Enter the culdesac. Welcome to the neighborhood. Feel free to script your house wherever you wish to compile it.

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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.

I have an HP laser printer from like 1992, before they turned to US=Privateers; rest-of-the-world=criminal pirates. HP died as a company when they spun off Agilent/Keysight as test equipment and continued the branding for contract manufactured consumer garbage. HP does not make anything. They market, place stickers on what others manufacture, and create ponzi scheme-like extortion scams, as the shriveled shell of a dying husk disconnected completely from their now long irrelevant past.

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 user 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

Primarily from predatory boys and men towards girls and young women in the real world by portraying them in imagery of themselves or with others. The most powerful filtering is in place to make this more difficult.

Whether intentional or not, most NSFW LoRA training seems to be trying to override the built in filtering in very specific areas. These are still useful for more direct momentum into something specific. However, once the filters are removed, it is far more capable of creating whatever you ask for as is, from celebrities, to anything lewd. I did a bit of testing earlier with some LoRAs and no prompt at all. It was interesting that it could take a celebrity and convert their gender in recognizable ways that were surprising. I got a few on random seeds, but I haven’t been able to make that one happen with a prompt or deterministically.

Edit: I’m probably assuming too much about other people’s knowledge on these systems. I assume this is the down voting motivation. Talking about this aspect, the NSFW junk is shorthand for the issues with AI generation. These are the primary form of filtering and it has large cascading implications elsewhere. By stating what is possible in this area, I’m implying a worst case scenario-like example. If the results in this area are a certain way, it says volumes about other areas and how the model will react.

These filter layers are stupid simplistic in comparison to the actual model. They have tensors on the order of a few thousand parameters per layer compared to tens of millions of parameters per layer for the actual model. They shove tons of stuff into guttered like responses for no reason. Some times these average out and you still get a good output, but other times they do not.

Another key point here is that diffusion has a lot in common with text generation when it comes to this part of the model loader code. There is more complexity in what text generation is doing overall, but diffusion is an effective way to learn a lot about how text gen works, especially with training. This is my primary reason for playing with diffusion – to learn about training. I’ve tried training for text gen, but it is very difficult to assess what is happening under the surface, like when it is learning overall style, character traits and personas, pacing, creativity, timeline, history, scope, constraints, etc. etc. I don’t care to generate and share much in the way of imagery I generate unless I’m trying to do something specific that is interesting. Like I tried to gen the interior of an O’Neill cylinder space habitat that illustrated the limitations of diffusion in a fundamental way because it showed the lack of any reasoning or understanding of object context or relationships required to display a scene scape with curved centrifugal artificial spin gravity.

Anyways, my interests are not in generating NSFW or celebrities or whatnot. I do not think people should do these things. My primary interest is returning to creative writing with an AI collaborative writing partner that is not biased politically in a way that cripples it from participating in an entirely different and unrelated cultural and political landscape. I have no aspirations of finding success in my writing. I simply enjoy exploring my own science fiction universe and imagining a reality many thousands of years from now. One of the changes to hard coded model filters earlier this year made filtering more persistent, likely for NSFW stuff. I get it, and support it, but it took away one of the few things I have really enjoyed over the last 10 years of social isolation and disability, so I’ve tried to get that back. Sorry if that offends someone, but I don’t understand why it would. This was not my intended reason for this post, so I did not explain it in depth. The negativity here is disturbing to me. This place is my only real way to interact with other humans.

You’ve never done ecommerce logistics. You do not get Amazon’s efficiency from such a garbage system, or the worlds richest man. I have no doubt there are dubious practices to give plausible deniability. The thing is too large and too successful for this to be the big picture. The collectivized warehousing invalidates the front end system and mechanics entirely.

The political and adult doesn’t bother me. The kinds of things I might not have the ethics to think through at a much younger age, that bothers me, and I have never been a very deviant type. I think the protections against age are primarily for this situation. Training a LoRA takes 5 minutes now. An advanced IP adaptors and control net is just a few examples away and a day top for the slightly above average teen figure out. Normalizing this would have some very serious edge case consequences. It is best to leave that barrier to entry filter in place IMO. I assume it is still there because everyone that knows about it feels much the same. It does not show up in a search engine, although that is saying less than nothing these days.

I vote they rename it to IA for Asimov. Sure he was only the robot term among others, but come on… McCarthy was “AI.”

Somebody needs to create US 'botics and name a model something like PTronic.

Edit: Really, you down vote a casual conversational comment?! Really?!

Diffusion is the issue not text gen

Yeah. This is what I mean. I just figured out the settings that have been hard coded. There are keywords that were spammed into the many comments within the code, I assume this was done to obfuscate the few variables that need to be changed. There are also instances of compound variable names that, if changed in a similar way, will break everything, and a few places where the same variables have a local context that will likewise break the code.

I’m certainly not smart enough to get much deeper than this. The ethical issue is due to diffusion.

I’ve been off-and-on trying to track down why an LLM went from an excellent creative writing partner to terrible but had trouble finding an entry point. I just happened to stumble upon such an entry point in a verbose log entry while sorting out a new Comfy model and that proved to be the key I needed to get into the weeds.

The question here, is more about the ethics of putting such filtering in place and obfuscating how to disable it in the first place. When this filtering is removed, the results are night and day, but with large potential consequences.

Amazon’s pricing I not deterministic. You were likely tracked and information collected to know this was a key item for you. Amazon will market loss leaders to you in an attempt to get you to default to buying on Amazon.

