I spent months on the wrong half. Better prompts, retries, error handling, a cleaner graph. The pipeline got solid and the output was still something nobody acted on. What changed it was the input. I stopped feeding it everything I could scrape and started feeding it only what is actually moving in the niche right now, with the reason it worked next to it. That part runs on an AI app now, but by hand it is the same thing: ten pieces in a doc, and one line each on what they promise. Three things I would check before building the next one: - Write in one sentence the decision this thing is supposed to make easier. If you cannot write it, you are building a demo. - Feed it less. Ten good examples with the reason they worked beat a thousand scraped rows. - Check the input by hand once a week for a month. A broken input never throws an error, it just produces confident nonsense. On my side this feeds content, and last month it put me past a million views while recording less than before. If anyone is building something similar, ask here and I will tell you where I went wrong first.

I've got a bunch of clients running Make.com and n8n accounts with my automations. Is there a way I can monitor them without having to switch between all their accounts.

Hello. I am Aaron. I lead a five-person team based in the Philippines. We have over the years made our money by landing projects from clients and agencies in the US, Latin America and Europe. We used platforms like Upwork, Freelancer and Toptal to find this work. As you may have noticed, the rise of AI agents and the spread of corporate automation have changed things. Many developers have lost their jobs. These developers are now competing in the freelance market, which has made it harder for us to find work. The space for us has gotten smaller. Because of this our revenue has not dropped over the past few years but has also become very unpredictable. Income levels for developers in Asia are still much lower than what developers earn in the US, Latin America and Europe. At the time the number of companies and clients who were once actively looking for developers from Asia has gone down. The talent pool from the US, Latin America and Europe has grown. Now more competition is coming from those regions. We used to handle these challenges when the work was steady.. Now things are very different. After thinking it through we’ve realized our way of working can no longer support stable long-term income. So we are now looking at two paths: First shifting our focus to AI specialization. Second, expanding our operations into high-revenue markets like the US, Latin America and Europe. But here’s the problem: we don’t have the skills to carry out these plans. Even though we want to move into the AI field we haven’t found the right people to make this shift happen. Our team has technical skills in AI automation. We’ve worked with AI and Large Language Models (LLMs). We believe there is a lot of profit to be made by using AI training platforms like Mercor, AfterQuery, DataAnnotation, Outlier, Turing and Snorkel. Projects in this space often require expertise, in natural sciences not just software engineering. That means we are missing the kind of talent. We also face limitations because of where we're located. We can’t easily bring in people with the background.

Hi everyone, I’d like some advice on the project I’m describing below. For the past weeks, I’ve been building a WhatsApp chatbot demo in n8n to handle appointments and answer FAQs for a medical clinic. I’m using Google Sheets and Google Calendar. I don’t have much experience building this kind of thing in n8n, and I’ve noticed that the AI ​​model (Gemini) makes several errors in its responses. It mixes up dates and times, and sometimes claims there are no free slots when there actually are. I know this is part of the troubleshooting and fine-tuning process, but I already have a fairly extensive System Message with many rules designed to prevent these errors, and it keeps making them. So, my question is: for a WhatsApp chatbot, is it better to use fixed options (like a menu) to avoid these errors, rather than having the AI ​​respond with free-form text? My goal is really to give customers the feeling that they’re talking to a person, not a robot. However, if the AI ​​is prone to so many errors, I’m wondering if it would be better to use buttons, fixed menus, or a hybrid approach?m What do you recommend? Thanks in advance.

I would not sell founder weekly operations as 'AI automation.' For Brendan's AI Community, whose audience is AI voice agents, Claude Code, and n8n; 26.9k members, I would center the lesson on implementation boundaries and recovery evidence. I would sell a specific outcome: a founder operations queue covering follow-ups, calendar conflicts, forms, and approval-ready actions. The operating workflow would review connected inbox and calendar context, prepare a weekly action queue, continue longer research in the background, and request approval before sensitive actions. The reason Muse fits this test is a dedicated secure VM, connected apps, background work, sensitive-action approval, and an audit trail. The implementation question is whether state, credentials, retries, and the failure path remain observable. A sensible starting offer is a one-time setup and operating-playbook package; commercial operating terms must be verified before sale. This is pricing logic, not a guaranteed result. Human control remains visible: Access is least-privilege, sensitive actions require approval, and the audit trail is reviewed weekly. What would an agent need to prove before you connected your inbox or calendar?