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# Entry
A job interview isn’t about memorizing random questions. It’s about demonstrating clear thinking, forceful fundamentals and the ability to reason under pressure. The fastest way to build that confidence is to learn from resources that have helped thousands of engineers succeed.
In this article, we will analyze the 10 most trusted GitHub repositories for tech interview preparation, covering coding interviews, system design, backend and frontend roles, and even machine learning interviews. Each repository focuses on what actually matters in intelligence, from data structures and algorithms to scalable system design and real-world trade-offs.
# GitHub repositories containing interviews with Acing Tech
// 1. jwasham/university-coding-interview
University of Coding Talks is a checklist-based, multi-month software engineering interview study plan focusing on the key CS topics that matter most (data structures, algorithms, Massive-O, and problem-solving practice). It started as the author’s personal roadmap and has evolved into a structured repository of resources, daily tips, and a clear path to preparation for companies like Google, Amazon, and Microsoft.
// 2. donnemartin/backing-design system
The System design primer is a structured, open-source guide to learning how to design scalable systems and preparing for system design interviews. It organizes distributed “systems at scale” concepts in one place, with clear trade-offs (like latency vs. bandwidth and consistency vs. availability), practical building blocks (CDN, load balancers, caches, databases, queues), and hands-on interviews with sample solutions, diagrams, and Anki flashcards to repeat at intervals.
// 3. Yangshun/Technical Interview Manual
Technical interview guide is a free, carefully curated interview prep guide for busy engineers, created by the author of Blind 75/Grind 75. It covers the entire interview journey from start to finish, including interview coding best practices, curated problem lists and patterns, algorithm cheat sheets, resume and behavioral preparation, and even front-end resources, with most of the content saved directly in the repository (not just links) and open to community contributions.
// 4. kdn251/interviews
Interviews is a comprehensive programming interview curated by Kevin Naughton Jr. and trusted by tens of thousands of engineers. It combines clear explanations of fundamental data structures and algorithms with categorized problem implementations, live coding practice, mock interview platforms, and educational resources, making it a practical, comprehensive FAANG-style interview preparation guide.
// 5. ashishps1/awesome-leetcode-resources
This Awesome LeetCode DSA resources the repository is an organized collection of high-quality materials for mastering data structures, algorithms and popular LeetCode patterns. Its focus on pattern-based learning, core concepts, curated problem lists such as the Blind 75 and Top Interview sets, as well as templates, articles, videos, books, and visual tools make it a hands-on hub for successful coding interview preparation.
// 6. binhnguyennus/incredible scalability
This Reading list for designing scalable systems is a curated, well-organized library of articles, talks, books, and real-world case studies that explain how large-scale systems remain rapid, reliable, and resilient as they grow from thousands to billions of users. It is based on practical results: diagnosing ponderous systems (scalability and performance), preventing and restoring failures (availability and stability), preparing for system design conversations (notes, architectures, diagrams), and even scaling the engineering organization itself (hiring, management, culture).
// 7. DopplerHQ/Awesome Interview Questions
Amazing interviews is a “meta-list” of technical interviewing resources: rather than being a single question bank, it contains many high-quality lists of interview questions covering a wide range of topics. It is intended to aid you quickly find interview questions for a specific stack or domain without having to search the Internet. The repository is also marked as no longer actively maintained, so consider it a vast snapshot of links that are still useful but may contain older/dated resources.
// 8. Chalarangelo/30 seconds of interviews
30 seconds of interviews is a community-curated collection of common interview questions with miniature, clear answers, designed for quick review before your interview. Focuses on practical, frequently asked topics in JavaScript, React, HTML, CSS, accessibility, node, and security. Instead of detailed tutorials, it emphasizes quick memorization, real-world understanding, and confidence under the pressure of a job interview, making it ideal for last-minute preparations.
// 9. arialdomartini/back-end developer interview questions
Back-end developer interview questions is a discussion-based collection of open-ended questions about back-end engineering, system design, databases, distributed systems, architecture, security, and team practices. It deliberately provides no answers, encouraging deep technical conversations rather than rote answers. These resources are best suited to initiating thoughtful dialogue and assessing real-world reasoning, design trade-offs, and engineering maturity, rather than conducting checklist-style interviews.
// 10. Khangi/Machine Learning Interview
Minimum viable study plan for machine learning interviews is a practical roadmap that “focuses on what actually shows up” when interviewing ML engineers and data scientists. It combines case studies on ML system design (recommendations, feed ranking, advertising, search), ML fundamentals (statistics, classical ML, deep learning), and interview prep exercises (SQL, some LeetCode if needed), all supported by curated readings, quizzes, and real-life interview stories.
# Final thoughts
If I’ve learned anything, it’s that good interview preparation isn’t about accumulating resources, but about consistently using the right ones. These repositories cover coding, backend fundamentals, system design, scalability, and machine learning in a way that actually reflects real-world interviews.
My advice is plain: attend as many job-related mock interviews as possible. Learn sample answers, understand what’s behind them, and develop the habit of practicing about 20 questions every day. When it’s time for the interview, your answers won’t feel memorized or forced, but will come naturally and confidently.
Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. Currently, he focuses on creating content and writing technical blogs about machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a Bachelor’s degree in Telecommunications Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.