Focused on AI programming, sharing ready-to-use development efficiency solutions.
This guide walks through installing Certbot on Ubuntu, obtaining a Let’s Encrypt certificate for both the root and www domains in under three minutes, automatically configuring Nginx, enabling systemd‑based 90‑day renewal, and avoiding common pitfalls, with an optional acme.sh alternative.
The article introduces seven essential Model Context Protocol (MCP) servers—Filesystem, Git, Fetch, Memory, Sequential Thinking, Time, and MCP Apps—explaining their configurations, practical use cases, and security tips, showing how each can significantly boost AI workflow efficiency.
This guide walks through installing Claude Code, obtaining a DeepSeek V4 API key, configuring the CC Switch tool, verifying the model connection, and using the integrated model inside VSCode, while highlighting common pitfalls and cost considerations.
This article walks through turning a local MCP demo into a production‑grade server, covering project layout, uv + FastMCP setup, authentication options, error‑code handling, structured logging, Docker containerization with health checks and load balancing, and comprehensive testing using Inspector and pytest.
The article introduces the Model Context Protocol (MCP), an open standard that unifies AI‑to‑external‑system communication like a USB‑C connector, explains its core concepts and roles, walks through building a Python weather server with FastMCP, and outlines the growing ecosystem of MCP servers and apps.
The article explains why the old LangChain memory classes are deprecated, breaks down the new 1.x memory architecture into three independent components, and shows how to replace ConversationSummaryBufferMemory with a concise six‑line agent setup that supports multi‑user isolation, persistence, and summarization middleware.
The article compares LangChain, LangGraph, and Deep Agents, outlining their roles, core differences, strengths, and weaknesses, and then maps typical LLM‑agent development scenarios to the most suitable framework, providing a concise decision matrix for developers.
This article walks through a lightweight, runnable knowledge‑base question‑answering skeleton built with Deep Agents, explains its current capabilities and limitations, shows the project structure and core code, and outlines a step‑by‑step upgrade path toward a production‑grade RAG system.
The article explains how to transition Deep Agents from a local CLI debugging tool to production-ready callable services, comparing CLI, Python SDK, LangGraph service, and ACP, and provides practical code examples, deployment steps, and a pre‑launch checklist for secure enterprise integration.
The article explains how Deep Agents’ skills and the interrupt_on (HITL) configuration let teams embed professional procedures and require human review for high‑risk tool calls, detailing the file‑based skill format, appropriate use cases, and how permissions, sandboxing, and logging complete the security stack.
