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You’ve got to do some manual config. I know about it but don’t use it. You can redirect home folders with the container in the distrobox create flags. I think the better option is to use the user/groups/SELinux context in addition to the container as this will show up in ownership and is more easy to trace. One of my main problems is how packages have Python installation requirements that by default try to break pip out of any containerized context and create their own venv setup. It totally screws up the whole distrobox container setup and separation from the base system.
gource
on the command line. Gource creates a 3d visualization of the project over time and its commit history. Have a look at the Linux kernel some time or just watch a video of someone that has uploaded the visualization: https://www.youtube.com/watch?v=5iFnzr73XXk With the actual visualization, you can zoom in and select the individuals or watch branches specifically. The trick is to get an idea of who the main contributors are in the various spaces and how consistent they are. Find who is working on what hardware and how they are working on it. Some times you’ll see a person comes in and only makes a single commit or a few that contain everything for a device and then they disappear. These are often subcontracted devs that a company hires and gives a checklist. Issues, bugs, and unsupported features are unlikely to get fixed unless you see someone else that is making commits in this space. What you’re really looking for is one of the main project devs that makes ongoing commits to some specific hardware over longer amounts of time and fairly recently. It means they have the device in question. That generally means the device has or will have excellent support in the long term. It also generally means the person either really liked the product or the company is smart enough to supply the dev with the device or supporting documentation.
Sorry if this seems unsolicited. It took me a long time to break out of the hardware spec shopping fallacy and all of the troubles it can cause. Prioritizing true ownership and shopping for the software first is a far more enjoyable life experience. It likely won’t help in this niche, but for computers in general use: https://linux-hardware.org/
You will likely find that search engines attempt to obfuscate this information. Expect that. Use offline open source LLM’s, ask the community, or more advance searching methods to find relevant info. Both m$ and the goo are the two biggest beneficiaries of the proprietary software ecosystem and they are the only two web crawlers that exist at relevant scale. All search engines use one or both of these sources either directly or by proxy.
TBH: tl;dr (…but read ~1/4 and skimmed the rest.)
Emacs can likely do most, if not all, of what you’re looking for.
As far as distros, go with either Fedora Workstation or Silverblue. If you can run SB, try to avoid messing with the base system as much as possible, skip using the toolbox containers system and just use distrobox. With distrobox, you have almost all Linux distros available as containers, so you build on them. The only exception I know of is NIX. You can’t run NIX in distrobox. You probably could run the NIX package manager, but that involves this weird setup where a under owned directory exists in /
root. Personally, this is just too weird for me to use it. I expect all user activity and configuration files to be confined to /home/$USER/
Fedora just works, but try and lag behind the release cycle a little bit. Like right now F40 is pretty solid, but there were some issues in the first month or so after F40 first came out. I have lagged in every release since ~F28 and never had issues. I switched to F40 within the first week or so and a few packages were wonky. Basically Python was super fresh and did some odd stuff with containers where it did not work without manually removing and replacing Python in each container. I think that was the only manual intervention issue I’ve had with Fedora. I have a 3080Ti laptop with the 16 GB GPU. The Anaconda system in Fedora builds the Nvidia kernel module automatically in the background each time the kernel is updated. It works flawlessly, even with secure boot enabled.
The 4004 is that tiny dip-16 chip the the top left, second box marked CPU. A working 4004 is worth around $300+ https://www.ebay.com/sch/i.html?_nkw=intel+4004
They are quite the collectors item as the first microprocessor
You using this in a toolchain? I haven’t tried any of the Qwen models yet, or Yi for that matter. I tried at one point early on, but they were not working well with my stuff and I had no complaints with Mistral stuff. I like some underlying things with a MoE for speed and underlying entity/realm stuff I can access in my favorite.
I’m curious if anyone has constructive contextual feedback about what makes these unique or worth exploring.
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Yes .docx.
It appears as though the encoding is missing in such a way that nothing in Linux recognizes the file. The underlying CLI tools don’t have a way of converting the file. I tried with Python’s docx tool and with iconv. It has to be encoding related because some tools initially load the file with several sets of Asian characters instead of English. However, there is no hexadecimal or sections of entirely binary looking data. Archiving tools do not open up the the file to reveal anything else like a metafile or header. Neo vim shows garbled nonsense throughout. Bat warns of binary. Python won’t load the file, nor will Only Office. Libre Office and Abi Word load initially with Asian characters before crashing.
