I recommend a used 3090, as that has 24 GB of VRAM and generally can be found for $800ish or less (at least when I last checked, in February). It’s much cheaper than a 4090 and while admittedly more expensive than the inexpensive 24GB Nvidia Tesla card (the P40?) it also has much better performance and CUDA support.
I have dual 3090s so my performance won’t translate directly to what a single GPU would get, but it’s pretty easy to find stats on 3090 performance.
The above post says it has support for Ollama, so I don’t think this is the case… but the instructions in the Readme do make it seem like it’s dependent on OpenAI.
16 GB of RAM, though? Is it even optimized for the Ryzen 9950X3D?
And a 4 TB SSD - not even necessarily NVME?
Doesn’t seem high powered to me.
Are you saying that NAT isn’t effectively a firewall or that a NAT firewall isn’t effectively a firewall?
Is there a way to use symlinks instead? I’d think it would be possible, even with Docker - it would just require the torrent directory to be mounted read-only in the same location in every Docker container that had symlinks to files on it.
Under notes, where you said my name, did you mean “Hedgedoc?”
local docker hub proxy
Do you mean a Docker container registry? If so, here are a couple options:
Oh 100% agreed - in this instance, it’s clear that OBS has a well maintained package that should be prioritized. But they could keep their repo first and remove OBS (and other known-to-be-well-maintained apps) from it to accomplish that.
They put their repo first on the list.
Right. And are we talking about the list for OBS or of repos in general? I doubt Fedora sets the priority on a package level. And if they don’t, and if there are some other packages in Flathub that are problematic, then it makes sense to prioritize their own repo over them.
That said, if those problematic packages come from other repositories, or if not but there’s another alternative to putting their repo first that would have prevented unofficial builds from showing up first, but wouldn’t have deprioritized official, verified ones like OBS, then it’s a different story. I haven’t maintained a package on Flathub like the original commenter you replied to but I don’t get the impression that that’s the case.
Why did Fedora make their packages take priority? Is it because the priority is otherwise random and if you don’t have a priority set, that leads to the issue they mentioned? Because if so, that sounds like a reasonable action by Fedora and like the real culprit is Flathub.
I primarily use Standard Notes. It’s a fantastic tool and I can use it anywhere, online or offline. It’s not great for collaboration, though, and it doesn’t have a canvas option. But I use it for scratch pads, for todo lists, for project tracking, for ideas, plans, plotting for my tabletop (Monster of the Week) game, software design and architecture, for drafting comments, etc…
Standard Notes also has a ton of options for automated backups. I get a daily email with a backup of my notes; I can host my notes on my home server and the corporate one; I can also set up automated backups on any desktop.
I don’t use it for saving links. I’m still using Raindrop.io for that, even though I’m self-hosting both Linkding and Linkwarden.
For sharing and collaboration, I either publish to Listed with Standard Notes or use Hedgedoc, which is great for collaboration and does a great job presenting nodes, too.
For canvas notes, I use GoodNotes on a tablet or the Onyx Boox’s default Notes app. I’d love a better FOSS, self-hosted option, especially for the Boox, but my experiences thus far have been negative (especially on the Boox).
I’ve been trying out SilverBullet lately, since I want to try out cross-note querying and all that, but I’m too stuck in my habits and keep going back to Standard Notes. I think I’ll have better luck if I choose one app and go with it.
I also have a collection of Mnemosyne notebooks that I use with fountain pens (mostly the Lamy 2000, but also quite commonly a Platinum 3776 or a Twsbi). Side note: the Lamy 2000 was my first fountain pen and after getting it I went deep into fountain pens. I explored a ton of different options, found a lot of nice pens across a number of brands… and yet how I still haven’t found something that I consistently like more. The Pilot VP is great but deceptive; a fancy clicky pen that only holds 30 minutes of ink (in a converter, at least) is decidedly inconvenient.
I’ve also been checking out Obsidian on my work computer. So far I haven’t seen anything that makes me prefer it over my existing set of tools.
Hedgedoc is fantastic. If you’re okay with your notes app being web-only (without an app or even a PWA) and you don’t need canvas notes or multi-note queries, you should check it out.
First, every note is Markdown, but it supports a ton of things natively. It has native Vim, Emacs, and Sublime (the default) editors and it’s built to be great for collaboration (if you want).
It also has
And best of all, they have a Hedgehog for the icon! (I may be biased.)
Clearly they’re cosplaying as a Canonical engineer whose internal explanation and pleas for them to not take this approach fell upon deaf ears /j
If you’re a C developer who doesn’t know Rust, yes.
