Its called that because you have a new appreciation for your life after coming back from that alive… “Thank God I didn’t drop to my death!”

during the time I was born TVs were small square boxes powered by glass tubes and turny knobs. I want to say 480p but tbh if you were using a junky 10 inch display at the turn of the century on satallite it was closer to like 240p. The jump from square 480p to widescreen 720/1080 was an actual graphical revolution for most people in a very big way, especially for watching movies that were shot in wide. In terms of games 1080p is both where 16:9 took off and the point where realistic looking graphics meet acceptable resolution for like skin pours and godrays shit like that. GTA5, TLOU and RDR are the examples that come to mind from the AAA 1080p era and their original states still probably hold up today.

When the 4k stuff finally came around and it was advertised as the next revolution I was excited man. However compared to going from 480 to 1080 it wasn’t a huge change tbh. It seems once you’re already rendering skin detail and individual blades of grass, or simulating atmospheric condition godrays, there isn’t much more that can be drastically improved just by throwing a billion more polygons at a mesh and upscaling textures. The compute power and storage space required to get these minimal detail gains also starts escalating hard. Its such bullshit that modern AAA games are like 80gb minimum with half of that probably being 4k textures.

I will say that im like the opposite of a graphics snob and slightly proud of it so my opinions on 4k and stuff are biased. Im happy with 1080p as a compromise between graphical quality and compute/disk space required. Ive never played a 1080p at maximum graphics and wanted for more. Im not a competitive esports player, im not a rich tech bro who can but the newest upgraded gpu and 500tb of storage. I don’t need my games to look hyperrealistic. I play games for the fun gameplay and the novel experiences they provide. Some of the best games I’ve ever played look like shit and can be played on a potato. Most of the games I found boring were AAA beautiful open worlds that were as wide and pretty as an ocean but gameplay wise it was as deep as a dried up puddle. I hopped off the graphics train a very long time ago, so take my cloud yelling with a grain of salt.

“I use Arch bt-”

“ITS SHiTE!”

“…excuse me?”

" YOUR BLOODY ROLLING RELEASE DISTRO IS FUCKING RAW. HOW MANY TIMES HAVE YOU RECOOKED IT AFTER A DEPENDENCY PACKAGE BROKE?"

“B-bhut chef… Its a rolling release bleeding distro that expects users to compile with the help of a wik-”

“I ASKED HOW MANY TIMES YOU HAD TO RECOMPILE IT THIS YEAR YOU FUCKING DONKEY”

“5 times sir.”

“FIVE FUCKING TIMES??? JESUS CHRIST DID I ASK FOR CONSTANT MAINTENANCE WITH A SIDE OF COMPUTER PROGRAMS IN BETWEEN? IF I WANTED A RAW OPERATING SYSTEM I WOULD HAVE BECOME A FLAGSMAN INSTEAD OF A CHEF AND ASKED FOR A DISH OF “GENTOO”. COOK ME A REAL OPERATING SYSTEM.”

Ken Cheng is a great satirist and probably knows thats not how it works anymore. Most model makers stopped feeding random internet user garbage into training data years ago and instead started using collections of synthetic training data + hiring freelance ‘trainers’ for training data and RLHF.

Oh dont worry your comments are still getting scraped by the usual data collection groups for the usual ad selling and big brother bs. But these shitty AI poisoning ideas I see floating around on lemmy practically achieve little more than feel good circle jerking by people who dont really understand the science of machine learning models or the realities of their training data/usage in 2025. The only thing these poor people are poisoning is their own neural networks from hyper focusing defiance and rage on a new technology they can’t stop or change in any meaningful way. Not that I blame them really tech bros and business runners are insufferable greedy pricks who have no respect for the humanities who think a computer generating an image is the same as human made art. Also its bs that big companies like meta/openAI got away with violating copyright protections to train their models without even a slap on the wrist. Thank goodness theres now global competition and models made from completely public domain data.

Some games just aren’t meant for you and thats okay. For example I spent a few hours playing civ enough to understand the experience it offers. I did not enjoy a single moment of its gameplay or strategy layers at any point. Apparently its a good enough game for many people to put hundreds/thousands of hours into and buy again every few years+dlc. I just didn’t pick up what it was putting down.

I have no issue with remakes themselves. Games are a kind of art, and good art should be kept alive for the next generations to enjoy. The problem to me is:

1. the only thing big studios now want to put out remakes/remasters of the backlog they already made because its a safe and easy cash grab. One of the top comments about there being 7 skyrims and 2 oblivions before ES6 is soo real man. Its like all the people who founded the companies who were responsible for creative novel design/story that gave big titles their soul in the 2000s no longer exist in the industry except a few indie devs. Now all big game companies are just run by business associates without a shred of humanity outsourcing everything for a quick buck.

2. Graphics have plateud from late 2010s and onward. Remastered and remaked stuff made a lot more since for the ps2/xbox and backwards, with the ps3/x360 1080p resolution it made a little less sense but I could still understand them porting like TLOU to ps4 at 4k or whatever. But now were remastering games that came out 5 years ago at 4k and trying to sell it as some huge graphical overhaul worth the asking price. Maybe im insane or old but my eyes can barely tell the difference between 1080p and 4k, going from 4k to 8k is like the same picture with slightly different shaders.

