alt Hacker News> I think LLMs have changed that equation in a way that makes the original dream much more feasible.
> Several times a day I'll prompt regular Claude chat to "Clone x/y from GitHub and tell me how Z works".
Still highly dependant on one's access to SOTA AI models (availability and funding). Most people praising LLMs publicly for this sort of use case, are the ones with unlimited access to tokens / AI credits, or simply with a lot of money to burn.
But reality is that between using one's limited employer-sponsored quota of tokens to do their 9-5 business logic coding maintenance job, versus exploring 3rd-party software as end-users, I am sure of which one their managers will prefer.
I believe it will eventually happen, whether with SOTA local models on highly capable local hardware, or super cheap inference APIs... or both.
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I've gone back and forth on this.
A couple of years ago LLM prompts really were incredibly cheap and falling in price. OpenAI's own models had fallen in cost by a factor of about 1000x since GPT-3.
They were also cheap because many of the things you might want to do with an LLM took in the order of a few thousands tokens, at most.
Then coding agents happened, and suddenly we had a reason to burn 100,000s or even millions of tokens on a single task. Stuff got expensive!
(Classic Jevon's paradox right there.)
It feels to me like that's trending back down again though. DeepSeek and Kimi are massively less expensive than OpenAI and Anthropic, and almost as useful.
OpenAI dropped the price of Luna by 80% the other day, and it's proving very capable of exploring codebases and generating quite competent code.
So I don't think advanced AI that can help debug and maintain software will stay out of the reach of most people for very long.