https://www.loom.com/share/a32d04f79ab94269ae65a0af337aad02 https://dashboard.n8n-mcp.com/ https://github.com/czlonkowski/n8n-mcp/tree/main Below is a structured "Deep Dive" blueprint designed to be a definitive resource for self-hosting **n8n + MCP**. --- ## 🏗️ The "Direct Pipe" Architecture Guide This setup ensures a 100% stable, unbuffered stream between your Raspberry Pi and Google Antigravity. ### **Phase 1: The Docker Engine (.env & Compose)** The goal here is to stop n8n from "over-processing" the data before it leaves the Pi. * **Key `.env` Nuances**: * `N8N_SKIP_RESPONSE_COMPRESSION=true`: **Crucial.** Prevents n8n from gzipping the stream, which causes "Connection Closed: EOF" errors in strict AI agents. * `N8N_TRUST_PROXY=all`: Tells n8n to trust the headers (like `Authorization`) passed by Nginx and Cloudflare. * `N8N_CORS_ALLOWED_ORIGINS=https://n8n.yourdomain.com`: Prevents security rejections when the agent initiates the handshake. * **Docker Compose Secret**: * Ensure your `N8N_ENCRYPTION_KEY` is at least 32 characters. If you recreate your container without this being static, your MCP tokens will instantly become invalid. --- ### **Phase 2: The Nginx Location Block (The Pipe)** Standard Nginx settings "buffer" data, which is death for real-time MCP streams. You need a dedicated location block for the `/mcp-server/` path. ```nginx location ^~ /mcp-server/http { proxy_pass http://n8n:5678; proxy_set_header Host $host; proxy_set_header Authorization $http_authorization; # --- SSE STABILITY --- proxy_http_version 1.1; proxy_set_header Connection "keep-alive"; # Keeps the pipe open during AI "thinking" proxy_buffering off; # Forces data to flow instantly proxy_cache off; # Prevents serving stale tool definitions gzip off; # Essential: compression breaks SSE

Most people use Google's Antigravity to build apps and websites. I use it to build AI employees. Here's how: I took the framework from this post put it in Qwen “thinking mode” ended up being about 1445 (idk how he got 2590, I’m sure I’m missing something) lines when I was done. (You have to give you the output in small chunks otherwise gets cut off but it will produce it) If you connected this with Claude code I’m sure it would do wonders, I’m cheap I use Gemini for now. 1. I need you to design a complete blueprint for building AI employees - a 2,590-line instruction (roughly) file called AGENTS.md. Think of it as the DNA for my AI workforce. 2. I will upload it to Google's Antigravity and I want to give it one simple prompt: "Setup the infrastructure using AGENTS.md" 3. I want to be able to give it another prompt (example): "Build a LinkedIn content creator". 4. The AI should be able to take over completely. I can answer questions like "What should the LinkedIn creator do?" or "Do you have examples?" I will also provid API keys when it asked. 5. I should be able to start testing and fine-tuning using plain English - no code, just conversations. 6. What it should be able to do: The AI logs every run, and after about 20 attempts, it analyzes the patterns and suggests improvements to its own instructions based on what worked and what didn't. 7. When those recommendations come in, I review them. If I approve, it rewrites its own instructions. I don't have to do anything else - it literally makes itself better over time. And now that the infrastructure is set up? If I want to build another AI employee, I just tell the AI what I want in plain English. I had Qwen build out the file, have it output it in sections otherwise it will get cut off. Now I have Antigravity + Ralph Loop + GSD (Get $h!t Done) + Antigravity Skills + the n8n-mcp.com + Jules + LLM of choice = An amazing AI Employee(s). Make sure you have it integrate all your tools and skills and everything is mapped, always ask/audit for gaps ie ways to improve/more robust. This does an amazing job my Employee helps me build workflow, test troubleshoot front end issue with React for webapp builds, and a huge headache saver.

I've been working on automating my blog publishing flow. Took me longer to document than to build. 😅 The system: - Airtable as my CMS - n8n for all the automation logic - WordPress as the final destination - Python API for featured image generation The workflow: 1. Create featured image in Airtable → auto-generates 1200×628 image in a couple of seconds 2. Write in Notion → syncs to Airtable automatically 3. Change Status to "Post to WordPress" → live blog post in 15-20 seconds The post I'm working on breaks down: - How the Variables table lets me switch between test and production with one checkbox - The Airtable automation script that routes everything to n8n - Why HTTP Request nodes in n8n kept breaking my multi-image handling (and how I fixed it) - How I made Rank Math SEO fields accessible via REST API - The complete markdown → Gutenberg conversion logic Once I publish, I'll drop the blog post here and the complete implementation (n8n workflow JSONs, Airtable scripts, base template, WordPress config, Python API) will go into the premium tier. Happy to answer questions if anyone's building something similar. The multi-image URL mapping was the trickiest part to figure out.

Here is a rough overview of a automated ebook / digital product creation workflow(s). Feel free to ask questions. https://www.loom.com/share/dcd9a5c0d8164bc0a208f47092ddc60d Here the ebook for review - https://pub-0efcc41bf8d3495e831acec3c597d433.r2.dev/ebooks/the-stress-free-digital-product-launchpad-go-from-idea-to-profit-even-if-you-hate-tech-get-stuck-on-every-word-final.pdf Now remember at no point was there any human interaction in creating any of this content? It's all AI obviously you can change the styling and quality of images -but I feel like being able to create Content from basically an idea and have a engine do it all autonomously and this could apply to really anything was pretty cool

Before you dive into workflows and Docker containers, let’s get to know each other. Drop your intro below - keep it simple. 🎯 What to share: - Your name (first name is fine) - What you’re currently automating (or trying to) - Your biggest automation pain point right now - One thing you hope to build here No need to write an essay. A few sentences works. I’ll go first: I’m Kjetil from Stavanger, Norway 🇳🇴 Principal Consultant with 15 years in IT - specializing in Azure Cloud, DevOps, and automation. These days I’m deep into self-hosted automation with n8n, running everything from a $10 Hetzner server. I got tired of watching SaaS bills climb every month and losing control over my data and workflows. That’s when I went all-in on self-hosting. Now I run n8n workflows, TTS, video processing, and S3 storage on a single server. I use Airtable as my CRM and orchestration hub, but everything else is self-hosted. I've been experimenting with NocoDB (open-source Airtable alternative) and replicated about 80% of my Airtable workflows - completely self-hosted. I'll be sharing those templates as I build them out. Outside work, I love tinkering with Docker, GitHub, and homelab projects. When I’m not deploying containers, I’m hanging out with my wife and two kids. Why I built this community: Honestly, the self-hosted automation space is a mess. You’re piecing together info from 47 different YouTube videos, praying your Docker containers don’t break at 2am, and there’s no clear path from “hello world” to production. I want to change that. My goal: Help you build automation that saves money, runs reliably, and doesn’t hold you hostage to subscription pricing. Now you - drop your intro below! 👇