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 👇

I used to stay busy with AI tools but wasn’t making consistent money because I didn’t have a real system. Once I focused on one niche, one offer, and one clear acquisition process and used AI to support that structure income became predictable. What’s your biggest struggle with AI marketing right now?

If you had a magic marketing genie and could "automate away" any marketing task or job, what would it be? Whatever you choose, would run on autopilot and just be done automatically Lets hear your magic wishes !

Most people struggle with AI because they focus on using many tools without a clear system or goal. I was in the same situation until I shifted to a simple workflow focused on one income-producing objective and used AI to support it. That change made my results consistent and started generating real income. Question: What is the main thing currently preventing you from getting consistent results with AI?

Introducing Mercury 2: The Fastest Reasoning LLM Mercury 2 is a new reasoning language model built for real production environments where speed actually matters. Modern AI systems are no longer single prompt, single response. They run in loops with agents, retrieval pipelines, tool calls, and background jobs. In these systems, latency compounds across every step. Traditional LLMs decode one token at a time, which creates a built-in speed bottleneck. Mercury 2 changes the architecture. Instead of sequential decoding, it uses diffusion-based generation. It produces multiple tokens in parallel and refines them over a few steps. Think less typewriter and more editor revising a full draft at once. The result is over 5x faster generation and a fundamentally different speed curve. Key highlights: - 1,009 tokens per second on NVIDIA Blackwell GPUs - $0.25 per 1M input tokens and $0.75 per 1M output tokens - 128K context window - Tunable reasoning - Native tool use - Structured JSON output - OpenAI API compatible The bigger shift is in the reasoning trade-off. Normally, better reasoning requires more test-time compute, which increases latency and cost. Diffusion-based reasoning delivers reasoning-grade quality within real-time latency budgets. Where Mercury 2 shines: - Coding and autocomplete where flow cannot be interrupted - Agent workflows with many chained inference calls - Real-time voice interfaces with tight latency constraints - Search and RAG pipelines where multiple steps stack delay Mercury 2 is built for production AI systems that need responsiveness under high concurrency, stable throughput, and consistent performance. It is available now via early access and integrates into existing OpenAI-compatible stacks without rewrites. The core idea is simple: faster reasoning unlocks better systems. This will be interesting for building marketing ai agents. what uses do you see for it ?