It’s really more of a proxy setup that I’m looking for. With thunderbird, you can get what I’m describing for a single client. But if I want to have access to those emails from several clients, there needs to be a shared server to access.
docker-mbsync might be a component I could use, but doesn’t sound like there’s a ready-made solution for this today.
Yeah, they are ideally the same mailbox. I’d like a similar experience to Gmail, but with all the emails rehomed to my server.
Steam + Proton works for most games, but there are still rough edges that you need to be prepared to deal with. In my experience, it’s typically older titles and games that use anti-cheat that have the most trouble. Most of the time it just works, I even ran the Battle.net installer as an external Steam game with Proton enabled and was able to play Blizzard titles right away.
The biggest gap IMO is VR. If you have a VR headset that you use on your desktop and it’s important to you, stay on Windows. There is no realistic solution for VR integration in Linux yet. There are ways that you can kinda get something to work with ALVR, but it’s incredibly janky and no dev will support it. There are rumors Steam Link is being ported to Linux, nothing official yet though.
On balance, I’m incredibly happy with Mint since I switched last year. However, I do a decent amount of personal software development, and I’ve used Linux for 2 decades as a professional developer. I wouldn’t say the average Windows gamer would be happy dealing with the rough spots quite yet, but it’s like 95% of the way there these days. Linux has really grown up a lot in the last few years.
Yeah, I don’t fully understand why Nvidia cards have this problem on first setup with so many distros. On Windows, the default display driver can at least boot with reduced resolution on most cards made in the last 15 years until you install proper drivers. It seems like the Linux kernel and common desktop environments ought to be able to do the same.
Maybe this is better in the 6.x kernel, I haven’t tried it. I’m not too much of a tinkerer, so the bleeding edge doesn’t interest me. I just want a good shell, POSIX for personal coding projects, and the ability to play games on Steam. Mint is great for that once you get past the initial display driver issues.
I’ve been using Mint for about 6 months now and it works with Nvidia just fine BUT the new user experience isn’t great. You have to use the nomodeset kernel option and install Nvidia drivers, otherwise you’ll boot to a black screen.
Helpful guide: https://forums.linuxmint.com/viewtopic.php?t=421550
I do quite like the stability of Cinnamon/Debian, and I think this problem is solvable (even if I have to solve it myself). I generally do not want to spend a lot of time futzing around with my desktop environment, but this is one thing I need to have.
I saw that and tried it pretty early on. That just moves the screen, it doesn’t fill the quadrant.
Updated to be specific, I’m using Cinnamon. Muffin is the builtin tiling window manager for Cinnamon and it does exactly what you’re describing. The problem is that it moves tiles, it doesn’t absolutely position them. You have to keep moving tiles around to get them where you want them, Rectangle just has hotkeys to immediately place and resize to fit the active window for each quadrant that it supports:
ctrl+cmd+left: top left quadrantctrl+cmd+right: top left quadrantshift+ctrl+cmd+left: bottom left quadrantshift+ctrl+cmd+right: bottom left quadrantalt+cmd+left: left halfalt+cmd+right: right halfalt+cmd+up: top halfalt+cmd+left: bottom halfalt+cmd+f: full screenIt’s hard to express how natural that feels after using it for a bit, and I’m still using a Macbook for work so the muscle memory is not going away.
I didn’t say it wasn’t amazing nor that it couldn’t be a component in a larger solution but I don’t think LLMs work like our brains and I think the current trend of more tokens/parameters/training LLMs is a dead-end. They’re simulating the language area of human brains, sure, but there’s no reasoning or understanding in an LLM.
In most cases, the responses from well-trained models are great, but you can pretty easily see the cracks when you spend extended time with them on a topic. You’ll start to get oddly inconsistent answers the longer the conversation goes and the more branches you take. The best fit line (it’s a crude metaphor, but I don’t think it’s wrong) starts fitting less and less well until the conversation completely falls apart. That’s generally called “hallucination” but I’m not a fan of that because it implies a lot about the model that isn’t really true. Y
You may have already read this, but if you haven’t: Steven Wolfram wrote a great overview of how GPT works that isn’t too technical. There’s also a great sci-fi novel from 2006 called Blindsight that explores the way facsimiles of intelligence can be had without consciousness or even understanding and I’ve found it to be a really interesting way to think about LLMs.
It’s possible to build a really good Chinese room that can pass the Turing test, and I think LLMs are exactly that. More tokens/parameters/training aren’t going to change that, they’ll just make them better Chinese rooms.
Maybe this comment will age poorly, but I think AGI is a long way off. LLMs are a dead-end, IMO. They are easy to improve with the tech we have today and they can be very useful, so there’s a ton of hype around them. They’re also easy to build tools around, so everyone in tech is trying to get their piece of AI now.
However, LLMs are chat interfaces to searching a large dataset, and that’s about it. Even the image generators are doing this, the dataset just happens to be visual. All of the results you get from a prompt are just queries into that data, even when you get a result that makes it seem intelligent. The model is finding a best-fit response based on billions of parameters, like a hyperdimensional regression analysis. In other words, it’s pattern-matching.
A lot of people will say that’s intelligence, but it’s different; the LLM isn’t capable of understanding anything new, it can only generate a response from something in its training set. More parameters, better training, and larger context windows just refine the search results, they don’t make the LLM smarter.
AGI needs something new, we aren’t going to get there with any of the approaches used today. RemindMe! 5 years to see if this aged like wine or milk.
Part of the reason these rules are similar is because AI-generated images look very dreamlike. The objects in the image are synthesized from a large corpus of real images. The synthesis is usually imperfect, but close enough that human brains can recognize it as the type of object that was intended from the prompt.
Mythical creatures are imaginary, and the descriptions obviously come from human brains rather than real life. If anyone “saw” a mythical creature, it would have been the brain’s best approximation of a shape the person was expecting to see. But, just like a dream, it wouldn’t be quite right. The brain would be filling in the gaps rather than correctly interpreting something in real life.
figma balls