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Learn what's new in Visual Studio Code 1.122 (Insiders) Read the full article Post Updated on May 27, 2026 at 06:00PM Thanks for reading from devamazonaws.blogspot.com

Introduction When multiple engineers on a team use AI coding tools independently — without shared context about the project's architecture or conventions — the output quality is inconsistent. One developer gets a well-structured endpoint; another gets generic boilerplate that needs to be rewritten. The AI is capable, but it is working blind. This post describes a pattern the ISE team applied on a customer engagement to solve this problem: AGENTS.md files for project-level context and reusable skills for task-level instructions, both consumed automatically by GitHub Copilot CLI . The pattern is both language and framework agnostic. The examples here are drawn from an Azure-deployed polyglot stack (Python/FastAPI backend, Next.js/React frontend, Terraform infrastructure), but the approach applies to any codebase. The Problem: Ad-Hoc AI Usage Doesn't Scale On an ISE engagement involving two Azure-deployed SaaS products, each living in its own polyglot monorepo (infrastructure...

The Win32 TreeView control in the common controls library provides two ways of sorting elements. TVM_­SORT­CHILDREN : Sorts children alphabetically by name. TVM_­SORT­CHILDREN­CB : Sorts children via custmm callback. The custom callback is provided the lParam of the two tree items being compared. But what if you want to sort by a combination of both the text and the lParam ? How do you get both? There are two general designs for using UI controls that represent collections. One model is for the UI control to be the data repository. Everything you need to know about the item resides in the UI control, somewhere in its name, its check state, its selection state, whatever. If you need to know something about an item, you ask the UI control for the information. The second model is for the data repository to be some sort of object that itself does not have any UI. (This is known in the biz as a "data model".) You then construct UI elements to be the representation of ...

Error code 7 is ERROR_ ARENA_ TRASHED . What does this mean? It sounds like a heavy metal band ran amok and made a mess of the performance area that they rented. This error message was inherited from MS-DOS. MS-DOS internally kept track of memory in the form of a sequence of variable-sized memory blocks, each prefixed by a 16-byte block known as an arena : arena STRUC arena_signature DB ? ; 4D for valid item, 5A for last item arena_owner DW ? ; owner of arena item arena_size DW ? ; size in paragraphs of item arena ENDS The arena_owner is the PDB of the process that allocated the memory, or zero if the memory is free. Each arena signature is 0x4D (ASCII capital M), except for the final one which is 0x5A (ASCII capital Z). Yes, those are the initials of Mark Zbikowski. When walking through the memory blocks, say, when searching for memory to satisfy an allocation request, if MS-DOS saw that the signature was nei...

Amazon SageMaker HyperPod now supports data capture for inference workloads, enabling customers to record inference request and response payloads for model monitoring, compliance, debugging, and offline analysis. Organizations deploying generative AI and machine learning models on HyperPod need systematic visibility into the inputs flowing into their models and the outputs returned to clients to detect model drift, satisfy regulatory audit requirements, debug production issues, and build ground-truth datasets for fine-tuning. Previously, customers had to either accept limited operational visibility into their inference workloads or build expensive custom logging pipelines outside the HyperPod Inference Operator. With data capture, you can choose to record inference traffic at the SageMaker endpoint, at the load balancer, or at the model pod, depending on the level of visibility you need, and combine these options for layered observability. Captured data is delivered asynchronously to...

Amazon Managed Grafana now supports dual-stack connectivity, enabling workspaces to communicate over both Internet Protocol version 4 (IPv4) and Internet Protocol version 6 (IPv6). Dual-stack mode is available for workspaces running Grafana version 10.4 or later. With dual-stack support, customers can simplify their network stack by eliminating the need to manage overlapping address spaces in their VPCs. Customers migrating to IPv6 can connect to their Grafana workspaces over IPv6 while maintaining IPv4 compatibility, and those not yet on IPv6 can continue using IPv4-only connections. This is especially beneficial as the continued growth of the internet exhausts available IPv4 addresses.  Support for dual-stack connectivity on Amazon Managed Grafana is available in all  regions  where the service is generally available. To get started, update your workspace configuration via the Amazon Managed Grafana console, API, or ...