🔥 Trending Repository: claude-skills
📝 Description: 65 Specialized Skills for Full-Stack Developers. Transform Claude Code into your expert pair programmer.
🔗 Repository URL: https://github.com/Jeffallan/claude-skills
📖 Readme: https://github.com/Jeffallan/claude-skills#readme
📊 Statistics:
🌟 Stars: 498 stars
👀 Watchers: 6
🍴 Forks: 56 forks
💻 Programming Languages: Python - JavaScript - HTML - Astro - Shell - Makefile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: 65 Specialized Skills for Full-Stack Developers. Transform Claude Code into your expert pair programmer.
🔗 Repository URL: https://github.com/Jeffallan/claude-skills
📖 Readme: https://github.com/Jeffallan/claude-skills#readme
📊 Statistics:
🌟 Stars: 498 stars
👀 Watchers: 6
🍴 Forks: 56 forks
💻 Programming Languages: Python - JavaScript - HTML - Astro - Shell - Makefile
🏷️ Related Topics:
#ai_agents #claude #claude_code #claude_skills #claude_marketplace
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
🔹 DATA SCIENCE – INTERVIEW REVISION SHEET*
*1️⃣ What is Data Science?*
> “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.”
Difference from Data Analytics:
- Data Analytics → past & present (what/why)
- Data Science → future & automation (what will happen)
*2️⃣ Data Science Lifecycle (Very Important)*
1. Business problem understanding
2. Data collection
3. Data cleaning & preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature engineering
6. Model building
7. Model evaluation
8. Deployment & monitoring
Interview line:
> “I always start from business understanding, not the model.”
*3️⃣ Data Types*
- Structured → tables, SQL
- Semi-structured → JSON, logs
- Unstructured → text, images
*4️⃣ Statistics You MUST Know*
- Central tendency: Mean, Median (use when outliers exist)
- Spread: Variance, Standard deviation
- Correlation ≠ causation
- Normal distribution
- Skewness (income → right skewed)
*5️⃣ Data Cleaning & Preprocessing*
Steps you should say in interviews:
1. Handle missing values
2. Remove duplicates
3. Treat outliers
4. Encode categorical variables
5. Scale numerical data
Scaling:
- Min-Max → bounded range
- Standardization → normal distribution
*6️⃣ Feature Engineering (Interview Favorite)*
> “Feature engineering is creating meaningful input variables that improve model performance.”
Examples:
- Extract month from date
- Create customer lifetime value
- Binning age groups
*7️⃣ Machine Learning Basics*
- Supervised learning: Regression, Classification
- Unsupervised learning: Clustering, Dimensionality reduction
*8️⃣ Common Algorithms (Know WHEN to use)*
- Regression: Linear regression → continuous output
- Classification: Logistic regression, Decision tree, Random forest, SVM
- Unsupervised: K-Means → segmentation, PCA → dimensionality reduction
*9️⃣ Overfitting vs Underfitting*
- Overfitting → model memorizes training data
- Underfitting → model too simple
Fixes:
- Regularization
- More data
- Cross-validation
*🔟 Model Evaluation Metrics*
- Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC
- Regression: MAE, RMSE
Interview line:
> “Metric selection depends on business problem.”
*1️⃣1️⃣ Imbalanced Data Techniques*
- Class weighting
- Oversampling / undersampling
- SMOTE
- Metric preference: Precision, Recall, F1, ROC-AUC
*1️⃣2️⃣ Python for Data Science*
Core libraries:
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
Must know:
- loc vs iloc
- Groupby
- Vectorization
*1️⃣3️⃣ Model Deployment (Basic Understanding)*
- Batch prediction
- Real-time prediction
- Model monitoring
- Model drift
Interview line:
> “Models must be monitored because data changes over time.”
*1️⃣4️⃣ Explain Your Project (Template)*
> “The goal was _. I cleaned the data using _. I performed EDA to identify _. I built _ model and evaluated using _. The final outcome was _.”
