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

ExternalSoftware Engineering
DZone

Amazon OpenSearch Vector Search Explained for RAG Systems

In this article, we will understand how vector search works in Amazon OpenSearch and how to use it as the retrieval layer in a retrieval-augmented generation (RAG) system. The article is meant for software engineers. We will not stop at theory. We will build a small, working example that you can run on your own machine and follow along step by step. By the end, you will have a small document search service that takes a user question, finds the most relevant text using vector similarity, and prep

DZoneRead original
ExternalCybersecurity
BleepingComputer

XBOW tests Anthropic's Mythos Preview for offensive security

Anthropic's Mythos Preview was highly effective at finding vulnerability candidates, especially when analyzing source code. XBOW explores how the model performed across exploit discovery, reverse engineering, and live-site validation. [...]

Security
BleepingComputerRead original
ExternalAI
AWS Machine Learning Blog

Build an agentic incident triage assistant with Amazon Quick and New Relic

This post shows engineering teams how to apply that principle to one of the most time-sensitive workflows in engineering: incident triage. You will build a custom incident triage assistant agent using Amazon Quick that orchestrates a response with the New Relic Model Context Protocol (MCP) Server and Asana through native integrations. From a single prompt, the Amazon Quick agent investigates the incident, assembles a root cause analysis (RCA) brief with evidence links, and creates a tracked Asan

Amazon Quick SuiteCustomer Solutions
AWS Machine Learning BlogRead original
ExternalCloud
Google Cloud Blog

Report: GKE Inference Gateway delivers up to 92% faster AI responses

As generative AI moves from experimental pilots to massive production environments, the efficiency of your infrastructure  becomes the ultimate differentiator. One way to get the most out of it and minimize costly accelerator idle time is to leverage the Google Kubernetes Engine (GKE) Inference Gateway, which intelligently routes generative AI workloads based on real-time model server metrics. Instead of relying on traditional, naive round-robin load balancing — which frequently triggers expensi

NetworkingAI & Machine LearningAI infrastructure
Google Cloud BlogRead original

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