> You're not interested in addressing customers' needs

I would love to support Wayland, but it is my position that it is impossible to "support Wayland" for Talon. I can only support a subset of the features and only on specific compositors, and it would be a lot of work.

> or giving them ways to address their needs themselves

As I said at the top of the message you are replying to, I believe today users already have the tools to address their needs themselves with about the same level of jank I'd be able to provide on Wayland. If this is a veiled hard line on open source being the only way for users to address their needs themselves, we have a philosophical difference that won't be sorted out in this thread.

> If slack interactions are so unpleasant, why do you direct all support through it?

That's a whole new sentence. I was specifically referring to the support requests for Wayland, which in the long tail have been more hostile toward me than is likely warranted.

> "yes this is a hack, but we'll live with it for now until an actually good solution is available"

The hack is switching to X11, which is fully supported, or working around it in your user scripts, which has already been done by some users for their specific environment.

It's possible to do the simple compositor specific hacks from Talon's scripting system to give yourself partial Wayland support at roughly the quality I'd be able to provide myself, and I know of a couple efforts to do this.

The tentative plan for "dropping support for X11" is just to do one more public Linux X11 release, stop there, leave it available to download, and make it very clear what to expect when you download for Linux or run on Wayland. I plan to continue supporting X11 on the paid version indefinitely.

Most requests relating to Wayland on the Slack have not been offers to help, and way too many have ended up being unpleasant conversations.

I have a standing offer to reconsider my stance if someone can show the vast majority of the necessary APIs are available and well supported without compositor specific hacks.

For some of the other points above, consider me disappointed but not surprised.

e.g. dual channel zen 1 showing 25GB/s on a single core https://stackoverflow.com/a/44948720

I wrote some microbenchmarks for single-threaded memcpy

    zen 2 (8-channel DDR4)
    naive c:
      17GB/s
    non-temporal avx:
      35GB/s

    Xeon-D 1541 (2-channel DDR4, my weakest system, ten years old)
    naive c:
      9GB/s
    non-temporal avx:
      13.5GB/s

    apple silicon tests
    (warm = generate new source buffer, memset(0) output buffer, add memory fence, then run the same copy again)

    m3
    naive c:
      17GB/s cold, 41GB/s warm
    non-temporal neon:
      78GB/s cold+warm

    m3 max 
    naive c:
      25GB/s cold, 65GB/s warm
    non-temporal neon:
      49GB/s cold, 125GB/s warm

    m4 pro
    naive c:
      13.8GB/s cold, 65GB/s warm
    non-temporal neon:
      49GB/s cold, 125GB/s warm

    (I'm not actually sure offhand why asi warm is so much faster than cold - the source buffer is filled with new random data each iteration, I'm using memory fences, and I still see the speedup with 16GB src/dst buffers much larger than cache. x86/linux didn't have any kind of cold/warm test difference. my guess would be that it's something about kernel page accounting and not related to the cpu)

https://bochs.info/vec2jpg/

This was a very basic experiment. I expect you could perform the DCT more intelligently on the vector dimensions instead of trying to pack the embeddings into pixels, and get higher quality semantic compression.

Neverball was working in the original glshim project before ptitseb forked it to gl4es. (Not to discount the significant work he's put in since, including the ES2 backend)

If you encounter what feels like poor recognition in Talon, I recommend enabling Save Recordings and zipping+sharing some examples on the Slack and asking for advice.

The current command set is definitely harder to learn than a system designed for chat/email where "what you say is what you get", but it's much more powerful for tasks like programming once you learn it.

I'm dubious about what kind of general command accuracy Numen is able to get with the Vosk models, as Vosk to my understanding is more designed for natural language than commands.

Assuming you mean speaking in natural language, that's slower to say, and likely less precise and predictable if you want to be able to just say "anything" any have a result.

You need a command system either way. If you want to express some precise intention, you need to understand what the command system will do.

There is a combined "mixed mode" system I've been testing in the talon beta where you can use both phrases and commands without switching modes.

It's also worth saying that if you only tried things out briefly, there are a handful of reasons recognition may have seemed worse. Talon uses a strict command system by default, because that improves precision and speed for trained users, but the tradeoff there is it's more confusing for people who haven't learned it yet.

For example, Talon isn't in "dictation mode" by default, so you need to switch to that if you're trying to write email-like text and don't want to prefix your phrases with a command like "say".

The timeout system may also be confusing at first. When you pause, Talon assumes you were done speaking and tries to run whatever you said. You can mitigate this by speaking faster or increasing the timeout.

The default commands (like the alphabet) may also just not be very good for some accents, and that will be the case for any speech engine - you will likely need to change some commands if they're hard to enunciate in your accent.

I recommend joining the slack [1] and asking there if you want more specific feedback. I definitely want to support many accents and even have some users testing Talon with other spoken languages.

[1] https://talonvoice.com/chat

Due to whisper's weakly supervised training on a large amount of automatically scraped data and reliance on a bigger language model, it's far more likely whisper had seen some of the test data before.

https://ai.googleblog.com/2021/01/improving-mobile-app-acces...

[1] https://twitter.com/lunixbochs/status/1574848899897884672