You should take some time to read @3liza's post documenting the Phantom Report Bug (which she deserves praise for doing, thank you eliza) and see how fucking broken Tumblr's report tool is.
I also want to reiterate something she is once again correct about: no one files bug reports. I have first hand experience working at Tumblr and I remember having to tell web devs on Staff "i saw a post about someone talking about a bug" and they were unaware because no one followed through to file a bug. I have fixed bugs that I saw people posting about that were in my domain (I'm a mobile dev) but were not in the system.
No this is not an endorsement of "complain about it enough and eventually someone will see it", this is an endorsement of "file a bug report directly to computer companies and people will most likely read it and probably fix it". I mean it this is not a Tumblr-only thing. I've seen this at every company I've worked for. Just fucking file a bug report please I beg you, software gets complicated and the devs are just unaware that there's a bug until you bring it to your attention. And they want to fix the bug! I promise!
She got the idea for the study while walking with her advisor at Stanford to discuss her thesis topic, and the paper she eventually published in the Journal of Experimental Psychology in 2014 is sharp enough that it should have ended the seated meeting on the day it came out.
She ran 4 experiments on 176 people. Same person tested twice. Once sitting, once walking. The creativity tasks were the standard ones psychologists have used for decades to measure how good a brain is at generating novel useful ideas.
81% of participants in the first experiment produced more creative ideas while walking than while sitting. In the second experiment, 88%. In the third, 100%. Every single person walked into a more creative version of themselves. On average, people generated 60% more novel useful ideas the moment their legs started moving.
The skeptical question is the obvious one. Maybe it was the fresh air. Maybe it was the scenery passing by. Maybe it was the change of environment doing the work, not the walking itself.
Oppezzo killed every one of those explanations with one experimental decision. She put people on a treadmill facing a blank wall. No scenery. No fresh air. No environmental change. Just legs moving in place while staring at white drywall. The 60% boost held.
Then she ran the experiment that closed the case completely. She took participants outside in two conditions. Half of them walked through a Stanford courtyard. The other half were pushed through the exact same courtyard in a wheelchair. Same outdoor stimulation. Same scenery passing at the same speed. The only difference was whether the legs were moving.
The walkers produced dramatically more novel high-quality ideas than the wheelchair group. The outdoors did almost nothing on its own. The walking did everything.
She also tested the opposite kind of thinking. Convergent thinking. The kind where there is one right answer and you have to narrow down to it. Word puzzles where 3 words share a hidden fourth word that connects them. The seated participants did slightly better on these. Walkers got slightly worse.
Walking is not a general intelligence enhancer. It does one specific thing. It opens up the divergent search inside your brain. The part that generates options. The part that produces unexpected connections. The part that takes a problem and finds five ways into it instead of one.
When you need to converge on the single right answer, sit down. When you need to find the answer in the first place, get up.
The mechanism is now well understood. Walking selectively activates what neuroscientists call the default mode network, the system inside your brain that runs when you are not consciously focused on anything. The DMN is where mind-wandering happens. Where memories cross-reference each other. Where ideas that have been sitting in separate folders inside your head finally bump into each other.
When you sit at a desk and force yourself to concentrate, you suppress the DMN. When you walk at a natural pace, the executive part of your brain gets just busy enough handling the walking that the DMN comes online and starts doing the work that focus was blocking.
The most useful finding in the entire paper is the one almost nobody quotes. The boost did not turn off the moment people stopped walking. Participants who walked first and then sat back down stayed elevated. Their next round of seated creativity work was still significantly better than people who had been sitting the whole time. The rest lingered for at least several minutes after the legs stopped moving.
You do not need to do creative work while walking. You need to walk before the creative work. The brain holds the state.
In a parent group for babies, we had been chatting about how disruptive the brain chemistry is of a baby who’s overtired (it is a catastrophe), when one very tired soul asked for advice:
“So what can I do when he’s overtired? I’ve tried everything I can think of and everything I’ve read. But once he’s overtired, it takes hours sometimes to get him down.”
