Github Top Repositories

🔥 Trending Repository: moltbot 📝 Description: Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞 🔗 Repository URL: https://github.com/moltbot/moltbot 🌐 Website: https://molt.bot 📖 Readme: https://github.com/moltbot/moltbot#readme 📊 Statistics: 🌟 Stars: 89.6K stars 👀 Watchers: 432 🍴 Forks: 12.2K forks 💻 Programming Languages: TypeScript - Swift - Kotlin - Shell - CSS - JavaScript 🏷️ Related Topics:

#ai #personal #assistant #own_your_data #crustacean #clawd #moltbot #molty

================================== 🧠 By: https://t.me/DataScienceM

200$ to 20k$ SOL Challenge! As promised, i will do another challenge for those who missed the previous one! Last one we completed in 6 days, let’s do this one even quicker! Join my free group Before closing 👇 https://t.me/+DAKLP7eUy9Y3ZjY0 #ad InsideAds

7 Golden Rules to Crack Data Science Interviews 🧑‍💻 1️⃣ Master the Fundamentals ⦁ Be clear on stats, ML algorithms, and probability ⦁ Brush up on SQL, Python, and data wrangling 2️⃣ Know Your Projects Deeply ⦁ Be ready to explain models, metrics, and business impact ⦁ Prepare for follow-up questions 3️⃣ Practice Case Studies & Product Thinking ⦁ Think beyond code — focus on solving real problems ⦁ Show how your solution helps the business 4️⃣ Explain Trade-offs ⦁ Why Random Forest vs. XGBoost? ⦁ Discuss bias-variance, precision-recall, etc. 5️⃣ Be Confident with Metrics ⦁ Accuracy isn’t enough — explain F1-score, ROC, AUC ⦁ Tie metrics to the business goal 6️⃣ Ask Clarifying Questions ⦁ Never rush into an answer ⦁ Clarify objective, constraints, and assumptions 7️⃣ Stay Updated & Curious ⦁ Follow latest tools (like LangChain, LLMs) ⦁ Share your learning journey on GitHub or blogs

❗️LISA HELPS EVERYONE EARN MONEY!$29,000 HE'S GIVING AWAY TODAY! Everyone can join his channel and make money! He gives away from $200 to $5.000 every day in his channel https://t.me/+HDFF3Mo_t68zNWQy ⚡️FREE ONLY FOR THE FIRST 500 SUBSCRIBERS! FURTHER ENTRY IS PAID! 👆👇 https://t.me/+HDFF3Mo_t68zNWQy

🔥 Trending Repository: Vision-Agents 📝 Description: Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses Stream's edge network for ultra-low latency. 🔗 Repository URL: https://github.com/GetStream/Vision-Agents 🌐 Website: https://visionagents.ai 📖 Readme: https://github.com/GetStream/Vision-Agents#readme 📊 Statistics: 🌟 Stars: 3.9K stars 👀 Watchers: 43 🍴 Forks: 347 forks 💻 Programming Languages: Python 🏷️ Related Topics:

#ai #realtime #tts #agents #stt #ai_agents #video_ai #voice_ai #vision_ai #agentic_ai #video_agents

================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: BambuStudio 📝 Description: PC Software for BambuLab and other 3D printers 🔗 Repository URL: https://github.com/bambulab/BambuStudio 📖 Readme: https://github.com/bambulab/BambuStudio#readme 📊 Statistics: 🌟 Stars: 3.7K stars 👀 Watchers: 62 🍴 Forks: 580 forks 💻 Programming Languages: C++ - C - JavaScript - HTML - Perl - CMake 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: PS2Recomp 📝 Description: Playstation 2 Static Recompiler & Runtime Tool to make native PC ports 🔗 Repository URL: https://github.com/ran-j/PS2Recomp 📖 Readme: https://github.com/ran-j/PS2Recomp#readme 📊 Statistics: 🌟 Stars: 1.2K stars 👀 Watchers: 52 🍴 Forks: 24 forks 💻 Programming Languages: C++ 🏷️ Related Topics:

#tool #reverse_engineering #ps2 #recompile #static_recompilation

================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: lobehub 📝 Description: The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction. 🔗 Repository URL: https://github.com/lobehub/lobehub 🌐 Website: https://lobehub.com 📖 Readme: https://github.com/lobehub/lobehub#readme 📊 Statistics: 🌟 Stars: 70.7K stars 👀 Watchers: 285 🍴 Forks: 14.5K forks 💻 Programming Languages: TypeScript - HTML - JavaScript - Shell - Gherkin - MDX 🏷️ Related Topics:

