99 related articles

Learn AI Agent core principles from scratch: understand how Agents differ from LLMs, their execution mechanisms, why rule design matters, and find the right learning path for your goals.

A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.

Deep analysis of LLM agent long-term memory security threats, covering persistence, statefulness, and propagation of memory poisoning, with a six-stage lifecycle defense framework.

A beginner's guide to AI Agents: understand core principles, how Agents differ from LLMs, their execution mechanisms, and get tailored learning path recommendations.

A Reddit user tested Gemini 3.5 Pro in Arena and found it generates 20+ files with hundreds of lines of code per file in a single pass, with no lazy shortcuts or placeholders.

A recursive technical proposition: Can we build a "meta-Skill" that auto-transforms any Skill into a Dify workflow? This article dissects the boundary between deterministic orchestration and autonomous Agent decisions.

Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.
ChatGPT Work Deep Dive: The Cloud-Loca…
ChatGPT Work runs in the cloud on web/mobile but accesses local files on desktop — and they don't sync. A deep dive into the split design, UX tradeoffs, and broader AI agent challenges.

Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.

Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.

A complete guide to deploying Dify 1.8.0: Docker setup, environment config, five app types explained, and workflow-building tips for beginners.

Hands-on Qwen review: Alibaba's free AI platform offers image generation, video creation, web dev, voice/video chat & travel planning — rivaling ChatGPT Plus.

A developer's hands-on account of building a brief-to-storyboard video Agent: JSON errors, missing fields, pacing issues — and how JSON Schema, retry loops, and MCP tools solved them.
AI Agent Autonomous Repair Systems: Wh…
When AI Agents are authorized to autonomously repair production systems, what real risks lurk behind "pray-and-operate"? A deep dive into loss-of-control dangers and practical safety principles including least privilege, human-in-the-loop, and rollback.

Kimi K3 sets a new open-weight record at 2.8T params, GrokBuild pivots to local-first after a privacy crisis, Open Interpreter rewrites in Rust — a deep dive into five major AI coding agent developments and the shift toward harness transparency.

A four-stage AI Agent development roadmap: from core theory and ReAct paradigm to multi-agent collaboration and production deployment. Covers DeepSeek, Coze, Dify, and more.

A deep dive into Coze 3.0's multi-agent collaboration, covering project workspace architecture, credit economics, local tool integration, and a Dify comparison.
Anthropic Open-Sources CWC Workshops: …
Anthropic open-sources cwc-workshops on GitHub — a TypeScript-based, structured workshop covering Prompt design, Tool Use, and Agent orchestration to help developers master Claude integration.

A structured 4-week AI Agent learning roadmap: Week 1 covers LLMs & Prompt engineering, Week 2 ReAct paradigms, Week 3 RAG memory systems, Week 4 multi-agent architectures.

How can frontend engineers transition into AI development? This guide covers four agent development directions: RAG, workflow agents, vertical agents, and general-purpose agents — with framework picks like LangChain.js.