We tested S3 Express for our search engine quickwit[0] a couple of weeks ago. While this was really satisfying on the performance side, we were a bit disappointed by the price, and I mostly agree with the article on this matter. I can see some very specific use cases where the pricing should be OK but currently, I would say most of our users should just stay on the classic S3 and add some local SSD caching if they... - Source: Hacker News / 6 months ago

Quickwit (https://quickwit.io/) | Paris, France | Onsite and remote (based in Europe) | Full-time The company is fully remote but we also have a small office in Paris. We prefer candidates based in Europe but can make exceptions for the right profiles. - Senior Software Engineer 80-110k€ + 0.25-1% equity based on experience.

    We’re looking for a senior software engineer to contribute to...

- Source: Hacker News / 9 months ago

- Another nice comment seen on HN « it seems to be very easy to run, not very IO intensive, and running fine on a single node with modest hardware with >2 billion log rows. It has a really cool dynamic schema feature too.» [9] Fun fact: at least 4 users are using Garage[10] as the object storage, this OSS project looks really promising and made the HN front page a few months ago[11], we really cherish the OSS for... - Source: Hacker News / 12 months ago

The github repository (⭐ are welcome ❤️) Https://github.com/quickwit-oss/quickwit. Source: 12 months ago

Quickwit is an open source Loki alternative too. Like said in one comment here, it works on billions of logs on one modest instance. And Grafana integration is on the way :) https://github.com/quickwit-oss/quickwit (disclaimer: I'm one of the cofounders). - Source: Hacker News / about 1 year ago

AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector. - Source: dev.to / 10 days ago

Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker. - Source: dev.to / 21 days ago

I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours. - Source: dev.to / 28 days ago

I'm currently looking to implement locally, using QDrant [1] for instance. I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2]. [1]. https://qdrant.tech/. - Source: Hacker News / about 2 months ago

There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb. - Source: Hacker News / 2 months ago