#agent #ai #mcp #gemini #openai #gpt #knowledge_base #claude #chatgpt #deepseek #agent_collaboration #agent_harness

================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: ingress-nginx 📝 Description: Ingress NGINX Controller for Kubernetes 🔗 Repository URL: https://github.com/kubernetes/ingress-nginx 🌐 Website: https://kubernetes.github.io/ingress-nginx/ 📖 Readme: https://github.com/kubernetes/ingress-nginx#readme 📊 Statistics: 🌟 Stars: 19.3K stars 👀 Watchers: 288 🍴 Forks: 8.5K forks 💻 Programming Languages: Go - Lua - Shell - Go Template - Dockerfile - Makefile 🏷️ Related Topics:

#nginx #kubernetes #ingress_controller

================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: kimi-cli 📝 Description: Kimi Code CLI is your next CLI agent. 🔗 Repository URL: https://github.com/MoonshotAI/kimi-cli 🌐 Website: https://moonshotai.github.io/kimi-cli/ 📖 Readme: https://github.com/MoonshotAI/kimi-cli#readme 📊 Statistics: 🌟 Stars: 4.3K stars 👀 Watchers: 31 🍴 Forks: 430 forks 💻 Programming Languages: Python 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: vault 📝 Description: A tool for secrets management, encryption as a service, and privileged access management 🔗 Repository URL: https://github.com/hashicorp/vault 🌐 Website: https://developer.hashicorp.com/vault 📖 Readme: https://github.com/hashicorp/vault#readme 📊 Statistics: 🌟 Stars: 33.9K stars 👀 Watchers: 779 🍴 Forks: 4.5K forks 💻 Programming Languages: Go - JavaScript - Handlebars - TypeScript - HCL - Shell 🏷️ Related Topics:

#go #vault #secrets

================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: pi-mono 📝 Description: AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods 🔗 Repository URL: https://github.com/badlogic/pi-mono 📖 Readme: https://github.com/badlogic/pi-mono#readme 📊 Statistics: 🌟 Stars: 2.5K stars 👀 Watchers: 11 🍴 Forks: 316 forks 💻 Programming Languages: TypeScript - JavaScript 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

Data Science Interview Questions with Answers Part-1 1. What is data science and how is it different from data analytics? Data science focuses on building predictive and decision-making systems using data. It uses statistics, machine learning, and domain knowledge to forecast outcomes or automate actions. Data analytics focuses on analyzing historical and current data to understand trends and performance. Analytics explains what happened and why. Data science focuses on what will happen next and what decision should be taken. 2. What are the key steps in a data science lifecycle? A data science lifecycle starts with clearly defining the business problem in measurable terms. Data is then collected from relevant sources and cleaned to handle missing values, errors, and inconsistencies. Exploratory data analysis is performed to understand patterns and relationships. Features are engineered to improve model performance. Models are trained and evaluated using suitable metrics. The best model is deployed and continuously monitored to handle data changes and performance drift. 3. What types of problems does data science solve? Data science solves prediction, classification, recommendation, optimization, and anomaly detection problems. Examples include predicting customer churn, detecting fraud, recommending products, forecasting demand, and optimizing pricing. These problems usually involve large data, uncertainty, and the need to make data-driven decisions at scale. 4. What skills does a data scientist need in real projects? A data scientist needs strong skills in statistics, probability, and machine learning. Programming skills in Python or similar languages are required for data processing and modeling. Data cleaning, feature engineering, and model evaluation are critical. Business understanding and communication skills are equally important to translate results into actionable insights. 5. What is the difference between structured and unstructured data? Structured data is organized in rows and columns with a fixed schema, such as tables in databases. Examples include sales records and customer data. Unstructured data does not follow a predefined format. Examples include text, images, audio, and videos. Structured data is easier to analyze, while unstructured data requires additional processing techniques. 6. What is exploratory data analysis and why do you do it first? Exploratory data analysis is the process of understanding data using summaries, statistics, and visual checks. It helps identify patterns, trends, outliers, and data quality issues. It is done first to avoid incorrect assumptions and to guide feature engineering and model selection. Good EDA reduces modeling errors later. 7. What are common data sources in real companies? Common data sources include relational databases, data warehouses, log files, APIs, third-party vendors, spreadsheets, and cloud storage systems. Companies also use data from applications, sensors, user interactions, and external platforms such as payment gateways or marketing tools. 8. What is feature engineering? Feature engineering is the process of creating new input variables from raw data to improve model performance. This includes transformations, aggregations, encoding categorical values, and creating time-based or behavioral features. Good features often have more impact on results than complex algorithms. 9. What is the difference between supervised and unsupervised learning? Supervised learning uses labeled data where the target outcome is known. It is used for prediction and classification tasks such as churn prediction or spam detection. Unsupervised learning works with unlabeled data and focuses on finding patterns or structure. It is used for clustering, segmentation, and anomaly detection.

