From five-figure months to first clients and real-world AI builds - this week inside AIS+ showed what happens when people stop waiting and start executing. Here are this week’s highlights inside AIS+ 👇 👉 @Sef El is collecting $25K this month from client invoices - built solo, step by step, through consistency and patience. 👉 Jan Goergen-Makinson launched his first voice agent, “Kora,” handling appointments using Retell - a big leap into real client-facing AI. 👉 Prakhar Dubey built his first client workflow for a UK-based NGO supporting SEND children - turning learning into meaningful impact. 👉 @Eduard Friesen broke into the All-Time Top 10 leaderboard and landed his first paid client - momentum stacking fast. 👉 @Abel Alvarado shipped an AI Social Media Intern - from research to auto-image generation and multi-platform posting, all automated. 🎥 Super Win Spotlight of the Week: Nick Mohler | Partnerships Through Community Nick didn’t join AIS+ just for tools - he showed up, participated, and shared openly. That single decision led to meeting a business partner, multiple collaborations, and new client opportunities - all through community connection. 🎥 Watch his quick story 👇 Nick’s journey is proof that when you put yourself out there, the right connections find you. ✨ Want to see more wins like these every week? Join the builders inside AI Automation Society Plus - where consistency, community, and action turn learning into real results 🚀

i just wanted to introduce myself to you all. I am a poet and an author. My goal is to automate my book contents on multiple platforms, so i can execute the next step.Your input is greatly valued and immensely respected.

I’ve been building an AI‑assisted system for my niche (commercial real estate tax strategy) and wanted to share it as a live example others can adapt. I’m calling it the Tax Logic Advertising Machine – a proof‑driven content loop that turns tax wins into daily visibility for brokers without paid ads.The stack:Proof creation with AI. Use AI to draft mini case studies and “what‑if” scenarios from real Tax Logic work: property type, strategy (cost seg, bonus depreciation, etc.), and cash‑flow impact, then refine manually so it stays accurate.Distribution engine. Schedule 3 posts a day across multiple platforms with Metricool, all built from those proof pieces and aligned around one weekly theme like “turn listings into after‑tax ROI stories.” Central hub + tracking. Every post points to a single “Tax Logic” hub link where brokers can see a sample after‑tax OM page and book a call, so all the AI‑generated content routes to one measurable outcome. The experiment is simple: use AI to reduce the friction of turning complex CRE tax strategy into a steady stream of broker‑friendly proof, then let scheduling/analytics tools tell me what’s working. If anyone wants to see the prompts or workflow I’m using, I’m happy to share and compare notes on how you’d adapt this pattern to your own niche.

we are moving to the new year full of new opportunities and what are the biggest challenge we will face in the AI race. Curious what you think and why

Had this project over the weekend with my team and here's some of the Challenges faced The main challenge was building a single automated system that could consistently generate high quality social media content across multiple platforms while avoiding duplicate posts and maintaining each platforms unique tone and format. Another challenge was coordinating AI text generation image generation posting schedules and storage in a way that was scalable reliable and easy to manage inside n8n. Handling validation errors API limits and image formatting differences between platforms also required careful workflow design. Solution offered I designed and implemented an automated social media content engine entirely in n8n. The workflow starts with a defined posting schedule and AI content parameters then pulls relevant articles and filters out duplicates by platform. I configured platform specific AI prompts for LinkedIn Instagram and Bluesky using an LLM and added JSON validation to ensure clean structured outputs. For visuals I integrated an image generation service then handled base64 conversion image resizing and storage in ImgBB. Each post and image is saved to Airtable for tracking and reuse. The workflow dynamically adapts content and images per platform while running fully automatically. Final outcome The final result was a fully automated multi platform content engine that produces consistent high quality text and images on schedule with zero manual input. The client can now scale social posting effortlessly reduce content creation time by several hours per week and maintain a strong brand presence across platforms. The system is stable easy to extend and ready for additional platforms or content sources as the business grows. Want a fully automated social media content engine like this for your business DO YOU WANT TO SEE A LIVE DEMO?