I'll reblog everything I post there onto this blog, but if you only want to see my art follow that one instead.
About me
Feel free to refer to me with the name of my art blog or simply the letter K
An adult zoomer. Had internet access a bit too early so my attitude towards tech generally aligns with that of Tumblr's average age demographic
Languages: Georgian, German, English, N4 level JP. I struggle with all of them.
Names, faces, and other important info tend to escape me. I try but, y'know ¯\_(ツ)_/¯
Interests: art, animated works, linguistics (to ease my suffering), tech (when it's genuinely useful / funny), and being silly and relaxed
Definitely a weeb but not really keeping up with newer stuff the way I used to
This blog is reblog-heavy for fandom stuff I enjoy, art, as well as classic style textposts. Sometimes I'll write down some thoughts. You might also encounter some rambles about my health, mostly just lamenting my vitamin deficencies.
ok the source of the last RB I only read a bit but if you turn your brain off since it's all in on the physical part it's really good??? the girl is sooo cute like hella cute and the guy's also drawn quite well. gorge artstyle maybe a bit too exaggerated of a size for my suspension of disbelief but eh
love that it's got a body swap element this is how straight smut can still be interesting 😛
I'm an artist and medical student, and I use art to help me pay some bills.
I built a free, helpful tool because to help prevent other talented creatives from undercharging, as I really see this a lot online.
It's a calculator with a built in reality check
Input your survival costs and expenses
True billable hours
Get the rate you actually need to charge to hit a 20% (or whatever you choose) profit margin.
It generates the rate, a template negotiation email + final invoice.
Plan to keep this tool free, ad-free, and open to everyone.
🔗 Check your math: fairpaycalc.artres.xyz
If the "Thriving Rate" calculation empowers you to double your quote on your next job, please consider hitting the "Buy me a coffee"button. It keeps the server running and the code flowing <3
I am an artist and medical student and creator of Art-Res, a blog where I write and curate art resources. Hopefully you find art that bring
I’m not super proud of how this turned out but after 11 hours I finally finished it!! One of these days I’ll get better at drawing environments and backgrounds
Alright, fine, here's the backstory about replacing my Microsoft Copilot key with a picture of a carp. Warning: that's the whole story.
When I bought my latest laptop, which coincidentally is already losing functionality in multiple keys, I noticed that the right Control button was no longer a right Control button. This was a bad sign for me, someone who fairly frequently used the right Control button. Worse still, it now bore the Microsoft Copilot logo and would open, when pressed, Microsoft Copilot. Not having personal interest in that particular robot, I was now in the market to rebind that key to something else.
I initially considered setting it up as my dedicated The Sims 2 button, but not only do I not yet have The Sims 2 installed on this computer, the way I play that game, it takes upwards of 15 minutes to boot up. I knew I would be constantly accidentally hitting this key, and I did not want to be constantly accidentally opening The Sims 2.
My partner immediately suggested that I set it to open "a jpeg of a fish." I Googled "carp," found an image, and set my right Control key to open a tiny browser window with a link to said fish. Though it is a .png file from pngtree labeled "pngtree-rohu-carp-fish-png-png-image_4022775.png", in an effort to display matrimonial piety, I dutifully labeled it "fish jpeg."
I now accidentally open this link multiple times a day, which is great, because it means I have a lot of opportunities to see a fish. Sometimes I accidentally hit it several times in quick succession, which means I get to see several fish! When I close all my open programs to shut down my computer, I usually find at least one forgotten fish. Things are working out beautifully, and everyone is happy.
The other day I brought up this story to a friend and relayed the saga of my success to her through her obvious confusion. At the end of my tale she asked me, "Why didn't you just bind it back to right Control?" and I had to admit to her that it honestly never occurred to me.
I’m not set up right now to make this like a truly expansive and thorough post but, I’m constantly experiencing cognitive whiplash every time I’m reminded that—put in a complete vacuum, contextually divorced from all societal, political, and environmental implications—generative AI is just… a kind of cool technology. It’s “what if Cleverbot was good.” It’s “hey isn’t this so neat that we have the technology to make electricity come up with an answer like it’s talking to us.” It’s “wow I see how that could be convenient for brainstorming and doing some menial tasks.”
And then the absolute firestorm of social, political, economic, and environmental context encompassing this thing and growing more malignant from 2022 until now has made it such an absolute fucking nightmare beast.
Like extremely extremely smart people have created the technology to make a predictive model that’s really good at saying hi to you and look what’s been done with it. Look what everything has done.
I'm also of the opinion this came at a uniquely bad time. Late 2022 was in fact a really terrible time for this kind of paradigm-altering technology to hit the scene. I'm of the opinion that the suffocating dominance of AI in the public consciousness would not have been nearly as bad if this had debuted in, say, 2018.
And this has so very much to do with the stock price of big tech companies.
I've put the tickers for Microsoft, Google, Amazon, and Facebook together in this chart.
I feel like mentioning inside the tech world, the initial dip at the start of the Covid pandemic was "scary" for those obsessed with their stock's value. You'll notice that is essentially a blip in early 2020, which then led into even more aggressive, even more meteoric stock rises through 2022.
You'll then notice all these companies spent the entirety of 2022 careening downward, because "recession" was the scary talk of the town, and tech stocks tend to be highly speculative, which lends them to behaving really well then things look good, and really poorly when things look bad.
ChatGPT debuted at the very end of 2022. And it debuted into an environment where every single massive tech company was panicking.
And it was weird to witness this from the inside, honestly. It's not a secret that I work for Microsoft. Microsoft's blue line in this chart was confidently up-up-up from when I joined in 2018 through 2022. And the climate, and the direction, of things was steady. The business model was steady. Big decisions were not flung around at random. There was pacing and planning around things.
But 2022 was a freefall panic across big tech. And AI was the one golden rope to pull themselves up with (or hang all of us with. Same thing. Really.)
ALL of that recovery from early 2023 through now? That is ALL riding on AI. The eggs are so thoroughly in one basket. This was a panic decision to pump everything everything everything into AI because it made investors excited and all all the big tech leaders were acting like scared beaten dogs fresh off their 2022 whipping, with only a single-minded purpose to yank themselves up on the AI bandwagon and try to do it better than everyone else.
It's also no secret I hate my job right now because I'm one "Touched Stove, Burned Hand" too deep into this with big asks and big products with tight deadlines getting scrapped into mulch because no one actually thought this through. The only driving force is to do AI faster and more than everyone else. Everyone is actively searching for problems to exist that AI can be slapped onto because it's the entire economy's bet right now. It needs to exist to retroactively justify the investment and keep the Atlassian weight of the economy propped up on AI shoulders.
Anyway. Anyway. I don't think this absolute blind panic, blind adoption, integration into Everything Everywhere As Much As Possible would have happened to NEARLY this scale if ChatGPT came out in 2018, or 2019, or 2020.
I do still think it would be widely used and widely popular, but perhaps more in the vein of smart phones becoming popular and adopted. There would have been a moment to breathe here and there. Every tech CEO would probably not be such an absolute sputtering madman about it, not nearly to this degree.
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
