In the fast-evolving AI era, we thoroughly explain stable technical foundations.
This article walks you through the complete process of creating a Telegram bot, from finding BotFather and issuing the /newbot command to naming the bot, setting a unique username, retrieving the access token, and securely storing it in your environment variables.
This tutorial walks through creating a full‑featured code‑review Agent with Claude Agent SDK, covering installation, TypeScript setup, the SDK‑managed agent loop, structured JSON output, permission handling, sub‑agents, session management, hooks, custom MCP tools, cost tracking, and a production‑grade example.
The author created a file‑based Personal Brain OS stored in a Git repository, using a three‑layer context architecture, JSONL/YAML/Markdown formats, and custom agent skills to give AI continuous awareness of style, goals, contacts, and workflows without any database or API keys.
In this final lesson of a nine‑day Agent engineering series, the author integrates the full Agent Loop, tools, MCP, skills, RAG, context handling, multi‑turn dialogue, and multi‑agent coordination to create a runnable RSS news‑briefing Agent that fetches feeds in parallel, filters content with LLMs, summarizes articles, and outputs a markdown report.
This lesson explains how to extend a single‑agent system with multi‑agent collaboration, covering context isolation, Handoff and Router patterns, flat coordinator architecture, code examples, task decomposition, and practical run‑time demos for building complex AI workflows.
This tutorial shows how to equip an AI agent with long‑term memory using Retrieval‑Augmented Generation (RAG), covering the concepts of vector embeddings, FAISS indexing, building and querying a knowledge base, and providing complete Python code examples.
This tutorial shows how to extend the MiniManus agent framework with Skill support, explains why Skills are needed compared to plain MCP, details the Claude Skill specification, provides concrete command‑line operations, code implementations, and demonstrates Skill‑MCP collaboration through practical examples.
This tutorial explains how the Model Context Protocol (MCP) standardizes AI agent integration by replacing custom tool code with a JSON‑RPC based, auto‑discovered ecosystem, walks through configuration, core loading logic, code implementation, a runnable example, and compares MCP with traditional tool use.
This tutorial introduces the foundational Agent Loop that powers modern AI agents, explains why it is needed, breaks down its four core components, compares workflow‑based and agent‑based designs, and provides a minimal Python implementation with code, pitfalls, and a concrete RSS‑news use case.
This tutorial extends a minimal LLM Agent loop by introducing Tool Use (function calling) to give the agent actionable capabilities—searching the web, retrieving the current datetime, and performing mathematical calculations—while explaining the BaseTool architecture, registration process, system‑prompt adjustments, and practical execution examples.
