What a misleading, clickbait title:

Mozilla moves away from open source

When the author really meant:

Mozilla does a thing I don’t like

Did you turn it off by using Invidious?

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.

Is your goal to create things that can be published or used in a project, or to create audiobooks for yourself to listen to?

For voiceovers for text, I use Kokoro Fast API, which has a web frontend. The frontend is only compatible with Chromium browsers on desktop or Android, which sucks as my daily driver is Firefox and an iPhone (there are workarounds in the thread) but it supports voice mixing, speed changes, etc… It also has an issue where it keeps the models (about 3GB) in memory; I keep the CPU version loaded normally and swap to the GPU version if I need it to be faster. If you want something similar for Bark, check out Bark-GUI.

I’ve also dabbled a bit in some TTS features that have Comfy nodes, though at this point mostly just in terms of getting them set up. For my purposes thus far Kokoro has been fine (and I prefer the FastAPI project over the Comfy nodes for most of my uses), but I’ve found nodes for Kokoro, Dia, F5 TTS, Orpheus, and Zonos.

Autiobooks and audiblez both look promising. A few weeks ago, I used the Kokoro FastAPI web frontend to create an audiobook for an ebook I worked on that used entirely self-hosted AI generation for the outlining and prose. Audiblez, which I found about like two days after that, looks like it would have simplified that process substantially. Still, I’d personally like something more like an audiobook studio, where I can more easily swap voices back and forth, add emotions, play with speed on a more granular level, etc… I’m thinking about building something that contains that at some point myself, but it’ll be a minute - hopefully someone else will beat me there.

I posted a comment here a few weeks back on a similar topic. I’ve since used OpenReader-WebUI and like it, though that’s not for producing audiobooks, but for a read-along experience. Reproducing the comment below in case it’s helpful for you:

If you want to generate audiobooks using your own / a hosted TTS server, check out one of these options:

• OpenReader-WebUI - this has built-in read along capability and can be deployed as a PWA that can allow you to download the audiobooks to your phone so you can use them offline
• p0n1/epub to audiobook
• ebook2audiobook If you don’t have a decent GPU, Kokoro is a great option as it’s fast enough to run on CPU and still sounds very good. If you’re going to use Kokoro, Audiblez (posted by another commenter) looks like it makes that more of an all-in-one option. If you want something that you can use without an upfront building of the audiobook, of the above options, only OpenReader-WebUI supports that. RealtimeTTS is a library that handles that, but I don’t know if there are already any apps out there that integrate it. If you have the audiobook generation handled and just want to be able to follow along with text / switch between text and audio, check out https://storyteller-platform.gitlab.io/storyteller/

Your comment wasn’t in a meta discussion; it was on a post where they were venting about people complaining about them having a women’s only space. There was certainly no indication that the regular community rules didn’t apply, nor any invitation for men to comment.

Commenting that it’s hostile for them to have a women’s only space might be ironic, but couldn’t possibly be good faith, in that context. And if the same mod banned you from multiple communities, then either it was out of line and you could appeal it, or it was warranted due to the perceived likelihood of you causing problems in those other communities and the perceived low likelihood of you contributing anything of value to them.

Even now, you’re acting like the mod(s) banned you because of her / their emotions. You don’t see how that’s misogynistic?

It makes logical sense for bad actors to be preemptively banned. Emotions have nothing to do with it.

Right now I have Ollama / Open-WebUI, Kokoro FastAPI, ComfyUI, Wan2GP, and FramePack Studio set up. I recently (as in yesterday) configured an API key middleware with Traefik and placed it in front of Ollama and Comfy, but currently nothing is using them yet.

I’ll probably try out Devstral with one of the agentic coding frameworks, like Void or Anon Kode. I may also try out one of the FOSS writing studios (like Plot Bunni) and connect my own Ollama instance. I could use NovelCrafter but paying a subscription fee to use my own server for the compute intensive part feels silly to me.

I tried to use Open Notebook (basically a replacement for NotebookLM) with Ollama and Kokoro, with Kokoro FastAPI as my OpenAI endpoint, but turns out it only supported, and required, text embeddings from OpenAI, so I couldn’t do that fully on my local. At some point, if they don’t fix that, I’m planning to either add support myself or set up some routes with Traefik where the ones OpenNotebook uses point to the service I want to use.

ETA: n8n is one of the services I plan to set up next, and I’ll likely end up integrating both Ollama and Comfy workflows into it.

It’s the new hyped up version of “no-code” or low-code solutions, but with AI so you have more flexibility to footgun.

Not any lazier. Script kiddies didn’t write the code themselves, either.

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.

Assuming you’re using ollama (is there another reason to use ollama.com?), you can use compatible files from huggingface directly in ollama. The model page will give you the instructions for the command to run; I always change ollama run to ollama pull , though. Instructions: https://huggingface.co/docs/hub/ollama

You should be able to fit Qwen3 32B at Q4_K_M with an acceptable context, and it did very well on math benchmarks (with thinking enabled). You can disable thinking by including /no_think at the end of your prompt to speed up responses, but I’m not sure how well it handles math under those circumstances. I wouldn’t even consider disabling thinking unless you were grading one question per prompt.

The ollama Qwen3 page is https://ollama.com/library/qwen3:32b and the default 32B quant is Q4_K_M. I personally am using the Q6_K quant by unsloth, and their quants have been great (when supported by ollama), often being the first to fix bugs impacting other quantizations.

I’m not sure if Q4_K_M is the optimal quant style for Intel Arc, but the others that might be better are not supported by ollama, anyway, as far as I know.

Qwen3’s real world knowledge is bad, so if there are questions that rely on that you may need to include the relevant facts as part of the prompt or use an ollama frontend that supports web searches.

Other options: This does seem like something Gemma3 27B would be good at, so it’s too bad you can’t use it. Older Gemmas may be good, but I’m not sure. Llama3.3 70B is also out, unless you have a decent amount of system RAM and are okay with offloading less than half to GPU. I could see it outperforming my recommendation below but I would be very surprised for the 8B version to outperform it. Older Qwen2.5 is decent at math but unless you grab QwQ doesn’t include thinking.

According to https://www.nextdiffusion.ai/blogs/hidream-the-new-top-open-source-image-generator it’s an uncensored image generation model developed by Vivago. In the benchmarks they highlighted - DPG-Bench, GenEval, and HPSv2.1 - it was ranked number 1. It’s said to be very good at following complex prompts.

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.

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.

If they do the form correctly, then it’s just an extra step for you to confirm. One flow I’ve seen that would accomplish this is:

1. You enter your address into a form that can be auto-filled
2. You submit the address
3. If the address validates, the site saves the form and shows you the address in a more readable format. You can click Edit to make changes.
4. If the address doesn’t validate, the site displays a modal asking you to confirm the address. If another address they were able to look up looks similar, it suggests you use that instead. It’s one click to continue editing, to use the suggested address, or to use what you originally entered.

That said, if you’re regularly seeing the wrong address pop up it may be worth submitting a request to get your address added to the database they use. That process will differ depending on your location and the address verification service(s) used by the sites that are causing issues. If you’re in the US, a first step is to confirm that the USPS database has your address listed correctly, as their database is used by some downstream address verification services like “Melissa.” I believe that requires a visit to your local post office, but you may be able to fix it by calling your region’s USPS Address Management System office.