#LLM agent
2416 related articles
·7 min
AI Agent Runs a Fully Automated Chess Channel: A Real-World Practice of Combining LLMs with Engines
A German engineer built a fully automated chess YouTube channel with an AI Agent, combining LLMs and chess engines to auto-generate explainer videos nightly, reaching 500K views. Here's the tech architecture, tool design, and real costs.
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·5 min
Getting Started with LangChain: LLM Invocation, Agent Practice, and Environment Setup Guide
Starting from the three limitations of LLMs, this guide systematically explains LangChain's core positioning, environment setup, API key prep, model init, and the message system. Learn init_chat_model and AIMessage/HumanMessage/SystemMessage.
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·3 min
How to Choose a Practical LLM/RAG/AI Agent Course: Four Key Criteria
Learn how to pick the best LLM, RAG, and AI Agent courses. Discover 4 key criteria for hands-on AI learning and top resources for developers.
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·3 min
From Raw LLMs to Agents: A Complete Guide to Enterprise-Grade AI Development
Prompt engineering and RAG are just the basics. Real enterprise AI runs on Agents. Explore the 4 stages of LLM deployment, Agent core capabilities, and industry trends.
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·7 min
Getting Started with LangChain 1.3: Core Concepts of LLMs and Agents
An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.
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·7 min
Tencent DiscoBench: The First Benchmark for Evaluating LLM Search Agents' Ambiguity Clarification Capabilities
Tencent Hunyuan and Tsinghua jointly release DiscoBench, the first benchmark evaluating search agents' dynamic ambiguity clarification. Covering 463 ambiguity instances across 11 domains, it reveals real weaknesses of mainstream LLMs.
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·7 min
LangChain Hands-On Guide: From LLM Calls to Agent Development
Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.
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·2 min
Andrew Ng on AI Agent Planning Pattern: How LLMs Autonomously Create Execution Plans
Andrew Ng explains the AI Agent Planning Design Pattern: how LLMs autonomously create step-by-step execution plans using tools, with real-world examples and current limitations.
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Building a Coding Agent with LLM: A De…
·3 min
Building a Coding Agent with LLM: A Deep Dive into Simon Willison's llm-coding-agent Experiment
Simon Willison built llm-coding-agent — an open-source Claude Code-style agent — using just two prompts and TDD. Explore its tool design, bootstrapped dev process, and real-world test results.
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Payload-Less Skills: The Hidden Supply…
·3 min
Payload-Less Skills: The Hidden Supply Chain Threat Targeting LLM Agents
Payload-Less Skills are a stealthy new attack targeting LLM agent supply chains — no malicious code required. Learn how they work and how to defend against them.
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·6 min
What Is Vibe Coding? A Complete Breakdown of the LLM + Agent + Workflow Three-Layer Architecture
Vibe Coding, coined by ex-Tesla AI Director Karpathy, redefines AI programming. This article breaks down the LLM + Agent + Workflow three-layer architecture.
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·4 min
LLM Job Interviews: Multi-Agent and Harness Engineering Are Now Must-Know Topics
Deep analysis of LLM job interview essentials: Multi-Agent architecture, Harness engineering, Agent Loop, sandbox isolation, and memory management with career transition tips.
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·2 min
mini-SWE-agent Roulette Mode: Why Randomly Switching Between LLMs Actually Performs Better
SWE-agent team finds mini-SWE-agent randomly switching between GPT-5 and Claude Sonnet 4 outscores either model alone on SWE-bench. Exploring the diversity hypothesis behind Roulette Mode.
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·2 min
LangChain + MCP Agent Development: A Practical Guide to LLM Selection and Common Pitfalls
A deep dive into LLM selection for LangChain and MCP agent development, comparing DeepSeek V3/R1 vs Qwen3 on Function Calling and MCP support with practical tips.
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·1 min
OpenAgent: A Deep Dive into the Open-Source AI Personal Assistant Built on LLM + RAG
Deep dive into OpenAgent, an open-source AI assistant featuring Computer Use, Browser Use, and Coding Agent capabilities, built on LLM + RAG + Agent Loops.
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·2 min
CreateNow Agent Configuration Tutorial: One-Click Integration with DeepSeek and Other Major LLMs
Step-by-step guide to configuring CreateNow agents with DeepSeek, Kimi, and Xiaomi LLMs. Covers one-click setup, custom model integration, and API Key acquisition for building AI digital employees.
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·3 min
LangChain from Beginner to Agent Development: A Complete Learning Path for LLM Application Development
A systematic guide to LangChain LLM application development, covering environment setup, core components (RAG, Chain, Memory), and Agent development to help developers master LLM app building.
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Tutorials·3 min
Agent Tuning: A Complete Guide to Training LLMs with Agent Capabilities
A deep dive into Agent Tuning principles and practices, covering why Agent training is needed, the evolution from Prompt to RAG to Agent, development workflows, and cost assessment for private deployment.
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Tutorials·1 min
AI Agent Learning Roadmap: A Complete Guide from LLM Fundamentals to Enterprise-Level Project Implementation
A systematic AI Agent learning roadmap covering Python setup, Prompt Engineering, RAG, LangChain, multi-Agent collaboration, with enterprise medical consultation system case study and phased learning plan.
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Tutorials·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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