When you're learning tools like n8n, it's easy to spend all your time building workflows and experimenting with different AI agents. But building the automation is only half the game. The real challenge is finding a business problem that people are actually willing to pay to solve. For example, instead of saying, "I build AI automations," imagine telling an agency: "I can automate your lead research, qualify potential clients, verify contact details, and organize everything in Google Sheets so your team spends less time on manual work." Same technical skills, but a much clearer business outcome. My biggest takeaway: Don't just build cool automations. Build solutions that save businesses time, reduce costs, or help them make more money. Curious to hear from everyone here: What's been harder for you so far — building the automation or finding someone willing to pay for it? 🚀

Recently I created an app on LinkedIn Developer portal and man, what this platform provides to you is insane. You can use MCP from Microsoft Learn to directly create an app from your choice of IDE and plug and play! Manual creation is also too easy to setup, you just need to know what products you want, what kind of audience you are going to target, and then the necessary scopes for these actions!! After doing this basic setup and then using an HTTP request node on n8n editor to GET basic profiel data, my eyes are next set on getting enriched information on analytics using Apify. Until then, let's buidl and explore! 🚀💪

Unfortunately Muse is not supported in my region currently its only supported in Canada and the US. Does anyone know a way around it? Thanks

You don't need the perfect plan, the perfect skills, or the perfect circumstances to get started. You just need to take the first step. Most people spend so much time thinking about what they want to achieve that they forget to actually work towards it. Start small. Stay consistent. Learn as you go. Your progress might be slow today, but small actions repeated every day can lead to incredible results. Focus on becoming 1% better every day. 💯

Here’s a mistake I think a lot of businesses are going to make as they start automating their sales processes. They’ll build an AI agent that responds to every new lead instantly, sends follow-ups, and books meetings. Sounds great, right? But there’s a problem. Not every lead should receive the same follow-up sequence. Think about two people who fill out a form on your website. Person A: Wants to get started immediately, has a budget, and is ready to book a call. Person B: Is researching options, doesn’t have a timeline, and just wants some information. If your automation sends both people the exact same five-message sequence pushing them to book a call, you’re probably handling at least one of them incorrectly. And this is where I think the difference between basic automation and genuinely useful AI agents starts becoming clear. Your agent shouldn’t just know WHEN to follow up. It should know WHY. For example, a better lead follow-up system could work like this: 1. Identify intent: Analyze the inquiry to determine whether the person is ready to buy, comparing options, or simply researching. 2. Choose the next action: Send a booking link to someone ready to talk, but provide useful information to someone still evaluating. 3. Monitor engagement: If someone clicks your pricing page after receiving a follow-up, that’s a different signal than someone who hasn’t opened anything. 4. Change the sequence: Adjust the next message based on what the person actually does instead of blindly sending message #3 because 48 hours have passed. 5. Know when to stop: If someone says they’re not interested, the system should recognize that and stop pushing. You don’t need AI making every decision. Simple rules can handle things like timing, unsubscribe requests, and stopping a sequence when someone books. AI becomes useful when the system needs to interpret something less predictable, like what a prospect actually means in their reply. The goal isn’t to replace a basic five-email sequence with a more expensive AI-powered five-email sequence.