LeetCode Trainer with AI

This project is personal LeetCode training repository configured to use an AI as a senior software engineer and interview coach. The AI is instructed to use the Socratic method, guiding you to solutions through questioning rather than providing direct answers.

🚀 How to Set Up the AI Trainer

The AI trainer is powered by a specific set of instructions located in .agent/rules/leetcode-trainer.md.

To ensure the AI follows these rules:

1. Instruction File: Ensure .agent/rules/leetcode-trainer.md exists with the "always_on" trigger.
2. AI Configuration: If using a tool (like Cursor, Windsurf, or Antigravity) that respects .agent/rules, the AI will automatically adopt the "Interview Coach" persona when you work on algorithmic problems.
3. Manual Setup: If your tool doesn't support .agent/rules automatically, copy the content of .agent/rules/leetcode-trainer.md into your system prompt or custom instructions.

🧠 Socratic Thinking Process

To find all reachable solutions and deeply understand the problem, follow this structured thinking flow with the AI:

1. Problem Translation: Explain the problem in your own words to the AI.
2. Examples & Edge Cases: Discuss various inputs, including empty values, large numbers, or negative values.
3. Brute Force Exploration: Propose a simple, first-instinct solution. The AI will acknowledge it and ask about its limitations.
4. Guided Optimization: Through targeted questions (e.g., "How could we reduce the lookup time?"), the AI helps you discover more efficient data structures or algorithms (Sliding Window, Two Pointers, Dynamic Programming, etc.).

📝 Thinking Journal (*_thinking.md)

For every problem, maintain a companion thinking file (e.g., BinaryTreeInorderTraversal_thinking.md) to capture the journey:

• Thinking Journal: Record your initial ideas, the AI's hints, and the refinements you make.
• Reachable Ways: List different approaches discovered (e.g., Iterative vs. Recursive, Time vs. Space trade-offs).
• Key Learnings & Summaries: Once the optimal solution is reached, the AI will provide a summary of patterns and takeaways. Save these at the end of the file for future review.
• Example Structure:

  # Thinking: [Problem Name]
  ## Approach 1: Brute Force
  - Thinking: ...
  - Complexity: ...
  ## Approach 2: Optimal
  - Thinking: ...
  - Complexity: ...
  ## Key Learnings
  - Pattern: ...
  - Takeaway: ...

🛠 Python Environment Setup

The project uses uv for dependency management and pytest for testing.

1. Install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh

2. Initialize/Sync Virtual Environment:

   uv sync

3. Run Tests: Tests are located alongside the source code or in specific folders. Run them using:

   uv run pytest

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Happy Coding! Use the AI to challenge your thinking, not just to get the green checkmark.