Kevin Hillinger

Dr. Daniel Amen dropped some eye-opening brain science on Chris Williamson: “Women have 52% less serotonin than men on average.” That’s one reason women are roughly twice as likely to suffer from depression and tend to ruminate more when upset — their brain gets “stuck” on negative thoughts. On the flip side, women show significantly better frontal lobe function, which helps explain why they go to jail about 14 times less than men. Testosterone and higher male risk-taking play a big role too. Fascinating how biology shapes these patterns. How much do you think these kinds of innate brain differences actually influence behavior and mental health between men and women?

How a car differential works, 1937

Este video explica a la perfección porque la equidad en los recursos, no garantiza la igualdad en las condiciones

A community college professor taught the same study skills lecture for 30 years, and the video quietly became one of the most watched educational recordings on the internet. His name is Marty Lobdell. He spent his career as a psychology professor watching students fail not because they were lazy, but because nobody had ever taught them how their brain actually works under the pressure of learning something hard. The lecture is called "Study Less Study Smart." Over 10 million views. Passed around in Reddit threads, Discord servers, and university study groups for over a decade. And the core insight buried inside it has been sitting in cognitive psychology research for years, waiting for someone to explain it in plain language. Here is the framework that completely changed how I think about effort. Your brain does not sustain focus the way you think it does. Studies tracking real students found that the average learner hits a wall somewhere between 25 and 30 minutes. After that, efficiency doesn't just decline. It collapses. You're still sitting at your desk, still looking at the page, but almost nothing is going in. Lobdell illustrated this with a student he knew personally. She set a goal of studying 6 hours a night, 5 nights a week, to pull herself out of academic probation. Thirty hours of studying per week. She failed every single class that quarter. She wasn't failing because she lacked effort. She was failing because she had confused time spent near books with time spent actually learning. The 25-minute crash hit her at 6:30pm every night. She spent the next five and a half hours sitting in the wreckage of her own focus and calling it studying. The fix sounds almost too simple. The moment you feel the slide, stop. Take five minutes. Do something that actually gives you a small reward. Then go back. That five-minute reset returns you to near full efficiency. Across a six-hour window, the difference is not marginal. It is the difference between thirty minutes of real learning and five and a half hours of it. The second thing he taught destroyed something I had believed about how memory actually works. Highlighting feels productive. Going back over your notes and recognizing everything feels like knowing. But recognition and recollection are two completely different cognitive processes, and your brain is very good at making you confuse them. You can see something you've read before and feel completely certain you understand it, even when you couldn't reconstruct a single sentence from memory if the page were blank. He proved this live in the room. He read 13 random letters to his audience. Almost nobody could recall them. Then he rearranged the same 13 letters into two words: Happy Thursday. The whole room got all 13 without effort. Same letters. Same count. The only thing that changed was meaning. The brain stores meaning. Not repetition. The moment new information connects to something you already understand, the retention changes entirely. This is what the cognitive psychology literature calls elaborative encoding, and it is the mechanism underneath every effective study technique. The third principle was the one that hit me hardest, and the one almost nobody applies. Lobdell cited research showing that 80 percent of your study time should be spent in active recitation, not passive reading. Close the material. Say it back in your own words. Teach it to someone else, or to an empty chair if no one is around. The struggle of retrieval is where the actual learning happens. Reading your notes again is watching someone else do the work. His parting line has stayed with me longer than almost anything else I have read about learning. He told the room that if what he shared didn't change their behavior, they hadn't actually learned it. It would just live in their heads as something they had heard once and felt good about. He was right. And most people leave every lecture exactly like that. The students who remember everything aren't putting in more hours. They stopped confusing the feeling of studying with the fact of it.

His name is Suchir Balaji. He was 26 years old. Indian-American. An AI researcher at OpenAI for 4 years. He helped build GPT-4. One of the most powerful AI systems in the world. In October 2024 he quit OpenAI and gave an interview to the New York Times. He said OpenAI was training its AI on copyrighted content without permission. He said it was illegal. He was named as a key witness in the NYT lawsuit against OpenAI. He wrote in his journal: “I am a credible threat because of New York Times.” One month after the interview he was found dead in his San Francisco apartment. The official ruling: suicide. His parents say: murder. His pen drive was missing. His computer was left open. CCTV footage for only 2 of 7 days was provided. GHB was found in his system. His cheekbone was broken. A second bullet was allegedly found lodged in his skull by independent radiologists. Elon Musk replied to his mother’s post: “This doesn’t seem like a suicide.” Tucker Carlson said in a September 2025 interview with Sam Altman: “He was definitely murdered.” The SF police closed the case. His mother Poornima Ramarao is still fighting. Alone. From India. Against one of the most powerful companies in the world.

Harvard circa 1700s: "No student shall be admitted unless they can translate Greek and Latin authors such as Tully, Virgil, The New-Testament, & Xenophon." Harvard circa 2026: "We can't assign whole novels anymore."

Everyone obsesses over whether aliens built the Nazca Lines, completely ignoring the true engineering miracle hiding right beneath them: a 1,500-year-old network of spiraling aerodynamic wind pumps that turned the world's driest desert into a permanent oasis. Modern concrete crumbles after a few decades, yet 32 of these ancient, stone-lined subterranean aqueducts are still successfully irrigating Peruvian farms today. If a pre-industrial society could engineer a self-sustaining desert water grid using nothing but rock, wood, and wind, what exactly is modern infrastructure's excuse?

Software horror: litellm PyPI supply chain attack. Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords. LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm. Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks. Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages. Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.