I’ve heard of Intel Arc users for instance not able to play certain games because it checks for AMD/nVidia, so you’d have to fake the GPU vendor to get it to work.
Eg see stuff like this: https://www.phoronix.com/news/Intel-Graphics-Hogwarts-Legacy
Or https://www.phoronix.com/news/The-Finals-Intel-Arc-Graphics
Before y’all get excited, the press release doesn’t actually mention the term “open source” anywhere.
Winamp will open up its code for the player used on Windows, enabling the entire community to participate in its development. This is an invitation to global collaboration, where developers worldwide can contribute their expertise, ideas, and passion to help this iconic software evolve.
This, to me, reads like it’s going to be a “source available” model, perhaps released under some sort of a Contributor License Agreement (CLA). So, best to hold off any celebrations until we see the actual license.
You can already run Linux apps using Termux and Termux-X11, and I’d say the performance would be better than this demo, because this is running in a virtual machine and uses it’s own kernel, whereas with Termux you’re running your apps directly on top of the Android Linux kernel. Also, you don’t have the overhead of running ChromeOS on top of Android.
This is a general tech community, mostly centered around news and end-user technology discussions, so it’s very unlikely you’ll get an answer here. Might want to try asking on Reddit or some dedicated Datto/Connectwise forum.
Nice, great to see the continued development of an old-school, lightweight browser. We need more active alternatives to the bloated duopoly.
Since you’re on Linux, it’s just a matter of installing the right packages from your distros package manager. Lots of articles on the Web, just google your app + “ROCm”. Main thing you gotta keep in mind is the version dependencies, since ROCm 6.0/6.1 was released recently, some programs may not yet have been updated for it. So if your distro packages the most recent version, your app might not yet support it.
This is why many ML apps also come as a Docker image with specific versions of libraries bundled with them - so that could be an easier option for you, instead of manually hunting around for various package dependencies.
Also, chances are that your app may not even know/care about ROCm, if it just uses a library like PyTorch / TensorFlow etc. So just check it’s requirements first.
As for AMD vs nVidia in general, there are a few places mainly where they lagged behind: RTX, compute and super sampling.
For RTX, there has been improvements in performance with the RDNA3 cards, but it does lag behind by a generation. For instance, the latest 7900 XTX’s RTX performance is equivalent to the 3080.
Compute is catching up as I mentioned earlier, and in some cases the performance may even match nVidia. This is very application/library specific though, so you’ll need to look it up.
Super Sampling is a bit of a weird one. AMD has FSR and it does a good job in general. In some cases, it may even perform better since it uses much simpler calculations, as opposed to nVidia’s deep learning technique. And AMD’s FSR method can be used with any card in fact, as long as the game supports it. And therein lies the catch, only something like 1/3rd of the games out there support it, and even fewer games support the latest FSR 3. But there are mods out there which can enable FSR (check Nexus Mods) that you might be able to use. In any case, FSR/DLSS isn’t a critical thing, unless you’re gaming on a 4K+ monitor.
You can check out Tom’s Hardware GPU Hierarchy for the exact numbers - scroll down halfway to read about the RTX and FSR situation.
So yes, AMD does lag behind in nVidia but whether this impacts you really depends on your needs and use cases. If you’re a Linux user though, getting an AMD is a no-brainer - it just works so much better, as in, no need to deal with proprietary driver headaches, no update woes, excellent Wayland support etc.
I based my statements on the actual commits being made to the repo, from what I can see it’s certainly not “floundering”:
In any case, ZLUDA is really just a stop-gap arrangement so I don’t see it being an issue either way - with more and more projects supporting AMD cards, it won’t be needed at all in the near future.
It’s not “optimistic”, it’s actually happening. Don’t forget that GPU compute is a pretty vast field, and not every field/application has a hard-coded dependency on CUDA/nVidia.
For instance, both TensorFlow and PyTorch work fine with ROCm 6.0+ now, and this enables a lot of ML tasks such as running LLMs like Llama2. Stable Diffusion also works fine - I’ve tested 2.1 a while back and performance has been great on my Arch + 7800 XT setup. There’s plenty more such examples where AMD is already a viable option. And don’t forget ZLUDA too, which is being continuing to be improved.
I mean, look at this benchmark from Feb, that’s not bad at all:
And ZLUDA has had many improvements since then, so this will only get better.
Of course, whether all this makes an actual dent in nVidia compute market share is a completely different story (thanks to enterprise $$$ + existing hw that’s already out there), but the point is, at least for many people/projects - ROCm is already a viable alternative to CUDA for many scenarios. And this will only improve with time. Just within the last 6 months for instance there have been VAST improvements in both ROCm (like the 6.0 release) and compatibility with major projects (like PyTorch). 6.1 was released only a few weeks ago with improved SD performance, a new video decode component (rocDecode), much faster matrix calculations with the new EigenSolver etc. It’s a very exiting space to be in to be honest.
