this post was submitted on 27 Jun 2026
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So, serious question then:
If code has a grammatically correct or correct syntax form to use, how is AI generated code detected from human created code? Wouldn't they both follow similar structuring?
I know sometimes a long line of code can be "abbreviated" (lack of a better understanding or term ) so that it saves a few bytes and eliminates possible steps or errors, but is AI writing out the long version of code with a lot of comments or commented out instructions or verbiage?
I guess I don't understand outside of, AI being: 'this is a very long sentence that I don't know how to structurally shorten" vs human "I shortened it"something along those lines...
AI is incapable of making engineering decisions based on context unless you very specifically ask it to do something a certain way so for example I was writing a cli tool to help automate some processes but knowing this tool is throwaway I didn’t bother making it like the most robust opting for clarity and easier modification. Another colleague tried to write the same tool with github copilot because they can’t handle the fact theyre useless at everything and it came out functionally the same sure but somehow many hundreds to thousands of lines more verbose.
Somehow idk how this guy does it but all the comments in the code are complete gibberish and will also be massively verbose over a single line comment I would put in to be more meaningful. The best part is i still wrote mine faster manually because he would hit errors and then ask the AI to fix these errors where because the context of its prior code has so much nonsense in it further loses the plot and breaks it more. And then he ran out of credits and simply stopped doing anything. I don’t run out of credits though.
Businesses only really care about the output but having a simple application bloat into many thousands of lines more is a debt you really dont want to pay back later. Its easy to see a codebase that had a human make human decisions in it down to the “TODO: Fix this later”
Also I swear most AI companies tell the llm to produce more to drive up token usage cos some of the things it comes up with. My attempts at using this myself usually devolves into frustration because its like asking a junior dev to do something and I end up getting annoyed and doing it myself faster. Just because syntax is correct and the code runs doesnt mean the application is good. Oh and another thing is it prioritises older standards all the time. I know python inside out upside down and its vast library ecosystem it generally trends towards using libraries and standards that are out of date or even unsupported targetting something closer to 3.10 when im using 3.14. If you aren’t interrogating your dependencies you may as well be asking for critical failure later but nobody cares about the future anymore!
Oh and last one as the codebase grows its concept of consistency falls off massively and it starts treating the new functionality separated from the rest. As a result you get duplicated logic all over the place. Now im not the biggest believer in solid principles it can produce some over engineered solutions but repeating the same logic over and over is a problem you cant easily get yourself out of.
This is something that no one seems to get (at least no one who doesn't actually write code). Technically correct and actually correct are in two different universes.
I spent two months re-writing our entire codebase from scratch last year because it had built up ~5 years of tech debt and was using systems and APIs that are no longer supported. There were a few concepts worth keeping, but not a single file was allowed to survive until it was rewritten.
Now that Python's type hints are more mature, that was a big one. The old code was using either no hints or old hints, and if you've ever worked in a fully typed (as in literally every line including private code) codebase, it's hard to go back. AIs kinda suck at that though because the vast majority of Python code is unhinted.
Mypy gives me a headache for some things but its invaluable. It kinda just makes me wish for a strictly typed python tbh. Its also one of those languages you can fuck yourself into a corner real badly if you don’t have some strict rules for it. One guy I worked with loved using language features for no actual good reason until the codebase was practically its own domain specific language the IDE just highlighted in bulk as “idk wtf this is”. It would take days to understand to make a change and all it was doing was running a regression model on a date and value lmao
Yeah, my entire codebase is ~50-75k lines and it runs most of the company. I have a sort of "DSL" that's more just a wrapper library for the APIs we need to use. Means I can implement the optimal way to do something and have the production code just be a function/method call. That's incredibly useful when you are writing design automations that need to be very clear to read and change as a project evolves.
I've had a couple juniors over the years, and even without AI it was amazing just how much code they'd write. Like a tool script that kind worked and is 2000+ lines, but after my review/rewrite it's more generalized with error handling and it's 300 lines.
LLMs are trained on existing data. There is going to be less data bout new stuff than there is about older stuff.
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Yes an llm fundamentally only looks backwards it cant look forwards. It relies on nerds like me utilising new language features properly to produce relevant slop but honestly i feel like that kind of person is a dying breed and higher level languages are being treated no differently to assembly where its just machine output thats never questioned or even looked at.
Been seeing a big comeback of basic exploits like sql injection its kinda funny. Its kinda funny how many “engineers” i now work with that dont know what that even is
It's a lot easier for the LLM to lose the plot when they're writing. So when the sentence is like "I'm hungry, so I'm going to put some waffles in the toaster" and the goal is "expand this out so I can put anything in the toaster. But write it in a way that makes it so I can use any other appliance later." The output would be like "I'm hungry so I'm going to put some silently approved bacon into the toaster once it's also an approved appliance."
So it could be grammatically correct. It might even work, but you accumulate these little kinks in the codebase.
2 prompts in its already lost the plot on a really basic frontend application. I think at best I’ve had it create the first draft of a greenfield project for me cos I dont really like doing that and then I just take it from there
Hah, so in a formatting or logical sense, it works, but it's obviously not written how humans talk and so appears robotic... Got it, thank you that was easier to understand than I thought lol.
I have unfortunately had to vibe code a few things for work since there's a lot going on, and I don't have uninterrupted times to work on stuff, so concentration is almost nonexistent. Using ai to patch or fix or fill has been helpful! But I can see for community projects are problematic!
Yes, the context of what you're doing certainly matters. I had to get into an archive file that I didn't know what to do with one time. I used AI to build me a little app to visualize its content. Was it perfect? I doubt it. Did it function and get me the info I was looking for? Yeah!
As a rule of thumb, would you trust Momo Yaoyorozu to build you a solution or do you need to be able to blame someone when the solution blows up regardless of having a human build it?