*1️⃣5️⃣ HR-Style Data Science Answers*
Why data science?
> “I enjoy solving complex problems using data and building models that automate decisions.”
Biggest challenge:
“Handling messy real-world data.”
Strength:
“Strong foundation in statistics and ML.”
*🔥 LAST-DAY INTERVIEW TIPS*
- Explain intuition, not math
- Don’t jump to algorithms immediately
- Always connect model → business value
- Say assumptions clearly
*1️⃣ What is Data Science?*
> “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.”
Difference from Data Analytics:
- Data Analytics → past & present (what/why)
- Data Science → future & automation (what will happen)
*2️⃣ Data Science Lifecycle (Very Important)*
1. Business problem understanding
2. Data collection
3. Data cleaning & preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature engineering
6. Model building
7. Model evaluation
8. Deployment & monitoring
Interview line:
> “I always start from business understanding, not the model.”
*3️⃣ Data Types*
- Structured → tables, SQL
- Semi-structured → JSON, logs
- Unstructured → text, images
*4️⃣ Statistics You MUST Know*
- Central tendency: Mean, Median (use when outliers exist)
- Spread: Variance, Standard deviation
- Correlation ≠ causation
- Normal distribution
- Skewness (income → right skewed)
*5️⃣ Data Cleaning & Preprocessing*
Steps you should say in interviews:
1. Handle missing values
2. Remove duplicates
3. Treat outliers
4. Encode categorical variables
5. Scale numerical data
Scaling:
- Min-Max → bounded range
- Standardization → normal distribution
*6️⃣ Feature Engineering (Interview Favorite)*
> “Feature engineering is creating meaningful input variables that improve model performance.”
Examples:
- Extract month from date
- Create customer lifetime value
- Binning age groups
*7️⃣ Machine Learning Basics*
- Supervised learning: Regression, Classification
- Unsupervised learning: Clustering, Dimensionality reduction
*8️⃣ Common Algorithms (Know WHEN to use)*
- Regression: Linear regression → continuous output
- Classification: Logistic regression, Decision tree, Random forest, SVM
- Unsupervised: K-Means → segmentation, PCA → dimensionality reduction
*9️⃣ Overfitting vs Underfitting*
- Overfitting → model memorizes training data
- Underfitting → model too simple
Fixes:
- Regularization
- More data
- Cross-validation
*🔟 Model Evaluation Metrics*
- Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC
- Regression: MAE, RMSE
Interview line:
> “Metric selection depends on business problem.”
*1️⃣1️⃣ Imbalanced Data Techniques*
- Class weighting
- Oversampling / undersampling
- SMOTE
- Metric preference: Precision, Recall, F1, ROC-AUC
*1️⃣2️⃣ Python for Data Science*
Core libraries:
- NumPy
- Pandas
- Matplotlib / Seaborn
- Scikit-learn
Must know:
- loc vs iloc
- Groupby
- Vectorization
*1️⃣3️⃣ Model Deployment (Basic Understanding)*
- Batch prediction
- Real-time prediction
- Model monitoring
- Model drift
Interview line:
> “Models must be monitored because data changes over time.”
*1️⃣4️⃣ Explain Your Project (Template)*
> “The goal was _. I cleaned the data using _. I performed EDA to identify _. I built _ model and evaluated using _. The final outcome was _.”
*1️⃣5️⃣ HR-Style Data Science Answers*
Why data science?
> “I enjoy solving complex problems using data and building models that automate decisions.”
Biggest challenge:
“Handling messy real-world data.”
Strength:
“Strong foundation in statistics and ML.”
*🔥 LAST-DAY INTERVIEW TIPS*
- Explain intuition, not math
- Don’t jump to algorithms immediately
- Always connect model → business value
- Say assumptions clearly
❤4
🔥 Trending Repository: Personal_AI_Infrastructure
📝 Description: Agentic AI Infrastructure for magnifying HUMAN capabilities.
