Query execution, benchmarks, indexing, and concurrency work.
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LoraDB has moved quickly since the public release, so it is worth stepping back from the version numbers and looking at the journey.
The individual posts tell the detail of each release. This one is the map: why the releases landed in this order, what each one proved, and how the current work fits the long arc from "fast local graph engine" to "database people can trust in the product loop."
LoraDB v0.5 is the release where the engine starts to breathe under larger result sets.
The first public releases were about making the model real: an in-memory graph, Cypher-shaped queries, vectors, snapshots, and then a WAL. v0.5 moves a level deeper. It changes how rows move through the executor, how common property lookups avoid scans, and how bindings expose results without requiring every query to become one large JSON payload.
The product promise is still the same: keep the graph close to the application, make the hot path fast, and keep the system small enough to understand. v0.5 makes that promise more practical once the graph stops being a demo-sized toy.