Weaver, a fast and scalable store for dynamically changing graphs
Weaver, a fast and scalable store for dynamically changing graphs launched on December 16, 2014 with 23 votes and more than 75% of that year's launches.
In plain words
Weaver is a database designed to store and manage graphs that change frequently and at scale. Built for developers and systems engineers who need to work with dynamic network data, it prioritizes speed and the ability to handle large volumes of evolving connections and relationships. The system is optimized for applications requiring real-time updates to complex interconnected data structures.
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HelixDB – A graph database built on object storageJun 2026 · github.com · ▲159Hey HN, it’s been just over a year since we launched HelixDB (https://news.ycombinator.com/item?id=43975423), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS). Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven applications attempt to combine all of these functionalities by stitching together…
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HelixDB – Open-source vector-graph database for AI applications (Rust)2025 · github.com · ▲237Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
Weave2025 · app.workweave.ai · ▲237Hey HN, We’re building Weave: an ML-powered tool to measure engineering output, that actually understands engineering output! Why? Here’s the thing: almost every eng leader already measures output - either openly or behind closed doors. But they rely on metrics like lines of code (correlation with effort: ~0.3), number of PRs, or story points (slightly better at ~0.35). These metrics are, frankly, terrible proxies for productivity. We’ve developed a custom model that analyzes code and its impact directly, with a far better 0.94 correlation. The result? A standardized engineering output…
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