Implementing a function isn't for a "fancy autocomplete", it's for a brain to do. Unless all you do is reinventing the wheel, then yeah, it can generate a decent wheel for you.

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

Fuck no. If it gets the test wrong, it won't necessarily fail. It might very well pass even when it should fail, and that's something you won't know unless you review every single line it spits out. That's one of the worst areas to use an LLM.

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

I'm not sure what you mean by that.

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

fill lines of data based on other data structures: it can transform text quicker than you can write regex and i’ve never had it fail at thisI'm

not sure what you mean by that.

Not speaking for them, but I use LLMs for this. You have lines of repetitive code, and you realize you need to swap the order of things within each line. You could brute force it, or you could write a regex search/replace. Instead, you tell the LLM to do it and it saves a lot of time.

Swapping the order of things is just one example. It can change capitalization, insert values, or generate endless amounts of mock data.