Yeah fake. No way you can get 90%+ using chatGPT without understanding code. LLMs barf out so much nonsense when it comes to code. You have to correct it frequently to make it spit out working code.
If we’re talking about freshman CS 101, where every assignment is the same year-over-year and it’s all machine graded, yes, 90% is definitely possible because an LLM can essentially act as a database of all problems and all solutions. A grad student TA can probably see through his “explanations”, but they’re probably tired from their endless stack of work, so why bother?
If we’re talking about a 400 level CS class, this kid’s screwed and even someone who’s mastered the fundamentals will struggle through advanced algorithms and reconciling math ideas with hands-on-keyboard software.
Are you guys just generating insanely difficult code? I feel like 90% of all my code generation with o1 works first time? And if it doesn’t, I just let GPT know and it fixes it right then and there?
I just generated an entire angular component (table with filters, data services, using in house software patterns and components, based off of existing work) using copilot for work yesterday. It didn’t work at first, but I’m a good enough software engineer that I iterated on the issues, discarding bad edits and referencing specific examples from the extant codebase and got copilot to fix it. 3-4 days of work (if you were already familiar with the existing way of doing things) done in about 3-4 hours. But if you didn’t know what was going on and how to fix it you’d end up with an unmaintainable non functional mess, full of bugs we have specific fixes in place to avoid but copilot doesn’t care about because it doesn’t have an idea of how software actually works, just what it should look like. So for anything novel or complex you have to feed it an example, then verify it didn’t skip steps or forget to include something it didn’t understand/predict, or make up a library/function call. So you have to know enough about the software you’re making to point that stuff out, because just feeding whatever error pops out of your compiler back into the AI may get you to working code, but it won’t ensure quality code, maintainability, or intelligibility.
My first attempt at coding with chatGPT was asking about saving information to a file with python. I wanted to know what libraries were available and the syntax to use them.
It gave me a three page write up about how to write a library myself, in python. Only it had an error on damn near every line, so I still had to go Google the actual libraries and their syntax and slosh through documentation
Yeah fake. No way you can get 90%+ using chatGPT without understanding code. LLMs barf out so much nonsense when it comes to code. You have to correct it frequently to make it spit out working code.
Two words: partial credit.
Usually this joke is run with a second point of view saying, do I tell them or let them keep thinking this is cheating?
If we’re talking about freshman CS 101, where every assignment is the same year-over-year and it’s all machine graded, yes, 90% is definitely possible because an LLM can essentially act as a database of all problems and all solutions. A grad student TA can probably see through his “explanations”, but they’re probably tired from their endless stack of work, so why bother?
If we’re talking about a 400 level CS class, this kid’s screwed and even someone who’s mastered the fundamentals will struggle through advanced algorithms and reconciling math ideas with hands-on-keyboard software.
Are you guys just generating insanely difficult code? I feel like 90% of all my code generation with o1 works first time? And if it doesn’t, I just let GPT know and it fixes it right then and there?
I just generated an entire angular component (table with filters, data services, using in house software patterns and components, based off of existing work) using copilot for work yesterday. It didn’t work at first, but I’m a good enough software engineer that I iterated on the issues, discarding bad edits and referencing specific examples from the extant codebase and got copilot to fix it. 3-4 days of work (if you were already familiar with the existing way of doing things) done in about 3-4 hours. But if you didn’t know what was going on and how to fix it you’d end up with an unmaintainable non functional mess, full of bugs we have specific fixes in place to avoid but copilot doesn’t care about because it doesn’t have an idea of how software actually works, just what it should look like. So for anything novel or complex you have to feed it an example, then verify it didn’t skip steps or forget to include something it didn’t understand/predict, or make up a library/function call. So you have to know enough about the software you’re making to point that stuff out, because just feeding whatever error pops out of your compiler back into the AI may get you to working code, but it won’t ensure quality code, maintainability, or intelligibility.
A lot of people assume their not knowing how to prompt is a failure of the AI. Or they tried it years ago, and assume it’s still as bad as it was.
My first attempt at coding with chatGPT was asking about saving information to a file with python. I wanted to know what libraries were available and the syntax to use them.
It gave me a three page write up about how to write a library myself, in python. Only it had an error on damn near every line, so I still had to go Google the actual libraries and their syntax and slosh through documentation
Garbage for me too except for basic beginners questions
deepseek rnows solid, autoapprove works sometimes lol