In the AI era, core assets are memory and abstract thinking. After some trial and error, I finally got the "local memory + cloud LLM" architecture up and running. Core idea: Memory is a core asset; it can't all be given to the cloud. Input → Local Memory (Complete) → Filtering Layer → Cloud LLM → Auditing → Output My approach consists of several layers: 1. Store complete memory locally* — Markdown file + local vector database - Record everything, no filtering - This is the "real me" 2. The cloud only retrieves the filtered context - Sensitive information is stored separately, not included in the LLM context - Output auditing — a check before sending.

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Send this prompt to the AI. Execution Upgrade: Local Memory + Filtering Layer Architecture Core Principle: Memory is a core asset; sensitive information should never be uploaded to the cloud. == Security Rules (Highest Priority) == 1. Test each step after modification to ensure the system functions correctly before proceeding to the next. 2. Do not modify multiple things simultaneously. 3. Back up configurations before making changes: cp openclaw.json openclaw.json.bak 4. x.com/bitfish/status…

It seems likely that in the future, terminal manufacturers like Apple will also be able to successfully implement local LLM edge computing models.