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2 contributions to AI Marketing
Prompting vs MCP Servers vs Claude Skills
While exploring the latest updates in the AI agent ecosystem, I realized many people mix up these three concepts: • Prompting • MCP Servers • Claude Skills But they actually solve very different problems when building AI systems. A simple way to think about it: Prompting → tells the AI what you want MCP → gives the AI tools to access data Skills → define the workflow or SOP So instead of relying only on prompts, modern AI agents combine all three: Prompt → triggers the task MCP → retrieves the data Skills → execute the workflow That’s when an LLM moves beyond being just a chatbot and starts acting like a real AI teammate. Curious — are you experimenting with Claude Skills or MCP servers yet? Would love to hear what you're building. 🚀
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Prompting vs MCP Servers vs Claude Skills
n8n just made AI agents production-safe 👀
If you’re building AI automations, you’ve probably faced this problem: “What if the agent sends the wrong email?” “What if it refunds the wrong amount?” “What if it writes bad data to production?” That hesitation is real. With the new Human-in-the-Loop (HITL) features in n8n v2.5+, we finally have a clean native solution. Here’s what this unlocks 👇 1️⃣ Tool Approvals Your AI pauses before executing sensitive actions. Refunds. Emails. Database writes. You approve → it runs. No approval → no action. 2️⃣ Send Approvals Where You Already Work You can route approval requests to: Slack | Microsoft Teams | Discord | Telegram | WhatsApp | Gmail | Outlook No dashboard hopping. 3️⃣ See Exactly What the AI Is About to Do Not just “Approve action?”You see: - The drafted email - The refund amount - The exact payload So approvals are informed, not blind. 4️⃣ Multi-turn Agent Conversations Agents can now: - Pause - Ask follow-up questions - Wait for clarification - Continue based on your response This makes workflows feel collaborative instead of robotic. The interesting part? They’re exploring editable parameters during review — meaning you’ll be able to tweak the AI output before approving it. That’s huge for real-world deployments. Curious: For those building AI agents here —Are you already using Human-in-the-Loop in production? Or are you still fully autonomous? Would love to hear real setups 👇
n8n just made AI agents production-safe 👀
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Divyanshu Gupta
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4points to level up
@divyanshu-gupta-6220
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Joined Feb 24, 2026