SR-SI context savings scale progressively
SR-SI doesn’t just reduce AI re-orientation — it scales with repo complexity. Small repos save 10–20%. Large repos can reach 40–60% when governance stays tight.
SR-SI doesn’t just reduce AI re-orientation — it scales with repo complexity. Small repos save 10–20%. Large repos can reach 40–60% when governance stays tight.
Better specs don’t fix AI projects. Context decay does. SR-SI compresses architectural memory into a shallow index the AI consults and maintains to prevent drift.
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 V2 is live: 106x Token Coherence improvement, near-zero marginal upkeep through agent-operated maintenance, and new findings on functional identity over long-running workflows.
Most AI-augmented development workflows break when they crash into the context wall.
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.
AI context drift isn’t a model limitation—it’s an architectural failure. SR-SI replaces brute-force context with indexing, enabling persistent coherence across long-running projects.
The problem with most AI workflows isn’t missing information. It’s missing navigation. SR-SI works because it gives AI a compact index, not a bloated encyclopedia.
A concrete look at how SR-SI works in practice: what the context document contains, how it’s structured, and how it replaces the hidden re-explanation overhead of AI-assisted development.