spr

What problem does it solve?

spr turns any provided text into a compact Sparse Priming Representation so a different LLM can reconstruct the original meaning with minimal tokens and low drift.

Core Features & Use Cases

• Sparse priming conversion: Converts each paragraph’s information into one-or-more non-overlapping, informationally dense priming statements.
• Structure mirroring: Preserves the source’s structured layout using semantic group headings and markdown subheadings when the input is organized.
• Recoverability-first output: Maximizes semantic recoverability while staying strictly within the source’s scope.
• Use case: When you need to transfer knowledge to another LLM that never saw the source (e.g., for downstream summarization, retrieval, or task execution), generate an SPR from the source text.

Quick Start

Convert the following text into an SPR with one priming statement per distinct concept and preserve the original structure: paste your text here.