Learning Programming in an Age of LLMs

This is an issue that's very real for me right now because I'm in the middle of teaching my own kids programming, and we've been working on it for years at this point. I'm watching AI seemingly invalidate the premise behind learning all of it. It's been a pretty depressing change to be honest, because I love programming and watching this happen is hard.

So, I've changed my approach with my own teaching. I'm having them do three things that I'm hoping will prepare them to work in the world they're going to be entering soon:

1. Always have a handmade project going, and work on it without AI assistance even if it's painfully slow.

2. When working with AI, treat it as a compiler that operates on data structures, algorithms, and architectural requirements rather than source code. You have to understand the theoretical pieces of what you're building before you instruct it to assemble them.

3. Use the AI as a powerful tool to grow your own knowledge of algorithms and troubleshooting. Whenever a problem crops up, it's an opportunity to come up with a few hypotheses of what the actual issue is. Don't let the model do it first. I have them actually write these down in our custom kanban tool as issue cards, and then they use the model to investigate how close they came to the true issue.

I'm hoping this will give them something similar to my experience of debugging over a lifetime of coding. The knowledge that seems to be most valuable that I bring to the table when coding with AI is in recognizing quality/maintainable code architectures, and "seen that before" debugging experience.

We just started this so I don't have much in the way of results yet, and it feels risky to even allow it into the curriculum. I don't think that it makes sense to hide from it though, so this is our best shot.

> Interesting article. My advice is to do it the old fashioned, hard way. There’s no royal road to knowledge, skill, and learning.

I had a colleague tell me "it's not vibe coding when a programmer does it."

I fully believe there's still value in understanding the internals of software and the patterns developers use to structure it, but I do wonder for how much longer any of this knowledge will be more than of academic interest.

Well.. I think this is a big open problem in philosophy.

On one hand, you have things like Lean (calculus of inductive constructions), these are relatively simple formal logics (just in more practical notation) that let you define any conceivable type, which is akin to specification.

On the other hand, there is a rich set of modal and fuzzy logics that can help with aspects of reasoning in natural language. I think these can be defined in the former, but nobody has really made a good agreement as to how.

So the main difficulty is for any such language to gain traction, people who speak it.

Instead, we trained LLMs and they came up with something (evolved to reason). I think the future philosophical research will need to answer what exactly do LLMs bring to the table in terms of formalization of natural language.

I came back from retirement for a couple reasons, one as a hedge against the stock market due to AI. Two, the camaraderie built by working with other people. Three, the opportunity to mentor young people.

AI has made the whole situation suck. Stock market is stressful due to energy and AI. AI has left people working alone managing agents. Any advice regarding development best practices I give to young people might have made sense 3 years ago, but hardly matters now. I look around at all these young people and wonder if they are going to be okay. It has only served to make me feel depressed and put a name and face to any future displacement.

It all seems so pointless.