Developer Builds Rust-Based Object-Shaped Memory Engine to Reduce AI Agent DB Friction

A developer behind a project called 'thingd' has designed a new memory engine aimed at eliminating the architectural friction that traditional SQL and NoSQL databases create for AI agents. The core argument is that AI agents operate in terms of state, context, and entities rather than rows, columns, or nested JSON blobs, making conventional database paradigms a poor fit. To address this, thingd uses SQLite as its underlying storage engine but exposes it as a high-performance object store, where every data point is treated as a 'thing' with its own identity, properties, and relationships. The engine is written in Rust to leverage zero-cost abstractions and is designed to be local-first, eliminating the network latency overhead of remote cloud database calls. The approach also sidesteps common ORM problems such as context bloat, schema inflexibility, and slow query overhead during autonomous agent loops.