39 related articles

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.

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.

A fresh grad interviewing for a GenAI Trainer role faced prime number coding and activation function questions while the interviewer used Gemini to generate questions live — exposing AI hiring chaos.

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.

How can Java engineers transition to AI Architect? This article breaks down three core capability layers — AI app development, production RAG, and AI Agent orchestration — using Spring AI Alibaba and LangChain4j to turn your Java foundation into a competitive edge.

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.

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.

A structured zero-to-one roadmap for AI Agent development: Phase 1 covers Python & LLM basics, Phase 2 tackles five core Agent capabilities and LangChain/LangGraph, Phase 3 delivers hands-on RAG projects.
5 Web Search APIs Compared: How to Cho…
A deep comparison of 5 mainstream Web search APIs across latency, result quality, and pricing — helping AI app developers find the best data source for RAG and LLM use cases.

A deep dive into Claude Code, the definitive course from DeepLearning.AI and Anthropic: from agentic principles and context optimization to three hands-on cases—RAG chatbot, Figma-to-frontend, and data analysis. Master AI-assisted coding methodology.

What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.

Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.

Airtel partnered with Perplexity to offer users a free one-year Pro subscription, but the renewal charging mechanism worries many. This article breaks down the billing logic of operator-bundled subscriptions and offers practical self-protection tips.

A complete AI Agent learning roadmap covering BDI theory, core components (Perception/Planning/Execution), AutoGen multi-agent frameworks, and DeepSeek RAG projects for beginners.

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.

FDE (Forward Deployed Engineer) is the hottest emerging role in the AI deployment wave, combining a technical CTO, full-stack AI engineer, and business consultant. Learn the two FDE tracks, core skills, and how to transition into one.

A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.

Embedded Linux or AI Agent development? This in-depth comparison covers salary, job availability, and career stability to help developers pick the right path.

A systematic AI Agent learning roadmap in four progressive stages: fundamentals → ReAct core paradigm → memory & tools → multi-agent collaboration. Master LangChain, AutoGen, and more, growing from beginner to practical developer in three months.