224 related articles

How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.

Deep dive into Agent skill routing: comparing pure model vs. pure retrieval approaches, with a detailed two-stage layered architecture balancing accuracy, latency, and cost.

Build an enterprise RAG knowledge base Q&A system using Spring AI 2.0, Cursor AI programming, Ollama local deployment, and Redis vector storage. Runs on just 4GB VRAM.

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.

A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.

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.

Are Cursor referral codes and discount codes still valid? This guide analyzes Cursor's pricing model, promotional policy trends, and practical savings strategies including annual billing, education discounts, and competitor comparisons.

A deep dive into an AI paper writing system built with FastAPI + Vue3, covering multi-agent collaboration, RAG, streaming output, and full academic workflow automation.

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.

A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.

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.
Kronos Financial Foundation Model: Usi…
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.
Human-Centered AI: Real-World Implemen…
An MSR workshop reveals the truth about AI deployment: from a $20 corneal diagnostic device to expert-in-the-loop chatbots, researchers share real-world experiences of AI in healthcare and design within resource-scarce environments.

Java developers can build AI apps too! Learn LangChain4j fundamentals including RAG, Agents, Function Calling, and hands-on projects — no Python required.

A beginner's guide to Dify: build RAG knowledge bases visually with zero coding. Covers agents, workflows, and why private deployment makes Dify ideal for enterprise AI.

CogniCore asks: should persistent memory, context engines, and state management be standalone AI infrastructure or in-app features? A deep dive into 5 key directions and the missing middleware of the agent era.

A deep dive into two enterprise RAG knowledge isolation strategies: physical isolation vs. adaptive soft boundaries — covering metadata tagging, dynamic user-profile filtering, hybrid retrieval architecture, and data quality best practices.