Photo by the Author
# Introduction
Technical interviews are not about memorizing random questions. They are about demonstrating clear thinking, strong fundamentals, and the ability to think under pressure. The fastest way to build that confidence is to learn from resources that have helped thousands of developers succeed.
In this article, we'll explore the 10 most reliable GitHub repositories for technical interview preparation, including coding interviews, system architecture, back-end and front-end roles, and machine learning interviews. Each repository focuses on topics of discussion, from data structures and algorithms to incremental system design and real-world transactions.
# GitHub repositories for Acing Tech discussions
// 1. jwasham/coding-interview-university
Coding Interview University is a checklist, multi-month study program for software engineering interviews, focusing on the most important core CS topics (data structures, algorithms, Big-O, and problem solving). It started as a personal guide for the author and grew into a structured repo with resources, daily guidance, and a clear way to prepare for companies like Google, Amazon, and Microsoft.
// 2. donnemartin/system-design-primer
System Design Primer is a structured, open-source guide to learning how to design scalable systems and preparing for a system design interview. It organizes the concepts of “systems at scale” in one place, with clear trade-offs (such as latency vs throughput and consistency vs availability), functional building blocks (CDNs, load balancers, caches, databases, queues), and interactive practice with examples of solutions, diagrams, and Anki cards for space replication.
// 3. yangshun/tech-interview-handbook
The Tech Interview Handbook is a free, curated technical interview guide for busy engineers, created by the author of Blind 75/Grind 75. It covers the full end-to-end interview journey, including coding interview best practices, checklists of selected problems and patterns, algorithm cheatsheets, resume and behavior modification, and even the best resources for community written content and community-only open offerings.
// 4. kdn251/talks
Interviews is a comprehensive coding interview prep repo curated by Kevin Naughton Jr., trusted by tens of thousands of developers. It includes clear explanations of core data structures and algorithms with step-by-step problem implementations, live coding, mock interview scenarios, and learning resources, making it a handy, all-in-one reference for FAANG-style interview preparation.
// 5. ashishps1/awesome-leetcode-resources
This repository of Awesome LeetCode DSA Resources is an organized collection of high-quality materials for managing data structures, algorithms, and common LeetCode patterns. It focuses on pattern-based learning, basic concepts, a list of selected problems such as the Blind 75 and Top Interview sets, as well as templates, articles, videos, books, and visual tools, making it an effective hub for preparing well for a coding interview.
// 6. binnguyennus/awesome-scalability
This Scalable Systems Design reading list is a curated, well-organized library of articles, lectures, books, and real-world case studies that explain how large systems remain fast, reliable, and robust as they grow from thousands to millions of users. It is designed for practical results: identifying slow systems (scalability vs performance), preventing and recovering from outages (availability and stability), preparing system design discussions (notes, structures, diagrams), and even measuring the engineering organization 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 high-quality lists of interview questions across a wide range of topics. It is designed to help you quickly find interview questions for a specific stack or domain without having to hunt across the internet. The repo is also marked as deprecated, so think of it as a big summary of links that are still useful, but may include old/outdated resources.
// 8. Chalarangelo/30-second-interview
30 Seconds of Interviews is a community-curated collection of common interview questions with short, clear answers, designed for quick review before interviews. It focuses on practical, frequently asked topics across JavaScript, React, HTML, CSS, accessibility, Node, and security. Instead of intensive studies, 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 an interview-driven collection of open-ended questions covering back-end engineering, system architecture, databases, distributed systems, architecture, security, and team processes. It doesn't intentionally give answers, it encourages deeper technical discussions than head answers. The resource is best used to spark thoughtful dialogue and explore real-world thinking, design trade-offs, and engineering maturity rather than a test-style interview.
// 10. khangich/machine-learning-interview
The Minimum Viable Study Plan for Machine Learning Interviews is a practical, “focus on what's real” interview for ML Engineer and Data Science. It includes case studies on ML system design (recommendations, feed rate, ads, search), ML fundamentals (statistics, classical ML, deep learning), and interview preparation exercises (SQL, LeetCode where needed), all supported by selected readings, questions, and real interview stories.
# Final thoughts
If there's one thing I've learned, it's that good interview preparation isn't about gathering resources, it's about consistently using the right ones. These repositories cover coding, backend basics, system design, scaling, and machine learning in a way that reflects real-world conversations.
My advice is simple: go through as many work-related mock interviews as possible. Read the sample answers, understand the reasoning behind them, and build a habit of practicing 20 questions every day. When the time comes for the interview, your answers will not feel memorized or forced, they will come naturally and confidently.
Abid Ali Awan (@1abidiawan) is a data science expert with a passion for building machine learning models. Currently, he specializes in content creation and technical blogging 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 idea is to create an AI product using a graph neural network for students with mental illness.
