Hey I went there
this post was submitted on 07 Jul 2025
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Why would they be right beyond word sequence frecuencies?
I use it for very specific tasks and give as much information as possible. I usually have to give it more feedback to get to the desired goal. For instance I will ask it how to resolve an error message. I've even asked it for some short python code. I almost always get good feedback when doing that. Asking it about basic facts works too like science questions.
One thing I have had problems with is if the error is sort of an oddball it will give me suggestions that don't work with my OS/app version even though I gave it that info. Then I give it feedback and eventually it will loop back to its original suggestions, so it couldn't come up with an answer.
I've also found differences in chatgpt vs MS copilot with chatgpt usually being better results.
For me as a software developer the accuracy is more in the 95%+ range.
On one hand the built in copilot chat widget in Intellij basically replaces a lot my google queries.
On the other hand it is rather fucking good at executing some rewrites that is a fucking chore to do manually, but can easily be done by copilot.
Imagine you have a script that initializes your DB with some test data. You have an Insert into statement with lots of columns and rows so
Inser into (column1,....,column n) Values row1, Row 2 Row n
Addig a new column with test data for each row is a PITA, but copilot handles it without issue.
Similarly when writing unit tests you do a lot of edge case testing which is a bunch of almost same looking tests with maybe one variable changing, at most you write one of those tests, then copilot will auto generate the rest after you name the next unit test, pretty good at guessing what you want to do in that test, at least with my naming scheme.
So yeah, it's way overrated for many-many things, but for programming it's a pretty awesome productivity tool.
Keep doing what you do. Your company will pay me handsomely to throw out all your bullshit and write working code you can trust when you're done. If your company wants to have a product in the future that is.
Lmao, okay buddy, based on how many interviews I have sat on in, the chances that you are a worse programmer than me are much higher than you being better than me.
Being a pompous ass dismissive of new tooling makes you chances even worse 😕
The person who uses fancy autocomplete to write their code will be exactly the person who thinks they're better than everyone. Those traits are correlated.
Do you use an IDE for writing your code or do you use a notepad like a "real" programmer? An IDE like Intellij has fancy shit like generating getters, setters, constructors, equals hashscode, you should never use those, real programmers write those by hand.
Your attention detail is very good btw, which I am ofc being sarcastic about because if you had any you'd have noticed I have never said I write my code with chat gpt, I said Unit tests, sql for unit tests.
Ofc attention to detail is not a requirement of software engineering so you should be good. (This was also sarcasm I feel like you need this to be pointed out for you).
Also by your implied logic that the code being not written by you = bad, no company should ever hire Junior engineers, I mean what are you gonna do? Fucking read the code they wrote?
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I’ve been in the industry awhile and your assessment is dead on.
As long as you’re not blindly committing the code, it’s a huge time saver for a number of mundane tasks.
It’s especially fantastic for writing throwaway tooling. Need data massaged a specific way? Ez pz. Need a script to execute an api call on each entry in a spreadsheet? No problem.
The guy above you is a nutter. Not sure if people haven’t tried leveraging LLMs or what. It has a ton of faults, but it really does speed up the mundane work. Also, clearly the person is either brand new to the field or doesn’t even work in it. Otherwise they would have seen the barely functional shite that actual humans churn out.
Part of me wonders if code organization is going to start optimizing for interpretation by these models rather than humans.
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30% might be high. I've worked with two different agent creation platforms. Both require a huge amount of manual correction to work anywhere near accurately. I'm really not sure what the LLM actually provides other than some natural language processing.
Before human correction, the agents i've tested were right 20% of the time, wrong 30%, and failed entirely 50%. To fix them, a human has to sit behind the curtain and manually review conversations and program custom interactions for every failure.
In theory, once it is fully setup and all the edge cases fixed, it will provide 24/7 support in a convenient chat format. But that takes a lot more man hours than the hype suggests...
Weirdly, chatgpt does a better job than a purpose built, purchased agent.