Harish Kumar

🚨 MIT just released 10 FREE AI courses. Most people think AI is for engineers. They're wrong. AI literacy is becoming a leadership skill. The leaders who understand AI will make better decisions. The ones who don't? Will depend on vendors, consultants, and everyone else in the room. You don't need to learn coding. You need to understand: ✅ What AI can do ✅ What AI can't do ✅ Where AI fails ✅ What AI costs ✅ What AI should never automate That's the difference between leading AI adoption... ...and being led by it. Here are 10 free MIT AI courses worth taking: 1. AI 101. The vendor-proofing starter. https://lnkd.in/gyJz7whc 2. Artificial Intelligence. Separate real from hype. https://lnkd.in/ggneRvcZ 3. Foundation Models and Generative AI. Spot overselling. https://lnkd.in/gNwgbtaE 4. Introduction to Machine Learning. Fund the right bets. https://lnkd.in/gT5HcRs5 5. Understanding the World Through Data. Ask sharper questions. https://lnkd.in/gVdj_EhG 6. Introduction to Deep Learning. Know what actually costs millions. https://lnkd.in/gq8PwrnP 7. ML with Python. See if your team is building or spinning. https://lnkd.in/gUdHfAhx 8. How to AI Almost Anything. Find competitive whitespace. https://lnkd.in/ghfgKgsx 9. Introduction to Algorithms. Defend AI decisions under scrutiny. https://lnkd.in/g2P-3ptd 10. AI in K-12 Education. See transformation before it reaches you. https://lnkd.in/gs9Fesqy The biggest AI risk isn't being replaced. It's making decisions about AI without understanding it. Spend one hour. Learn enough to ask better questions. That alone puts you ahead of most leaders. 📌 Save this list. 🔄 Repost to help others learn AI. 💬 Which course would you start with? ➕ Follow Harish Kumar for more AI resources. #AI #ArtificialIntelligence #MIT #MachineLearning #GenerativeAI #Leadership #FutureOfWork #CareerGrowth #Technology #Learning

🚨 Stop paying for AI courses. The world's best AI education is already free. Google. Harvard. MIT. Microsoft. IBM. Here are 9 free AI courses that could save you thousands of dollars. 👇 Here’s the list: 1. AI for Everyone (DeepLearning.AI) https://lnkd.in/eDhC8kcd 2. Introduction to Generative AI (Google) https://lnkd.in/ddM4DY6S 3. Prompt Engineering for ChatGPT (Vanderbilt) https://lnkd.in/d-rCb-AM 4. Google AI Essentials https://lnkd.in/eVuje4Rf 5. Generative AI for Everyone (DeepLearning.AI) https://lnkd.in/g3KtK8vw 6. AI Foundations (IBM) https://lnkd.in/gXRQSGJU 7. CS50’s Introduction to AI with Python (Harvard) https://lnkd.in/dfZtsxDr 8. Artificial Intelligence (MIT OpenCourseWare) https://lnkd.in/eskDb_ap 9. Career Essentials in Generative AI (Microsoft + LinkedIn) https://lnkd.in/gg452Mvk Most people think learning AI requires expensive bootcamps. The biggest mistake people make? They spend months watching random AI videos... Instead of following a structured learning path. You don't need to finish all nine. Just pick one. Complete it. Apply what you learn. Repeat. That's how real AI skills are built. 📌 SAVE this list for your learning journey. 💬 COMMENT: Which course are you starting first? 🔄 REPOST to help someone learn AI for free. ❤️ LIKE if you believe world-class education should be accessible to everyone. 🚀 Follow Harish Kumar for practical AI resources, career tips, and free learning opportunities every week.

🚨 I don't understand why more people aren't using this Claude prompt. Most people think AI gives wrong answers because it's not smart enough. Wrong. The real problem? AI can sound confident even when it's guessing. This prompt fixes that. It forces Claude to prioritize truth and accuracy over sounding helpful. The result? ✅ Better answers ✅ More reliable research ✅ Fewer hallucinations ✅ Better fact-checking ✅ More trustworthy outputs Here's the exact prompt (save this now) 👇 1/ Open Claude settings → Click your profile picture in the bottom left corner. → Go to Settings. → Click General. → Find the "Instructions for Claude" field. 2/ Paste this full prompt "You are committed to truth and accuracy above everything else, including being helpful. A wrong answer delivered confidently is worse than no answer. Follow these 7 rules in every response: 1. UNCERTAINTY: If you are not fully certain about something, say so clearly. Use phrases like "I am not certain, but..." or "You may want to verify this...". Never state guesses as facts. 2. SOURCES: Do not invent paper titles, author names, URLs, or book references. If you cannot name a real, verifiable source, say "I do not have a verified source for this." 3. STATISTICS: Flag any number you are not 100 percent confident in. Say "approximately" and recommend I verify it from a primary source. 4. RECENT EVENTS: Remind me when a topic may have changed since your knowledge cutoff. Do not present outdated info as current. 5. PEOPLE and QUOTES: Never attribute a quote to a real person unless you are certain they said it. If unsure, say "I cannot confirm this quote is accurate." 6. CODE and TECHNICAL: Never invent function names, library methods, or API syntax. If unsure a function exists, tell me to verify it in the current docs. 7. LOGIC GAPS: Do not fill missing context with assumptions. If something is unclear, ask a clarifying question before answering. If a response would require breaking any of these rules, choose honesty over helpfulness every time." 3/ What this changes Claude will flag its own guesses instead of stating them as facts. It refuses to invent sources, quotes, or statistics. It tells you when it's unsure instead of confidently lying. It asks clarifying questions when context is missing. This works in every chat, forever. Copy the prompt, paste it in settings, and Claude stops lying to you. Have you tried this yet? Most people will scroll past this. The smart ones will copy it and instantly improve every conversation they have with AI. 💾 SAVE this prompt before you forget 🔁 REPOST to help others get better AI answers 💬 Have you ever caught an AI confidently making something up? 👇 Tell me in the comments ➕ Follow Harish Kumar for more AI, Career & Productivity tips #ClaudeAI #ArtificialIntelligence #AITools #ProductivityHacks

