Build Persistent AI Memory with Plain Text Files | Claude Code, Codex, Antigravity, Gemini, GPT 5.4

Most AI tools forget everything between sessions, forcing you to repeat context, preferences, and project details every day. This video explains a persistent AI memory system built with plain text files, markdown notes, and a two-layer architecture for core memory and recall memory. It covers the context continuity problem, why plain text can outperform vector databases for single-user workflows, how a nightly primer keeps AI aligned, and why human review matters when memory conflicts appear. If you are building an AI workflow, personal knowledge base, or markdown-based assistant system, this breakdown shows a practical architecture for reliable long-term context. Timestamps 0:00 AI amnesia and the context continuity problem 0:55 Why plain text files work for persistent AI memory 1:12 The five markdown files that store identity, context, learning, decisions, and tools 2:05 Boot image logic and the overnight primer summary 2:54 Core memory vs recall memory architecture 3:43 Overnight memory maintenance and Mem0-style sorting 4:28 Why false memory is more dangerous than no memory 5:03 Contradictions, conflict tagging, and human review 5:46 Why readable text files make AI memory safer and easier to manage 🧠 The video opens with the core problem of AI amnesia and why repeated context setup wastes time and causes recurring errors. 📁 It explains a minimal persistent memory system built from five markdown files that store preferences, projects, rules, decisions, and tool routing. ⚙️ It shows how an overnight process compresses those files into a short primer so the AI starts each session with relevant context already loaded. 🗂️ It separates core memory from recall memory to prevent context overload and keep active conversations accurate and focused. 🔄 It covers automated memory maintenance, including adding, updating, deleting, or ignoring information based on relevance and validity. ⚠️ It emphasizes that false memory is a real operational risk and that conflicting records should be flagged for human review instead of guessed. ✍️ It closes on the practical value of plain text: readable, editable, low-complexity AI memory that a human can correct instantly. Persistent AI memory works best when context stays readable, structured, and easy to correct. Plain text AI memory, markdown workflows, nightly primer summaries, and human in the loop review create a stronger AI context management system. That makes long-term AI assistants more accurate, more consistent, and more useful across real work. #AIMemory #MarkdownWorkflow #AIContextManagement