A Hoard Of Woodpeckers

[neriedar]

Welcome to my Blog!

Here you will find my fandom sillies and theories as well as things that generally tickle my fancy. Occasionally there will be more serious items as well as potentially uncomfortable topics for those squeamish of biology, body horror and other topics. I work in Veterinary Medicine, you kinda get a bit desensitized to certain things and lose your filter a bit.

Feel Free to use my Brain Rot however you see fit ( Barring A.I. uses).

[loontattoo]

see unfortunately I have this condition where if I am not explicitly told that I am a part of the ingroup then I will assume I must be part of the outgroup

[neriedar]

Okay, that stream today really put some thoughts in my head. The books heavily imply that the last corruption incursion . I don’t think those dragon voices from the wheel are alone. The corruption had all its mana ripped away by iron. If we take iron drinks a bit literally it holds mana. If the corrupted mana is being held in iron with the dragon mana they might be going a bit crazy. “ They don’t care about us……” I think that might not be a truly honest statement. Me thinks manipulation is a foot. Get the one person who can free you on your side, separate him from the people who could help him see the truth.

[depsidase]

[depsidase]

[depsidase]

[abbiistabbii]

New Windows AI feature records everything you’ve done on your PC

Recall uses AI features "to take images of your active screen every few seconds."

I think every computer user needs to read this because holy fucking shit this is fucking horrible.

So Windows has a new feature incoming called Recall where your computer will first, monitor everything you do with screenshots every couple of seconds and "process that" with an AI.

Hey, errrr, fuck no? This isn't merely because AI is really energy intensive to the point that it causes environmental damage. This is because it's basically surveilling what you are doing on your fucking desktop.

This AI is not going to be on your desktop, like all AI, it's going to be done on another server, "in the cloud" to be precise, so all those data and screenshot? They're going to go off to Microsoft. Microsoft are going to be monitoring what you do on your own computer.

Now of course Microsoft are going to be all "oooh, it's okay, we'll keep your data safe". They won't. Let me just remind you that evidence given over from Facebook has been used to prosecute a mother and daughter for an "illegal abortion", Microsoft will likely do the same.

And before someone goes "durrr, nuthin' to fear, nuthin to hide", let me remind you that you can be doing completely legal and righteous acts and still have the police on your arse. Are you an activist? Don't even need to be a hackivist, you can just be very vocal about something concerning and have the fucking police on your arse. They did this with environmental protesters in the UK. The culture war against transgender people looks likely to be heading in a direction wherein people looking for information on transgender people or help transitioning will be tracked down too. You have plenty to hide from the government, including your opinions and ideas.

Again, look into backing up your shit and switching to Linux Mint or Ubuntu to get away from Microsoft doing this shit.

[solunas-insomnia]

Steps to Disable or Uninstall 'Recall' in Windows 11 24H2

there are multiple options here depending on how comfortable you are digging into your computer's registry. You can either simply disable it surface level through settings or excise it entirely from the system registry

[muddlemore]

reblogging again as a cautionary tale to please PLEASE fucking make a system restore point before you do anything. i consider myself tech savvy and still nearly bricked my computer. and make sure you know how to access safe mode

[kawuli]

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)

[yomikoreadsbooks]

Keeping a list of Google alternatives just in case…

[saxifraga-x-urbium]

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

[justalurkr]

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.

[justalurkr]

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.

[irispurpurea]

This is the paper. It's excellent, highly recommend reading it.

I remember reading about Gebru's firing but I had no idea this was the paper she was fired over.

[republicansaretheproblem]

[snowkissedmonsters]

Also on age verification: I have been on this website since 2011. Unless you think I started blogging at age 2, you KNOW I'm an adult.

