And I should mention that I have 30+ years of programming experience.
I’ve stopped using llms to generate architecture, which i design and write myself and let the machine pattern match the gaps. I also use it to review issues which I lot of the times push back against.
I’m working on a stateful application sitting on top of a data warehouse and have to implement a stream of messy half defined feature requests and navigate on top of an ever changing infrastructure layer. LLMs rarely get the infra layer even if it is written as code and have hard time grasping how to deal with tech debt, when and how to re-architecture parts of the stack or even implement stuff based on a detailed openspec design.
Usually, it produces code that would take three or four humans days to figure out—in just 20 minutes.
Even the professors and PhDs who hire me all use AI. Honestly, they hold PhDs and professorships, which puts them in a league I can't even touch—and even they use it. AI just does it really well.
Honestly, I learned from your book, 'rossant'—I never expected a programmer like you to say something like that. I thought my perspective was because I'm only an intermediate-level programmer. But you're in the 1% expert category I mentioned
.... and in the and you end up with a very deep prompt that exactly specifies the behavior. This is what a programming language is.
I'd rather describe a data structure in a language designed for this task, than a prompt the might be interpreted in many different ways.
I imagine at competitive coding the goal is quite clear, but in a real world project, the goal is not always so clear, and especially in hobby projects the ideas and goals are not that clear. I get inspiration on how to improve my project or its usability, not the LLM. I instruct it to do something a specific way, because it doesn't do anything on its own, and I need to tell it what to generalize, which it failed to see, because it didn't consider a simplification which is technically less precise, but due to user context and human nature doesn't matter (in this case it was interpreting "now" to mean the current second, which is a small time range, instead of a mere point in time).
So it still takes a ton of hand holding in a more open project. I imagine, one could also code it up in the same amount of time. But it is good for generating tons of test cases. Though one will have to review those, and impose a test style on them, give examples and so on.
Beyond hobby projects, most clients often don't really know what they want. And that's generally what we call domain modeling. This is definitely an area where AI is weak. As you know, it mainly pulls from generic patterns.
When there are specific constraints, AI struggles with core business logic. And as you said, it's also weak at choosing the right direction or the goal to pursue. But as you also know, 80% of programming is built on what others have already created. Originality is only about 20%. And in that 80%, AI is absolutely dominant. I agree with you and I've upvoted your comment.
I really like your perspective