313 related articles

Deep dive into MCP (Model Context Protocol): clarifying the three-layer relationship between MCP, Function Calling, and Agents, covering protocol roles, call flows, transport choices, and production security for AI developers.

Deep dive into the Cocos Creator AI Agent Plugin: three-level context compression, tool approval, multi-agent parallelism, and DeepSeek-powered Mario scene building — all in one test.

Ditch complex workflow nodes. Use Agent Skill packs to dynamically load AI capabilities, build stable intelligent automation, and understand RAG, LLM limits, and Scale Agent plugin setup.

The open-source project "Interview System" offers 204 RAG interview questions, 12 architecture approaches, and deep analysis of 6 failure modes. Prepare systematically for RAG engineer roles.

A proven 4-step roadmap to becoming an AI Agent engineer: stable LLM calls, tool use (RAG + Function Calling), production engineering, and resume optimization.

A systematic breakdown of the AI agent development learning path, covering four stages: fundamentals, RAG knowledge bases, tool use, multi-agent collaboration, and hands-on projects.

A deep dive into the LLM Wiki: how Agents auto-build indexes and bidirectional links to solve slow, Token-heavy retrieval in growing knowledge bases. Full breakdown of its three-layer structure.

How do you choose the right memory strategy for an AI agent? This article uses a decision-tree methodology to analyze the use cases and trade-offs of short-term memory, vector retrieval, and structured summaries.

What is an AI Agent's harness? This article systematically dissects the core components of agent frameworks: context management, tool use, control loops, and caching strategies—revealing why the same model performs so differently across harnesses.

India's AI/data science postings hit 11,557 this week, down 5% from last week, but the skill demand structure barely changed. Python, ML, and SQL remain top skills while GenAI/LLM demand keeps rising.

Master LangGraph core concepts: nodes, edges, and routing functions. Learn StateGraph, MemorySaver, and ToolNode through a weather-query Hello World example, and understand how LangGraph relates to LangChain and powers Agent workflows.

Want to build an AI Agent but don't know where to start? This guide covers the complete seven-step workflow—from requirements analysis, platform selection, prompt engineering, data storage, and UI building to testing and deployment.

Learn how to use AI Agents to link the entire research pipeline—from literature management, data analysis, and paper writing to scientific illustration and dissemination—building a reusable research automation workflow with NotebookLM, N8N, and Ollama.

LangChain releases four major updates: OpenWiki for auto-generating codebase docs, voice agent tutorials, Harbor evaluation integration, and deepagents programmable sub-agents.
Apple Sues OpenAI Over Trade Secret Th…
Apple has filed a trade secret lawsuit against OpenAI, fracturing the once-deep partnership between the two tech giants. A deep analysis of talent mobility, tech competition, and AI coopetition.

Anthropic's real-name policy takes effect July 8. Learn what's actually affected: cloud API channels like AWS Bedrock, Azure, and Snowflake are fully exempt. Plus: Fable 5 export controls lifted globally.

A real NCA-GENL study journal from an IT-support-turned-AI-engineer: 50+ scenario questions, 7-week prep, and a brutal 40% on Trustworthy AI. Covers Transformer concepts, NVIDIA tools, and what actually works.

OpenAI confirms GPT-5.6 as the preferred model for Microsoft Copilot 365, responding to "breakup rumors." A deep dive into the strategy, multi-model trends, and AI productivity commercialization.

OpenAI officially releases the GPT-5.6 series with three models: flagship Sol, balanced Terra, and economy Luna. A deep dive into its core breakthroughs—a step change in design judgment and enhanced computer-use capabilities.

A clear, in-depth guide to how AI Agents work: the paradigm shift from traditional programs, the perception-decision-action loop, and the four pillars—LLMs, tool calling, memory, and RAG.