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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
ExternalAI
AWS Machine Learning Blog

Accelerate ML feature pipelines with new capabilities in Amazon SageMaker Feature Store

Today, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0. In this post, we walk through each capability with code examples you can use to get started. For complete end-to-end walkthroughs, see the accompanying notebooks for Lake Formation governance and Iceberg table properties in the SageMaker Python SDK repository.

Amazon Machine LearningAmazon SageMakerAnnouncements
AWS Machine Learning BlogRead original
ExternalAI
AWS Machine Learning Blog

Implementing programmatic tool calling on Amazon Bedrock

In this post, we show three ways to implement Programmatic tool calling (PTC) on Amazon Bedrock: a self-hosted Docker sandbox on ECS for maximum control, a managed solution using Amazon Bedrock AgentCore Code Interpreter, and an Anthropic SDK-compatible path through a proxy for teams that prefer that developer experience.

Amazon BedrockAmazon Bedrock AgentCoreTechnical How-to
AWS Machine Learning BlogRead original
ExternalTechnology Trends
HN RSS Best

I’ve joined Anthropic

https://xcancel.com/karpathy/status/2056753169888334312 https://www.axios.com/2026/05/19/anthropic-openai-karpathy-a..., https://archive.ph/h6T3X Comments URL: https://news.ycombinator.com/item?id=48194352 Points: 1409 # Comments: 604

HN RSS BestRead original
External
AWS Architecture Blog

How Synthesia optimizes generative AI video inference on Amazon EC2 G7e instances

This post introduces a video decoding optimization technique that we have ideated in collaboration with Synthesia Research Engineering team, which we call Asynchronous Frame Generation Pipeline. Adopting this technique allows you to overlap GPU compute, device-to-host (D2H) data transfer, and host-side post-processing. In this post, we apply this technique to the VAE decoder of a Wan video generation model as an example, where our benchmarks on G7e show increased GPU kernel utilization from 82%

Amazon EC2Customer SolutionsGenerative AI
AWS Architecture BlogRead original
ExternalSoftware Engineering
DZone

Lambda-Driven API Design: Building Composable Node.js Endpoints With Functional Primitives

“Lambda-driven API design” fits naturally with Node.js because a Lambda handler can be treated as a small, explicit function boundary: an event arrives, a response is returned, and everything else becomes an implementation detail that can be composed. The core challenge is not producing a response object, but scaling many endpoints without turning each handler into a copy-pasted blob of parsing, validation, authorization, logging, and error mapping.  AWS has increasingly nudged Lambda Node.js wo

DZoneRead original

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