You can control that with a setting. In Settings - Privacy, turn on “Query in the page’s title.”
My instance has a magnifying glass as the favicon.
Giant squids are the bears of the ocean
Giphy has a documented API that you could use. There have been bulk downloaders, but I didn’t see any that had recent activity. However you still might be able to use one to model your own script after, like https://github.com/jcpsimmons/giphy-stacks
There were downloaders for Gfycat - gallery-dl supported it at one point - but it’s down now. However you might be able to find collections that other people downloaded and are now hosting. You could also use the Internet Archive - they have tools and APIs documented
There’s a Tenor mass downloader that uses the Tenor API and an API key that you provide.
Imgur has GIFs is supported by gallery-dl, so that’s an option.
Also, read over https://github.com/simon987/awesome-datahoarding - there may be something useful for you there.
In terms of hosting, it would depend on my user base and if I want users to be able to upload GIFs, too. If it was just my close friends, then Immich would probably be fine, but if we had people I didn’t know directly using it, I’d want a more refined solution.
There’s Gifable, which is pretty focused, but looks like it has a pretty small following. I haven’t used it myself to see how suitable it is. If you self-host it (or something else that uses S3), note that you can use MinIO or LocalStack for the S3 container rather than using AWS directly. I’m using MinIO as part of my stack now, though for a completely different app.
MediaCMS is another option. Less focused on GIFs but more actively developed, and intended to be used for this sort of purpose.
Wouldn’t be a huge change at this point. Israel has been using AI to determine targets for drone-delivered airstrikes for over a year now.
https://en.m.wikipedia.org/wiki/AI-assisted_targeting_in_the_Gaza_Strip gives a high level overview of Gospel and Lavender, and there are news articles in the references if you want to learn more.
This is at least being positioned better than the ways Lavender and Gospel were used, but I have no doubt that it will be used to commit atrocities as well.
For now, OpenAI’s models may help operators make sense of large amounts of incoming data to support faster human decision-making in high-pressure situations.
Yep, that was how they justified Gospel and Lavender, too - “a human presses the button” (even though they’re not doing anywhere near enough due diligence).
But it’s worth pointing out that the type of AI OpenAI is best known for comes from large language models (LLMs)—sometimes called large multimodal models—that are trained on massive datasets of text, images, and audio pulled from many different sources.
Yes, OpenAI is well known for this, but they’ve also created other types of AI models (e.g., Whisper). I suspect an LLM might be part of a solution they would build but that it would not be the full solution.
Thanks for clarifying! I’ve heard nothing but praise for Kagi from its users so that’s what I was assuming, but Searxng has also been great so I wouldn’t have been too surprised if you’d compared them and found its results to be on par or better.
By the way, if you’re self hosting Searxng, you can use add your own index. Searxng supports YaCy, which is an actively developed, open source search index and crawler that can be operated standalone or as part of a decentralized (P2P) network. Here are the Searxng docs for that engine. I can’t speak to its quality as I still haven’t set it up, though.
Look up “LLM quantization.” The idea is that each parameter is a number; by default they use 16 bits of precision, but if you scale them into smaller sizes, you use less space and have less precision, but you still have the same parameters. There’s not much quality loss going from 16 bits to 8, but it gets more noticeable as you get lower and lower. (That said, there’s are ternary bit models being trained from scratch that use 1.58 bits per parameter and are allegedly just as good as fp16 models of the same parameter count.)
If you’re using a 4-bit quantization, then you need about half that number in VRAM. Q4_K_M is better than Q4, but also a bit larger. Ollama generally defaults to Q4_K_M. If you can handle a higher quantization, Q6_K is generally best. If you can’t quite fit it, Q5_K_M is generally better than any other option, followed by Q5_K_S.
For example, Llama3.3 70B, which has 70.6 billion parameters, has the following sizes for some of its quantizations:
This is why I run a lot of Q4_K_M 70B models on two 3090s.
Generally speaking, there’s not a perceptible quality drop going to Q6_K from 8 bit quantization (though I have heard this is less true with MoE models). Below Q6, there’s a bit of a drop between it and 5 and then 4, but the model’s still decent. Below 4-bit quantizations you can generally get better results from a smaller parameter model at a higher quantization.
TheBloke on Huggingface has a lot of GGUF quantization repos, and most, if not all of them, have a blurb about the different quantization types and which are recommended. When Ollama.com doesn’t have a model I want, I’m generally able to find one there.