Every RAG stack in production runs on one primitive: retrieval. retrieval.ml available. #AI #RAG #vectorsearch #LLM #domains

LLM direct calls vs vector search — CrowdSnap tested both on real data. 400x faster queries. $0.11 vs $15 per 1K analyses. Break-even at 51 queries. The answer? Use both. Here's when → ow.ly/8xVK50Yyuni #TiDB #VectorSearch

At #NDC London, #OpenSearchAmbassador @horovits discussed search methodologies, with special emphasis on semantic search, and what the OpenSearch Project provides, fully open source under The Linux Foundation. bit.ly/4uPsMG4 #VectorSearch #AIApps

Milvus • Chroma • Faiss • MongoDB Vector SearchFour of the most popular vector databases in one frame — each bringing its own strength to AI-powered search, embeddings, and semantic retrieval.Which one is your go-to for vector search? #VectorDatabase #VectorSearch #Milvus #Chroma #Faiss #MongoDB #AI #MachineLearning #GenAI #LLM

Kimberly and #AIJoe Tech Talk: 11 - Designing for Embeddings using Partitioning youtu.be/Xd8RtRE7j2c via @YouTube #VectorSearch #Data #AI #SQLServer2025

Google Research Unveils TurboQuant for Extreme AI Model Compression (Source: Research) Google Research introduces TurboQuant for extreme LLM and vector search compression. #AICompression #LLMOptimization #VectorSearch #GoogleAI #MachineLearning 🤔 How will extreme compression techniques like TurboQuant reshape the economic and environmental footprint of large-scale AI? s.dailyaiwire.news/74usKpr

Introducing VectorClust: A new theoretical framework for capacity-constrained vector search. By modeling hardware limits as a k-clustering problem, it achieves an optimal (3+O(ε)) approximation for any monotone symmetric norm. ​#AI #VectorSearch #ComputerScience #AGI

I benchmarked 7 RAG strategies across 17000 chunks. The search engine matters least. Start with BM25, invest in your agent and ingestion pipeline. - hackernoon.com/why-your-rag-s… #vectorsearch #ragoptimization

#NvidiaGTC 2026 exclusive: theCUBE x @NYSE Wired’s @GemmaAllenSays speaks with @elastic’s @skearns64 & @DellTech’s Vrashank Jain about their partnership on democratizing #vectorsearch capabilities. 💡 Get more insights! thecube.net/events/nvidia/… #MetaData #DellPartners #TechNewsl

一般論への思考ベクトルを強制削除する試行 ​An attempt to forcibly delete the thought vector toward generalities. #AlgorithmDesign #VectorSearch #AIAlignment #ZeroVariable #LogicalPurity #TEDPI #CognitiveScience

𝗪𝗵𝗮𝘁’𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗻𝗼𝘄 𝗮𝘁 @awscloud? AWS is showing how streaming data + #vectorsearch + #ApachePinot are powering a new generation of AI applications. Because in AI systems, context 𝘥𝘦𝘭𝘢𝘺𝘦𝘥 is 𝘷𝘢𝘭𝘶𝘦 𝘭𝘰𝘴𝘵. 𝗧𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Modern AI applications depend on fast-changing signals:   • Customer conversations   • Product catalogs   • Operational data   • Market sentiment   • Supply chain signals But most vector databases still update in batches. That means AI systems are often retrieving stale context, not what’s happening right now. And when context is stale, AI decisions lag behind reality. 𝗧𝗵𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁 AWS demonstrated how real-time vector pipelines solve this problem. Streaming data flows through #Kafka or #Kinesis. Data is embedded with models like Amazon Titan. Those embeddings are ingested, indexed, and made available for vector search in Apache Pinot in real-time. The result is AI that retrieves live context, not yesterday’s embeddings. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁 Applications that understand what’s happening right now:   • Live deep learning recommendation engines   • Customer support copilots with fresh context   • Real-time sentiment analysis from social platforms Ultimately, faster responses to customer sentiment, market changes, and operational events. Because in the AI-native era: It’s not just what you know. It’s how fast you know it—and act on it. 𝗙𝘂𝗹𝗹 𝘀𝘁𝗼𝗿𝘆 → 𝗵𝘁𝘁𝗽𝘀://𝘀𝘁𝗿𝗲𝗲.𝗮𝗶/𝟰𝟬𝗘𝗙𝗲𝗲𝟰