2025-11-30 18:13:27
Published on November 30, 2025 10:13 AM GMT
As I mentioned in my previous articles, I learned a lot of bullshit during my time at university. This is by no means an isolated case, but a widespread phenomenon.
Where Our Engineering Education Went Wrong — LessWrong
Chinese universities are wasting students’ time on a massive scale. Quite a few students have already awakened to this reality and are stepping forward to point it out.
The author of today’s article, like me, is a computer science student.
And just like him, what I have learned from working far exceeds what I learned from university.
I hope our experiences can serve as a wake-up call for more university students.
Jarrett Ye
2025-11-30
Author: Cinea4678
Link: https://zhuanlan.zhihu.com/p/690074253
Source: Zhihu
Copyright belongs to the author. For commercial reprints, please contact the author for authorization. For non-commercial reprints, please indicate the source.
I haven't written a new article in over two weeks, but today, rather than talking about technology, I want to talk about the three years of my life that were wasted by university.
Let me give a brief self-introduction. I am a third-year student at Tongji University,[1] majoring in Software Engineering. My GPA ranks in the bottom 10%,[2] I have never received a scholarship during my university years, and I am currently interning at a quantitative trading firm. I am an "engineer" type of talent, not particularly skilled in "scientist" type abilities like mathematical derivation, calculation, and memorization, but I am quite adept at engineering and technology.
My parents are both mechanical engineering majors and know nothing about programming. However, my father enjoyed tinkering[3] with computers in his youth, so our home was filled with books on how to mess with Windows 98, and these books became the key that opened the door to computing for me. In the third or fourth grade, I started buying books to teach myself programming. The first compiler I ever used was the ancient relic[4] Turbo C, introduced in an old C textbook. In high school, I first encountered the Informatics Olympiad (OI),[5] and won a provincial second prize[6] after just two months. But six years ago, Yunnan province had no real competitive environment for informatics, so after my regular academic[7] grades plummeted, I caved to the pressure around me and gave up on OI, making a hasty exit.
After the Gaokao, I listed "Information" at Tongji University as my first choice. Although this broad category[8] is called "Information," only four of its majors are directly related to computer science (Computer Science, Software Engineering, Big Data, and Information Security). The rest are related to electronic information, and it even includes Optoelectronic Engineering and Surveying & Mapping—two majors that have almost nothing to do with "computer" or "information." This laid the groundwork for me to "slip"[9] in the major selection process later on.
I was successfully admitted to the Information category at Tongji University and became an undergraduate in the freshman college. I thought a wonderful university life was about to begin, but instead, I was held back for three full years by an unreasonable educational system. In my first year, I "slipped" during major selection and was placed in Surveying & Mapping. Fortunately, thanks to Tongji's lenient major-transfer policy, I managed to escape to Software Engineering. In my third year, despite winning a national first prize and several Shanghai municipal awards, I still couldn't get even the lowest-tier scholarship. The specialized courses I took in my second and third years were almost entirely spent writing "reports" and "reading notes." Even doing a couple of labs from an online course from abroad would have been ten thousand times more useful than attending these classes. It's safe to say that to have gotten where I am today, the only help the university provided was the relatively dazzling halo of its prestigious name. What truly supported me was my own inner drive, which helped me acquire my professional and technical abilities.
First, the major selection system does not assess students' actual abilities. It is based solely on GPA,[10] rampant with formalism, and the official guiding principle of "interest-driven, preference-first, academics-led, and comprehensive evaluation" is a piece of absolute bullshit.[11] According to the official narrative, the major selection system is implemented to "give students ample time to discover the major they truly want to study." While there's nothing wrong with this statement itself, it deliberately ignores a crucial problem: students may be able to discover the major they want, but the university offers no guarantee that they can actually get into it. What if a student wants to study their preferred major? Their only option is to get their GPA high enough.
I don't deny that GPA can reflect a student's learning ability and overall quality, but a student with a low GPA does not necessarily lack solid professional skills. What's more, of the courses in the first year, nearly half are ideological and political education, and another large portion are unrelated to the target major (for example, the University Physics and Circuit Theory I took in my first year have almost nothing to do with computer science). Using such criteria to judge whether a student is fit to study computer science, while appearing to be about "comprehensive quality," is in reality still just a way to screen for good test-takers.
Our university's major selection score also includes 10% for an interview and 10% for "Five Educations" score.[12] Never mind that there are no uniform grading standards for the 10-point interview, and under special circumstances, the interview is canceled altogether (for example, during the Shanghai yiqing[13] in spring 2022). This just shows how little the university values it. The "Five Educations" score is an even more cringeworthy[14] thing, the king of formalism. As the saying goes, even Stephen Hawking would have to put down his research to go grind[15] for "Five Educations" points in all sorts of meaningless activities.
Second, the scheduling of foundational courses is chaotic, delayed, and illogical. To accommodate the major selection system, many courses are not taught in the proper sequence. For example, the Circuit Theory course I just mentioned. This course is very valuable for students in electronic information, but it is completely meaningless and unhelpful for computer science students (and it's even more cringeworthy because this course drags down your GPA for major selection). Another example is Discrete Mathematics, a course that should be taught in the second semester of the first year but is now scheduled for the first semester of the second year because of the major selection system, delaying the teaching progress. What's even more terrifying is that students will be taking Discrete Mathematics at the same time as subsequent courses like Data Structures. This is like building the first and second floors of a house simultaneously—such an unimaginable thing is actually happening in the real world.
What's even more absurd is that the cohort after me learns C++ in the second semester of their first year, and then in the first semester of their second year, they have to take a course where the final project is "to use Cocos to create a game similar to Teamfight Tactics."[16] And that course teaches neither game development nor Cocos, but rather programming paradigms. Never mind the questionable relevance between the homework and the project. While self-learning ability is indeed necessary for computer science students, you don't even teach them how to self-learn and just demand they build a rocket from the get-go[17]. What's the point of this other than to torture students?
