One startling thing nobody ever expected an agent to do was to take notes about what it was doing for future agents to read. And, again: look at these tree-traversing programs, traversing trees!
So okay, so to take a evolutionary view, right? That new viewpoint prediction is exactly evolution had to solve by making animals move. You you nature give animals eyes. But nature didn't give trees eye. Eyes. Why? Because when you move, you see a new viewpoint.
Since 2025, MAGA ideologues have been using this alleged plot as an excuse to dismantle government programs, stifle research, intimidate American citizens, and even distort American foreign policy.
I think this is one of the great tragedies of the moment, is the falling birth rates are making it more difficult for people to be excited about a more prosperous future that is difficult to get to. Because if we're planting trees of which our children will not sit in the shades, I don't know, maybe we should just make some lumber and burn it all down, right? I think there's a nihilism that can sneak into society much more easily when we are not DNA invested in the future
Structure and Interpretation of Computer Programs (SICP) by Gerald Jay Sussman and Hal Abelson. SICP is my all-time favourite programming book. It's also where I learned about the power of wishful thinking when coding. SICP uses wishful thinking as a design tool: write code as if the abstraction already existed. Pretend. Then, once the ideal abstraction has taken shape, you go ahead and implement those functions. This meta-level is what made SICP so valuable. Ultimately, it's a book that teaches how to think about code and problem solving. I'm tempted to even say that SICP is more a work of art than a pure coding book, but I won't go there. A beautiful book.
You look under the underneath though at the code and it was just a horrible unmaintainable mess. But the fact that people could program the programs that they wanted was a a significant step forward as opposed to I'm going to write a thousandpage requirements document and then wait 8 years and not get what I want which was the alternative that we were offering at the at the time.
So a a big challenge in object-oriented programming is dividing the responsibilities because you're moving the computation to where the data is. Saying, "Well, this object does this and that object does that is a really critical decision because you want to you want the computation near to the data so that there's less coupling between them." which is a lesson that I I think uh kind of got lost in the noise. That's the fundamental design move in ob in designing object-oriented programs and I I think I stand behind that.
part of the goal of programs is to communicate intent to other human beings and now to models as well which which is a much more open-ended problem. We understand a lot more about how to communicate to other human beings whether we apply that understanding or not. We don't understand at all how to communicate effectively to models
While there are certainly some benefits to these programs, YIMBYs are right to doubt that housing subsidies alone are a scalable solution to affordability challenges.
Which actually goes to show you that you there's not a correct huristic for science. You actually just need multiple independent research programs with their own huristics.
In truth, rather rapid electrification projects, without decade-by-decade continuity, exist outside the UK and are often quite reasonable in their costs, as in Denmark, Israel, and especially India.
do not say AI because what we don't want to do is use a big umbrella to describe what you're doing. Let's get concrete details. These are computers. These are computer programs.
If AI-driven labor displacement ends up being large in magnitude and permanently drives down the demand for labor, it will likely be necessary to go beyond mere incentive programs to long-term income support for a significant fraction of the labor force.
That was always the idea, and that that goes back even to the predecessor of Turbo Pascal, this idea that it's not just a compiler. It's an experience, right? I mean, you don't just compile your programs. You also edit them. You also run them. You also debug them. You also have a runtime library. It all has to like fit together.
because if you were to force AI to write a type annotation on everything, then it would probably get it wrong more often because now it has to keep track of all these types and and it and it has to just repeat itself over and over and over, right? And so, types are important where there's no context.
so many of the best practices that your lawyers, your bankers, whoever advisers you have, they're going to be pushing best practices on you that are younger than the trees in your local park.
I'm willing to buy it sort of low enough down when, you know, progress is relatively simple, um, but but higher up you start to get to shape the way in which you you do the exploration and it's interesting, you know, people we are starting to shape it in in in interesting ways.
Um, there's just too many interesting ways of combining things. There's too many sort of deep ideas waiting to be uh, discovered and when it you know, not only we, but but nobody ever is going to to discover most of them. So, choices about how to make how to do the exploration actually matter quite a bit.
With Claude, I've built a host of software like this. Mostly small tools for myself — programs that instantly append copy buffers to text files (I keep a running file of nice things people write to me called notapieceofshit.txt) or quickly perform live currency conversions.
and that's in some ways what allows it to be very sturdy so I Think today management is really about this idea of like be sturdy while being flexible and that is a very hard thing to pull off
This book gave me both insight and a deeper curiosity about the subject. This book introduces the SPIN model checker and exposes the reader to the nuances of verifying programs using SPIN. A nice feature of the book is how it explains concepts using thoughtfully developed, freely available tools (such as jSpin) that the author created for pedagogical purposes. It's a demanding book, but the content was interesting. The book presents details about concurrency, temporal logic, non-determinism, advanced SPIN topics, and even case studies. I've already written a few blog posts about the topics I explored while reading this book, and I plan to write more. Overall, it's a good book. I'm sure I'll revisit the latter half again.
The selling point of generative A.I. is that these programs generate vastly more than you put into them, and that is precisely what prevents them from being effective tools for artists.
Well, not just at Area 51, but in all the different national security and military intelligence and intelligence programs. And I personally have no reason to believe that aliens have ever visited Earth. That’s just me personally.
Well, first of all, yes, they're connected, but that network stops when there is a physical gap. That network does not necessarily enable the whales in the Dominican to connect with an olive tree in Israel to connect with a weeping willow in Montana.
I occasionally use these Adobe CS4 programs: Dreamweaver, Photoshop, Acrobat, Illustrator, and Flash. I don't use any of them enough to justify a subscription to their latest cloud offerings.
Today, it is increasingly apparent that both of these views of the nature of human intelligence - either a collection of special-purpose programs or a general-purpose Tabula Rasa - are likely incorrect
The Unix Programming Environment is a book you can't read too soon. It's something I wish I'd come across years before I actually did. An authoritative and comprehensive manual for using Unix as a programmer, it clearly explains concepts such as inodes, hard and soft links, system calls, and the power of joining programs together via stdin and stdout. By the time I came across this book I already knew these concepts, but had never studied them before in one place, and in such a coherent manner.
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