memory over a very long time period. The
results will be used to improve the scheduling algorithms behind the
software even further.
2014-03-25 02:55:25 +08:00