Dear video essay creators. A video analysis is when you analyze a piece of media. No no look at me. A summary, no matter how thorough, is not an analysis. An analysis requires you to draw conclusions about the media such as authorial intent, real-world parallels, discussion about themes/worldbuilding/character motivation, and so much more. You have to stop summarizing something and saying that’s analysis. The Gaylors are doing more critical analysis than you. Is that who you want to lose to? The gaylors?
･ ⟢ ⋮ Free to use, credit not required but appreciated. Reposts/Edits/Recolours/adding to Masterlists or hoards allowed. Please do not modify my work with AI.
IM SO EXCITEDDD FOR ARTFIGHT OMGGGGG!! Which team will you choose? Miami Dolphins vs Evil Intimidating Horse vs Fake Smile Girl!!!!
Thin people are absolutely relentless with this. "But I have to TELL them!" "I can't just not SAY something!" "We can't just PRETEND it's fine to be fat!"
They really seem to think the main reason people are fat is because we're simply drowning in approval and no one has been brave enough to inform us that it's better to not be fat, actually
the issue with growing up in the 2000s and 2010s was like there was this really big push toward "accepting your weirdness" overall but they meant like idk wearing mismatched socks or something not being tangibly beyond the norm in any way shape or form
random PSA, I know a lot of people use duckduckgo as a Google alternative search engine, but it always kind of annoyed me when I was using it because it felt like No Name Brand Google
I have switched to using Startpage.com and vastly prefer it. for one thing, instead of displaying an "AI summary" at the top of the search results (unless you turn it off, yes I know), it displays the first paragraph of the Wikipedia article, with link, whenever it finds one that's relevant.
also a waaayyyyy better sense of design than duckduckgo
also private, European based, least annoying search I've used lately (RIP old "don't be evil" Google)
i have one of those, scraped from multiple different rec posts:
Search Engines
Infinity Search is an alternative search engine with a special focus on privacy
DuckDuckGo is a popular search engine for those who value their privacy and are put off by the thought of their every query being tracked and logged. Uses bangs, ![site] for in-page search (sells your data to microsoft and draws from fucking bing)
WolframAlpha is a privately owned search engine that allows you to “compute expert-level answers using Wolfram’s breakthrough algorithms, knowledgebase, and AI technology.” A data search engine.
Boardreader is a search engine for forums and message boards. It allows you to search forums and then filter down results by date and language.
Based in France, Qwant is a privacy-based search engine that won’t record your searches or use your personal details for advertising. Uses “&” as a bang search.
Another privacy-based search engine is Search Encrypt, which uses local encryption to ensure that users’ identifiable information cannot be tracked. Metasearch across multiple engines.
Offering unbiased results from several sources, SearX is a metasearch engine that aims to present a free, decentralized view of the internet. Can be self-hosted.
Gibiru’s tagline is “Unfiltered private search” and that’s exactly what it offers. Requires AnonymoX Firefox add-on for privacy.
Disconnect allows you to conduct anonymous searches through a search engine of your choice.
Swisscows provides fully encrypted searches to protect your privacy and security. Built-in violence/porn filter cannot be overridden.
MetaGer offers “Privacy Protected Search & Find” through its anonymised search. A plugin will allow it to be made a default.
Gigablast is a private search engine that indexes millions of websites and servers real-time information without tracking your data, keeping you hidden from marketers and spammers. Variety of filtration and refinement options for searching.
Oscobo is a search engine that protects your privacy while you search the web. By not using any third-party tools or scripts, your data is protected from hacking and misuse. Has a Chrome extension to allow use in toolbar.
https://search.marginalia.nu/ an independent DIY search engine that focuses on non-commercial content, and attempts to show you sites you perhaps weren't aware of in favor of the sort of sites you probably already knew existed. Use old-school searching rather than query-based for the best results.
https://www.mojeek.com/
https://wiby.me/ - It’s goal is to index as many personalized websites as possible, and NOT commercial sites.
https://4get.ca/ it works a lot like SearX, but honestly better. It doesn’t have its own index, but pulls from many others. I think it’s the best for research, since it allows you to search for answers from different indexes, is easy to configure, add free, and avoids censorship as much as it can.
https://www.searchenginemap.com/ for more on how search engines relate to each other.
https://yep.com/ is a crawler
https://www.etools.ch/ retrieves from Google, Mojeek, Bing, and Yandex, like Searx
https://www.dogpile.com/
https://searxng.org/ (next gen Searx)
https://luxxle.com/ - possibly conservative?
https://presearch.com/ - good for academic?
https://kagi.com/smallweb - free/randomised Kagi.
Other Searchers
www.refseek.com - Academic Resource Search. More than a billion sources: encyclopedia, monographies, magazines.
www.worldcat.org - a search for the contents of 20 thousand worldwide libraries. Find out where lies the nearest rare book you need.
https://link.springer.com - access to more than 10 million scientific documents: books, articles, research protocols.
www.bioline.org.br is a library of scientific bioscience journals published in developing countries.
http://repec.org - volunteers from 102 countries have collected almost 4 million publications on economics and related science.
www.science.gov is an American state search engine on 2200+ scientific sites. More than 200 million articles are indexed.
www.base-search.net is one of the most powerful researches on academic studies texts. More than 100 million scientific documents, 70% of them are free.https://cosine.club/ is an electronic music similarity search engine
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.
the weird thing about being a leftist is the government calling you a radical extremist and your family believing that youre a radical extremist and the whole times your main political beliefs are shit like "we live in a world where we could very easily end world hunger, homelessness, most disease, poverty, ect. and the people in power are choosing not to, and thats evil and should change" and that bigotry is bad
