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.

This week taught me one important lesson: progress doesn’t come from feeling ready; it comes from showing up anyway. 🚀 I’ve been developing a tool that I keep referred to in the video as an “automation” tool. However, I know better it’s a custom quote generator. 🧾 This tool features reusable templates, allowing companies to avoid the tedious process of rebuilding quotes from scratch. They won’t have to jump into Canva, place images, format text, and repeat that entire process each time. Or constantly looking for files to copy paste The contents to not have to write it down every time The real value lies not in buzzwords, but in the time saved. ⏱️ I’m new to this, and things break; some features only work halfway. My laptop moves as if it's on vacation! 💻🐢 However, I still committed. 🔁 Here’s what I learned: - Consistency beats talent when talent gets tired. 💪 - Confidence comes after taking action. 💼 - Broken things are feedback, not failure. 🧠 Nothing is perfect yet. The user interface still needs improvement, and the templates aren’t finished. But I’m not the same person I was two weeks ago Heck! I’m not even the same person I was a year ago. 🌟 This journey isn’t just about building one app; it’s about developing discipline. 📈 If I occasionally use the wrong words, understand that my intentions are right. I might call it “automation,” but what I really mean is saving people time and reducing stress. 😊 And if my Accent sounds different, that’s just the Caribbean in me! 🌴 I’m focused on fixing code, not my accent. So for those that might have an accent remember what matters most is your hard work and dedication 😂🫡👊🏾 Week two is complete, and week three is on the way. I’m not stopping! 🚀