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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

Hey, Is anyone building for LLM-native systems and AI agents? 1. LLM analytics & intelligence: Traditional tools like CleverTap or Google Analytics track events and drop-offs, but it won't work in conversational systems. When users interact with LLM

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

Everyone talks about AI agents. Very few people understand what’s actually happening under the hood. Here’s the vocabulary that shows up constantly when working with agent systems. First, the core ideas. An agent is software that observes informat