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

TOTP can be backed up and used on several devices at least.

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.

If config prompt = system prompt, its hijacking works more often than not. The creators of a prompt injection game (https://tensortrust.ai/) have discovered that system/user roles don’t matter too much in determining the final behaviour: see appendix H in https://arxiv.org/abs/2311.01011.

Like Firefox ScreenshotGo? (I think it only supports English though)

Huh, it’s actually a thing.

xkcd.com is best viewed with Netscape Navigator 4.0 or below on a Pentium 3±1 emulated in Javascript on an Apple IIGS at a screen resolution of 1024x1. Please enable your ad blockers, disable high-heat drying, and remove your device from Airplane Mode and set it to Boat Mode. For security reasons, please leave caps lock on while browsing.

• https://xkcd.com

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.

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.

You’re welcome!

As far as I understand, all of them can be made to work locally (especially if your local model is served via an OpenAI-compatible API, e.g. see llama.cpp’s server binary) with varying degrees of effort required.

• https://github.com/neuml/txtai
• https://docs.llamaindex.ai/en/stable/
• https://docs.gpt4all.io/gpt4all_chat.html#localdocs-plugin-chat-with-your-data
• https://github.com/atisharma/llama_farm
• https://github.com/jonfairbanks/local-rag
• https://docs.useanything.com/
• https://python.langchain.com/docs/get_started/introduction
• https://docs.neum.ai/get-started/introduction

Never ran RAG, so unfortunately no. But there’re quite a few projects doing the necessary handling already - I’d expect them to have manuals.

I’m using local models. Why pay somebody else or hand them my data?

• Sometimes you need to search for something and it’s impossible because of SEO, however you word it. A LLM won’t necessarily give you a useful answer, but it’ll at least take your query at face value, and usually tell you some context around your question that’ll make web search easier, should you decide to look further.
• Sometimes you need to troubleshoot something unobvious, and using a local LLM is the most straightforward option.
• Using a LLM in scripts adds a semantic layer to whatever you’re trying to automate: you can process a large number of small files in a way that’s hard to script, as it depends on what’s inside.
• Some put together a LLM, a speech-to-text model, a text-to-speech model and function calling to make an assistant that can do something you tell it without touching your computer. Sounds like plenty of work to make it work together, but I may try that later.
• Some use RAG to query large amounts of information. I think it’s a hopeless struggle, and the real solution is an architecture other than a variation of Transformer/SSM: it should address real-time learning, long-term memory and agency properly.
• Some use LLMs as editor-integrated coding assistants. Never tried anything like that yet (I do ask coding questions sometimes though), but I’m going to at some point. The 8B version of LLaMA 3 should be good and quick enough.

The exact definition of sanity is a cultural choice.

Rounding?

it cuts out the middle man of having to find facts on your own

Nope.

Even without corporate tuning or filtering.

A language model is useful when you know what to expect from it, but it’s just another kind of secondary information source, not an oracle. In some sense it draws random narratives from the noosphere.

And if you give it search results as part of input in hope of increasing its reliability, how will you know they haven’t been manipulated by SEO? Search engines are slowly failing these days. A language model won’t recognise new kinds of bullshit as readily as you.

Education is still important.

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

I’ve never encountered a keyboard app with UI/UX comparable to Fleksy, so that’s what I use (and UI/UX is everything for a keyboard).

The settings became a bit silly in terms of UI in the course of updates though, I mean specifically the keyboard itself.

There’s good journalism as well, e.g. Quanta Magazine, Scientific American &c.