Introduction to IoT Hub

IoT Hub is a fully managed service from Microsoft Azure  as part of Azure IoT Suite that enables reliable and secure bi-directional communications between millions of IoT devices and your solution back end. Azure IoT Hub are designed to provide following capabilities: Multiple device-to-cloud and cloud-to-device communication options, including one-way messaging, file transfer, and request-reply […]

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AI Agent Architectures: From ReAct to Multi-Agent Systems

Introduction: AI agents represent the next evolution of LLM applications—systems that can reason, plan, and take actions to accomplish complex tasks autonomously. Unlike simple chatbots that respond to single queries, agents maintain state, use tools, and iterate toward goals. This guide covers the architectural patterns that make agents effective: the ReAct framework for reasoning and […]

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Azure IoT Hub Device Management–Released to Public

Today Microsoft has announced general availability of Azure IoT Hub Device Management. With this release Azure IoT Hub subscribers/customers will be able to get access to following features and functionalities: Device twin. Use a digital representation of your physical devices to synchronize device conditions and operator configuration between the cloud and device. Direct methods. Apply […]

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Microsoft Visual Studio 2015 Update 3 (KB3165756) – Cumulative Servicing Release – 14.0. 25431.01

As per Microsoft ” This cumulative servicing release provides fixes to Microsoft Visual Studio 2015 Update 3. These fixes address high-impact bugs that were either found by the product team or reported by the community.” Download the latest from: here , and you can fine the detailed list of fixes on the same site.

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Embedding Models Deep Dive: From Sentence Transformers to Production Deployment

Introduction: Embeddings are the foundation of modern AI applications—they transform text, images, and other data into dense vectors that capture semantic meaning. Understanding how embedding models work, their strengths and limitations, and how to choose between them is essential for building effective search, RAG, and similarity systems. This guide covers the landscape of embedding models: […]

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