Which ones are not actively spending an amount of money that scales directly with the number of users?

Most of these companies offer direct web/api access to their own cloud supercomputer datacenter, and All cloud services have some scaling with operation cost. The more users connect and use computer, the better hardware, processing power, and data connection needed to process all the users. Probably the smaller fine tuners like Nous Research that take a pre-cooked and open-licensed model, tweak it with their own dataset, then sell the cloud access at a profit with minimal operating cost, will do best with the scaling. They are also way way cheaper than big model access cost probably for similar reasons. Mistral and deepseek do things to optimize their models for better compute power efficency so they can afford to be cheaper on access.

OpenAI, claude, and google, are very expensive compared to competition and probably still operate at a loss considering compute cost to train the model + cost to maintain web/api hosting cloud datacenters. Its important to note that immediate profit is only one factor here. Many big well financed companies will happily eat the L on operating cost and electrical usage as long as they feel they can solidify their presence in the growing market early on to be a potential monopoly in the coming decades. Control, (social) power, lasting influence, data collection. These are some of the other valuable currencies corporations and governments recognize that they will exchange monetary currency for.

but its treated as the equivalent of electricity and its not

I assume you mean in a tech progression kind of way. A better comparison might be is that its being treated closer to the invention of transistors and computers. Before we could only do information processing with the cold hard certainty of logical bit calculations. We got by quite a while just cooking fancy logical programs to process inputs and outputs. Data communication, vector graphics and digital audio, cryptography, the internet, just about everything today is thanks to the humble transistor and logical gate, and the clever brains that assemble them into functioning tools.

Machine learning models are based on neuron brain structures and biological activation trigger pattern encoding layers. We have found both a way to train trillions of transtistors simulate the basic information pattern organizing systems living beings use, and a point in time which its technialy possible to have the compute available needed to do so. The perceptron was discovered in the 1940s. It took almost a century for computers and ML to catch up to the point of putting theory to practice. We couldn’t create artificial computer brain structures and integrate them into consumer hardware 10 years ago, the only player then was google with their billion dollar datacenter and alphago/deepmind.

Its exciting new toy that people think can either improve their daily life or make them money, so people get carried away and over promise with hype and cram it into everything especially the stuff it makes no sense being in. Thats human nature for you. Only the future will tell whether this new way of precessing information will live up to the expectations of techbros and academics.

Theres more than just chatgpt and American data center/llm companies. Theres openAI, google and meta (american), mistral (French), alibaba and deepseek (china). Many more smaller companies that either make their own models or further finetune specialized models from the big ones. Its global competition, all of them occasionally releasing open weights models of different sizes for you to run your own on home consumer computer hardware. Dont like big models from American megacorps that were trained on stolen copyright infringed information? Use ones trained completely on open public domain information.

Your phone can run a 1-4b model, your laptop 4-8b, your desktop with a GPU 12-32b. No data is sent to servers when you self-host. This is also relevant for companies that data kept in house.

Like it or not machine learning models are here to stay. Two big points. One, you can self host open weights models trained on completely public domain knowledge or your own private datasets already. Two, It actually does provide useful functions to home users beyond being a chatbot. People have used machine learning models to make music, generate images/video, integrate home automation like lighting control with tool calling, see images for details including document scanning, boilerplate basic code logic, check for semantic mistakes that regular spell check wont pick up on. In business ‘agenic tool calling’ to integrate models as secretaries is popular. Nft and crypto are truly worthless in practice for anything but grifting with pump n dump and baseless speculative asset gambling. AI can at least make an attempt at a task you give it and either generally succeed or fail at it.

Models around 24-32b range in high quant are reasonably capable of basic information processing task and generally accurate domain knowledge. You can’t treat it like a fact source because theres always a small statistical chance of it being wrong but its OK starting point for researching like Wikipedia.

My local colleges are researching multimodal llms recognizing the subtle patterns in billions of cancer cell photos to possibly help doctors better screen patients. I would love a vision model trained on public domain botany pictures that helps recognize poisonous or invasive plants.

The problem is that theres too much energy being spent training them. It takes a lot of energy in compute power to cook a model and further refine it. Its important for researchers to find more efficent ways to make them. Deepseek did this, they found a way to cook their models with way less energy and compute which is part of why that was exciting. Hopefully this energy can also come more from renewable instead of burning fuel.

Hell yes! Scream it from rooftops with your menthol laced breath. The celestial calendar fortold that 2025 would truly be the year of the linux ™ the time is now!

Redditor chud behavior: gets bothered by a single downvote, edits comment to ask why they were down voted turning into a rant about the NPCs and sheeples who disagree with them. Subconsciously worried about karma points and awards.

Lemming chad behavior: Gets 20 upvotes and 10 down votes, happy 30 people reads their thing and glad to have put a controversial opinion into the world that might make someone think a new way.