Every day there’s more big job cuts at tech and games companies. I’ve not seen anything explaining why they all seam to be at once like this. Is it coincidence or is there something driving all the job cuts?
Every day there’s more big job cuts at tech and games companies. I’ve not seen anything explaining why they all seam to be at once like this. Is it coincidence or is there something driving all the job cuts?
Can you elaborate on this part? What’s your idea of proper usage?
So maybe I don’t know what I’m talking about. I will only share what I have experienced from using them. In particular I haven’t messed with Copilot very much after the upgrade to GPT-4, so maybe it’s a lot more capable now.
In my experience, Copilot does a pretty poor job at anything except writing short blocks of new code where the purpose is pretty obvious from context. That’s, honestly, not that helpful in a lot of scenarios, and it makes the flow of generating code needlessly awkward. And at least when I was messing with it there didn’t seem to be a way to explicitly hint to it “I need you to look at this interface and these other headers in order to write this code in the right way.” And, most crucially, it’s awkward to use it to modify or refactor existing blocks of code. It can do small easy stuff for you a little faster, but it doesn’t help with the big stuff or modifying existing code, where those are most of your work day.
To me, the most effective way to work with AI tools was to copy and paste back and forth from GPT-4 – give it exactly the headers it needs to look at, give it existing blocks of code and tell it to modify them, or have it generate blocks of boilerplate to certain specifications (“make tests for this code, make sure to test A/B/C types of situations”). Then it can do like 20-30 minutes’ worth of work in a couple of minutes. And critically you get to hold onto your mental stamina; you don’t have to dive into deep focus in order to go through a big block of code looking for things that use old-semantics and convert them to new-semantics. You can save your juice for big design decisions or task prioritization and let it do the grunt-work. It’s like power tools.
Again, this is simply my experience – I’ll admit that maybe there are better workflows that I’m just not familiar with. But to me it seemed like after the GPT-4 transition was when it actually became capable of absorbing relatively huge amounts of code and making new code to match with them, or making modifications of a pretty high level of complexity in a fraction of the time that a human needs to spend to do it.
I wonder if it might be the specific type of work that you do that allows for this. I don’t pay for ChatGPT, so I wouldn’t know the quality of the code it outputs with GPT-4, but I personally wouldn’t blindly trust any code that comes out of it regardless, meaning I’d have to read through and understand all the generated code (do you save time by skipping this part maybe?), and reading code always takes longer and is overall more difficult than writing it. On top of that, the actual coding part only accounts for a small fraction of the work I do. So much of it is spend deciding what to code in order to reach a certain end goal, and a good chunk of the coding (in my case at least) is for things that are much easier to describe with code than words. So I’m still finding it hard to imagine how you could possibly get anything more than a 1.5x output improvement.
The main time savings I’ve found with generative AI is in writing boilerplate code, documentation, or writing code for a domain that I’m intimately familiar with since those are very easy to skim over and immediately know if the output is good or not.