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4 ways how AI systems communicate and coordinate. I've explained each one in detail below. 1. 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) • User submits a request: “Summarize today’s Slack messages.” • MCP Client receives input: Interface b

Your AI sucks because it’s stuck at Level 1. You can easily take it to Level 3. I've explained below. 𝗦𝘁𝗲𝗽 1 – 𝗕𝗮𝘀𝗶𝗰 𝗟𝗟𝗠 (𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴) • This is the simplest level of AI systems. • You give input text or a docu

I’m starting to build a series of AI agents designed to solve real-world problems. Imagine saying: ✈ “Find me the cheapest round-trip flight from Delhi to Bangalore next weekend, book it, and email me the ticket.” 🗞 “Summarize today’s top tech h

🧠 AI Agents: Explained for Non-Tech Builders A timeline breakdown of what they are, how they work, and why they matter (with real examples). (00:00–01:22) | LLMs: Smart but Passive Tools like ChatGPT and Claude are built on LLMs. They’re great at

Real Agentic AI is a multi-layered system, where each layer solves a specific challenge from reasoning to compliance: 1. LLM (Core Reasoning) – Handles language understanding and generation. Alone, not enterprise-ready. 2. RAG (Retrieval Layer) – G

𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻: 𝗧𝗵𝗲 𝗙𝗮𝘀𝘁𝗲𝘀𝘁-𝗚𝗿𝗼𝘄𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 LangChain is quickly emerging as the go to framework for developing AI agents and intelligent LLM-powered applications. In a short

Enterprise AI agents are systems, not simple prompts. Some teams use a single agent with tools. This works well for simple automation tasks. Structured work often uses agents in sequence. Each agent handles one clear stage. At scale, tools are cen

Sharing a selection of AI courses I've mastered some of them, and others are in the backlog. HuggingFace Agent Course - https://huggingface.co/learn/agents-course/en/unit0/introduction MCP with Anthropic - https://www.deeplearning.ai/short-course