KV cache is the memory wall that limits context length on consumer hardware. TurboQuant shrinks it 5x with minimal quality loss — here’s a ready-to-run build that packages llama.cpp with TurboQuant KV compression into a single conda install.
RBAC tells you if a role can access a table. But can this agent invoke this tool on this data for this purpose? The industry is building the pieces — Cedar, Proofpoint, Cisco, Immuta — but the unified policy engine that evaluates all attributes across all layers doesn’t exist yet.
Google’s TurboQuant compresses embedding vectors to 3-4 bits with under 2% recall loss — no training required. Here’s why that matters for AI agent memory systems.
A pluggable semantic memory layer for AI agents inspired by the Zettelkasten method — auto-linking, importance scoring, and graph traversal across CrewAI, LangGraph, and Claude Code.