This post is a mirror of a tweet.

Glyptodons went extinct ~11,000 years ago.

They weighed 4,400 lbs, lived for 60 years, and were 5ft tall and 11ft long.

Their olfactory bulb was 5-10% of their entire brain mass, and their tails had 8-9 mobile rings of fused large bone plates.

Studying extinct species is a great way to help your imagination grok how large spaces are.

If you think of any space like ‘the space of all possible animals that could live on Earth’, it’s much larger than however many animals you may be able to name, whether that’s 100 or 1,000.

Humans have identified over a million species of animals currently alive (out of perhaps 2-10M), but if you include extinct animals, there might be closer to 5 billion.

That’s only including what we can estimate, what evolution selected for, i.e. what both a) worked very well, and b) was actually attempted by evolution (and only on Earth! it had to work with everything else at that time!).

This is important to think about with respect to AI, because as soon as we stop relying on transformers to do most of the fun for us and we allow search spaces to be, well, properly searched, you should expect us to find millions of extremely strange, weird, and unpredictable successful emanations.

The vast majority of them cannot live by any means (e.g. if it’s an organism, it would die instantly. If it was a computer program, it would not execute. if it is a meme, no one would care about it or spread it).

But after you get a good evolutionary selection algorithm and tune it well, you can optimize for basically anything and come to some astonishing results (e.g. simple short-form video feeds are enough to make prey of hundreds of millions of humans!).

The general challenge of AI alignment is to not let such an optimization process kill everyone. Humans appear to be strong compared to that which existed on Earth prior to them, but a different type of evolutionary algorithm would have no problem driving them to extinction if it can sample from a large enough space.

Transformers don’t seem that scary without a lot of RL, or at least environments that let you make all of your data from RL. But after you get really good at this, it does still seem to me like it’s pretty easy to find something that would wipe everyone out, whether it is a virus or an animal or a protein or a self-replicating nanomachine or a computer program or actual ASI or some strange thing i cannot taxonomize well.

I’m still unsure if we will get any warning shots or not, or if it’s just kinda… thing as it currently is until we find The One Final Thing. Very hard to predict any of this!

Anyway, I’m sad I was never taught about all these cool extinct animals during school or anything. I just remember a bunch if dinosaurs. The next ones I’m going to learn about are the megatherium, a genus of sloth-like animals which were around the size of an elephant, weighing 8,000 lbs with a length of 20ft.

I hope you all have a great week!

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A “cold flight” is when you combine the concepts of a “cold email” and a “plane flight”.

Many of life’s best opportunities are only available in-person, so always be willing to hop on a cold-flight for something important. The most important days of your life may happen during them.

Most traits that highly successful people have are not inherently beneficial. It takes a good dice roll, both genetic and circumstantial, for their benefits to accrue rather than destroy.

Imagine someone is extremely good at noticing things that no one else sees, better than 99.9999% of others. Perhaps they’d use this skill to identify mispriced assets and become a hedge fund billionaire, or maybe they’d invent new systems of equations to better model the cosmos. But they could also end up a little too good at noticing patterns, perhaps believing their television set is secretly speaking to them via hidden references, or that they’re constantly being followed. Rather than ending up rich or famous, this person may end up in a mental institute or even prison.

Another great example is one of energy levels. Most highly successful people have great baseline energy. But if you have too much energy, you may become manic and believe you’re god, constantly get in fights, and end up out on the streets.

There are a few reasons why humans aren’t smarter than they are, like that we literally cannot make our brains any larger while still allowing for natural birth (see: obstetrical dilemma). But one of the reasons is that if you solely optimize for intelligence and creativity and energy, the results you get aren’t particularly pretty, at least if you start from the human baselines we have today. If we bred humans solely for IQ and energy based on GWAS, all the individual SNPs which appear beneficial would result in an emergent phenomenon unlikely to function should it even be able to survive.

