I use chatgpt to write code. If it gets a bit better and can connected to an editor to create files, update existing files developer productivity will go through the roof
I have a website in development aidev.codes that automatically saves the output from OpenAI queries to files you specify (hosts them in a web page if it's that type of file). GitHub integration is one of many things I have planned. Today fixing some bugs with the knowledgebase stuff I just added yesterday and also putting in a template system. It has an !!edit command.
Also if you use vim you can try my npm package `askleo` `npm i -g askleo` (not tied to the website but requires your own OpenAI API key) with `:r ! askleo Go function to reverse a list of numbers` or whatever .
What sorts of things are you using it (successfully) for? I've gotten it to write a script or two for me, but it feels like usually I have to type out so much context (or domain/business knowledge) that it doesn't end up saving much effort.
I use it as an instant StackOverflow for the most part to get around new libraries or libraries/languages I don't use that often. Also generating custom bash one liners or small scripts. It is priceless for this use case. Yeah, sometimes it is wrong, but in my exp. less than 5%. Also we are lucky that for our purposes we can almost always validate the answer almost instantly and without incurring any cost.
Because you are a developer. Replace yourself with a product manager, and maybe you will start seeing things differently.
I think in the long run LLMs will enable the real-deal NoCode solutions. You would probably need to write an essay to get it right, but you will just need to know a human language understood by the LLM and the business domain.
Thanks! That really had nothing to do with what I asked tho... This bit of thread is literally about using it to write code, so saying "pretend you're a PM" is not really relevant at all
What I meant is that your (our) job as a developer (in a mid-term future, I would say 10-12 years) will be made largely irrelevant, because a PM will be able to program by explaining what they wants to a LLM. That's why you are not seeing any real benefit from it now.
Right that's great. I get that. It's gonna put me out of a job in 10-12 years. I'll worry about that later.
The person I responded to was saying that they (as a dev I believe) have been seeing huge productivity gains _right now_ and that's what I'm interested in.
I use it to generate whole test suites from function definitions. Not one test, but dozens, covering various inputs and edge cases. TDD purists will balk at that, but it saves serious amounts of typing out boilerplate. You have to recheck its suggestions, of course.
I couldn't get Copilot to spew anything like that (a single simple test at best, and it fails at that more frequently than it produces something useful).
It's also quite good at converting relatively simple programs or configs between languages. For example, I used it to convert PostgreSQL DDL queries into Hibernate models (and also in reverse), JS snippets into OCaml, XML into YAML, maven pom.xml into gradle build scripts, and a few more.
Different use case I think. With chatGPT you write something like:
write a function using the python requests library which makes a get request to the URL example.com, parses the JSON response and returns the value of the "foo" field. Throw an exception if the get request fails, the response is not JSON or is invalid JSON, or if the foo field is not present in the response.
I just tried this and got a correct (and reasonable) function on the first try.
This kind of high level description to low-level implementation is a huge timesaver but it saves time in a different way than copilot.