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.

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.

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.

Good point!

If OP is hourly, those 3 hours should be billed as work - probably under a generic HR-related category if one is available.

If OP is salaried exempt, then this would fall under “doing any work at all” (all that’s needed to be paid for the day) and if sick time is tracked by day and not by hour, then OP doesn’t need to use one. If it’s tracked hourly then OP should make sure to only use 5 sick hours (or less, depending on how long the work-related conversations took) and depending on employer policies may not need to use any sick time at all.

This also cut into the time OP could have been using to rest. It would be very reasonable for OP to need an extra day to recover, as a result.

Don’t forget to file an expense report for the co-pay. It’s a business expense that was required by company policy, as communicated by HR, and is therefore the company’s responsibility to cover.

Generally, usage of the term “gentrification” refers to the improvement of neighborhoods - or other places where people live, like apartment complexes - and, due to increased cost of living, the displacement of the people who used to live there. Displacement of less wealthy current residents when gentrification occurs is so common that it’s implied. If it weren’t, people wouldn’t have such low opinions of gentrification.

If a forest has been gentrified, therefore, then - if you interpret “gentrified” in the same way - it follows that people who have been living there have been displaced. And since those people were living in a forest - not in a cabin in a forest - they’re necessarily homeless. Since OP didn’t say that they were building houses or apartments in the forest, that would mean that the wealthier people who displaced them were also homeless.

Since the context was another commenter calling “gentrified forest” a cursed phrase, I don’t think I’m alone in thinking that.

You should look up the definition of “gentrification.” There are a ton of options that don’t suggest that the homeless people in the forest are being forced out and replaced with wealthier homeless people.

Maybe “commercialized” would be a better word choice? Alternatively, “developed” or “sanitized?”

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.

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

It doesn’t read like AI to me, but their takeaways about copyright made me think the author had read an AI summary rather than the actual source material.

It’s okay, the author of the article didn’t actually read (or understand) the Copyright Office’s recommendations. They are:

Based on an analysis of copyright law and policy, informed by the many thoughtful comments in response to our NOI, the Office makes the following conclusions and recommendations:

• Questions of copyrightability and AI can be resolved pursuant to existing law, without the need for legislative change.
• The use of AI tools to assist rather than stand in for human creativity does not affect the availability of copyright protection for the output.
• Copyright protects the original expression in a work created by a human author, even if the work also includes AI-generated material.
• Copyright does not extend to purely AI-generated material, or material where there is insufficient human control over the expressive elements.
• Whether human contributions to AI-generated outputs are sufficient to constitute authorship must be analyzed on a case-by-case basis.
• Based on the functioning of current generally available technology, prompts do not alone provide sufficient control.
• Human authors are entitled to copyright in their works of authorship that are perceptible in AI-generated outputs, as well as the creative selection, coordination, or arrangement of material in the outputs, or creative modifications of the outputs.
• The case has not been made for additional copyright or sui generis protection for AI- generated content.

Pretty much everything the article’s author stated is contradicted by the above.