One of the things that I love about Keyboard Maestro is the ability to chain together disparate technologies to achieve some automation goal on macOS.
In most of my previous posts about Keyboard Maestro macros, I’ve used Python or shell scripts, but I decided to draw on some decades-old experience with Perl to do a little text processing for a specific need.
Background I want this text from Wiktionary:
to look like this:
It seems like the command line is one of those places where you can accomplish crazy efficient things with one-liners.
Here’s a perfect use case for a CLI one-liner:
In Anki, I often add lists of synonyms and antonyms to my vocabulary cards, but I like them formatted as a bulleted list. My usual route to that involves Markdown. But how to convert this:
известный, точный, определённый, достоверный
to
- `известный` - `точный` - `определённый` - `достоверный` After trying to come up with a single text replacement strategy to make this work, the best I could do was this:
The ability to execute Javascript in Anki card templates offers users flexibility in displaying data. In Anki 2.1, though, the asynchronous execution of Javascript means that user script functionality is not entirely predictable. This post on r/Anki discusses an approach for dynamically loading Javascript resources and ensuring that they are available when the card is displayed. Since I modularize my Javascript code so that it can be flexibly deployed to different card types, I extended this method to allow the template developer to load multiple scripts in one <script> block.
It’s possible to use cloze deletion cards within standard Anki note types using the Anki Cloze Anything setup. But additional scripts are required to allow it to function seamlessly in a typical language-learning environment. I’ll show you how to flexibly display a sentence with or without Anki Cloze Anything markup and also not break AwesomeTTS.
Anki’s built-in cloze deletion system The built-in cloze deletion feature in Anki is an excellent way for language learners to actively test their recall.
I write in Markdown because it’s much easier to keep the flow of writing going without taking my hands off the keyboard.
I also like to write content in Anki cards in Markdown. Over the years there have been various ways in of supporting this through add-ons:
The venerable Power Format Pack was great but no longer supports Anki 2.1, so it became useless. Auto Markdown worked for a while but as of Anki version 2.
W3schools.com has a CSS library that’s quite nice. I often use Bootstrap; but I like some of the visual features here better. For example, I like their tags because they have more flexible use of colour.
If you want to fetch from a Python dictionary, but you need a default value, this is how you do it:
upos_badge = {'noun': 'lime','verb': 'amber', 'adv': 'blue',} badge_class_postfix = upos_badge.get(value.lower(), 'light-grey') I recently learned about DeepL as an alternative to Google Translate.
Recently I hit an extra digit when setting up a custom new card session and was stuck with hundreds of new cards to review. Desparate to fix this, I started poking around the Anki collection SQLite database, I found the collection data responsible for the extra cards. In the col table, find the newToday key and you’ll find the extra card count expressed as a negative integer. Just change that to zero and you’ll be good.
There’s a phenomenon that verteran Anki users are familiar with - the so-called “Anki hell” or “ease hell.”
Origins of ease hell The descent into ease hell has to do with the way Anki handles correct and incorrect answers when it presents cards for review. Ease is a numerical score associated with every card in the database and represents a valuation of the difficulty of the card. By default, when cards graduate from the learning phase, an ease of 250% is applied to the card.
In a previous post I described macros to support certain tasks in generating source material for L2 chorus repetition practice. Today, I’ll describe two other macros that automate this practice by slowing the playback speed of the repetition.
Background I’ve described the rationale for chorus repetition practice in previous posts. The technique I describe here is to slow the sentence playback speed to give the learner time to build speed by practicing slower repetitions.
Achieving fluid, native-quality speech in a second language is difficult task for adult learners. For several years, I’ve used Dr. Olle Kjellin’s method of “chorus repetition” for my Russian language study. In this post, I’m presenting a method for scripting Audacity to facilitate the development of audio source material to support his methodology.
Background For detailed background on the methodology, I refer you to Kjellin’s seminal paper “Quality Practise Pronunciation with Audacity - The Best Method!