Technology

I use it as a time-saving device. The hardest part is spotting when it's not actually saving you time, but costing you time in back-and-forth over some little bug. I'm often better off fixing it myself when it gets stuck.

I find it's just like having another developer to bounce ideas off. I don't want it to produce 10k lines of code at a time, I want it to be digestible so I can tell if it's correct.

For me it's because if the AI does all the work the person "coding" won't learn anything. Thus when a problem does arise (i.e. the AI not being able to fix a simple mistake it made) no one involved has the means of fixing it.

• AI Code suggestions will guide you to making less secure code, not to mention often being lower quality in other ways.
• AI code is designed to look like it fits, not be correct. Sometimes it is correct. Sometimes it’s close but has small errors. Sometimes it looks right but is significantly wrong. Personally I’ve never gotten ChatGPT to write code without significant errors for more than trivially small test cases.
• You aren’t learning as much when you have ChatGPT do it for you, and what you do learn is “this is what chat gpt did and it worked last time” and not “this is what the problem is and last time this is the solution I came up with and this is why that worked”. In the second case you are far better equipped to tackle future problems, which won’t be exactly the same.

All that being said, I do think there is a place for chat GPT in simple queries like asking about syntax for a language you don’t know. But take every answer it gives you with a grain of salt. And if you can find documentation I’d trust that a lot more.

A lot of people spent many many nights wasting away at learning some niche arcane knowledge and now are freaking out that a kid out of college can do what they can with a cool new machine. Maybe not fully what they do but 70% there and that makes them so hateful. They'll pull out all these articles and studies but they're just afraid to face the reality that their time and life was wasted and how unfair life can be

The other day we were going over some SQL query with a younger colleague and I went “wait, what was the function for the length of a string in SQL Server?”, so he typed the whole question into chatgpt, which replied (extremely slowly) with some unrelated garbage.

I asked him to let me take the keyboard, typed “sql server string length” into google, saw LEN in the except from the first result, and went on to do what I'd wanted to do, while in another tab chatgpt was still spewing nonsense.

LLMs are slower, several orders of magnitude less accurate, and harder to use than existing alternatives, but they're extremely good at convincing their users that they know what they're doing and what they're talking about.

That causes the people using them to blindly copy their useless buggy code (that even if it worked and wasn't incomplete and full of bugs would be intended to solve a completely different problem, since users are incapable of properly asking what they want and LLMs would produce the wrong code most of the time even if asked properly), wasting everyone's time and learning nothing.

Not that blindly copying from stack overflow is any better, of course, but stack overflow or reddit answers come with comments and alternative answers that if you read them will go a long way to telling you whether the code you're copying will work for your particular situation or not.

LLMs give you none of that context, and are fundamentally incapable of doing the reasoning (and learning) that you'd do given different commented answers.

They'll just very convincingly tell you that their code is right, correct, and adequate to your requirements, and leave it to you (or whoever has to deal with your pull requests) to find out without any hints why it's not.

Now, I obviously didnt tell it to write the entire code by itself. [...]

I am fairly competent in writing programs.

Go ahead using it. You are safe.

When it comes to writing code, there is a huge difference between code that works and code that works *well." Lets say you're tasked with writing a function that takes an array of RGB values and converts them to grayscale. ChatGPT is probably going to give you two nested loops that iterate over the X and Y values, applying a grayscale transformation to each pixel. This will get the job done, but it's slow, inefficient, and generally not well-suited for production code. An experienced programmer is going to take into account possible edge cases (what if a color is out of the 0-255 bounds), apply SIMD functions and parallel algorithms, factor in memory management (do we need a new array or can we write back to the input array), etc.

ChatGPT is great for experienced programmers to get new ideas; I use it as a modern version of "rubber ducky" debugging. The problem is that corporations think that LLMs can replace experienced programmers, and that's just not true. Sure, ChatGPT can produce code that "works," but it will fail at edge cases and will generally be inefficient and slow.

Its not just AI code but AI stuff in general.

It boils down to lemmy having a disproportionate amount of leftist liberal arts college student types. Thats just the reality of this platform.

Those types tend to see AI as a threat to their creative independent business. As well as feeling slighted that their data may have been used to train a model.

Its understandable why lots of people denounce AI out of fear, spite, or ignorance. Its hard to remain fair and open to new technology when its threatening your livelihood and its early foundations may have scraped your data non-consentually for training.

So you'll see AI hate circle jerk post every couple days from angry people who want to poison models and cheer for the idea that its just trendy nonesense. Dont debate them. Dont argue. Just let them vent and move on with your day.

• issues with model training sources
• business sending their whole codebase to third party (copilot etc.) instead of local models
• time gain is not that substantial in most case, as the actual "writing code" part is not the part that takes most time, thinking and checking it is
• "chatting" in natural language to describe something that have a precise spec is less efficient than just writing code for most tasks as long as you're half-competent. We've known that since customer/developer meetings have existed.
• the dev have to actually be competent enough to review the changes/output. In a way, "peer reviewing" becomes mandatory; it's long, can be fastidious, and generated code really needs to be double checked at every corner (talking from experience here; even a generated one-liner can have issues)
• some business thinking that LLM outputs are "good enough", firing/moving away people that can actually do said review, leading to more issues down the line
• actual debugging of non-trivial problems ends up sending me in a lot of directions, getting a useful output is unreliable at best
• making new things will sometimes confuse LLM, making them a time loss at best, and producing even worst code sometimes
• using code chatbot to help with common, menial tasks is irrelevant, as these tasks have already been done and sort of "optimized out" in library and reusable code. At best you could pull some of this in your own codebase, making it worst to maintain in the long term

Those are the downside I can think of on the top of my head, for having used AI coding assistance (mostly local solutions for privacy reasons). There are upsides too:

• sometimes, it does produce useful output in which I only have to edit a few parts to make it works
• local autocomplete is sometimes almost as useful as the regular contextual autocomplete
• the chatbot turning short code into longer "natural language" explanations can sometimes act as a rubber duck in aiding for debugging

Note the "sometimes". I don't have actual numbers because tracking that would be like, hell, but the times it does something actually impressive are rare enough that I still bother my coworker with it when it happens. For most of the downside, it's not even a matter of the tool becoming better, it's the usefulness to begin with that's uncertain. It does, however, come at a large cost (money, privacy in some cases, time, and apparently ecological too) that is not at all outweighed by the rare "gains".

https://www.techrepublic.com/article/ai-generated-code-outages/

I use ai, but whenever I do I have to modify it, whether it's because it gives me errors, is slow, doesn't fit my current implementation or is going off the wrong foot.

Personally, I've found AI is wrong about 80% of the time for questions I ask it.

It's essentially just a search engine with cleverbot. If the problem you're dealing with is esoteric and therefore not easily searchable, AI won't fare any better.

I think AI would be a lot more useful if it gave a percentage indicating how confident it is in its answers, too. It's very useless to have it constantly give wrong information as though it is correct.

We built a Durable task workflow engine to manage infrastructure and we asked a new hire to add a small feature to it.

I checked on them later and they expressed they were stuck on an aspect of the change.

I could tell the code was ChatGPT. I asked "you wrote this with ChatGPT didn't you?" And they asked how I could tell.

I explained that ChatGPT doesn't have the full context and will send you on tangents like it has here.

I gave them the docs to the engine and to the integration point and said "try using only these and ask me questions if you're stuck for more than 40min.

They went on to become a very strong contributor and no longer uses ChatGPT or copilot.

I've tried it myself and it gives me the wrong answers 90% of the time. It could be useful though. If they changed ChatGPT to find and link you docs it finds relevant I would love it but it never does even when asked.