When I tried to “talk” to gpts, it was always hard to formulate my requests

I used to think like you until I actually tried copilot. I don’t even write much boilerplate code (it’s python, and the framework is already setup). It is still an effective and useful tool. It saves mental bandwidth, and the code it writes is exactly what I was intending to write, so it turns me into a reviewer than a coder and thus makes it easier to get the job done (I’m still confirming the code does what I want). This is just gpt-3. With chatGPT i finally managed to get unit test backbones for the most annoying methods in our code base that stuck our coverage at 92%. Once we get full copilot gpt-4 integration we will likely get to 98% coverage in a days time. That’s not nothing.

Totally. All I want is an easier time navigating code; not getting lost in tabs and page scrolling, nested class structures and function references, interrupted by context-switching text searches. It feels like I spend 75% of my brain capacity managing my short term memory. Your buddy mentor is a great formulation of the kind of AI aid I need. As for generating code, that's the least of my concerns. I'm actually happy when I get the opportunity to just hammer down some brain dead lines for a change.

A large part of software development is documentation. It is often overlooked or not kept up to date. I think the great advancement of AI is that we can now more closely link and validate one with the other. You can easily summarize some code with chatGPT, and also provide some structure of code based on documentation (as an outline, or first cut). However, this is the state of the art today. In the future, the training set will be based on the prompt-result output and refinement process, leading me to believe that next generation tools will be much better at prompting the user to provide the details. I've already seen this enhancement in gpt4 recently, I think this is a common and interesting use case. Overall, these tools will become more and more advanced and useful for developers. Now is a great time to become proficient and start learning how the future will require you to adapt you…

I think it has to do with how invested you are in the appearance of the final code. If you're very focussed on getting a result and no more, then GPT is probably very powerful. But if you also care about the "how" of the implementation then you'll probably get frustrated micromanaging GPT. I know I do, but I do find its pretty useful when my memory of how to achieve something is blurry. I know what looks wrong but I don't remember how to make it right.

As an infra dude it saves me loads of time. Most of the code I write is stuff where I already know what needs to happen, I already know how the process looks un-automated, and I'm not going to learn a whole lot writing more boilerplate, or learning the names of all the endpoints for a dumb api and parsing the text. It's still mental work. You still have to read the code over and edit stuff. It helps me save lots of time so I can do quality things like comment on hn during work..

I'm not super impressed yet either, but I do think there's a lot of value. I tried a few times to get it to write some simple python scripts, and usually it'd get some things wrong. But it did help to get the basics of the script started. If you haven't already, look into trying out Copilot (I don't think there's a free version anymore?) or Codeium (free and has extensions for a lot of editors, just be careful with what code you're okay sending to a third party). Using AI prompts as a fancy auto complete is what has been giving me the most benefit. Something simple like a comment "# Test the empty_bank_account method" in an existing file using pytest, I go to the next line and then hit tab to auto-complete a 10-15 line unit test. It's not always right, but it definitely helps speed things up compared to either typing it all out or copy/pasting. My biggest annoyance so far, at least with …

Everyone projects their difficulties on to you, so here's mine: A specification can be rigorous, structured, precise and formal: a declarative description of what is to be done - not how. It is a very different perspective from designing algorithms - even one that proves the thing to be done, is done. But I think trusting the results is misplaced. It's more like being a partner in a law firm, where the articled clerks do all the work - but you are responsible. So you'd better check the work. And doing that efficiently is an additional skill set.

I feel like the real value is when working with tools you're not proficient with. I.e. for a backend dev that hardly knows react and needs to write some, who usually gets by doing small modifications and copy-paste-driven development there, ChatGPT / Copilot is basically a 10x productivity multiplier. I agree that I've not been feeling that much value yet when working in areas I'm proficient in.