15kingben/googleprep

This repository is not an application codebase; it is a single-page learning and interview-prep playbook. The entire “architecture” lives in README.md, so the main boundaries are organizational rather than technical.

1. Primary purpose

- The repo is a curated study guide for algorithm practice, competitive programming, and technical interviews.

- It is meant to be consumed by a human, not executed by software.

2. Content hierarchy

- The README is organized into three major layers:

- learning resources and concept recommendations

- contest participation guidance

- interview preparation scripts and practice questions

- This makes it a knowledge base with a progression from concept acquisition to application.

3. Concept curriculum subsystem

- The “Algorithms Live!” section is the core syllabus.

- It highlights algorithm families such as Fenwick trees, segment trees, graph connectivity, SCCs, topological sort, DP variants, max flow, binary lifting LCA, and geometric sweeps.

- These topics form the conceptual backbone of the repo.

4. Practice-to-reinforcement loop

- The README strongly emphasizes solving problems immediately after learning a concept.

- The intended flow is: watch an episode, solve the related problem independently, then skim the coding technique afterward.

- This creates a tight feedback loop between theory and implementation practice.

5. Contest learning subsystem

- The “Learning From Contests” section defines a second major input stream: contest failures.

- The repo recommends reviewing post-contest solutions, extracting unfamiliar techniques, and converting mistakes into future study topics.

- This is the repo’s main mechanism for continuously expanding the concept catalog.

6. Contest cadence and external platforms

- The repo names specific competitive programming platforms and schedules: LeetCode, Codeforces, CodeChef, Google Code Jam, and clist.by.

- These are not integrated services in a software sense, but they are the external execution environments where the study plan is applied.

7. Interview workflow subsystem

- The “Interview Script” section is a procedural boundary for problem solving.

- It prescribes a sequence: clarify requirements, propose brute force, test on a small case, optimize, code, and enumerate edge cases.

- This is effectively the repo’s operational model for live technical interviews.

8. Problem catalog

- The “Practice Interview” section is a handpicked list of representative problems.

- The problems are grouped implicitly by technique: graph traversal, union-find/BFS/DFS, topological sorting, reverse reasoning, DP, bitmask DP, monotonic structures, greedy scheduling, and binary search.

- This section acts like a test suite for validating mastery of the learning objectives.

9. Behavioral and meta-skills boundary

- The README separates algorithmic skill from communication skill.

- It explicitly calls out articulation, avoiding dead air, and asking interviewer questions as part of the overall system.

- In architecture terms, this is the “human interface” layer of the repo.

10. External knowledge dependencies

- The repo depends on external content rather than local code: YouTube episodes, blog posts, and LeetCode/contest problem statements.

- The repository itself contains no implementation artifacts, build tooling, or runtime dependencies.

11. Information flow

- The dominant flow is: curated concept → practice problem → contest experience → retrospective learning → deeper concept mastery.

- A second flow is interview-oriented: concept study → interview script rehearsal → selected practice questions → communication practice.

12. Overall structure for diagramming

- If you were drawing an architecture diagram, represent this repo as a documentation-driven learning system with:

- a concept library

- an exercise/problem bank

- a contest feedback loop

- an interview process playbook

- external learning and evaluation platforms

- There are no internal code modules, services, or data stores beyond the README content.