1. W Which German councils are discussing wind power? Poliscope answers in one search. 4,800 council ... Which German councils are discussing wind power? Poliscope answers in one search. 4,800 council portals, 130M points, one self-hosted Qdrant node. Searches went from up to 20 s to 10/19 18:00 ♡
2. H https://t.co/uXP12n7r2A qdrant.tech/blog/pplx-embe… 💬 0 🔄 0 ❤️ 11 👀 511 📊 2 ⚡ Powered by xgo.ing 10/14 15:06 ♡
3. 2 21× smaller vectors. 99.9% of baseline retrieval quality retained with rescoring. We tested Perplex... 21× smaller vectors. 99.9% of baseline retrieval quality retained with rescoring. We tested Perplexity’s pplx-embed-v2-late with Qdrant’s TurboQuant across 4 ViDoRe v3 datasets. 85 10/14 15:05 ♡
4. R Read the benchmarks: https://t.co/ZlnUTTHGYw Read the benchmarks: qdrant.tech/documentation/… 💬 0 🔄 0 ❤️ 4 👀 224 📊 1 ⚡ Powered by xgo.ing 10/13 17:31 ♡
5. 780 ms during background optimization. 4.3 ms after it finished. Same Qdrant collection, very diffe... 780 ms during background optimization. 4.3 ms after it finished. Same Qdrant collection, very different search latency. We tested 13 optimizer configurations on 1.76M vectors to un 10/13 17:29 ♡
6. L LangGraph docs: https://t.co/r7Wns5XOME Qdrant integration: https://t.co/6rxYzLD573 LangGraph docs: docs.langchain.com/oss/python/lan… Qdrant integration: pypi.org/project/langgr… 💬 0 🔄 0 ❤️ 3 👀 254 📊 1 ⚡ Powered by xgo.ing 10/09 16:20 ♡
7. Your AI agents need memory that lasts beyond a single conversation. LangGraph supports long-term me... Your AI agents need memory that lasts beyond a single conversation. LangGraph supports long-term memory, allowing agents to store information and recall it across conversations. No 10/09 16:20 ♡
8. Take the free course on @DeepLearningAI today, and learn how to give your robots real memory with #Q... Take the free course on @DeepLearningAI today, and learn how to give your robots real memory with #QdrantEdge DeepLearning.AI @DeepLearningAI This assistant remembers where you lef 10/09 13:49 ♡
9. 77× less vector RAM. 94.5% of baseline retrieval quality. We got early access to Google EmbeddingGe... 77× less vector RAM. 94.5% of baseline retrieval quality. We got early access to Google EmbeddingGemma 2 and tested how far its Matryoshka-compatible embeddings could be compressed 10/07 21:05 ♡
10. What happens when engineers are given a controversial topic and told to debate it? We’re bringing s... What happens when engineers are given a controversial topic and told to debate it? We’re bringing something a little different to San Francisco Tech Week. Join Qdrant × @neo4j for 10/05 21:30 ♡