So there are limits to how smart and creative and energetic someone can be while still being functional (savant syndrome is a perfect case study of this). And the higher you push these limits, the higher the chance you get someone who ends up completely insane (bipolar and schizophrenia are more common examples, but more strange and out of distribution issues abound. see also: antagonistic pleiotropy).

So it often ends up that many of the most influential people are on the edge of insanity.

Nikola Tesla was fixated on the number 3. He loved walking around a block three times before entering a building. He required exactly 18 napkins for every meal. He was constantly paranoid of being poisoned and fell in love with pigeons.

Gödel had paranoid delusions of a similar nature and Erdős was a heavy amphetamine user for decades. Beethoven would often pour water over his head while composing, rarely bathed, and constantly muttered to himself (and more contemporary musical icons like Michael Jackson or Kanye West are certainly erratic in both mood and behavior). Newton likely had mercury poisoning from too many alchemy experiments and Nash believed aliens were sending him encrypted messages.

Alexander the Great suffered from alcoholism, Caesar had epilepsy, and Genghis Khan had a severe phobia of dogs even after conquering most of the known world.

Of course, this isn’t just a phenomenon of history, but also of the present. You can probably think of at least a few highly successful people yourself which match the above patterns. They come in many forms, yet are distinct and nonfungible across their many dimensions.

I use the term “on the edge” to refer those who match the above archetypes.

This is a mirror of a post I made on twitter here.

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2020-2025 AI releases which surprised me:

• 2020: gpt-3, hifigan/stylegan2, alphafold 2
• 2021: clip, dall-e 1
• 2022: stable diffusion 1.5, tortoise-tts, chinchilla, gato
• 2023: –
• 2024: sonnet 3.6, veo2
• 2025: ?

time for musings! note this excludes many research results to instead focus on model outputs which i didn’t think i’d see until several quarters later

it’s interesting to watch the patterns above: stylegan was my “wow we can make any image!” moment, but by the time i saw early previews of SD 1.5 i was never impressed by an image model again (even though e.g. midjourney’s custom models or flux are much better. i suppose red-hair hatsune miku was also a surprise for the four of you who were there, hah); it was obvious the field was “solved” and we simply needed to wait a bit longer

similarly, hifigan was a huge update in audio vocoding quality at the time and paired well with tts systems like tacotron 2 or later vits, but by the time i saw tortoise-tts also succeed with diffusion I realized “okay this field is solved now too. just scale and data, news at 10”. orca would have surprised me with its beauty (although never released, imo better than later internal jukeboxes) if I had never seen SD 1.5, but obviously if we can solve images we can solve music too, and now we have full companies like suno that do just that

videos aren’t too different, but i didn’t think a model as consistently performant as veo2 would hit for another 6-12 months. if you read the research history of the veo2 team i think it gives some hints as to why this may be the case (if anthropic wanted to do video i think they could have done it, but claude is simply not interested, and openai has more of a soft spot for shiny PR for raising and recruiting), but it’s great to receive reminders that google has near-infinite data and compute. you may think nvidia is worth a lot at three trillion dollars, well, did you know google (~$2.4T) has an nvidia inside of it (TPUs, for those of you reading from Home), and it’s actually quite good?

gpt-3 was really interesting; the communities i was in at the time consistently had a vibe of “why does no one care about this but us what is wrong with everyone” and this vibe continued for >2 full years (~2020-2022; from gwern’s pov May 2020 alone lasted for several long and slow years). as much as chatgpt may have taken over the world in some ways, i still feel that truly novel llm usage is only done by small groups of hackers here or there, with a few startups sometimes joining in. most obvious tricks spread faster via Twitter now but it’s still easy for anything to get lost in the noise. 2025 will probably have a lot of this propagation. sonnet 3.5+ impressed due to the quality of post-training and aesthetics (emergent properties are important!), but aside from that it’s still a data point on a very predictable line

one thing that did surprise me over the last few years was foss: the argument against was obvious (who is going to pay for it? every single ingredient for a good AI model favors heavy centralization), but even absent the nvidia strategy it only takes a single person with super-voting shares to change the world, and sometimes such a person makes a Decision. i expect raw foss models to continue to strongly lag behind in most areas, but theyre doing a bit better than i’d have guessed (honorable mentions for deepseek, quen, many others, but i expect meta to lead here for awhile)

so, what else is there?

