2 videos in one day?! I just couldn't wait on this one. Been having so much fun building with Claude Code lately. More content coming soon. In this video, I’ll walk complete beginners through setting up Claude Code so it can actually build n8n workflows and agents for you. You’ll see how to use Claude Code inside Visual Studio Code, connect it to the n8n MCP server so it can search nodes, configurations, and existing workflow templates, and give it the ability to create, edit, and manage workflows directly in your n8n instance. We also connect n8n skills so Claude understands how nodes work together and how to use the MCP server properly. The result is a supercharged Claude setup that’s loaded with n8n knowledge, so even if Claude Code feels intimidating, this tutorial gets you fully set up and ready to start building workflows much faster.

A practical way to overcome the memory limits of AI models (Claude, ChatGPT, Gemini) by externalizing memory into files, without writing code. Problem - AI models have limited memory. - When processing many files (e.g., dozens of transcripts), they “forget” parts of the content, lose context, or hallucinate. - Even large files that are “accepted” are not always read in full. Solution - Use a tool that allows the AI to read and write local files (e.g., Claude Desktop / Claude Code). - These files act as persistent notes, allowing the AI to resume work after its internal memory is “reset.” The 4 components of the system 1. Data: files to be processed (transcripts, emails, tickets, documents, etc.). 2. Context (context.md): describes the main objective of the task. 3. Checklist / To-dos (todos.md): list of steps/files to process, marked as progress is made. 4. Insights (insights.md): where the AI continuously saves extracted results. How the cycle works - The AI processes the files. - It continuously updates the three documents. - When internal memory runs out: - The process continues until everything is completed, maintaining quality. Setup (no code) - Install Claude Desktop. - Use Code mode to allow local read/write access. - Select the folder containing the files. - Use a structured prompt that specifies: Standard prompt structure - Goal: what to analyze/extract. - Before you start: create the three files. - As you work: update insights and checklist. - After memory reset: reread context and to-dos. - Final constraint: continue until everything is completed. Use case examples - Extract customer language for marketing and copywriting. - Create real FAQs from conversations. - Map sales objections. - Identify churn signals. - Prioritize leads in old email archives. - Generate feature ideas from recurring requests. Conclusion - “Unlimited memory” comes from external files, not from the model itself. - With context + checklist + insights, AI can work for hours without losing quality. - The method is reusable for virtually any type of data or objective.