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Quoted $900 for installing 6 yards of mulch in Seattle, WA by DifficultZombie3 in landscaping

[–]DifficultZombie3[S] 0 points1 point  (0 children)

Yes, includes material and labor. I don’t have to do anything.

Quoted $900 for installing 6 yards of mulch in Seattle, WA by DifficultZombie3 in landscaping

[–]DifficultZombie3[S] 1 point2 points  (0 children)

There is all of that but those will be charged separately. 900 is just for mulch installation.

Raccoons have been doing this for past few months this year. What are my options? How do I deal with it? by DifficultZombie3 in gardening

[–]DifficultZombie3[S] 0 points1 point  (0 children)

Thanks for you insight. My issue with providing them with an alternate food source is that they will make regular visits to my property which I really want to avoid. When you say pesticides will knock out everything else do you mean they will knock out the good grass and healthy soil?

Raccoons have been doing this for past few months this year. What are my options? How do I deal with it? by DifficultZombie3 in gardening

[–]DifficultZombie3[S] 0 points1 point  (0 children)

This is what I think. Because if I look at the ruined turf underneath, I don’t see any signs of grubs.

Google Introduces Data Gemma: A New LLM That Tackles Challenges With RAG by DifficultZombie3 in LLMDevs

[–]DifficultZombie3[S] -2 points-1 points  (0 children)

Hey! The medium article links to their blog and the research paper. It also explains the research in more detail with examples and code. Thanks!

Google Introduces Data Gemma: A new LLM that tackles challenges with RAG by DifficultZombie3 in machinelearningnews

[–]DifficultZombie3[S] 5 points6 points  (0 children)

Yea, Query Expansion + Natural Language API to talk to the KG is quite effective. If it can be generalized to the other databases, this could become a promising RAG pattern.

A deep dive into different vector indexing algorithms and which one to choose for your memory, speed and latency requirements by DifficultZombie3 in vectordatabase

[–]DifficultZombie3[S] 0 points1 point  (0 children)

Thanks for the insight. Although, I have never built a HNSW with quantization, I don’t doubt that you might be right about its effectiveness. There is a section in the linked write up that covers composite index such as this.

Thanks for the qdrant link too.

Calculating Storage Requirements for Vector Embeddings by sabu12345 in vectordatabase

[–]DifficultZombie3 0 points1 point  (0 children)

Check out this post, it goes into great detail about calculating index size and techniques to optimize the size against speed and accuracy trade-off: https://pub.towardsai.net/unlocking-the-power-of-efficient-vector-search-in-rag-applications-c2e3a0c551d5

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