A modern, AI-assisted coding problem workspace for interview prep, algorithm mastery, and collaborative learning. This project provides:

• A large, organized set of coding problems (including LeetCode and custom challenges)
• Automated workflows for generating, testing, and documenting problems using AI tools (like Cursor, Copilot, or ChatGPT)
• Scripts and context files for seamless onboarding, code quality, and reproducible practice
• Support for dev containers, Docker, and modern Python tooling

Whether you're preparing for interviews, practicing algorithms, or sharing with friends, this workspace helps you focus on learning and problem-solving—not boilerplate setup.

To load project context into your AI assistant, you have two options:

1. Copy and paste the onboarding prompt below (the code block) into your AI agent when you open this project.
2. Or run:

   make print-ai-onboarding

   This will print the same onboarding prompt below, which you can then copy and paste into your AI agent.

Onboarding prompt:

Please read `ai_context/README.md` and all referenced files in `ai_context/` before assisting. Use these as your context for all workflows, code generation, and automation in this project.

• This ensures the AI understands all project-specific workflows, code style, and automation policies.
• For more details, see ai_context/README.md.

Note: The checked-in codebase on GitHub includes solutions implemented by the author (jcranston) as part of his own interview prep and workflow. If you want to start fresh (with only problems and tests, no solutions or explanations), see the Wipe Solutions Script below.

• AI Context-Driven Workflow: Use AI context files to automate and standardize problem creation, solution, testing, and documentation.
• Flexible LeetCode Problem Addition:
  • Add problems by LeetCode number (e.g., 217) or by name (e.g., "contains duplicate").
  • The system will map user-supplied names to the correct LeetCode problem number and canonical name (e.g., "triplet sum to zero" → LeetCode 15, "3Sum").
  • The README for each problem will include the LeetCode number, canonical name, and your supplied title.
  • See ai_context/leetcode_problem_mapping.md for details.
• Scripted Utilities: Scripts for problem status, batch addition, and more.
• Consistent Directory Structure: All problems follow the same structure for easy navigation and automation.

You can use this codebase in several ways:

• Add new problems:
  • By LeetCode number or name (see examples above)
  • By running scripts (see below)
  • By using the AI (Cursor, VS Code Copilot, etc.)
• Generate explanations:
  • Ask the AI to generate an EXPLANATION.md for any problem.
• Check problem status:
  • Use the problem_status.py script or ask the AI to list unimplemented problems, missing explanations, etc.
• Run and test solutions:
  • Use pytest or make test to run all tests.
• Work in a container or dev container:
  • Use the provided scripts or open in Cursor/VS Code for a seamless dev environment.
• Use Makefile commands:
  • See below for a list of common commands.

For detailed workflows and advanced usage, see ai_context/README.md.

• Recommended: Open the project in Cursor or VS Code and select “Reopen in Container.”
• Manual:
  • Build: make build or ./scripts/run_in_container.sh build
  • Run: make run or ./scripts/run_in_container.sh run
• All dependencies and tools are pre-installed in the container.

• You can use AI assistants to:
  • Add new problems (by name or number)
  • Generate explanations
  • Refactor or review code
  • Clarify problem statements
• See ai_context/README.md for prompt examples and detailed workflows.

• See How to Use This Project and ai_context/README.md for workflow guidance and context files.
• Use the provided scripts and AI prompts to automate your workflow.

Feel free to contribute or suggest improvements!

If you want to start with only the problems and tests (no solution implementations or explanations), you can use the provided script:

# Dry run (shows what would be wiped)
python scripts/wipe_solutions.py --dry-run

# Actually wipe all solution implementations (keeps docstrings, stubs out with 'pass')
python scripts/wipe_solutions.py --yes

# Also remove all EXPLANATION.md files
python scripts/wipe_solutions.py --yes --wipe-explanations

# Backup all wiped files to a .backup directory before deleting
python scripts/wipe_solutions.py --yes --backup

# Combine options as needed
python scripts/wipe_solutions.py --yes --wipe-explanations --backup

Warning: This is a destructive operation! Only run it if you want to start fresh.
By default, the script runs in dry-run mode and will not delete or overwrite anything unless you use --yes.