> Once the data structures are set the coding is easy.
I don't always find this, because there's a lot of "inside baseball" and accidental complexity in modern frameworks and languages. AI assist has been very helpful for me.
I'm fairly polyglot and do maintenance on a lot of codebases. I'm comfortable with several languages and have been programming for 20 years but drop me in say, a Java Spring codebase and I can get the job done but I'm slow. Similarly, I'm fast with TypeScript/CDK or Terraform but slow with cfndsl because I skipped learning Ruby because I already knew Python. I know Javascript and the DOM and the principles of React but mostly I'm backend. So it hurts to dive into a React project X versions behind current and try to freshen it up because in practice you need reasonably deep knowledge of not just version X of these projects but also an understanding of how they have evolved over time.
So I'm often in a situation where I know exactly what I want to do, but I don't know the idiomatic way to do it in a particular language or framework. I find for Java in particular there is enormous surface area and lots of baggage that has accumulated over the years which experienced Java devs know but I don't, e.g. all the gotchas when you upgrade from Spring 2.x to 3.x, or what versions of ByteBuddy work with blah maven plugin, etc.
I used to often experience something like a 2x or 3x hit vs a specialised dev but with AI I am delivering close to parity for routine work. For complex stuff I would still try to pair with an expert.
This matches my experience. In practice there's just a lot of stuff (libraries, function names, arguments that go in, library implementation details etc.) you need to remember for most of the programming I do day to day, and AI tools help with recalling all that stuff without having to break out of your editor to go and check docs.
For me this becomes more and more relevant as I go into languages and frameworks Im not familiar with.
Having said that you do need to be vigilant. LLMs seem to love generating code that contains injection vulnerabilities. It makes you wonder about the quality of the code it's been trained on...
> I don't always find this, because there's a lot of "inside baseball" and accidental complexity in modern frameworks and languages. AI assist has been very helpful for me.
My use of esoteric C++ has exploded. Good thing I will have even better models to help me read my code next week.
The much lowered bar to expanding one’s toolkit is certainly noticeable, across all forms of tool expansion.
Can't express it more clearly than this. Data structures are just one part of the story not the only spot where the rubber meets the road IMO too. But going back to top of the thread, for new projects it is indeed steps 2 and 3 that consume most time not step 3
>In October 2015, Byte Buddy was distinguished with a Duke's Choice award by Oracle. The award appreciates Byte Buddy for its "tremendous amount of innovation in Java Technology". We feel very honored for having received this award and want to thank all users and everybody else who helped making Byte Buddy the success it has become. We really appreciate it!
Don't misread me. It's solid software. And an instance of a well structure objet-oriented code base.
But it's impossible to do anything without having a deep and wide understanding of the class hierarchy (which is just as deep and wide). Out of 1475 issues on the project's Github page, 1058 are labelled as questions. You can't just start with a few simple bricks and gradually learn the framework. The learning curve is super steep from the get go, all of the complexity is thrown into your face as soon as you enter the room.
I don't always find this, because there's a lot of "inside baseball" and accidental complexity in modern frameworks and languages. AI assist has been very helpful for me.
I'm fairly polyglot and do maintenance on a lot of codebases. I'm comfortable with several languages and have been programming for 20 years but drop me in say, a Java Spring codebase and I can get the job done but I'm slow. Similarly, I'm fast with TypeScript/CDK or Terraform but slow with cfndsl because I skipped learning Ruby because I already knew Python. I know Javascript and the DOM and the principles of React but mostly I'm backend. So it hurts to dive into a React project X versions behind current and try to freshen it up because in practice you need reasonably deep knowledge of not just version X of these projects but also an understanding of how they have evolved over time.
So I'm often in a situation where I know exactly what I want to do, but I don't know the idiomatic way to do it in a particular language or framework. I find for Java in particular there is enormous surface area and lots of baggage that has accumulated over the years which experienced Java devs know but I don't, e.g. all the gotchas when you upgrade from Spring 2.x to 3.x, or what versions of ByteBuddy work with blah maven plugin, etc.
I used to often experience something like a 2x or 3x hit vs a specialised dev but with AI I am delivering close to parity for routine work. For complex stuff I would still try to pair with an expert.