Language-specific interview questions

This project is a comprehensive technical interview preparation resource and computer science interview guide. It serves as an educational reference for developers to study core software engineering fundamentals and common coding patterns required for employment screenings. The repository provides detailed guides and references covering data structures and algorithms, networking and security, operating systems, and web development. It specifically focuses on the implementation and complexity analysis of sorting, searching, and graph algorithms. The material encompasses a wide breadth of computer science domains, including software engineering principles like SOLID and design patterns, language fundamentals across Java, C, and C++, and system architecture. It also covers database design and scaling, concurrency and multithreading, and frontend development lifecycles. The project is primarily written in Java and is structured as a knowledge base for mastering technical interviews.

This project is a collection of comprehensive guides and reference materials designed for technical interviews, machine learning system design, and professional development. It serves as a technical knowledge base and a career coaching manual, providing structured resources to help candidates navigate the machine learning hiring landscape. The resource distinguishes itself by offering detailed frameworks for comparing industry roles, analyzing company types, and planning long-term career progression. It provides specific guidance on evaluating employer organizational health, identifying research labs, and differentiating between roles such as research scientists, machine learning engineers, and software engineers. The content covers a broad capability surface, including technical interview preparation across computer science fundamentals, mathematics, and machine learning theory. It also includes detailed strategies for job search tactics, compensation analysis, and the design of technical hiring pipelines. The materials are organized as a structured repository of reference guides and curricula.

Type-challenges is a community-driven learning platform and programming playground designed to help developers master advanced TypeScript type systems. It provides a collection of interactive exercises that focus on complex type-level logic, allowing users to practice and refine their skills through hands-on problem solving. The project distinguishes itself by focusing on the boundaries of the language, requiring users to employ recursive conditional types, mapped transformations, and variadic tuple manipulation to solve curated coding puzzles. By working through these challenges, developers gain experience in constraint-based narrowing, template literal parsing, and recursive unrolling, which are essential for building expressive and reusable code architectures. Beyond the exercises, the repository serves as an educational resource library, aggregating articles, books, and official documentation to support deep technical learning. The platform encourages collaborative growth, offering shared solutions and explanations that assist in technical interview preparation and overall language proficiency.

This project is a structured educational framework designed to guide developers through the core concepts of JavaScript programming and software engineering. It functions as a comprehensive training resource, providing a logical roadmap for mastering web development, from fundamental language syntax to full-stack application architecture. The platform utilizes a markdown-based documentation system that organizes technical learning materials into a clear, hierarchical curriculum. By employing a static site generator, the project transforms these plain-text educational modules into a collection of portable HTML files, ensuring consistent delivery and accessibility across the learning path. The content is structured to support professional skill development by emphasizing industry-standard workflows and programming principles. The entire curriculum is maintained through a version-controlled repository, allowing for collaborative updates and a modular approach to organizing complex technical topics.

This project is a professional development repository that provides structured learning paths for individuals pursuing careers in data-centric engineering and artificial intelligence. It functions as a competency benchmarking framework, defining the core knowledge areas and technical milestones required to achieve proficiency in specialized domains. The repository distinguishes itself through hierarchical knowledge graphing, which organizes complex technical subjects into nested tree structures to create clear, progressive learning sequences. By centralizing curated educational resources and industry-standard curricula, it streamlines the process of self-directed study for roles ranging from data engineering to deep learning. The content is maintained using markdown-based storage, allowing for version control and consistent updates across multiple technical roadmaps. These roadmaps cover a broad capability surface, including the design of scalable data systems, the application of statistical models, and the mastery of foundational mathematical and database principles.

This project is a comprehensive functional programming curriculum and learning resource for Haskell. It provides sequenced educational paths and technical reference guides designed to take developers from beginner to advanced levels of proficiency. The project distinguishes itself through a deep focus on theoretical and technical foundations, offering detailed studies on type theory, category theory, and runtime internals. It includes a dedicated performance handbook for optimizing execution speed and memory management, as well as an ecosystem guide for managing development tools and editor configurations. Its technical coverage extends to advanced functional patterns and architectural strategies, including monad transformers, recursion schemes, and lens utilities. The materials also cover practical implementation areas such as parser combinators, property-based testing, concurrency models, and the design of domain-specific languages. The resource also aggregates external guides, university courses, and multilingual materials to support a broad range of learners.

This project is an interactive programming curriculum and educational system designed to teach computer science and software engineering. It provides a structured set of courses and professional roadmaps focused on backend engineering, DevOps, and systems fundamentals. The platform is distinguished by an AI-powered coding tutor that provides Socratic guidance and contextual hints to help students find solutions independently. It features a browser-based code sandbox using WebAssembly to eliminate local environment setup, alongside automated test-based grading and spaced-repetition logic to reinforce difficult concepts. The curriculum covers a broad range of technical domains, including programming languages such as Go, Python, and TypeScript, as well as relational database design, container orchestration with Kubernetes, and cloud operations. It also includes professional development resources for technical interview preparation and portfolio construction. Learning engagement is managed through gamified incentives like experience points and leaderboards, while progress is tracked via sequenced learning paths and AI-generated coding challenges.