Furthermore, the content of my university courses was outdated and inappropriate. Students waste time grinding out reports[18] and writing reading notes, while the quality of the project itself is the least valued aspect. The PowerPoints have been passed down for ten years, the textbooks are old editions, and the professors have been detached from the industry[19] for over a decade. These problems are common to almost all universities in the country. Many people on Zhihu have complained[20] about this better than I can, so I won't elaborate further. The professor for my Data Structures course even said when discussing the final exam, "The questions are set at the graduate school entrance exam[21] standard, maybe even harder." But the problem is, not all students take this course to prepare for the graduate entrance exam. If I wanted to prepare for that, why would I bother taking your class and doing your homework? There are tons of online courses for "408"[22] available online;[23] I have far better options.
Regarding projects, let me take two courses I took in my second year as examples: Computer Organization Lab and Operating Systems Course Design. The former required submitting a "handwritten report," with the stated reason being to prevent copy-pasting. At the same time, however, the professor provided model reports from previous students and hinted that we only needed to write the conclusion in our own words, while the rest could be fully "referenced." It turns out the professor didn't want electronic reports not to prevent plagiarism, but to prevent students from finishing the assignment too easily. As for Operating Systems Course Design, the course offered four project options:
Unsurprisingly, the entire grade chose xv6 because it only required submitting a report, and being able to successfully run one lab during the defense was enough to meet the requirements. To make their reports stand out from the other "xv6-ers," students had to compete on page count and content, wishing they could write a whole thesis on every single command involved, discussing its past, present, and precautions. During my defense, the teaching assistant told me that out of more than 220 students in the entire grade, I was the only one who chose to implement my own operating system. One can't help but sigh that one of the "three great romances of a programmer"[24] has become an option no one is interested in. However, this experience ultimately became my stepping stone into the quantitative trading firm. Readers can judge for themselves which of the two choices is better.
Finally, there is absolutely no room for "engineer" type talent in the various systems for awards and honors. Awarding scholarships based on GitHub stars and contributions might be too progressive for Chinese universities, but to still be unable to get even the lowest-tier scholarship after winning a national-level award is truly hard to accept. I admit my award is not a heavyweight one like XCPC,[25] but it is, after all, a competition whose results can be recognized for graduate school recommendations. This incident made me doubt myself for a while, and it was only with the help of my friends that I came to recognize my own value again.
Whether universities should cultivate "engineers" or "scientists" is not a question I can answer, but I believe universities should not stifle the space for "engineers" to grow, and they certainly shouldn't completely ignore the existence of "engineers" in their evaluation systems. After all, my major is called "Software Engineering." Isn't it only natural[26] to recognize the value of engineers?
Fortunately, after starting my internship, I finally found my value, which is why I enjoy working. At work, I can leverage my outstanding abilities and unique insights, and I can continuously learn cutting-edge technologies and knowledge. At school, I have to worry about things that hijack students under the guise of "well-rounded development," like "labor education hours," "Five Educations points," "innovation credits," "high-quality general education courses," "volunteer activities," and "social activities." But at the company, all I need to do is work hard and do my job well. This can't help but make me feel that my first three years were truly wasted.
There are also some of my personal experiences that were a significant reason why my three university years were wasted. Within the university, many people are enthusiastic about doing "innovation projects." I also spent over a year doing a "Shanghai Innovation Project" with them. Looking back on that time, having weekly group meetings and wasting so much time on work that was completely useless for my future employment, I just feel that I was so stupid and ridiculous back then.
This article was written out of a burst of frustration and is mainly based on my personal experiences. Perhaps it can represent the situation in the Software Engineering department at Tongji University and at some other universities. However, my school is not entirely without merit. Its lenient major-transfer policy allowed me to escape a major I wasn't interested in. Its logistical support doesn't have much for me to criticize. Its cafeterias haven't left me with bad memories either. More importantly, whenever I swipe my card to enter the campus during cherry blossom season and see the school full of tourists, I still feel proud to be a member of Tongji.
I hope all my friends who have read this far can find their own paths and move confidently towards their goals.
Tongji University (同济大学, Tóngjì Dàxué): A prestigious "Project 985" university in Shanghai, especially renowned for engineering.
GPA ranking 90% (GPA排名90%): This means the author's GPA is in the 90th percentile, i.e., the bottom 10% of the class.
Tinkering (折腾, zhēteng): A colloquial verb meaning "to tinker with," "to fiddle with," or "to mess around with," often with a positive connotation of playful experimentation.
Ancient relic (上古 / 文物, shànggǔ / wénwù): "Ancient" / "cultural relic." Used humorously to describe Turbo C as extremely outdated.
Informatics Olympiad (OI竞赛 / 信奥, OI jìngsài / xìn'ào): OI is the standard abbreviation for the competitive programming Olympiad in Informatics.
Provincial second prize (省二, shěng èr): Short for "省级二等奖" (shěngjí èrděngjiǎng), a provincial-level second-class award.
Regular academic (文化课, wénhuàkè): "Culture classes." Refers to the standard academic curriculum, as distinct from specialized training like sports or, in this case, competitive programming.
Broad category (大类, dàlèi): A system where universities admit freshmen into a general field of study. Students then compete based on first-year grades to get into their preferred specific major.
"Slip" (滑档, huádàng): A common term meaning to fail to get into one's desired school or major and "slip" down to a lower-preference choice.
GPA-ism (唯绩点论, wéi jìdiǎn lùn): A pejorative term for a system that judges students solely based on their GPA.
Absolute bullshit (一纸屁话, yī zhǐ pìhuà): Literally "a piece of paper of fart-talk." A very blunt and coarse expression for "worthless nonsense on paper."
"Five Educations" (五育, wǔ yù): The official national policy for developing students' Moral, Intellectual, Physical, Aesthetic, and Labor education. The author sees its implementation as a box-ticking exercise.
yiqing (艺晴, yì qíng): A likely euphemism or coded way of writing "疫情" (yìqíng), meaning "epidemic," used to avoid censorship when discussing the COVID-19 pandemic.
Cringeworthy (难蚌, nán bèng): A popular internet meme short for "蚌埠住了" (Bèngbù zhùle), a pun on "绷不住了" (bēng bu zhùle), meaning "can't hold it in anymore." Used for something so awkward or ridiculous that one can't help but react.
Grind (刷, shuā): "To brush." Slang for accumulating points or completing requirements in a repetitive, mechanical way.
