The conversation around AI coding is still stuck on velocity and its completely missing the real operational bottleneck -> DEBUGGING
I use a combination of tools like GitHub Copilot, Cursor, and generic agentic code gen tools(whichever give me the most credits that week) , dropping a 300-line functional block from a natural language prompt takes about a minute. On paper, developer velocity should have been increased by 69 times.
but i feel like the bottleneck hasn't disappeared; it just shifted down the pipeline. Like i traded manual work for incredibly frustrating debugging.
LLM code looks fine on surface but like when u go through line to line, you feel like its built on sand i mean sure if it works it works but like one thing i struggle with is ghost features, like if i accidentally suggest a feature then the LLM is gonna shove it in my code, even if i say no later on. (if someone knows how to fix do dm)
idk about ya'll but i'd much rather have a ai llm that takes like 1 hour to write 500 lines of code if that means i have to debug less.
another thing how are you handling validation boundaries? are u using runtime timeout scripts or smth open source like gitagent?
also this is gonna sound weird but i kinda have trust issues when a llm spits like 300-400 lines in under a minute (idk why)
sorry for my bad english, im not a native speaker
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