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1. Automate the creation of handout notes using Amazon Bedrock Data Automation

   In this post, we show how you can build an automated, serverless solution to transform webinar recordings into comprehensive handouts using Amazon Bedrock Data Automation for video analysis. We walk you through the implementation of Amazon Bedrock Data Automation to transcribe and detect slide changes, as well as the use of Amazon Bedrock foundation models (FMs) for transcription refinement,...

2. Streamline GitHub workflows with generative AI using Amazon Bedrock and MCP

   This blog post explores how to create powerful agentic applications using the Amazon Bedrock FMs, LangGraph, and the Model Context Protocol (MCP), with a practical scenario of handling a GitHub workflow of issue analysis, code fixes, and pull request generation.

3. Mistral-Small-3.2-24B-Instruct-2506 is now available on Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

   Today, we’re excited to announce that Mistral-Small-3.2-24B-Instruct-2506—a 24-billion-parameter large language model (LLM) from Mistral AI that’s optimized for enhanced instruction following and reduced repetition errors—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. Amazon Bedrock Marketplace is a capability in Amazon Bedrock that developers can...

4. Generate suspicious transaction report drafts for financial compliance using generative AI

   A suspicious transaction report (STR) or suspicious activity report (SAR) is a type of report that a financial organization must submit to a financial regulator if they have reasonable grounds to suspect any financial transaction that has occurred or was attempted during their activities. In this post, we explore a solution that uses FMs available in Amazon Bedrock to create a draft STR.

5. Fine-tune and deploy Meta Llama 3.2 Vision for generative AI-powered web automation using AWS DLCs, Amazon EKS, and Amazon Bedrock

   In this post, we present a complete solution for fine-tuning and deploying the Llama-3.2-11B-Vision-Instruct model for web automation tasks. We demonstrate how to build a secure, scalable, and efficient infrastructure using AWS Deep Learning Containers (DLCs) on Amazon Elastic Kubernetes Service (Amazon EKS).

6. How Nippon India Mutual Fund improved the accuracy of AI assistant responses using advanced RAG methods on Amazon Bedrock

   In this post, we examine a solution adopted by Nippon Life India Asset Management Limited that improves the accuracy of the response over a regular (naive) RAG approach by rewriting the user queries and aggregating and reranking the responses. The proposed solution uses enhanced RAG methods such as reranking to improve the overall accuracy

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

   In this post, we demonstrate how to create a powerful research assistant for drug discovery using Strands Agents and Amazon Bedrock. This AI assistant can search multiple scientific databases simultaneously using the Model Context Protocol (MCP), synthesize its findings, and generate comprehensive reports on drug targets, disease mechanisms, and therapeutic areas.

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

   In this post, we’ll walk through what makes Nova Act SDK unique, how it works, and how teams across industries are already using it to automate browser-based workflows at scale.

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

   In this post, we explore how Crypto.com used user and system feedback to continuously improve and optimize our instruction prompts. This feedback-driven approach has enabled us to create more effective prompts that adapt to various subsystems while maintaining high performance across different use cases.

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

    This post explores how the AWS Serverless MCP server accelerates development throughout the serverless lifecycle, from making architectural decisions with tools like get_iac_guidance and get_lambda_guidance, to streamlining development with get_serverless_templates, sam_init, to deployment with SAM integration, webapp_deployment_help, and configure_domain. We show how this conversational AI...