Teamfight Tactics (金铲铲之战, Jīn Chǎnchǎn zhī Zhàn): Literally "Battle of the Golden Spatulas." The official Chinese name for the popular auto-battler game.
"Build a rocket from the get-go" (一上来就要学生造火箭): A common metaphor for setting an impossibly difficult task for a beginner without providing the necessary foundational knowledge.
Grinding out reports (卷报告, juǎn bàogào): The verb "卷" (juǎn, to roll/involve) is used here to mean engaging in intense, often meaningless, competition or busywork.
Detached from the industry (脱离生产, tuōlí shēngchǎn): Literally "detached from production." A phrase for being out of touch with real-world industry practices.
Complain (吐槽, tùcáo): A very popular slang term borrowed from the Japanese tsukkomi, meaning to roast, to complain about, or to point out the absurdity of something.
Graduate school entrance exam (考研, kǎoyán): The national postgraduate entrance examination.
408: The code for the notoriously difficult national unified computer science subject test for the postgraduate entrance exam.
"Grab a huge handful online" (网上一抓一大把, wǎngshàng yī zhuā yī dà bǎ): An idiom meaning something is extremely common and easy to find.
"Three great romances of a programmer" (程序员三大浪漫, chéngxùyuán sān dà làngmàn): A saying in the Chinese programming community referring to writing one's own compiler, operating system, and database—seen as fundamental and deeply rewarding projects.
XCPC: A common acronym for the ICPC (International Collegiate Programming Contest) and its regional variants in China, the most prestigious programming contest for university students.
Only natural (天经地义, tiānjīng dìyì): An idiom meaning "perfectly justified," "unalterable principles," or "as it should be."
2025-11-30 16:20:28
Published on November 30, 2025 8:20 AM GMT
Here’s a fun format: get a big white board, and write the years of the 21st century. Write a category; something that has many variations come out every year. Next, write your picks or favourites. Now invite everyone attending to replace a year’s pick if they want and replace it with something they like.
This was a rolling game played last month at Lighthaven. I put up “Best blog post of every year” when the opportunity arose.
This Joel SPolsky piece is applicable to far more than software. It’s been the foundation or ancestor of all my good project management.
Billy Joy writes with foresight about new technologies and their ethical dimensions.
A stream of conciousness, half manic fever dream, half calculatingly ruthless ode to victory. Don’t worry about the Magic: The Gathering details, they don’t matter.
Paul Graham had not yet learned brevity, but he had learned design. This is a beachhead of aesthetic - perhaps just the legible outcrop into software engineering.
Ah, Cory Doctorow. Never change. Here is the first high profile use of creative commons, which (along with other licenses) should be internet lore.
Paul Graham taking an early swing at intellectual conformity. I like it as a message in a bottle to today’s culture wars.
Paul Graham taking a swing at communism. Wealth creation is cool, liberalism lifts people out of poverty and elevates humanity. Most saliently; you can create wealth! Go out and make something people want!
A fairy tale of defying our limitations. A bit preachy, and I wish there was more characterization, but it has a nice cadence.
For me, this is the central post of the entire rationality project. Everything else is cruft, addons that either distract from or build on this thesis.
Robin Hanson introduces the concept of tugging the policy rope sideways. It’s neat to look back and fee footprints of people doing this.
A masterful pastiche of engineering blog posts. Plus burritos make everything better. Macej Ceytowski hits it out of the park.
Bruch Schnier is the information security blogger, and this is the post on thinking through the security implications of the world. CW: Ants.
Eliezer’s Best post - the prereq to all advanced study of language.
Shamus Young lays out, in an understated warning, the dangers of learning the wrong way. It’s insidious! Once you have bad habits, fixing them can be harder than starting fresh.
Wanna be happier? Course you do. Alicorn’s capstone of the Luminosity sequence lays out how she step by step built a happier life. I can’t promise it will work for you - but it worked for me!
Luke makes an underrated point here about how to work on hard problems. There’s a lot of things to big to eat in one bite - that’s why we can chew!
TLP is a generational blogger- no spoilers
This. This fucking post right here. This is what I think someone should have been following up on full time, or at least 2x a year. If true, then why? If false, how to fix it?
Patrick McKenzie gives career advice to SWE’s that generalizes to workers in many disciplines. A large portion of my net worth (and current income) is downstream of this post, which also serves as a practical guide to “understanding your counterparty and their incentives.”
Scott manages to succinctly describe a really relevant dynamic for a big community of nerds. Many of us have that one fascination.
The idea of a control group for science is still a funny idea, and also useful! Talking about parapsychology is a good innoculation against trusting everything in a journal article.
Moloch, whose mind is pure machiner!
Moloch, whose blood is running money!
There, in Las Vegas, I saw Moloch.
Nate Soares outlines a common. . . is it a motivation? This persistant ‘should’ that crops up in our internal monologues and explanations of our behavior?
Nate Soares writes a version of Fable of the Dragon Tyrant but EA: To save lives and defeat the dragon, you have to put a price tag on a life saved, and do what life savers need and want, including the whole economy. (This description does not give it justice.)
Scott Alexander manages to give useful advice for conspiracy theories, normal science, and also a good idea for a fiction novel!
Meditation stuff tells you how to win at life with marginal effort.
Raemon points at a problem across many domains, one which only rears its head when its too late for an organizing team to fix.
Sara Constintin’s tight, hard hitting meditation on the rubber hits the road, gearsy, final boss rationality of human survival if its inherent humanism. The real world can kill you. Say something to your fellow travelers.
Self plug, deal with it, I made the list.
This is my standing answer to people asking why I’m writing about something someone else covered.
Zvi writes a lot of text. I would argue under all those words he has two through lines, and how good things come to times and places that don’t prevent them is one.
Here’s a post that some people need printed out, rolled up, and whapped with like adog with a newspaper. It’s common to make this mistake!
Eliezer, having seen the outcome a decade on from the community he created, has some pointed advice on being happier and saner.
I think about this post twice a week. I catch like 10% of the opportunities in my life to look stupid, and it has made me smarter and more effective. Onward and upward!
