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465 points 0x63_Problems | 1 comments | | HN request time: 0.935s | source
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perrygeo ◴[] No.42138092[source]
> Companies with relatively young, high-quality codebases benefit the most from generative AI tools, while companies with gnarly, legacy codebases will struggle to adopt them. In other words, the penalty for having a ‘high-debt’ codebase is now larger than ever.

This mirrors my experience using LLMs on personal projects. They can provide good advice only to the extent that your project stays within the bounds of well-known patterns. As soon as your codebase gets a little bit "weird" (ie trying to do anything novel and interesting), the model chokes, starts hallucinating, and makes your job considerably harder.

Put another way, LLMs make the easy stuff easier, but royally screws up the hard stuff. The gap does appear to be widening, not shrinking. They work best where we need them the least.

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cloverich ◴[] No.42139189[source]
For me same experience but opposite conclusion. LLM saves me time by being excellent at yak shaving, letting me focus on the things that truly need my attention.

It would be great if they were good at the hard stuff too, but if I had to pick, the basics is where i want them the most. My brain just really dislikes that stuff, and i find it challenging to stay focused and motivated on those things.

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1. davidsainez ◴[] No.42140617[source]
Yep, I'm building a dev tool that is based on this principle. Let me focus on the hard stuff, and offload the details to an AI in a principled manner. The current crop of AI dev tools seem to fall outside of this sweet spot: either they try to do everything, or act as a smarter code completion. Ideally I will spend more time domain modeling and less time "coding".