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You start off with what seems like the obvious solution to a multi-tenant SaaS application. We have tenant A. We have tenant B. We have one application and one database. Within that database, for every structure, whether that is a table, collection, or stream, we segregate things by a tenant ID. It is simple. It… Read …
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Most "AI agent" demos forget everything the moment the process exits. That's fine for a toy project, but useless for anything real. An agent that helps you write, triage, or support needs two things a language model alone can't give it: durable state and the ability to recall the right context by meaning. …
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Some time ago, I discussed how to extract the function pointer from the control flow guard check. I gave the code for LdrpValidateUserCallTarget, but there's another ve Link to article: https://devblogs.microsoft.com/oldnewthing/20260708-00/?p=112510
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Cut through the hype with a practical decision framework for choosing between monolith vs microservices in C#. Learn when each architecture wins for .NET developers. Link to article: https://www.devleader.ca/2026/07/08/monolith-vs-microservices-in-c-a-decision-framework-for-net-developers
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AI agents are changing software development. Learn why your laptop is becoming the new production environment and why runtime governance matters. Link to article: https://www.docker.com/blog/your-laptop-is-the-new-production-environment/
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This is the seventh article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. …
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Python's agent-framework-orchestrations package is now 1.0.0. That puts Microsoft Agent Framework's orchestration layer at 1.0 across Python and .NET. Sequential, concurrent, group chat, handoff, and magentic orchestration are now stable in both SDKs. You can pick the coordination pattern that fits your problem instead …
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Agentic coding benchmarks are getting closer to real-world software development. For Kotlin teams, the most important question is how reliably AI agents can complete end-to-end Kotlin tasks, from reading an issue to producing a solution that passes validation. We’re taking the first step in addressing that gap by …
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Model Context Protocol explained for C# and .NET developers: what MCP is, why it matters, and how hosts, clients, servers, tools, resources, and prompts fit. Link to article: https://www.devleader.ca/2026/07/08/model-context-protocol-mcp-explained-for-c-and-net-developers
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Set up a private NuGet feed with Azure Artifacts or GitHub Packages. Learn nuget.config setup, authentication, and consuming private packages in .NET. Link to article: https://www.devleader.ca/2026/07/07/private-nuget-feeds-in-net-azure-artifacts-and-github-packages
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Object detection powers transformative applications, from autonomous vehicles navigating city streets and security systems identifying threats in real time to retail analytics tracking inventory and medical imaging detecting tumors. But choosing the right model for your computer vision project can be challenging, …
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In this post, we walk through what Dataset Enrichment is, how it differs from legacy Topics, and provide three migration scenarios with step-by-step guidance so you can move your business context into the dataset layer with confidence. Link to article: …
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Today, we are excited to announce Multi-Dataset Relationships in Amazon Quick Sight. This new capability lets you define logical relationships between Quick Sight datasets and perform runtime joins at query time. Instead of flattening tables ahead of time, you keep each table as its own Quick Sight dataset and declare …
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In this post, we shift from concepts to patterns. For each schema, you’ll find a table structure, use cases, implementation steps, and sample SQL queries. We also cover workarounds for advanced scenarios that require extra modeling steps, and close with a summary of current limitations. Link to article: …
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This post is for data architects, business intelligence (BI) engineers, and analytics engineers building or optimizing Quick Sight Topics for natural-language Chat-based exploration. Link to article: https://aws.amazon.com/blogs/machine-learning/multi-dataset-topic-best-practices-for-amazon-quick-chat/
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In this post, we walk through how multi-dataset Topics work, explain how the chat agent uses defined relationships to generate cross-dataset queries, and demonstrate an end-to-end implementation using a retail analytics scenario in Quick Sight. Link to article: …
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This post walks through building a serverless image editor where users upload a photo, describe an edit in plain English, and receive the result in seconds. The agent runs on AgentCore harness without custom orchestration code. We deploy the full solution, including authentication, encrypted storage, three image …
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Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use open source Evidently together with Amazon SageMaker AI to generate monitoring reports, organize and compare the results in …
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In this post, you build an AWS Support Companion using Amazon Bedrock AgentCore. The agent uses Strands Agents as the orchestration framework and connects to AWS services through the Model Context Protocol (MCP). By the end, you have a working agent that can analyze CloudWatch logs, search AWS documentation, query …
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In this post, we show how AWS Finance used chat agents and Flows in Amazin Quick to transform two of their most time-consuming workflows. Link to article: https://aws.amazon.com/blogs/machine-learning/how-aws-finance-teams-reclaimed-hundreds-of-hours-with-amazon-quick/
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