This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
brinicle is a disk-first HNSW retrieval engine for vector search, structured item search, hybrid search, and autocomplete. On 1.2M Amazon products, brinicle achieved sub-ms P99 hybrid search (lexical, semantic) while using substantially less search memory than Weaviate, OpenSearch, Typesense, and Meilisearch. The report: https://brinicle.bicardinal.com/search_benchmark
I wanted to design an extremely fast vector search engine that does not blow your RAM, while performs competitive in terms of accuracy. So, I built brinicle. brinicle supports: ANN Vector Engine. Item Search Engine. And Autocomplete Engine. I compared it against Milvus, Chroma, Qdrant, and Weaviate in different datasets and published results here: brinicle.bicardinal.com/benchmark Then, I created a benchmark for its item search ability on two datasets and compared it against Weaviate, OpenSearch, Typesense, and Meilisearch. brinicle outperforms in terms of search latency, and memory consumption, while keeping the accuracy, or outperforming in some metrics. Results, and the approach are explained in this comprehensive report: brinicle.bicardinal.com/search_benchmark
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About brinicle on Product Hunt
“Extremely fast/RAM-friendly search engine”
brinicle was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #60 on the daily leaderboard. brinicle is a disk-first HNSW retrieval engine for vector search, structured item search, hybrid search, and autocomplete. On 1.2M Amazon products, brinicle achieved sub-ms P99 hybrid search (lexical, semantic) while using substantially less search memory than Weaviate, OpenSearch, Typesense, and Meilisearch. The report: https://brinicle.bicardinal.com/search_benchmark
brinicle was featured in Developer Tools (514k followers), Artificial Intelligence (471k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 195.1k products, making this a competitive space to launch in.
Who hunted brinicle?
brinicle was hunted by Saeed Dehqan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Saeed Dehqan
I wanted to design an extremely fast vector search engine that does not blow your RAM, while performs competitive in terms of accuracy. So, I built brinicle.
brinicle supports:
ANN Vector Engine.
Item Search Engine.
And Autocomplete Engine.
I compared it against Milvus, Chroma, Qdrant, and Weaviate in different datasets and published results here: brinicle.bicardinal.com/benchmark
Then, I created a benchmark for its item search ability on two datasets and compared it against Weaviate, OpenSearch, Typesense, and Meilisearch. brinicle outperforms in terms of search latency, and memory consumption, while keeping the accuracy, or outperforming in some metrics. Results, and the approach are explained in this comprehensive report: brinicle.bicardinal.com/search_benchmark