The only option is likely gong to be setting up the W10 machine and converting a bunch of files within it.
Ultimately, my old man thinks he can be an author all of the sudden and is trying to write. He’s not very capable of learning. I’m not confident that he can learn to use FOSS to do the same thing he has been doing. This post was just to see if there are options I am not already aware of that might actually work in practice. I can easily do everything I need in FOSS. I can do everything he needs to do. I’m more concerned about becoming his tech support when he forgets how to copy pasta. He already fails to separate the internet hardware connectivity from the web browser and operating system within his mental model of technology.
All of the office suites seem to use either Python 3 docx or iconvert under the surface. These tools do not support whatever default encoding m$ is using. It is clearly a font encoding issue, but I won’t know what that font is until my back is in good enough shape to setup a desktop at my bedside workstation.
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Too many documents and Office 365 is a $10 month sub scam.
Distilbert https://huggingface.co/distilbert/distilroberta-base
…was setup for something like that here but note that the repo that runs this has an “unsafe” warning that I have not looked into: https://huggingface.co/spaces/nasrin2023ripa/multilabel-book-genre-classifier
https://huggingface.co/spaces/nasrin2023ripa/multilabel-book-genre-classifier/tree/main
It might be fine or whatnot, I’m on mobile and can’t see the file in question. The associated Python code might be a helpful starting point.
In my experience, most models intentionally obfuscate copyright sources. They all know the materials to various degrees, but they are not intended to replicate sources. They all have strong interference in place to obscure both their recognition and reproduction potential. If, for instance, you can identify where errors are inserted and make a few corrections, they often continue adding a few details that are from the original source. If this is done a few times in a row, they tend to gain more freedom before reverting to obfuscation again. This is the behavior I look out for. It is a strong tool too if you get creative in application.
Perhaps someone posts an API to look up the library of congress classification of a work based on a few lines or something. GL
There are likely automatic checks in the startup script. I don’t use A1111 any more in favor of Comfy and I only have a 3080Ti with 16 GB (mobile version). I can run within issues. The only time I have issues with anything AI related is when I need Nvidia’s proprietary compiler nvcc. I need nvcc to hack around with things like llama.cpp. With nvcc, it can have issues with the open source driver
So software like CAD is funny. Under the surface, 3d CAD like FreeCAD or Blender is taking vertices and placing them in a Cartesian space (X/Y/Z - planes). Then it is building objects in that space by calculating the mathematical relationships in serial. So each feature you add involves adding math problems to a tree. Each feature on the tree is linearly built and relies on the previously calculated math.
Editing any changes up tree is a massive issue called the topological naming problem. All CAD has this issue and all fixes are hacks and patches that are incomplete solutions, (it has to do with π and rounding floating point at every stage of the math).
Now, this is only the beginning. Assemblies are made of parts that each have their own Cartesian coordinate planes. Often, individual parts have features that are referencing other parts in a live relationship where a change in part A also changes part B.
Now imagine modeling a whole car, a game world, a movie set, or a skyscraper. The assemblies get quite large depending on what you’re working on. Just an entire 3d printer modeled in FreeCAD was more than my last computer could handle.
Most advanced CAD needs to get to the level of hardware integration where generalizations made for something like Wayland simply are not sufficient. Like your default CPU scheduler, (CFS on Linux) is setup to maximize throughput at all costs. For CAD, this is not optimal. The process niceness may be enough in most cases, but there may be times when true CPU set isolation is needed to prevent anything interrupting the math as it renders. How this is split and managed with a GPU may be important too.
I barely know enough to say this much. When I was pushing my last computer too far with FreeCAD, optimising the CPU scheduler stopped a crashing problem and extended my use slightly, but was not worth much. I really needed a better computer. However looking into the issue deeply was interesting. It revealed how CAD is a solid outlier workflow that is extremely demanding and very different from the rest of the computer where user experience is the focus.
My 16 GB 3080Ti is only annoying with Flux gens right now. Those take like 1.5-2 minutes each and need a lot of iterations. My laptop heat saturates from Flux. It could get better if the tools support splitting the workflow between GPU and CPU like with Textgen, but at the moment it is GPU only and kinda sucks. Stuff like Pony runs fast enough at around 20-30 seconds for most models.