If you're writing anything involving cons, scams, heists, or morally questionable characters who are very good at lying, here are some free resources I've been using for research. Saving you the "why is this in my search history" anxiety.
1. The FBI's Famous Cases & Criminals archive (fbi.gov/history/famous-cases) has detailed breakdowns of real fraud cases, Ponzi schemes, and confidence operations. The language they use is clinical and precise, which is perfect for getting the procedural details right.
2. The FTC Consumer Sentinel Network publishes annual reports on the most common fraud tactics in the US. Great for understanding how modern scams actually work and what makes people fall for them.
3. The Smithsonian's American Art Museum has a free digital collection of forgery case studies. If your character forges documents or art, this is gold.
4. Court Listener (courtlistener.com) is a free legal database where you can read actual court transcripts from fraud trials. Want to know how a real con artist talks under oath? This is where you find out.
5. The Internet Archive's collection of old newspaper crime sections. Search for "confidence man" or "swindle" in papers from the 1920s through 1960s and you'll find incredible real stories that would feel too dramatic for fiction.
Bonus: The Psychology of Fraud section on the Association for Psychological Science website has accessible articles about why people trust, how deception works cognitively, and what makes someone a convincing liar. Essential reading if you want your con artist characters to feel psychologically real.
Reblog to save for later. Your WIP will thank you.
celestia is such a funny character like she's constantly manipulating twilight and friends to do shit instead of just asking and you could arguably frame that as being bc she's a "god" and pushing fate to her design or whatever, except that she engages with the group like a normal and relatable person, which makes it more like villainous machinations, except 90% of this manipulation goes towards things like "I don't want my party to be boring shit again. put my little country girl blorbos in there with zero prep so they fuck it up bad"
Celestia instantly makes more sense as a character when you ignore the princess stuff and remember that she's a 1000+ years old wizard. Of course she does manipulative trickster stuff to teach moral lessons and/or cause chaos to amuse herself, that's classic wizard behavior. Of course sometimes she's actually socially awkward and bad at personal relationships and has bad ideas that she thought were good that result in her eating shit embarrassing style, that's classic wizard behavior. Of course she lets the aristocrats and nobles run around being assholes she's still running on wizard advisor programming, she's basically trying to merlin the entire upper class of equestria instead of just a king and some knights. "Yeah uuhhh we'll release the incarnation of chaos himself from his ancient prison because we think this shy girl can be friends with him", terrible plan if you're thinking like a ruler, amazing plan if you're thinking like a wizard. Just look at Canterlot 'Castle' for five seconds and ask yourself if that's in any way a castle. No. Wizard tower, yes. Wizard.
A couple scenes from a little gay vampire story I was kicking around a couple years ago! I sometimes do little pencils-only scenes for things and never post them. But I was looking back at these and was like why not? They're cute!
I have a whole story for these two, but just for funsies, something to do every now and then when I need to let loose.
Man, as the whitest Irishman that ever whited, I could write a fuckin thesis on the 'Rocky Road to Dublin' scene from Sinners. Or any scene with that Irish weirdo to be honest.
I'd have great fun writing about how they specifically used the first verse where the lyrics talk about leaving home to find fortune, then skipped all the ones where other Irish people looked down on him, and jumped right to the one about being mildly persecuted in the place he got off the boat until the singer forms a mob of fellow Irishmen to enact violent revenge on the people there who looked down on him/cast him out because of what he is. Ya know, almost exactly how he's doing in the film.
Not to mention how he has been weaponising Irishness to try ingratiate himself into a persecuted community but purely for his own benefit.
And then, when hes got enough of that community under his control, he turns around and makes them do a funny little dance to a tune from a different culture, with all the terrifying implications and undertones, to a song that was originally Irish/Scotts but was taken by an English man, given English lyrics, and then he used it to make money in English dancehalls without giving credit to its origin. Like, you see what I'm getting at, right? Using the stolen and commercialised concept of Irishness to oppress and over run the will of different and already oppressed community? Like, we don't even know if hes actually Irish at all. Its very possible hes just weaponising it because it makes white people overlook you as harmless, while also potentially automatically ingratiating you to oppressed communities.
The fuckin layers in that scene, never mind the whole movie, are just so delicious. I want to chew on it like a tasty steak and explain all the nuanced flavours from an Irish perspective from the small things like how he purposefully exaggerates his accent when its useful, which I've also done on occasion when its useful, and on all the way up to the ways that Irish people have perpetuated oppression for our own benefit while at the same time playing the angle of "ah sure we're irish, we're a great bunch a lads! Don't worry about us, we're very silly, 'diddely fiddely eye', we'll beat the shit out of anyone but not you cuz we're having a drink together, waaaheey".
I think one of the funniest abortion stances I've heard was from my parents neighbor. He's a like, hard-core libertarian viking larper guy who is very tall and very fat and very bald.
He believes a fetus is human with a soul, but also its "basically attacking the woman's body" so if she wants to get rid of it, that's "basically self-defense". He compared it to shooting a home invader. So he supports abortion not as healthcare, but as killing a baby in self-defense
[Image: Post by Time Capsule Tales: "Rick Astley &the Foo Fighters perform "Never Gonna Give You Up" in the style of "Smells Like Teen Spirit", after Dave Grohl spotted Rick on the side of the stage aand just pulled him up to do the song." Video is as described by the post.]
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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