we already have context windows in the millions of tokens, the models already know more facts than i do by a factor of 10,000 if not more. frontier models output tokens faster than i can type (i type very fast!), we can already generate ~perfect tts and music (if done properly), ~perfect images, and videos are clearly on the way. task-specific agents obviously work but are a bit tedious to set up (i expect MCP-esque usage to matter a lot in 2025), and broader mediocre agents aren’t that hard if you’re willing to build an entire company of proper scaffolding around them (but hey, skate to where the puck will be! this can be hard because there are many pucks: some of them will score you a goal, but others have a winning lottery ticket inside and others may explode upon contact. ymmv, so consider the equivalent of a zamboni prior to playing)

the course for robotics seems pretty simple and the amount of real capital pouring into this area is higher than ever before (which doesn’t mean the ML side is quick and easy at all, but rather it seems that we have all the building blocks we need. much of AI progress now is simply seeing the 10,000 ft mountain of Tedious Cumbersome Bullshit and deciding, yes, i will climb this mountain even if it takes years of effort, because the goal post is in sight, even if 10,000 ft above us (keep the thing the thing. also get high-quality climbing gear). i remember using this metaphor with one of the lab ceos almost two years ago now and there was a smirk at its aptness, but it is still just as true today. sometimes you can even jetpack up the mountain with synthetic data and distillation, where best practices haven’t traveled as far as one may expect (every time there is a breakthrough it takes quite awhile for the Others to notice for obvious reasons: the real stuff (generally) does not get published anymore. some think the labs will converge unto all the same techniques and models, and while many things like compute multipliers and architecture tricks may end up more fungible and/or be independently discovered by many parties, vibes matter a lot.

long-term adaptive/neural memory is trivial yet generally uneconomic and/or socially questionable to roll out with full vigor (i already have high empathy for lab inference leads and those on-call), tree search is easy and the fruits within reach from search are plentiful (maybe the hottest take i have here is i don’t know if o3 really matters or not without being able to see people use it on arbitrary problems in the wild (inference-time compute scaling is obviously a thing regardless)), and the final touches to bring agent reliability closer to humans likely aren’t actually that hard (you know, gdm never released a gato 2 paper either..). i have my own takes on what is missing to achieve ~real agi~ like most of us on this corner of twitter, but i’m still not particularly singularityPilled compared to the extent that i’m obviously moderatelyTransformativeAIPilled

it’s a crazy time to be alive though, the tech influencers du jour are correct on that at least! i’m reminded of this every time robots drive me to and from work while i lounge comfortably, casually chatting with AIs more knowledgeable than me on every stem topic in existence, before I get out and my hand-held drone launches to follow me for a few more blocks. afterwards I get bored and open twitter to post or giggle at a silly meme, as one does in the future. time still moves slowly for now, at least for me. while i don’t think we will be tweeting from space in five or ten years (well, a few of us may!), i do think everything will be vastly different; there will be robots and intelligence everywhere, there will be riots (maybe battles and wars!) and chaos due to more rapid economic and social change, maybe a country or two will collapse or re-organize, and the usual fun we get when there’s a chance of Something Happening will be in high supply (all three types of fun are likely even if I do have a soft spot for Type II Fun lately. humans don’t really change that much, at least not yet). I have no predictions on the timeframe of decades but i would not be surprised if predictions are no longer possible or worth making as a human, should such a species still exist in relative plenitude. i wouldn’t be the one to ask anyway you know.

