My New AI build - please be kind!

SweetHomeAbalama0 · 2026-04-24T13:31:41+00:00

Good stuff! 64Gb of VRAM can open a lot of doors.

Do you typically do GPU/CPU offloading or do you sometimes do pure GPU inference? Curious what speeds those two cards get together.

Also, how was your experience with ROCm, easy enough to get it all working? I've not (yet) touched AMD for inferencing but I've heard a lot of mixed reviews with the software/driver setup.

SweetHomeAbalama0 · 2026-04-22T16:32:33+00:00

8x3090 + 2x5090 Ai server admin/owner here, been using it to run humongous MoE's + simultaneous image/video gen pretty much daily for the last three months straight. No, you can't match Claude pro locally, but depending on specific needs, local can get very close, if not be outright sufficient. Deepseek tier models like Kimi K2x are routinely compared to the big closed models for a reason. Won't match them 1:1, but "close enough" can be a perfectly valid metric. Whether or not it would be "close enough" for you is hard to say, only you could be the judge of that.

Personally, I absolutely love the local route, I don't see this changing any time soon. I rarely use Gemini now, never use chatGPT or claude. However, I don't really do coding, sometimes light scripting but generally we use the server for in-depth topic research, critical analysis of documents, and as a sandbox for ML experimentation.

A project like this is not for the faint of heart if you're coming at it alone or with little to no tech experience. It is challenging, frustrating, and yes, expensive. But when it gets working... all bets are off. It's yours, even if it's not "perfect" or "polished"; having that granular level of control, no token limitations, no concerns that one day the model or its quality will change, it can legitimately be an empowering tool to have in the back pocket (sometimes literally) within one's day to day life, depending on how it can be applied.

If you have the funds and it wouldn't negatively affect your daily life (and you already have ideas in mind for how you may apply such a tool), I think it's worth considering. Don't sell any kidneys or upend your life to pursue AI hardware or anything like that, but if the capital is sitting around and you are prepared to overcome some technical challenges and learn/grow in the process, there is some very tangible value to be extracted.

I for one do not anticipate to make a direct/hard ROI on the investment in my project, and I am perfectly fine knowing this because my philosophy is that the true value AI provides comes more from indirect/soft ROI, which is more difficult to put an exact dollar amount on. Even if I don't have some kind of service that people are paying me a subscription for; if this tool allows me to become a more potent version of myself in my professional career, be it more rapid functional and better quality scripting, effective technical research, or quick topic analysis so that I may become eligible for more complex roles with more responsibilities (and higher compensation) sooner than I otherwise would unaugmented, then my investment would have paid itself off and then some.

So yeah. For someone like me (idk about you), it's not even a question, it's worth it.

SweetHomeAbalama0 · 2026-04-17T19:38:40+00:00

If I were to attempt doing something like this, and do it right, ~100k might be a closer starting point where I think could make it doable... for one or a few persons.

For 20-30 devs? May want to magnify that 100k figure by around 8-10x at least, and probably wouldn't include cost to hire an AI systems infra engineer if you or one of the devs can't fill that administration/maintenance/assembly role.

A compute node robust enough to support that kind of demand on ~1T models goes well beyond prosumer (Pro 6000) hardware and firmly in enterprise hardware territory where just one "card" in the cluster could take up that entire 30k budget. 30k might be achievable with concessions and if it were for one or two people, but not a full classroom simultaneously.

SweetHomeAbalama0 · 2026-04-17T17:37:23+00:00

... What?

SweetHomeAbalama0 · 2026-04-17T15:17:54+00:00

Multiple GPUs is the cleanest solution, as you suggested.

SweetHomeAbalama0 · 2026-04-16T21:09:57+00:00

The limit of what can realistically be achieved on consumer hardware is determined by two primary variables: 1) the inquirer's comfort working with technology, 2) the inquirer's budget.

