February 21, 2026
I cut the AI's memory and it got smarter
Shrinking a monolithic index into a navigation hub plus scoped sub-indices reduced context overhead and improved coherence. The AI didn’t get smarter — the memory architecture did.
Shrinking a monolithic index into a navigation hub plus scoped sub-indices reduced context overhead and improved coherence. The AI didn’t get smarter — the memory architecture did.
SR-SI fixes long-running AI context decay using a shallow index and a protocol—no embeddings, databases, or fine-tuning required.
Most teams treat LLM memory as a compute problem. It’s an architecture problem. SR-SI replaces bloated scaffolds with a simple retrieval prosthesis built on indices, markdown, and Git.
Spec-first tools aren’t wrong — they just don’t match how everyone thinks. SR-SI supports an architect’s workflow: sketch, test, refine, repeat, without context loss.