akshatsinghnayal/leetcode

1. This repository is a problem-solution archive, not an application. It primarily contains LeetCode and interview-practice answers, generated with LeetHub v2 and organized as a catalog of independent algorithm implementations.

2. The main top-level subsystem is the LeetCode collection: folders named by problem number and slug, such as 0001-two-sum, 0207-course-schedule, or 3946-maximum-number-of-items-from-sale-i. Each folder is a self-contained solution unit.

3. Inside most LeetCode folders there are two artifacts:

- a C++ source file with the same problem slug

- a local README.md containing the problem statement or notes

A few problems have only a README, indicating missing or not-yet-committed code.

4. The second major subsystem is a categorized interview-practice area under Difficulty/. This is split into Basic, Easy, Medium, and Hard, and then further into named problem folders. These are also independent solution units, but they follow a different naming scheme from the LeetCode-numbered archive.

5. The codebase is intentionally flat and modular at the folder level. There is no visible shared library, service layer, test harness, or cross-folder dependency graph. Architecture-wise, every folder stands alone and solves one problem in isolation.

6. Languages are problem-driven rather than system-driven. Most implementations are in C++, reflecting algorithmic interview practice. A small number of SQL problems are included as .sql files, which are separate from the C++ problem set and represent database-query solutions.

7. The repository root README.md acts as an index rather than documentation for a runtime system. It enumerates LeetCode topics and links each topic to the corresponding problem folder. This creates a taxonomy layer across the independent solutions.

8. The topic index is the most important “architecture” layer beyond folders. Problems are grouped by algorithmic patterns such as Array, String, Linked List, Tree, Graph, Dynamic Programming, Greedy, Heap, Binary Search, Stack, Sliding Window, and others. For a diagram, this is a many-to-many classification map from problem folders to topics.

9. Several problems appear under multiple topics, which is expected for algorithm exercises. For example, a single solution may belong to both “Tree” and “Depth-First Search,” or both “Array” and “Two Pointers.” The repository is therefore better modeled as a tagged solution corpus than a hierarchical app.

10. The stats.json file likely serves as repository metadata/summary generated by tooling rather than by the solutions themselves. It is a supporting artifact, not part of the solution execution path.

11. There is no runtime data flow between problems. The only real flow is external: LeetCode problem statement → local README/source file → optional metadata classification in the root README. No services, APIs, databases, or queues are part of the solution architecture.

12. The clean architectural boundary is therefore: problem folder = unit of implementation; root README = index and taxonomy; Difficulty/ = alternate categorization for non-LeetCode practice; SQL files = separate query-solution branch. An accurate diagram should emphasize catalog structure, classification overlays, and the absence of shared execution dependencies.