Inefficiency and frustration in using LLMs for software development due to poor output quality and the time required for cleanup

Signals over time

2 observed signals across 1 sources, tracked for 5 days. Confidence: low.

What people are saying Observed

• “I recently spent a day coding by hand. I was able to build a project, about 200-300 lines in python. Took me half day. But I could still do it. LLM would have done this in less than half an hour. After this I realized why I personally choose LLM to write code: it is simply more convenient. As a parallel, once you learn how to drive you stop walking, as for the primary mode of transportation.”

  Hacker News · frustration

• “Subscribed for work, but using it mostly for one-shot utilities and fixes, very infrequently. I'm also running local models on my gaming laptop, but just out of curiosity, for testing how far local models are coming along. Despite my negative experiences (below) I still think the technology is cool, and it's even cooler I can run it on my not very cutting edge laptop. I give local models a set of tasks and see how far along each new one comes. Rant: For work, I've mostly given up on using LLMs for large features, in all cases I've tried in the last 2 years, the time it takes to clean up their output is about the same I would have spent writing and debugging the feature myself. There's only a single major feature I've shipped that was mostly written by an LLM, and that was back in May. All other major tasks I've tried, even somewhat trivial ones, led to unusable code, filled with comment slop and workarounds, and hidden bugs. This is probably due to the sheer amount of legacy code our app has, but even trying to develop more or less self-contained components was very difficult. I do use LLMs daily, but mostly through a chat interface, as a somewhat”

  Hacker News · complaint