I Built a Memory System for Claude That Solves the Context Loss Issue
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The author built a memory system for Claude, an AI model, to address context loss issues, sparking interest and discussion on its potential applications and limitations.
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How does buildautomata_memory_mcp differ in functionality and implementation from recall, and post-ECAN OpenCog STM/LTM decay?
How to detect when this occurs?
"A small number of samples can poison LLMs of any size" (yesterday) https://news.ycombinator.com/item?id=45529587
That would probably prevent waste and could scale.
From https://news.ycombinator.com/context?id=45561039 :
> /? llm firewall: https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
There's probably already a name for 'Sensitivity analysis'; how does the output vary with/without particular context?
Dual-layer architecture: SQLite + Qdrant vector search Git-style versioning: Full history with diff tracking Memory access patterns: Importance decay + usage tracking Timeline visualization: See how knowledge evolved CLI + MCP: Works with Claude Desktop AND Claude Code
Already validated: 100+ memories, 0.9MB database, working in production across three Claude interfaces (Desktop, Code, Cursor). GitHub: [https://github.com/brucepro/buildautomata_memory_mcp] Gumroad: [https://brucepro1.gumroad.com/l/zizjl] Happy to answer questions about the architecture or the Ego persona system it evolved from (Memoir v1 → BuildAutomata v2).