I posted on When did Math get so hard? a commenter pointed out that one can also ask When did Computer Science Theory Get so Hard? For t...
The fall of the theorem economy
How AI could destroy mathematics and barely touch it
</> htmx ~ Yes, and...
In this essay, Carson Gross discusses his advice to young people interested in computer science worried about the future given the advancements in AI.
Teach Yourself Programming in Ten Years
The conclusion is that either people are in a big rush to learn about programming, or that programming is somehow fabulously easier to learn than anything else. Felleisen et al. give a nod to this trend in their book How to Design Programs, when they say "Bad programming is easy. Idiots can learn it in 21 days, even if they are dummies." The Abtruse Goose comic also had their take.
"Code was never the hard part" is an insult to all programmers — Senko Rašić
The software development profession is in the midst of upheaval. Nobody knows how the AI revolution will play out in the end, but it is c...
bliki: Two Hard Things
There are only two hard things in Computer Science: cache invalidation and naming things -- Phil Karlton (bonus variations on the page)
A Fuller Statement About My Bluesky Posts
Let me paint a picture of what it was like to be a woman math major at the University of Chicago circa 2004.
Programming (with AI agents) as theory building
Back in 1985, computer scientist Peter Naur wrote “Programming as Theory Building”. According to Naur - and I agree with him - the core output of software engineers is not the program itself, but the theory of how the program works. In other words, the knowledge inside the engineer’s mind is the primary artifact of engineering work, and the actual software is merely a by-product of that.
Field Theory: AI as Social Science Question, Object & Tool
Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.
NP-overrated
If you learned about NP-hard problems in university, your takeaway was probably this:
Modular: The Claude C Compiler: What It Reveals About the Future of Software
Compilers occupy a special place in computer science. They're a canonical course in computer science education. Building one is a rite of passage. It forces you to confront how software actually works, by examining languages, abstractions, hardware, and the boundary between human intent and machine execution.
Is AI causing a repeat of Frontend’s Lost Decade? | Mastro Blog
AI is doing to programming what framework-brain did to the frontend before. Deskilling, or just working at a higher level of abstraction?
When did Google get so f-ing weird?
I recently had an experience while doing a simple Google search that was so profoundly weird that it stopped me in my tracks. Understanding this Google se...
Why the fuss about conversational programming?
First, there isn’t much fuss … yet. But there will be.
