Nice. Some thoughts. Look at PPM. Your prediction model would work better with personalised data. PPM is efficient (nb. I see you are using python - look at th

Nice. Some thoughts. Look at PPM. Your prediction model would work better with personalised data. PPM is efficient (nb. I see you are using python - look at this https://github.com/willwade/pylm https://github.com/willwade/pylm - although be warned - i think my code is not quite right..) Layout shifting for finger movement - well its great if you didnt have to look. The time for visual processing the letters adds a significant lag (its why typical word prediction isnt used that much and when it is - not over 3 predictions (I have papers on this if you are interested). But its not all bad.. Switch users who need next letter prediction this could dramatically support their rate of input. (view https://youtu.be/Bhj5vs9P5cw?si=VnytfH_vdEUWuLok&t=73 https://youtu.be/Bhj5vs9P5cw?si=VnytfH_vdEUWuLok&t=73 - now note how the keyboard blocks the scan up. But imagine if it just scanned each letter first by next most likely - or heck - like this repo - actually changes button position and kept the scan pattern the same. It would be a ton more efficient) (and a bit of a rabbit hole.. What if keys had word predictions on them? This is basically the end result of ACE-LP: https://discovery.dundee.ac.uk/en/publications/ace-lp-augmenting-communication-using-environmental-data-to-drive https://discovery.dundee.ac.uk/en/publications/ace-lp-augmen...)