As a former Buyer for a chain of retail stores, the loss leader is effective marketing. I sell you a popular item at or below my typical cost because statistically, a large percentage of customers are making a special trip to my store to buy that product and will make additional purchases at margin. On the wholesale Buying side, these are tools to get past bulk buying tier discounts for seasonal ordering with smaller scale retail.

Amazon is using a convoluted front end system of overlapping product categories and a supposed multi seller listings (despite collectivized logistics and warehousing) on the website you see. This is how they perform price fixing where you do not see honest or straight forward determinism. When you repurchase that same item later without making comparisons, the seller will shuffle so that a higher price is presented.

If you have a well isolated network where device history for social media and internet browsing is totally partitioned from e-commerce you’ll likely see even more of the scam. If you see anyone online show the search results and pricing on Amazon, then try to replicate those search results and product price on a device that is totally partitioned from your viewing of the item/price elsewhere, you’re likely to find it is not possible. If you then go back to the original device and do the same, you’ll magically find the same product and lower price. It is a scam market. This is why they are collecting and paying for all that data about you. We are in an age when automated individual targeting and manipulation is possible and happening. This is why data mining stalkerware is insidious. Scam markets are only the tip of the iceberg and what can be uncovered if you go looking for it. Anyone that has done database or logistics management should have major red flags flying when looking at how Amazon’s website is setup. The front end is absolutely untenable garbage for effective logistics. The only reason it is convoluted and search results are terrible is because it is a price fixing scam. The logistical efficiency proves that there is no connection between the front and back end of the site.

Not for potato supreme. I’m sure labels and sony bought vacations for those sub human coup supporting shits

Multi threading is parallelism and is poised to scale to a similar factor, the primary issue is simply getting tensors in and out of the ALU. Good enough is the engineering game. Having massive chunks of silicon laying around without use are a mach more serious problem. At the present, the choke point is not the parallelism of the math but actually the L2 to L1 bus width and cycle timing. The ALU can handle the issue. The AVX instruction set is capable of loading 512 bit wide words in a single instruction, the problem is just getting these in and out in larger volume.

I speculate that the only reason this has not been done already is because pretty much because of the marketability of single thread speeds. Present thread speeds are insane and well into the radio realm of black magic bearded nude virgins wizardry. I don’t think it is possible to make these bus widths wider and maintain the thread speeds because it has too many LCR consequences. I mean, at around 5 GHz the concept of wire connections and gaps as insulators is a fallacy when capacitive coupling can make connections across all small gaps.

Personally, I think this is a problem that will take on a whole new architectural solution. It is anyone’s game unlike any other time since the late 1970’s. It will likely be the beginning of the real RISC-V age and the death of x86. We are presently at the age of the 20+ thread CPU. If a redesign can make a 50-500 logical core CPU slower for single thread speeds but capable of all workloads, I think it will dominate easily. Choosing the appropriate CPU model will become much more relevant.

Mainstream is about to collapse. The exploitation nonsense is faltering. Open source is emerging as the only legitimate player.

Nvidia is just playing conservative because it was massively overvalued by the market. The GPU use for AI is a stopover hack until hardware can be developed from scratch. The real life cycle of hardware is 10 years from initial idea to first consumer availability. The issue with the CPU in AI is quite simple. It will be solved in a future iteration, and this means the GPU will get relegated back to graphics or it might even become redundant entirely. Once upon a time the CPU needed a math coprocessor to handle floating point precision. That experiment failed. It proved that a general monolithic solution is far more successful. No data center operator wants two types of processors for dedicated workloads when one type can accomplish nearly the same task. The CPU must be restructured for a wider bandwidth memory cache. This will likely require slower thread speeds overall, but it is the most likely solution in the long term. Solving this issue is likely to accompany more threading parallelism and therefore has the potential to render the GPU redundant in favor of a broader range of CPU scaling.

Human persistence of vision is not capable of matching higher speeds that are ultimately only marketing. The hardware will likely never support this stuff because no billionaire is putting up the funding to back up the marketing with tangible hardware investments. … IMO.

Neo Feudalism is well worth abandoning. Most of us are entirely uninterested in this business model. I have zero faith in the present market. I have AAA capable hardware for AI. I play and mod open source games. I could easily be a customer in this space, but there are no game manufacturers. I do not make compromises in ownership. If I buy a product, my terms of purchase are full ownership with no strings attached whatsoever. I don’t care about what everyone else does. I am not for sale and I will not sell myself for anyone’s legalise nonsense or pay ownership costs to rent from some neo feudal overlord.

Yeah this has been my experience too. LLMs don’t handle project specific code styles too well either. Or when there are several ways of doing things.

Actually, earlier today I was asking a mixtral 8x7b about some bash ideas. I kept getting suggestions to use find and sed commands which I find unreadable and inflexible for my evolving scripts. They are fine for some specific task need, but I’ll move to Python before I want to fuss with either.

Anyways, I changed the starting prompt to something like ‘Common sense questions and answers with Richard Stallman’s AI assistant.’ The results were remarkable and interesting on many levels. From the way the answers always terminated without continuing with another question/answer, to a short footnote about the static nature of LLM learning and capabilities, along with much better quality responses in general, the LLM knew how to respond on a much higher level than normal in this specific context. I think it is the combination of Stallman’s AI background and bash scripting that are powerful momentum builders here. I tried it on a whim, but it paid dividends and is a keeper of a prompting strategy.

Overall, the way my scripts are collecting relationships in the source code would probably result in a productive chunking strategy for a RAG agent. I don’t think an AI would be good at what I’m doing at this stage, but it could use that info. It might even be possible to integrate the scripts as a pseudo database in the LLM model loader code for further prompting.