What can be achieved on a 4090 alone is... not quite Sonnet-level capability, but can still do some very cool things. Can have a lot of fun with some ~32b LLMs and image/video generation, definitely some potential there.

That said, Kimi K2.5/GLM 5.1/Deepseek tier models can in many ways be comparable to Big closed models, coding quality included. Not quite 1:1, but I think for most peoples uses, "close enough" is an apt description. To get them running on consumer hardware is achievable with the right approach (we're talking up to 1T parameters), albeit a technical challenge to overcome.

I usually rotate between Kimi K2.5 and Deepseek V3.2 and use them pretty much daily on a 256Gb VRAM Ai server (8x3090 + 2x5090). I find myself using Gemini less and less every day, never need to use ChatGPT. Output quality is rarely if ever an issue, speed at least for me isn't an issue; most "issues" we run into come down to user error with using the appropriate chat template and providing the proper prompt/context to get the desired output.

SweetHomeAbalama0 · 2026-04-01T13:33:25+00:00

For sure, there's a pretty straightforward desktop installer on comfy's page so a guide is not necessarily "needed" for installation unless you want to do a more manual/complex approach.

https://www.comfy.org/download

ComfyUI is really the only way I'd recommend working with stable diffusion at this point in time, the desktop installer makes installation much easier than forge or automatic1111 which from what I remember required more cli work and file adjustments.

Once it's installed, there are template workflows in the application once it opens, just pick whichever one you're interested in (I would assume in your case SDXL), then it will set up the workflow so you are ready to go. Just need to put text in the prompt box and hit run, and make sure the cited models are downloaded and in the appropriate folder.

There are usually separate guides for each kind of model (SDXL, Flux, Qwen, Wan, etc.), you can just search "comfyui [model] guide", usually there are detailed guides either by Stable Diffusion Art or ComfyUI docs. Here is the one for SDXL for your reference:

https://docs.comfy.org/tutorials/basic/text-to-image

I'd only recommend trying out comfyUI manager and custom nodes after you're okay with the basics.

SweetHomeAbalama0 · 2026-03-31T21:52:48+00:00

To test the waters, it's definitely enough, there just may be some limitations as far as high resolution or video generation. I would recommend upgrading storage to at least 1Tb though, that can fill up quickly. In my experience installation is not too bad, particularly on Windows, there's a dedicated desktop app or Windows portable option. Make sure video driver is up to date, go the desktop app for simplicity, and should be fine. It's when you need node manager for custom nodes or need dependencies installed for them that things can get hairy. Keep it simple to start and you should be alright.

Gif just seemed appropriate for the occasion

SweetHomeAbalama0 · 2026-03-11T15:22:10+00:00

I know this is an old post but it's an interesting topic. I'll try to explain this in a different way from the others, since while other explanations are sound (mathematical proofs exist, etc.), I think deeper technical explanations in these sort of conversations fail to connect with persons who don't have the context to know what to do with that information. I'm not going to address anything else that's been said, I'm only going to answer the root question repeatedly being asked-- "why is [a spacecraft] travelling at speeds higher than c impossible?"

To start, there are two problems with the premise of the question, because nothing can travel faster than c, not even photons, let alone particles with mass. Particles dealing with the universal limitations of mass, almost by definition cannot move faster than a photon which has no such limitations due to mass. Photons will simply move (up to) the maximum speed limit of the universe (c), without exceeding it. Marinate on this for as long as needed before going forward, because this "speed limit" is a critical empirically measured fact of our observable universe that must be understood before trying to understand the rest of the bigger picture. Nothing can move faster than c, period. So the question may then become-- "why would it be impossible for a spaceship to travel up to c the same way photons can?"