An essay on essay writing. Too much of this is self indulgent. Sazen comes with an advice and a warning of how you can misunderstand and be misunderstood
Jenn’s piece here is worth it if it only had the line “what is your fantasy complement organization?” And then there are lots more good bits! A great bridge between two worlds.
Leopold Aschenbrenner changed the posting game with a giant, one-site one-essay work that came out of nowhere. This is a high production value blog post. It comes to put AI on a government radar, and succeeds.
By internet princess. There is a narrative you might encounter on the internet and inside of you. It insists you must be a healed person before you get to participate in society. Reyne lays out why this is wrong, and why it’s important not to shut yourself away, even if it means that others can hurt you further, and even if you might end up hurting them back. After all, we are no good alone.
Not about cults!
This is a frank discussion on communication within and without the public eye.
Hopefully! Write good posts!
2025-11-30 15:58:44
Published on November 30, 2025 7:58 AM GMT
(with apologies to Alicorn)
I have begun to wonder whether the word "lie" is useful as an umbrella term covering many instances that do not resemble each other strongly.
This is particularly noticeable in marginal and edge cases, where it seems to me that people put a lot of weight and emphasis on particular statements and interpretations. I think it is often worth dissolving the question and tabooing the word (e.g. diagnosing situations as "this person messed up here and here, that person is likely to be untrustworthy in the future, the third person is probably in the ethical clear but doesn't communicate clearly" or whatever.)
I don't expect anyone who reads this post to find all of these examples ambiguous, but I think probably some of them will seem so. They get weird in places. I ran out of letters of the alphabet before I ran out of ways to make the concept of a lie confusing, so feel free to add more ideas.
As you're going along, I suggest keeping a notepad open and given your answers. "Yes" or "no" is fine, don't feel like you need to write a lot.
1. Bob is on the couch watching TV. Alice asks Bob to get off the couch and turn the TV off, and Bob says "I will in a couple seconds." This is possible to do in a couple seconds. Bob takes about a hundred and twenty seconds to turn the TV off and get up. Did Bob lie?
2. Alice asks Bob to finish moving the couch into the living room then come to the kitchen, and Bob says "I'll be there in a couple seconds." This is not really possible to do in a couple seconds. Bob takes about a hundred and twenty seconds to get to the kitchen. Did Bob lie?
3. Alice asks Bob to come from the living room to the kitchen, and Bob says "I'll be there soon." Bob takes about three minutes to get to the kitchen. Did Bob lie?
4. Alice asks Bob to come from the living room to the kitchen, and Bob says "I'll be there in a second." Bob finishes the game of Risk he was playing with some friends and then goes to the kitchen, which takes another hour. Did Bob lie?
5. Caleb recently read a book about space, and the book incorrectly said that the moon is about 240 miles from the earth. Caleb tells Debbie the incorrect 240 miles fact. Did Caleb lie?
6. Same as above, but Caleb is an astronomer and is pretty sure the book is wrong.
7. Caleb is an amateur who recently read a book about space, and the book correctly said the moon is about 240,000 miles from earth but Caleb really wasn't paying much attention. Caleb tells Doug that the moon is about 240 miles from earth. Did Caleb lie?
8. Same as 7, but Caleb said "I remember reading in a book that the moon is about 240 miles from earth." Did Caleb lie?
9. The book was wrong, Caleb is an astronomer who knows better, and he was paying attention enough to catch the error. Caleb says "I remember reading in a book that the moon is about 240 miles from earth." Did Caleb lie?
10. Elise and Frank work together. Elise is Frank 's boss, and Frank is an engineer. Elise asks when Frank will be finished with an important project, and Frank says "By the end of March." The project is not done by halfway through April. Did Frank lie?
11. Same as above, but Frank claims he meant March of next year, not this year. The project is done by March of next year.
12. Same as 10, but halfway through March Elise tells Frank there's a more important project that just came in, drop everything to work on that, and Frank does.
13. Same as 10, but Frank has been tracking his estimations and is aware that projects usually take three or four times as long as the initial estimate. He has not adjusted his estimates to account for this.
14. George writes "He got it all right. Okay, not literally all, he got this and that and the other thing wrong." Is George lying in that first sentence?
15. Harriet quotes George, specifically just the line "He got it all right" with no further part of the quote, then talks about how George is totally wrong, here's all the things that weren't right, George must be lying or an idiot. Is Harriet lying?
16. George and Harriet are moderators of a subreddit, they get in an argument, and George says he'll step away from moderating if Harriet does. Harriet reports to the other mods that George said he'd step away from moderating, so the problem should be settled. Is Harriet lying?
17. Isabelle cooks dinner for John. It’s mediocre. John, out of appreciation for the work Isabelle did in cooking it, says it’s great, thank you so much. Is John lying?
18. Same as 17, but John is a professional food critic and Isabelle was really looking for some useful feedback.
19. Same as 17, but the food isn’t mediocre, it’s terrible.
20. Same as 17, but Isabelle understands that John is saying it out of politeness and knows if he actually liked it he would have said he loved it and asked for the recipe. She’s correct, that’s what he would have said.
21. Kriss is a fiction author. He writes a story about space wizards, and says in the story it took place a long time ago in a galaxy far far away. Is he lying?
22. Same as 21, but the story is about a suburban family, is told in first person, uses real details from the author’s life such as his religion and the town he lives in, and no obviously fictional elements appear until halfway through the book. When they do appear the fictional elements are very obviously fictional, like zombies rising from the grave.
23. Same as 22, but the fictional elements are actually plausible like a child getting hit by a car and the parents having to deal with the grief.
24. Same as 23, but this piece isn't clearly labeled as fiction or nonfiction. (It's a blog post on a blog that sometimes does fiction and sometimes does nonfiction, or article in a magazine that publishes both.)
25. Leah says “I helped organize that big conference” meaning that they sent a couple emails to committee and volunteered for an hour or two to help put chairs away afterwards. Others, listening, assume Leah was a larger participant than they actually were, and Leah doesn’t clarify. Did Leah lie?
26. Morgan, who knows exactly how involved Leah was, says “No you didn’t” in response to Leah’s statement. Did Morgan lie?
27. Nancy and Omar are stuck in traffic. Nancy rolls her eyes and says “I love driving in Boston, it’s so fast,” with heavy sarcasm in her voice. It is not actually fast to drive in Boston. Omar, who is six, believes her. Did Nancy lie?
