---
title: "AI memory architectures: a Claude conversation"
url: https://memory.wiki/yt2ZZqyI
updated: 2026-05-14T17:52:48.410Z
hub: https://memory.wiki/hub/demo
bundle_count: 1
concept_count: 12
source: "memory.wiki"
---
# AI memory architectures: a Claude conversation

> Captured from a working session with Claude Opus, 2026-03-12. Cleaned, structured, and saved as a permanent URL so the next AI session can pick up where we left off.

## The question

What architecture should a personal memory layer use? Three patterns are in production today:

1. **Vector recall** — every message goes through an embedding model, gets stored, retrieved by cosine similarity on demand. ChatGPT memory beta works this way.
2. **Episodic snapshots** — full conversation transcripts are stored verbatim, indexed by date and topic. Claude Projects does this.
3. **Hub-shaped memory** — the user authors structured notes; the AI reads them as URL-addressable resources.

## What Claude argued

> Vector recall trades precision for breadth. Episodic snapshots trade verbosity for fidelity. Hub-shaped memory trades automation for author-control.

The third pattern wins for one reason: **the human stays the author**. Vector + episodic both let memory drift — once stored, the user can't easily edit or curate without leaving the AI's UI. Hub-shaped puts the artifact in a place the user already lives (a document) and the AI reads from there.

## Takeaway for mdfy

This is the existing direction. Worth checking against the spec page — `/spec` already documents the URL contract. No code change needed; this conversation just validates the choice.

## Related concepts

- Vector recall, episodic snapshot, hub-shaped memory
- Forgetting as a feature
- URL-addressable knowledge


---

## Concepts in this document
- **mdfy** _(entity)_
  A tool that stores project context and decision history, integrated into Cursor via custom rules.
- **Claude** _(entity)_
  Anthropic's AI model cited as an example of vendor lock-in through projects and memory features.
- **ChatGPT** _(entity)_
  Example of an AI provider whose memory feature is intentionally confined to its own product.
- **Hub-shaped memory** _(concept)_
  The chosen architecture where users author structured notes as URL-addressable resources that the AI reads, preserving human control over memory curation.
- **Knowledge Management** _(tag)_
  Domain of organizing, storing, and retrieving information for human and AI use.
- **Vector recall** _(concept)_
  Memory pattern using embeddings and cosine similarity retrieval; trades precision for breadth but risks memory drift beyond user control.
- **URL-addressable knowledge** _(concept)_
  Design principle enabling AI systems to read structured user-authored content from persistent, addressable locations outside the AI interface.
- **Episodic snapshots** _(concept)_
  Memory pattern storing full conversation transcripts indexed by date and topic; trades verbosity for fidelity but also risks uncontrolled memory drift.
- **Composable scopes** _(concept)_
  Three-tier architecture (doc, bundle, hub) that scales from single notes to full knowledge graphs.
- **Andrej Karpathy** _(entity)_
  The originator of the LLM wiki concept presented in a 2024 Twitter thread.
- **AI Memory** _(tag)_
  Broad category encompassing how AI systems maintain and access knowledge across sessions.
- **Human-authored memory** _(concept)_
  The core principle that durable AI memory comes from deliberate human writing, not lossy inference from chat transcripts.

## Concept relations (within this doc's concepts)
- **Vector recall** implemented by **ChatGPT**
- **Episodic snapshots** implemented by **Claude**
- **Hub-shaped memory** implemented by **mdfy**
- **Composable scopes** enables pattern **URL-addressable knowledge**
- **Hub-shaped memory** implements principle **Human-authored memory**
- **Composable scopes** extends architecture **Hub-shaped memory**
- **URL-addressable knowledge** enables approach **Hub-shaped memory**
- **mdfy** implements pattern **Hub-shaped memory**
- **mdfy** provides architecture **Composable scopes**
- **mdfy** integrates with **Claude**
- **mdfy** solves problem for **ChatGPT**
- **Hub-shaped memory** depends on **URL-addressable knowledge**

## Bundles containing this document
- [AI Memory Research](https://memory.wiki/b/wa-K_2rF)
  > Captured conversations + external reading on how AI memory architectures actually work. Reading order: the Claude conversation lays out the three patterns, the Karpathy summary names the philosophical

_Hub canonical:_ https://memory.wiki/hub/demo
_Concept digest:_ https://memory.wiki/raw/hub/demo?digest=1&compact=1