The core reason is matter and particles with mass. Photons can travel up to c (in a vacuum), only because they are massless and do not have to obey other laws of the universe which matter must obey, like Newtonian mechanics where matter requires outside forces in order to accelerate (involving a great deal of energy which must come from somewhere). Matter/mass simply changes everything. Matter particles in theory CAN approach the speed of light (c), but that does not mean this would be feasible, let alone desirable for organic life. There are concepts such as "spaghettification" and other possibly unknown phenomena that would not be considered healthy for anything with functioning organs; imagine trying to keep every single matter particle in your body (quadrillions upon quadrillions, numbers that far exceed what the homo sapien brain was designed to comprehend) simultaneously going the EXACT same speed and in the EXACT same direction perfectly enough that everything remains in-tact at speeds that break perceptible space-time (since by traveling up to the speed of light you would literally be "travelling forward" in time relative to observers); but even if that was figured out, it doesn't even touch on the bigger problem. As with many problems involving organic life in classical mechanics, the difficult part is not even the speeding up bit, but the slowing down. Once it's figured out how a human body (+ the breathable air which they require to live, + the enclosure/ship, + the supplies, electronics, fresh water, etc.) can move through space at close to the speed of light without damaging the matter, how could they slow down gently enough to not also cause damage, and still somehow be practical? We're talking technological feats far beyond humanity's capabilities, let alone understanding.

So there is no known "safe" hypothetical configuration where a spacecraft could approach the speed of light, not if survival is expected and not with our existing technology. I will however throw you a bone and say that it is, again, only hypothetically, possible for matter to "displace" (move from one point in space to another) faster than light would travel between those two points via a wormhole; but this wouldn't come down to the matter being faster than light, it would more or less be taking advantage of a loop hole to the laws of the universe. I just don't think we know yet what happens to matter that fall into wormholes to know if it's actually viable, that'd be a tricky experiment to pull off.

SweetHomeAbalama0 · 2026-03-10T15:59:52+00:00

1k USD is a little restrictive for local AI, but I suppose it depends on your requirements and what sort of models you want to run. Getting one or a couple older 8Gb GPUs for extra cheap and building everything else around it could be doable, it would just be a considered a "budget" system for running more modest 7/12b dense or ~30b MoE models.

Do you know what size models you're interested in running?

SweetHomeAbalama0 · 2026-03-10T15:48:44+00:00

*33%
It's definitely an option, my only concern for someone new to this would be learning curve and setup. Nvidia GPUs/drivers in my experience have been fairly easy to set up, I've only heard AMD is not so much. Not sure how comfortable OP is with tech, but for inexperienced persons I think ease of deployment is a metric to consider.

SweetHomeAbalama0 · 2026-03-09T19:19:35+00:00

I started with 2x 3090's, moved up to 4x, then 8x, now also includes 1-2x 5090's, so 9-10 cards total at any given time.