28. Same as 27, but Omar is twenty-six and just kind of bad at noticing sarcasm.
29. Same as 28, but Nancy does it in a normal tone of voice and without the eye roll.
30. Same as 29, but it’s April Fools day.
31. Peter and Rebecca are stuck in traffic. Peter gets frustrated and says “Urgh, I swear nobody in Boston can drive.” Did Peter lie?
32. Rebecca is trying to give directions. She points out a turn to Peter, saying “I’m a hundred percent sure that’s the right turn.” It’s not, but she was very sure of herself. Did Rebecca lie?
33. As with 32, but Rebecca is a good rationalist who knows that 0 and 1 are not probabilities.
34. Samuel is a schoolteacher, and preaches the virtue of honesty and hard work. He tells his students it's the best way to get a good life. He secretly lies to his husband about doing the yardwork and plays videogames instead. Is Samuel lying to his students?
35. Tammy is a professional poker player. She tells her friends about how to see good spots and about what the odds are in a poker game, and advises them not to deviate from playing by the numbers. One day, Tammy goes against the textbook plays on a hunch someone else is bluffing. Was Tammy lying to her friends?
36. Urist is dating a woman, and he says after a month or two that she’s the most beautiful woman in the world, and that he’ll love her forever. Six months later, they’ve broken up, and Urist no longer agrees with those statements. Did he lie?
37. Urist is dating a woman, and after three years they get married. He says at the altar in front of both their families and a priest of their religion that he takes her as his wife to have and to hold, in sickness and in health, for richer and poorer. Five years after that, they’ve divorced, and Urist no longer wants to have anything to do with her. Did he lie?
38. Urist is dating a man. They live in the United States in 2001, where gay couples can’t get legally married but can get the same benefits through a civil union. Urist and his partner say at an altar in front of both their families and a priest of their religion that they take each other as husbands to have and to hold, in sickness and in health, for richer or poorer. They live together in a house where both their names are on the deed. Other than the legal distinction and the gender of the two, Urist behaves like a traditional married couple. In conversation, Urist says that he’s married. Did he lie?
39. It’s 2025 and gay marriage is legal in the US. Urist got drunk and married a man in Vegas he’d just met, with no vows and only the barest bit of paperwork or ceremony. The next morning, filled with regrets, he’s on his way to get divorced. Standing in line to do just that, he says in conversation that he’s married. Did Urist lie?
40. Winson puts a gun to Yara's head, cocks it, and whispers that Yara needs to call Victor on the phone and say everything's fine. Yara obeys. Did Yara lie?
41. As 40 but Winson wasn’t willing to commit murder. The gun wasn’t even loaded, though it was a real gun. Did Winson somehow lie by using the gun, implying something he wasn’t going to do?
42. Zachary is writing an essay that’s mostly a collection of hypothetical ways people can lie. Zachary said at the beginning that they ran out of letters in the alphabet before running out of ways people could say false things, but actually Zachary was one short, then had to wrack their brains to finish out the alphabet. Did Zachary lie?
43. As 42, but the thing Zachary actually said was “ways to make the concept of a lie confusing”, not “ways people could say false things”, and just wants to mess with the readers a little. He explains (albeit via a hypothetical example) that he didn’t actually say “ways people could say false things” so hopefully nobody is confused, though the hypothetical example is kind of meta and possibly very confusing. Now did Zachary lie?
44. As 43, but this time Zachary is wearing a "Might be lying" sign as he writes the essay. Which of course nobody can see, because it's a written essay.
45. As 44, but this time Zachary includes a picture.
46. As 45, but his name isn't Zachary. Though he never exactly said it was either.
2025-11-30 15:13:45
Published on November 30, 2025 7:13 AM GMT
Over the last two years I ran 20+ meetups, most with 40-90 people. I also attended a few dozen more.
A typical meetup starts with a talk, followed by socialising: people mixing and mingling in loose circles.
People really like the idea of talks — it’s great to have a specific reason for coming to a meetup. But think about the most fun and useful meetups you’ve attended: were they great because of the talk, or because of the conversations you had afterwards?
For me, the answer is the conversations. It’s amazing when the talk is engaging, but if the conversations afterwards lack depth, I might as well have watched a video on YouTube.
Meetup organisers significantly underinvest in making sure the post-talk conversations are great and people connect with each other. And there’s a simple way to upgrade that socialising part: turn it into an unconference.
An unconference is usually defined as “a participant-driven conference with a write-in schedule on a wall”.
I ran 10+ of these and here is how they look. There are a few tables labeled A, B, C, …. On a wall there is a schedule made from large sticky notes (A5 / 15×21 cm) arranged in a rectangular grid — the start times are at the top, and the table labels are on the left. There are plenty of markers nearby so people can write things on the schedule — and there is a note encouraging them to do so.
At any point of the event, anyone can claim an empty slot and propose a session: a discussion on a topic, a mini-talk, playing a board game or even silently meditating together.
In my experience, 95% of sessions end up being discussions around a topic. Despite having the word “conference” in the name, an unconference ends up very different from its parent concept. The schedule becomes a matchmaking service to help people find others who want to talk about the same things. Attending sessions is optional — if you run into someone you want to continue talking, you can skip the next session.
I find that people put a lot of sessions in the early slots, and the later slots end up being mostly empty. People find circles they enjoy and just keep going. This is a feature and not a bug: there’s no need for this structure once attendees find interesting people and engaging conversations. Essentially, unconferences “default” to regular old unstructured discussion circles when this makes sense.
Typically, the person who proposes a topic also ends up running the session. But a classic unconference experience is arriving at a table and finding that the original proposer isn’t there. This rarely a problem — people figure this out, often the person most knowledgeable on the topic volunteers to be a moderator.
In my experience, the minimum meetup size where an unconference makes sense is about 25 people. Below that, there isn’t much advantage to it: there will be very few sessions, and everyone can talk to everyone anyway. And ideally, you need 40+ attendees — that’s when you get a diversity of topics on the schedule and the unconference format really starts to shine.
