gk1

29 ms·

631.
On-demand drug information with AI(inpharmd.com)
2 points by gk1 3y ago|0 comments
632.
Gallery of Embedding Models(pinecone.io)
2 points by gk1 3y ago|0 comments
633.
Don't Design Your Emails (2016)(gkogan.co)
32 points by gk1 3y ago|10 comments
634.
by gk1 3y ago
I work for Pinecone so my bias is obvious, but objectively: Pinecone is ranked as the most popular vector DB according to multiple sources [1][2][3][4], the best funded ($100M Series B), and is used in production by the likes of Shopify, Go
635.
Show HN: Reference architecture for RAG applications on AWS(github.com)
1 points by gk1 3y ago|0 comments
636.
Vector Databases: The Engine Behind Today's AI Revolution(medium.com)
3 points by gk1 3y ago|0 comments
637.
Making Stable Diffusion Faster with Intelligent Caching – Pinecone(pinecone.io)
2 points by gk1 3y ago|0 comments
638.
Canopy: Build your own AI assistant(github.com)
2 points by gk1 3y ago|0 comments
639.
Show HN: Canopy – RAG framework and context engine powered by Pinecone(github.com)
2 points by gk1 3y ago|1 comments
640.
OpenAI Assistants API vs. Canopy: A Quick Comparison – Pinecone(pinecone.io)
1 points by gk1 3y ago|0 comments
641.
by gk1 3y ago
Besides the cost factor, stuffing the context window can actually make the results worse . https://www.pinecone.io/blog/why-use-retrieval-instead-of-la...
642.
by gk1 3y ago
Hey Ozzie, have a promo code for HNers looking to switch?
643.
by gk1 3y ago
- Intuit's revenue was $14.4 billion last year. An app making "millions" per month is a drop in the bucket. - Intuit paid $4.7 billion for Credit Karma, and only $170M for Mint. - Credit Karma had expected revenues of $1.5 bi
644.
Llama 2: The Missing Manual(pinecone.io)
10 points by gk1 3y ago|2 comments
645.
Advanced RAG: Rerankers and Two-Stage Retrieval(pinecone.io)
5 points by gk1 3y ago|0 comments
646.
Princeton Cos 597A: Vector Search and Databases(github.com)
3 points by gk1 3y ago|0 comments
647.
RAG: Reduce LLM Hallucinatons(pinecone.io)
1 points by gk1 3y ago|0 comments
648.
Purpose-Built vs. Bolted-On Vector Databases(pinecone.io)
1 points by gk1 3y ago|0 comments
649.
Great Algorithms Aren't Enough(pinecone.io)
2 points by gk1 3y ago|0 comments
650.
Rerankers and Two-Stage Retrieval for Rag(pinecone.io)
1 points by gk1 3y ago|0 comments
651.
RFC 9457: Better information for bad situations(redocly.com)
1 points by gk1 3y ago|0 comments
652.
Jina Launches Open-Source 8K Text Embedding(jina.ai)
2 points by gk1 3y ago|0 comments
653.
by gk1 3y ago
James Briggs? He's awesome and you can find lots of his articles + videos here: https://www.pinecone.io/learn/ https://www.youtube.com/@jamesbriggs
654.
Why vector databases are the engine of the AI era(runtime.news)
3 points by gk1 3y ago|0 comments
655.
Guardrails make RAG go faster(pinecone.io)
1 points by gk1 3y ago|0 comments
656.
How Shortwave (smartest email) works(shortwave.com)
2 points by gk1 3y ago|0 comments
657.
Sync Postgres to Pinecone(pinecone.io)
1 points by gk1 3y ago|0 comments
658.
by gk1 3y ago
I'm from Pinecone. We use proprietary indexes. We could've used HNSW but decided the high memory consumption (ie, costly at scale) and slow index updates (ie, data gets stale) won't cut it for production use cases.
659.
by gk1 3y ago
What you’re describing is easily done in Pinecone, and in other solutions as well. See: https://docs.pinecone.io/docs/metadata-filtering
660.
by gk1 3y ago
The “force merge” process in an Elastic vector index took as long as 18 hours in one test. That’s a lot longer than a few hops across the network. This is not even a dig at Elastic. The problem is deeper than that… It’s an issue with the un

More ›