🚀 After trying different ways to self-host n8n, I think I’ve finally found the most affordable, beginner-friendly, and headache-free option. Spoiler: it’s Hostinger VPS. I’ve tried running n8n locally (too slow, too messy), tested Render (hours wasted setting env variables just to pay $7 for 0.5 CPU and 512 MB RAM — barely usable), and gave Elestio and Railway a shot (not bad, but more technical, tricky pricing, and, eventually, more time-consuming). In the end, Hostinger was an offer I “couldn’t refuse”: the $5 plan already gives you 1 vCPU + 4 GB RAM, and the $7 plan doubles that to 2 vCPU + 8 GB RAM — which outperforms Render’s $85 “Pro” tier by miles. The setup was straightforward, and honestly, the value speaks for itself. 👉 Want the step-by-step video guide on how to get started on Hostinger? Just drop a comment or DM me. 💬 Curious — where are you hosting your n8n instance right now, and how’s it been treating you?

Here’s a system prompt I’ve been experimenting with: it makes ChatGPT output clear, blunt, and predictable responses without fluff or filler. Perfect if you want direct answers instead of conversational tone. 👉 Find the full system prompt in the first comment below.

n8n just released a brand-new feature: native data tables. This makes it possible to store and manage data directly inside your n8n environment without needing an external database or constant API calls. In this video, I walk you through how these tables work, how to set them up, and how you can connect them to your workflows or AI agents in just a few clicks. I also cover the pros and cons, and when you might still want to use a traditional database instead. If you’re building quick proof of concepts, demos, or just want a simple way to manage data without latency or rate limit issues, this feature is a game-changer. The google sheets templates: Contacts Sales Test

In this video I show a new, faster way to build RAG agents in n8n using the Pinecone Assistant to handle the heavy lifting, no preprocessing pipeline, no manual chunking, no custom embedding flow. You simply drop your documents in and Pinecone takes care of ingestion, splitting, and indexing on the back end. Then we wire it up in n8n to get accurate answers with trustworthy, page-level citations and exact text quotes pulled straight from your knowledge base. If you’re a beginner, this is the easiest path I’ve found to stand up a production-ready RAG agent; if you’re more advanced, it’s a huge time-saver for rapid prototyping. Follow along step-by-step as I connect n8n to Pinecone Assistant, load docs, test grounded responses, and return clean sources you can show to clients or stakeholders, all with a simple, no-code build that you can replicate in minutes.

In this video, we’re diving into the winning submission from our very first AI Automation Society Plus community hackathon, where the top prize was $5,000. Out of hundreds of participants, the winner was @Azim K, a high school student who had only been experimenting with AI and automation tools for about six months. In our interview, Azim walks us through the mental health web app he built, which used Vapi for the voice agent, n8n for the back-end workflows, and Bolt.new for the front-end web app. He explains how he approached the challenge, the decisions behind his build, and what he plans to do next. This conversation is both inspiring and practical, showing what’s possible when you combine creativity with the power of AI automation. Want to compete for $6,000 total in cash prizes every month? Become a Plus member