An unconference requires minimal preparation. But there is a bit of a learning curve when it comes to actually running one: you have to keep time and explain the format. Here are my tips that’d make your first unconference a smoother experience.
An unconference is a low-tech and powerful way to make a meetup more serendipitous by increasing chances like-minded people find each other. If you are a meetup organiser — consider running an unconference at your next event.
And if you’re an attendee, consider sending this post to your local organisers. I myself will be definitely doing this. I wrote this post for selfish reasons: I want better, deeper conversations at the meetups I go to.
2025-11-30 15:05:41
Published on November 30, 2025 7:05 AM GMT
I have made many many feedback forms for events I have run or been a part of. Here are some simple heuristics of mine, that I write for others' to learn from and for my collaborators in the future. Most of my events have had between 50 and 500 people in them, that's the rough range I have in mind.
1. The default format for any question is a mandatory multiple-choice, then an optional text box
Most of your form should be 1-10 questions! (e.g. "How was the food, from 1-10?") Then next to it give people an optional space to provide additional text.
All forms I make primarily look like a stack of these questions.
This is because you can get a lot of signal cheaply through getting ~100 people giving a 1-10 on how the food was, or how the talks were, or some other thing. An average of 7/10 is very different from an average of 4/10, and the latter suggests you screwed up and need to do better.
Most of the time asking for text is very costly and takes much more time, and isn't relevant. The text box is there for if they need to tell you something more.
And it's common that they want to! A common experience when someone has something to say is that they feel the number is insufficient to convey their experience, and are compelled to use the free text box.
"How was the food? Oh dear, I got a terrible allergy from something that was poorly labeled, yet overall it was very tasty, healthy, and voluminous. I'm going to pu 2/10 because of my terrible reaction, but I have more to say than a simple number!"
This person uses the text box, but most people don't.
Also, sometimes people let you know some important reason why you shouldn't count their datapoint. For example, someone might rate the food 1/10, which sounds terrible, but then they'll clarify that they weren't there during mealtimes and didn't eat the food, and just gave it 1/10 because it was mandatory! This is rarely predictable, but especially with autistic people you occasionally get odd edge-cases like this.
2. All the areas of participant experience, and all areas you put serious work into, should have a multiple-choice question, and probably that should be 1-10.
Which areas?
Anything that cost a lot of money, or took a lot of staff time, or that was a big part of the participant experience.
Examples of things that I have asked about:
Yes sponsorships! If you sold sponsors part of your event, find out how positive/negative it was! This can end up being positive or negative and it's worth checking.
3. Whatever the key reasons are you ran the event, or whatever makes this event different from other events, should also have multiple choice questions!
The most important parts of your event also probably just need a single 1-10 question.
Don't ask for free-text. You won't have enough time to keep reading them all, also it will be hard to get an aggregate sense.
As an example, after the FTX explosion I ran a rationalist town hall to discuss it. Surely I wanted to ask for mini-essays from everyone about their feelings and how the event shifted them? No, not really. Here were the main questions:
To be clear, I missed rule number 1, I didn't give both optional fields. Partly this is because I field bad about taking up space in google forms; that's one way airtable is better (has better layout/density).
4. Ask how good the event was overall!
People sometimes don't ask this. It's an important question, the difference between an average of 9.2 and 5.6 is big. It helps to compare with other events too (e.g. if you end up running an event series, or an annual event, or just you run 3 different events and are curious which ones people liked more).
It also helps a lot when interpreting other questions. "They made lots of picky comments about the food and venue, yet overall gave it a 9/10, which suggests it wasn't a big determinant of their experience."
Extension: Having a consistent question between feedback forms is similarly good. I almost always use the exact wording of the NPS question, which isn't a great question, but helps me do comparisons with events run by other people (who often use the same question). I would like to hear other proposals for good questions to have over all of my events.
5. There should be basically up to 3 free-text questions that are mandatory, all other free texts should be deleted or clearly marked optional.
Free text fields are very costly in terms of time. The only time to have more than three is if you're paying people to fill it out (e.g. they're staff you employ, or the form is a paid survey for science or something).
6. My standard 3 free-text questions are "Best", "Worst", and "Change"
In essentially all user interviews I do, about any product, service, or event, I ask
This gives me a ton of detail.
7. "Name" should either be mandatory and first, or optional and last.
Either let them know up front that the info is going to be de-anonymized, or let them fill it all out and then get to reflect on whether they're happy to share. It sucks to get to the end of a form where you complain a lot and judge other people, only to find out your name is going to be attached. And it's hard to decide at the top of a form whether to add your name, you want to see what information you're sharing first.
8. Ask about the best people and worst people. Same for sessions.
Here is a set of questions I've begun to ask in my feedback forms:
Why?
Well, how much people contribute to others' experience is heavy tailed. You can find out who are the people providing a ton of value and exploit them. Turns out there's like a few people you should make sure are always at your events because they provide a ton of value.
And for the bad? Most events, basically nothing comes up. Hurrah! But then sometimes it does, and it was super helpful that you got the flag. I had light concerns about someone at an event, and then got a lot of flags, causing me to investigate further, and now I've uninvited them from further events. This was really useful for telling me to do that.
9. Babble then prune. First write a form that is too long.
I wrote out like 50+ questons for my first feedback form for Inkhaven, over the course of the first week, before cutting most of them for not being worth everyone's time. This helped me find the right ones that I didn't normally need and weren't obvious to me, like:
These all helped me identify people struggling and make an effort to help them.
10. Make sure someone in the target reference class fills it out in full before you send it out for the people.
Make sure they fill it out fully.
Make sure that they can submit the form. (I once had a form that would not submit on airtable, an hour before the closing session would start for ~400 people. I spent the entire time recreating it fresh in a format that would work. It was stressful.)
Make sure that it takes them a reasonable amount of time, without you forcing them to go fast. Ideally it should take 5-10 mins, not more than 15.
Bonus Tips
1. You should have a section in your closing session for filling it out for like 10 mins. Then you actually get serous amounts of people filling it out.
2. Make an interface for the aggregate data, and show it live as it comes in! This makes the experience rewarding for the people because literally anything at all happens to them as a result. (If you're concerned about goodharting, you can just show it after all the data comes in.)
