The AIMLInterviews repository is primarily an interview preparation tool that centers around an MCP (Model Context Protocol) server. This server turns any MCP-compatible AI assistant into a spoiler-free interview coach by providing tools to list problems, give progressive hints, review answers, and generate learning or company-specific preparation plans. The server dynamically builds an in-memory catalog by scanning markdown files in the src/ and cn/ directories, extracting problems and sections based on heading patterns. Beyond the server, the repository contains extensive educational content including markdown guides, Jupyter notebooks for ML algorithm implementations, and solution code with tests, all aimed at AI/ML interview readiness.
This is a pnpm monorepo whose dominant application is apps/portal, a Next.js + tRPC + Prisma community platform for tech interview preparation. It delivers three interactive products: an anonymous salary/offer comparison tool with percentile analysis and currency conversion, a resume review system with PDF uploads, comments, stars and gamified badges, and a crowdsourced interview question bank with voting, full-text search and list curation. Authentication is NextAuth via GitHub, file uploads use Supabase storage, and all application logic is exposed through a tRPC router tree backed by PostgreSQL. apps/website is a Docusaurus static site that publishes the interview handbook content, blog posts and practice lists such as Grind 75; it is content-delivery rather than dynamic. The repo has some data/research flavour because it ships Prisma seed datasets (countries, states, companies, salary XLSX) and offer-analysis logic, but those are reference data and application features, not an analytical pipeline. The engineering follows T3 conventions: Zod-validated routers, React Hook Form, a custom UI kit and shared config packages. Strong reusable pieces include an accessible typeahead, a compound radio list, a currency exchange utility and an optimistic vote hook. Weaknesses are long and repetitive tRPC routers (especially the offers-profile update mutation), duplicated voting mutations across routers, inconsistent use of protected routers, hardcoded assumptions in seeds, and no visible automated test suite. The repository is actively maintained with recent commits and CI workflows, but it has medium technical drift: the portal pins older major versions of Next.js, tRPC and React Query, and it depends on a custom vite-plus toolchain. The Prisma schema has many 2022-era migrations and substantial complexity. It is best used as a reference architecture for building community-driven interview-prep features and as a source of isolated UI/utility patterns rather than as a drop-in application.
This repository is a collection of competitive programming solutions and learning resources accumulated during interview preparation for a Microsoft SWE role. It contains implementations of algorithms and data structures from platforms like LeetCode, HackerRank, CodeChef, and others, organized by problem source and topic. The repository serves as both a personal record of solved problems and a reference for common coding interview patterns. The codebase consists of standalone solution files that implement specific algorithms to solve well-defined problems. Rather than a unified application, it's an educational repository where each file represents a self-contained solution to a particular coding challenge. The solutions demonstrate various techniques including two-pointer approaches, stack-based algorithms, in-place array modifications, and thread synchronization patterns. While the repository doesn't contain a single cohesive software system, it does include several reusable algorithmic patterns that could be extracted for use in other projects. Notable examples include the stack-based parentheses validation, two-pointer technique for sorted arrays, in-place array modification algorithms, and thread coordination mechanisms using Events and Semaphores. These components represent fundamental computer science concepts implemented with attention to correctness and efficiency.
This repository is an educational resource focused on teaching low-level design (LLD) and object-oriented design (OOD) principles through runnable code examples. It contains implementations of the Gang of Four design patterns (creational, structural, behavioral) across multiple programming languages including C++, Java, C#, Python, Go, and JavaScript/TypeScript. Additionally, it provides problem statements and solutions for common LLD interview questions such as parking lot systems, ATM implementations, vending machines, and social networking platforms. The repository serves as both a reference guide and a practical learning tool. Each design pattern is demonstrated with minimal, focused examples that isolate the pattern's core mechanics. The problem/solution sections show how these patterns can be applied in real-world scenarios, complete with class diagrams and explanations. While the code is functional and demonstrative, it prioritizes clarity of concept over production robustness, making it ideal for learning but requiring adaptation for actual deployment. Although primarily educational, the repository contains executable code that could be considered a collection of mini-applications. Each pattern implementation and problem solution can be run independently to observe the behavior. The multi-language approach allows developers to compare how the same pattern is expressed in different ecosystems, highlighting both universal principles and language-specific idioms.
This repository is an educational reference and study guide for system design interviews. It is not a deployable application or data pipeline. It provides a structured index of system design topics, solution sketches for common design problems (e.g., pastebin, Twitter, web crawler), and Python code snippets illustrating key patterns like LRU caching, hash tables, BFS on social graphs, and MapReduce. The material is oriented toward preparing engineers for technical interviews, with back-of-the-envelope calculations, REST API designs, and scalability discussions. The code examples are minimal, self-contained, and demonstrate fundamental computer science patterns, but are not production-grade software. The repository also includes Anki flashcard decks for spaced repetition.
This repository is a curated reading list organized as a GitHub Pages site using Jekyll. It contains no application logic, no data pipelines, and no executable code. The entire substantive content is a single README.md file with approximately 1,500 categorized links to external articles, talks, and case studies about scalable system design. The repository's value lies entirely in its curation quality and organization, not in any software it produces. The architecture is a static Jekyll site served via GitHub Pages, configured through _config.yml with the 'jekyll-theme-modernist' theme. The only other files are standard GitHub community templates (CONTRIBUTING.md, SECURITY.md, issue templates, CODE_OF_CONDUCT.md, LICENSE, FUNDING.yml). There are no tests, no CI/CD pipelines, no package manifests, and no source code in any programming language. The last commit was in January 2026, indicating the repository is still receiving updates. The content covers distributed systems patterns across major tech companies (Netflix, Uber, Facebook, Google, LinkedIn, Pinterest, etc.) organized into categories: principles, scalability, availability, stability, performance, intelligence/ML, architecture, interview prep, organization, and talks. The breadth of coverage is impressive but the curation is entirely manual link collection with no original analysis or tooling.
This repository is a comprehensive collection of Android interview questions and answers, organized into topics such as Java basics, Android fundamentals, advanced Android, computer networks, operating systems, databases, data structures and algorithms, and non-technical interview questions. It is purely a documentation resource with no executable code, build scripts, or data pipelines. The content was last updated around 2020 and is now deprecated, with the author promoting a paid version in a separate knowledge planet. While the repository provides a broad coverage of interview topics, it lacks any software components, making it useful only as a reference for interview preparation. It is not a software application or a data research project.
This repository consists solely of a README.md file containing 100 Golang interview questions and answers for 2026. It serves as an educational resource covering Go language fundamentals like workspace architecture, GOPATH, slices, goroutines, channels, error handling, and data types. The content explains concepts with code snippets but does not include any executable source files, project structure, or buildable components. Despite the recent commit activity indicating maintenance, the absence of actual code means the repository functions only as static documentation rather than a runnable application or reusable library.
This repository contains a single README.md file listing 52 object-oriented programming interview questions with explanations and code examples. It is a static educational resource hosted on Devinterview.io, not a software application or data pipeline. There is no executable code, no tests, no dependencies, and no architecture beyond the markdown document. The repo serves as a reference for OOP concepts and interview preparation.