[ignoreusernames]

I think this argument only holds if you believe that LLMs are at a point where it can handle any combination of craziness that you throw at it.

From my own experience working with agents is that there’s “snowball of shit” effect. Small little mistakes that compound on each other. You can either

- review the code and try to prune some of the shit occasionally - let the LLM handle everything

As of the current status of the industry it’s very hard for me to not see option 2 as extremely irresponsible. Coding agents limits are not well defined and unless you’re running an open weight model locally (most people aren’t) you just gave up all control over your code to a third party. If running local models were the norm, the argument that LLM are just another layer of abstraction would hold a little better. Reusing the compiler analogy from the post, it’s like depending on a compiler where you pay a monthly premium to compile your code. Those did exist a while ago with closed licenses, but I think the majority of deployed code nowadays is on open-ish platforms. This walled garden development paradigm already lost once