4022 related articles

GitHub trending Aug 1: ByteDance's deer-flow SuperAgent, Microsoft's GenAI course, 3D generation, voice cloning, and privacy-first tools shape the AI landscape.

Fluree AI replaces traditional RAG by directly querying structured data, giving AI agents cited, verifiable, and permission-controlled enterprise context via MCP protocol integration.

Fluree AI replaces traditional RAG by querying structured data directly, giving AI agents cited, verifiable, and permission-controlled enterprise context via MCP protocol.

In-depth analysis of AI agent memory systems: examining whether current improvements represent real progress or just RAG repackaged, and what architectural changes are truly needed.

Microsoft open-sources agent-governance-toolkit covering all OWASP Agentic Top 10 risks through policy enforcement, zero-trust identity, execution sandboxing, and reliability engineering for production AI Agent deployment.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

Build high-quality AI projects on a budget. Learn how to use Ollama, Groq, Chroma, and other free open-source tools to build RAG systems and multi-Agent workflows from scratch.

From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.

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 comprehensive guide to AI-native application architecture: LLM inference, RAG retrieval (vector DB/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability — end-to-end.

RL3 is a zero-code, browser-based reinforcement learning platform featuring drag-and-drop environment design, visual reward configuration, and Q-learning/PPO training. Built by an indie developer over 15 months to make RL accessible to everyone.

An in-depth look at the core tech behind AI Agents: how the HNSW, IVF, and PQ vector search algorithms power RAG and long-term memory. Understand where a model's "memory" and "knowledge" come from.

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.

A comprehensive guide to modern AI-native system architecture: LLM reasoning, three RAG paradigms (vector/knowledge graph/BM25), Agents, MCP tool calling, AI gateways, and observability for enterprise AI.

A comprehensive guide to LangChain: core concepts, RAG applications, Agent development, version selection (0.3/1.0), and career opportunities for Java/Python developers entering LLM development.

An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.

Oragent (Dingyi ORA Agent) is an AI agent built for foreign trade, generating in-depth market analysis reports covering product selection, regulatory risks, and marketing calendars in just 5 minutes.

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.

Want to break into AI application development? This guide covers the full learning path — from Agents and RAG to Prompt Engineering — helping you master LLM engineering skills and land the job.

Open-source AI Agent tutorial project with 2600+ GitHub Stars covering multi-agent systems, memory, planning, and reasoning loops via Jupyter Notebooks for hands-on learning.