So you’d have to be blind to not notice these rapid changes that’s really happening. And yes, right now it’s still very, very early days for AMD and they’ve got a lot of catching up to do, and there’s a lot of scope for improvement too. But it’s happening for sure, AMD + the community isn’t sitting idle.
The best option is to just support the developer/project by the method they prefer the most (ko-fi/patreon/crypto/beer/t-shirts etc).
If the project doesn’t accept any donations but accepts code contributions instead (or you want to develop something that doesn’t exist), you can directly hire a freelancer to work on what you want, from sites like freelancer.com.
I personally use a ThinkPad Z13 (all AMD; it’s nice but pricey), but I’d recommend getting a Framework (which wasn’t an option for me back then). I think modular and repairable laptops are cool, plus they seem to be well supported by the Linux community.
Samsung midrangers like the A15 / M15 / A25 etc have both the jack as well as an sdcard slot. There’s also the Motorola G34 and the G84. And finally, there’s also a flagship - the ASUS ROG Phone 8 Pro. And a whole bunch of Chinese phones which I won’t bother mentioning.
Considering that predicting the next word from context is the one thing LLMs are really good at, I just don’t understand how none of these developments have found their way into predictive keyboards.
The problem is that LLMs require a considerable amount of computing power to run, unlike the simple markov chain predictions that keyboards use. You could use a cloud-based service like ChatGPT or something, but most people wouldn’t want their keyboards to send all their keystrokes to a remote server… and even if they didn’t know or care, the response time wouldn’t be good enough for real-time predictions.
Now smartphone SoC makers like Qualcomm have started adding NPUs (neural processing units) with their latest chips (such as the SD8 Gen 3, featured in the most recent flagship phones), but it’s going to take a while before devices with NPUs become commonplace, and it’ll take a while for developers to start making/updating apps that can make use of it.
But yeah the good news is that it is coming, it’s only a matter of “when” - I suspect it won’t be long before the likes of SwiftKey start to take advantage of this.
That 240W is only for SuperVOOC. PD however goes only upto 65W. Plus, being that big and single-port only makes this thing completely pointless for everyone except VOOC users.
Kvaesitso. Opensource, minimal, search-based launcher. My only complaint is that it’s not optimised for foldables, otherwise it’s a great launcher.
Thanks! I did that, but it doesn’t get seem to get rid of them from “trending”.
Anyone here know how to disable shorts? I could’ve sworn there was an option to disable it previously, but I can’t find it any more.
if any one of my Windows or Android units got stolen and somehow cracked into or something.
This shouldn’t be a concern if you’re using disk encryption and secure passwords, which is generally the default behaviour on most systems these days.
On Android, you don’t need to worry about anything as long as you’ve got a pin/password configured, as disk encryption has been enabled by default for like a decade now.
On Windows, if you’re on the Pro/Enterprise edition, you can use Bitlocker, but if you’re on Home, you can use “device encryption” (which is like a lightweight Bitlocker) - but that requires a TPM chip and your Windows user account linked to a Microsoft account. If that is not an option, you could use VeraCrypt instead, which is an opensource disk encryption tool. Another option, if you’re on a laptop, could be Opal encryption (aka TCG Opal SED), assuming your drive/BIOS supports it.
TL;DR: Encrypt yo’ shit, and you don’t need to worry about your data if your device gets stolen.
Actually this is probably a good time for you to get into Rust, since everyone is still at the starting point (ish), and you, unlike others, don’t have decades of C/C++ habits ingrained in you. So if you’re starting off fresh, Rust is not a bad language to dip your toes into. :)
It’s easiest to just register a domain name and use Couldflare Tunnels. No need to worry about dynamic DNS, port forwarding etc. Plus, you have the security advantages of DDoS protection and firewall (WAF). Finally, you get portability - you can change your ISP, router or even move your entire lab into the cloud if you wanted to, and you won’t need to change a single thing.
I have a lab set up on my mini PC that I often take to work with me, and it works the same regardless of whether it’s going thru my work’s restricted proxy or the NAT at home. Zero config required on the network side.
In the footnotes they mention GPT-3.5. Their argument for not testing 4 was because it was paid, and so most users would be using 3.5 - which is already factually incorrect now because the new GPT-4o (which they don’t even mention) is now free. Finally, they didn’t mention GPT-4 Turbo either, which is even better at coding compared to 4.