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.

Why should we know this?

Not watching that video for a number of reasons, namely that ten seconds in they hadn’t said anything of substance, their first claim was incorrect (Amazon does not prohibit use of gen ai in books, nor do they require its use be disclosed to the public, no matter how much you might wish it did), and there was nothing in the description of substance, which in instances like this generally means the video will largely be devoid of substance.

What books is the Math Sorcerer selling? Are they the ones on Amazon linked from their page? Are they selling all of those or just promoting most of them?

Why do we think they were generated with AI?

When you say “generated with AI,” what do you mean?

• Generated entirely with AI, without even editing? Then why do they have so many 5 star reviews?
• Generated with AI and then heavily edited?
• Written partly by hand with some pieces written by unedited GenAI?
• Written partly by hand with some pieces written by edited GenAI?
• AI was used for ideation?
• AI was used during editing? E.g., Grammarly?
• GenAI was used during editing?E.g., “ChatGPT, review this chapter and give me any feedback. If sections need rewritten go ahead and take a first pass.”
• AI might have been used, but we don’t know for sure, and the issue is that some passages just “read like AI?”

And what’s the result? Are the books misleading in some way? That’s the most legitimate actual concern I can think of (I’m sure the people screaming that AI isn’t fair use would disagree, but if that’s the concern, settle it in court).

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.

From https://www.yalemedicine.org/news/covid-vaccines-reduce-long-covid-risk-new-study-shows

At the pandemic’s onset, approximately 10% of people who suffered COVID-19 infections went on to develop Long COVID. Now, the risk of getting Long COVID has dropped to about 3.5% among vaccinated people (primary series).

…

Then, the team conducted analyses to uncover the reasons for the observed decline in Long COVID cases from the pre-Delta to Omicron eras. About 70% of the decline was attributable to vaccination, they found.

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.

It’s more likely that this is being done to either:

• Evade hiring restrictions
• Interview on someone else’s behalf

Depending on setup this can be true with Jellyfin, too. I have a domain registered, use dynamic DNS, and have Traefik direct a subdomain to my Jellyfin server. My mobile clients are configured using that. My local clients use the local static IP.

If my internet goes down, my mobile clients can’t connect, even on the LAN.

Orcas are dolphins, though. Do you mean bottlenose dolphins specifically?

Do you mean like a FOSS version of https://soundiiz.com/transfer-playlist-and-favorites?

Or at a song/album level, a FOSS version of https://odesli.co/?

Under notes, where you said my name, did you mean “Hedgedoc?”

If you have a lot of books to download, check out https://github.com/treetrum/amazon-kindle-bulk-downloader

From the feature comparison at https://github.com/meichthys/foss_note_apps only two FOSS apps support handwriting: Joplin (with a plugin) which gets a subjective 6/10 score, and TriliumNext, which gets a subjective 2/10 score. I personally dislike Joplin but many people love it, so I recommend giving it a shot. EDIT: I installed Joplin using the APK from the site and both the handwriting and Excalidraw plugins were “not available on mobile,” so I have to rescind my recommendation. On my iOS device, the plugins didn’t even show up in the search.

I think TriliumNext is great, but the mobile experience is still lacking (though they are tracking several issues to improve here). There’s no dedicated mobile app but they at least have a PWA. It also needs to be self-hosted, but doing so is straightforward if you’re already using Docker. The handwriting is done via a built-in Excalidraw integration.

Here are some options not captured in that list:

Obsidian is not open source, but also has an Excalidraw plugin. I’ve not used it yet but I’ve seen multiple discussions saying that it’s very well done and has additional functionality on top of base Excalidraw. There’s also an open source (MIT) plugin for Obsidian that adds support for handwritten notes. I only use Obsidian on my work computer and haven’t used it either, though I plan to install the Excalidraw plugin Monday.

StylusLabs Write is FOSS (AGPL 3.0), multiplatform, and has a free Android apk available. Note that the Google Play version has had updates suspended. I just learned about it and don’t know how it otherwise measures up. I’m planning to check it out, though.

You can use any note app that has Excalidraw support, so long as you don’t need your handwritten text to be OCRed. That means that the following are all options:

• The Nextcloud Excalidraw integration
• Standard Notes, using https://github.com/nienow/sn-excalidraw (or if you pay for Standard Notes, official Excalidraw editor)
• The Excalidraw PWA
• A self-hosted fork of Excalidraw with Firebase replaced with something else (see https://github.com/beltebelt/excalidraw for an example)

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/

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.