11–20 of 225 posts
What value does a developer deliver when their entire process is done by an LLM? Is there no desire for a creative process?
Is there no desire for creative process if we use compilers?
What value does a developer deliver when their entire process is done by an LLM? Is there no desire for a creative process?
By this argument we should be writing in machine code. Is there no desire for creative process if we use compilers?
What value does a developer deliver when their entire process is done by an LLM? Is there no desire for a creative process?
People had the same thoughts about cake mixes in the 40s. Oh you can just buy a cake mix? That's not cooking anymore444 https://www.youtube.com/watch?v=r6wKaLQ66r8
Earlier quoted context omitted.
By this argument we should be writing in machine code. Is there no desire for creative process if we use compilers?
This is not even wrong. The level of analogies people use in the first few months of $hype_cycle is mind boggling.
You don’t mention it explicitly, but I assume you’ve manually copied and pasted all the code, as well as the various patches with updates? In my experience, that quickly makes new suggestions from ChatGPT go out of sync with the actual state of the code. Did you occasionally start the conversation over and pasted in all the code you currently had, or did this not turn out to be an issue for you?
I had a spooky experience with a project that was written almost entirely by GPT. I gave it the skeleton and one method and asked it for a modification. It gave it and also said "don't forget to update this other method", and showed me the updated code for that too.
The spooky part is, I never told it the code for that method, but it was able to tell from context what it should be. (I told it that it itself had written it, but I don't know if that made any difference: does GPT know how it "would" have done things, i.e. can predict any code it knows that it wrote?)
It's good at writing new code, with sufficient prompting. But the big open question as of now for engineering orgs is - can it edit existing code like developers, just by instructions. Is there any hands on experience anyone has on copilot-x?
You don’t mention it explicitly, but I assume you’ve manually copied and pasted all the code, as well as the various patches with updates? In my experience, that quickly makes new suggestions from ChatGPT go out of sync with the actual state of the code. Did you occasionally start the conversation over and pasted in all the code you currently had, or did this not turn out to be an issue for you?
I asked GPT to write a program which displays the skeleton of a project, i.e. folders, files, functions, classes and methods. I put that at the top of the prompt. I had a spooky experience with a project that was written almost entirely by GPT. I gave it the skeleton and one method and asked it for a modification. It gave it and also said "don't forget to update this other method", and showed me the updated code for…
Often, it will posit the existence of a function that is named slightly differently. This is great for helping you find corners of an API with functionality you didn't know existed, but insanely frustrating when you're just trying to get code working the first time. You end up manually verifying the API calls.
It's good at writing new code, with sufficient prompting. But the big open question as of now for engineering orgs is - can it edit existing code like developers, just by instructions. Is there any hands on experience anyone has on copilot-x?
Copilot without -x does this relatively well. But it's a hands-on process. I can't just give it some source files and say "go". But it can easily make you 10x. I often spend more time tab-completing than writing.