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?

A convincing demo is not yet a client handoff. For a private OpenClaw support assistant, I would ask the builder to pass four tests before calling it deployed. 1. The input is limited to an approved channel and a named policy source. 2. A routine question produces a draft with its source; a missing or ambiguous policy produces an escalation, not a guess. 3. Refunds, account changes, promises, and uncertain answers cannot be completed without a human. 4. After a timeout or restart, an operator can see the request state, retry history, credentials used, and whether a reply was actually sent. The handoff should include the knowledge boundary, escalation rules, and an operator runbook so someone else can recover the system. That is where the service value lives—not in a one-off answer. The image sketches the automated-versus-human split; it is an illustration, not measured performance. Which test would you insist on before deploying this for a client?

I've been noticing a weird pattern lately when prompting LLMs for complex workflows. When I type out a prompt, I naturally compress things, I edit out edge cases, skip background context, and try to make the text clean before pressing enter. Lately I started just speaking to the AI using voice input instead of typing. Because speaking takes less effort than typing out a polished paragraph, I end up leaving in all the messy context, small nuances, and specific constraints I would usually cut. The output quality jumps up massively just because the context window actually gets the raw, unedited picture of what I'm trying to solve. Anyone else shifted away from typing out their prompts for longer tasks? Curious if you're seeing a similar difference in how the model responds.