🔥 Trending Repository: openclaw
📝 Denoscription: Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
🔗 Repository URL: https://github.com/openclaw/openclaw
🌐 Website: https://openclaw.ai
📖 Readme: https://github.com/openclaw/openclaw#readme
📊 Statistics:
🌟 Stars: 106K stars
👀 Watchers: 506
🍴 Forks: 15K forks
💻 Programming Languages: TypeScript - Swift - Kotlin - Shell - CSS - JavaScript
🏷️ Related Topics:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
🔗 Repository URL: https://github.com/openclaw/openclaw
🌐 Website: https://openclaw.ai
📖 Readme: https://github.com/openclaw/openclaw#readme
📊 Statistics:
🌟 Stars: 106K stars
👀 Watchers: 506
🍴 Forks: 15K forks
💻 Programming Languages: TypeScript - Swift - Kotlin - Shell - CSS - JavaScript
🏷️ Related Topics:
#ai #personal #assistant #own_your_data #crustacean #molty #openclaw
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
❤1
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
The problem with classic OCR is that it "extracts letters", but breaks the meaning:
- tables lose their structure (including merged cells)
- the connections "chart
- 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/
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🔥 Trending Repository: 99
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: claude-plugins-official
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: termux-app
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: mermaid-ascii
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: flowsint
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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
==================================
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🔥 Trending Repository: cline
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
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
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
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GitHub
GitHub - ashishps1/learn-ai-engineering: Learn AI and LLMs from scratch using free resources
Learn AI and LLMs from scratch using free resources - ashishps1/learn-ai-engineering
❤4
🔥 Trending Repository: Maestro
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: calibre
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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
==================================
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🔥 Trending Repository: vibetunnel
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: CodexBar
📝 Denoscription: 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: 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://news.1rj.ru/str/DataScienceM
🔥 Trending Repository: prek
📝 Denoscription: ⚡ 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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: ⚡ 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
==================================
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🔥 Trending Repository: Stable-Video-Infinity
📝 Denoscription: [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:
==================================
🧠 By: https://news.1rj.ru/str/DataScienceM
📝 Denoscription: [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
==================================
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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?
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?
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