In the terminal session you launched firefox from:

Ctrl-Z  # Temporarily suspend process
bg      # Put that process into the background
exit    # Cleanly close the terminal session

Or launch Firefox (or whatever application) with a ’&’ at the end to put it in the background from the start.

I think I’ve had the opposite experience. I use W11 for my day-job with a laptop connected to 2 monitors. It could just be the archaic painful apps that my employer uses, but it routinely moves windows to different screens if I lock the system and return a few mins later. I set the taskbar on each screen to only show the windows that are open on each screen, but often a window will be open on one screen but the taskbar icon for it is on another. To work around that I developed a routine when I return from my breaks - I move every window to a different screen, then back again, and that ‘fixes’ it - it feels so stupid to have to do this on an OS that’s built by one of the biggest companies on earth.

I think the equivalent issue on Linux might be due to Wayland and/or the desktop environment not keeping track of window positions, and there’s ample developer ‘debate’ about if/how that gets handled.

The encoding settings you’ve given for AOM and SVT don’t appear equivalent, so the speed, file-size and probably the ‘perceptual’ picture quality will be different. It’s probably possible to make the AOM encoder much faster, or the SVT encoder output smaller, just by adjusting a few key settings. You’ll need to fine-tune these to your own preferences based on your source video.

For the SVT-AV1 encode, you’re using “-preset 12”. That preset is a combination of settings designed to focus on encoding speed instead of quality. If you changed to “-preset 0” or “-preset 1”, the encoder would be considerably slower, but would give you better picture quality and probably better file-size. The equivalent for AOM encoder is “cpu-used” (0 = slowest but high quality; 8 = fastest but low quality). This page shows what each preset does in SVT, but there’s probably an equivalent page for the AOM encoder.

In your SVT settings, you’re using “-qp 32” and “-crf 39” for AOM. These settings are for defining the desired picture quality and bit-rate trade-off, but they work in different ways. For each, a higher number means lower-quality & file-size. When using ‘QP’ mode, the encoder usually applies the same quality setting for every frame of video. That means parts of your video will sometimes get more (or less) compression than they need, and the output could look uneven. ‘CRF’ mode lets the encoder vary the quality for each frame, trying to account for human perception, and making the output appear more even. Depending on how the encoder works internally, CRF is probably slower than QP, but it’s what I normally choose for archiving.

Your other settings (“tile-columns”, “tile-rows”, “threads”, “row-mt”) are more specific to the way the encoders work internally, and how they spread the processing load on your CPU. They could be useful if you need to limit the amount of cores/threads used, or ensure more are used, but I’d suggest adjusting those kinds of settings later on, after you find your desired quality/file-size/speed preference.

I’m more familiar with SVT-AV1, but my recommendation is to try benchmarking your settings with just one encoder at a time, focus on the presets first (maybe start around 4-6 if using SVT), then try different CRF values until you see the difference.

As for hardware encoding, your GTX 970 sadly can’t help. For AV1 encoding on consumer-level graphics cards, you’d need Nvidia 40 series, AMD 7000 series, Intel Arc/Xe2, or higher. Encoding with them is usually faster and more efficient, but I’ve found the quality is usually lower, even when requesting the highest-quality modes available. The options available also depend on chip maker – I can use QP quality mode with my AMD card, but not CRF, for example.

TLDR: try different presets to alter the speed, and then CRF values to determine your preferred quality/file-size trade-off.

https://github.com/Manish7093/IBus-Speech-To-Text

I tried this in Fedora/Wayland previously, and it seems to work in most applications. It uses “VOSK” models which the GUI can download automatically - you just pick your language and desired model size when setting it up.

When I was exploring this a few months ago, I noticed speech recognition models have moved on quite recently (e.g. https://github.com/openai/whisper which can be run locally) but didn’t see anything integrating it into an input-method like the above.

I think the best advice is to experiment with different approaches and find what works best for you. I’ve been working on a novel for ~7-8 years in my spare time, and I’m probably only ~25-33% done, so maybe take my advice with a pinch of salt.

The problem when I was starting out was inertia – I had a clear idea of what I wanted to write, and made copious notes about the plot, characters, and other elements. Once I’d finished that part, I came to a realisation that it would just take too much of my time, and I already had other priorities with my work and personal life. My thinking was that I’d need to set myself a strict schedule, abandon other interests entirely, commit to writing x paragraphs per day, and set a deadline to finish it. Due to my other commitments at the time, that wasn’t viable for me, so I felt the project was already a failure.

It took a while to get over that, but once I’d started, I found myself continually re-wording sentences and paragraphs, focussing too much on grammar and vocabulary choices than actually progressing the story. So I sat back and realised that I needed to change my approach again. I know my writing style and quality probably isn’t going to be the greatest for a first draft of a first novel, so rather than attempting perfection sentence-by-sentence, I let the quality slide a bit and pressed on. Now, I just leave myself hints to go back and re-work the problematic sections later – for me, I compartmentalise by highlighting those sections in a gaudy yellow. Only when I’m happy with the changes will I take the highlighting off.

Regarding pacing, the thing that works best for me is to set aside a specific time of day for writing, maintain that, and try to remove any distractions for that period. It definitely helps keep momentum, and if it’s a day when I’m not up to writing anything ‘new’, I can go back to the highlighted sections and fix them, or add more planning notes about the following chapters. Progress is progress, regardless if the ‘word count’ isn’t going up, and consistency matters more than intensity.

I also experimented using text-to-speech, but found it wasn’t a good fit for me. My typing speed is good enough, and text-to-speech seems to work better when you don’t ‘um’, ‘err’, and correct yourself as much as I do. I suppose I could try it again at some point, but I’d need to learn how to use it properly with editing commands like ‘go back’ or ‘delete that’. If you’re the kind of person that can speak in entire paragraphs without verbally stumbling, its worth a try.

I know everyone’s got an opinion on AI and particularly Generative AI these days, so make whatever decisions you feel are appropriate for you. I’ve experimented with local LLMs, noticed there are models that are probably more ‘ethical’ than others, and that I’d prefer a local one over something remote/cloud/subscription based. I’d be reluctant to ask AIs to “write a story about…” though. If your intention is creative writing, and you might want it published some day, then you do need to put in the work. Readers want ideas and creative effort, not regurgitated ‘slop’ that can be generated in a few seconds, and publishers have tools to detect AI-generated content. I’d also be cautious about asking AI chatbots to critique your work – most tend to be far too ‘agreeable’ out-of-the-box. Aside from that, LLMs can be useful for the writing process – You could write a prompt to ask it to paraphrase sections of your own work, or to change the tone. Maybe it’ll give you crap, just a few useful sentences, or changes that you hadn’t even thought of – it’ll be up to you if you want to use some/all/none of it.

As I said earlier, what works for me might not work for you, and you’ll probably want to experiment with different approaches. Don’t be afraid to pivot to something else if it’s not working or not sustainable.

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“One of the characters of your chosen password was used in the same position 12 password-changes ago, but I won’t tell you which”