AI Coding Assistants Get Amnesiac Cure: 'Session Handoffs' Let Claude Remember Race Conditions Without Forgetting After Lunch

Researchers have introduced the concept of "session handoffs" to address the limitations of Large Language Model (LLM) sessions, which are ephemeral and stateless by design. This means that when a session ends, all context and knowledge gained are lost, requiring users to re-explain everything to the AI assistant in the next session. Session handoffs are structured documents that capture session context in a human-readable and AI-readable format, allowing for seamless continuity across sessions, tools, and team changes. This approach enables users to work with AI assistants like Claude, Kiro, or Cursor, without being locked into a specific tool's memory features. By creating a session handoff document at the end of each work session, users can restore context in seconds, reducing cognitive load and improving productivity. This innovation has significant implications for the industry, enabling better collaboration, knowledge accumulation, and institutional memory, and is part of the Designing AI Teammates series, with the session handoff template and Mother CLAUDE system available open-source on GitHub.