ContextLattice v3.2.3: Faster Agent Memory with Go/Rust Runtime and Staged Retrieval

I shipped ContextLattice v3.2.3, a local-first memory/context layer for apps and agents. I improved retrieval speed across all database backends and deep-source coverage for ContextLattice. Key runtime design: Go handles ingress/orchestration Rust handles retrieval/memory hot paths interfaces with cli, desktop & web apps, claw/messaging platform-ready Retrieval design: fast lane: topic_rollups + qdrant + postgres_pgvector deep continuation lane: mindsdb + mongo_raw + letta + memory_bank Performance highlights: v3.2.0 Go lane mean 0.157s vs Python lane 0.255s (38.547% faster) earlier runtime cutover benchmark: ~4.94x faster mean vs legacy lane Project: https://github.com/sheawinkler/ContextLattice https://contextlattice.io Next phase: data compression, data expansion control interactive monitoring dashboard debugging agent & subagent orchestration challenges future: decrease personal computer requirements obsidian plug-in or similar/more performant knowledge visualizer