Typing applications has been an extremely crowded space since all the way back in the Mavis Beacon days - good luck!

Small bit of feedback that might help it stand out more from the crowd and appeal to HN users: Add in training for Colemak, Dvorak, and maybe regional variants like AZERTY.

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absoluteunit1 12 minutes ago | parent [-]

Thank you!

I definitely approached it from an engineering perspective - building what I thought was missing in the space that I wanted. In hindsight, it may not be the best business to quit a FAANG job for haha.

> Colemak, Dvorak...

We have these :). Have QWERTZ and British QWERTY as well. Going to add AZERTY next and a few others.

I an also exploring more uniqie/niche ones like Kinesis keyboards, etc.but no plans for this just yet.

> good luck!

Thank you!

	▲	vunderba 7 minutes ago \| parent [-]
		Haha - well hopefully coming from a FAANG you've got some monetary runway at last :) Some more feedback I'm a fairly fast typist (~120 wpm) so I didn't dig too deep into your tool, but I did see that you seem to have a metric around highlighting for letters where people might be making mistakes. You might already be doing this, but my back-of-the-envelope thought is that you really don’t want to think about typing in terms of individual letters; you want to think in terms of clusters. Ideally, you’d use some kind of frequency corpus or Markov model or something similar to break text into commonly occurring constituent clusters, e.g. the -ing in a gerund. It's essentially the equivalent of "memory chunking" but applied to typing analysis. That’s not to say letter-level analysis is not useful (especially for hunt and peckers learning where the keys are) but as the person progresses, you’d want to introduce this idea of statistical feedback on common sequential clusters.