steno-dictionaries VS autowrite

https://steno.sammdot.ca/emily-symbols.png

for these, where it says starter, you just press all those keys down, and then on the other side you press the keys listed for what you want. so for example, I can enter like ~104 symbols without moving my hands. the average sybmol layer has like 20. the crossplatform movement dict lets me move around much easier in any text field. (note that you don't really even need to know what the key names you are pressing are as its all a pattern) I currently have six other dictionaries that I use some of the time. you can see more here: https://www.openstenoproject.org/stenodict/.

any cli program would be very easy to add most of the commands to a dictionary if you wanted. for example, a basic git dictionary: https://github.com/didoesdigital/steno-dictionaries/blob/mas...

plover has made using a computer much more fun. its a bit of a hard sell for a lot of people, but I recommend trying out some of the other dictionaries to see what you can do besides type words fast. its seriously really crazy that we are only pressing one key at a time using a keyboard.

In the spirit of reduction, I look another look at https://github.com/didoesdigital/steno-dictionaries/blob/master/dictionaries/dict.json. In the same way that I asked how many of the 128 left-hand (four fingers only) states are actually used by the dictionary, I can also ask how many of the 4 million available chords are actually used?

Another day, another story on HN that will have me down a rabbit hole (yesterday's was a three-hour tangent into ternary bit compression after the Microsoft paper) :D

Your project is delightful -- thank you for sharing. I have explored this realm a bit before [0] [1], but in Python. The tool I made was for personal use, but streaming every keystroke through a network connection added a lot of unnecessary latency.

I used word surprisals (entropy) to calculate the most likely candidates, and gave a boost to words from my own writing (thus, the predictive engine was "fine-tuned" on my writing). The result is a dictionary of words with their probabilities of use. This can be applied to bigrams, too. Your project has me thinking: how could that be pruned, massively, to create the smallest possible structure. Your engine feels like the answer.

My use case is technical writing: you know what you want to say, including long words you have to repeat over again, but you want a quicker way of typing.

[0]: https://jamesg.blog/2023/12/15/auto-write/

[1]: https://github.com/capjamesg/autowrite

3. Use that as advanced autocomplete.

I got a working solution here: https://github.com/capjamesg/autowrite/blob/main/autocomplet...

(No docs yet -- coming in the next few days. Leave a GitHub Issue if you want to chat about it!)