These are all additions to the existing treaty that could have been added without the drama, anger and stupid memes, without Trump blustering about "deals" and without terrifying the inhabitants of Greenland.
Nobody knows for sure. We're in uncharted waters here, and I think even the LLM skeptics would have to say that the technology has taken us far past what many originally thought possible.
It's absolutely wild how many times Volvo has managed to refresh this platform. They are basically the same cars as in 2015/2017 but now they have 150km range plus electric and they still look amazing.
rice, grains, seeds and potatoes all used to be peasant foods And the kings actually just ate a lot of fresh meat like beef, venison, wild boar, swan, and peacock, and imported fruits and spices like saffron, cinnamon, nutmeg, and pepper A king's diet was high in protein A peasant's diet was low in protein
Large language models are “grown, not built”. Researchers run training data through a neural network. Eventually this creates a working AI; nobody really knows how.
In practice, Hochman's hire is another nauseating sign that the Trump administration is destroying the bureaucracy in favor of some corrupt, neo-royalist husk of an apparatus that mostly posts memes and occasionally deploys the 82nd Airborne to extract rents.
And that's the problem in our current education system today is we are taking eighth graders and we are delivering them eighth grade content because that's what an eighth grade teacher does by law. They need to stand up and deliver the eighth grade content.
We talked in an earlier chapter about Resistance being the strongest at the finish line. It is. Resistance knows we’re about to defeat it. It summons all its genius and all its power to stop us.
No one was happy. Because no one knows what they want until they receive it. You don't know what a program should do until you play with it. So in the agentic age, you should resist the temptation to be overly specific upfront. Be as vague as you can to manifest something, then interact with the something. The way you arrive at good software is you write a little bit of software, and then you try to use it. It is in the process of using software that you discover what you really want.
it was actually being damaged by overly prescriptive humans. And any programmer who's had a pointy-haired boss knows exactly what that is like. When the boss walks into the room, doesn't know anything, starts telling you how to program, how to code. What do you do? You sulk. You write shittier code if you're mandated to do things that are against your better judgment. Why would an agent not be the same?
if you look at the nuclear industry outside of Valor, it's mostly a modeling and simulation uh industry. Like when you think of a nuclear company, right, there's like nuclear companies out there that everyone knows the name of. And you kind of look under the cover. It's actually a modeling and simulation company, right? They produce um very very precise what we call paper reactors which have really good predictions of how a theoretical thing might behave.
Anyone using GitHub knows that it isn't cut out for agents. The workflow, the review process, the merge queue and many things are made for the previous era.
And so if nobody knows how much an experiment costs, like every time you like grow up a new cell line or every time we make a new a new probe in in the fab, how much does that loop cost? Nobody knows. Therefore, experiments are free. Um, it doesn't cost dollars. It costs media. And media comes from the fridge.
Um, and sometimes that's because the model is trying to do something it doesn't have a lot of experience doing. So it's been trained on a whole set of things and as soon as you get a little bit off the distribution of things it knows how to do then like most machine learning models it will you know its performance will suddenly will start to degrade and the farther you get off the comfort zone of what it knows how to do the the more likely it is to to not work as well.
we believe that everybody in the world, you know, all the billions of people in the world are going to have a super intelligence that is adapted and tailored to them, that is enables them to accomplish their goals, knows their context, and ultimately is an expander of their own agency.
we want we want to encourage everybody and every company to build their own AIs. And and and who knows what innovation will come from the fact that it's open source.
I think the challenge is that everyone can now build apps But 1) almost nobody has distribution (like an audience), or 2) the money to pay for distribution (ads or UGC), or 3) the creative genius to get distribution for free (classically called guerilla marketing)
Their words now
I thought it'd be distribution but who knows, obviously creativity and ideas, but if you can copy a successful app in an hour, then how does that differentiating work?
I think context engineering has been so long lived because it's it's grounded in the fundamentals of how transformer attention works and until we have post transformer models or linear attention or whatever it is which who knows when that's going to happen context engineering will be interesting and important to anyone building on AI
And I think, you know, most people when they talk about aging, they're so worried about dementia and, you know, Alzheimer's disease as a specific form, but balance is even more important, I think. And most people aren't even aware of their balance.
Nobody knows now. That playbook has been wiped clean and people whose identity is I know the playbook are now terrified. Who who am I? Now, it turns out that the skill of writing a playbook is completely different than the skill of applying a playbook.
And so you kind of end up in this this bad equilibrium where everybody kind of knows that it's a bad equilibrium, but like nobody wants to break out. And I I felt like, okay, well, if I just hopefully come out and say like, look guys, let's all recognize that we're in a bad equilibrium and let's move to this different equilibrium where we're we're plotting things with an X-axis
One is of course everybody knows power constraint. Some country the power they just don't have that. They get impacted. And then secondly, a lot of people didn't realize the helium impact can be also very significant for semiconductor. And then the thirdly, is everybody know right now memory is a bigger shortage.
But if you ever want to get better at anything, and sometimes that depth, that nuance is the thing that leads to an invention, right? If you know how a compiler works, if you know how memory management works, that might give you enough information to say, "Oh, I can make a new programming language."
The point is a user is looking for help, and a maintainer is willing to help and knows a solution. They are all real humans. You guys are hurting them by closing and deleting the question.
And one of the things I noticed is for 25 years we've kind of had the answers. Somebody comes to us and says we have too many bugs or like, all right, well, here's how you write tests. Oh, I can't write tests. Well, here's how you design so you can write tests. It's just kind of press play uh on the recorder. And the thing that's changed is at this moment nobody knows the answers to anything.
