<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://brucebarrera.github.io/</id><title>Bruce's Tech Blog</title><subtitle>A blog about coding, tech, and life.</subtitle> <updated>2025-07-16T23:02:23-04:00</updated> <author> <name>Bruce Barrera</name> <uri>https://brucebarrera.github.io/</uri> </author><link rel="self" type="application/atom+xml" href="https://brucebarrera.github.io/feed.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://brucebarrera.github.io/"/> <generator uri="https://jekyllrb.com/" version="4.4.1">Jekyll</generator> <rights> © 2025 Bruce Barrera </rights> <icon>/assets/img/favicons/favicon.ico</icon> <logo>/assets/img/favicons/favicon-96x96.png</logo> <entry><title>Implementation guide for integrating xAI's Grok API into a C#/.NET application</title><link href="https://brucebarrera.github.io/posts/implementation-guide-for-integrating-xAI-grok-API-into-C-application/" rel="alternate" type="text/html" title="Implementation guide for integrating xAI&amp;apos;s Grok API into a C#/.NET application" /><published>2025-06-19T20:58:00-04:00</published> <updated>2025-06-25T22:20:17-04:00</updated> <id>https://brucebarrera.github.io/posts/implementation-guide-for-integrating-xAI-grok-API-into-C-application/</id> <content type="text/html" src="https://brucebarrera.github.io/posts/implementation-guide-for-integrating-xAI-grok-API-into-C-application/" /> <author> <name>Bruce Barrera</name> </author> <category term="AI" /> <summary>In this article I’ll show an example of an implementation for integrating xAI’s Grok API into a C#/.NET application, focusing on a practical use case: generating text completions using the Grok API. This article includes setup instructions, a sample C# code snippet, and error handling, leveraging the API’s compatibility with OpenAI’s SDK format. The example assumes you have an API key from xAI’...</summary> </entry> <entry><title>Using Azure AI Vision to perform image analysis and gender prediction</title><link href="https://brucebarrera.github.io/posts/azure-ai-vision-image-analysis-gender-prediction/" rel="alternate" type="text/html" title="Using Azure AI Vision to perform image analysis and gender prediction" /><published>2025-06-19T20:58:00-04:00</published> <updated>2025-06-25T22:20:07-04:00</updated> <id>https://brucebarrera.github.io/posts/azure-ai-vision-image-analysis-gender-prediction/</id> <content type="text/html" src="https://brucebarrera.github.io/posts/azure-ai-vision-image-analysis-gender-prediction/" /> <author> <name>Bruce Barrera</name> </author> <category term="AI" /> <summary>This article is an example of a C# .NET program that uses Azure AI Vision (formerly Azure Cognitive Services) to perform image analysis and attempt to recognize whether a human in an image is male or female based on facial attributes. Azure AI Vision provides pre-trained models for face detection and attribute analysis, including gender prediction. This approach is practical for rapid developme...</summary> </entry> <entry><title>Simplifying SQL Data Retrieval with SafeGetT Extension Method in C#</title><link href="https://brucebarrera.github.io/posts/simplifying-sql-data-retrieval-with-safegetT-extension-method/" rel="alternate" type="text/html" title="Simplifying SQL Data Retrieval with SafeGetT Extension Method in C#" /><published>2025-05-15T19:58:00-04:00</published> <updated>2025-06-25T22:19:25-04:00</updated> <id>https://brucebarrera.github.io/posts/simplifying-sql-data-retrieval-with-safegetT-extension-method/</id> <content type="text/html" src="https://brucebarrera.github.io/posts/simplifying-sql-data-retrieval-with-safegetT-extension-method/" /> <author> <name>Bruce Barrera</name> </author> <category term="C#" /> <summary>When working with SQL data in C#, developers often need to handle data retrieval from an IDataReader safely, accounting for potential null values or missing columns. In this article we will implement a SqlDataExtensions static class that provides a useful extension method called SafeGetT that simplifies this process by safely retrieving typed data from an IDataReader with proper error handling....</summary> </entry> <entry><title>Zero-shot image classification using C# and .NET with a hypothetical pre-trained CLIP model via ONNX Runtime</title><link href="https://brucebarrera.github.io/posts/zero-shot-image-classification-using-c-clip-onnx-runtime/" rel="alternate" type="text/html" title="Zero-shot image classification using C# and .NET with a hypothetical pre-trained CLIP model via ONNX Runtime" /><published>2025-04-05T20:58:00-04:00</published> <updated>2025-06-27T17:30:19-04:00</updated> <id>https://brucebarrera.github.io/posts/zero-shot-image-classification-using-c-clip-onnx-runtime/</id> <content type="text/html" src="https://brucebarrera.github.io/posts/zero-shot-image-classification-using-c-clip-onnx-runtime/" /> <author> <name>Bruce Barrera</name> </author> <category term="AI" /> <summary>In this article I’ll provide a C# implementation for zero-shot image classification using ONNX Runtime with a hypothetical CLIP model, along with unit tests. Assumptions A pre-trained CLIP model (ONNX format) is available, with inputs for an image (preprocessed to 720x480 pixels, normalized) and text embeddings for labels. The image is a 720x480 RGB image containing giraffes, an elephan...</summary> </entry> <entry><title>Harnessing AI APIs in C# and .NET: Common Use Cases and Implementation Guide</title><link href="https://brucebarrera.github.io/posts/harnessing-AI-apis-C-common-use-cases-and-implementation-guide/" rel="alternate" type="text/html" title="Harnessing AI APIs in C# and .NET: Common Use Cases and Implementation Guide" /><published>2025-01-09T20:18:00-05:00</published> <updated>2025-06-25T22:19:56-04:00</updated> <id>https://brucebarrera.github.io/posts/harnessing-AI-apis-C-common-use-cases-and-implementation-guide/</id> <content type="text/html" src="https://brucebarrera.github.io/posts/harnessing-AI-apis-C-common-use-cases-and-implementation-guide/" /> <author> <name>Bruce Barrera</name> </author> <category term="AI" /> <summary>AI APIs are widely used in C# and .NET applications to integrate advanced AI capabilities without building models from scratch. In this article it’ll be shown below common use cases and how they are typically implemented in C#/.NET: Common Usages of AI APIs in C#/.NET Natural Language Processing (NLP) Use Cases: Text generation, sentiment analysis, chatbots, text summarization, ...</summary> </entry> </feed>
