Current generation iPad Pros and Airs have the same processing power as Apple Silicon Macs. That’s more than enough for Blender. Even the base iPad and the iPad Mini likely have enough processing power - though I don’t think the base iPad has enough RAM.

Does mirroring a screen (or adding a screen) from a computer or connecting to a computer via remote desktop count?

if everyone thought like you no one would create digital media

This is obviously incorrect.

I thought Hue bulbs used Zigbee?

The up arrow moves through the letters, e.g., A->B->C. The down arrow moves to the next character in the sequence, e.g., C->CA->CAA. If you click past the correct letter, you’ll have to click all the way through again. And if you submit the wrong letter, you have to start all over (after it takes twenty seconds attempting to connect with the wrong password and then alerts you that it didn’t work, of course).

Fair point, I should have asked about commercial games in general

That said I didn’t mean that the game studio itself would do the AI training and own their models in-house; if they did, I’d expect it to go just as poorly as you would. Rather, I’d expect the model to be created by an organization specialized in that sort of thing.

For example, “Marey” is one example I found of a GenAI model that its creators are saying was trained ethically.

Another is Adobe Firefly, where Adobe says they trained only on licensed and public domain content. It also sounds like Adobe is paying the artists whose content was used for AI training. I believe that Canva is doing something similar.

StabilityAI is also doing something similar with Stable Audio 2.0, where they partnered with a music licensing company, AudioSparx, to ensure that artists are compensated, AI opt outs are respected, etc…

I haven’t dug into any of those too deep, but they seem to be heading in the right direction at the surface level, at least.

One of the GenAI scenarios that’s the most terrifying to me is the idea of a company like Disney using all the material they have copyright for to train their own, proprietary GenAI image, audio, and video tools… not because I think the outputs would be bad, but because of the impact that would have on creators in that industry.

Fortunately, as long as copyright doesn’t apply to purely AI generated outputs, even if trained entirely on your own content, then I don’t think Disney specifically will do this.

I mention that as an example because that usage of AI, regardless of how ethically the model was trained, would still be unethical, in my opinion. Likewise in game creation, an ethically trained and operated model could still be used unethically to eliminate many people’s jobs in the interest solely of better profits.

I’d be on board with AI use (in game creation or otherwise) if a company were to say, “We’re not changing the budget we have for our human workforce, including for contractors, licensed art, and so on, other than increasing it as inflation and wages increase. We will be using ethical AI models to create more content than we otherwise would have been able to.” But I feel like in a corporate setting, its use is almost always going to result in them cutting jobs.

Are you okay with AAA studios using GenAI that was trained only on licensed works?

Copyright applies to unfinished works, too. There are many reasons it might not protect an unfinished work, but those reasons are still relevant even for finished works.

If someone steals your physical drawing, that’s theft. If they take a picture of it, then use the picture - or your picture + modifications - without your permission, particularly in a commercial work, then that’s copyright infringement, but not theft. If they steal your physical drawing and then take a picture and so on, then it’s both theft and copyright infringement.

Most likely this wasn’t considered copyright infringement because the allegedly copied art isn’t copyrightable, e.g., game mechanics; or the plaintiff didn’t own the copyrights themselves and thus couldn’t sue (possibly the arts were still copyrighted by the original artists, having never been purchased; possibly they were stock assets that were re-purchased by the defendant). There are any number of reasons. However, “the work wasn’t published” isn’t one of them.

On the other hand, it’s quite likely they were able to sue for theft of trade secrets for that very reason. And they might have chosen to do that simply because proving copyright infringement is much more difficult.

This happened because the developers allegedly used assets from a game called P3, which was never released, and therefore not subject to copyright infringement claims.

That isn’t how copyright works. Copyright is awarded upon creation of a work, not upon release.

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.

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/

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.

You don’t have to finish the file to share it though, that’s a major part of bittorrent. Each peer shares parts of the files that they’ve partially downloaded already. So Meta didn’t need to finish and share the whole file to have technically shared some parts of copyrighted works. Unless they just had uploading completely disabled,

The argument was not that it didn’t matter if a user didn’t download the entirety of a work from Meta, but that it didn’t matter whether a user downloaded anything from Meta, regardless of whether Meta was a peer or seed at the time.

Theoretically, Meta could have disabled uploading but not blocked their client from signaling that they could upload. This would, according to that argument, still counts as reproducing the works, under the logic that signaling that it was available is the same as “making it available.”

but they still “reproduced” those works by vectorizing them into an LLM. If Gemini can reproduce a copyrighted work “from memory” then that still counts.

That’s irrelevant to the plaintiff’s argument. And beyond that, it would need to be proven on its own merits. This argument about torrenting wouldn’t be relevant if LLAMA were obviously a derivative creation that wasn’t subject to fair use protections.

It’s also irrelevant if Gemini can reproduce a work, as Meta did not create Gemini.

Does any Llama model reproduce the entirety of The Bedwetter by Sarah Silverman if you provide the first paragraph? Does it even get the first chapter? I highly doubt it.

By the same logic, almost any computer on the internet is guilty of copyright infringement. Proxy servers, VPNs, basically any compute that routed those packets temporarily had (or still has for caches, logs, etc) copies of that protected data.

There have been lawsuits against both ISPs and VPNs in recent years for being complicit in copyright infringement, but that’s a bit different. Generally speaking, there are laws, like the DMCA, that specifically limit the liability of network providers and network services, so long as they respect things like takedown notices.

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.

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

Further, “Whether another user actually downloaded the content that Meta made available” through torrenting “is irrelevant,” the authors alleged. “Meta ‘reproduced’ the works as soon as it made them available to other peers.”

Is there existing case law for what making something “available” means? If I say “Alright, I’ll send you this book if you want, just ask,” have I made it available? What if, when someone asks, I don’t actually send them anything?

I’m thinking outside of contexts of piracy and torrenting, to be clear - like if a software license requires you to make any changed versions available to anyone who uses the software. Can you say it’s available if your distribution platform is configured to prevent downloads?

If not, then why would it be any different when torrenting?

Meta ‘reproduced’ the works as soon as it made them available to other peers.

The argument that a copyrighted work has been reproduced when “made available,” when “made available” has such a low bar is also perplexing. If I post an ad on Craigslist for the sale of the Mona Lisa, have I reproduced it?

What if it was for a car?

I’m selling a brand new 2026 Alfa Romeo 4E, DM me your offers. I’ve now “reproduced” a car - come at me, MPAA.