One thinking is most people writing software who are not software engineers prefer using AI because they don't think software is valuable in itself, it's only a way to solve a problem. So there are two camps, the other being people who like to solve "software problems". But this latter has been solved by AI
But good data structure is not always evident from the get go. And if your types are too specific it would make future development hard if the specs change. This is what I struggle with
I have started using chatgpt for everything from financial planning to holiday planning to product purchase. Whenever I think I hit something useful I add it to memory. I'm a "go" plan user because they had a promotional offer that gave me free access to the plan for a year. Will I continue after one year? Truth is nothing I have in chatgpt cannot be recreated elsewhere. But if I care about keeping those memories I might. I think the real challenge for me now is finding back out conversations, it seems their history search is quite bad.
Wake up and smell the coffee: fsharp is dead. Look at the release notes of fsharp. It's laughable for a major version update. It's maintained by like 5 people working in eastern Europe and they only do maintenance updates basically. C# is getting all its features and DotNet is basically c# oriented anyway. So even in the past you had to learn C# to program in F#
That's not my point. My point is it's unreliable at best.
strikes me that if you developed your skill set around using AI more effectively, you could have both developed a deep understanding and gotten what you wanted, and done it in less time and at higher quality than you could have done solo.
Why is this a given? I don't think there's a secret incantation that could have gotten better results. It's an inherent limitations of the system. If you have resources for me to learn I'm open to discover
There's no skill in using AI. I spent 3 hours trying to build something like a table visualiser that creates a visualization of SQL schema relationship. I wrote simple prompts, tailored them using LLMs and fed them back into another LLM. Went on about 2 hours iterating on outputs until it looked like what I want. Result? It produces an output. The outputs worked well for most part but the results were variable. The arrows would sometimes not be in place. Sometimes you get 100 instead of 1. It was slow. And what I did learn from this that I didn't already know? Zero! On the other hand if I'd tried to figure out myself how to do it, I would have built something not only deterministic and faster , but I'd have gained some new experience and skills along with it of solving a problem.
I don't really understand this comment. Are you saying dogs made way for nuclear families? Why would it be impossible? In India for example pet ownership is very low. Much lower than prevalence of nuclear families
Java type shenanigans are endless if you want some fun but baseline you can cast to arbitrary types at runtime and completely bypass all compile time checks.
For this reason Java is a bad example of a typed language. It gives static typing a bad rep because of its inflexible yet unreliable type system (only basic type inference, no ADTs, many things like presence of equality not checked at compile time etc ) Something like ocaml or fsharp have much more sound and capable type systems.