lil bit shy, but i’ll say hi back!!! i don’t interact a ton on tumblr outside of blank reblogs, but if you wanna chat more feel free to ask for my discord where i’m more active!
if you’re the type to moralize about fiction (kinkshaming, pro/antiship bs whatever, etc.) then i invite you to reconsider why you believe that fiction is representative of someone’s actual, real life actions. if you’re polite you can even ask me about it.
Sideblogs:
@end-of-the-art : My art sideblog, i reblog my art from here.
@a-dragon-hoard: my Hoard, a collection of reblogged dragons from everywhere on the site I can find. Queue is kept full, but I prolly won’t be interacting much over there.
Other Stuff:
my site! stuff i make, and links to my other socials!!
therian/dragonkin playlists
Tags:
#reblog: on any post I reblog, usually along with some comment or something (if i rember) (queued posts don’t have this)
#end.art: my art tag, this has all my art (though you should check my sideblog @end-of-the-art!)
#end.txt: for original text posts and additions in the tags
#end.info: for information/resources I might share (I have a habit of collecting, if you have a request feel free to let me know!)
#end.mp3: for my music related stuff, I sometimes post songs or playlists
dont trace reference images. dont even LOOK at reference images. in fact, don't ever look at anything that exists in the world, in person or in photos or in videos, even when you're not drawing, because you will still be able to use the memories of what something looks like as a reference when you're drawing it later. yes that includes yourself. destroy all the mirrors in your house. don't look down at your hands or feet. don't look. close your eyes. close them tight. forget everything. it's okay, embrace the darkness. just forget.
unironically i think we need to bring back computer labs because APPARENTLY some people WERENT taught basic computer literacy and internet safety in school
things about computers/the internet i think kids should be formally taught in schools because theyre important to know and the amount of soon to be grown adults i know who know NOTHING about any of these is quite frankly almost all of them (and resources to learn if you dont know these things, because its never to late to get better with computers)
how to troubleshoot by yourself when you have a technical problem
what common file types are
some very basics on how to use ""developer tools"" on your computer (because i cant think of a better way to refer to them) like task manager and command prompt (and their mac equivalents, terminal and activity monitor ofc)
how to read and understand a privacy policy and what your personal data is, as well as what it being collected actually means and steps you can take to keep it private
how to understand terms of service
(hey. if you have trouble with reading legalese and worry about being able to understand these policies anyways, here's a site that gives basic summaries of privacy policies and ToS)
what a cookie actually is
internet privacy and your digital footprint!! seriously i dont know why we stopped teaching people that they shouldnt be putting their entire real identity online in a world where your online actions can ruin you irl
basic safety measures like antivirus software (and why you should use it or if the built in one on windows or mac is enough for you) and backing up your computer (also a mac guide)
common keyboard shortcuts (and on mac)
as an additional note: things i think everyone should know on computers and the internet but schools may bit hesitant to teach about for whatever moral/legal standards schools pretend to operate on
vpns and adblockers! (btw for most of these where you can pay for things im purposefully not recommending any specific software but seriously just use ublock origin for an adblocker)
how to not get a virus while pirating something
what a temporary email is and when to use one
red flags that you shouldn't trust a website (and how to quickly check the security of a site)
what javascript on a website does and how to disable it to get around paywalls
ok one last addition! if you want to take it one level higher, i think learning the very basics of at least one programming language is good for people. it makes computers less scary and it makes you feel very cool, and a lot of people get discouraged about it because it seems overly complicated and hard to learn outside a formal classroom setting, so heres some resources for learning the very basics of python (because i consider it the easiest language to learn and knowing one language will make it easier to learn others)
an online compiler so you dont need to download anything or worry about running code directly on your computer if that makes you nervous
a basic video guide to introduce you to python and walk you through beginner steps
a guide to some syntax and commands you should know (this was literally my lifeline in my first CS class)
some performance tasks to give you things to code to practice and assess yourself
From what I've come to understand, Millennials were the only generation to be taught basic computer literacy in grade school and high school.
My Gen X high school computer exposure was punch card FORTRAN (no, I don't remember any of it) and the bleeding-edge TRS-80 and Apple ][.
(My Boomer sisters didn't even get that much.)
Despite being a STEM major, I never touched a computer during my first ill-fated stab at college in the 1980s; at that point, computers were still for dedicated computer science majors, or were expensive toys that didn't do much. Returning to school a decade later got me access to some very thorough computer and library science classes that provided systematic rigor to what I'd learned messing around on my own on a 286 DOS machine.
By my understanding, in the 1990s, most of the information that @dreamsy990 provides above was fairly common curricula in US primary and secondary education.
(On the other claw, I had several Millennial classmates in fairly advanced courses in the late '90s who had to be slowly coached through things like "open a folder on your computer" before we could actually start learning the software.)
Things changed around the turn of the century, though -- not just because "No Child Left Behind" disrupted the entire US educational system, but because there was an assumption that "this is the first generation that grew up with computers, so they must instinctively know How This Stuff Works."
Unfortunately, that ignored the fact that the tech industry, through both operating system design and the advent of device-based apps, spent those years deliberately obfuscating "How This Stuff Works."
@dreamsy990 also noted:
seriously i dont know why we stopped teaching people that they shouldnt be putting their entire real identity online in a world where your online actions can ruin you irl
Simple: the commercialization and monetization of the Internet relied upon people buying into the Panopticon. As I said back in the days of the Nym Wars: They needed to make you easier to track. They needed to make you a product.
When Facebook's CEO claimed that "the social Web can't exist until you are your real self online", I replied:
The logical fallacy, of course, is the conflation of "real self" with "legal name". You can't be your "real self" if you're always wondering, "what would my family think of this? What if my boss Googles me?"