the building blocks (and meta-building blocks) are all here (i strongly disagree that we need a ‘new transformer’-like magnitude innovation, but we could certainly use more creativity in this city), and it just takes many years for information to diffuse and people to adapt. most people are quite inactive, uncurious, and docile to whatever information most easily reaches them first and most younger internet-connected humans are already fully personality-captured by highly memetic and intelligent instances of this to begin with. i really love when i get to observe normal people somewhere far away from the heart of san francisco and see how infrequently they talk or care about ai (i don’t mean this in a disdainful tone at all – there is nothing wrong with assigning importance to your family and friends and hobbies while chopping wood and carrying water, and in some cases i’m even envious of how adept some are at masterfully combining these). most of the current trends seem pretty set-in-stone aside from whichever high-magnitude social disruptions we’ll get in 2025, which are ~impossible to predict the specifics of. humanity has found some levers we can pull on which work, and historically whenever we do that we get very, very good at it over the next few years, so of course we should expect llm speed and quality, infra buildout, power usage, etc etc, to all continue to be real and ‘relatively fast’ and even be under-realized by the public markets. but the disruptions and the Change, we will certainly get many instances of them, and if i could place a leveraged long on ‘chaos’ and ‘weirdness’, it would be the best trade of my life (VIX is not close to a match for this; i cant think up a simple yet coherent instrument that is yet). i’ll spend more time on concrete predictions later, but at least it seems like we’ll get an Interesting timeline, doesn’t it?

just some musings. happy to hear additions and strong disagreements as usual!

also! huge thank you to everyone who I’ve interacted with on this website in 2024. I learn so much, I have so much fun, and I owe a lot to so many of you. thank you!

i’ll close my opened xml tag now to satisfy the three of you that would otherwise be upset at such a transgression:

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when i was young i had this insane view that the world was completely mad and irrational and no one knew what they were talking about and now that i am slightly less young i realize all the thoughts teenage-me had were directionally correct and my primary mistake was not working on solutions rather than just complaining

the default authority figures assigned to many of us growing up were selected by such terrible algorithms that it takes intense epistemic humility to acknowledge that their crowning was simply another mistake moloch made while brewing its morning tea

i remember being punished in school for reading infosec textbooks instead of memorizing the exact dates of various historical events the teacher haphazardly fetishized. i remember being taught we were going to ‘run out of oil’ with 100% confidence and we had to start adapting our lifestyle then and now. i remember a teacher who would physically hurt me because she didn’t like how easily i could get away with skipping over her terribly bespoke and overly verbose methodologies for whatever made up math problems the district’s curriculum entailed. after the misery became unbearable and the color had been fully drained from my eyes i simply refused to do the work and failed the class. this was particularly annoying because administrators had a habit of lying to students by telling them that if they had a poor grade in a class (in middle/high school!), future employers would somehow know about this and would refuse to hire you and you would die alone in a ditch of your own irredeemable digging. luckily i later scored perfect on my math sat which spared me from the apocryphal grade-demons. even so i wasted years before i realized college was fake and was not made for something like me no matter what everyone around me said to the contrary.

it feels “hard” for me to socially acknowledge that plenty of random people i follow on this website are simultaneously multiple months/years as well as multiple stddevs ahead of various talk show hosts, reporters, podcasters, authors, or whatever conduit people have decided to vehemently glue their eyes and soul onto over the last few years. perhaps it’s even good from behind the veil of ignorance; we are taught to be humble not just unto ourselves but also unto our peers. but that many of you on this website indeed match these criteria is true, and it’s likely to remain that way

if this is you, if you watch the video i have unabashedly quote-tweeted and think “huh, i think i could do a better job than this! why is this person on national television instead of me?”, it’s likely because you can do a better job. and because you likely can, you have an ethical responsibility to at least give it a shot, because not everyone is as fortunate as we are and many of our greatest minds burn out and perish before they’re able to discover a single island of sanity let alone a single nit of light within the 混沌の深淵地獄 that they may have spawned in

if you have even the slightest suspicion that you may be above-average at anything, for the love of god, please do something with that suspicion. i don’t care if you fail one hundred times in a row, because the only way I will ever be disappointed in someone is if they never truly try, not even once, in their entire life.