🔥 Trending Repository: ai-data-science-team 📝 Description: An AI-powered data science team of agents to help you perform common data science tasks 10X faster. 🔗 Repository URL: https://github.com/business-science/ai-data-science-team 📖 Readme: https://github.com/business-science/ai-data-science-team#readme 📊 Statistics: 🌟 Stars: 3.9K stars 👀 Watchers: 78 🍴 Forks: 741 forks 💻 Programming Languages: Python 🏷️ Related Topics:

#data_science #machine_learning #ai #openai #gpt #copilot #agents #data_scientist #ml_engineering #ai_engineer #ai_engineering #ml_engineer #generative_ai

================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: video2x 📝 Description: A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. 🔗 Repository URL: https://github.com/k4yt3x/video2x 🌐 Website: https://docs.video2x.org 📖 Readme: https://github.com/k4yt3x/video2x#readme 📊 Statistics: 🌟 Stars: 17.9K stars 👀 Watchers: 173 🍴 Forks: 1.6K forks 💻 Programming Languages: C++ - CMake - Just - Python - Dockerfile - Shell - C 🏷️ Related Topics:

#machine_learning #vulkan #neural_networks #frame_interpolation #anime4k #rife #upscale_video #realcugan #realesrgan #super_resoluion

================================== 🧠 By: https://t.me/DataScienceM

🚨 XAU/USD | GOLD VIP SIGNALS 🚨 💥 Gold is not about luck — it’s about strategy. 📊 Clear entries. Controlled risk. Real execution. ━━━━━━━━━━━━━━ 🎯 Every signal includes: ✅ 1 Take Profit 🛑 1 Stop Loss ⚖️ 50 pips risk management ━━━━━━━━━━━━━━ 🆓 1 FREE signal every day 👑 VIP channel: 2–3 premium signals daily Subscribe NOW 👉 Forex Hollywood VIP #ad InsideAds

🔥 Trending Repository: conduit 📝 Description: Conduit React Native app 🔗 Repository URL: https://github.com/Psiphon-Inc/conduit 📖 Readme: https://github.com/Psiphon-Inc/conduit#readme 📊 Statistics: 🌟 Stars: 78 stars 👀 Watchers: 8 🍴 Forks: 37 forks 💻 Programming Languages: TypeScript - Java - Swift - Go - JavaScript - Makefile 🏷️ Related Topics: Not available ================================== 🧠 By: https://t.me/DataScienceM

🔥 Trending Repository: czkawka 📝 Description: Multi functional app to find duplicates, empty folders, similar images etc. 🔗 Repository URL: https://github.com/qarmin/czkawka 📖 Readme: https://github.com/qarmin/czkawka#readme 📊 Statistics: 🌟 Stars: 28.3K stars 👀 Watchers: 147 🍴 Forks: 925 forks 💻 Programming Languages: Rust - Fluent - Slint - Python - Just - Nix 🏷️ Related Topics:

#rust #gtk_rs #duplicates #cleaner #multiplatform #similar_images #similar_music #similar_videos

================================== 🧠 By: https://t.me/DataScienceM

Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment. I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion" Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention. High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments. 24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time. II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open. Low Initial Investment Risk. Bot development and testing incur significant costs. While rental fees are required, in the early stages of the project, the company prioritizes market expansion and brand awareness over short-term profits. If you are interested, please join my Telegram group for more information and leave a message: https://t.me/+lVKtdaI5vcQ1ZDA1