162 related articles

Hubbele is an open-source note-taking app designed for both humans and AI Agents, supporting self-hosted deployment. This article analyzes its Agent-native design philosophy and implications for the future of knowledge management.

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.

Deep dive into the AmneziaVPN open-source client: AmneziaWG anti-DPI obfuscation, self-hosted VPS deployment, multi-protocol support, and cross-platform privacy.

A recursive technical proposition: Can we build a "meta-Skill" that auto-transforms any Skill into a Dify workflow? This article dissects the boundary between deterministic orchestration and autonomous Agent decisions.

NVIDIA CEO Jensen Huang says US companies should absolutely be allowed to use Chinese open-source AI models like DeepSeek and Kimi, calling backdoor fears a misunderstanding and arguing great models drive more compute demand.

An in-depth analysis of the three-layer GTM Agent architecture—the Signal, Buyer Intelligence, and Action layers—revealing how context graphs identify anonymous visitors and capture purchase intent.
Mindwalk: Replaying AI Coding Agent Be…
Mindwalk renders codebases as 3D maps, visually replaying the full operation trajectories of AI coding agents like Claude Code and Cursor. A deep dive into its core ideas, use cases, and the future of agent observability tools.

A Vue3 beginner tutorial centered on "learn just enough, apply immediately." A three-stage path covers reactivity, Composition API, Element Plus, and data visualization, culminating in an enterprise-grade AI health monitoring system with blood sugar management, RAG consultation, and doctor-patient collaboration.

A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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.

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.

A data-deletion disaster reveals the biggest AI Agent risk: the problem isn't the model, it's Harness design. Learn context management, process standards, and permission isolation.

Skip the dry theory and get hands-on! This article demonstrates step by step how to build a working AI Agent from scratch in 30 minutes using AI coding tools—covering the agent skeleton, tool system, memory mechanism, Flask web UI, and DeepSeek API integration.

Cheap Cursor tools hide ban risks and privacy traps. Learn how client tampering and API relays create vulnerabilities, and how to identify trustworthy tools.

A systematic overview of the AI Agent tech stack: RAG retrieval, Agent planning, MCP protocol, AI Gateway, and observability — helping developers build production-grade AI systems.

Microsoft Power Platform's Dataverse plugin for coding agents supports GitHub Copilot, Claude Code, and more — enabling natural language data modeling, queries, security config, and docs generation.

Deep dive into langgraph-agent-stack: per-run dollar budget control, canary traffic routing, Mock testing mode, and 800+ test cases to safely deploy AI Agents from demo to production.

No coding required! This guide breaks down the complete Claude workflow: custom Projects, batch SEO content, one-sentence tool building with Artifacts, and Claude Code terminal ops—with real traffic-growth cases.

From SHRDLU to modern neuro-symbolic AI: explore procedural semantics, CCG grammars, semantic parsing, and interactive fiction engines in today's NLP landscape.