Github Top Repositories
前往频道在 Telegram
Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
显示更多📈 Telegram 频道 Github Top Repositories 的分析概览
频道 Github Top Repositories (@githubre) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 13 288 名订阅者,在 教育 类别中位列第 15 339,并在 印度 地区排名第 32 388 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 13 288 名订阅者。
根据 11 六月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 383,过去 24 小时变化为 5,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 1.11%。内容发布后 24 小时内通常能获得 0.75% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 148 次浏览,首日通常累积 99 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 1。
- 主题关注点: 内容集中在 repository, fork, programming, statistic, description 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
凭借高频更新(最新数据采集于 12 六月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。
13 288
订阅者
+524 小时
+837 天
+38330 天
帖子存档
13 288
🔥 Trending Repository: skills
📝 Description: Skills Catalog for Codex
🔗 Repository URL: https://github.com/openai/skills
📖 Readme: https://github.com/openai/skills#readme
📊 Statistics:
🌟 Stars: 2.6K stars
👀 Watchers: 26
🍴 Forks: 166 forks
💻 Programming Languages: Python - Shell - JavaScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: review-prompts
📝 Description: AI review prompts
🔗 Repository URL: https://github.com/masoncl/review-prompts
📖 Readme: https://github.com/masoncl/review-prompts#readme
📊 Statistics:
🌟 Stars: 192 stars
👀 Watchers: 9
🍴 Forks: 29 forks
💻 Programming Languages: Python - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: rag-from-scratch
📝 Description: No description available
🔗 Repository URL: https://github.com/langchain-ai/rag-from-scratch
📖 Readme: https://github.com/langchain-ai/rag-from-scratch#readme
📊 Statistics:
🌟 Stars: 6.8K stars
👀 Watchers: 60
🍴 Forks: 1.8K forks
💻 Programming Languages: Jupyter Notebook
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: nanochat
📝 Description: The best ChatGPT that $100 can buy.
🔗 Repository URL: https://github.com/karpathy/nanochat
📖 Readme: https://github.com/karpathy/nanochat#readme
📊 Statistics:
🌟 Stars: 41.4K stars
👀 Watchers: 289
🍴 Forks: 5.4K forks
💻 Programming Languages: Python - Jupyter Notebook - HTML - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: qui
📝 Description: A fast, single-binary qBittorrent web UI: manage multiple instances, automate torrent workflows, and cross-seed across trackers.
🔗 Repository URL: https://github.com/autobrr/qui
🌐 Website: https://getqui.com
📖 Readme: https://github.com/autobrr/qui#readme
📊 Statistics:
🌟 Stars: 2.6K stars
👀 Watchers: 8
🍴 Forks: 74 forks
💻 Programming Languages: Go - TypeScript - CSS - Python - Makefile - HTML
🏷️ Related Topics:
#go #golang #qbittorrent #libtorrent #workflows #qbit #cross_seed #cross_seeding================================== 🧠 By: https://t.me/DataScienceM
13 288
Top 100 Data Science Interview Questions ✅
Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?
Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?
Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?
Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?
Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?
Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?
Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?
Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?
Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics?
13 288
🔥 Trending Repository: Stable-Video-Infinity
📝 Description: [ICLR 26] Stable Video Infinity: Infinite-Length Video Generation with Error Recycling
🔗 Repository URL: https://github.com/vita-epfl/Stable-Video-Infinity
🌐 Website: https://stable-video-infinity.github.io/homepage/
📖 Readme: https://github.com/vita-epfl/Stable-Video-Infinity#readme
📊 Statistics:
🌟 Stars: 1.6K stars
👀 Watchers: 30
🍴 Forks: 128 forks
💻 Programming Languages: Python - Shell
🏷️ Related Topics:
#dance_generation #long_video_generation #audio_driven_talking_face #video_diffusion_transformers #end_to_end_filming================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: prek
📝 Description: ⚡ Better `pre-commit`, re-engineered in Rust
🔗 Repository URL: https://github.com/j178/prek
🌐 Website: https://prek.j178.dev/
📖 Readme: https://github.com/j178/prek#readme
📊 Statistics:
🌟 Stars: 4.1K stars
👀 Watchers: 13
🍴 Forks: 126 forks
💻 Programming Languages: Rust
🏷️ Related Topics:
#git #pre_commit #git_hooks================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: CodexBar
📝 Description: Show usage stats for OpenAI Codex and Claude Code, without having to login.
🔗 Repository URL: https://github.com/steipete/CodexBar
🌐 Website: https://codexbar.app
📖 Readme: https://github.com/steipete/CodexBar#readme
📊 Statistics:
🌟 Stars: 3.6K stars
👀 Watchers: 14
🍴 Forks: 250 forks
💻 Programming Languages: Swift - Shell - JavaScript
🏷️ Related Topics:
#swift #ai #codex #claude_code================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: vibetunnel
📝 Description: Turn any browser into your terminal & command your agents on the go.