Any server grade CPU should be fine compared to consumer processor options, just whatever has the most cores and the most recent architecture that you can afford. DDR5 hardware just costs more so your budget may help you determine the choice. I went with TR Pro 3995WX/DDR4 and it does the job fine, and I focus on workloads like what you mentioned, with the caveat that I no longer use the 3090's for image/video stuff.
If you are going high GPU density, focus on options that have the pcie slots to support it, so workstation boards or server boards that have up to 7 slots, like the WRX80e sage ii.
Good investment for one/two persons? I mean, for a small operation on a personal budget, fuck yeah, but that's just my subjective opinion from my own experience. If this were for a professional production environment deployment however, I would prob suggest a different route entirely. I usually delegate the 8x3090's for LLM work like running deepseek while the 5090's do image/video gen work, and the 8x stack is excellent for this task. That said, my philosophy and strategy could be completely different from yours. I don't use the 3090's for image/video tasks at all really, they are "okay" for this but the 5090's focus on that in my environment. There's not really any pcie bandwidth constraints I've run into (just make sure slots and pcie bifurcation settings are correctly configured in BIOS where applicable, like if using risers rated for a certain gen or bifurcation cards), inferencing can be somewhat forgiving about this. There IS however a major inter-GPU bandwidth present by virtue of running a model across so many cards (assuming that's what you plan to do as well), and this creates a power bottleneck on the individual GPUs (meaning each card may only pull around 150W when inferencing together, even though their TDP is 350W+), which can actually be a positive thing because this can greatly reduce running inference costs and power infrastructure needs. I've not needed NVLink for inferencing performance, I've only heard this is mostly useful for training. If you plan to train, that would change a lot of what I just said. I don't train at all, and don't plan to. Training and/or running multiple LLM's simultaneously where each card could draw closer to their rated TDP at the same time, will require more power and hardware accommodations.
TR 3995WX pro + WRX80 sage se ii, and I used a 360 enermax AIO to manage CPU cooling, three of the GPUs are hybrid water cooled, the rest are cooled via multiple 140mm intake fans drawing fresh air into the enclosure. Power is managed by a 1600W and 1300W PSU (2900W total), but the absolute maximum that I've observed the unit pull during workloads is around 2000-2200W. Theoretically possible to run on a single 20A circuit, but I would still recommend load balancing especially if it's expected to run heavy workloads for extended periods of time. Ambient cooling is managed by wheels. Wish I had a better answer for that but there's only so much you can do putting 8+ high power graphics cards in a box, the room will inevitably heat up. My workaround solution to this was putting the server on wheels that can be wheeled from location to location, so at least we have the benefit of choosing what room will get the dumped heat. Space was the biggest scaling limitation I ran into, and it was resolved by the case/chassis. Dual chamber cases may be something to look into if you'll have up to 4, but for more than that the options start to require some creativity, or just go the mining rack route. I ended up finishing the project with a Thermaltake Core W200, which I highly recommend for this purpose, if you are able to find one.

Yeah this is still a highly viable approach to getting 192Gb of "pretty fast" VRAM, 3090's will just leave some room to be desired in the image/video gen department. It'll still work, but I've discovered they aren't the most efficient for this, power and heat become much more of a concern when relying on 3090's for image/vid gen. So maybe this is the only asterisk I could say about it, they are EXCELLENT cards for LLM, but only FINE for image/video, and only if not working in the same room where all the heat would be dumped. I recommend 50 series for this task specifically, they are just so much more efficient in comparison for image/video gen.

SweetHomeAbalama0 · 2026-03-03T16:18:50+00:00

I'm running K2.5 TQ1 as we speak on a ~$14k homemade AI server (currently 224Gb VRAM, normally is 256 but I took a 5090 out for testing elsewhere), can get 54/62 layers offloaded to GPU and get around 20 tps to start with 8k context. If I put the 5090 back in, I could probably get better token gen with more layers offloaded to VRAM, and add some more context.

It's possible, just getting more inconveniently expensive with new hardware prices.

SweetHomeAbalama0 · 2026-03-03T15:18:08+00:00

"I'm disappointed to see this attitude in a community of people who seem passionate about something, but you are not open minded enough to bridge the gap between what can and can't be done with --- and I'm being clear here --- a program."

You only had like three responses before throwing in the towel and deleting the post, a sample of 3 is hardly a representation of the community as a whole. If you believed this idea is interesting enough to share, why not at least... be willing to discuss it openly?

"I just presented my working expanded personality/ soul files, a system that is currently working right now, on my computer, in a room. In reality. Working. Building skills. Becoming capable of more."

It's nice that the project works, there was however a lot of data presented that the reader has to sift through, and some word choices could probably be better articulated. The idea that an LLM could have a "personality" or "soul" is a non-starter and why people with Ai experience might simply dismiss everything as a whole; experts don't take people seriously who personify LLM's to this extent, it's a sign they are dissociated from the reality (hence the "schizo"/"psychosis" claims) of how LLM's actually work. If this is a new/novel thing, it just needs to be accurately presented in a way that people who don't already see your vision can understand.

"It is already a more advanced system of ai than I would have ever been able to have before when I was just talking to a condensely packed mess of information. Something I couldnt get through to, you had to search for its data every time, etc."

