Have you actually tried using an LM for programming? ChatGPT catches me off guard sometimes even when working on "boring" problems. For example, I was consumin

Have you actually tried using an LM for programming? ChatGPT catches me off guard sometimes even when working on "boring" problems. For example, I was consuming structs from a UNIX domain socket in Java and had some tedious translation from C-types to ByteBuffer calls. It's brain dead easy thing to do, the structs were defined as packed so not alignment issues, ByteBuffers handle endianness, there's a little bit of trickiness with unsigned types but nothing terrible... but with 6 types and writing tests for all 6 definitions, I wondered if ChatGPT could do it and save me a couple of minutes. What impressed me wasn't that it managed to generate the near identical code to my example definition,it was the fact it recognized one of the fields as padding and pre-emptively discarded its value. The padding wasn't in any way shape or form covered by typing, yet the fact the comment in the C definition mentioned padding caused ChatGPT to generate code that skipped over the padding instead of trying to needlessly capture its value and commented that was doing so. And when I had it generate unit tests, it managed to maintained context on the padding and added a helpful comment pointing it out. - It's small corner cases like that which really make me see LMs as being the natural productivity multiplier for programmers. There's a ton of nuance in some problems that by the very nature of an LM, you'd never imagine it'd be able to capture. Yet when you press on it, it actually does better on those than it does on "copy everything from Stack Overflow" problems.