Harish kumar

Harish kumarNov 11, 2026

🚨 Your AI agent is MORE than just an LLM. 5 building blocks explain how modern AI agents actually work. 👇 Most people talk about AI agents. But understanding what's happening behind the scenes is what separates AI users from people who can build AI systems. Here's the breakdown. 🧵 🧠 1. LLM = THE BRAIN The Large Language Model understands instructions, reasons through problems, and generates responses. But an LLM alone doesn't automatically know your private business data or have access to every tool. 📚 2. RAG = THE KNOWLEDGE LIBRARY Retrieval-Augmented Generation helps an LLM retrieve relevant information from external knowledge sources. Instead of relying only on its existing knowledge, it can use your documents and data to generate more grounded answers. 🔌 3. MCP = THE CONNECTION LAYER Model Context Protocol provides a standardized way for AI applications to connect with external tools and data sources. Think of it as a bridge between your AI application and the systems it needs to access. ⚡ 4. SKILLS = REUSABLE KNOW-HOW Skills package instructions and workflows that can be reused for specific tasks. They help an AI system follow a consistent process without requiring you to explain every step from scratch. 🤖 5. AI AGENT = THE BRAIN IN ACTION An AI agent combines a model with instructions, context, and available tools to work toward a goal. It can plan steps, use tools, evaluate results, and continue working when necessary. 🔥 PUT IT ALL TOGETHER: 🧠 LLM → Understands and reasons 📚 RAG → Retrieves relevant knowledge 🔌 MCP → Connects tools and data ⚡ Skills → Provides reusable workflows 🤖 AI Agent → Coordinates actions toward a goal Important: These components serve different purposes. Not every AI agent needs all five, and MCP itself doesn't make an application an agent. 📌 Save this infographic as your AI agent architecture cheat sheet. 🔄 Like, repost, comment, and save to help others understand AI. 👇 LWhich should I break down next: RAG vs. Agentic RAG, MCP, or AI Skills? Follow Harish Kumar for more AI & technology insights.

Harish kumarNov 10, 2026

🚨 Google has a collection of AI and tech courses worth exploring in 2026. You don't need to spend thousands on learning AI. Google offers beginner-friendly courses covering Generative AI, LLMs, Prompt Engineering, Machine Learning, Cybersecurity, and more. Here are 10 courses to add to your learning roadmap. 👇 📚 1. Introduction to Generative AI Learn what Generative AI is, how it works, and how to build AI applications using Google tools. 🔗 https://lnkd.in/eiFtRAUV 📚 2. Introduction to Large Language Models Understand LLMs, their applications, and how prompt tuning improves model performance. 🔗 https://lnkd.in/edr5JNvU 📚 3. Google Cybersecurity Learn cybersecurity fundamentals, SIEM tools, Python, Linux, and SQL. 🔗 https://lnkd.in/e_NrQpu9 📚 4. Introduction to Image Generation Explore diffusion models and understand the technology behind AI image generation. 🔗 https://lnkd.in/eDhXTAu2 📚 5. Google Prompting Essentials Learn practical prompting techniques to improve productivity, data analysis, and everyday work. 🔗 https://lnkd.in/e5B-gd_U 📚 6. Cloud Engineering Get hands-on experience with Google Cloud, IAM, networking, and Kubernetes. 🔗 https://lnkd.in/eVGcC8FM 📚 7. Machine Learning Specialization Learn machine learning with NumPy, scikit-learn, TensorFlow, neural networks, and decision trees. 🔗 https://lnkd.in/eHTkTxyu 📚 8. Introduction to Responsible AI Understand responsible AI, ethical AI development, and Google's AI principles. 🔗 https://lnkd.in/e2J4EzFx 📚 9. Google Data Analytics Capstone Apply your data analytics knowledge through a practical case study. 🔗 https://lnkd.in/e94A83mE 📚 10. Data Analysis with Python Learn Python libraries and techniques for analyzing and visualizing data. 🔗 https://lnkd.in/eDymjp9b 🔥 WHAT SHOULD YOU LEARN FIRST? If you're a beginner, follow this order: 1. Generative AI 2. Large Language Models 3. Prompt Engineering 4. Machine Learning 5. Data Analysis 6. Responsible AI Don't try to learn everything at once. Pick one skill. Complete the lessons. Build practical projects. Apply what you learn. That's how you turn free learning resources into valuable career skills. 📌 Save this post for your next learning session. ♻️ Repost this to help students, freshers, and professionals discover these resources. 💬 Which course are you starting with? Follow Harish Kumar for more AI tools, free learning resources, and career opportunities. #Google #ArtificialIntelligence #FreeCourses #GenerativeAI #MachineLearning

Harish kumarNov 11, 2026

🚨 How to clone yourself into Claude in just 48 hours. Imagine an AI assistant that knows your writing style, understands your preferences, remembers your workflows, and helps you get things done. Here's how to build one in 7 steps. 👇 1. Tell Claude who you are Share: → Who you are and what you do → Your goals and priorities → How you communicate → How you make decisions → What you expect from AI Use Claude's memory features to retain useful context and preferences. 2. Teach it your writing style Share your emails, LinkedIn posts, reports, and messages. Ask Claude to analyse your tone, vocabulary, sentence structure, and formatting. Create a reusable style guide so future content sounds more like you. 3. Build Projects, not endless chats Create separate Projects for: 📌 Content – Ideas, posts, writing guidelines 📌 Research – Reports, industry trends, analysis 📌 Business – Proposals, plans, meeting notes 📌 Learning – Courses, study plans, technical notes Add relevant files and instructions to each Project. 4. Connect your tools Explore available integrations: → Gmail → Google Drive → Calendar → Notion → Slack Also explore Claude in Chrome, computer use, and Claude Desktop. 5. Ask for finished work, not just answers Don't just ask for ideas. Turn them into complete deliverables: → Documents → Presentations → Spreadsheets → Research reports → Client proposals Give Claude clear instructions about the format, structure, and result you expect. 6. Create reusable Skills Turn repetitive tasks into repeatable workflows. Examples: → /weekly-report → /content-brief → /client-proposal Use Claude's Skills features to create and reuse workflows. 7. Schedule repetitive tasks Automate recurring tasks where available: → Monday briefings → Weekly reports → Daily research updates → Inbox summaries Let automation handle repetitive work while you focus on important decisions. Here's what you've built: → An AI that understands your preferences → An AI that follows your writing style → An AI that knows your workflows → An AI that helps turn ideas into finished work Your 48-hour challenge: Day 1: Teach Claude who you are and how you work. Day 2: Create Projects, add useful files, and build one repeatable workflow. Start small. Improve as you go. 📌 Save this roadmap. ♻️ Repost to help others work smarter with AI. 💬 Which step will you try first? Follow Harish Kumar for more AI tools and workflows. __ Bonus 🎁 Cyberman is the best place to get tech news, tips, tutorials and resources. Join today and receive: • AI Job Guide • AI Sidehustle Ideas • 70+ free AI courses and more 100% FREE 👉 cyberman.ai/join #ClaudeAI #AITools #ArtificialIntelligence #Productivity #AIWorkflow