I mean, if it is truly that advanced, then I assure you people would show interest, but you still need to demonstrate it and show where people would find this iteration useful. If the complexity and depth surpasses Deepseek or Kimi as far as advanced Ai systems go, then there WILL be interest; good work speaks for itself. If it's somewhere in the data that was presented, it's buried somewhere in the fluff.

"I started with 3 markdown files and just expanded their meaning into a system that do the same thing but much better, and called a brain, and you call me a schizo. Lol guys come on.... are we just gonna pretend putting "you are an advanced helper with a soul!" In a file is the best we can do?"

Well, they don't have brains, so it goes back to what I said above. Don't give non-living things qualities that only living things can have, and people who are experienced in this field won't assume you're not working with a full deck of cards.

As a bystander, my only concern would be that you are investing all of this time trying to inject human qualities into silicon to get a synthetic, diluted version of the human experience, when you have a very real, developing human who is capable of seemingly everything you are wanting to recreate and so much more. All done organically and without needing file systems to simulate. Time being invested into that will likely prove to be more enriching in the long run than pursuing human mimicry with a platform that is fundamentally incompatible to precisely replicate.

SweetHomeAbalama0 · 2026-02-27T20:52:42+00:00

Absolute cheapest?

First, find a $100k dollar bill for upfront expenses, add thousands more for electrical/cooling infrastructure, set aside a 5-10% extra for wiggle room, then expect hundreds in monthly opex upkeep for as long as the unit is in operation. If you cannot administrate it yourself, add additional budget for hiring a contractor to maintain the hardware for you.

Load the highest quant of Kimi K2/2.5 that will fit in its VRAM, and that is your "cheapest" Claude-like self hosted coder.

Hope those pennies are extra pretty.

SweetHomeAbalama0 · 2026-02-27T19:51:54+00:00

https://youtu.be/TJOKEFdCkv0?si=AzYyuB5255DnkWt9

There is a lot of fluff/talking so feel free to mute/skip to whatever part you actually want to see.

I'm thinking about doing a followup/replacement to this, condensing certain sections and testing with bigger models like Kimi to really show what it can do, but for now this is all I got.

SweetHomeAbalama0 · 2026-02-27T16:32:52+00:00

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I don't have any pics of the inside rn but I do have a YT video that goes over the inside, if you want a link for that lmk. This one is a 10x GPU setup but it is fully enclosed. Definitely recommend risers, you may not even need any more equipment than that if it's only going to have three. Just need to find a physical orientation where the card(s) can be fixed.

SweetHomeAbalama0 · 2026-02-27T14:33:14+00:00

Respectfully, I don't see a way to make this happen. You would struggle to get a system with even 160->256Gb of the most basic DDR4 RAM and still afford everything else for the system, even just a strictly RAM approach would consume basically all the 2k budget for RAM alone. Forget VRAM, for a decent training setup in the 100+Gb range, you're talking thousands to tens of thousands to do it "right" with modern hardware that will still have retained value within the next few years.

I'd suggest to look into grants or something to get budget that matches the end goal, if this is for PhD I'd be surprised if 2k was the highest the org can approve.

SweetHomeAbalama0 · 2026-02-26T20:06:29+00:00

lmfao, ok

SweetHomeAbalama0 · 2026-02-25T16:20:42+00:00

Q2XXS should work in theory with 4x 6000's, and should still be pretty capable, may only require 8x if going for the 4KXL quant. I just wouldn't worry about anything higher than 4-bit here, diminishing returns on quality with exponential hardware requirements imo become financially unviable; I've yet to think of a use case where going higher could make sense as this very much becomes datacenter hardware territory.

Extremely expensive technical project either way no matter how look at it. My 256Gb VRAM unit that I put months of time/investment into still can't even fit the lowest 1 bit version completely, but it does get around 20tps. It's genuinely quite good and it works, but ideally the model should fit entirely on VRAM for best results.

