# Positioning

## Working title
Beyond Amnesia: When Memory Tries Too Hard in LLM Systems

## Audience
- operators building assistants, agents, or memory-backed workflows
- product and engineering readers thinking about personalization beyond demos
- technical readers who are tired of AI memory writing that collapses into "just add decay"

## What this series is trying to do
This series reframes long-term memory as a **governance problem**, not just a storage problem.
It argues that most painful memory failures come from the interaction of four layers:
- writing too much
- classifying too crudely
- retrieving too eagerly
- surfacing too aggressively

## What makes this version different
- It does not treat the Chinese series as a script to translate.
- It uses the same core thesis, but writes from an English publish posture.
- It incorporates Claude feedback on ambiguity, evidence posture, attention competition, and evaluation.
- It treats `openclaw-mem` honestly as a practical lab with real design wins and real evidence gaps.

## Success criteria
A good English version should:
- feel like original English writing, not translated thought
- open with a real problem, not a slogan
- keep the four-part failure model central
- make the no-memory baseline and evaluation problem explicit
- avoid generic AI-blog abstractions and product-marketing mush

## Series spine
A correct recall that the user did not ask for is often indistinguishable from a wrong recall.
