It certainly looks interesting, how does this differ from a plan.md?
replies(1):
The deeper problem
1. Not portable – context is vendor-locked; nothing travels across tools.
2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance.
3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried.
Demo video: https://youtu.be/iANZ32dnK60
Repo: https://github.com/RedPlanetHQ/core
What we built
- CORE (Context Oriented Relational Engine): An open source, shareable knowledge graph (your memory vault) that lets any LLM (ChatGPT, Cursor, Claude, SOL, etc.) share and query the same persistent context.
- Temporal + relational: Every fact gets a full version history (who, when, why), and nothing is wiped out when you change it—just timestamped and retired.
- Local-first or hosted: Run it offline in Docker, or use our hosted instance. You choose which memories sync and which stay private.
Try it
- Hosted free tier (HN launch): https://core.heysol.ai
CORE lets you - Automatically extracts and stores facts from conversations - Builds intelligent connections between related information - Answers complex queries ("What did I say about something and when?") - Detects contradictions and explains changes with full context
For simple fact recall, plan.md should work but for complex systems a relational memory should be able to help better.