#LLM application
80 related articles
Tutorials·3 min
Hermes Agent Framework Practical Guide: Building AI Agents from Scratch
A detailed guide on ByteDance's Hermes Agent framework covering core features, deployment, custom tool development, and enterprise-grade AI Agent applications.
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Industry Insights·2 min
Two Major Career Paths in AI Large Language Models: Engineering Implementation vs. Algorithm Research
In-depth analysis of two core AI LLM career paths: engineering implementation vs. algorithm research. Compare education requirements, skills, and job prospects for programmers transitioning to AI.
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Tutorials·3 min
Can You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
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Tutorials·3 min
Hermes Agent in Practice: A Complete Breakdown from ReAct Loop to Autonomous Skill Evolution
Deep dive into Hermes Agent's four progressive cases: terminal ReAct loop, Feishu AI assistant, four-layer persistent memory, and three-stage Skill evolution with DeepSeek support.
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Tutorials·3 min
The Complete Guide to Claude Code Commands: 10 Core Commands & Practical Workflows
A systematic guide to Claude Code's 10 core commands, keyboard shortcuts, CLI parameters & 4 practical workflows covering context management, code review, model switching & cost optimization.
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Tutorials
Build a Claude Code Agentic OS in 3 St…
·3 min
Build a Claude Code Agentic OS in 3 Steps — Stop Using AI Like a Slot Machine
Learn how to transform Claude Code from a random prompting tool into a systematic Agentic OS through three layers: architecture, Obsidian memory, and an observability dashboard.
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Industry Insights
Deep Dive into Three Major LLM Career …
·3 min
Deep Dive into Three Major LLM Career Paths: Requirements, Tech Stacks, and Career Prospects
Deep analysis of three core LLM roles—Application Engineer, Development Engineer, and Algorithm Engineer—covering technical requirements, salary thresholds, and career prospects including RAG, fine-tuning, and inference deployment.
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Tutorials
LangGraph 0.5.3 + MCP Agent Developmen…
·2 min
LangGraph 0.5.3 + MCP Agent Development in Practice: Security Authentication & Deployment Guide
LangGraph 0.5.3 introduces MCP server security authentication and agent deployment solutions. Combined with Qwen3 models, it provides a complete production-grade AI agent development stack.
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Tutorials
Getting Started with LangChain: Core C…
·3 min
Getting Started with LangChain: Core Concepts and Hands-On Guide for LLM and Agent Development
A systematic guide to LangChain's core features, covering LLM vs. Agent concepts, unified interface design, multi-provider support, environment setup, and hands-on code examples for AI app development.
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Tech Frontiers
Generic Agent: A Self-Evolving AI Agen…
·3 min
Generic Agent: A Self-Evolving AI Agent Built with Just 3,000 Lines of Code
Generic Agent builds a self-evolving AI agent with just 3,000 lines of code, 9 atomic tools, and a five-layer memory architecture — using only one-sixth the tokens of competitors.
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Tutorials
Why Qwen3 Is the Best Open-Source Mode…
·2 min
Why Qwen3 Is the Best Open-Source Model for MCP Agent Development
Analysis of Qwen3's advantages for MCP agent development, comparing DeepSeek R1's lack of Function Calling, covering MoE architecture and thinking mode switching.
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Tutorials·2 min
Learning AI Large Language Models from Scratch: A Guide to Learning Paths, Hardware, and Programming Languages
A beginner's guide to learning AI large language models — covering learning paths, hardware requirements, Python essentials, and cloud services for learners at every level.
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Tutorials
Five Core Capabilities of GitHub Copil…
·2 min
Five Core Capabilities of GitHub Copilot's MCP Integration in JetBrains
Deep dive into GitHub Copilot's MCP integration in JetBrains IDEs: Agent Mode, Sampling model scheduling, MCP Prompts, Resources context injection, and Elicitation structured dialogue guidance.
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Tutorials·3 min
LangGraph Multi-Agent Architecture: Graph Structure Principles and Enterprise-Level Implementation Guide
Deep dive into LangGraph's core graph structure design, single and multi-agent collaboration patterns, MCP protocol integration, and Time Travel fault-tolerance, with enterprise-level hybrid multi-agent architecture implementation.
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Tutorials·3 min
Claude Agent SDK Python in Practice: Custom Tools, Multi-Turn Conversations & Hooks Security Control
A deep dive into Claude Agent SDK for Python: Query basics, custom Tools with MCP Server, CloudSdkClient multi-turn sessions, and Hooks security control with full code examples.
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Tutorials·2 min
Career Progression in AI/LLM Application Development: Four Stages from Beginner to 40K Monthly Salary
A systematic four-stage career path for AI/LLM application development: from RAG and Agent fundamentals to architecture design, helping developers transition to AI roles targeting 40K+ monthly salary.
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Tutorials·3 min
SubAgent Architecture: The Core Implementation for Context Isolation
Deep dive into SubAgent context isolation architecture, covering parent-child Agent roles, tool definitions, run_subagent implementation, and differences from TodoList and Agent Teams.
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Deep Dives·3 min
Claude Sub-agents vs Agent Teams: How to Choose Between Two AI Multi-Agent Collaboration Patterns
Deep comparison of Claude sub-agents vs agent teams: architecture differences, use cases, and real-world results. A Pokémon RPG case study shows how agent teams achieve 95%+ feature completeness.
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Tutorials·2 min
Complete Breakdown of Andrew Ng's LangChain Course: Five Core Components and Hands-On Guide
Deep dive into Andrew Ng and Harrison Chase's LangChain course, covering the five core components—Models, Prompts, Indexes, Chains, and Agents—to help developers master LLM app development.
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Tutorials·3 min
Complete Breakdown of Andrew Ng's Agent Course: A Practical Guide to the Five Core Modules of AI Agent Development
Deep dive into Andrew Ng's viral AI Agent course covering five core modules: Reflection, Planning, Tool Use, Multi-Agent Collaboration, and Memory, with practical learning paths for LLM agent development.
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