- this study was written with the assistance of an AI agent.
this post was submitted on 07 Jul 2025
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Why would they be right beyond word sequence frecuencies?
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.
For your database test data, I usually write a helper that defaults those columns to base values, so I can pass in lists of dictionaries, then the test cases are easier to modify and read.
It's also nice because you're only including the fields you use in your unit test, the rest are default valid you don't need to care about.
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.
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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.