The other day I was surfing the internet and I found this specialized painting colour wheel, it shows how real paint colours relate to each other.
Outside: the purest/brightest colours from the tube.
Inside: naturally muted or earthy colors, like browns and ochres.
The Center: dark neutral tones used for mixing shadows.
The Lines: two equilateral triangles. One shows the triad of primary colors, and the other shows the triad of secondary colors. Triads are traditionally used to create vibrant yet perfectly balanced compositions.
Complementary colors: are those located directly opposite each other on the wheel.
Shadows I:
If you want to achieve rich, dark neutrals, the standard rule is to mix a color with its direct complementary (opposite) tone. However, doing this can often make your shadows look flat or muddy.
Instead of using the exact complementary color, the real trick is to use one of the colors right next to it (either to the left or the right). By doing this, you avoid a dull mix and introduce a beautiful, subtle color bias into your shadow, keeping it clean and full of life. You can see an example of this in the second image.
I want to share this with you because I think it is really illustrative!
Reference: “Quiller Wheel” by Stephen Quiller (👈link)
Thought crimes aren't real, telepaths don't exist, and even if they were and do, having your head constantly play "Fly me to the moon/Let me kick its fucking ass/Let me show you what I learned/In my moon jiu-jitsu class" on repeat while out and about in public isn't illegal
If a fantasy world has an ancient tree of wisdom, that means it must also have young trees that are dumb as shit. Just giving terrible advice like, "the evil wizard is kinda hot"'
I WAS FUCKING WONDERING WHAT THOSE DIGITAL PRICE TAGS WERE ABOUT SUDDENLY i had hoped they were so the workers didn't have to finagle those little papers into the slider part anymore 😭
Hi, yes, that is the OFFICIAL excuse made to me by the guy replacing the paper tags with digital ones at my local Walmart, but the end goal is to remove the numbers off the shelf entirely, replacing them with QR codes that you have to scan with the app…. Which requires your login information….. and also stores your card information so even if you didn’t use your Walmart account at the physical checkout, if you used a card they recognize, they assign that purchase to your Walmart account purchase history.
I explained very clearly to the manager my issue with the meat section not having the price tags listed, and they claimed it was only going to be for the meat, since meat is by weight, and the price of each item is printed on the packs of each item.
Sure. That’s how they get their foot in the door. Fast forward not even two weeks, and here we are:
Bar codes. No prices, no item descriptions. No price stickers on the individual items. Heck, not even the name of the item that is SUPPOSED to be there.
No. The only way to see the price is to scan it on your phone app, which is also recording what you looked at recently, as a way of gauging what you might be looking for in the future.
So here’s what we’re gonna do gang:
Every time you go into a store that has implemented these price-less tags:
Take 1-3 items up to the cash register. Ask the cashier for the price, or hit the price check item on the self checkout, which will likely call over the attendant.
Express that you didn’t actually want it, you just couldn’t see on the shelf how much it was.
POLITELY, AND WITH A THANK YOU FOR THE PRICE CONFIRMATION, Give the items to the cashier or attendant to put back.
When they inevitably try to push the app, politely decline. If pressed for why not, say you don’t want to have to carry your phone in-hand the whole time you are shopping in order to see how much things cost. (Not having cell service or data to use the app is NOT a valid excuse, as stores already often have complimentary WiFi AND more stores will provide WiFi rather than give up on this push for surveillance pricing)
If it’s a shelf-stable item, the cashier will have to set it aside, taking up room in their limited operating space, and eventually pass it off to someone to put in a holding area to put back later. If it’s a fridge/freezer item, it might have to get tossed due to food product sale regulations.
In either case, you are making it a pain in the ass for them to have these digital bar codes. Tie up the checkouts. Give the employees more busywork that the company has to pay them to do. Hurt their bottom line having to toss the pint of ice cream you carried around in your cart for 20 minutes before giving it back to the cashier.
Yes, call your reps. Yes, push for more legislation like this in more places. But also take an extra minute out of your shopping trip to MAKE IT HURT for companies to pull this shit.
I've seen some people in the notes express (very fair) concern that this is only going to inconvenience already under-paid laborers, and not have any impact on corporate. While I can't speak for every company or every store, I do work in a grocery store and I can tell you this is precisely the kind of thing that would have an impact, especially if people are doing it en masse. Stores absolutely track their shrink numbers, and they do draw distinctions between what gets stolen, damaged, or wasted for other reasons. If people are making it clear that the reason they're bringing things to the cashier is that the prices are not adequately represented on the displays, and rather than improving business it's wasting product, slowing down transactions, and causing confusion and mistrust in customers, that is a language that shareholders speak.
I worked in retail for years. If this had happened while I was working retail, I would have been delighted and felt great solidarity with anyone who was wasting my employer's time and money and giving me busy work as an act of protest. In point of fact every moment the employee spends carting items back to the shelves is a moment not spent standing at a register.
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.
turnnoffyourmind
dededos
theartofmadeline
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