Hi team. I can't believe I've wasted a whole day on this. I've worked with Claude Chat, Claude Cowork, Claude Chrome and ChatGPT and they've ALL FAILED to deliver a working solution; i.e a clean Google Doc output format path for a clean WordPress input format path. They had no restrictions on modules to use, or number of modules, but despite showing them the EXACTLY the output they needed from the input provided, I'm still no closer to success than when I started! FYI, here is the Google Doc which I've shared publically for now: https://docs.google.com/document/d/1pziCm9wIIvtKvzt10IRc_jk-5pl1pn0rd8RVrjsBOLM/edit?usp=sharing PS. Here is how Cowork described the clean input that is working inside the scenario: https://us2.make.com/93886/scenarios/4492233/edit?folder=63806 Note I don't know if the following IS the reason it works. All I know is that the single module scenario above with the clean "content" inside it, works. Claude trying to self-diagnose: "When you gave me the blueprint with the content already populated, it worked. Here's why: The "content" value is plain HTML — but with a very specific character encoding approach. Here's what's actually in there: What it IS: - Raw HTML tags: <h1>, </h1>, <p>, </p>, <h2>, <ol>, <li>, <table>, <tr>, <td>, <br>, <hr>, etc. - All on a single continuous line — zero newlines, zero line breaks anywhere - No escaped characters like \" — double quotes simply don't appear inside the content value at all What it uses for special characters: - Named HTML entities for typographic characters: &ldquo; &rdquo; &rsquo; &ndash; &nbsp; &pound; &reg; - Numeric entity &#39; for apostrophes (instead of a literal ') - &gt; for > where needed - &quot; for quote characters where needed The critical difference from what LLMs produce: When an LLM (or a Make module) processes text from a Google Doc, it almost always produces one or more of these problems:

Hey everyone. If you’ve ever had a reel take off and suddenly found yourself drowning in comments and DMs, this video is for you.That’s exactly what happened to me. Most people were asking the same things — links, resources, or what to do next — and there was no realistic way to reply to everyone manually. So I built a simple automation using n8n that now handles it in the background. Here’s what the setup does: 1. Reads every new comment on your post 2. Understands what the person is actually asking for 3. Replies in a natural, human tone 4. Sends personalised DMs automatically when needed 5. Lets you control everything through a simple Google Sheet Once it’s live, it just runs on its own. In this video, I walk through the entire workflow — how the logic works, which n8n modules are used, and how you can build the same setup for your own content. If Instagram is an important part of your lead generation or community growth, this can save you hours every week. 🎥 Watch the full walkthrough here 📘 Get the blueprint for this inside the Classroom If you try it or want to customise it further, drop a comment — happy to help.

Hey everyone, We’ve have created an n8n blueprint for the Instagram Comments + DMs automation. Along with that, we’ve set up a classroom that walks you through the entire setup. This is the same system that: - Replies like a human - Handles comments + DMs automatically - Saves everything to Google Sheets - Turns engagement into real opportunities If you’re using n8n and want a clean, practical implementation — this is for you. 👉 Access the n8n blueprint from classroom and try it out right away!! Let us know if you’d like more n8n automations like this — happy to build what actually helps you.

Today was a mess. Everything I tried to rebuild went completely wrong. I thought I could trigger a subworkflow using the “call n8n workflow” node.Simple idea. But nothing worked.Nothing was passed through, nothing arrived, nothing happened. And I have no clue why. I searched and searched and searched. Still nothing.I even used Gemini to try to debug it, but no luck there either. I wanted to spend one hour.I ended up at one and a half. I even got up early to do this. And let’s not forget Christmas is coming. I still need to buy gifts, breathe a little and not lose my mind. Today the morning is finished. So here is my question for you. Do you have any good examples in the classroom or anywhere else that show how to use the “call n8n workflow” node properly? Did anyone yoused this node in an AI Agent node? I clearly need to understand this better. Not every day is progress. Todaywas pure frustration. It was nothing... But that is part of the journey.

Today I remembered something important. Google Gemini offers a File Search feature that is supposed to be much cheaper than Pinecone or Supabase. So I made it my goal for today to test it and see how it works inside n8n. From what I have read, it is a complete simplification of the whole RAG workflow. Here is what makes it different. With Pinecone or Supabase you have to build the entire RAG pipeline yourself. That includes: • Chunking the documents • Managing embeddings • Calculating and storing vectors • Handling metadata With Google File Search, all of that shall be handled automatically. We will see what will happen. You shall upload a file and that it is. No chunking, no embedding model selection, no vector Google does all of this internally. I am curious to see if it really is that simple. The setup First I had to get an API key, which meant creating a project in Google Cloud and adding a billing account.Once that was done, I had my key and could start. In n8n I created a new credential and added everything into the HTTP Request node. Then I created the store, which is basically the container where the files will be indexed. Next I added a form trigger to upload the document, followed by an HTTP Request for the file upload and another HTTP Request to import the file into the store. And here is the interesting part: My full patent file with all images was accepted by Google without any issues... interesting. Nut i am not sure. I am curious to see the results later, but not today.This setup already took more time than expected. Still, I am happy that everything worked so smoothly. Another good step in the journey.