Tag: AI Agent
All the articles with the tag "AI Agent".
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Claude Code's Five-Layer Architecture Explained: How MCP, Skills, Agent, Subagents, and Agent Teams Work Together
Anthropic officially describes Claude Code as a five-layer architecture: MCP for connectivity, Skills for task knowledge, Agent as the main worker, Subagents for parallel isolation, and Agent Teams for coordination. This post breaks down each layer's role and collaboration patterns, with a real-world example from my blog's blog-preflight Skill showing three layers working together.
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Frontman Deep Dive: What an AI Agent Can Do When It Sees Your Code from the Browser, Paired with Frontend Skills
Cursor and Claude Code both start from source code, but a frontend engineer's real work happens in the browser—the actual color on hover, the real DOM after SSR, the re-render triggered by the third useState. Frontman works in the opposite direction: from the browser back to the code. This post breaks down its architecture and combines it with Anthropic's frontend-design Skill and others into a complete frontend AI workflow.
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Claude Code Skills in Practice: Building a Reusable Cross-Project Skill from Scratch
Pull your repetitive playbooks, checklists, and multi-step workflows out of CLAUDE.md and turn them into Skills. Using a "pre-publish blog check" as the running example, this post covers the SKILL.md structure, every frontmatter field, context fork isolation, the boundaries with Slash Commands and Sub Agents, plus debugging and sharing.
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AI Agent Success Rates Jumped from 12% to 66%: How Frontend Developers Should Prepare for the Era of 'Usable' Agents
Stanford's 2026 AI Index report shows AI agent success rates on real computer tasks jumped from 12% to 66% in a single year—just 6 percentage points shy of the human baseline. Here's what that means for frontend developers and how to adjust your workflow to take advantage of this inflection point.
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One CLAUDE.md File, 44K Stars in a Week: Karpathy's Four Principles for AI Coding
A breakdown of how the forrestchang/andrej-karpathy-skills repo gained 44K stars in a single week: Karpathy's four principles for AI coding (think before coding, simplicity first, surgical changes, goal-driven execution), and how to use them directly in Claude Code.
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Inside the Axios Poisoning: How a North Korean APT Infected Millions of Developer Environments in 3 Hours
In March 2026, the axios npm package was hijacked by a North Korean state-level APT, planting a RAT into millions of developer environments within 3 hours. This post breaks down two separate but related incidents: the full supply-chain poisoning attack chain, and the technical mechanics and real-world exploitability debate around CVE-2026-40175 (CVSS 10.0).
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The AI Agent Security Landscape: From the ClawHavoc Poisoning to Cisco DefenseClaw and Microsoft's Governance Toolkit
A ClawHavoc-style supply chain attack poisons 1,184 agent skills and hits 300,000 users; within two weeks, Cisco and Microsoft ship agent security tooling. This post breaks down the threat model, compares the two defense architectures, and walks through real integration code.
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Getting Started with Claude Managed Agents: Let Anthropic Run Your Agent Loop
Claude Managed Agents, which entered public beta in April 2026, moves the agent loop, tool execution, and sandboxed runtime entirely into Anthropic's cloud—three API calls are all it takes to get an autonomous agent running. This post walks through the core concepts, demonstrates the full workflow with real code, and compares it against building your own.