Of course:

The rest of the instructions are all valid n-controlled Toffolis and Hadamards, but of course mostly Toffolis since it’s replicating a classical algorithm. There is no quantum advantage, it’s just a classical algorithm written in a format compatible with a quantum computer.

Add small errors to the quantum simulator (quantum computers always have those) and all’ll break entirely - apparently (1) no error correction was used and (2) it’s just logic gates for Doom rewritten as quantum gates. No wonder the author got bored, I’d be bored too.

It would. But it’s a good option when you have computationally heavy tasks and communication is relatively light.

Once configured, Tor Hidden Services also just work (you may need to use some fresh bridges in certain countries if ISPs block Tor there though). You don’t have to trust any specific third party in this case.

CVEs are constantly found in complex software, that’s why security updates are important. If not these, it’d have been other ones a couple of weeks or months later. And government users can’t exactly opt out of security updates, even if they come with feature regressions.

You also shouldn’t keep using software with known vulnerabilities. You can find a maintained fork of Chromium with continued Manifest V2 support or choose another browser like Firefox.

If your CPU isn’t ancient, it’s mostly about memory speed. VRAM is very fast, DDR5 RAM is reasonably fast, swap is slow even on a modern SSD.

8x7B is mixtral, yeah.

Mostly via terminal, yeah. It’s convenient when you’re used to it - I am.

Let’s see, my inference speed now is:

• ~60-65 tok/s for a 8B model in Q_5_K/Q6_K (entirely in VRAM);
• ~36 tok/s for a 14B model in Q6_K (entirely in VRAM);
• ~4.5 tok/s for a 35B model in Q5_K_M (16/41 layers in VRAM);
• ~12.5 tok/s for a 8x7B model in Q4_K_M (18/33 layers in VRAM);
• ~4.5 tok/s for a 70B model in Q2_K (44/81 layers in VRAM);
• ~2.5 tok/s for a 70B model in Q3_K_L (28/81 layers in VRAM).

As of quality, I try to avoid quantisation below Q5 or at least Q4. I also don’t see any point in using Q8/f16/f32 - the difference with Q6 is minimal. Other than that, it really depends on the model - for instance, llama-3 8B is smarter than many older 30B+ models.

Have been using llama.cpp, whisper.cpp, Stable Diffusion for a long while (most often the first one). My “hub” is a collection of bash scripts and a ssh server running.

I typically use LLMs for translation, interactive technical troubleshooting, advice on obscure topics, sometimes coding, sometimes mathematics (though local models are mostly terrible for this), sometimes just talking. Also music generation with ChatMusician.

I use the hardware I already have - a 16GB AMD card (using ROCm) and some DDR5 RAM. ROCm might be tricky to set up for various libraries and inference engines, but then it just works. I don’t rent hardware - don’t want any data to leave my machine.

My use isn’t intensive enough to warrant measuring energy costs.

Disabling root login and password auth, using a non-standard port and updating regularly works for me for this exact use case.

I have a MediaWiki instance on my laptop (I’ve found the features of all other wikis/mindmaps/knowledge databases decisively insufficient after having a taste of MW templates, Semantic MediaWiki and Scribunto).

Also some smaller things like pihole-standalone, Jellyfin and dictd.