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.
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.
To be clear, I’m measuring the relative humidity of the air in the drybox at room temp (72 degrees Fahrenheit / 22 degrees Celsius), not of the filament directly. You can use a hygrometer to do this. I mostly use the hygrometer that comes bundled with my dryboxes (I use the PolyDryer and have several extra PolyDryer Boxes, but there are much cheaper options available) but you can buy a hygrometer for a few bucks or get a bluetooth / wifi / connected one for $15-$20 or so.
If you put filament into a sealed box, it’ll generally - depending on the material - end up in equilibrium with the air. So the measurement you get right away will just show the humidity of the room, but if the filament and desiccant are both dry, it’ll drop; if the desiccant is dry and the filament is wet, it’ll still drop, but not as low.
Note also that what counts as “wet” varies by material. For example, from what I’ve read, PLA can absorb up to 1% or so of its mass as moisture, PETG up to 0.2%, Nylon up to 7-8%… silica gel desiccant beads up to 40%. So when I say they’ll be in equilibrium, I’m referring to the percentage of what that material is capable of absorbing. It isn’t a linear relationship as far as I know, but if it were, that would mean that: if the humidity of the air is 10% and the max moisture the material could retain is 1%, then the material is currently retaining 0.1% moisture by mass. If my room’s humidity is kept at 40%, it’ll absorb moisture until it’s at 0.4% moisture by mass.
That said, this doesn’t measure it perfectly, since while most filament materials absorb moisture from the air when the humidity is higher, they don’t release it as easily. Heating it both allows the air to hold more moisture and allows the filament (and desiccant) to release more moisture.
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.
What have you done to clean the bed? From the link to the textured sheet, you should be cleaning it between every print - after it cools - with 90% IPA, and if you still have adhesion issues, you should clean it with warm water and a couple drops of dish soap.
Has the TPU been dried? I don’t normally print with TPU but my understanding is that it needs to be lower humidity than PLA; I use dryboxes for PLA and target a humidity of 15% or lower and don’t use them if they raise above 20%. The recommendation I saw for TPU was to dry it for 7 hours at 70 degrees Celsius, to target 10% humidity (or at least under 20%) and to print directly from a drybox. Note that compared to other filaments, TPU can’t recover as well from having absorbed moisture - if the filament has gotten too wet, it’ll become too brittle if you dry it out as much as is needed. At that point you would need to start with a fresh roll, which would ideally go into a dryer and then drybox immediately.
You should be able to set different settings for the initial layer to avoid stringing, i.e., slower speeds and longer retraction distance. It’s a bit more complicated but you can also configure the speed for a specific range of layers to be slower - i.e., setting it to slow down again once you get to the top of the print. For an example of that, see https://forum.prusa3d.com/forum/prusaslicer/bed-flinger-slower-y-movement-as-function-of-z/
What’s the max speed you’re printing at? My understanding is that everything other than travel should all be the same speed at a given layer, and no higher than 25 mm/s. And with a bed slinger I wouldn’t recommend a much higher travel, either.
In addition to a brim, have you tried adding supports?
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:
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.
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.
I downgraded Firefox once last year, but after the next major version a couple weeks later, I was able to upgrade again. Never had to downgrade it before that, though.
I forget why, though. I think it was a pretty niche issue.
Ah, I assumed there were some areas where Firefox had been found lacking relative to Chromium browsers.
For me, the current version of any major browser or fork with consistently applied security updates and capability of running the full version of Ublock Origin is sufficiently secure for my threat model. Given that, and that they all offer the feature set I want, wouldn’t it be reasonable to avoid Chromium browsers because I don’t want to encourage the Chromium monopoly?
That’s only a small fraction of why I use Firefox, to be fair, but suppose for argument’s sake that I don’t care about MV3 extensions, Firefox Containers, etc… Would be it be so wrong for not wanting there to be a Chromium monopoly to be why I chose Firefox or one of its forks?
Use the better, more secure technology
More secure according to what?
Under notes, where you said my name, did you mean “Hedgedoc?”
Sure, but the license is limited to uses that “help you navigate, experience, and interact with online content as you indicate with your use of Firefox.”
Not sure how ads would help with that.
AI? Sure, if an AI solution did those things. But it wouldn’t be them training on your data. This would be them using your data in AI-powered services, whether that be search (especially relevant if Google is mandated to stop paying them to default to Google); automatic categorization of your web browsing to make Containers more streamlined and effective; or even just having a completely opt-in AI assistant chatbot that can access data entered elsewhere in Firefox once you activate it.
Worst case I suspect whatever they add will be things you can simply turn off in settings. Ideally it would be opt-in, of course, or at least prompted-opt-out and disabled until first use.
And there are plenty of things that aren’t ad or AI-related that this could apply to. Heck, this could be part of a step to consolidate licenses for other products - VPN, Pocket, email anonymizers, etc. - and to enable deeper integration of those into Firefox.
If you have a lot of books to download, check out https://github.com/treetrum/amazon-kindle-bulk-downloader
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.