Slskd - https://github.com/slskd/slskd/
It uses the soulseek network (an old favourite of mine) installed though docker accessible through a browser.
If you want a desktop app, I recommend Nicotine+ for Linux or visit https://www.slsknet.org/news/node/1 for Windows.

The artists I like don’t come around where I live, so I can’t support through live music. I’ve done it in the past when I lived in a large city. In the end we’re all trying our best. And we all have our use cases, there’s no right way to listen to music.

The best way might not be a perfect one. Still a little bit better than most other options.

I live in Europe. Had Spotify for about 5 years, stopped paying and using 6 months ago. I usually buy from Bandcamp, mostly non mainstream music, and download in FLAC and store it on my server. I can stream through the app on my phone when I’m out.
For the ones I can’t find on Bandcamp, or albums from major labels, I tend to find it on Qobuz in MP3. Pricing trends to be similar everywhere.
My pirating nowadays is mainly for old music or establish artists.

Edit: autocorrect

Petition to ban all people who think differently from me

Eu uso este https://github.com/dullage/flatnotes.
Eu não tenho nenhuma nas minhas notas, mas na página diz que tem suporte para imagens

I’ve been using the Asus BT400 without any issues in different distros. But I’d always recommend a cable mouse if you have a stationary setup

NixOS is the sort answer. It’s reproducible across hardware. But I’ve never tested it.

Do you have desktop or laptop?
For desktop, I recommend getting an HDD for storage. They’re cheaper, I bought a 4tb Seagate Ironwolf for less than 100€.
I also have a 1tb nvme, where I store things that require fast reads like my gaming collection while the system is installed in an SSD. (The parts were not bought at the same time). For laptop, you might want to stick to what you have inside and just get a good external USB. The usual brands are still good and I think prices have flatten across the industry.

The “statement” was taken from the study.

We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI’s codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants’ language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.