🔗 Repository URL: https://github.com/danielmiessler/Personal_AI_Infrastructure
📖 Readme: https://github.com/danielmiessler/Personal_AI_Infrastructure#readme
📊 Statistics:
🌟 Stars: 7.2K stars
👀 Watchers: 120
🍴 Forks: 1.1K forks
💻 Programming Languages: TypeScript - Vue - Python - Shell - CSS - Handlebars
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Agentic AI Infrastructure for magnifying HUMAN capabilities.
🔗 Repository URL: https://github.com/danielmiessler/Personal_AI_Infrastructure
📖 Readme: https://github.com/danielmiessler/Personal_AI_Infrastructure#readme
📊 Statistics:
🌟 Stars: 7.2K stars
👀 Watchers: 120
🍴 Forks: 1.1K forks
💻 Programming Languages: TypeScript - Vue - Python - Shell - CSS - Handlebars
🏷️ Related Topics:
#productivity #ai #humans #augmentation
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: rowboat
📝 Description: Open-source AI coworker, with memory
🔗 Repository URL: https://github.com/rowboatlabs/rowboat
🌐 Website: https://www.rowboatlabs.com
📖 Readme: https://github.com/rowboatlabs/rowboat#readme
📊 Statistics:
🌟 Stars: 4.9K stars
👀 Watchers: 38
🍴 Forks: 388 forks
💻 Programming Languages: TypeScript - CSS - MDX - Python - JavaScript - Dockerfile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Open-source AI coworker, with memory
🔗 Repository URL: https://github.com/rowboatlabs/rowboat
🌐 Website: https://www.rowboatlabs.com
📖 Readme: https://github.com/rowboatlabs/rowboat#readme
📊 Statistics:
🌟 Stars: 4.9K stars
👀 Watchers: 38
🍴 Forks: 388 forks
💻 Programming Languages: TypeScript - CSS - MDX - Python - JavaScript - Dockerfile
🏷️ Related Topics:
#productivity #open_source #ai #orchestration #multiagent #agents #ai_agents #llm #generative_ai #chatgpt #opeani #ai_agents_automation #claude_code #agents_sdk #claude_cowork
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: cinny
📝 Description: Yet another matrix client
🔗 Repository URL: https://github.com/cinnyapp/cinny
🌐 Website: https://cinny.in
📖 Readme: https://github.com/cinnyapp/cinny#readme
📊 Statistics:
🌟 Stars: 2.8K stars
👀 Watchers: 19
🍴 Forks: 385 forks
💻 Programming Languages: TypeScript
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Yet another matrix client
🔗 Repository URL: https://github.com/cinnyapp/cinny
🌐 Website: https://cinny.in
📖 Readme: https://github.com/cinnyapp/cinny#readme
📊 Statistics:
🌟 Stars: 2.8K stars
👀 Watchers: 19
🍴 Forks: 385 forks
💻 Programming Languages: TypeScript
🏷️ Related Topics:
#client #reactjs #matrix #hacktoberfest #matrix_client #matrix_org #cinny #cinnyapp
==================================
🧠 By: https://t.iss.one/DataScienceM
The biggest surprise for our valued audience: we are offering 40 paid courses completely free.
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We use a spam/flood protection system to ensure that all registered users are real people.