I am my "real self" online, and my "social Web" is woven among those who know me as "Athelind" and "Your Obedient Serpent".
That other name?
That's not my "real self", Ms. Sandberg.
That's my banking information, and I know why you want it.
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.
at some point in your life you will be boiling fruit, water, sugar, and lemon juice in a pot to make a syrup or jam. the instructions will tell you to simmer for a certain amt of time. your timer will go off and you will look at the pot and go, "hm, this doesn't look thick enough. maybe i'll let it go for another 10 minutes." this is the devil speaking. it's only so liquid right now because it is at boiling point. it will thicken when it cools down. learn from the follies of my youth and do not let this happen to you
at some point in your life you will be making a sauce or a stew in which you need to add cornstarch to thicken it. and you will prepare a slurry of starch in cold water and think "this looks like way too little starch to thicken this amount of liquid." this is the devil speaking. cornstarch instantly polymerizes at 95°C and if you add too much it will turn into an impossibly thick goop.
at some point in your life you will be making some sort of cream based dessert that requires gelatin to thicken it. and you will soak some gelatin sheets in water and think "this is too few gelatin sheets for this amount of cream." this is the devil speaking. it will thicken in the fridge and if you add too much you will end up with milk jelly
at some point in your life you will be baking cookies. you will take the sheet out after twelve minutes as the recipe instructs and the cookies will still be glistening and soft. "these don't seem cooked enough," you will think to yourself, "i should place them back into the oven until their edges are nice and golden." this is the devil talking. this is how you get dry, overdone cookies. the cookies will continue to bake on the warm sheet for several more minutes and then harden up after sitting on a rack for a while. trust the process. trust the process.
at some point in your life you will be adding a small pasta to a soup and you will think "that is not enough small pasta." this is the devil talking. the pasta will absorb the stock and expand. this is how you end up with a soup that is a solid mass of soggy ditalini.
At some point in your life you will be adding garlic to a dish and you will think "that is not enough garlic." These are angels speaking. They are correct. Add more garlic.
Never forget that the physicians and "detransitioners" they bring to speak against trans healthcare are paid enormous sums to give false testimonies. They're not legitimate sources and shouldn't stop you from living your best life (source).
They're just not there to platform in the first place.
This is not something that stories about "concerns" tend to highlight, but the number of examples this very motivated nationwide search for "transition ruined my life and maimed my body and it should be illegal" testimonies has found is like...a literal handful.
Set aside percentages of detransitioners broadly defined and poorly researched. If you're talking about people who claim that they have been harmed by transition, the number is essentially nil.
Also, this can't be emphasized enough: of that literal handful, a significant number have come back out as trans and retransitioned (or de-detransitioned). Like, it's not just a small number of anti-transition detransitioners, it's a number that is shrinking because people keep crossing back over to the pro-transition side.
I also usually chose a random commenter I have seen often deduce we have similar tastes and start trusting them with my life (reading fics I would have never picked up by myself)
THIS is the way!! it has been my experience that authors very often do not like to read the same things they like to write, and vice versa. sometimes they will even read completely different pairings and fandoms than they write!!
going through author bookmarks may or may not yield good results; going through the bookmarks of fellow commenters almost always will!
I know i've said it before, but if you are concerned it could be real and not a scam, the best way to avoid getting scammed is to return contact separately.
Here's how that works:
say you get a text from your internet provider, let's say it's Comcast (whom i hate). So you have this text that says it's from Comcast about your bill with a contact number and a clickable link -- could be real, could be a scam.
Don't touch anything about this text. Open a web browser and look up the customer service number for Comcast. Or get the number from the bill they send you. However you do it, get the contact info for Comcast from a trusted source, like an official phone directory or the Comcast website itself.
Get in touch with them using that information.
So. Let's run the example both ways it could go.
If it IS a scam: you reach out to Comcast and tell them you were contacted about a problem with your bill, they look you up in their customer database, and they tell you there is no problem with your bill.
If it's NOT a scam, you do the same thing, they look you up, and they explain the problem. In this case, neither Comcast nor the employees involved give a single shit whether or not you clicked the link in the text vs. going through their official website.
This works the same for the your bank, the IRS, Amazon, political causes, charities, everything.
By handling any questionable incoming calls to action this way, you significantly protect yourself from scams and malware and shit
i need to mention that while i did get a lot of planning done for a possible neverending legacy rework in the last few days i'd like to dip back into that audio program i've been making before i get started on any big game stuff. rationale being that once it's usable i can produce all the sound effects my games will ever need and ooo perhaps low-bandwidth in-browser sequenced music too
particularly excited to no longer have to rely on other people's software for audio stuff. please i just need to reinvent one more wheel. just this one wheel and it's gonna be so good it's gonna be the best wheel because it's gonna be mine #mywheel
for those like me who cannot install alternative android forks on their phone because the phone in question is thoroughly unrootable, I would recommend downloading anyapk on your phone while you still can. In their own words:
anyapk is a lightweight Android application installer that bypasses Google's developer verification requirements by using local ADB (Android Debug Bridge) connections. Smoothly install any APK file on your device without restrictions, gatekeepers, or corporate approval.
If you're reading this after Google's lockdown date and are unable to install anyapk the regular way, there is a method outlined on the github linked above which tells you how to install anyapk on your phone by plugging it into a computer with ADB installed on it. Once you have anyapk on your phone, you will not have to do that ever again (unless you delete anyapk off your phone)
[Description for the second image: a post from jrepin that reads:
"Sideloading" is the rentseeker word for "being able to run software of your choosing on a computing device you purchased". There is no reasonable case for an operating system developer having a say over what programs you run on your hardware.
--Eugen Rochko of Mastodon https://mastodon.social/@Gargron/115093185284473606]
