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.
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:
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.
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.
On the other hand it is a conduit for censorship. If an admin doesn’t like what you post on another instance, then they can censor you everywhere.
Such a user can
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.
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?”
They didn’t say it was a Linux problem; they said it was a mobile problem
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:
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:
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.