[dreamerinsilico]

#the fact that 'can prove access to an online account at least 12 years old' or even 'account to be verified is itself fully 18 years old'#AREN'T accepted methods of age verification is such a telling sign of what the real purpose of age-gating laws is:#data harvesting and deanonymization and the buildout of state-controllable ways to restrict both content and internet access itself en masse (via @shinelikethunder )

⬆️

[w1f1n1ghtm4r3]

saw some people on twt turning the flight dragons into humanoids and i couldnt resist doing it with drop and aster. the blurple sisters

[depsidase]

[barkingbarghest]

Here is an article from NPR about it (May 22, 2026):

Carolina Milanesi, an independent technology analyst, said Google is trying to make its cash cow business — search — richer and more personalized, and it will make shopping easier. But there is a risk that users may have fewer choices about what to click. "Right now it's: I ask a question, I get a bunch of answers and I feel that I'm in control as to which answer I take, or if I'm looking for something, which product I'm going to end up buying. That is going to be less so going forward," she said. Milanesi envisions AI-enabled search and agents proposing products to consumers — perhaps even those they have requested — but with less clarity or choice around where it's coming from. "If you're going to say: 'I want a pair of Jordans, go find them,' you're not necessarily sure what steps have been taken and whether the AI has used a source or a store that was paid for and therefore came up in the search results," she said, "or if AI actually went and did their due diligence and picked the best for me as a customer."

And here's one from Time magazine (May 20, 2026):

While Google already has “AI Mode,” the company will now power the whole search bar through its new Gemini 3.5 Flash model.  Instead of the classic list of blue links, Google Search will now also generate a custom page with an AI-generated summary of what you’re searching about, which will then trigger a conversation with AI Mode on the main page, allowing users to ask follow-up questions—similar to the kind of layout you would see when opening ChatGPT.

And a little more from Time's article on how this may affect the websites that we are trying to search for:

When Google first started implementing AI-assisted results, news publishers warned of “catastrophic” impacts on the industry, much of which relies on Google search to drive users to their websites.  Last year, news websites saw significant traffic declines as chatbots increasingly replaced Google search as the primary way to find sites and ask questions.  Small businesses also noted drops in traffic to their sites from Google, which has traditionally delivered customers.   Lily Ray, vice president of SEO strategy & research at Amsive, a digital marketing agency, warned as early as last year that Google’s planned changes to search are “going to have a devastating impact on the Internet.”  “It will severely cut into the main source of revenue for most publishers and it will disincentivize content creators who rely on organic search traffic, which is millions of websites, maybe more,” she told Technology Magazine.  

[anarchopuppy]

noai.duckduckgo.com blocks all AI content in search results automatically

[neriedar]

Okay, the Professor gives me complex motives vibes. Their initial interactions seem that of someone being forced to do something, which for a mighty dragon would chafe something terrible. The fact they also seem to have partial mind reading power( hinted at by Legundo’s latest conversation) means they know much more than they let on yet rarely interfere. The one factor that I keep coming back to for this behavior is that we do not know if they are bonded to a person or who that is. From Jibble(Jubble?) we see that dragons that are unbonded are either incredibly slow growing or are limited by a lack of magic in an area (or he has dwarfism of some variety). If the Professor was not around for the predecessors time then they are likely bonded to someone. The most likely candidate is the Queen or a high ranking official. The wildest potential bond would be Junipers mother considering what Juniper said about dragon bonding showing hereditary tendencies. If the Professor is bonded to the queen and she has , let’s say less than genuine intentions for the academy they would know. There is a whole thing about Summer/ Winter or Seelie/ Unseelie fae and how they trick and interact with mortals that are pinging for me with the Realm of Summer and Realm of Eternal Storm. Summer fae on the surface can be inviting and sweet but are prone to truly horrible deals when they want; sweet words to horrible traps. Winter fae are also capable of this but are more outwardly horrifying, they don’t hide their brutality;if they are gonna screw you over they are more straightforward. The strange thing is the winter fae are said to be the ones to bargain with if you are in the unenviable position of needing to. Winter fae are transactional in bargains ,they will ruthlessly enforce that bargain but they are less likely to bargain for frivolous things so you better have something they deeply want or need. Less likely to add flowery worded traps to the bargain but still capable.The Queen gives summer fae vibes 100% . I think there are some ancient wrongs fueling this conflict and the corruption that she knows about.

[aninvisibleghosttt]

Gadget stole a knife...

[neriedar]

Child has learned this is an appropriate hoard item

[neriedar]

Dragon Natures?

Does anyone have a list of the dragons names and natures? I’m trying to gather thoughts on dragon behavior( scaley cats?dogs birds? Do they teeth?) Like I feel we are kinda neglecting them as a fandom; there is so much potential here for shenanigans, it’s like a small child and a pet combined.