That incredibly sympathetic group of mostly moms, normally full of sympathetic answers, just looked at each other, silent for probably 20 long seconds.
Finally one mom answered, with kindness and deep resignation:
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.
Look closely and you’ll see that every part of the text is not quite right.
Eve Fairbanks writing for The Atlantic:
So we end up with canned perfection—writing that can’t really be argued with, because it has no underlying deliberative reasoning process, no train of thought. As I wrote on X recently, AI writing is almost impossible to edit, because even when it sounds plausible, a closer look will show that every element is equally off: The tone is bland; individual word choices are baffling; the structure lacks sense; key pieces of the argument are missing; facts are false. Working on AI text, as an editor, is like trying to operate on a body whose skin, muscles, veins, bones, and organs are all compromised. There’s nothing to leave intact, nowhere to begin.
i need data for a statistics project for school, so be my sample data, worms. i need thirty people minimum so if there aren't enough voters yet i'd love if you could help. thank you very much. worms.
take this test (https://www.keithcirkel.co.uk/whats-my-jnd/), then come back here:
what's your JND?
.00030-.00099
.0010-.0017
.0017-.0024
.0024-.0031
.0031-.0038
.0038-.0045
.0045-.0052
.0052-.0059
.0059-.0066
.0066-.0073
.0073-.0080
.0080 or greater
Voting ended onMay 13
it doesnt have to be a good score, you dont have to take it multiple times, you dont have to get on a good screen, etcetera. just gimme your score please this is my final project grade :)
““Democratic accounts were shown significantly more anti-Democratic content than Republican accounts were shown anti-Republican content. The algorithm wasn’t just giving people what they want; it was giving one side more of what the other side says about them.” To conduct the study, researchers created hundreds of TikTok accounts, used virtual private networks to geolocate said accounts to specific cities, and trained said accounts to watch videos aligning with either pro-Democratic or pro-Republican Party content. The dummy accounts trained on pro-GOP content were delivered by TikTok’s algorithm with 11.5% more content aligning with their views when compared to the pro-Democratic Party content. “Our findings show partisan imbalances in political information exposure on a platform dominated by algorithmic recommendations, with implications for platform governance and democratic discourse,” the peer-reviewed study reads.”
— Researchers expose algorithm skew that boosted Trump in 2024
What to actually do
If you get one of these, the answer is boring and it works every time:
Don't call the number. Don't reply. Don't click links in the email — not even the unsubscribe link. Open a fresh browser tab, type paypal.com yourself, and log into your account. Check your activity. You'll see either nothing, or a tiny incoming payment from a stranger that you can ignore.
Then forward the original email as an attachment to [email protected] and delete it. If you want to go a step further, report the phone number to the FTC at reportfraud.ftc.gov — every report makes it slightly harder for these operations to keep running.
And if you've already called? Don't beat yourself up — these scams are designed by professionals to fool smart people. Hang up, run a malware scan if you installed anything they asked you to install, change your PayPal and bank passwords from a different device, and call your bank's real fraud line (the number on the back of your card) to flag your accounts. Move fast, but you don't need to panic.
from the above linked article. For the UK the email to forward phishing scams to is [email protected], texts can be forwarded on to 7726 (for free!) and as a victim of fraud you can report it here (or here for Scotland)
— If an email recently landed in your inbox with a subject line like "Pending charge of USD 987.90 for account activation. Questions? Call (855) 629-1161" — don't call that number. Don't click anything. And whatever you do, don't panic-dial to "stop the charge."
You're being targeted by one of the cleverest scams going right now, and the reason it works is uncomfortable: the email genuinely came from PayPal.
The trick is in the subject line, not the email
When most people think "phishing email," they picture sketchy senders, broken English, and links to weird domains. This scam is the opposite. The email passes every authenticity check — SPF, DKIM, DMARC, all green. It comes from PayPal's actual mail servers. The fonts are right. The footer is right. The unsubscribe link works. If you forwarded it to a security expert and asked "is this really from PayPal?" they'd have to say yes.
