How do you compre to https://slapdash.com? - Source: Hacker News / over 1 year ago

Maybe slapdash is what you are looking for. Source: over 2 years ago

Slapdash (YC W19) - Remote - https://slapdash.com/ We have built a low-latency file system for your cloud application as well as the fastest interface in the world - the Command Bar - to interact with it. It's a new type of type of tool that makes modern computer work less tedious and more fun. We are hiring for three engineering roles:

  * Infrastructure Engineer - build infra that turns cloud apps...

- Source: Hacker News / almost 3 years ago

I tried out Superpowered and while it's more polished than its competitors, it has a few downsides other commenters have already touched upon (pricing, memory use, and personally, the name hits too close to home to Superhuman.) I've found that the best alternative is Slapdash. [0] Connect your calendar and then just hit cmd + J and boom -- Zoom is running. Many more connections are amazing as well, eg. Deep... - Source: Hacker News / over 3 years ago

Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 4 months ago

Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 5 months ago

Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 6 months ago

A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 6 months ago

To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 8 months ago