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Build a drug discovery research assistant using Strands Agents and Amazon Bedrock

Build a drug discovery research assistant using Strands Agents and Amazon Bedrock

Drug discovery is a complex, time-intensive process that requires researchers to navigate vast amounts of scientific literature, clinical trial data, and molecular databases. Life science customers like Genentech and AstraZeneca are using AI agents and other generative AI tools to increase the speed of scientific discovery. Builders at these organizations are already using the fully …

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Amazon Nova Act SDK (preview): Path to production for browser automation agents

Amazon Nova Act SDK (preview): Path to production for browser automation agents

In early 2025, we introduced the Amazon Nova Act SDK as a research preview to help developers build agents that reliably complete tasks in a web browser. Now, we are excited to work with customers to take their agents to production in a limited preview, using new AWS integrations including AWS Identity and Access Management …

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Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency

Optimizing enterprise AI assistants: How Crypto.com uses LLM reasoning and feedback for enhanced efficiency

This post is co-written with Jessie Jiao from Crypto.com. Crypto.com is a crypto exchange and comprehensive trading service serving 140 million users in 90 countries. To improve the service quality of Crypto.com, the firm implemented generative AI-powered assistant services on AWS. Modern AI assistants—artificial intelligence systems designed to interact with users through natural language, answer …

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Build modern serverless solutions following best practices using Amazon Q Developer CLI and MCP

Build modern serverless solutions following best practices using Amazon Q Developer CLI and MCP

Building modern serverless applications on AWS requires navigating best practices to manage the integration between multiple services, such as AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon EventBridge. Security considerations, performance optimization, and implementing a comprehensive monitoring systems adds further requirements to build a serverless architecture while adhering to AWS best practices. Amazon Q Developer CLI with Model Context Protocol …

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Build an intelligent eDiscovery solution using Amazon Bedrock Agents

Build an intelligent eDiscovery solution using Amazon Bedrock Agents

Legal teams spend bulk of their time manually reviewing documents during eDiscovery. This process involves analyzing electronically stored information across emails, contracts, financial records, and collaboration systems for legal proceedings. This manual approach creates significant bottlenecks: attorneys must identify privileged communications, assess legal risks, extract contractual obligations, and maintain regulatory compliance across thousands of documents …

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How PerformLine uses prompt engineering on Amazon Bedrock to detect compliance violations 

How PerformLine uses prompt engineering on Amazon Bedrock to detect compliance violations 

This post is co-written with Bogdan Arsenie and Nick Mattei from PerformLine. PerformLine operates within the marketing compliance industry, a specialized subset of the broader compliance software market, which includes various compliance solutions like anti-money laundering (AML), know your customer (KYC), and others. Specifically, marketing compliance refers to adhering to regulations and guidelines set by …

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Boost cold-start recommendations with vLLM on AWS Trainium

Boost cold-start recommendations with vLLM on AWS Trainium

Cold start in recommendation systems goes beyond just new user or new item problems—it’s the complete absence of personalized signals at launch. When someone first arrives, or when fresh content appears, there’s no behavioral history to tell the engine what they care about, so everyone ends up in broad generic segments. That not only dampens …

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Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto

Benchmarking Amazon Nova: A comprehensive analysis through MT-Bench and Arena-Hard-Auto

Large language models (LLMs) have rapidly evolved, becoming integral to applications ranging from conversational AI to complex reasoning tasks. However, as models grow in size and capability, effectively evaluating their performance has become increasingly challenging. Traditional benchmarking metrics like perplexity and BLEU scores often fail to capture the nuances of real-world interactions, making human-aligned evaluation …

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