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Curated links from external sources — not 360Softy original articles.

ExternalSoftware Engineering
DZone

S3 Vectors: How to Build a RAG Without a Vector Database

Every RAG tutorial follows the same script: embed your documents, spin up a vector database (Pinecone, Weaviate, pgvector, OpenSearch), manage its infrastructure, and pray the costs don't spiral. For most internal AI apps, this is overkill. Amazon S3 Vectors changes the equation. It's native vector storage built into S3 — no clusters, no provisioning, no idle compute. You store vectors like you store objects, query them with sub-100ms latency, and pay per use. It went GA in December 2025 and now

DZoneRead original
ExternalDatabase
AWS Database Blog

Automated JDBC query caching with the AWS Advanced JDBC Wrapper

Today, we’re announcing the Remote Query Cache Plugin for the AWS Advanced JDBC Wrapper. The plugin handles query caching automatically. It intercepts JDBC queries, caches results in Amazon ElastiCache for Valkey, and serves subsequent identical queries from cache. Your only application change is prefixing queries with SQL hints. In this post, we show you how to use Amazon CloudWatch Database Insights to identify queries to cache, configure the Remote Query Cache Plugin in your Java applications

Amazon AuroraAmazon CloudWatchAmazon ElastiCache
AWS Database BlogRead original
ExternalSoftware Engineering
DZone

Key Takeaways From Integrating a RAG Application With LangSmith

In this article, I am sharing what I learned while integrating a RAG-based application with LangSmith. It covers how the integration works and the key insights gained from using LangSmith for observability and evaluation. LangChain LangChain is a framework for building applications powered by large language models in a more structured and modular way. It helps developers connect LLMs with prompts, tools, memory, agents, and external data sources to create more capable applications. In simple ter

DZoneRead original
ExternalAI
AWS Machine Learning Blog

Scalable voice agent design with Amazon Nova Sonic: multi-agent, tools, and session segmentation

In this post, you’ll learn how to use Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands BidiAgent to build scalable, maintainable voice agents that handle these challenges efficiently, resulting in more responsive and intelligent customer interactions. We’ll explore three popular architectural patterns for voice agents, highlighting their trade-offs and best practices for minimizing latency.

Amazon BedrockAmazon Machine LearningAmazon Nova
AWS Machine Learning BlogRead original
External
AWS Machine Learning Blog

Extending conversational memory in Kiro CLI using Amazon Bedrock AgentCore Memory

In this post, we demonstrate how you can extend the conversational memory of Kiro CLI by implementing a custom Model Context Protocol (MCP) server that integrates with Amazon Bedrock AgentCore Memory. You can use Kiro CLI to interact with AI agents of Kiro directly from your terminal. Amazon Bedrock AgentCore Memory is a fully managed service that allows AI agents to retain information from past interactions, creating more intelligent and context-aware conversations. By implementing a custom MCP

Advanced (300)Amazon Bedrock AgentsKiro
AWS Machine Learning BlogRead original

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