Hey everyone! I’m excited to share my final result after participating in this Christmas Challenge. It’s been an intense process bringing this story to life over the last few days, and it’s finally ready. For my project, I chose the 𝗦𝘂𝘀𝗽𝗲𝗻𝘀𝗲 / 𝗜𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗖𝗮𝗯𝗶𝗻 𝘀𝘁𝗼𝗿𝘆𝗹𝗶𝗻𝗲. As you’ll see in the video, my main focus was on lighting and atmosphere. I wanted to step away from the traditional cozy Christmas aesthetic to create something more cinematic and tense—playing with harsh shadows, flashlight beams cutting through the darkness, and the subtle contrast of dying embers. This definitely won't be a "𝗦𝗶𝗹𝗲𝗻𝘁 𝗡𝗶𝗴𝗵𝘁"... 𝗛𝗼𝗽𝗲 𝘆𝗼𝘂 𝗲𝗻𝗷𝗼𝘆 𝗶𝘁! 👇⚡🔦

v1 is on the left, v2 is on the right... This should be interesting.

Hey everyone! 👋 You’ve probably heard the noise by now, but I wanted to drop the real value bomb here. n8n Version 2 is out, and honestly, it’s the quality leap we’ve been waiting years for. If you’ve ever lost an entire complex workflow because Chrome decided to update itself or you accidentally closed a tab... today is a day to celebrate. 🍾 Here is the no-fluff summary of why this changes the game for us: 1. 🧠 AUTO-SAVE (The MVP of the update) Yes. It’s real. No more trauma from lost work. This feature alone makes the update worth it. 2. 🚀 Insane Speed The new interface isn't just prettier (better dark/light mode), it flies. If you have workflows with 200+ nodes, you will feel the difference instantly. No more lag. 3. 🤖 Ready for AI Agents If you are building systems with LLMs (like many of us here are doing), V2 handles memory, prompts, and AI error handling much better. It’s built for the future of agents. 4. 🔐 Enterprise-Level Security For those of you selling services to clients: n8n now has better logging, credential management, and error controls. No more "hacky" workarounds to make clients feel safe. 5. 🔗 Rock-Solid Integrations They’ve rewritten core nodes like Google, Slack, HubSpot... fewer "ghost" errors and much more stable connections. 🧐 My Take: n8n has stopped being just that "low-code tool for hackers" and has become a robust platform ready for serious production. If you sell automation services, your offering just increased in value. 👇 Question for the group: Which improvement was most urgent for you? The auto-save or the stability in AI nodes? Let me know in the comments. 💬

In this video, I break down n8n’s new instance-level MCP and show you why it is a big upgrade from running your own MCP Server Trigger inside a workflow. I explain what it is, how it works, and the key differences you need to know so you can get the most out of it. I also walk through how to connect the new MCP to tools like ChatGPT, Claude, and Lovable so your AI agents can take real actions inside your n8n instance. You’ll see practical examples, simple use cases, and a full demo of how to think about using this in your daily life to automate tasks, speed up your workflow, and make your AI tools much more useful. If you want to understand how to unlock the full power of AI agents inside n8n, this video will show you exactly how to get started.

Hello community! 👋 The AI automation landscape is moving at breakneck speed. One of the current keys is equipping our agents with specific knowledge (RAG) without dying trying. Until recently, this required setting up complex pipelines with Pinecone, Supabase, etc. But recently, both 𝗚𝗼𝗼𝗴𝗹𝗲 (𝗚𝗲𝗺𝗶𝗻𝗶) and 𝗢𝗽𝗲𝗻𝗔𝗜 have launched solutions that promise to drastically simplify this process. I've been analyzing both options thoroughly, comparing costs, ease of use, and integration (especially thinking about workflows like n8n), and here is the final verdict. 👇 🔥 𝗧𝗵𝗲 𝗠𝗶𝗹𝗹𝗶𝗼𝗻 𝗗𝗼𝗹𝗹𝗮𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: W𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿? The short answer is: It depends on your priority. Are you looking for maximum economy and speed, or the most elegant integration into your workflow? 🥇 𝗚𝗲𝗺𝗶𝗻𝗶 𝗙𝗶𝗹𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜 (𝗧𝗵𝗲 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 & 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆) If your priority is low cost and getting it running fast, Gemini is unbeatable. - 💸 𝗔𝗹𝗺𝗼𝘀𝘁 𝗭𝗲𝗿𝗼 𝗖𝗼𝘀𝘁: Storage is currently free. Indexing a 120-page PDF costs less than $0.15. It's ridiculously cheap compared to the competition. - 🚀 𝗡𝗼 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Forget about managing external vector databases. Google takes care of chunking, embedding, and storage. Just upload the file and you're done. 🥈 𝗢𝗽𝗲𝗻𝗔𝗜 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗲 (𝗧𝗵𝗲 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻) If you value a clean workflow and integrated tools within the OpenAI ecosystem. - 🧠 𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: It shines especially if you use n8n. You access the vector base directly via the OpenAI API, without weird extra nodes. - 🛠️ "𝗕𝘂𝗶𝗹𝘁-𝗶𝗻" 𝗧𝗼𝗼𝗹𝘀: It allows adding file or web search directly in the agent configuration, eliminating the need for external tools like Perplexity in many cases. It's a much more elegant design. 📊 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 (𝗘𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗜𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀) I have prepared detailed infographics for the community that visually summarize: - The quick verdict. - The brutal difference in prices. - How they differ from traditional RAG and their limitations (watch out, they aren't magic!).