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学习资源 · GitHub

发现精选资源、教程、学习路线与编程实践项目。

实用 GitHub 项目

2,637 个项目
4,168星标+2024小时
SDE-Interview-and-Prep-Roadmap preview

欢迎来到 SDE 面试准备路线图!这个代码仓库不仅记录我的个人历程,也是一个共同学习的协作空间。在准备软件开发工程师(SDE)面试的过程中,我整理了一份全面的清单,帮助自己规划备考。

  • Structured Learning Path: Follow a well-organized roadmap covering key domains such as Data Structures, Algorithms, System Design, Operating Systems, Networking, Databases, and more.
  • Checklist-based Approach: Track your progress systematically with checklist-style items for each topic, ensuring thorough coverage of concepts and practical skills.
3,626星标+2024小时
awesome-design-skills preview

面向 Claude Design、Google Stitch、Codex、Cursor 等智能体工具及其他 AI 工具的 67 个精选 DESIGN.md 和 SKILL.md 设计技能文件列表

74,381星标+1724小时

一份精选的优秀 C++(或 C)框架、库、资源及其他亮眼内容清单。灵感来自 awesome-... 系列。

4,513星标+1724小时
awesome-claude-design preview

Awesome Claude Design:68 个可直接使用的设计系统灵感示例,采用 DESIGN.md 格式。放入一个示例,即可一次性搭建完整的 UI。

1,346星标+1624小时
awesome-reverse-engineered preview

Don't reverse engineer it twice. 17,000+ open-source tools built by reverse engineering closed apps, APIs, devices, protocols, file formats, games and AI tools. Search site + agent skill + MCP server, refreshed nightly.

awesome-opencode

awesome-opencode

10,967星标+1624小时
awesome-opencode preview

https://opencode.ai 的精选插件、主题、智能体、项目和资源列表。

学习资源JavaScriptCC0-1.0GitHub
3,030星标+1424小时
awesome-decomp-games preview

经过评分并自动刷新的游戏列表,收录反编译、重新编译和逆向分析的游戏:反编译版本、静态重新编译版本、源码移植版、引擎重制版和浏览器移植版。

学习资源PythonCC0-1.0GitHub

awesome-quant

wilsonfreitas

30,592星标+1424小时

一份面向量化从业者(量化金融)的超赞库、软件包与资源精选列表。

zphisher

htr-tech

17,228星标+1324小时
zphisher preview

一款包含 30 多个模板的自动化钓鱼工具。本工具仅用于教育目的!作者不对任何滥用本工具包的行为负责!

  • Latest and updated login pages.
  • Beginners friendly

IsaacLab

isaac-sim

8,635星标+1324小时
IsaacLab preview

支持多物理引擎/渲染器的统一机器人学习框架

  • Robots: A diverse collection of robots, from manipulators, quadrupeds, to humanoids, with more than 16 commonly available models.
  • Environments: Ready-to-train implementations of more than 30 environments, which can be trained with popular reinforcement learning frameworks such as RSL RL, SKRL, RL Games, or Stable Baselines. We also support multi-agent reinforcement learning.