Back

Hands down the simplest explanation of AI agents using LLMs, memory, and tools. A user sends an input → the system (agent) builds a prompt and may call tools and memory-search (RAG) → agent decides and builds an answer → the answer is returned to th

🚨 BREAKING: Anthropic just dropped the tutorial on "Building AI Agents with Claude Agent SDK" Here's what it covers: > Agent Loop Gather context → Take action → Verify work → Repeat. Your agent searches files, executes tasks, checks its output, t

Greg Isenberg just shared 23 MCP STARTUP IDEAS TO BUILD IN 2025 (ai agents/ai/mcp ideas) and its amazing: "1. PostMortemGuy – when your app breaks (bug, outage), MCP agent traces every log, commit, and Slack message. Full incident report in seconds.

AI Agents now have muscle memory. This Python SDK records agent tool-calling patterns, replays them for repeated tasks, and falls back to agent mode for edge cases. 100% Opensource. Read more here: https://www.theunwindai.com/p/muscle-memory-for-a

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 friends, I’ve been building something close to my heart — a *portfolio project* that reimagines how AI can work as your startup team. Introducing *AgentFlow* — a *virtual office of autonomous AI agents* that think, plan, and collaborate like a le

Deconstructing How Agentic AI Actually Works We’ve all experienced what Large Language Models can do — but Agentic AI is the real leap forward. Instead of just generating responses, it can understand goals, make decisions, and take action on its own

Most of us rely on prompting to get the work done through ChatGPT. Even I used to follow this technique until one of my friends taught me how to master Context Engineering. Here's how it works: Prompting vs Context Engineering Prompting is about a

Most non-tech people learning AI don’t get this. I've explained it in a simple way below. 1. 𝗨𝘀𝗲𝗿 Everything starts with the 𝗨𝘀𝗲𝗿. • The user wants something done • Example: “𝘍𝘪𝘯𝘥 𝘵𝘩𝘦 𝘣𝘦𝘴𝘵 𝘭𝘢𝘱𝘵𝘰𝘱 𝘶𝘯𝘥𝘦𝘳 $1000” or “𝘞𝘳�

Most people overlook these basics of AI Agents. I've explained it in a very simple way below. 1. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 An AI system that observes its environment, information, makes decisions, and takes actions to achieve a goal. 2. 𝗟𝗟𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