jeevan chand @jeevan36

@ThiruShobz Ott ?

@twtayaan Second and third GitHub links are not working .. can you check ?

@Pranj_Sometimes Terraform

This is a beginner-friendly guide for Terraform. If you need it, just drop “Terraform” in the comments and I’ll send it to you!

@sauravstwt K8

@dkare1009 Learn claude

Learn Claude Code properly, not randomly. 1 playlist. 13 videos. full system. Most people open Claude Code, try a few prompts, then quit. Not because it’s hard. Because there is no structure. This fixes that. Here’s the actual roadmap from the playlist: 1. Introduction and setup 2. System setup on your machine 3. Slash commands 4. Making real code changes 5. Context window management 6. Claude.md file 7. Spec-driven development 8. Plan mode and thinking 9. Custom slash commands 10. Skills 11. Subagents 12. GitHub workflows 13. Final usage patterns This is not random learning. This is how you move from trying Claude → to actually using it. What you will actually learn: → How to run Claude Code inside your system → How to control context instead of losing it → How to structure work using plan mode → How to use MCP, skills, and subagents properly → How to build real AI agents, not prompt chains ✦ This is layered learning Each video builds on the previous one If you want the full playlist: 1. Like this post 2. Comment “Learn Claude” 3. Connect with me, I will send it to your DMs

@dkare1009 LINUX

Stop wasting hours trying to learn Claude Code. I’ve already done it for you. With one list. Zero confusion. And no fluff. I’ve put together all the resource links to help you get started instantly: 1️⃣ Like & repost 2️⃣ Comment “LINUX” 3️⃣ Follow me so I can DM you the guide Save this list for later. Share it with a friend by ♻️ reposting this image..

@ajay_2512x Please share

@dkare1009 NCP

If your AI agent can’t use tools properly, you’re building it the wrong way. I just went through this MCP (Model Context Protocol) guidebook from Daily Dose of Data Science, and it’s not just theory. It actually shows how modern AI systems should be built. Here’s what makes this guide worth your time: 1. Clear fundamentals (no fluff) → What MCP really is and why it exists → The real problem with M × N integrations → How MCP simplifies AI + tool communication 2. Architecture that actually makes sense → Host, Client, Server explained simply → How AI apps connect with tools in a standard way → Why MCP acts like a universal connector for AI systems 3. Core building blocks you must understand → Tools (actions AI can execute) → Resources (data AI can read) → Prompts (structured workflows for AI behavior) 4. 11 practical MCP projects (this is the best part) → Build a 100% local MCP client → Create agentic RAG with fallback to web search → Build a financial analyst agent → Voice agents, deep research agents, and more → Even RAG over videos and audio analysis This is not just learning. This is how you start building real AI agents that interact with systems, data, and tools properly. If you’re: • AI engineer • Developer • Or building AI products You need to understand MCP. Because this is where AI systems are heading next. 📌 Save this post for later 🔁 Repost to help others learn To get it instantly: 1️⃣ Like & Repost 2️⃣ Comment “MCP” 3️⃣ Follow (so I can DM you the guide)

@dkare1009 Claude Code

Learn Claude Code 0% → 100% in 11 videos. 1 YouTube Playlist Most people try Claude Code once, then quit because it “looks technical”. This fixes that. This is a complete roadmap: 1. Setup and introduction 2. CLAUDE md and memory 3. Context handling 4. Tools and permissions 5. Planning and thinking 6. Slash commands 7. MCP servers 8. Subagents 9. GitHub integration 10. Final tips What you will actually learn: → How to set up Claude Code inside your IDE → How to make it remember your workflows → How to use MCP servers and subagents → How to build real AI agents, not chat prompts ✦ This is structured learning Each video builds on the previous one Most people: → Jump between random tutorials, → Copy prompts and, → Stay stuck only Few people: → Follow a system → Understand workflows → Build leverage If you want the full tutorial playlist: 1. Like this post 2. Comment “Claude Code” 3. Connect with me, I will send it to your DMs

@sauravstwt OSI model

@dkare1009 LLM

💥 Top 50 LLM Interview Questions & Answers Get ready for your next AI / ML / LLM interview with this power-packed Q&A guide covering: ✅ Prompt Engineering ✅ Fine-tuning & RAG ✅ Transformer Architecture ✅ Tokenization & Attention ✅ Real-world LLM Scenarios Perfect for anyone preparing for AI Engineer, ML Researcher, or LLM Developer roles. 🚀 1️⃣ Like & Repost 2️⃣ Comment “LLM” 3️⃣ Follow (so I can DM you the guide) #LLM #ArtificialIntelligence #MachineLearning #InterviewPrep #AIGuide #Giveaway #PromptEngineering #AICommunity

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@dkare1009 Google

Google just dropped a detailed technical guide on building AI agents. And honestly... this is the clearest signal yet of where products are heading. Not “AI features.” Not “copilots.” Agentic systems. After going through it, one thing stands out: We’re shifting from interfaces → workflows → autonomous systems As an AI Product Manager, this changes how I think about product design entirely. A few things this guide makes very explicit: • AI agents are not just LLM wrappers They’re systems built on models + tools + memory + orchestration • The real value is in multi-step execution → not single prompt → response flows • Grounding (RAG) is foundational → accuracy is a product requirement, not a nice-to-have • The future is multi-agent + multi-model systems → optimize for cost, latency, and reasoning — not just intelligence • Orchestration is the product → how your system plans, acts, and recovers is what users actually experience Curious how others are thinking about this are you still building features, or starting to design agents? To get it: 1. Follow me (So that I can DM) 2. Like & Repost 3. Reply "Google"

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@dkare1009 AI