Train Memory Curves, Smart Prioritization, Flexible Review, Code Smarter!
训练记忆曲线，智能评估优先级，灵活复习，更聪明地刷题！
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After Installation: A
ratebutton will appear at the bottom-right corner of LeetCode problem pages (draggable to adjust position). After solving a problem, click the button to rate your mastery level (1-5 points). The system will intelligently schedule reviews based on your rating. -
Open Plugin Homepage: Click the LMS icon in your browser toolbar to view today's problems for review. The system automatically calculates memory retention rate for each problem based on the forgetting curve, prioritizing problems you're likely to forget.
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Flexible Review Volume: Adjust your daily review count through the gear buttons on the plugin homepage to fit your schedule. The FSRS algorithm supports intermittent learning - it intelligently adjusts the review plan even after a few days' break.
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Complete Reviews: Choose either method:
- Click ✓ directly in the plugin popup to mark as reviewed
- Click the problem to enter LeetCode page and complete review via the
ratebutton
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Problem Management: In the Problems tab, view all problems, add notes, search/filter, and export notes as Markdown files.
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Cloud Sync (Optional): Configure Nutstore WebDAV in Settings to enable multi-device data synchronization for reviewing anywhere, anytime.
💡 Core Concept: Transform knowledge from short-term to long-term memory through scientific spaced repetition!
- 安装插件后：在 LeetCode/力扣题目页面右下角会出现
rate按钮（可拖动调整位置）。完成题目后点击按钮，为掌握程度评分（1-5分），系统会根据你的评分智能安排复习时间。 - 打开插件主页：点击浏览器工具栏的 LMS 图标，查看今日待复习题目。系统根据遗忘曲线自动计算每道题的记忆保持率，优先展示容易遗忘的题目。
- 灵活调整复习量：通过插件主页的齿轮按钮调整每日复习数量，适应你的时间安排。FSRS 算法支持间歇学习，即使中断几天也能智能调整复习计划。
- 完成复习：两种方式任选其一：
- 在插件弹窗中直接点击 ✓ 标记已复习
- 点击题目进入 LeetCode 页面，通过
rate按钮评分完成复习
- 题目管理：在 Problems 标签页查看所有题目，支持添加笔记、搜索筛选、导出笔记为 Markdown 文件。
- 云同步（可选）：Settings 中配置坚果云 WebDAV，实现多设备数据同步，随时随地复习。
💡 核心理念：通过科学的间隔重复，让知识从短期记忆转化为长期记忆！
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| 任务/功能 | 状态 | 备注 |
|---|---|---|
| 多设备数据云同步 | ✅ 已完成 | Edge、Chrome |
| 监控提醒 | ✅ 已完成 | bilibili、youtube |
| url添加力扣题目 | ✅ 已完成 | 配合 IDE 刷题，工位摸鱼专用 |
| url添加自定义卡片 | ✅ 已完成 | 用于记录面试手撕题、其他刷题网站用户暂时替代方案 |
| 提供笔记功能 | ✅ 已完成 | 题目列表中新增笔记按钮，支持导出所有笔记为Markdown |
| 收集Anki fsrs 训练数据 | ✅ 已完成 | 待用于测试fsrs官方端口训练 |
| 接入Anki fsrs官方训练端口 | ✅ 已完成 | 目前仅支持本地复习记录训练（云同步用户可能存在影响） |
| 扩展webdav云同步服务 | ✅ 已完成 | 接入坚果云，无上限记录题目数据 |
| 支持语言切换 | ❌ 待完成 | 待完成 |
| 不同网站题目数据源切换 | ❌ 待完成 | 待完成（目前仅支持力扣国际站和**站，待兼容洛谷等） |
| 兼容火狐 | ❌ 待完成 | 待完成 |
兼容ctrl + enter |
❌ 待完成 | 目前优先级较低 |
| Task/Feature | Status | Remarks |
|---|---|---|
| Multi-device cloud sync | ✳️ Completed | Edge, Chrome |
| Monitoring reminders | ✳️ Completed | bilibili, youtube |
| Add LeetCode URL | ✳️ Completed | For IDE coding practice, perfect for working |
| Add custom card URL | ✳️ Completed | For recording interview problems, alternative for other coding websites |
| Provide note-taking feature | ✳️ Completed | Add note button in problem list, support exporting all notes to Markdown |
| Collect Anki FSRS training data | ✳️ Completed | To be used for testing FSRS official training endpoint |
| Integrate Anki FSRS official training endpoint | ✳️ Completed | Currently supports training with local review records (may affect cloud sync users) |
| Expand webdav cloud sync service | ✳️ Completed | integrated with Nutstore |
| Support language switching | ❌ Pending | Pending completion |
| Switch data sources for different websites | ❌ Pending | Pending completion (currently only supports LeetCode international and Chinese sites, to be compatible with Luogu, etc.) |
| Compatibility with Firefox | ❌ Pending | Pending completion |
Compatibility with ctrl + enter |
❌ Pending | Lower priority for now |
We welcome every user to try LMS! If you find it helpful, please give our GitHub repository a Star - it's the greatest support for our work. If you encounter any issues or find bugs during use, feel free to submit an Issue and we'll address it as soon as possible.
我们欢迎每一位用户试用LMS！如果你觉得它对你有帮助，请为我们的GitHub仓库点一个Star，这将是对我们工作的最大支持。如果你在使用过程中遇到任何问题或发现bug，欢迎随时提交Issue，我们会尽快为你解决。
This project is based on PMCA (Practice Makes Code Accepted), with improvements to its codebase. While maintaining the core concept of spaced repetition learning, we have optimized it specifically for time-constrained review scenarios. The improvements include smarter algorithm implementation for priority assessment and more flexible user interaction logic to help users make the most of their limited study time.
本项目基于 PMCA (Practice Makes Code Accepted) 的代码开发，在保持重复复习的核心理念的同时，我们特别针对有限时间内的复习场景进行了优化。改进包括更智能的优先级评估算法实现，以及更灵活的使用交互逻辑，帮助用户在有限的学习时间内获得最大收益。