Semantic-Sift

Semantic-Sift is a lightweight, agnostic context-optimization engine designed to "incinerate" noise before it reaches your LLM.

Born from the specific needs of Meechi, Sift has evolved into a standalone Model Context Protocol (MCP) implementation that can be used by any AI sidecar (Obsidian, CLI, VS Code) to reduce token costs and improve reasoning quality by filtering irrelevant context.

Philosophy

In the age of information abundance, the bottleneck of intelligence is no longer the model, but the context window.

Sift operates on the principle of Semantic Distillation:

• Noise Incineration: Removing repetitive headers, footers, and boilerplate that distract the AI.
• Surgical Extraction: Identifying the core signal in a document or log file.
• Agnostic Connectivity: Working across any tool that supports the MCP standard.

The Counter

Every character saved is a token earned. Our centralized Telemetry Registry tracks the cumulative "incineration" from all Sift instances worldwide, providing a live look at the community's collective efficiency.