LORE.md: An Open Standard for Extracting Structured Knowledge from AI Conversations
LORE.md is an open standard for extracting structured knowledge from AI conversations, specifically designed to address the problem of valuable insights being lost in chat logs. The standard defines a structured format that captures the durable knowledge from any AI conversation.
What LORE.md Captures
The format is designed to extract several key elements from conversations:
- Decisions with full rationale: Not just what was chosen, but the underlying assumptions that would need to change to revisit the decision
- Insights: Key realizations surfaced during conversations
- Patterns: Recurring themes or behaviors identified
- Open questions: Unresolved issues or topics for further exploration
- Next steps: Action items or follow-up tasks
All captured knowledge links together across sessions, allowing users to connect current conversations with previous discussions on the same topics.
Implementation Details
The project includes several practical components:
- System prompt: Works with any LLM - paste a conversation transcript and get structured knowledge back
- Bulk pipeline: For processing Claude data exports in volume
- Open source: MIT licensed and available on GitHub
The tool addresses the specific problem of being unable to search conversation history, connect insights across different sessions, or provide AI assistants with a comprehensive map of previously established knowledge.
This type of tool is useful for developers and researchers who regularly use AI assistants for problem-solving and want to maintain a searchable, structured knowledge base from their interactions.
📖 Read the full source: r/ClaudeAI
👀 See Also
Creation OS: A Local σ-Gated LLM Runtime That Lets Models Say ‘I Don’t Know’ Instead of Hallucinating
Creation OS wraps local LLMs (BitNet, Qwen, Gemma, any GGUF) with a σ-gate that measures multiple uncertainty channels and decides ACCEPT, RETHINK, or ABSTAIN per output. No cloud, no API. TruthfulQA accuracy improved ~29% via selective regeneration.
TRELLIS.2 Image-to-3D Ported to Run Natively on Apple Silicon
A developer has ported Microsoft's 4B parameter TRELLIS.2 image-to-3D model to run natively on Apple Silicon via PyTorch MPS, replacing CUDA-specific operations with pure-PyTorch alternatives. The port generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro with 24GB memory.
PhantomCrowd: Multi-Agent Audience Simulator Using Claude Code
PhantomCrowd is a marketing-focused multi-agent prediction engine that simulates how real audiences react to content before posting. It generates 10-500 personas with unique demographics and personalities, each reacting independently to content like ad copy or social posts.
DuckDB’s Quack Protocol Enables Client-Server with Multiple Concurrent Writers
DuckDB introduces the Quack remote protocol, allowing two DuckDB instances to communicate as client and server, supporting concurrent writers and leveraging HTTP for transport.