OP is also in the allegedly ultra rare camp of “successfully configured Jellyfin and lived to tell the tale.” Not what I’d expect of someone unable to configure Plex correctly. I’ve not set up a Plex server myself but my guess is it wasn’t clear that it was misconfigured - it did work previously, after all.

If they’re calling it remote streaming when you’re on the same (local) network, that’s not exactly intuitive. I’d say OP’s phrasing was fair.

You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.

Wow, there isn’t a single solution in here with the obvious answer?

You’ll need a domain name. It doesn’t need to be paid - you can use DuckDNS. Note that whoever hosts your DNS needs to support dynamic DNS. I use Cloudflare for this for free (not their other services) even though I bought my domains from Namecheap.

Then, you can either set up Let’s Encrypt on device and have it generate certs in a location Jellyfin knows about (not sure what this entails exactly, as I don’t use this approach) or you can do what I do:

1. Set up a reverse proxy - I use Traefik but there are a few other solid options - and configure it to use Let’s Encrypt and your domain name.
2. Your reverse proxy should have ports 443 and 80 exposed, but should upgrade http requests to https.
3. Add Jellyfin as a service and route in your reverse proxy’s config.

On your router, forward port 443 to the outbound secure port from your PI (which for simplicity’s sake should also be port 443). You likely also need to forward port 80 in order to verify Let’s Encrypt.

If you want to use Jellyfin while on your network and your router doesn’t support NAT loopback requests, then you can use the server’s IP address and expose Jellyfin’s HTTP ports (e.g., 8080) - just make sure to not forward those ports from the router. You’ll have local unencrypted transfers if you do this, though.

Make sure you have secure passwords in Jellyfin. Note that you are vulnerable to a Jellyfin or Traefik vulnerability if one is found, so make sure to keep your software updated.

If you use Docker, I can share some config info with you on how to set this all up with Traefik, Jellyfin, and a dynamic dns services all up with docker-compose services.

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:

• q4_K_M (the default): 43 GB
• fp16: 141 GB
• q8: 75 GB
• q6_K: 58 GB
• q5_k_m: 50 GB
• q4: 40 GB
• q3_K_M: 34 GB
• q2_K: 26 GB

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.

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.

Not directly, but indirectly. You gerrymander the district-based positions, which allow you to pass legislation enabling you to suppress enough votes to win the statewide elections, too.

That sounds like something someone who’s never heard of gerrymandering or voter suppression would say.

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.

Are you saying that NAT isn’t effectively a firewall or that a NAT firewall isn’t effectively a firewall?

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:

• Use the official Docker registry: https://www.docker.com/blog/how-to-use-your-own-registry-2/
• Self-host forgejo or gitea and use the included package registry, which is automatically enabled. Details: https://forgejo.org/docs/latest/user/packages/

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.

there is a better open source meta search engines

I already use Searxng and have never used Kagi, but I’m curious why you say that Searxng is “better.” Are you saying that because the quality of the searches is better, because it’s open source and Kagi isn’t, or for some other reason?

Do you only experience the 5-10 second buffering issue on mobile? If not, then you might be able to fix the issue by tuning your NextCloud instance - upping the memory limit, disabling debug mode and dropping log level back to warn if you ever changed it, enabling memory caching, etc…

Check out https://docs.nextcloud.com/server/latest/admin_manual/installation/server_tuning.html and https://docs.nextcloud.com/server/latest/admin_manual/installation/php_configuration.html#ini-values for docs on the above.

Your Passkeys have to be stored in something, but you don’t have to store them all in the same thing.

If you store them with Microsoft’s Windows Hello, Apple Keychain, or Google Password Manager, all of which are closed source, then you have to trust MS/Apple/Google. However, Keychain is end to end encrypted (according to Apple) and Windows Hello is currently not synced to the cloud, so if you trust those claims, you don’t need to trust that they won’t misuse your data. I don’t know if Google’s offering is end to end encrypted, but I wouldn’t trust it either way.

You can also store Passkeys in a password manager. Bitwarden is open source (though they did recently introduce a proprietary, source available SDK), as is KeepassXC. 1Password isn’t open source but can store Passkeys as well.

And finally, you can store Passkeys in a compatible security key, like the YubiKey 5 series keys, which can each store 100 Passkeys. This makes them basically immune to being stolen. Note that if your primary interest in Passkeys is in the phishing resistance (basically nearly perfect immunity to MitM attacks) then you can get that same benefit by using WebAuthn as a second factor. However, my experience has been that Passkey support is broader.

Revoking keys involves logging into the particular service and revoking them, just like changing your password. There isn’t a centralized way to do it as far as I’m aware. Each Passkey is only used for a single service, after all. However, in the same way that some password managers will offer to automatically change your passwords, they might develop a similar for passkeys.