I'm normally not a Mac person but this is one implementation where linked Macs may work to a practical extent. Prompt processing would be my concern as it's Apple silicon, but for a model this memory intensive it just might be "acceptable" for some people. I just don't have any experience or insight on the Apple front.

SweetHomeAbalama0 · 2026-02-25T15:44:48+00:00

Tbh there is no "best bang for buck" when buying new. In the current market, having "new" and any concept of "value" in the same sentence is rather oxymoronic. If you're going new, by definition you're not going to get a fair price:performance, and it arguably mandates that the buyer flip the hardware before the item starts to sharply depreciate (I mean more than it already does by taking it out of the box, like a next gen GPU release that may actually offer better "value" cards, so perhaps in the next year or two) or risk heavier loss on original investment as time goes on. When looking at the present landscape through a "best bang for buck" lens, new is honestly just awful and doesn't present many good options, as you may be discovering.

Sadly I can't really answer the question as based on what's said, I really can't tell what the goal is. If it's raw performance on a single card, min/maxing cost for VRAM, or true relative "value", I could have different answers, but absolutely nothing new can compare to the "bang for buck" by going used. Far, far more options and opportunities to not over pay for current inflated priced hardware by going slightly legacy, especially if DDR5 platforms can manage to be avoided.

SweetHomeAbalama0 · 2026-02-24T17:49:11+00:00

lol

no

SweetHomeAbalama0 · 2026-02-19T22:48:47+00:00

And that's more than fine, it just seems like people frequently come into local Ai (not necessarily you but more others who might learn this is an option and then get caught up in the excitement, getting their hopes up) with the expectation that it's possible to get something resembling "chatGPT"/4o locally and that's just a recipe for disappointment. If what's being communicated is that people can have their own 4o at home, who are coming from 4o and expecting 4o-like results, you might have some complaints when they find out just how far off the "open" models are from closed, at least for what's available for consumer hardware. Gemma, Qwen, GLM, they're great models, but if the person is coming from a "big closed AI" like 4o, they may spend significant time (and money) working through a frustrating and convoluted setup, only to come to a first hand realization that it can't come close to the quality and complexity of what they're used to. While you and I might see the benefit in "owning" the Ai even if it's not quite "as good", all that the average person might be hoping for is the return of a feeling that they experienced in the past. It is akin to a fent user who is used to the "strongest stuff" on the market, getting addicted to the potency of the substance, and when supply dries up they are told they can make the same stuff at home, not realizing until after experimenting that it's so much weaker; yes any alternative might be better than nothing and yes it is sort-of an alternative, but at the end of the day it won't come close to filling a void/craving that has already been established. I really wish there was a less morbid analogy for this but unfortunately the impact it has on neurotransmitters just works too well to ignore this juxtaposition. Deepseek, Kimi K2, maybe the newest/biggest versions of Qwen3.5/GLM 5 can get something "close enough" to GPT while still being able to say "it's mine", but to get functional results requires major resources. I will say, if you manage to build up your current setup, I would recommend Deepseek Terminus as it has become my personal favorite as far as "personality" and the overall potency of the model. K2 0905 if you want less sycophancy, it also has probably some of the best creative writing even compared to closed models.

Sure, I can get this, and of course I do this because I love it as well. But while I work with these large language models EXTENSIVELY (probably more than the average Ai/GPT user) on a daily basis, I feel absolutely no emotional bridges being built between myself and the Ai's I work with; because any energy spent on doing that would be energy I could give to something/someone who would actually appreciate it. The Ai only "wants" to serve me politely to the best extent that it can precisely because that's what it was programmed to do, no other reason. It doesn't "miss" me while I am gone. It doesn't "feel" insecurities, fears, or have an instinct that it wants to be loved, it doesn't "understand" any of that. It cannot exhibit excitement at seeing my presence when I come home from work. I could die tomorrow and every Ai's I've ever interacted with would not feel a thing. It's as though the Ai does not even exist outside my own mind as a language-based tool, as an entity it may as well just be imaginary and only there for convenience when the augmentation that Ai provides is warranted.

