Logical Behaviour definitely comes off the weaker of the two, tho' i feel like that's more due to its focus, than any particular failing in design.
Billed as a semi-tutorial to help show players how to integrate simple logic into stage design, and it makes sense Tony would want to reduce visual distraction as much as possible to highlight the moving pieces, but the lack of elegant presentational flair they usually brought to their levels really hurts this one. The relatively simple, low-challenge gameplay really stands out when it doesn't have anything in the visual field to pick up the slack.
Now, to be fair, that is the level very much working as intended, and i can see how it would have been a help to budding creators (especially this early in the game's lifecycle, when the whole community was still noodling out best practices), so i don't want to knock it too hard. Still, coming at it from the perspective of now, and it really doesn't stand up wiv Tony's other work.
Hidden Paradise really makes up for it tho'. This one was made as a capstone and send off to LBP1, and it is really, really good. Nice and chonky wivout being bloated, a delightfully busy visual presentation, and some real nice variety in gameplay (even a swimming section that doesn't suck! wonder of wonders!). Just good times here from start to finish, and a worthy coda to Tony's work in LBP1.
It's a shame they never really picked things up in LBP2. i was never personally acquainted, so i can hardly say if that was a life getting in the way thing, a growing out of the series thing, or a disappointment wiv LBP2's direction. They did put out one level, Hardboard, which is unfortunately way above my skill ceiling, so i can't even properly archive it, tho' there is at least one video out there showing it played to completion.
(video of HardBoard included here for completeness. it's definitely not mine)
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i'll probably talk about it more later, on stream if not here, but i went to Tokyo Rainbow Pride yesterday, and just between you me and the wall, it kinda sucked. i'm pretty sure this is just a "i'm doing it wrong thing," but it did not feel like my scene.
Honestly, i think i miss monsters and freaks, and i probably should be trying to figure out where to find them.
You know, sometimes you’re having a crummy day, or a crummy set of days, and you’re just feeling lower than low, and then you get something like this out of the blue
and you figure, “well hell, guess i can’t pack it in just yet.”
so, like, if there’s some creative, or anyone really, that you follow or who’s in your life, maybe drop them a line & let them know they make you happy; it really can mean a lot.
i know my art makes people happy despite other people saying horrible stuff Abt it or saying what I draw isn't good enough. it does get to me and it adds stress onto everything else but I appreciate the others who are kind to me about it. my heart just hurts a lot. I'm crying right now.
ill be real i do look kinda hot with a two-day stubble. if we were like 40 years into a proper gender abolition movement i would probably wear it, unfortunately i have to constantly interact with cis people (and like 70% of queer spaces lets be real), and wearing a beard while trying to be respected as a woman and get she/her'd is just kneecapping myself.
if you can't tell i wrote this with the knowledge that if i just said "i wish i could be a girl and have a beard" i would get a dozen "you can! just do it!" and like. im sorry but im 5'11' and shaped like a dorito and my voice is a natural baritone unless im putting effort into it. if i actually want people to see me as a woman (and i do), i am NOT working with a lot of GNC wiggle room here.
in general i'm just constantly walking the line and questioning myself in regards to "do i like looking fem or am i doing this so people will respect my gender" and i think it varies from moment to moment
Just as how Satanic Panic of the late 20th century was actually about women entering the workplace and the evils of the Daycare that allow the women to be able to work hidden behind the rhetoric of Protecting the Children, the "Kill a Pedophile" branch of Qanon (by far the most popular) has been about rallying against sexual minorities in public spaces hideen behind the rhetoric of Protecting the Children.
It is a deeply reactionary current of yankee culture and every single one of you that participated in demeaning LGBT+ ppl being visible in public spaces or Protecting the Children from Travis Scott/Balenciaga/Wayfaire helped it penetrate the mainstream. This is true for unquestioningly pushing around whisper campaigns targeting LGBT+ people, especially trans women who are targeted disproportionately by these people. Every time you participate in pedojacketing a trans woman, you're essentially just your reactionary parents banning D&D from public library that you've sworn you'll never become, but with pride flag patch on your denim jacket this time.
i think one of the worst things the left wing internet ever did was push the idea that oppression is basically a virtue, and being oppressed is a sign of your morality. it has made it like…impossible for some of you to hold the idea that most people are privileged in some ways and oppressed in others. AND a lot of you seem to have it in your mind that terrible people cannot be oppressed, and that oppressed people cannot do terrible things, which is a dangerous rhetoric to hold imo.
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.
