Descriptions:
Dylan Davis demonstrates a practical workaround for one of the most frustrating AI limitations: context window caps that cause models to lose track of earlier work mid-session. Using Claude Code—which requires no programming knowledge despite the name—he shows how to process 50 meeting transcripts in a single continuous session by externalizing the model’s working memory to a set of plain markdown files on disk.
The approach creates three persistent files the AI writes and maintains itself: a context file storing the original goal, a checklist tracking progress across every file being processed, and an insights file updated incrementally after each transcript. When Claude’s in-context memory fills and resets, it reads these files to resume exactly where it left off—preserving quality across the full dataset rather than degrading halfway through.
Davis walks through the exact prompt structure he uses, organized into three phases: setup instructions before starting (create the three files in markdown format), working instructions (update the checklist and insights after each item), and output constraints defining what counts as a valid finding. He demonstrates the workflow on synthetic call transcript data, filtering for signals of frustration, stress, fear, and confusion. The same framework applies equally to email inboxes, support tickets, and any large document collection, and Davis notes it works with OpenAI Codex and Gemini CLI as drop-in alternatives to Claude Code.
📺 Source: Dylan Davis · Published January 13, 2026
🏷️ Format: Tutorial Demo
