A specification and compiled Rust CLI describe an in-container LSP daemon that intercepts stdio JSON-RPC streams from IDE/agent loops to perform structural AST pruning and local execution for non-cognitive tasks. The design uses a Tree-sitter AST engine, a Cognitive Dependency Index (CDI) to decide what requires cloud inference, and zero-knowledge SHA-256 symbol masking so proprietary code never leaves the workspace. By filtering import/header/comment noise and routing low-cognitive work to a local execution grid, the system claims SOC2-ready privacy, zero host installation in containers or devboxes, and deterministic local parse latency guarantees (sub-50ms, often <10ms on common files).
Benchmarks presented simulate a 25-step refactoring loop on a 150-file TypeScript/Python codebase and report input token spend reduced from 1,450,000 to 280,000 (75.2% reduction), per-step latency falling from 1,240ms to 115ms (10.7x faster), and estimated cost per 1,000 runs dropping from $21.75 to $4.20 (≈80.6% margin expansion). Token reduction varies by file density (15-88% range). The repo includes an unlimited-bench binary to reproduce parsing and caching behavior, plus notes on distributed in-VPC AST state caching to eliminate redundant parsing and edge egress; commercial licensing and contact info for the author are provided.
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Sergey Brin states that Google's AI models are entering a phase of recursive self-improvement, where AI helps develop better AI systems. This process could accelerate AI development significantly within the next year, according to industry experts. (businessinsider.com)
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