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# Introduction
Technical interviews are not about memorizing random questions. They are about demonstrating clear thinking, strong fundamentals, and the ability to reason under pressure. The fastest way to build that confidence is to learn from resources that have already helped thousands of engineers succeed.
In this article, we will explore 10 of the 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 interviews, from data structures and algorithms to scalable system design and real-world tradeoffs.
# GitHub Repositories for Acing Tech Interviews
// 1. jwasham/coding-interview-university
Coding Interview University is a checklist-based, multi-month study plan for software engineering interviews, focused on the core CS topics that matter most (data structures, algorithms, Big-O, and problem practice). It started as the author’s personal roadmap and grew into a structured repo with resources, daily guidance, and a clear path to prep for companies like Google, Amazon, and Microsoft.
// 2. donnemartin/system-design-primer
The System Design Primer is a structured, open-source guide for learning how to design scalable systems and preparing for system design interviews. It organizes the scattered “systems at scale” concepts into one place, with clear trade-offs (like latency vs throughput and consistency vs availability), practical building blocks (CDNs, load balancers, caches, databases, queues), and hands-on interview practice with example solutions, diagrams, and Anki flashcards for spaced repetition.
// 3. yangshun/tech-interview-handbook
Tech Interview Handbook is a free, curated technical interview prep guide for busy engineers, created by the author of Blind 75/Grind 75. It covers the full interview journey end-to-end, including coding interview best practices, curated problem lists and patterns, algorithm cheatsheets, resume and behavioral prep, and even front-end resources, with most content written directly in the repo (not just links) and open for community contributions.
// 4. kdn251/interviews
Interviews is a comprehensive coding interview prep repo curated by Kevin Naughton Jr., trusted by tens of thousands of engineers. It combines clear explanations of core data structures and algorithms with categorized problem implementations, live coding practice, mock interview platforms, and learning resources, making it a practical, all-in-one reference for preparing for FAANG-style interviews.
// 5. ashishps1/awesome-leetcode-resources
This Awesome LeetCode DSA Resources repository is a structured collection of high-quality materials for mastering data structures, algorithms, and common LeetCode patterns. It focuses on pattern-based learning, fundamental concepts, curated problem lists like Blind 75 and Top Interview sets, plus templates, articles, videos, books, and visual tools, making it a practical hub for efficient coding interview preparation.
// 6. binhnguyennus/awesome-scalability
This Scalable Systems Design Reading List is a curated, well-organized library of articles, talks, books, and real-world case studies that explain how large-scale systems stay fast, reliable, and resilient as they grow from thousands to billions of users. It’s structured around practical outcomes: diagnosing slow systems (scalability vs performance), preventing and recovering from outages (availability and stability), preparing for system design interviews (notes, architectures, diagrams), and even scaling the engineering org itself (hiring, management, culture).
// 7. DopplerHQ/awesome-interview-questions
Awesome Interviews is a “meta-list” of technical interview resources: instead of being a single question bank, it curates many high-quality lists of interview questions across a huge range of topics. It’s meant to help you quickly find interview questions for a specific stack or domain without hunting across the internet. The repo is also marked as no longer actively supported, so think of it as a large snapshot of links that’s still useful, but may include older/outdated resources.
// 8. Chalarangelo/30-seconds-of-interviews
30 Seconds of Interviews is a community-curated collection of common interview questions with short, clear answers, designed for fast revision before interviews. It focuses on practical, frequently asked topics across JavaScript, React, HTML, CSS, Accessibility, Node, and Security. Instead of deep tutorials, it emphasizes quick recall, real-world understanding, and confidence under interview pressure, making it ideal for last-minute preparation.
// 9. arialdomartini/Back-End-Developer-Interview-Questions
Back-End Developer Interview Questions is a discussion-driven collection of open-ended questions covering backend engineering, system design, databases, distributed systems, architecture, security, and team practices. It intentionally provides no answers, encouraging deep technical conversations rather than rote responses. The resource is best used to spark thoughtful dialogue and assess real-world reasoning, design tradeoffs, and engineering maturity instead of checklist-style interviewing.
// 10. khangich/machine-learning-interview
Minimum Viable Study Plan for Machine Learning Interviews is a practical, “focus on what actually shows up” roadmap for ML Engineer and Data Science interviews. It mixes ML system design case studies (recommendation, feed ranking, ads, search), core ML fundamentals (statistics, classical ML, deep learning), and interview prep drills (SQL, a bit of LeetCode where needed), all backed by curated readings, quizzes, and real interview stories.
# Final Thoughts
If there is one thing I have learned, it is that good interview prep is not about collecting resources, it is about using the right ones consistently. These repositories cover coding, backend fundamentals, system design, scalability, and machine learning in a way that actually reflects real interviews.
My advice is simple: go through as many job-relevant mock interviews as you can. Learn the sample answers, understand the thinking behind them, and build the habit of practicing around 20 questions every day. When interview time comes, your answers will not feel memorized or forced, they will come naturally and with confidence.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
