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      <image:title>News - Enhanced Exploration in Genuix Dimensions - Make it stand out</image:title>
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      <image:title>News - Enhanced Exploration in Genuix Dimensions - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>News - Enhanced Exploration in Genuix Dimensions - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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    <lastmod>2025-11-05</lastmod>
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      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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      <image:title>News - Teknikos at Ignite: Redefining AI Experiences with NPU and Phi Silica</image:title>
      <image:caption>Join us at Ignite as we showcase the power of local inferencing, and stay tuned as we count down to Genuix - the application layer that makes AI work for everyone.</image:caption>
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    <lastmod>2025-09-16</lastmod>
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    <lastmod>2025-09-11</lastmod>
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    <loc>https://teknikos.com/news/device-to-cloud-hackathon-in-mexico-city</loc>
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    <lastmod>2025-08-18</lastmod>
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      <image:title>News - Device to Cloud Hackathon in Mexico City - Recently, CTO and VP of Engineering Jonathan Khoo had the opportunity to participate in a Microsoft-led event in Mexico City focused on device-to-cloud AI (aka "hybrid AI"). HIs role was to design and lead a hands-on lab that demonstrated how cloud and on-device AI can work together seamlessly. The session followed excellent presentations from Azim Verjee (Global Black Belt North America, Surface devices) on Surface and NPUs, and from Beth Pan (Windows Partner &amp; Developer Experiences) on the Windows Copilot Runtime.</image:title>
      <image:caption>Photo courtesy Azim Verjee</image:caption>
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      <image:title>News - Device to Cloud Hackathon in Mexico City - The lab showcased how the same AI tasks can be executed both in the cloud and locally—using the same ONNX model in both scenarios. For this, we built a chatbot that processes user input, classifies the intent using AI, and responds as though it were an employee in the appropriate department. The classification task was executed via the same ONNX model file in the cloud and on the device, and the chat response was generated by a large language model in the cloud (GPT-4o-mini) or a small model on the device (Phi Silica). We began by discussing ONNX models and runtime, and how to use Jupyter notebooks and Python to perform inference with Hugging Face models—specifically the fine-tuned DeBERTa model by Moritz Laurer and its optimized ONNX version which we used for the zero-shot classification task. The lab walked through converting a Hugging Face model to ONNX, running it on-device with the QNN Execution Provider to tap into NPU acceleration, and then deploying the pre-optimized version to Azure Machine Learning. We compared performance and results across three environments: the original HF model, the ONNX model on-device, and the ONNX model deployed in Azure.</image:title>
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    <lastmod>2025-08-18</lastmod>
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