🔥 Trending Repository: aios-core
📝 Description: Synkra AIOS: AI-Orchestrated System for Full Stack Development - Core Framework v4.0
🔗 Repository URL: https://github.com/SynkraAI/aios-core
🌐 Website: https://github.com/allfluence/aios-core
📖 Readme: https://github.com/SynkraAI/aios-core#readme
📊 Statistics:
🌟 Stars: 291 stars
👀 Watchers: 29
🍴 Forks: 171 forks
💻 Programming Languages: JavaScript - Python - Shell - Handlebars - PLpgSQL - CSS
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Synkra AIOS: AI-Orchestrated System for Full Stack Development - Core Framework v4.0
🔗 Repository URL: https://github.com/SynkraAI/aios-core
🌐 Website: https://github.com/allfluence/aios-core
📖 Readme: https://github.com/SynkraAI/aios-core#readme
📊 Statistics:
🌟 Stars: 291 stars
👀 Watchers: 29
🍴 Forks: 171 forks
💻 Programming Languages: JavaScript - Python - Shell - Handlebars - PLpgSQL - CSS
🏷️ Related Topics:
#nodejs #cli #development #automation #framework #typescript #ai #orchestration #fullstack #agents #ai_agents #claude
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: MTProxy
📝 Description: No description available
🔗 Repository URL: https://github.com/TelegramMessenger/MTProxy
📖 Readme: https://github.com/TelegramMessenger/MTProxy#readme
📊 Statistics:
🌟 Stars: 5.8K stars
👀 Watchers: 233
🍴 Forks: 994 forks
💻 Programming Languages: C - Makefile
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: No description available
🔗 Repository URL: https://github.com/TelegramMessenger/MTProxy
📖 Readme: https://github.com/TelegramMessenger/MTProxy#readme
📊 Statistics:
🌟 Stars: 5.8K stars
👀 Watchers: 233
🍴 Forks: 994 forks
💻 Programming Languages: C - Makefile
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: superhuman
📝 Description: No description available
🔗 Repository URL: https://github.com/google-deepmind/superhuman
📖 Readme: https://github.com/google-deepmind/superhuman#readme
📊 Statistics:
🌟 Stars: 268 stars
👀 Watchers: 14
🍴 Forks: 21 forks
💻 Programming Languages: TeX
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: No description available
🔗 Repository URL: https://github.com/google-deepmind/superhuman
📖 Readme: https://github.com/google-deepmind/superhuman#readme
📊 Statistics:
🌟 Stars: 268 stars
👀 Watchers: 14
🍴 Forks: 21 forks
💻 Programming Languages: TeX
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
🔥 Trending Repository: slime
📝 Description: slime is an LLM post-training framework for RL Scaling.
🔗 Repository URL: https://github.com/THUDM/slime
🌐 Website: https://thudm.github.io/slime
📖 Readme: https://github.com/THUDM/slime#readme
📊 Statistics:
🌟 Stars: 4K stars
👀 Watchers: 16
🍴 Forks: 523 forks
💻 Programming Languages: Python - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: slime is an LLM post-training framework for RL Scaling.
🔗 Repository URL: https://github.com/THUDM/slime
🌐 Website: https://thudm.github.io/slime
📖 Readme: https://github.com/THUDM/slime#readme
📊 Statistics:
🌟 Stars: 4K stars
👀 Watchers: 16
🍴 Forks: 523 forks
💻 Programming Languages: Python - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
❤2
🔥 Trending Repository: DebugSwift
📝 Description: A toolkit to make debugging iOS applications easier 🚀
🔗 Repository URL: https://github.com/DebugSwift/DebugSwift
📖 Readme: https://github.com/DebugSwift/DebugSwift#readme
📊 Statistics:
🌟 Stars: 1.3K stars
👀 Watchers: 7
🍴 Forks: 118 forks
💻 Programming Languages: Swift
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A toolkit to make debugging iOS applications easier 🚀
🔗 Repository URL: https://github.com/DebugSwift/DebugSwift
📖 Readme: https://github.com/DebugSwift/DebugSwift#readme
📊 Statistics:
🌟 Stars: 1.3K stars
👀 Watchers: 7
🍴 Forks: 118 forks
💻 Programming Languages: Swift
🏷️ Related Topics:
#debugger #swift #debugging #ui #networking #log #analytics #analysis #view #cocoapods #sandbox #uikit #debug #performance_analysis #crashlytics #hacktoberfest #leak_detection #logs_analysis #layout_debugger #swift6
==================================
🧠 By: https://t.iss.one/DataScienceM
❤3
SQL 𝗢𝗿𝗱𝗲𝗿 𝗢𝗳 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻
1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).
𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓
↬ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
↬ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
https://t.iss.one/DataScienceM
1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).
𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓
↬ Find the second-highest salary:
SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);
↬ Find duplicate records:
SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;
https://t.iss.one/DataScienceM
❤1🔥1
🔥 Trending Repository: zvec
📝 Description: A lightweight, lightning-fast, in-process vector database
🔗 Repository URL: https://github.com/alibaba/zvec
🌐 Website: https://zvec.org/en/
📖 Readme: https://github.com/alibaba/zvec#readme
📊 Statistics:
🌟 Stars: 967 stars
👀 Watchers: 4
🍴 Forks: 56 forks
💻 Programming Languages: C++ - SWIG - Python - C - CMake - ANTLR
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A lightweight, lightning-fast, in-process vector database
🔗 Repository URL: https://github.com/alibaba/zvec
🌐 Website: https://zvec.org/en/
📖 Readme: https://github.com/alibaba/zvec#readme
📊 Statistics:
🌟 Stars: 967 stars
👀 Watchers: 4
🍴 Forks: 56 forks
💻 Programming Languages: C++ - SWIG - Python - C - CMake - ANTLR
🏷️ Related Topics:
#embedded_database #rag #vector_search #ann_search #vectordb
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: wifi-densepose
📝 Description: Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation system that enables real-time full-body tracking through walls using commodity mesh routers
🔗 Repository URL: https://github.com/ruvnet/wifi-densepose
📖 Readme: https://github.com/ruvnet/wifi-densepose#readme
📊 Statistics:
🌟 Stars: 6K stars
👀 Watchers: 39
🍴 Forks: 544 forks
💻 Programming Languages: Python - Rust - JavaScript - Shell - HTML - CSS
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Production-ready implementation of InvisPose - a revolutionary WiFi-based dense human pose estimation system that enables real-time full-body tracking through walls using commodity mesh routers
🔗 Repository URL: https://github.com/ruvnet/wifi-densepose
📖 Readme: https://github.com/ruvnet/wifi-densepose#readme
📊 Statistics:
🌟 Stars: 6K stars
👀 Watchers: 39
🍴 Forks: 544 forks
💻 Programming Languages: Python - Rust - JavaScript - Shell - HTML - CSS
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: unstract
📝 Description: No-code LLM Platform to launch APIs and ETL Pipelines to structure unstructured documents
🔗 Repository URL: https://github.com/Zipstack/unstract
🌐 Website: https://unstract.com
📖 Readme: https://github.com/Zipstack/unstract#readme
📊 Statistics:
🌟 Stars: 6.2K stars
👀 Watchers: 46
🍴 Forks: 588 forks
💻 Programming Languages: Python - JavaScript - Shell - CSS
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: No-code LLM Platform to launch APIs and ETL Pipelines to structure unstructured documents
🔗 Repository URL: https://github.com/Zipstack/unstract
🌐 Website: https://unstract.com
📖 Readme: https://github.com/Zipstack/unstract#readme
📊 Statistics:
🌟 Stars: 6.2K stars
👀 Watchers: 46
🍴 Forks: 588 forks
💻 Programming Languages: Python - JavaScript - Shell - CSS
🏷️ Related Topics:
#unstructured_data #etl_pipeline #llm_platform
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: letta-code
📝 Description: The memory-first coding agent
🔗 Repository URL: https://github.com/letta-ai/letta-code
🌐 Website: https://docs.letta.com/letta-code
📖 Readme: https://github.com/letta-ai/letta-code#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 7
🍴 Forks: 133 forks
💻 Programming Languages: TypeScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: The memory-first coding agent