So how is it a scam?
Scammers have figured out that PayPal lets anyone send small amounts of money to anyone else, and that PayPal will dutifully email the recipient a notification. The scammer sends you a payout of, say, one Hungarian forint — about a quarter of a cent. PayPal's system then automatically generates and sends you a real, legitimate, fully-authenticated email confirming the transaction.
Here's the catch: the email's subject line is whatever the scammer typed when they set up the payout. PayPal doesn't sanitize it. So they write something terrifying like "Pending charge of USD 987.90 — call this number with questions" and PayPal's servers cheerfully deliver that subject line straight to your inbox, wrapped in a perfectly legitimate-looking notification.
The actual transaction in the email body is for 1 forint. There is no $987.90 charge. There never was. But by the time most people read carefully enough to notice that, they've already dialed the number. —
I used to struggle with my finances. Every month I’d stress out about how I was going to make rent, pay the bills, and still have something to set aside for my future. I must have read every article and watched every webinar looking for advice on how to get ahead, but the most important thing I learned didn’t come from any expert. It was a lesson I had to teach myself—that the key to financial success lies in taking advantage of others.
Trust me, screwing people over is the best thing that ever happened to my bank account.
Many of us fall into the habit of treating those around us—friends, family, coworkers—with respect. Unfortunately, this all-too-common practice can be devastating to our financial wellness. The good news is that our prospects improve dramatically as soon as we learn to see other people as nothing but tools for our personal gain. It really is that simple. In my case, the moment I started following a basic plan of always manipulating everyone around me, I was on the road to prosperity.
“We talk a lot these days about how Donald Trump seems to think he owns the United States – he puts his brand, his likeness, his signature on everything. He talks about his generals, his military, etc. But there’s a more concrete and specific way this is true and it goes to the heart of what needs to be fixed about the American presidency and the whole constitutional system. […] An analogy illustrates the distinction. Say you’re a wealthy person with an estate and you hire someone to run it for you. Maybe you’re a true high roller and you summer there. But you need someone to run it, manage it. So we’ve found our estate manager and he’s running our estate. But now you heard that he’s tearing down the main house and replacing it with a roller skating rink. And there are three acres of land down at the bottom of a hill. Well, he sold those to someone else and he’s using the proceeds to build himself an ice cream factory on your estate. At some point you found out about all this stuff and you say, WHAT THE ACTUAL FUCK IS GOING ON HERE? I hired you to run my estate, to manage it, to do upkeep, manage the staff, keep the lawn mowed. I didn’t hire you to demolish it or sell off parts of it. You can see where I’m going here. The American people select a president to run the federal government, to execute the responsibilities the federal government has. That includes national security and foreign policy, administering a vast social safety net, running various duties the federal government has taken on — ag policy, biomedical research, grant research. The American people own the federal government. They are the sovereign. That sovereignty is expressed through laws passed by Congress. The president is elected to run it for four years. It’s not his. He can’t just lop off big parts of it, sell parts off, decide it should no longer do things it was designed to do.”
this is your regular reminder that if you're an iPhone user and you regularly go through and kill apps from the task switcher (swipe up on them to quit them) you're not saving any battery or memory. In fact you're using more battery/memory as the apps have to do a full launch again instead of a wake from termination
iOS already suspends apps and marks the memory as usable once they're in the background. They're no longer running, they're in suspension, and no it does not take up more ram/battery to track those than it does to kill them and re-launch them. when they're in suspension they can be re-launched more quickly and don't go through the full launch cycle, which requires re-init-ing the app from main, linking libraries, making fresh network calls/checks instead of reading cache, etc (depending on how the app is made).
if the memory your suspended app is taking up is needed by an active app, it will then be terminated automatically by the operating system
source: me, who has been unfortunately developing iOS apps for over a decade and hates this cursed operating system. If you want more sources, here's someone else that has been doing it longer than me, who in turn links even more sources affirming this.
