Scratchpad (https://scratchpad.com/) | REMOTE (US/Canada) | Full-time | Senior Backend Engineer, Senior Software Engineer - Backend Apply here: https://www.scratchpad.com/careers?ashby_jid=7b986939-6b0d-4365-98f8-9df4c6d83002 About Scratchpad At Scratchpad, our mission is to make salespeople happy. We believe we can do that by reducing complexity and creating delightful experiences, which has led us to pioneer the... - Source: Hacker News / over 1 year ago

You should definitely check out: https://scratchpad.com/. Source: about 2 years ago

Congrats on the new launch Iba and Syed, I still remember meeting you guys years ago in LA, you've come a long way! I'll echo some of the other comments here that most of the bigger companies I've worked with want a better JIRA but are weary of alternatives, data security, and the switching costs involved. Is there a way to offer this on top of the existing Atlassian tools? A similar model would be... - Source: Hacker News / almost 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 / 3 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 / 4 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 / 4 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 / 5 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 / 7 months ago