I do think we should expect more speciation in the intelligences. Um like, you know, the animal kingdom is extremely diverse in the brains that exist and there's lots of different niches of of nature and some animals have overdeveloped visual cortex or other part kind of parts and I think we we should be able to see more speciation and um you don't need like this oracle that knows everything.
a swarm of agents on the internet could collaborate to improve LLMs and could potentially even like run circles around frontier labs. Like who knows, you know? Um yeah, like maybe that's even possible. Like frontier labs have a huge amount of trusted compute but the earth is much bigger and has huge amount of untrusted compute.
There’s so many things that that game gets right that other games are lucky if they get one of those things right, and are… become best in their genre just for getting that one thing right. And Breath of the Wild does them all right and the best.
It is, it is powerful, but in many ways it's not much different than if I run cloud code with dangerously skipped permissions or codecs in YOLO mode, and every, every attending engineer that I know does that, because that's the only way how you can, you can get stuff to work.
Swift’s build infra is good enough for most things these days. codex knows how to run iOS apps and how to deal with the Simulator. No special stuff or MCPs needed.
The audience is waiting for you to get through this thing because they know nothing's going to happen. Like whatever's on that page, that's what's going to happen.
The US has a lot to learn from other countries for how to catch up to the efficient frontier, especially for certain types of construction (ie: transit) and in certain places in the country (ie: expensive coastal metros).
If you are interested in learning more about how humans communicate and work together, I highly recommend reading When Everyone Knows That Everyone Knows.
That's great, but I don't think it can replace a human poking at virtual machines; my experience with EC2 is that images break in all sorts of wild ways, and nothing beats a human for saying "huh, I have no idea what's going on here but something seems weird".
Luckily, if you want to do what we’re forced to do and also what I want to do, which is make new IP, you need single-player games. You can launch a multiplayer game with new IP. It’s just extremely hard.
the only thing that we know is that models will improve. Will it be incremental? Will it be exponential? I mean somewhere in between. Who knows? But uh what you have to believe is that you get better as models get better. Your organization gets better as models get better.
So it's extremely important to curate the information sources that you have, so that you wouldn't be somebody who is left to the will of AI-based algorithmic feed telling you what's important so that you end up consuming the same information, the same stuff, the same memes, the same news as everybody else.
We don't we don't really know what information they had prior. We are we have to guess because they've been fed so much. This is one reason why they're not a good way to do science. Uh it's just so uncontrolled, so unknown.
So here's what I would say that deep down at a very fundamental level the synthetic experience that you create yourself doesn't allow you to learn more about the world. It allows you to rehearse things. It allows you to consider counterfactuals but somehow information about the world needs to get injected into the system.
but something to remember is that when a pilot is using a simulator to learn to fly an airplane, they're extremely goal- directed. So, their goal in life is not to learn to use a simulator. Their goal in life is to learn to fly the airplane.
Uh so in order to effectively learn from your own experience, it turns out that it's really really important to already know something about what you're doing. Otherwise, it takes far too long.
Decades of grading data; standardized test scores; cross-sectional, longitudinal, observational, and experimental studies; along with many other types of ancillary and convergent evidence, ultimately tell the same story: education can raise the absolute performance of most students modestly, but it almost never meaningfully reshuffles the relative distribution of ability and achievement
will the error rate ever be controllable or manageable will you ever get to a model that knows when it's wrong which to me seems like given a stat statistical system seems like a contradiction in terms
No matter how much I know about a domain, the other person will always know far more about their own situation, context, beliefs, skills, preferences, etc than I do.
Roberts reflects on the wild problems we face in our lives and how to navigate them—like career changes, marriage, or children—that can't be solved on a spreadsheet.
But that is not going to help you get dozens of 70 ton granite blocks 300 feet in the air to form the roof of the King’s Chamber and the floor of the chamber above it, and the roof of that chamber, and the floor of the chamber above that, and so on and so forth. Wet sand never got those objects up there.
You Were Like a Wild Chrysanthemum (Japan, 1955, CC) 3.5 Chishu Ryu is one of my favorite actors, but he has a fairly small role. Very nice cinematography. Recommended for fans of nostalgia.
Oh, if it’s not chemical. So I think some of the things that have evolved on our biosphere I would call as much alive as chemistry, as a cell, but they seem much more abstract. So for example, I think language is alive, or at least life. I think memes are.
So abstract. Maybe the most abstract book you’ll ever read. Compares “finite” games with rules and winners, versus “infinite” games without winners where we can play with the game itself. Is it about a job versus a calling? Religion versus spirituality? A story versus story-telling? Who knows. Thought-provoking if you can apply the metaphor to whatever concerns you.
The answer is not more fields, which means destroying even more wild ecosystems. It is partly better, more compact, cruelty-free and pollution-free factories.
Their solutions, such as pasture-fed meat, with its massive land demand , are impossible to scale without destroying remaining wild ecosystems: there is simply not enough planet.
How to improve your HUGE major life decisions, like whether/who to marry, whether to have kids, where to live, career paths, and such — where you can’t use the usual checklist/data approach. This economist addresses self-identity, “deepest self”, and “something I was meant to do”. You can’t use a pro/con checklist with an item that says “lose respect for myself”.
I may have I may be an investor. I have a track record of calling the market right in the last five times. People say, "Wow, he really knows what he's talking about." But there are lots of people who are trying to predict which way the market is going to go. And some of them are going to get it right by chance. And people assume it's because they know what they're what they're doing, and in fact they're just the lucky ones.
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