What automations and use cases do you use Cloud for in your agency?
One of the big things we're using now is a humanization prompt. I created a statistical model of my tone of voice across all my writing on the internet, then use that model to iteratively produce sentence fragments for content. For each fragment I score the perplexity (statistical deviation) from my own writing and select the lowest‑perplexity options, resulting in content that sounds exactly like me. This lets me generate AI content in my own voice, avoiding the typical AI‑generated feel. You can also use AI to generate drafts and then reformat them to match your tone, which works fairly well despite occasional artifacts that appear only about once every few pages. This dramatically cuts down the time I spend on Q&A and content creation.
Beyond that, I'm thinking about more advanced loops to improve existing automations. For example, you could run a Claude code loop that scans your N8N or Claude routines and tries to make them 10 % more time‑ or cost‑efficient, logging results to a shared file. Over time the system can auto‑research, generate hypotheses, test them, and if it reaches the 10 % improvement target it stops. Running this continuously can re‑jig automation components, reduce module count, convert them into code blocks, and eventually offload them to serverless providers like Modal or Trigger, cutting costs by up to 90 % and making the automations run orders of magnitude faster.
The biggest pain point is content ideas. AI can help by researching what others are doing, finding unique angles, and creating a summary bot that compiles summaries into a Google Sheet, saving you daily research. It can also generate titles based on high‑performing templates, though you should still add your own human touch because titles and thumbnails are the highest‑leverage elements for virality. Another major pain point is repurposing. Repurposing long‑form content into short‑form, text, and distributing across platforms is valuable, especially when you take content from creators in adjacent niches — like video editors or personal trainers — who use AI terms. By scraping their reels, you get domain‑specific experts recommending AI tools, letting you create content that funnels their audience into your automation niche. Finally, AI can help generate outlines for content using deep research, allowing you to produce large amounts of content quickly.
I haven't done it because I find there's very little business utility—these tools tend to work only about 80% of the time and the response quality is pretty poor, so most of this functionality is below the level where businesses would pay for it. My channel is all about businesses, strategies, and systems that businesses will actually pay money for. That said, I could definitely show the simplest, straightforward way to get up and running with this on your own, whether you want it as a hobby project or see an opportunity to monetize it. Here’s how retrieval‑augmented generation works: you feed a bunch of text into a database; the database indexes the text—turning it into numbers, essentially a high‑dimensional vector representation. When you ask a question, the system searches that database for snippets of text similar to your query (like Googling over your own data), retrieves those matches, and inserts them into the prompt for a large language model. The model then answers based on both the retrieved data and its own built‑in intelligence. To illustrate, I drew a quick diagram: RAG stands for retrieval‑augmented generation. Retrieval is just Googling over your own dataset—user inputs a query, the query searches the database, we find however many matches we want, we insert those matches into the LLM’s prompt, and the LLM answers the question. There are many ways to implement this, but the simplest way I’ll cover is using the OpenAI Assistants playground. In the playground you define the assistant’s instructions, then under Tools you enable File Search, upload a text file (I used one of my blog posts, stripped of formatting), and the system automatically creates a vector store and handles the embedding and retrieval for you. As a demo, I asked the assistant, “What are three components to a great high‑ROI automation?” It returned: a clear deliverable, it solves a hot‑button pain point, and you can template most of it. I also showed how to define a “pressing problem” as anything that impacts your ability to generate revenue, and the assistant gave a relevant answer. The result is your own RAG‑enabled chatbot. There are various services—like Chatbase, Botpress, or others—that let you run assistants through their back ends, or they may have their own RAG implementations to charge you for. You can embed this onto your website or service in many ways. I don’t sell it to businesses because I don’t find it very valuable for them, but chatbots are growing in popularity and functionality, so there’s definitely potential there. Hopefully that answers your question—thanks for asking it!
Thanks for the request—I'd love to make a video on building a humanlike chatbot. Right now, the value of an AI assistant feels small because I can do tasks like scheduling appointments myself in just a few seconds, whereas earlier it would have taken much more day-to-day effort. The question is when the speed gap becomes so tiny that it's worth using an AI, or when the value becomes so high that I'd need an AI secretary. For me, it doesn't make much sense yet because I type extremely fast—about 186 words per minute—thanks to years of RuneScape grinding where I had to type the same thing repeatedly without copy‑paste. I'm also very fluid with computers and know hotkeys, so doing things myself is often faster than telling an AI to do them, especially given the current lag and the few words needed to prompt the model. That said, the landscape is changing: soon we'll be able to chain actions, so a single prompt could trigger dozens of steps like looking up flights, comparing costs, and booking them via APIs. We're approaching that point, but the models aren't there yet, which is why I've held back on chatbot videos. I will definitely build a humanlike chatbot for tasks like this in the future. A community member, Mehmud from Maker School, already sells Vapy chatbots and showed how powerful simple prompts can be, so a Vapy‑focused video might be worthwhile as well.
Maker School works like this: you join, then go through a pre‑program where I walk you through my methodology and mindset for building a new business, giving you my high‑level view of how I've approached every business I've run. Next, you choose a path. If you lack automation experience, you enter the automation program, which covers an end‑to‑end process including cloud code. I now have a full course on generating media because I see that as the next frontier now that text is saturated. After finishing that (if you still lack automation experience) you move into the main program; if you already have automation experience you can jump straight into the main program. The main program is simply a daily checklist: each day you show up and I tell you what to do—like 'do this, do that.' As part of the checklist I share a video where I actually perform the task, so you learn by reading and watching, then you apply it to your own business. It's not drip‑fed; you can only advance further if you ask me directly and show evidence that you already have a successful business or similar result. I don't let everyone move forward automatically because most people's biggest obstacle is themselves—they get stuck thinking about future content, feel overwhelmed, and lose focus on the daily actions.