3. Make the feedback form when you're first announcing and planning the event (e.g. 2 months ahead of time), so that it helps you think about what you're measuring.
2025-11-30 14:34:20
Published on November 30, 2025 6:34 AM GMT
cube_flipper of smoothbrains.net recently made something resembling the following argument in a talk. I like the argument because it uses tools of computationalism to argue against computationalism: it argues within the Solomonoff Induction framework, against the computationalist position on phenomenal consciousness.
This is my own interpretation of the argument; if you don’t like something about it, please don’t blame cube_flipper.
First, I should explain what is at stake in this argument.
The computationalist position views the brain as a computer, and claims that “what it is like” to be a brain (or anything else) depends entirely on what computations are implemented by that brain (or other device). This implies, for example, that if you could simulate a brain perfectly on a digital computer, then it would have exactly the same experiences as its non-digital analogue.
The physicalist position instead identifies conscious experience as some specific (but relatively simple) physical phenomena in the brain, such as the activity of the EM field or some quantum phenomena. Here’s cube_flipper’s write-up of the EM theory in particular.
The argument considered here argues in favor of physicalist theories, but does so from premises which I think will seem plausible to many computationalists.
Premise 1: Solomonoff Induction (SI) is a good normative theory of epistemic rationality; IE, if SI would believe something, so should we.
Premise 2: In order to make predictions about observations, we should (in practice, for our universe) make a model which consists of three parts:
Premise 3: Computationalist phenomenal bridges are complex relative to physicalist phenomenal bridges.[1]
Premise 4: Physical phenomenal bridges are at least as compatible with the data of experience as computationalist phenomenal bridges.
Premise 5: Our theory of phenomenal consciousness should be identified with our theory of phenomenal bridges.
Conclusion: We should prefer physicalist theories of consciousness to computationalist theories of consciousness.
Argument:
We should judge theories of consciousness in the same way that we judge theories of physics, IE, by balancing predictive accuracy with simplicity of the theory, as stipulated by SI.
When we do this, we come up with theories which describe physical reality (EG the Schrodinger Equation), plus theories which read off experiences from physical reality (EG neural correlates of consciousness), plus some probability distribution over who we are (EG the Born rule).
It might possibly be that computationalist theories are about equally good at fitting with the data of experience, but they aren’t better, at least not with respect to observations so far.
However, computational ways of building phenomenal bridges are going to be very complex compared to physicalist ways of doing so. Therefore, we should prefer the physicalist theories
I have difficulty accepting the conclusion, because my thinking on phenomenal consciousness frames it as a problem of mapping between our first-person perspectives (our direct experience) and a shared third-person perspective (the shared understanding of an objective world). A high-quality upload of a human could be a participant in that sense-making process, which suggests to me that a theory of phenomenal consciousness should be invariant to (some sort of) computational equivalence.
The assumptions run deep, so they are difficult to question. It feels clear to me, for example, that agentic desire is inherent to pain and suffering (there has to be a thing that wants not-that for it to count as pain/suffering). The physicalist view refuses to depend on such things.
Nonetheless, it is important to question one’s assumptions. So, what do I think of the premises of the arguments?
I don’t believe that SI is the correct normative theory precisely, but it does seem hard to get away from something roughly like SI. I prefer Garrabrant Induction as a normative theory (IE, I think it is closer to normative for us, since we are computationally bounded agents). This does have a somewhat different character (more inclined to invoke specialist theories for specific topics, for example, rather than one big unified theory).
Nonetheless, it seems difficult to deny the part of SI which is important for this argument, namely that simpler theories should be preferred. We can quibble over which notion of simplicity (in SI terms, which universal machine to use), and we can worry about malign prior arguments. I'd certainly prefer to have some stronger reason for a position. Still, simplicity is a very important heuristic.
One might wish to argue that simplicity is somehow being misapplied here. Perhaps physical theories should be simple, but anthropic theories (theories determining the probability that you are you as opposed to someone else) need not be? (EG, because there is no need for anthropic theories in one’s epistemology?) I think the grounding in a common theory of rationality (SI) addresses this complaint, however: differing opinions can either show how it does not work out as described within SI, or state their disagreement with SI (and ideally give an alternate rationality framework that escapes the argument).
The argument for premise 2 is an empirical one: it seems hard to credibly model the world in other ways than this.
I will divide the discussion of premise 2 into two parts:
I think this is essentially a question of physical reductionism. (Not the physicalist theory of consciousness we've been discussing; rather, physical reductionism being the claim that all things can be defined in terms of physical things.) One might argue: SI will split up the world into physics plus self-locator because physics is all there is, and SI is smart, so SI will figure out that physics is all there is.
I don’t think this is necessarily true, even if one believes physical reductionism. SI might squish things together for reasons of compression. The theories which compress most effectively are not necessarily easily interpreted into comprehensible parts.
However, this isn't an objection to the spirit of the claim, I think.
We might more plausibly invoke condensation, or some other theory which similarly does a better job of representing the normative pressure to separate out concepts in a comprehensible way. The spirit of the claim is that the physical world would pop out in such a theory. SI might not cleanly separate its physical hypothesis from its phenomenal hypothesis (the bridge) and anthropic uncertainty (the pointer), but if you picked apart its code, that division might still be what's going on in some sense.
So, as with premise 1, I think we can quibble about the theory of rationality, but doing so doesn’t undermine the plausibility of the position being expressed. I suspect many readers will find it very plausible that a superintelligence will model the world in this way.
One possible objection is that modeling physics and then finding ourselves in it is not a practical way to model the world for computationally bounded agents such as ourselves. It might be normative for agents with unbounded computing power, but we are not such agents.
I think physical reductionist will not be dissuaded by this argument. Non-physical-reductionists, on the other hand, probably should not buy premise 2a.
SI only cares about predicting observations well. We can accept 2a, yet only divide our world-model into two parts: the physical world calculation, plus the function which looks at the world and tells us our observation.
2b postulates that we can split the question into our location plus the function that turns a location into observations.
This bakes in two important ideas: that we have a location, and that other locations can also contain experiences (not directly accessible to us).
There’s a confusing question of what format the locations should be given in. Are they like single points? Are they areas? This doesn’t seem like a big obstacle to me, though.
