I find most developers fall into one of two camps: 1. You treat your code as a means to an end to make a product for a user. 2. You treat the code itself as your craft, with the product being a vector for your craft. The people who typically have the most negative things to say about AI fall into camp #2 where AI is automating a large part of what they considered their art while enabling people in group #1 to iterate…
We crossed a threshold in the past couple months where I'm not sure I'd agree that "libraries other devs depend on" is the right cutoff for LLMs. In many cases, LLMs will write more concise, faster, and more accurate code. Not by one-shooting, but through a deeper search of possibility space - not dissimilar to how stockfish and other pre-llm search algorithms worked Modern harnesses are systems built with LLMs as on…
if it really were fuzzing and finding different candidate spaces, then I'd expect it to be good at things like dynamic programming, where I've only seen it fail. usually i see it get stuck in a bad solution and just thrashes around in that minima. these are problems where we can construct a verifiable test space, and it will eventually wind up with a solution, but one that is thousands of lines long and uses no structure of the problem space