I’ve seen a few articles saying that instead of hating AI, the real quiet programmers young and old are loving it and have a renewed sense of purpose coding with llm helpers (this article was also hating on ed zitiron, which makes sense why it would).
Is this total bullshit? I have to admit, even though it makes me ill, I’ve used llms a few times to help me learn simple code syntax quickly (im and absolute noob who’s wanted my whole life to learn code but cant grasp it very well). But yes, a lot of time its wrong.
pretty much every line of code we write in modern software isn’t unique… we use so many orders of magnitude more lines of other people’s code than our own, we’re really just plumbing pipes together
most functions we write that aren’t business logic specific to the problem domain of our software (and even sometimes then) has been written before… the novel part isn’t in the function body: the low level instructions… the novel part is how those instructions are structured… that may as well be pseudocode, and that pseudocode may as well take the form of function headers
write tests, tests fail, write code, tests slowly start to pass until you’re done… this is how we’ve always done TDD because it ensures the tests fail when they should. this is a good idea with or without LLMs because humans fuck up unit tests all the time
for example, you have an external API of some kind with an enum expressed via JSON as a string and you want to implement that API including a proper Enum object… an LLM can more easily generate that code than i can, and the longer the list of values the more cumbersome the task gets
especially effective for generating API wrappers because they basically amount to function some_name -> api client -> call /api/someName
this is basically a data transformation problem: translate from some structure to a well-defined chunk of code that matches the semantics of your language of choice
this is annoying for a human, and an LLM can smash out a whole type safe library in seconds based on little more than plain english docs
it might not be 100% right, but the price for failure is an error that you’ll see and can fix before the code hits production
and of course it’s better to generate all this using swagger specs, but they’re not always available and tend not to follow language conventions quite so well
for a concrete example, i wanted to interact with blackmagic pocket cinema cameras via bluetooth in swift on ios: something they don’t provide an SDK for… they do, however document their bluetooth protocols
https://documents.blackmagicdesign.com/UserManuals/BlackmagicPocketCinemaCameraManual.pdf?_v=1742540411000
(page 157 if you’re interested)
it’s incredibly cumbersome, and basically involves packing binary data into a packet that represents a different protocol called SDI… this would have been horrible to try and work out on my own, but with the general idea of how the protocol worked, i structured the functions, wrote some test case using the examples they provided, handed chatgpt the pdf and used it to help me with the bitbanging nonsense and translating their commands and positionally placed binaries into actual function calls
could i have done it? sure, but why would i? chat gpt did in 10 seconds what probably would have taken me at least a few hours of copying data from 7 pages of a table in a pdf - a task i dont enjoy doing, in a language i don’t know very well