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Learn how to use MCP (Model Context Protocol) to run adversarial tests on AI agents in the terminal, covering prompt injection, privilege escalation, and dangerous command execution scenarios.

A structured AI Agent learning path covering core principles, prompt engineering, tool use, multi-agent systems, and frameworks like LangChain, CrewAI, and Dify for enterprise deployment.

LangChain4j is the AI application development framework built for Java engineers. Integrate DeepSeek, Qwen, and more into Spring Boot — no Python required.

Programmers transitioning to AI engineering aren't starting from scratch. Learn the 6 core skills — LLM APIs, RAG, prompt engineering, LLMOps — needed to make the leap.

A deep dive into a hands-on AI Agent development book covering component architecture, RAG, multi-agent systems, Function Calling, and production observability.

A deep dive into Spring AI 2.0: provider-agnostic APIs, RAG with vector databases, and how Java developers can build LLM apps using the Spring ecosystem.

Running Gemma 3 12B locally via Ollama and want to build an AI Agent? This guide covers tool calling, n8n/LangChain/CrewAI comparisons, context limits, and more.

Claude Code is Anthropic's local AI programming assistant that reads your entire codebase, auto-debugs, and delivers far higher accuracy than Cursor and Trae. Here's why it's the strongest AI coding tool today.

The Hermes Agent gets a major upgrade with eight new features: native iMessage, parallel background sub-agents, Unreal Engine MCP support, a self-evolving Skill Hub, and more. A hands-on breakdown of the core changes and their real impact on personal AI automation workflows.

Torn between Géron, Chollet, and Raschka? This article breaks down 4 classic ML books for self-learners aiming at finetuning and small language models (SLM), helping you find the best advanced path.

LangChain is the leading open-source framework for LLM application development, supporting GPT-4, GLM, and other mainstream models. This article dives into its three core concepts: Components, Chains, and Agents.

OpenAI releases the GPT-5.6 series, deeply merging ChatGPT with Codex as the Agent engine takes over the underlying architecture. A deep dive into its impact on AI coding toolchains and Agent applications.

AI Agents are reshaping software development with 42.8% market CAGR. Learn the difference between Agents and traditional AI, plus a complete LangChain-based curriculum to launch your career in intelligent agent development.

An AI research engineer with 3 years of experience sent 50 applications to FAANG with zero replies. This article breaks down the hidden barriers of top-tech AI roles, the truth about LinkedIn ghost jobs, and the MLE vs. Research Engineer divide.

GPT-5.6 (Sol, Terra, Luna) hands-on testing: a Hokkaido farmer controls a greenhouse with AI, a NYC small business builds custom software, and a Polish mathematician breaks a 3-year problem. A deep dive into end-to-end autonomous execution.

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.

OpenAI has dropped SWE-Bench Pro as a recommended AI coding benchmark, exposing deep issues like data contamination and metric limitations. We explore the trust crisis and where evaluation is headed.

A deep dive into AI Agent development: real architecture, entry barriers, and learning paths. From ReAct to multi-agent systems and LangChain — cut through the hype.

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.

Build an HR recruitment workflow Agent with Spring AI Alibaba Graph, covering resume parsing, job matching, tiered question generation, HITL checkpointing, and time travel state rollback across 20 core technical points.