Show HN: Learn LLMs LeetCode Style
(github.com)177 points by Exorust 3 days ago
177 points by Exorust 3 days ago
Most ML problems in real life don’t constrain you to use linear regression or a CNN either. But there will be some metric you need to optimize.
What would take this repo to the next level is to have a reproducible data generation function for each exercise as well as a reasonable metric which must be passed. I don’t see anything that requires my classification auc to be over 0.5 which would be a basic criteria of bug-free code.
> Avoid using GPT. Try to solve these problems on your own. The goal is to learn and understand PyTorch concepts deeply.
I mean...this entire project appears to be mostly GPT-generated?
One time my teacher used a computer to make a math test for me, but then told me I couldn't use my computer during the exam. I dropped out of school immediately.
Great analogy my brother there’s minimal difference between a word processing software and an LLM
The purpose of the project is learning. The author believes that avoiding GPT will help you learn more effectively and offers that as upfront guidance. In this case, “avoid using GPT” isn’t an ethical directive but simply a learning recommendation. The value of that advice isn’t tied to which tools were used to create the question set.
^ This is a bot/crackpot account whose only purpose is promoting jumbled words on HN. How/where to report it?
Reminds me of some of Xah Lee's earliest posts on the Python mailing lists. Many, myself included, thought he was a clever bot.
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This is decent for what it is. Some of the problems are pretty open ended which has pros and cons, but that is very different from leetcode, which has very specific data and test cases.
For example, implement linear regression but the example solution uses a random number generator without a fixed seed. It’s fine, reproducibility isn’t the point, but leetcode problems are more structured.
In leetcode they usually don’t tell you exactly what data structure you must use, only that it must pass certain test cases. By analogy this might not tell you which architecture to use but require that it passes certain eval metrics.