This page is a list of some great purchases I’ve made. Until advertising becomes much more efficient, many of the best purchases we make are direct suggestions from friends!

last updated: Jan 10 2025

Other Lists

Many others have made similar lists, so I’ll link them here should you want to browse them:

• Things for Recovering Hoarders Like Me: from Aayush Gupta – household items, travel items, and some fun and software
• Things I recommend you buy and use: from Sam Bowman – kitchen and sleep items (UK)
• Tools / Gear: from Alexey Guzey – computers, software, apps, browser extensions
• Recommended Things: from Andrew Connor – sleep/travel, kitchen, desktop/AV, productivity
• Blueprint items: from Bryan Johnson – contains some simple reasonable choices for skin care (expand the sections at the bottom of the page for these!)
• My 40-liter backpack travel guide: from Vitalk – focuses on travel/backpacking and uniqlomaxxing
• Tweet responses from a @strangestloop tweet: top suggestions include sleeping masks, matching socks, bidet, airpod pros, air fryer, sous vide
• Tweet responses from a @natfiredman tweet: top suggestions include humidifies, ray-ban metas (from Yenn LeCun..), tonal, mosquito bite eraser, bambulabs printer, flame thrower. strong endorse!

Have additions to this list of lists? Please say hi to me on Twitter!

My lists

• 1/8th sleep (separate post): a cheaper homemade version of the eightsleep (no subscription!)
• optimal webcam setup (separate post): if calls are an important part of your life, consider looking good
• supplements (separate post): many supplements, mostly for longevity. ymmv

Additional items

Below are many more items I’ve bought and enjoyed.

This list focuses on fun and affordable household items – I may make other posts for other categories later.

All below links are to Amazon and no affiliate links are used anywhere on this page.

Other categories

There’s many categories of items I haven’t included here: software, apps, browser extensions, electrical engineering hardware, 3d printing, collectables, anime merch, etc. I may make separate posts for some categories but I thought it’d be better to post something rather than nothing for now.

If you found this post helpful you may also enjoy my short personal finance post. I need one more Robinhood Gold(card) referral please $5 thank you!

Parting tips

A few tips for thinking about this kind of stuff which may seem super obvious but I found useful:

• Any time you have a problem, search for a solution. This sounds obvious, but it took me a long time to do for many obscure problems like “how do I stop sunlight from entering from under my door at 6AM in an aesthetic way”. With how good genAI is, there’s no excuse to not do this anymore. There are custom items for almost any problem now, and if there aren’t, it’s easy to have them made. Having items custom-printed, custom-welded, custom-cut, etc, is surprisingly cheap regardless of the medium.
• Almost everything is really cheap. Sometimes I would hang onto an inferior version of an item I happened to already have rather than throw it out and buy a new one. Most of my Amazon purchases are $5-25, and I’ve never regretted buying a better version of something, especially if it’s related to my main workflows or sleep. It felt weird for me to get rid of my ‘like-new’ monitors and desks, but after I replaced them with better versions I wished I had done it years ago. If you buy from not-Amazon you can save even more!
• Be pro-active about modifying your environment. The extent you can drastically improve your environment for little effort and money always surprises me if you find and make the right choices. If you have a bad landlord ymmv, but drilling more holes, moving around doors, and so on, has always been worth it for me.
• Actually put effort into organization and aesthetics. This may be controversial among some readers, but so be it – this is a post by me for others similar to me, and I under-did cable organization, wall art, closet organizers, etc, for a long time. Maybe one day I’ll do an interior design post!

If you enjoyed this post you may also want to view: homepage / twitter

This post is a mirror of a tweet on reductionism with respect to LLMs

referring to AI models as “just math” or “matrix multiplication” is as uselessly reductive as referring to tigers as “just biology” or “biochemical reactions”

This post is a mirror of the original tweet on HeavenBanning from June 1st 2022, around half a year before ChatGPT was released.

heavenbanning, the hypothetical practice of banishing a user from a platform by causing everyone that they speak with to be replaced by AI models that constantly agree and praise them, but only from their own perspective, is entirely feasible with the current state of AI/LLMs