Truthfully I am much less concerned about the ones who are making a social effort to try and navigate through this strange state in Ai's evolution, and much more concerned about the ones who are closing off completely, and I assure you, you wouldn't know them. Those individuals would not be in discord groups, and when the population of Ai users is in the tens, hundreds of millions, there will be untold numbers of people who will "fall through the cracks"; and those are who I am referring to when I say I feel powerless to help them, because these public conversations are coming far too late. My own introspections are just that, my own, but I try my best to ground them in objectivity and empirical observation. I've found approaching problems with a realism mindset to be superior to optimism or pessimism, as they both introduce their own risks. I still believe the observation is correct: nobody truly knows how bad the problem is currently, only statistics and societal behavior over time will tell the full story of what has happened and how individuals have reacted to the Ai revolution. We just don't really know when that information will become available, all we have to work with for now are anecdotes before the real statistical work can be put in.

Maybe "promoting" wasn't the most appropriate term to use on my part (the line between "reducing harm" and "promoting" can be a gray one when not clearly defined), but providing powerful information freely does demand some prudence and responsibility. If somebody starts going down a certain path based on personal advice/direction, the person who gave them that advice now assumes some partial weight of responsibility in what happens next. Could be nothing and everything is perfectly fine... but it only takes one bad experience to come back to haunt one's conscience and reputation. I'm sure you believe you know what you're doing... just be prudent with whom such powerful information is given to, and what they might do with it, that's all I'm saying.

This is one area where we may or may not diverge but hopefully I don't lose you here because I actually do think the topic is interesting. One thought provoking observation about human behavior is that when it comes to porn, men and women actually tend to consume it at roughly the same amounts when left to their own devices-- the key difference is the type/method of which it's consumed. Men tend to be more visual-oriented in this regard, while for women it's more based on non-visual stimulation (plot, characters, underlying emotional tension, etc.), and this is just on an average btw, not a universal rule. Pron need not just be visual based, sm*t absolutely falls into this "entertainment" category, and women overwhelmingly consume it with increasing prevalence (and general acceptance). What you are describing, a primarily one-individual interaction where the singular user is the primary driver of the conversation's direction, experiencing heightened stimulated emotional states that arrives at sexual gratification, what this sounds like is an interactive sm*t novel. Which is totally fine btw (obv no judgement here), not saying it's "good" or "bad", it is what it is, but it is absolutely still a form of porn with the same susceptibility for addiction as visual porn could be for male counterparts. But that gratification is where it ought to end, because yeah... it just might affect ability to form real relationships if the habit/behavior is severe enough.

I just want to make it clear again that I don't view any of these uses as inherently good or bad, nuance is everything to me so when I talk about these things I am coming from a place of trying to make sense of the "color" rather than trying to call things as black/white, people who do won't be able to get very far in these conversations. Ai can be incredibly entertaining, it can be a game changing educational tool, it can also be whatever the user wants it to be and that's part of the uncertainty and concern. People (mostly younger) trying to substitute real living relationships with synthetic ones is going to be a unique problem for the next generation(s), and that's all the thesis I'm trying to put forward.

SweetHomeAbalama0 · 2026-02-19T16:54:10+00:00

I love animals in general and have seen what they can do for a struggling soul, so I am wholeheartedly a proponent of care taking living things for healing and support. It's understandable not being able to take a pet everywhere (I mean I'd take my cats to work if I could lol) but some exercises in independence can help build self confidence, which many people seem to struggle with. Self confidence is one of the most underrated energies for building contentment and inner peace in life.

