We are building a Jackfruit business in Uganda and on the side, we started helping young, graduated students to become masters in AI. I basically give them my learning program and all the videos I watched including the input from community members. (I send all the video´s of @Nate Herk ) Our mission: Empowering the next generation of African tech innovators with cutting-edge skills in artificial intelligence and workflow automation. What we're covering: ✅ AI fundamentals and practical applications ✅ N8N workflow automation mastery including the excess to the platform ✅ Hands-on projects and real-world case studies ✅ Career-ready skills for the global tech market We believe Uganda has incredible untapped potential in tech, and we're committed to making advanced AI education accessible and practical. Looking for input from the community: What additional elements would you include in the perfect AI/automation learning plan? We want to ensure our students get the most comprehensive and valuable experience possible. Additional suggestions for your learning plan: · Portfolio development: Help students build a showcase of projects · Mentorship matching: Connect students with industry professionals · Soft skills training: Communication, project management, client relations · Local context projects: AI solutions for Ugandan/African challenges · Certification pathways: Industry-recognized credentials · Job placement assistance: Career guidance and networking opportunities · Community building: Create lasting peer networks for ongoing support What specific aspects of AI or N8N are you planning to emphasize most? Let me know if you have tools for those students so we can help and pass our experience to them Your suggestions could help shape the future of these amazing students! 💡 Big Thanks to @Ai Titus , Bauke Dekker on behave of the students 🙏yes just say thanks to @Ai Titus for the free n8n access for the students

In this video I show a new, faster way to build RAG agents in n8n using the Pinecone Assistant to handle the heavy lifting, no preprocessing pipeline, no manual chunking, no custom embedding flow. You simply drop your documents in and Pinecone takes care of ingestion, splitting, and indexing on the back end. Then we wire it up in n8n to get accurate answers with trustworthy, page-level citations and exact text quotes pulled straight from your knowledge base. If you’re a beginner, this is the easiest path I’ve found to stand up a production-ready RAG agent; if you’re more advanced, it’s a huge time-saver for rapid prototyping. Follow along step-by-step as I connect n8n to Pinecone Assistant, load docs, test grounded responses, and return clean sources you can show to clients or stakeholders, all with a simple, no-code build that you can replicate in minutes.