AI coding chats often contain proprietary code, internal URLs, credentials-adjacent snippets, debugging notes, architecture decisions, and emotional context from a late-night fix. That makes chat history useful, but also a poor default place to store project memory.
Why this matters.
Microsoft reported malicious AI assistant browser extensions that harvested LLM chat histories and browsing data. Separately, prompt-injection guidance from OpenAI describes how external content can manipulate agent behavior when it enters the conversation context. Both problems point to the same recovery rule: minimize what you carry forward and label where it came from.
What to preserve instead.
- Keep the diff. Changed files are more important than the full chat transcript.
- Keep verification state. Tests run, commands failed, and logs produced are durable evidence.
- Keep decisions. Summarize why a path was chosen without copying unnecessary sensitive text.
- Keep failed attempts. Record what not to repeat.
- Drop irrelevant conversation. Do not carry every prompt and response into the next tool.
What ShardStitch does here.
ShardStitch is local-first and zero telemetry. Its job is to turn messy chat-adjacent work into a smaller, labeled recovery packet: verified disk facts, inferred claims, failed attempts, decisions, and next action. That helps you restart or switch tools without preserving a sensitive transcript as project memory.
Source trail
- Microsoft Security: malicious AI assistant extensions harvest LLM chat histories
- OpenAI: understanding prompt injections