🔗 Repository URL: https://github.com/amantus-ai/vibetunnel
🌐 Website: https://vt.sh
📖 Readme: https://github.com/amantus-ai/vibetunnel#readme
📊 Statistics:
🌟 Stars: 3.4K stars
👀 Watchers: 11
🍴 Forks: 223 forks
💻 Programming Languages: TypeScript - Swift - HTML - Shell - JavaScript - Zig
🏷️ Related Topics:
#terminal #remote #vibecoding================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: calibre
📝 Description: The official source code repository for the calibre ebook manager
🔗 Repository URL: https://github.com/kovidgoyal/calibre
🌐 Website: https://calibre-ebook.com
📖 Readme: https://github.com/kovidgoyal/calibre#readme
📊 Statistics:
🌟 Stars: 23.5K stars
👀 Watchers: 385
🍴 Forks: 2.5K forks
💻 Programming Languages: Python - C - C++ - HTML - Shell - XSLT
🏷️ Related Topics:
#python #ebook #epub #kindle #ebook_manager #calibre #ebook_reader #ebooks #ebook_formats #epub_generation================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: Maestro
📝 Description: Agent Orchestration Command Center
🔗 Repository URL: https://github.com/pedramamini/Maestro
🌐 Website: https://RunMaestro.ai
📖 Readme: https://github.com/pedramamini/Maestro#readme
📊 Statistics:
🌟 Stars: 802 stars
👀 Watchers: 10
🍴 Forks: 108 forks
💻 Programming Languages: TypeScript
🏷️ Related Topics:
#opencode #codex #ai_agents #generative_ai #claude_code================================== 🧠 By: https://t.me/DataScienceM
13 288
Here: GitHub repository to learn AI Engineering.
It contains some of the best free courses, articles, tutorials, and videos on the following topics:
Mathematical foundation
Basics of AI and #ML
Deep Learning and specializations
Generative #AI
Large language models (#LLM)
Guides on #promptengineering
#RAG, #agents, and #MCP
See here: https://github.com/ashishps1/learn-ai-engineering
👉 @CODEPROGRAMMER
13 288
🔥 Trending Repository: cline
📝 Description: Autonomous coding agent right in your IDE, capable of creating/editing files, executing commands, using the browser, and more with your permission every step of the way.
🔗 Repository URL: https://github.com/cline/cline
🌐 Website: https://marketplace.visualstudio.com/items?itemName=saoudrizwan.claude-dev
📖 Readme: https://github.com/cline/cline#readme
📊 Statistics:
🌟 Stars: 57.3K stars
👀 Watchers: 266
🍴 Forks: 5.7K forks
💻 Programming Languages: TypeScript - Go - JavaScript - Python - Shell - CSS
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: flowsint
📝 Description: A modern platform for visual, flexible, and extensible graph-based investigations. For cybersecurity analysts and investigators.
🔗 Repository URL: https://github.com/reconurge/flowsint
🌐 Website: https://flowsint.io
📖 Readme: https://github.com/reconurge/flowsint#readme
📊 Statistics:
🌟 Stars: 2K stars
👀 Watchers: 25
🍴 Forks: 257 forks
💻 Programming Languages: TypeScript - Python - CSS - JavaScript - Makefile - Dockerfile
🏷️ Related Topics:
#python #osint #recon #investigation================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: mermaid-ascii
📝 Description: Render Mermaid graphs inside your terminal
🔗 Repository URL: https://github.com/AlexanderGrooff/mermaid-ascii
🌐 Website: https://mermaid-ascii.art/
📖 Readme: https://github.com/AlexanderGrooff/mermaid-ascii#readme
📊 Statistics:
🌟 Stars: 751 stars
👀 Watchers: 3
🍴 Forks: 35 forks
💻 Programming Languages: Go - CSS - JavaScript - Shell - Makefile - Nix - Dockerfile
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: termux-app
📝 Description: Termux - a terminal emulator application for Android OS extendible by variety of packages.
🔗 Repository URL: https://github.com/termux/termux-app
🌐 Website: https://f-droid.org/en/packages/com.termux
📖 Readme: https://github.com/termux/termux-app#readme
📊 Statistics:
🌟 Stars: 49.4K stars
👀 Watchers: 1.4k
🍴 Forks: 5.9K forks
💻 Programming Languages: Java - C++
🏷️ Related Topics:
#android #linux #terminal #termux #hacktoberfest================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: claude-plugins-official
📝 Description: Official, Anthropic-managed directory of high quality Claude Code Plugins.
🔗 Repository URL: https://github.com/anthropics/claude-plugins-official
🌐 Website: https://code.claude.com/docs/en/plugins
📖 Readme: https://github.com/anthropics/claude-plugins-official#readme
📊 Statistics:
🌟 Stars: 5.7K stars
👀 Watchers: 59
🍴 Forks: 570 forks
💻 Programming Languages: Shell - Python
🏷️ Related Topics:
#skills #mcp #claude_code================================== 🧠 By: https://t.me/DataScienceM
13 288
🔥 Trending Repository: 99
📝 Description: Neovim AI agent done right
🔗 Repository URL: https://github.com/ThePrimeagen/99
📖 Readme: https://github.com/ThePrimeagen/99#readme
📊 Statistics:
🌟 Stars: 1.9K stars
👀 Watchers: 26
🍴 Forks: 91 forks
💻 Programming Languages: Lua - Tree-sitter Query - Vim Script
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
13 288
LandingAI released a free course on Document AI. It teaches how to build document processing pipelines that extract text, tables, charts, and forms without losing the context of the markup.
The problem with classic OCR is that it "extracts letters", but breaks the meaning:
- tables lose their structure (including merged cells)
- the connections "chart ⬅️➡️ signature" fall apart
- the reading order in multi-column becomes a mess
The course shows how to build an agent-workflow that reads documents closer to how a human does it, through Agentic Document Extraction (ADE).
What's inside:
- why regular OCR fails on complex documents
- how layout detection + correct reading order preserve the structure
- how to parse PDF into Markdown/JSON and not lose the layout
- how to collect RAG with ADE and vector databases
- how to deploy event-driven document pipelines on AWS
3 hours, 6 practical code examples. Completely free.
https://www.deeplearning.ai/short-courses/document-ai-from-ocr-to-agentic-doc-extraction/
👉 @DataScienceN
现已上线!2025 年 Telegram 研究 — 年度关键洞察 
