Hey everyone who’s ready for codejuly!!!! The reason I made this was to make the tag more active again after getting inspired with the Danny Phantom’s phanniemay calendar~ (sorry for the lateness as usual i’m a certified mess™️✨)
The official tag is “#codejuly” also “#codejuly25” (no spaces or punctuation). Please use it so that people can block it if they don’t want to see it. If you are just talking about Code July, please do not tag your post with these tags
FAQ
I don’t have time to do this in July?/Can I post it on another day?
Even though the calendar is for July, you don’t have to do them all in July. You can do them on whatever day seem best for you.
I don’t get the theme for some of these days?
You can go ask me on my ask box if you are confused on a specific day. (btw each word is just an idea, so as long as you come up with something it doesn’t matter how far you stray from the literal meaning of the word)
Is this a drawing thing only?
No, you can do anything including fanfic, AMV, cosplay, 8tracks, and etc… the choice is yours
Do I have to do all the themes?
Nope, it’s your choice on what you want to do. If you don’t feel inspired by some you can skip them. (and if you get an idea later you can submit it late)
(But there is a special prize where I will give everyone who completes all the prompts a lil doodle of a character of their choice~ just make sure to let me know if you complete them all)
ONLY ENTRIES MAY BE POSTED IN THE “codejuly & codejuly25” TAGS! NO TEXT POSTS (except for Fanfiction)!
Hey, when you talk to children, you know you can explain things to them right? thats theyre capable of comprehension? In fact you should be explaining things so they understand comprehension better?
Earlier my little sister slammed her closet and room door-- not out of anger as far as im aware, she just pushed them too hard. I yelled at her to stop it.
she said she would, but it was clear from her tone she just said so out of obligation and was annoyed. i tell her to stop making loud noises a lot, so she probably assumed this was just another example of that.
realizing this, i explained her the reason: our rooms are right next to each other. When she slams doors, the wall shakes. When the wall shakes, my mirror shakes, and if it shakes too hard it can fall and break
after the explanation, she apologized genuinely and actually understood the reason instead of just thinking im nagging her or just want quiet. the fact that she knows the reason means shes more likely to remember, and she can apply the knowledge in different ways: "even if my older sibling isnt home, their mirror could still break, so i still shouldnt slam the door." She knows im not just trying to annoy her or assert dominance over her like a lot of rules and demands we give to children do- i just dont want my mirror to break. It helps her understand cause and effect.
Would this result be the same if i had just screamed at her or spanked her? Or did it make more sense to just explain? After all, it was a simple mistake. I could see my parents doing the same thing-- when you close a door, youre not usually thinking about the walls in the other room.
Children are humans, humans use logic. Use logic with children. Its simple.
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.
Do not forget that discord is still planning on moving forward with age verification and has only "delayed it" until "the later half of 2026." They are hoping you will forget while they quietly roll it out when no one is looking. Continue to message them about it. Continue to talk about it. Make it clear this is unacceptable. Discord is one of the only places left you can even talk about or share adult content in private at scale anymore. They will tell you "its not that bad if you dont use it for nsfw" but fuck them and fuck people who say that shit.
you don’t realize how important lunch is until you’re wandering around thinking about how unloveable and untalented and uniquely cursed you are and then it’s 4pm and you finally eat lunch and you go Oh. oh right.
