⚒️ The Artifact Foundry

(Working in progress)

A personal workshop for deliberate practice in machine learning, AI engineering, and software development.

I treat my career as a craft that requires deliberate practice and a constant return to the basics. Everything here is a result of me getting my hands dirty to understand how things actually work—not just how they are used. This is the structure that I use to keep pace with the field without losing sight of the foundations.

Table of Contents

• 📚 What's Inside
• 🏗️ Repository Structure
• 🚀 Getting Started
• 🤖 AI for Knowledge Building
  • Custom Agents
  • Available Skills
• 🤝 Contributing
  • How to Contribute
  • Questions or Suggestions?

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📚 What's Inside

This repository spans the full ML/AI engineering stack. Current focus: classical machine learning (tree-based methods, ensembles) and Python fundamentals (class systems, build tools).

What's here now:

• Deep-dive notes on ML algorithms, MLOps, and software engineering
• From-scratch implementations with tests and documentation
• Curated learning resources across all topic areas
• Interview prep cheatsheets for quick reference

Coming next:

• Neural networks and deep learning architectures
• Advanced NLP and LLM applications
• More production ML system patterns
• Data structures and algorithms deep dives

Topic areas: Foundations • Machine Learning • Deep Learning • ML System Design • AI Engineering • Data Analytics • Software Engineering • Productionization • Interview Prep

Each topic folder contains notes/, snippets/, and resources.md for organized learning.

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🏗️ Repository Structure

This repository follows a deliberate organizational structure:

Learning Materials (optimized for understanding):

• Comprehensive explanations with "why" and "how it works internally"
• Real-world context and applications
• Multiple approaches and trade-offs
• Mathematical foundations where relevant

Interview Prep (optimized for recall):

• Condensed, no-fluff cheatsheets
• Pattern-focused common question types
• Template solutions and quick lookup tables
• Formulas and algorithms ready for quick reference

Duplication is intentional - The same topic can exist in both places with different treatments.

For detailed organization guidelines, see .claude/ORGANIZATION.md.

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🚀 Getting Started

This repository uses Python 3.10+ with organized dependencies:

# Clone the repository
git clone https://github.com/whanyu1212/artifact-foundry.git
cd artifact-foundry

Using pip:

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Unix/macOS
# .venv\Scripts\activate   # On Windows

# Install dependencies (choose what you need)
pip install -e .                    # Core dependencies only
pip install -e ".[ml]"              # + Machine learning
pip install -e ".[dl]"              # + Deep learning
pip install -e ".[viz]"             # + Visualization
pip install -e ".[notebooks]"       # + Jupyter notebooks
pip install -e ".[dev]"             # + Development tools
pip install -e ".[all]"             # Everything

Using uv (faster alternative):

# Create and activate virtual environment
uv venv
source .venv/bin/activate  # On Unix/macOS
# .venv\Scripts\activate   # On Windows

# Install dependencies
uv pip install -e ".[all]"

Running tests:

pytest tests/

Browsing content:

• Check machine-learning/notes/ for ML deep dives
• Explore machine-learning/snippets/ for implementations
• Review interview-prep/notes/ for quick references

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🤖 AI for Knowledge Building

This repository leverages Claude Code with custom agents, skills, and coding standards for enhanced learning workflows:

Custom Agents

• learning-curator: Organizes notes and resources, ensures consistent structure
• snippet-reviewer: Validates code correctness and educational clarity

Available Skills

• /add-resource - Add learning materials to appropriate resources.md files
• /link-project - Track implementation projects in PROJECTS.md

Configuration & Standards

• .claude/CLAUDE.md - Project overview and AI assistant context
• .claude/ORGANIZATION.md - Content organization guidelines
• .claude/rules/python-snippets.md - Python code style standards

See AGENTS.md for detailed documentation on AI-assisted workflows.

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🤝 Contributing

While this is primarily a personal learning repository, contributions are welcome! If you'd like to collaborate:

How to Contribute

1. Resources: Add valuable learning materials (books, papers, courses, articles)

   • Edit resources.md files in the appropriate topic folder
   • Follow the format: - [Title](link) - Author/Source - Brief description
   • Keep entries organized by type and alphabetically sorted

2. Cheatsheets & Notes: Improve or create study materials

   • Focus on depth and practical understanding over breadth
   • Include code examples, formulas, and real-world applications
   • Cite sources and credit original authors

3. Projects: Share implementation examples

   • Add to PROJECTS.md with repository links
   • Include context: what you built, why, and key learnings

4. Code Snippets: Add practical implementations

   • Place in appropriate topic folders with clear documentation
   • Focus on educational value and correctness
   • Include comments explaining concepts and non-obvious decisions

Questions or Suggestions?

Feel free to open an issue or submit a pull request. All contributions should maintain the spirit of hands-on learning and deep understanding.

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Built with curiosity, maintained with discipline

View Documentation • Browse Notes • Explore Code