99.999% of this blog is feral ff6 content. my favorite thing is plant-based Jeff [完全菜食ジェフ], which is Edgar when you meet him in the World of Ruin. I'm 100% normal about him.
I included work from challenges I did (a few sketch a days on Sade's server, one I hosted on paintscroll's server, kimodameshi, ff6artober) that I've posted here and on bsky, plus some stuff I haven't posted (the cat with dandelions, a mandala, a boudoir pic of the protagonist from my novel, and a pic of Edgar I just finished).
Overall, I have learned SO much this year. Thank you all for being a part of the journey and liking and reblogging and encouraging me to keep going. <3 I hope to do more in 2026 and to learn even more new techniques!
The new overworld theme is hauntingly beautiful, and very fitting for this new world Celes has found herself in
I came across some enemies in the tiny desert and they immediately died?? Without me doing anything. Not sure if they were supposed to have died of exposure of exhaustion or something completely different. Either way it's not a good sign 😬
The visual parallel of Celes climbing the stairs to toss the bouquet over the edge when playing Maria, and now her climbing the cliffs to toss herself over the edge... Same musical theme playing. Sort of the same story theme too; now it feels like back in the play, the bouquet symbolized Maria's wish to escape her lonely fate, same as Celes now.
Also... Considering where Celes washed up on the shore... I imagine Cid would have woken up to quite horrific sights over the years considering what he said about the other people from the island. One by one, the living threw themselves over the cliffs. One by one, the dead washed up on the shore. One by one, he buried their bodies where he could.
Can't wait to see if this version of Locke's theme will be in Final Bar Line. It's so soft.
Someone in town mentioned there being eight Legendary Dragons........ I guess I know what I'll be doing eventually 🥲
Aw nooo apparently the eight dragons are sealing away some ancient mighty force. Guess I know what I'll be doing after I've fought the dragons 🫠
(Unless, if the dragons are bad, the Crusader turns out to be good. Unless unless the dragons were actually good all along and the Crusader is going to destroy me)
COMPLETE side track: I just remembered there was this one single time when I had Shadow on my team and we spent the night at an inn and it activated a flashback/dream of some kind?? And it never happened again?? I thought it would be connected to Shadow because I didn't recognize any of the people or names... I guess it shall remain a mystery for now 🤔
I want a remake just so I can see the biggest hug of relief and joy Celes must've given to Sabin when she realized he was alive, and consequently that the rest of them might be too. She's not alone anymore! 🥺
I'd better find Locke soon, there's so many creatures with good items to steeaal
TERRA!!
... MAMA????????
I have to admit I did NOT see that coming, not at all. Terra, single mother of nine kids. Not... exactly sure what to think about that just yet. I'll need to wait and see how they tie this into her journey of self discovery and personhood 🤔
Gerad... Did you just scramble the letters in Edgar to get that cover name? 😆
I know it's because the only guaranteed party member right now is Celes, but it feels so awkward to be arguing about whether or not Gerad is Edgar while his twin brother is standing right beside us 😂 Can't wait to go look for fanart of that moment later when I'm done with the game
I'll have to stop there for now, it's getting late. Next time, to Figaro Castle!
Pet peeve of the day is the way many fiber artists use "carpet wool" as a catchall derogatory term for any wool that is coarse and scratchy, with the implied meaning carpet wool=garbage wool. That is not what carpet wool is.
Carpet wool, as the name implies, is a term for the wool from sheep specifically, intentionally bred for centuries to produce wool that is ideal for carpets (historically meaning rugs or, less often, heavy outer blankets, not just carpet like the modern stretched kind stapled to the floor.)
The ideal fiber for an item that is going to be put on the floor and walked on needs to be durable and easy to clean, therefore each strand needs to be long, relatively thick (yes, that means coarse,) and not prone to easily felting.
What it DOESN'T need to be is particularly "soft." The long fibers will make a smooth enough surface to not irritate the soles of your feet and you're not generally going to be wearing a rug right against very sensitive skin all day where you can feel the prickly ends. "Carpet wool" breeds, also known as longwool breeds, have wool fibers that perfectly meet those criteria.
A rug made from wool from a hair sheep or some meat sheep that is coarse but also brittle and short is going to wear out and fall apart ao quickly it's not worth the effort to make it. Therefore, despite being coarse and "scratchy," it is NOT CARPET WOOL. Use that for stuffing pillows or compost it for fertilizer.
A fine, soft, Merino rug is also going to be a felted, pilled, worn-out mess in such a short time it's not going to be worth the effort to make. Save that for a beautiful lace scarf that's going to be directly against the skin of your neck all day. Judging by what's best for rugs, Merino is actually the "garbage" wool.
Anyway, humans spent millenia developing different fibers for different applications, and also words mean things and don't mean other things. Can we please stop calling Lincoln or Wensleydale wool "garbage" because it makes scratchy socks and can we also stop calling Katahdin or Dorper hair "carpet wool" please.
Also, the sheep that grow the wool are absolutely rocking the look.
Imagine telling these lovely Wensleydale ladies their hair isn't glam!
You are a long forgotten god. A small girl leaves a piece of candy at your shrine, and you awaken. Now, you must do everything to protect your High Priestess, the girl, and her entire kindergarten class, your worshipers.
The stone was immovable, in the past. Indestructible. A spire of granite no mortal hand could even alter.
But mortal hands build clever tools, and these last few hundred years I have lived in dread that they will break this, my sacred stone, the last link that preserves me, a faint shadow of a forgotten god. While my sacred stone stands, I do not, quite, fade away.
I am in a park, now, clipped and tamed, my forests long gone. But they landscape around me and my stone, admiring its beauty, so I do not complain. While they take pleasure in the stone, I am safe.
There is a playground a few lengths away, and the laughter and happy shrieking rouse me a little from my sleep. I watched over children, once. It’s nice to hear them again.
But I don’t truly awaken until the Offering is made.
Little hands touch my stone, with curiosity and a sort of reverence that only the very young feel now. For a child young enough the world is still a mystery, and even an ancient granite stone provokes wonder. So I stir, when she touches the stone, becoming hazily aware.
And then, solemnly, the child places a tiny colourful object in the roughly shaped alcove in the stone’s side, the place where offerings were laid two thousand years ago and more, and I awaken. Many people have put things in that alcove, of course… to take pictures, usually, these days, or putting a lost object where it will be seen. Merely to place an object in the alcove isn’t enough. A true offering is given as a gift, with intent.
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.
In case you just skimmed the post above and missed it, I want to reiterate and highlight Gebru's current position as Executive Director of Distributed AI Research Institute. If you're curious about what AI technology might look like when not applied in the horrifically unethical and damaging way it's currently applied, please check them out.
If we want to have nice things, decentralization is essential, and if we want to decentralize, we need to have our eyes on things that are beyond the scope of the current Big Tech narrative.
The Distributed AI Research Institute is a globally distributed organization of academics, activists, and engineers conducting community-roo
