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I feel the landing page needs a minimum example of how to get value, not only how to install it. Think stripe, that to this day shows how to integrate it with a

by lorey 4y ago

I feel the landing page needs a minimum example of how to get value, not only how to install it. Think stripe, that to this day shows how to integrate it with a few lines of code. When looking at the tutorial, I also feel a lot of steps necessary to check it out. Why not provide basic functionality out of the box or a quick start to git clone? On another note, having built startup search myself [1], I think the demo does not do the technology justice [2]. People will most likely judge the product based on it, but the offered startup search does not work as expected, e.g. when trying "food delivery". Feel free to get in touch, will happily provide some data :) [1] https://startupradar.co https://startupradar.co [2] https://demo.qdrant.tech/ https://demo.qdrant.tech/

kacperlukawski 4y ago
Thanks for the insights, really appreciate them! I work at Qdrant, and if there is any way to ease the start with using it, then we're eager to think about it. However, it's way easier to do it with a tool like Stripe, which essentially simplifies one thing. Qdrant should be rather compared to databases like Postgres or MongoDB, but designed to support quick vector search. There is plenty of possibilities of how to use it, including semantic search, image search, anomaly detection, etc. so we don't want to bring a single topic as if it was the main use case. That's why we rather focus on a variety of tutorials and demos, covering different domains. However, we can surely think of an example based on your data, let's talk! :)
andre-z 4y ago
I think the "value" is explained just below: "Make the most of your Unstructured Data Qdrant is a vector similarity engine. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending..." However, I agree that the learning materials could be organized better.

by lorey 4y ago

kacperlukawski 4y ago

Thanks for the insights, really appreciate them! I work at Qdrant, and if there is any way to ease the start with using it, then we're eager to think about it. However, it's way easier to do it with a tool like Stripe, which essentially simplifies one thing. Qdrant should be rather compared to databases like Postgres or MongoDB, but designed to support quick vector search. There is plenty of possibilities of how to use it, including semantic search, image search, anomaly detection, etc. so we don't want to bring a single topic as if it was the main use case. That's why we rather focus on a variety of tutorials and demos, covering different domains. However, we can surely think of an example based on your data, let's talk! :)

andre-z 4y ago

I think the "value" is explained just below: "Make the most of your Unstructured Data Qdrant is a vector similarity engine. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending..." However, I agree that the learning materials could be organized better.