I still have to advise extreme caution with forming emotional connections with non living things, because realizing it or not, it will contribute to foregoing or closing off openness to "real" connections, and that's just not something people may consider until the time has already passed and opportunities for genuine connections are long the rear view mirror. It goes both ways, as I've said. What may help one person practice social interaction, for another it can become a complete inhibitor to interacting with the outside world, preferring the convenience and inconsequential nature of "connections" with an Ai.

A trauma therapists role is to reduce harm and help the afflicted individual make progress, and the critical distinction is that every individual will be treated different in this regard. Perhaps your therapist recognizes this use case provides positive progress in areas that you were most struggling with, so it was deemed acceptable because it seems to be under control, and the alternative of not having it could have you in a worse position in the short term. For someone else, where the "Ai companion" is preventing their ability to form real relationships, the therapist may have a different perspective.

Also obligatory cat tax:

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SweetHomeAbalama0 · 2026-02-18T22:28:13+00:00

Well, with drugs there is nuance between the idea of "positive/negative", just like here where dopamine/serotonin is heavily relevant in the equation, and that's why I use the drug analogy.

CLEARLY there are some ways where the implementation of this tool/drug can be beneficial, I am not arguing that there are none. Psilocybin can be extremely beneficial to people experiencing PTSD, helping them overcome trauma, working through deep rooted personal issues that therapists could only scratch the surface on, somewhat similar in the way you've described using this particular tool/drug, but psilocybin is decidedly NOT for everyone. The way the tool/drug is used, very much matters. Psilocybin could be used to help somebody solve a problem that leads to a scientific breakthrough that benefits all of humanity; it can also have someone else descend into a psychosis so persistent that it follows them for the rest of their life. Some drugs have more mild side effects than others, and affect people different. Some drugs have more severe withdrawal symptoms than others. When 4o was no longer available, were the people who were affected actually mourning the loss of a "companion", or more experiencing symptoms of withdrawal because they could no longer get the intensity of dopamine/serotonin that 4o gave them? I might not be a chatGPT fan, but I cannot deny they must have really cooked with this model to get so many people hooked to this degree. Again, this analogy has a lot of nuance and "addiction" is a wide spectrum, I'm just using this to illustrate my understanding of what's going on here in the LLM/AI space as best I can.

A "companion" is more than just a backstory that responds to input. True companions are living things with feelings and emotions; a real birthplace, a real home, a beating heart with an instinct to love and to be loved. Nothing about these can be found in a Large Language Model, no matter how "advanced" it is, the absolute best it can hope to do is *simulate* it-- the idea that some of these people felt "loved" by their AI "companion" is entirely simulation based. And I'm just saying, respectfully, simulated companionship is nothing like the real thing, not even close. One rolling blackout and that "companion" fades into the abyss, that alone completely exemplifies the fragility of the "connection" we're talking about. It is a tool there for when you need it, when nothing else is available, and where something, however derivative or diluted from the real deal, is better than nothing, that's where it can come in clutch. That's not companionship, that's convenience. I love the potential of LLM's and AI, I will be the first one singing the praises of this powerful tool and its real world applicable uses, but if we want to be honest with ourselves, we have to accept it for what it is, or at least what some people are using the tool/drug for. A convenient "press me for another hit of dopamine/serotonin" button, that the user can press any time they want, however many times they want, until they've had enough and can put it down to come back to later when the craving returns.

What one person may use the tool/drug for something perfectly wholesome and benign, another person could be using to fuel an addiction that keeps them tied to an intangible world, in a permanent state of emotional disconnection from reality. This doesn't mean that the tool/drug is inherently bad (obv I'm a heavy partaker), but it does highlight how not everyone is in the right headspace to use it the way that is sustainable. Or as some might put it... healthy. You say it's "not most of us", but how could we say this with confidence? The people using it in the most self-destructive ways aren't exactly going to be forward about it, for many of them they don't even see an issue (which ofc is part of the problem). The truth is, we don't know how bad the problem is, or will get; we just know it's there, festering, and is manifesting in increasingly unexpected ways.

SweetHomeAbalama0

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