A while back I documented the full automation system I built to publish from Notion to WordPress. 51-node sync workflow, 5 Airtable tables, a Python/Pillow image generation API, and an image mapping cache to handle Notion’s expiring S3 URLs. It worked. Never broke. But maintaining it was a different story — every time I needed to change something I had to trace the entire flow to understand what I’d built. So I rebuilt it as a proper app. Same pipeline, proper code. - Notion webhook triggers a sync - Notion blocks converted to Gutenberg HTML - Inline images downloaded, uploaded to WordPress, URLs cached in Postgres - Featured image auto-generated - Rank Math SEO applied - WordPress draft created - Change status to Publish in Notion — goes live The full blog post with all the n8n workflows, Airtable templates, and implementation details is here (still available for premium members): 👉 https://kjetilfuras.com/automate-wordpress-blog-publishing/ The app I built from it is called Notipo. Open source, self-hostable with Docker, or hosted version with a 7-day free trial: 👉 https://notipo.com If anyone here runs a WordPress blog and writes in Notion — give it a try. Honest feedback welcome, especially if something breaks.

Just shipped two things today. 1. n8n Integration Notipo works as a drop-in publishing engine inside n8n. Instead of building 50 nodes to handle Gutenberg conversion, image uploads, featured image generation, and SEO metadata — you call one HTTP Request node and Notipo handles the advanced logic. Full editorial pipeline example: Airtable brief → AI Agent generates post → Notion page created → Notipo publishes to WordPress → Slack notifications if errors occur. Docs: notipo.com/docs/api/n8n 2. CLI for AI agents The Notipo CLI is built for agentic workflows — Claude, Cursor, or any AI agent that can run terminal commands can now create posts, trigger syncs, and monitor jobs directly: npx notipo posts create npx notipo sync npx notipo jobs All output is JSON so agents can parse and act on it programmatically. Docs: notipo.com/docs/api/cli

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.

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

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.