This repository serves as a centralized knowledge base for software engineering concepts, best practices, algorithms, design patterns, and career development resources. It supports my mastery-based learning approach by providing structured reference materials across multiple domains of software engineering.
The primary purposes of this repository are:
- Consolidate learning across multiple programming languages and paradigms
- Document best practices and design patterns for reference
- Create a searchable knowledge base for technical concepts
- Support career development with comprehensive resources
- Maintain reference materials for interviews and technical discussions
- Facilitate knowledge transfer between different technology stacks
- Ruby: Syntax, idioms, metaprogramming, performance optimization
- Python: Language features, libraries, data analysis, machine learning
- JavaScript: Modern JS, functional programming, async patterns
- TypeScript: Type system, interfaces, advanced types
- SQL: Query optimization, database design, indexing strategies
- Fundamental data structures with implementations
- Algorithm analysis and complexity
- Common algorithm patterns and techniques
- Problem-solving strategies
- Interview preparation materials
- Object-oriented design principles
- Functional programming concepts
- Design patterns with examples
- Architecture patterns and strategies
- System design considerations
- Terminal-centric workflow techniques
- Git workflows and advanced commands
- Test-driven development strategies
- Code review best practices
- Documentation standards
- Neovim configuration and plugins
- tmux setup and usage patterns
- Shell scripting techniques
- Build tools and automation
- Development environment optimization
- Technical interview preparation
- Resume and portfolio development
- Salary negotiation resources
- Career progression planning
- Professional networking strategies
Resources are organized by:
- Topic (language, concept, tool)
- Complexity level (beginner, intermediate, advanced)
- Application domain (web, data, systems)
- Resource type (cheatsheet, tutorial, reference)
Each reference includes:
- Concise explanations of concepts
- Practical code examples
- Context for when and how to apply
- Common pitfalls and edge cases
- Links to additional resources
- launch-school-coursework: Main repository for Launch School curriculum work
- learning-roadmap: Documentation of learning journey and plans
- terminal-setup: Development environment configuration
- ruby-fundamentals: Ruby programming concepts and exercises
- python-fundamentals: Python programming concepts and exercises
- js-fundamentals: JavaScript programming concepts and exercises
- python-data-analysis: Data analysis projects using Python
This project was developed with assistance from Anthropic's Claude AI assistant, which helped with:
- Documentation writing and organization
- Code structure suggestions
- Troubleshooting and debugging assistance
Claude was used as a development aid while all final implementation decisions and code review were performed by the human developer.
This repository is available under the MIT License. See the LICENSE file for details.