It does feel significant to assume that the function which decodes our consciousness out of physics can be meaningfully applied to other locations. It is like a physical law: translation-invariant. I'm told people interested in physicalist theories spend time thinking about what theories fit with physical invariances such as frame-invariance.
2b seems closely related to premise 4: one might argue that the phenomenal bridge should be a function of location because one wants to use it as a general theory of phenomenal consciousness. In other words, this is another argument from realism, but this time realism about consciousness: “I do think other people’s experiences are real, not just my own, therefore I think ideal rational reasoning would recognize that.”
Both 2a and 2b can also be framed in terms of what you want a theory of phenomenal consciousness to do for you. The point is to bridge between physics and qualia! The point is to tell us about the consciousness of other people!
Why should we expect computationalist bridges to be more complex than physicalist bridges?
A physicalist bridge needs to be able to pick out some physical phenomenon, such as patterns in the EM field.
A computational bridge needs to do that as well, to parse the physical model, but it also needs to contain the complexity of several layers of interpretation. For example, to interpret computers, we need to interpret floating-point arithmetic. This extra complexity penalizes the hypothesis.
You might want to argue that floating-point arithmetic isn’t really so complex. Ok. But computationalism supports arbitrary layers: logic gates are implemented out of electrical components; machine code is implemented out of logic gates; low-level languages are implemented out of machine code; high-level languages are implemented out of low-level languages; application programs are implemented from low- or high-level languages.
The computationalist theory of phenomenal consciousness doesn’t care about how many implementation layers are stacked on top of each other. There’s not supposed to be a penalty for that. Yet, if we have to represent each of those layers in the phenomenal bridge, description-length piles up.
If you deny premise 3, you must either think that physical phenomenal bridges are going to be very complex, or there is a simple specification of the computationalist phenomenal bridge. For example, I have a strong intuition that there is a simple theory of implementation (the question of whether a physical structure implements a computation). If true, then computationalist phenomenal bridges become easy to compute.
However, as far as I know, implementation is still an unsolved technical problem, even though I personally feel like there should be a solution. If there isn't, we shouldn't even believe computationalism for computers.[2] It seems quite possible that there is a simple notion of implementation, but that the definition is not computable (and therefore infinitely complex by the standards of SI).
Without a specific proposal, I am just flatly denying premise 3, which suggests I should be open to the alternative.
(Even with a specific proposal, one still has to believe that it is shorter than physicalist phenomenal bridges, which is hard to believe when we’re assuming physics is the basic data fed into the function. Otherwise premise 3 still holds.)
Differences in how well a theory of consciousness accounts for the data of experience can easily overpower differences in prior probability, so if computationalist theories had an edge in that respect, the debate about description length could easily be irrelevant.
A computationalist might argue: “It is easy to construct a counterexample. If we modify the brain in a way that keeps it computationally equivalent (typically easy to find in the computational realm) but which modifies the prediction of the physicalist theory, then of course the person will experience no change in their experience, disproving the physicalist theory.”
If I understand correctly, cube_flipper welcomes such an experiment (save for the fact that it seems far beyond our current technology), and anticipates having a different experience due to the modified physical field. I make the opposite prediction, myself, siding with the computationalist. However, I am not sure this would be as good a test as it sounds. A physicalist, if I understand correctly, could consistently claim that such an experiment is deluding the subject, essentially doing something like modifying the memory of the experience so that they inaccurately feel the same, when in fact there was a difference.
I do think it is true that we can be in better or worse positions to report our own experiences accurately, and modifying someone’s brain may put them in a worse position. (Still, the test seems important. Fitting people’s self-reports of conscious experience is an important feature of a theory of phenomenal consciousness, even if we must concede that there should be some failure cases.)
An earlier version of this assumption was “If it is rational for each of us individually to think our personal qualia work a specific way, then we should also think each other’s work that way.”
Granting premises 1-4, a conscious being should believe in a physicalist phenomenal bridge. You should anticipate things such as: changes to the EM field should change your conscious experience, even in cases where computational theories would not anticipate this.
Without premise 5, this doesn’t yet extend to other people. The fact that the phenomenal bridge can look at other people (premise 2b) doesn’t yet mean that it should. This is an anti-solipsism assumption, as well as an assumption against theories such as “physicalism for me, but computationalism for everyone else”.
I have nothing against premise 5. I think there might be more lurking under the surface if we were to dig into this premise more, but I don’t have much to say about it now.
I think this is a fun argument. For mental computationalists who are also physical reductionists, it highlights the importance of solving the implementation problem; without a simple theory of implementation, premise 3 seems difficult to deny. For those who find themselves in an uncomfortable position of believing the assumptions but denying the conclusion, remember that computationalism and physicalism are not the only two options when it comes to theories of consciousness.
cube_flipper also claims that computationalist theories have some related defects which don’t quite fit into the argument I’m articulating.
The computationalist picture isn’t just complex; it is also arbitrary, meaning it has a lot of degrees of freedom. For example, do we interpret a specific signal as a 1 or a 0?
When it comes to our own personal phenomenal bridges, of course, these choices are constrained by experience. More precisely: in order to apply the SI analogy, we have to choose some way of encoding our experience into bits. This then constrains all the arbitrary choices, since we are reasoning about our rationality as if we’re experiencing bits. This feature is shared with the physicalist theories, since we’re encoding everything into bits in order to apply SI as a theory of rationality.
However, when it comes to other people, 1 could just as well be 0, amongst other arbitrary mappings (110 could become 000, etc). The computationalist picture is committed to a sort of invariance, where the same abstract computation should have the same experience. Yet, if qualia are modeled via binary encodings (the SI picture), there’s got to be arbitrary choices here, which means you don’t necessarily trust other people’s reports of their qualia (they could be swapping 1 and 0 compared to your interpretation, or other such mappings).
This arbitrariness implies a lack of predictive power. This is the old “Is my red the same as your red?” problem.
Physicalist theories are (in my limited understanding) supposed to avoid this problem, because unlike computationalist theories, they’re happy to make specific claims like thus-and-sech configuration of the EM field corresponding universally to the experience of red.
This pithy observation is due to Mahmoud Ghanem.