Everytime ive tried to do anything of any complexity these things blow up, and i spend almost as much time toying with the thing as it would have taken to read docs and implement from scratch.
But what do I know? Shiny thing must be better because it’s new!
Also don't expect chatgpt to ever be as good as Claude for example . Oh and copilot is a joke for anything remotely serious.
“You’re just not doing it right. Have you tried upgrading to Claude 9000 edition/writing a novels worth of guardrails/using this obscure ‘AI FIRST’ IDE/creating a Goldberg machine of agents to check the code?”
There are some problems that you can't just "make smaller"
Secondly, I write VERY strongly typed code, commented && documented well. I build lots of "micro-pkgs" in larger monorepos. My functions are pretty modular, have tests and are <100-150 lines.
No matter how much I try all the techniques, and my baseline fits well into LLM workflows, it doesn't take away from the fact that It cannot one shot on anything over 1-2k lines of code. Sure, we can go back and forth with the linter until it pumps out something that will compile. This will take a while, in which I could have used something like auto-complete / co-pilot to just write the boilerplate, and fill it in myself in a shorter amount of time than it takes for the agent to reason about a large context.
Then if it does eventually get something "complex" to compile (after spending a ton of your credits/money) Often times, it will have taken a shortcut to do so and doesn't actually do what you wanted it to. Now I can refactor this into something usable and sometimes that is faster than doing it myself. But 8/10 times I waste 2 hours paying money for an LLM to gaslight me, throw out all the code and just write it myself in 1.5 hours.
I can break down a 1-2k line task into smaller prompts tasks too. but sorry I didn't learn to program to become a project manager for a "Artifically Intelligent" machine
Right now the agents are roughly equivalent to a technically proficient intern that writes code at 1000 wpm, loves to rewrite your entire code base, and is familiar with every library and algorithm written 2 years ago.
I personally find that I can do a lot with 5 concurrent interns matching the above description.
AI is still a great tool, but it needs to be learned.