my experience using LLM-powered tools (e.g. copilot in agent mode) has been underwhelming. like, shockingly so. like not cd-ing to the wrong dir where a script is located, and getting lost, disregarding my instructions to run ./tests.ps1 and running `dotnet test`, writing syntactically incorrect scripts and failing to correct them, particularly being overwhelmed by verbose logs. sometimes it even fails to understand the semantic meaning of my prompts.

whereas my experience describing my problem and actually asking the AI is much, much smoother.

I'm not convinced the "LLM+scaffolding" paradigm will work all that well. sanity degrades with context length, and even the models with huge context windows don't seem to use it all that effectively. RAG searches often give lackluster results. the models fundamentally seem to do poorly with using commands to accomplish tasks.

I think fundamental model advances are needed to make most things more than superficially automatable: better planning/goal-directed behavior, a more organic connection to RAG context, automatic gym synthesis, and RL-based fine tuning (that holds up to distribution shift.)

I think that will come, but I think if LLMs plateau here they won't have much more impact than Google Search did in the '90s.

I'm one of those people who thinks simultaneously that (a) current AI cannot replace developers, it just isn't good enough (and I don't think it's good for it to write much code), and (b) AI is simply an incredible invention and will go down as one of the top 5 or 10 in history.

I've said the same thing as you, that there is a LOT left to be done with current AI capabilities, and we've barely scratched the surface.

Creating oodles of new jobs in internally QAing LLM results, or finding and suing companies for reckless outcomes. :p