Hey everyone, I filmed a very special video today and wanted to share it with you here first. What did I do? I took 3 different n8n workflows and converted them to Claude Code. I tested them live, on camera. Made mistakes, fixed them, got shocked - you saw it all. 📊 Real Results: Workflow 1: Lead Generation - Before: 47 nodes, hours of setup - Now: "Do this" → 13 seconds → Found 20 leads - Source: Hacker News "Who is hiring" posts - Workflow 2: Email Finder - Using AnymailFinder API - Automatically found 14 people's emails - Just with natural language - Workflow 3: Email Campaign - Company research with Perplexity - Personalized email writing - Adding to Instantly campaign - All automatic - 🤯 Most Shocking Part I didn't connect a single node. Didn't configure HTTP requests. Didn't read API documentation. I just showed it my n8n workflow and said "run this in Claude Code." ⚠️ IMPORTANT: n8n Isn't Dying! I said this clearly in the video: - Health data → n8n - Legal documents → n8n - Critical data security → n8n Need control? n8n. Need speed? Claude Code. You must know both. 💬 LET'S DISCUSS: Question 1: Have you tried converting your n8n workflows to Claude Code? How were the results? Question 2: Which of your workflows do you think are most suitable for this transition? Question 3: Do you have concerns about data security? Which workflows would you prefer to keep in n8n? Question 4: Are your clients asking you for these kinds of solutions?

I have joined this community a while back but thought I would just say hello to everyone, I will be sharing a win hopefully soon just waiting for singed terms.

Which is the best-free cloud service for using n8n according to you?

Client sends 18-page RFP.😫 My marketing team agents (using community template) create great content. But I have to manually brief them on every new project. "Target audience is B2B SaaS buyers" "Key messages: innovation, efficiency, ROI" "Timeline: 3 weeks" "Budget: $8K-12K" Typing the same information to 3 different agents. Every. Single. Project. Then I realized: Why am I the middleman between the RFP and the agents? 💡THE DISCOVERY: The marketing template has this brilliant agent coordination system. Content strategist → Copywriter → Designer. Beautiful workflow. But project intake is manual. Someone (me) reads client brief and tells agents what to do. What if agents could READ the brief themselves? I added automatic RFP processing. Upload client brief PDF → Agents get briefed automatically → Campaign creation starts. Zero manual briefing. 🚀THE DIFFERENCE: BEFORE: - Client sends RFP PDF - I read 18 pages - Write brief for content strategist agent - Copy key points to Notion - Brief copywriter on messaging - Send timeline to project manager - Total time: 45 minutes per RFP AFTER: - Client sends RFP PDF - Upload to folder - Agents receive structured brief automatically - Review extracted info: 5 minutes - Agents start working 🤖WHAT GETS EXTRACTED: Client name, project scope, target audience, key messages, deliverables list, timeline, budget range, brand guidelines mentioned, success metrics. All formatted into agent-readable instructions. The content strategist agent receives: "Create strategy for [Client Name]. Target: [Audience]. Messages: [Key Points]. Deliverables: [List]. Timeline: [Dates]." Perfect brief. Zero typing. ✅ REAL EXAMPLE: Tech company RFP for product launch campaign arrives. 18 pages of requirements. Old way: Read entire document → Summarize key points → Brief each agent → 45 minutes New way: Upload PDF → Review extracted brief → Approve → 5 minutes Agents work with same level of detail. I just don't waste time being a human document parser.

Someone just asked about AI accuracy, and I realized this is something we all need to talk about. Because here's the thing: AI is incredibly powerful, but it's not perfect. And knowing how to work with that? That's the real skill. Let me share what I've learned about getting more accurate and reliable results from AI. The Truth About AI ChatGPT and other AI tools can absolutely give you wrong information sometimes. They can be confidently incorrect. They can make stuff up. This isn't a flaw you need to work around forever (probably), but it's reality right now. The good news? Once you know this, you can use some simple strategies to get way better results. Here's What Actually Works Make it show its work. Add phrases like "explain your reasoning" or "walk me through your thinking" to your prompts. When AI has to justify its answers, it tends to be more careful and accurate. It's like asking a student to show their math work. Get specific with your prompts. Vague questions get vague (and sometimes wrong) answers. Instead of "tell me about SEO," try "give me three SEO strategies for blog posts in 2025, with specific examples." The more detail you provide, the better the output. Double-check important stuff. If you're using AI for facts, dates, statistics, or anything critical, verify it. I always do a quick Google search for anything that matters. Takes 30 seconds. Saves potential headaches. Challenge it when something feels off. Just say "are you sure about that?" or "can you reconsider that answer?" You'd be surprised how often it'll correct itself and give you something better. Ask for confidence levels. This one's been huge for me. Add "rate your confidence in this answer from 1-10 and explain why" to your prompts. When it gives itself a 5 or 6, you know to dig deeper or verify. Turn on Deep Research. Most LLMs now have a deep research mode (ChatGPT, Claude, Gemini, DeepSeek, Perplexity). This feature actually goes out and finds real sources, validates information, and gives you thought-through links and content. It takes a bit longer, but the accuracy jumps way up. Game changer for anything factual.