I feel like I have gone from 100% hand written code -> 0% hand written -> 50% and climbing.
I think there are things LLMs are really good at coding up and experimenting with but I think there's still a need to understand the btoader context of your software so you need to dive in at some point.
It's very important to understand what's happening, sure. Blindly shipping AI-produced code without understanding is just irresponsible. But how does writing code helps with that? Reading helps a lot, but writing just slows things down in my experience so far.
LLMs will make tradeoffs without consulting you. This is sometimes a feature and often a bug. Many times a decision made up front can have non obvious implications down the line. This is one of the biggest values I bring to be table as a more experienced swe.
I think if I fully understand everything the agent is going to do and trivially verify the output quality, LLMs are a win. For one off prototypes, the same is true. For software which is unique, complex, and performing a task which has not been fully specified I find myself needing to drop into my editor more and more.
Recently I've been moved to a research focused team and I really need to have proof that something is happening. I've had Claude Opus 5 gaslight me by telling me it did something and when I read the code it obviously did not. This happens more and more with my tasks that are kind of complex.
I've found a lot of the claims by AI people have been 6mo - 2 years "ahead" of my experience. I think we are now in an era where harness engineering is highly valuable (making a test, looping an agent, manually annealing with new ideas) but the claims that no one writes code manually seems like it may not be fully there for all code.
gravypod · · focus · HN ↗
I think there are things LLMs are really good at coding up and experimenting with but I think there's still a need to understand the btoader context of your software so you need to dive in at some point.
dimonomid · · focus · HN ↗
gravypod · · focus · HN ↗
I think if I fully understand everything the agent is going to do and trivially verify the output quality, LLMs are a win. For one off prototypes, the same is true. For software which is unique, complex, and performing a task which has not been fully specified I find myself needing to drop into my editor more and more.
Recently I've been moved to a research focused team and I really need to have proof that something is happening. I've had Claude Opus 5 gaslight me by telling me it did something and when I read the code it obviously did not. This happens more and more with my tasks that are kind of complex.
I've found a lot of the claims by AI people have been 6mo - 2 years "ahead" of my experience. I think we are now in an era where harness engineering is highly valuable (making a test, looping an agent, manually annealing with new ideas) but the claims that no one writes code manually seems like it may not be fully there for all code.