As a content creator offering repurposing and scheduling services, what pain points could automation solve for me?
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.
Ideal customers are creators who are just starting to standardize their content, have around 10K–100K followers, are at the early stage of exponential growth, and are looking to outsource or work with a team to reduce costs. You can serve them by using a transcript to automatically generate posts for ten social platforms—either fully automated or with a person handling it. To find these leads, scrape Instagram, Twitter, and YouTube for channels in that follower range. For example, use a YouTube scraper with about fifty search terms to get roughly a thousand videos for $5, which corresponds to about six hundred creators. Take the top ten percent (about fifty to a hundred channels), sort them by subscriber count, and then use a YouTube channel scraper to pull contact details from video descriptions—Instagram, website, etc.—giving you Instagram, Facebook, X/Twitter, and LinkedIn profiles. With those contacts, take one of their long‑form YouTube videos and use your system to create customized content for each platform at a marginal cost of just a few cents. Finally, run personalized outreach based on the video or another asset, ensuring each message includes some level of customization.
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.
Good stuff. My recommendation for you, Curbman, is that we're not actually training the LLMs—that's a common misconception. What we do is applied AI: we take the work from companies like OpenAI, who build and train the models on vast datasets, and we sell it to businesses. We're not doing any training ourselves; we just give the model examples of what we want it to do, which some people call training but it's different from actual model training. Regarding testing the accuracy of the model as stated in the video, it's simple: set up an accuracy test prompt, run it multiple times (e.g., 10, 20, 50, 100, or 1,000 times) on slightly different inputs with the same prompt, score each run, then average the scores. For example, if you get scores like 93, 96, 95, 92, 97, 95, 93, 97, 97, 99, the average is 95.4, which exceeds a 95% threshold, so you can use the model. To be thorough, you can then test with a dumber model and continue down the list to ensure you stay above the threshold; this can be fully automated. I once set up such an automation for my content writing workflow. In terms of expectations, things usually take longer than you think, but once you get initial traction, results accelerate—it's asymmetrical. For me, I wasn't planning to make over $300,000 this month; I thought I'd make around 190k–200k, but I made 1.5 times that because I'd put in daily inputs long enough to be rewarded when it blew up. As for the biggest pain points in our automation services, idea generation was a major issue until we solved it by repurposing YouTube comments and using tools like '1 of 10' to find trending formats, then applying my industry knowledge to make them unique. One of my highest‑performing videos was a clone of a popular video, but I rebranded it to AI and automation—'Watch me do an AI version of this and sell AI.' Editing is also a big pain point; while you can't fully automate it, content repurposing helps. Creators often treat their main YouTube channel as the source and create short‑form offshoots on other platforms. I've tried to address this using creator rewards, though it's hard to measure attribution—we have around 300,000 views but can't track exactly how well it's working. I'll keep you posted.
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!