1. Any turing complete computation system can be made to simulate another turing complete system.

2. All Most modern programming languages are turing complete.

About a yard

The real answer is that the techie nerds willing to learn git and contribute to open source projects are likely to be hobbyist programmers cutting their teeth on bugfixes/minor feature enhancements and not professional programmer-designers with an eye for UI and the ability to make it/talk with those who can. Also in open source projects its expected that the contributor be able to pull their own weight with getting shit done so you need to both know how to write your own code and learn how to work with specific UI formspecs. Delegating to other people is frowned upon because its all free voulenteer work so whatever you delegate ends up eating up someone elses free time and energy fixing up your pr.

deleted by creator

We recently got an airfryer. it helps cut down prep time and ease of cleaning to the point I don’t get upset about it anymore. Would highly recommend if you haven’t got one already

You shouldn’t be sorry, you didn’t do anything wrong content wise. If anything you helped the community by sparking a important conversation leading to better defined guidelines which I imagine will be updated if this becomes a common enough issue.

I enjoyed the meme LadyButterfly. the Lemmy AI hate train has gone full circle-jerk meltdown with this one, sorry it happened to you. Thanks for the laugh.

Thank you for the explanation! I never really watched the Olympics enough to see them firing guns. I would think all that high tech equipment counts as performance enhancement stuff which goes against the spirit of peak human based skill but maybe sports people who actually watch and run the Olympics think differently about external augmentations in some cases.

Its really funny with the context of some dude just chilling and vibing while casually firing off world record level shots

Who are these people?

deleted by creator

Any device someone ask my help with figuring out. Its rarely the appliance that pisses me off and more the blatant learned helplessness and fundimental inability for fellow adults to rub two braincells together on figuring out a new thing or to troubleshoot a simple problem. A lifetime of being the techie fixer bitch slave constantly delegated the responsibility of figuring out everyones crap for them has left me jaded to the average persons mental capacity and basic logical application abilities.

There are some pretty close physical analogs that are fun to think about. You cant move a black hole by exerting physical force on it in the normal way so practically infinite gravity wells are like a immovable “object”, though if you’re sufficently nerdy enough you can cook some fun ways to harness its gravitational rotation into a kind of engine, or throw another black hole at it to create a big explosion and some gravitational waves which are like a kind of unstoppable force moving at the speed of light.

True! Most browsers don’t have native gemini protocol support. However a web proxy like the ones I shared allow you to get gemini support no matter the web browser. Gemtext is a simplified version of markdown which means its not too hard to convert from gemtext to html/webpage. So, by scraping information from bloated websites, formatting it into the simple gemtext format markdown, then mirroring it back as a simple web/html page, it works together nicely to re-render bloated sites on simple devices using gemini as a formatting medium technology. You don’t really need to understand gemini protocol to use newswaffle + portal.mozz.us proxy in your regular web browser

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.

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.

Theoretically you may be able to store the core seed information that encodes the starting constants that lead to the beginning of the universe. Its not really the same thing like the difference between a cake and the recipe used to make it. Information systems can be distilled to core seed equations and regenerated by iterating that equation many times. This is Barnsley’s collage theorem.

Lowtechmagazine wrote an excellent article about decadent mist showers that use many small nozzles spraying very fine mist particle sizes water on you as a much more efficient way to use water for showers. I would love to install and try out something like that one day.