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Daily GitHub Project Recommendation: Coding Interview University - 300K Stars, The Definitive Guide for the Software Engineer Path!

Today, we bring you a legendary GitHub project. It’s not a cool tool or framework, but a “learning map” that can truly change your career—jwasham/coding-interview-university. This computer science study plan, personally practiced by the author who successfully joined Amazon, boasts over 320,000 stars and 80,000 forks. It is undoubtedly an indispensable guide for anyone aspiring to join a top tech company and become an excellent software engineer.

Project Highlights

This project’s core value lies in providing a complete, systematic, and practice-oriented computer science learning path, specifically focused on technical interview preparation.

  • Comprehensive Knowledge System (Technical Perspective): It’s like a condensed CS textbook, covering everything from fundamental algorithm complexity (Big-O) analysis to various core data structures (arrays, linked lists, trees, graphs, hash tables), mainstream sorting and searching algorithms, and more advanced topics like dynamic programming, recursion, design patterns, processes and threads, caching, computer networks, and more. What’s even better, it also touches upon advanced concepts such as NP-completeness, bit manipulation, and Tries, ensuring you have a solid CS foundation.
  • Efficient Interview Preparation (Application Perspective): The project author explicitly states that this plan is designed to help you “avoid detours” and directly target the technical interview requirements of large tech companies (e.g., Amazon, Google, Facebook, Microsoft). It not only lists study topics but also provides specific learning resources (videos, books) and even includes non-technical but equally crucial job search guidance such as resume preparation, interview processes, and behavioral interview tips. It particularly emphasizes “coding practice while learning theory,” which is key to interview success.
  • Inspiration from Personal Success Stories: In the project’s README, the author shares his experience of successfully joining Amazon by following this plan, instilling strong confidence in all learners. He also generously shares mistakes he made during his learning journey and “pitfall avoidance guides” (such as using flashcards and practicing while learning), enabling subsequent learners to progress more efficiently.

How to Get Started / Links

Whether you are a CS student, looking to transition into software development, or aspiring to join a top tech company, this valuable resource can guide your way.

To begin your learning journey, please visit the project’s GitHub repository, carefully read the README file, and explore at your own pace:

GitHub Repository Link: https://github.com/jwasham/coding-interview-university

Call to Action

If you are preparing for software engineer interviews or want to systematically improve your computer science fundamentals, we highly recommend adding jwasham/coding-interview-university to your study list. Give it a Star, Fork it to your own repository, and start your learning plan! Perhaps the next success story will be yours!

Daily GitHub Project Recommendation: Flutter - One Codebase, Multi-Platform, Let Your Creativity Flourish on All Screens!

Hey, developers and tech enthusiasts! Today, we’re thrilled to introduce a star project that can truly revolutionize your development process—Flutter, from Google! If you’re still struggling with multi-platform development, Flutter is definitely your ultimate solution. It helps you build outstanding mobile, web, and desktop applications from a single codebase with amazing speed and beauty.

Project Highlights

Flutter’s power lies in being more than just a UI framework; it’s a complete SDK designed to help developers bring their ideas to life in the most efficient way:

  • True Cross-Platform King: Forget about building separate apps for iOS, Android, Web, macOS, Windows, and Linux. Flutter allows you to effortlessly deploy to all major platforms using a single Dart language codebase. This not only significantly saves development time but also ensures consistent brand visuals and user experience.
  • Extreme Beauty and Fluidity: Flutter’s rendering engines (Skia and Impeller) draw directly to the screen, offering pixel-perfect control. This enables you to create arbitrarily complex, smoothly animated, and visually stunning user interfaces. Whether it’s Material Design, Cupertino style, or a completely custom design, Flutter renders it perfectly.
  • Leap in Development Efficiency: Experience Flutter’s iconic “Hot Reload” feature! You can make changes in your code and instantly see the effects on the simulator or device, without restarting the application or losing its current state. This immediate feedback mechanism is undoubtedly a secret weapon for boosting development efficiency.
  • Native-like Performance: Flutter compiles your Dart code into native machine code for platforms like ARM, Intel x64, ensuring extremely fast application execution and performance comparable to native applications, bidding farewell to the stuttering impression of cross-platform apps.
  • Vast Ecosystem and Community: With over 170K stars, Flutter demonstrates its immense global influence and active community. Tens of thousands of ready-to-use packages and a robust plugin system allow you to easily integrate various functionalities and seamlessly interoperate with existing native code.