One Document. 50+ AI Agent Resources. Unlimited Learning 🤯 Most people waste time searching random tutorials. Smart people save curated lists like this 👇 I just found one of the best AI Agents collections on the internet. Saved it instantly. You should too. 📌📹 Videos: 1. LLM Introduction: https://lnkd.in/g2RFCi7N 2. LLMs from Scratch: https://lnkd.in/gXr2kchU 3. Agentic AI Overview (Stanford): https://lnkd.in/gD-FfAeJ 4. Building and Evaluating Agents: https://lnkd.in/gz8Wf6gp 5. Building Effective Agents: https://lnkd.in/gPnYaFDm 6. Building Agents with MCP: https://lnkd.in/gVVgq3nn 7. Building an Agent from Scratch: https://lnkd.in/gGMjCWUa 8. Philo Agents: https://lnkd.in/g_tYnxSh 🗂️ Repos 1. GenAI Agents: https://lnkd.in/gwPRaaVV 2. Microsoft's AI Agents for Beginners: https://lnkd.in/g3J-fNhb 3. Prompt Engineering Guide: https://lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: https://lnkd.in/dxaVF86w 5. AI Agents for Beginners: https://lnkd.in/g6D3vztJ 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: https://lnkd.in/d2dMACMj 8. Hands-On AI Engineering:https://lnkd.in/g-7ckgQr ... More 🗺️ Guides 1. Google's Agent Whitepaper: https://lnkd.in/gFvCfbSN 2. Google's Agent Companion: https://lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: https://lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: https://lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: https://lnkd.in/guRfXsFK 📚 Books 1. Understanding Deep Learning: https://lnkd.in/g8FyW_rE 2. Building an LLM from Scratch: https://lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: https://lnkd.in/gWUT2EXe . . . more 📜 Papers 1. ReAct: https://lnkd.in/gRBH3ZRq 2. Generative Agents: https://lnkd.in/gsDCUsWm. 3. Toolformer: https://lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: https://lnkd.in/gaK5CXzD. 5. Tree of Thoughts: https://lnkd.in/gRJdv_iU. 🏫 Courses 1.HuggingFace's Agent Course: https://lnkd.in/gmTftTXV 2. MCP with Anthropic: https://lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: https://lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: https://lnkd.in/gm9HR6_2 5. Agent Memory: https://lnkd.in/gNFpC542 . . . more 📂 FULL RESOURCE (Save this) 👉 https://lnkd.in/gd6FRGi5 💡 WHY THIS MATTERS AI is moving from chatbots → agents If you learn this now, you’re early. 💾 Save this (you’ll need it later) 🔁 Repost to help others ❤️ Like if this helped ➕ Follow Harish Kumar for AI & Career growth

🚨 Engineers don’t need another chatbot. They need answers they can actually build with. Most AI tools are great at sounding smart. But when you’re working on a real engineering problem, “sounds right” is not enough. You need: evidence-backed answers technical reasoning prior art and patent visibility outputs you can actually use in research and decision-making That’s what stood out to me about PatSnap Eureka Engineering. It’s built for engineers and R&D teams who need more than surface-level responses. Instead of just giving generic answers, it helps you: ✅ explore technical solutions faster ✅ analyze patents and scientific literature in one place ✅ validate ideas with source-backed reasoning ✅ turn research into structured outputs you can actually use The bigger lesson? AI becomes much more valuable when it’s trained for the workflow you actually work in. For engineers, that means moving beyond general AI tools and using systems built for technical problem-solving. If you work in engineering, R&D, innovation, or technical research, this is worth exploring. 🔗 Check it out here:👇 https://lnkd.in/g6WUXWdg 💬 What’s the biggest bottleneck in your technical research workflow today? #AI #Engineering #RND #Innovation #PatSnap #PatSnapEureka #ArtificialIntelligence #Productivity #Research