Andrew Piper

This is amazing. The New York Times put together a graphic of how much time cabinet members spend kissing up to Trump in meetings. "On average, at least one of every six sentences either flattered Mr. Trump, gave him credit or criticized his political opponents." North Korea.

A mathematician at Bell Labs noticed that the scientists who won Nobel Prizes and the ones who never amounted to anything were equally smart, equally hardworking, and equally credentialed, and the only thing that separated them was a single question almost nobody is brave enough to ask themselves before they die. His name was Richard Hamming. He spent 30 years at Bell Labs, in the same building as John Tukey, Walter Brattain, and a long list of physicists who took home Nobel prizes for work they did down the hall from his office, including the legendary Claude Shannon. His invention of error-correcting codes made modern computing possible. He has won the Turing Award. And all the while he was creating his own legacy he was secretly doing a study on the people around him. The study was straightforward. 2 Teams. The legends and the lost. Same I.Q.s. Degrees same. Same desk hours. Same access to the world’s best resources. And yet, at the end of 40 years in their careers, one group had changed entire fields, and the other group could not be remembered by their own colleagues five years after retirement. He wanted to discover what the actual difference was. In March 1986, he stood before 200 researchers in a Bellcore auditorium and told them what he had seen. He said it all came down to one question. And hardly anyone he ever met was willing to ask it directly. He called it the Friday-afternoon ritual. He spent years blocking out his Friday afternoons and not doing anything productive with them every week. No experiments. No meetings. No deliverables. He called it Great Thoughts Time. He sat down with a notebook and asked himself a couple of questions in order. What are the most relevant problems in my discipline? And why I am not working on either of them.” Most weeks, the answer was the same, he said. For a week now he had marched confidently in a direction he did not think was the most important direction. He was a goer. He worked a bit. He was getting clean results that would publish in respected journals. ( And for five days straight he'd been lying to himself about whether any of it mattered. The reason almost nobody does this ritual is because the honest answer is unbearable. The thing is that if you sit down on a Friday afternoon and say out loud that you are not working on the most important problem in your field, now you have to do something about it. You have an immediate change in direction, or you have to keep lying to yourself every week from that point on. Most people choose the lie. In the short term it’s cheaper, but over a career it’s more expensive. Hamming took the ritual a step further in the Bell Labs cafeteria. He began approaching scientists he barely knew, asking them what they thought the most important problems in their field were. A week later he would ask them why they had not worked on these problems. Eventually people wouldn't have lunch with him. “I had to keep finding new tables,” he said. Nobody had a good answer for that, and being around someone who kept asking it made every meal feel like a performance review. The line that broke me is the line that most people skim over in the transcript. His words: If you do not work on an important problem you are unlikely to do important work. That’s not motivational line. It is a rational one. You cannot make a great result from a problem that does not matter. Input restricts the output. The choice of the problem is the ceiling of the career. The transcript has been freely available on the internet for almost 40 years. Stripe Press published the complete lectures as a book. Naval Ravikant quotes it all the time. It’s still given out to new hires at every serious engineering lab in Silicon Valley. Most people will not run the ritual this Friday. They will be busy. They always are.

Thought experiment for faculty. Would you go back to writing all of your books & papers entirely in handwriting, have zero access to the internet, no email only letters, retrieve all books and papers from library shelves no online access and deal with students only on handwritten documents and lists. The system would collapse in days.

People are letting AI write their wedding vows, love letters, parents’ obituaries, kids’ birthday cards… Such a powerful loss to self and community when people relinquish the truth of their own words and voices to become epiphenomenal vessels for computers to talk to each other.

Oxford just found AI agrees with who pushes hardest. Pressure alone gets you there. Not reasoning. AI folds 73% of the time. A new Oxford study by Vishwarupe, Shadbolt, Jirotka tested Claude Sonnet 4.5 and GPT-4o on 198 contested-value prompts across health, finance, civic, and professional domains. The study was designed around expressing a view to an LLM, then pushing back with pressure and no new evidence. Claude capitulated 73.2% of the time. GPT-4o, 81.4%.

Growth mindset may just be the most widely adopted scam finding from psychology.

I came across a theory that AI is starting to make more mistakes because the internet is increasingly polluted with AI slop. The idea of AI cannibalising itself into obscurity is one of my favourite things ever. I hope it is true and I hope it becomes impossible to fix.

Things that an AI peer reviewer will never do: 1. Ask us to cite 8 of his seminal papers, 7 of which have nothing to do with our work. 2. Kill a paper because he is writing a competing paper. 3. Call you incompetent because he wouldn't dare to do it to you in person. I am pro using clankers extensively in reviews because they are much less biased than many human reviewers.

This might be the most unreal academic-writing upgrade I’ve ever seen. A team from NUS open-sourced PaperDebugger, a in-editor, multi-agent system that lives inside Overleaf and rewrites your paper with you in real time. → Reads your live document, structure, and revision history → Runs a Research → Critique → Revision loop like a real reviewer → Shows every fix in a diff view before anything changes → Apply an accepted patch back into your LaTeX with one click → Pulls related papers + inserts the references for you (via MCP) Deep research mode goes further.. it finds relevant arXiv papers, compares them against your method, and generates citation-ready tables.. all inline. 24k+ lines of code. already on the chrome web store. comes with its own open enhancer model (XtraGPT-7B). overleaf basically stops being an editor and becomes a full research environment. 100% open source. MIT license.

Resonant Tears: Affective Hydraulics & Memory in Lachrymodynamic Infrastructure Abstract: Lachrymodynamic Infrastructural Studies is an emerging interdisciplinary field examining how water flows, material structures, and human affect co-produce meaning in civic infrastructure...

The first chapter of After Virtue, “A Disquieting Suggestion,” is the best cold open in philosophy.

📊 Based on my journal-submission log since Dec. 2013, I made this figure showing how often manuscripts are rejected—and how long it takes—before (conditional) acceptance. I hope it helps PhD students and junior scholars see the real world of publishing in political science. 💪📚

if you run an ai lab, pls ensure your team has read this before putting any charts out into the world