Technical Details and Use Cases

Flutter uses Dart language, which is optimized for client-side development, focusing on rapid development and high performance. It is particularly suitable for projects that require fast iteration, complex UI demands, and the desire to cover multiple user groups simultaneously. Whether it’s an MVP for a startup or a core application for a large enterprise, Flutter provides a stable and efficient solution.

How to Get Started

Eager to experience Flutter’s charm? Visit the official website to easily get started:

Call to Action

Flutter is more than just a tool; it’s a vibrant ecosystem. Whether you are an experienced developer or a newcomer to the world of programming, we strongly recommend you explore Flutter. Give it a Star, try building your next project with it, or join the community to contribute! Let’s conquer all screens with a single codebase!

Daily GitHub Project Recommendation: Tongyi DeepResearch - Your Intelligent Information Exploration Tool!

Hello, GitHub explorers! Today’s featured project is a masterpiece from Alibaba Damo Academy—Alibaba-NLP/DeepResearch. This is a groundbreaking Agentic Large Language Model (LLM) designed specifically for deep information exploration and long-duration tasks, aiming to be your powerful assistant in the complex ocean of information. It has currently garnered 8,831 stars and 647 forks, demonstrating its strong appeal and community attention.

Highlight Analysis

DeepResearch is more than just a language model; it’s a “researcher” with autonomous exploration capabilities. Its core value lies in solving complex problems that require long-duration, multi-step information searching and integration.

  • Agent Core Functionality: DeepResearch is designed as an agent capable of simulating human research processes. It can autonomously perform information retrieval, analysis, and reasoning, and is particularly adept at handling “long-duration” tasks that involve finding answers across multiple information sources. Whether it’s answering complex questions, conducting market research, or summarizing professional literature, it provides powerful support.
  • Technical Breakthroughs and Outstanding Performance: This model boasts a total of up to 30.5 billion parameters, but only activates 3.3 billion parameters during each inference. This efficient sparse activation mechanism allows it to maintain powerful capabilities while achieving superior performance. It has achieved industry-leading results in multiple Agentic search benchmarks (such as Humanity’s Last Exam, BrowserComp, etc.), proving its exceptional performance.
  • Innovative Training Paradigm: The project’s highlights also include its fully automated synthetic data generation pipeline, large-scale Agentic data continuous pre-training, and end-to-end reinforcement learning based on a customized Group Relative Policy Optimization framework, all of which endow the model with powerful learning and adaptation capabilities.
  • Flexible Inference Compatibility: It is compatible with two inference paradigms: ReAct and the “Heavy” mode based on IterResearch. Users can choose according to their needs, flexibly assessing the model’s inherent capabilities or unlocking its maximum performance potential.

Deep Dive

The project is primarily developed using Python, and it is recommended to use Python 3.10.0 for environment configuration. The DeepResearch model is available in a 30B-A3B size version, supporting an ultra-long context length of 128K, which means it can process extremely large amounts of information. For developers and researchers who need to build intelligent search agents, automate information collection, or create complex decision support systems, DeepResearch provides a solid foundation.

Experience Now

Eager to experience the charm of DeepResearch for yourself?

  1. Environment Setup: It is recommended to use conda or virtualenv to create an isolated Python 3.10.0 environment.
  2. Install Dependencies: Easily install required libraries by running pip install -r requirements.txt.
  3. Configuration and Execution: The project provides a detailed run_react_infer.sh script to guide you through configuring model paths, datasets, API keys, etc., after which you can run inference.

GitHub Repository Link: https://github.com/Alibaba-NLP/DeepResearch

You can also download the model from HuggingFace and ModelScope .

Get Started

As a cutting-edge AI Agent project, DeepResearch not only showcases the model’s immense potential in deep information exploration but also provides new ideas for future AI application development. If you are interested in large model agents, automated research, or advanced information processing, feel free to click the link to explore further, or even contribute your code or ideas to collectively advance the development of AI agent technology!