2 posts tagged with "amazon bedrock"

Introduction​

Modern businesses need to analyze streaming data in real-time to make faster decisions. Whether it's monitoring IoT sensors, tracking user behavior, or processing financial transactions, the ability to query fresh data immediately is critical. However, building a streaming analytics pipeline traditionally requires managing complex infrastructure and dealing with data format conversions.

This solution shows how to build a serverless real-time streaming analytics pipeline using Amazon S3 Tables and Amazon Kinesis Data Firehose. By combining streaming ingestion with Apache Iceberg's analytics-optimized format, you can query data within minutes of generation—without managing any servers or data transformation jobs.

GitHub Repository: https://github.com/manu-mishra/s3table-firehose-lambda-terraform-demo

Introduction​

Modern large language models face a fundamental limitation: context windows. While frontier models now reach 1 million tokens (Nova Premier, Claude Sonnet 4.5), workloads analyzing entire codebases, document collections, or multi-hour conversations can easily exceed 10 million tokens—far beyond any single model's capacity.

This post demonstrates Recursive Language Models (RLMs), an inference strategy from MIT CSAIL research that enables scaling to inputs far beyond context windows. What makes this implementation special: Strands Agents and Amazon Bedrock AgentCore reduce what could be weeks of glue code and deployment work to just a few hours of development.