upskill with ai

LLMs reward expertise

In the 2010s, if you had technical gaps (say, you couldn’t write CSS), you had to either rely on a skilled colleague or just hope that the answer to your exact problem was out there on the internet. Today, everyone can write sort-of-okay CSS by delegating the task to an LLM. LLMs make everybody into a generalist.

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.

explain-diff

explain-diff · GitHub

AI Coding will Prevent Expertise | Lars Faye

The need for ongoing friction in long-term skill formation.

Cognitive Surrender

Cognitive offloading is delegating to the AI and still owning the answer. Cognitive surrender is when the AI's output quietly becomes your output and there i...

AI Dry July: Aborted

Reflections on trying to go a month without AI. I made it two weeks (sort of). 5.2k words, 23 minutes reading time.

AI Upskilling — AI @ UVA

List of workshops

The LLM Critics Are Right. I Use LLMs Anyway.

I almost agree with all of the LLM critics, yet I still use LLMs a lot. I know this sounds like I am delusional, but I don't think I am alone with it.

Arvind Narayanan (@aisnakeoil)

There’s a big, under-appreciated reason why people may have very different experiences and opinions about using AI for work — are they using it for tasks they’re already an expert at, or tasks they can’t do themselves? The former leads to a growth cycle and the latter leads to a dependence spiral. When I use AI to do something I’m an expert at, like coding, I treat it as a tool. I can build quickly, maintaining an understanding of the code, knowing that if necessary, I can fix the code myself. It feels empowering. It frees up my time to think about the complex, judgment-oriented parts of software engineering that I can’t or won’t delegate to AI. That means my own skills improve rapidly, and I get to climb the ladder of complexity and develop higher-level skills, much more so than when I write the code myself. I feel in control. I can lock in and achieve a flow state — when AI is working, I’m reviewing, building understanding, and planning the next steps. I never get the feeling that the tool is about to replace me. This is the growth cycle.  (Of course, the growth cycle is not automatic. I still need to exercise agency to use AI responsibly. But it’s the same challenge with any productivity-enhancing technology, and those who’ve navigated such transitions before are well-equipped to navigate it with AI as well.) On the other hand, if I use it for tasks I don’t understand and haven’t learned to perform myself, I have no choice but to treat it as a superintelligence. If something breaks, the best I can do is ask AI to fix it and hope for the best. I generally can’t evaluate the quality of the output myself. The only way to find out if it's any good is if and when the work is ultimately reviewed by an actual expert. The experience is confusing, unsettling and disempowering. And forget about flow state. By over-relying on AI, I risk losing whatever skill I had at the task in the first place, even if it boosts productivity in the short term. This is the dependence spiral. It’s no wonder that entry-level workers and students preparing to enter the workforce find themselves in a bind. To compete with the AI-enabled productivity of more seasoned workers, they must adopt AI themselves, but doing so risks the dependence spiral. I have some thoughts on solutions that I will share in later posts, but I think having a clear diagnosis of the problem is a useful first step.

Tropes - AI Writing Pattern Directory

The definitive reference for identifying AI writing patterns. Learn to spot the tropes that give away AI-generated text.

DrCatHicks/learning-opportunities

A Claude or Codex skill for deliberate skill development during AI-assisted coding

On Taste, Effort & Curiosity - again

When AI collapses how long it takes to ship, what’s left is judgment, experimentation, and knowing what not to build.

After AI Takes Everything | Airing

Prompted by letters from three engineers, the author asks what remains for humans as AI takes over execution, naming judgment, taste, and derivation as irreplaceable, and warns against the erosion of subjecthood.

Living documents and interactive schematics — Selin Jessa

Using LLMs to turn static figures from data analysis, schematics, and publications into interactive widgets as tools for thought