You are right, I missed some crucial details in the blog entry. I will definitely take your feedback into account for Part 2, where I want to do a more detailed deep dive into the prompting protocols (with maybe some exact examples) and my learning strategy.

To answer your questions:

1. By "generated" I mean that I prompted the LLM incrementally to provide me the list of the next LeetCode problems to do (without the deep research/search function)

2. Yes, the problem names are the exact names from LeetCode. Initially, the LLM suggested this format, and I later forced it to stick to real LeetCode problems.

This allowed me to verify some output independently of the LLM (avoiding hallucinations), cross-check solutions with other materials, and track my progress.

Interestingly, I realized later that the LLM was mostly pulling from the standard Blind 75 problem set, and almost all the problems are from that list.

3. About the "forgotten and forbidden" code: I probably phrased it poorly in the article. As you said, this algorithmic logic is abstracted away in standard libraries and data structures. The disconnect for me (and I suspect for many "business logic" developers too) is that our daily production code rarely requires writing these fundamental structures from scratch, so we do not see the patterns that can also be applied in more high-level business logic. But this is still an in-progress hypothesis in my mind, without detailed examples.