As a kid, I was really upset that Bill Watterson wouldn't license Calvin & Hobbes so I could have plushies or so there would be a Saturday morning cartoon. Now, I realize his resistance is the reason we don't have a Calvin & Hobbes DreamWorks movie starring Chris Pratt.
you don’t realize how important lunch is until you’re wandering around thinking about how unloveable and untalented and uniquely cursed you are and then it’s 4pm and you finally eat lunch and you go Oh. oh right.
we gotta get back to torrent distribution, i just watched someone eat eight grand in bandwidth charges because they ran a direct-download piracy site with local file hosting through cloudflare. torrents were invented literally for this exact reason
i have a file or folder on my pc that i want to share with other people. let's call it gayshit.mp3
unfortunately gayshit.mp3 is 750mb and im not paying for discord nitro so i need another way to send it
i put it into qbittorrent and it makes a torrent file. this is essentially a very small file that points to gayshit.mp3 so other computers can find it. kinda like a treasure map
i send this tiny file to my friend, who loads it into qbittorrent. their computer takes a moment to find mine over the vast expanse of cyberspace and then (as long as my pc is running and the file is still where it should be), it gets copied from my hard drive to theirs
this is the cool part: if somebody else loads that tiny file, they can download it from both of us. if i'm offline but my friend is on, the third person can still get it. this also means that if two people have separate halves of the file, they can download the other half from each other. as long as some combination of people have the pieces between them, they can all have the whole thing.
crucially this does not require a server!!! you can just upload the file to a few people and as long as they keep it, it's still accessible. as long as somebody, somewhere is still connected, it's available forever. the only way it goes away is if everybody disconnects from it.
How ATLUS Got Dante From Devil May Cry Into Shin Megami Tensei III: NOCTURNE
As I'm sure you can tell from the title, I just finished a video covering the publicly known behind the scenes details surrounding ATLUS and Capcom's collaboration on Nocturne Maniax edition. In my opinion this is a pretty interesting moment in ATLUS history, and I think even fans who are avid readers of staff interviews will learn something new.
Document Transcript
For anyone who requires a transcript to follow along with the reading of the document within the video:
Dante receives a request to "kill a fiend" from a gentleman in a suit who calls himself "Louis Cyphre" and lands in Tokyo, where he is dragged into the "Tokyo Conception". Then, while wandering around Tokyo, Dante encounters the true hero and engages him in a fight to the death in order to "kill the Fiend". However, Dante is convinced through the battle that there is an ulterior motive behind the request, and resolves to set out with the hero to discover the true purpose of the request and, possibly, to carry it out.
"…. I'm always ready to take you down"
Dante had lost his father's inherited magical power after being drawn into the Tokyo Conception. It is the magical power that activates the Devil Trigger and transforms him into a form that maximizes the power of majin [Fiend]. Thus Dante, in such a state, is in the greatest danger. Not satisfied with achieving creation in another world, Beelzebub, the "Tyrant of Chaos," appears in Tokyo, twisting the dimensions in his insatiable pursuit of power.
Beelzebub revived at each attempt, no matter how many times he tried to strike him down. Mentally and physically pushed to the edge of the cliff, Dante's father Sparda's voice rang in his ears.
"…Dante, leap into the chaos.
The true opponent you must defeat is not such a boy, nor such a revolting demon.
It is the fallen angel from the depths of chaos! Now leap in! I'll lend you my power!"
Dante, determined, accompanied Beelzebub to the dimensional hole that Beelzebub had distorted and expanded, and jumped into it himself.
"Boy, I won't let you die. After all, I'm the one who's going to kill you."
Dante was calm, realizing everything.
Throughout his journey with the boy, he learned that the request was for a "Fiend Battle Royal," a trap set by Louis Cyphre, and that by falling into the trap, he learned of the true enemy he was to defeat. In the depths of the converging dimensional hole, Dante activates his Devil Trigger, transforms into his true majin [Fiend] form, and is about to deliver the finishing blow to Beelzebub.
"Let's rock, baby!"
Right after that voice rang out, the dimensional hole was completely sealed, and the protagonist found an "event item (tentative) [menorah/candelabrum]" dropped nearby…
Do you think it's like a rite of passage for every new generation of xmen to momentarily feel like it's kind of fucked up to be trying to kick the shit out of a senior citizen until magneto crumples someone into a cube like a trash compactor in front of them and they're just like Oh Ok
On the one hand I'm sure everyone else is very heavily emphasizing that magneto is Theee big bad of all time and so on and so forth the whole time the new ones are training but also. Like. Imagine you're the new new new mutants or whatever and somebody manages to actually knock magneto over and he stays on the ground for a second and you're kind of looking at each other like. Guys isn't he *really* old what if we just killed magneto & then the entire city starts shaking while he's getting back on his feet & you're like ohhh he's just REALLY ANGRY now. Ok :) oh that's bad :/ oh shit. Uh oh
[ID: tags from @transguyhawkeye that read, "#having a panic attack over whether you just broke magnetos hip meanwhile he just separated wolverine into recycling and organics for the #third time this week" /end ID]
Oh, to be a little kitten who just got vaccinated and then taken to a high-end restaurant and tasted the best food the chefs could offer and then fell asleep in a basket.
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.
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