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One of the common uses I've heard is for generating boiler plate code. I have two thoughts on this. First, you actually have to understand what the boiler plate code is doing for it to be of any value. Second, there are already solutions for this that work just as well or better. Most of the major IDE's either support code templates or have extensions for that. You just have to be willing to take the time to create templates. I use Resharper with Visual Studio for this all the time.

I tried copilot free for a month and was not that impressed.

Absolutely has been my experience. It's actually slowed me down because of the slop it tends to throw in there as part of the hallucinations.

I used replit for education. At some point they added ai assistance. It was like pair programming with someone who is over eager and doesn't know why you're dying things.

When I'm teaching code, I don't need ai to finish my circle calculation program before I've explained the first line to students.

Honestly, it’s GREAT for replying to bull shit business emails.

Weed too. Programmers on weed think they're more productive but just introduce more bugs than sober programmers. Similar ratios as LLMs too IIRC

You're not doing it right if you're just straight up using whatever ChatGPT gives you. Use it to get the bulk of the code, edit it to work and follow project standards, then use it. CoPilot has certainly improved my productivity. There is a cost though. I am nowhere near as good as writing code from scratch as I was a year ago, because I haven't done it in around that long.

If people aren't getting anything out of llms they're using them wrong.

If you blindly copy paste code you don't understand from chatgpt there's obviously a problem

Get it to generate single functions at a time with the requirements clearly defined and then actually read over the code and it's an amazing tool

It doesn't do anything for me. It's good for simple code that I could've written myself in the time it takes me to make sure that what it generates is what I needed.

It helps me as autocomplete, but that’s about it. The increase in productivity is negligible. The problem I have is suddenly feeling dependent on it. It’s like with navigation software. We rely on it so much, suddenly we can’t navigate our way out of a paper bag when it’s not around. There’s something unsettling about having the floor pulled out from under you like that.

If I was simply someone trying to get a job done and drone through the day, sure, it’s probably fine. But I’m someone that needs to know and understand everything I put into my commits, and I actually enjoy coding.

The article mentioned in the screenshot.

I mean, if you're already competent in what you're doing, it probably won't help, but I've found they are very helpful when learning a new system.

Like it might turn 10 searches about how a language works into 1 or 2.

So I don't have to look up type conversions, or proper loop syntax or if have to pipe in a a blank object into this function or I can just leave it null etc. And can just look up hoe to fix that 1 error it gave me instead.

It's pretty much the exact same cost-benifit as stealing code off of stack overflow.

I don't know, I use GH copilot every day. Not to generate whole blocks of code but as autocomplete. It more often than not can finish my sentence for me. It's makes wrong assumptions some times but it most definitely saves me time. I was just using an IDE today not set up for copilot and it made me realize how much I miss it when its gone.

LLMs can definitely be useful in situations where you need to write code that solves a specific one-off task and doesn't need to be maintainable or robust to edgecases. Some prompts where LLMs saved me 15 minutes or so of work:

• "Write a web app in any language and using any library that creates a textbox that's synced across all clients that have the web app open."
• "Write a python PIL program that iterates over the pixels in an image... Now make it a command line tool with argparse that takes the image path as input"

Oh sweet irony... I remember butting heads with techbros on this very platform about their misguided intuition that people criticizing LLMs were going to be left out (assuming I or others had never tried them).

Yeah, I write enough bugs on my own, I'll pass on the 41% more, thank you very much.

Sure, I know this study needs to be replicated and should not be considered to be a holy truth... But the issues with the tech do pile up, and they're not just ethical concerns about resource usage. There's a new study like this one every week.

I wonder how they're measuring productivity, I don't think there's a good way to measure it tbh 😅

As a hobbyist with no production-environment or critical coding projects, Google Gemini has been great for generating a starting point for Arduino projects if I otherwise don't know how to get going.

That would be correct for me. If i know what question to ask i also know how to solve it. And its not good at solving beacuse it requires precision which is something that generative ai inherently dont poses. But its also why i think gen ai will reduce the number of artist very significantly beacuse art just need to be good enough. Or to be precise democrtize art. No longer will you need to have a band of 5 pepole and a smidge talent to make a metal song for yourself. Likewise if you want a sick reasonably specific wallpaper for your screen. Now idea will be good enough for a good enough result.

I got fed up with my boss coming around and saying “would this work go any faster with AI?”, but now I’ve become AI-Positive and I take her bits of technical debt we’d like to clean up saying “we could use AI on this!”.

Been using copilot and another smaller AI model for hobby projects. Both of them slowed me down as much as they helped me with my code.

Now I've switched to supermaven free tier, and it's actually net positive for me.

It's not like it's writing whole functions for me, but it's actually quite context sensitive when it comes to defining functions and using them with the same parameters and things like that. I won't say I am 50% faster, but I could easily attribute a 30% development boost to it. Now mind you, for bug fixes and bug prevention that's back down to a 5% boost in speed, but that's still a net positive, so I'll take that.

People should be using it to get their test suite correct > 90% first before attempting to put any ML generated code into production