🔗 Repository URL: https://github.com/letta-ai/letta-code
🌐 Website: https://docs.letta.com/letta-code
📖 Readme: https://github.com/letta-ai/letta-code#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 7
🍴 Forks: 133 forks
💻 Programming Languages: TypeScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
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📝 Description: The Ruby Programming Language
🔗 Repository URL: https://github.com/ruby/ruby
🌐 Website: https://www.ruby-lang.org/
📖 Readme: https://github.com/ruby/ruby#readme
📊 Statistics:
🌟 Stars: 23.3K stars
👀 Watchers: 1.1k
🍴 Forks: 5.6K forks
💻 Programming Languages: Ruby - C - Rust - C++ - Yacc - HTML
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: The Ruby Programming Language
🔗 Repository URL: https://github.com/ruby/ruby
🌐 Website: https://www.ruby-lang.org/
📖 Readme: https://github.com/ruby/ruby#readme
📊 Statistics:
🌟 Stars: 23.3K stars
👀 Watchers: 1.1k
🍴 Forks: 5.6K forks
💻 Programming Languages: Ruby - C - Rust - C++ - Yacc - HTML
🏷️ Related Topics:
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==================================
🧠 By: https://t.iss.one/DataScienceM
❤1
🔥 Trending Repository: gogcli
📝 Description: Google Suite CLI: Gmail, GCal, GDrive, GContacts.
🔗 Repository URL: https://github.com/steipete/gogcli
🌐 Website: https://gogcli.sh
📖 Readme: https://github.com/steipete/gogcli#readme
📊 Statistics:
🌟 Stars: 2.7K stars
👀 Watchers: 19
🍴 Forks: 242 forks
💻 Programming Languages: Go - HTML - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Google Suite CLI: Gmail, GCal, GDrive, GContacts.
🔗 Repository URL: https://github.com/steipete/gogcli
🌐 Website: https://gogcli.sh
📖 Readme: https://github.com/steipete/gogcli#readme
📊 Statistics:
🌟 Stars: 2.7K stars
👀 Watchers: 19
🍴 Forks: 242 forks
💻 Programming Languages: Go - HTML - Shell
🏷️ Related Topics:
#google #gmail #gdrive #gcal #gcontacts
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: moonshine
📝 Description: Fast and accurate automatic speech recognition (ASR) for edge devices
🔗 Repository URL: https://github.com/moonshine-ai/moonshine
📖 Readme: https://github.com/moonshine-ai/moonshine#readme
📊 Statistics:
🌟 Stars: 3.6K stars
👀 Watchers: 43
🍴 Forks: 175 forks
💻 Programming Languages: C - C++ - Python - Swift - Java - Jupyter Notebook
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Fast and accurate automatic speech recognition (ASR) for edge devices
🔗 Repository URL: https://github.com/moonshine-ai/moonshine
📖 Readme: https://github.com/moonshine-ai/moonshine#readme
📊 Statistics:
🌟 Stars: 3.6K stars
👀 Watchers: 43
🍴 Forks: 175 forks
💻 Programming Languages: C - C++ - Python - Swift - Java - Jupyter Notebook
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: brave-browser
📝 Description: Brave browser for Android, iOS, Linux, macOS, Windows.
🔗 Repository URL: https://github.com/brave/brave-browser
🌐 Website: https://brave.com
📖 Readme: https://github.com/brave/brave-browser#readme
📊 Statistics:
🌟 Stars: 21.5K stars
👀 Watchers: 388
🍴 Forks: 3K forks
💻 Programming Languages: Not available
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Brave browser for Android, iOS, Linux, macOS, Windows.
🔗 Repository URL: https://github.com/brave/brave-browser
🌐 Website: https://brave.com
📖 Readme: https://github.com/brave/brave-browser#readme
📊 Statistics:
🌟 Stars: 21.5K stars
👀 Watchers: 388
🍴 Forks: 3K forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#windows #macos #linux #browser #chromium #brave
==================================
🧠 By: https://t.iss.one/DataScienceM
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