News For Dev-ops

Amazon SageMaker Data Agent now integrates with SageMaker Catalog business context and metadata, enabling data practitioners to discover datasets and generate more accurate SQL and Python code using business terminology instead of cryptic technical table names. This integration allows the Data Agent to leverage the business context that companies have invested months curating in their SageMaker Catalog, including those synced from Collibra, Atlan, and Alation, to deliver more accurate data discovery and code generation. With this capability, data practitioners can ask questions like "Calculate customer retention rate" or "What data do I have on customer churn?" and the Data Agent will search glossary terms, custom metadata forms, asset summaries, and README content to identify the correct tables and columns. The agent generates more accurate code on first attempt by understanding business context, plans multi-step workflows with the correct sequence of tables and ...

This blog post summarizes changes to the vcpkg package manager as part of the 2026.05.25 registry release. No vcpkg-tool releases occurred during this period. This release includes major library updates for Boost 1.91, Qt 6.11, and OpenCASCADE 8.0, along with 27 new ports and over 500 port updates. There are now 2,824 total ports available in the vcpkg curated registry. A port is a versioned recipe for building a package from source, such as a C or C++ library. 27 new ports were added to the curated registry. 521 ports were updated in May . As always, we validate each change to a port by building all other ports that depend on or are depended by the library that is being updated for our main triplets . 85 community contributors made commits. The main vcpkg repo has over 7,500 forks and 27,100 stars on GitHub. vcpkg changelog (2026.05.25 release) Since no vcpkg-tool changes were released this month, this changelog highlights notable port updates: Boost...

Amazon Elastic Kubernetes Service (Amazon EKS) Capabilities can now be configured as log delivery sources using Amazon CloudWatch Vended Logs. This enables customers to monitor and troubleshoot their EKS Capabilities for Argo CD, AWS Controllers for Kubernetes (ACK), and kro (Kubernetes Resource Orchestrator) by monitoring logs collected from the managed controllers that run in AWS-managed infrastructure. Customers can enable log delivery for each capability using CloudWatch APIs or the AWS Console. Logs are configured as a CloudWatch Vended Logs delivery source, enabling reliable, secure log delivery to CloudWatch Logs, Amazon S3, or Amazon Kinesis Data Firehose destinations. This feature is available in all AWS Regions where the EKS Capabilities feature is supported. Standard CloudWatch Vended Logs pricing applies based on the chosen destination. There is no additional EKS charge. To learn more about EKS Capabilities, visit the Amazon EKS documentation . Post Updated on June ...

Learn what's new in Visual Studio Code 1.123 Read the full article Post Updated on June 3, 2026 at 06:00PM Thanks for reading from devamazonaws.blogspot.com

Why edge AI development is still hard  AI is no longer confined to cloud experiments. Developers are increasingly expected to deliver AI inside apps, devices, and edge systems where responsiveness, privacy, resilience, and local control are essential. But building those experiences for production is still difficult.   Teams often have to solve model packaging, runtime fragmentation, hardware differences, and deployment complexity before they can ship a single reliable feature. That slows iteration and makes it harder to move from prototype to product.   At Microsoft Build 2026, we’re announcing updates across Foundry Local and Foundry Local on Azure Local that help developers build once and run AI closer to where data is created and decisions are made. These updates expand platform support, improve control over inference and acceleration, add new on-device APIs, and simplify deployment across disconnected, regulated, and sovereign environments.   What’s new in Foundry Local ...

Introduction In a recent engagement, we worked with a customer who already had a similarity search system backed by LanceDB. The foundation was solid: vector search over embedded documents powering retrieval for end user similarity searches. But their use case wasn’t purely semantic. Users frequently searched by identifier numbers and exact fields. They valued that LanceDB could support hybrid retrieval, allowing them to directly pinpoint a specific result while also retrieving semantically similar ones. Sure, the search system worked. But as we enhanced their system, the conversation naturally evolved into: If we were to design this from scratch, would we choose Keyword Search, or Hybrid Search? Or, more broadly: How should enterprises decide between these two architectures? Rather than answer based on intuition, we developed a structured evaluation framework. Over time, that framework distilled into five measurable criteria we now use to guide this decision. While this dis...