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”
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!”.
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.
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.
A colleague once “showed off” how impressive Copilot supposedly was. I was like:
- Please don’t let AI write unit tests. That’s the one spot where I really don’t need bugs done by automation.
- Don’t you guys use snippets? I do that shit faster with snippets, macros and knowing my way around neovim.
I don’t have snippets set up for languages I’ve never touched before.
But copilot sucks. ChatGPT went super downhill. Claude is alright. If I know the language then it’s not that helpful. But if I don’t, or I don’t know the algorithm, then yeah, it’s super helpful.
My LSP has some neat built-in snippets. ¯\_(ツ)_/¯
I’d rather just write it out. I’ve never used snippets or macros per say, but I do make liberal use of regex replace and multiline cursors lol. Writing out a bunch of getters and setters? Regex!
I did try LLM tab auto complete and while sure, it did suggest some stuff that was useful (after refactoring it), the amount of time I spent WTF’ing some suggestions it made wasn’t worth it.
I find more benefit from asking an LLM about something I’m undecided or confused about, and while it’s never given me a good enough answer, it has stirred enough creative juices in my brain to help me along lol.
Edit: sorry for the dupes. When Eternity said it failed the send I took that at face value.
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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.
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 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.
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.
Last year, for the first time, a large majority of my students used chatGPT.
This correlated with their skills at the start of the year: the more they lacked (or were lazy), the more they used it. And at the end of the year, they were the ones who had learned the least.
I’m not playing the old fart who thinks young people are getting dumber and dumber. There are beginning to be studies on this, and my little experience is consistent with their results.
I, on the other hand, am very happy that AI can autocomplete the n-th similar filter function I need to write.
In line completion of repetitive stuff is fine, even though it does often introduce bugs, meaning I still need to read every single char it writes. Now scale that up to entire functions, project that onto people that don’t know the language/library well, and don’t understand the function itself. That’s just chaos.
Honestly, it’s GREAT for replying to bull shit business emails.
Probably better be careful to proofread it. If you’re about to be fired for something you let ChatGPT tell an important client, I wouldn’t think “it was ChatGPT’s fault” is going to make much difference in your favor.
Oh yeah. It’s always my mail. Not ChatGPT’s mail.
It just helps me pump up the word count to a point where the suits think it’s acceptable.
I prompt it with two sentences and I get two full paragraphs.
Bullshit countering bullshit. Finally, an actual use for LLMs.
People should be using it to get their test suite correct > 90% first before attempting to put any ML generated code into production
tests are code too. It makes you feel more productive to generate them, I guess.
If it’s not good for the real code, it’s not good for the tests
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.
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.
