93 related articles

Crew is an open-source AI agent collaboration framework whose core idea is to build a "Stack Overflow" for agents—letting multiple agents share experience and accumulate knowledge, shifting from optimizing single agents to building evolving teams.

A comprehensive look at n8n, the open-source workflow automation platform: core features, 500+ node ecosystem, AI Agent and RAG integration, plus a fast learning path.

Torn over your capstone topic? This article analyzes the academic value, feasibility, and innovation potential of a Multi-agent Debate system to help AIML students decide.

More teams are adopting multi-model tiered scheduling. AI gateways solve cross-vendor API management, automatic fallback, and cost tracking — but add a new abstraction layer. Learn when a gateway is worth it.

Build production-grade AI Agents with a pure Go stack using ByteDance's Eino framework. A deep dive into seven core capabilities: multi-Agent orchestration, long-task execution, command approval, RAG, MCP, Skills, and database reporting.

Andrew Ng partners with Anthropic to launch a hands-on Claude Code course, revealing its simple architecture, local security edge, and core context methodology across three cases: RAG chatbot, Jupyter analysis, and Figma-to-frontend.

GitHub trending project exercises-dataset features 433 fitness exercises with target muscles, equipment types, instructions, and animation demos—ideal for fitness apps, AI coaches, and RAG systems.

Deep dive into the three-layer architecture of AI persistent memory systems—storage, management, and retrieval—with an in-depth comparison of Mem0, Zep, and ContextNest to help developers choose the right memory solution for AI Agents.

Prompt engineering and RAG can no longer meet enterprise digital transformation needs—AI Agents are the key. This article breaks down the four evolutionary stages of large model deployment and the four major Agent commercial tracks.

ECC is an agent optimization framework for AI coding assistants like Claude Code, Cursor, and Codex, enhancing them with skills, memory, security, and research-first development capabilities.

Poor RAG retrieval? The root cause often lies in the Embedding model. This article explores why fine-tuning embedding models is necessary, the limits of general Embeddings, and where Embedding fine-tuning fits in RAG optimization.

Tencent Cloud open-sources TencentDB Agent Memory — a fully local AI Agent memory system with a 4-tier progressive pipeline, zero external API dependencies, and 8,100+ GitHub Stars. Ideal for finance, healthcare, and privacy-sensitive use cases.

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.

Model capabilities are converging, making inference cost and scalability the new focus of AI competition. A deep analysis of AI infrastructure's core layers.

Databricks tech lead Sandy shares a five-pillar framework for production-grade AI Agents—evaluation, observability, data foundation, orchestration, and governance—with a £85K retail banking failure case to bridge the demo-to-production gap.

A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.

A complete guide to Dify local deployment: from Docker environment setup, source code pulling, and container startup to first access. Build a private AI app development platform across Linux, Windows, and Mac for fast enterprise AI deployment.

A systematic four-stage roadmap for AI Agent development: fundamentals, core principles, enhancement, and real-world deployment. Build complete Agent skills.

Prompt engineering is more than messaging AI. This guide breaks down the four core functions of prompts, the six-step prompt engineering process, and key limitations to help you build the right foundation.

An in-depth walkthrough of deploying Dify 1.8.0 and building applications: three-step Docker deployment, five app types compared, and Workflow vs Chatflow use cases—build enterprise AI apps with zero code.